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docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/LICENSE

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+Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+
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docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/README.md

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+# Stable Diffusion 2.1 FineTuning with Low-Rank Adaptation of Large Language Models for PyTorch
+This directory provides scripts to fine-tune Stable Diffusion Model (2.1) which is based on latent text-to-image diffusion model and is tested and maintained by Habana.
+For more information on training and inference of deep learning models using Gaudi, refer to [developer.habana.ai](https://developer.habana.ai/resources/).
+
+  - [Model-References](../../../README.md)
+  - [Model Overview](#model-overview)
+  - [Setup](#setup)
+  - [Training and Examples](#training)
+  - [Supported Configuration](#supported-configuration)
+  - [Changelog](#changelog)
+
+## Model Overview
+
+This implementation is designed to fine-tune Stable Diffusion model (stabilityai/stable-diffusion-2-1-base) by Low-rank Adaptation which is considered to be very efficient. Here the pipeline is to fine-tune CLIP + Unet + token to gain better results.
+
+  More details about LoRA and its usage with diffusion can be found at [Blog](https://huggingface.co/blog/lora) and [examples](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image#training-with-lora)
+
+### How to use
+Users acknowledge and understand that the models referenced by Habana are mere examples for models that can be run on Gaudi.
+Users bear sole liability and responsibility to follow and comply with any third party licenses pertaining to such models,
+and Habana Labs disclaims and will bear no any warranty or liability with respect to users' use or compliance with such third party licenses.
+
+## Setup
+Please follow the instructions provided in the [Gaudi Installation Guide](https://docs.habana.ai/en/latest/Installation_Guide/index.html) to set up the environment including the `$PYTHON` environment variable. To achieve the best performance, please follow the methods outlined in the [Optimizing Training Platform guide](https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Optimization_in_Training_Platform.html).
+The guides will walk you through the process of setting up your system to run the model on Gaudi.
+
+### Clone Habana Model-References
+In the docker container, clone this repository and switch to the branch that matches your SynapseAI version.
+You can run the [`hl-smi`](https://docs.habana.ai/en/latest/System_Management_Tools_Guide/System_Management_Tools.html#hl-smi-utility-options) utility to determine the SynapseAI version.
+```bash
+git clone -b [SynapseAI version] https://github.com/HabanaAI/Model-References
+cd Model-References/PyTorch/generative_models/stable-diffusion-finetuning
+```
+### Install Model Requirements
+1. In the docker container, go to the model directory:
+```bash
+cd Model-References/PyTorch/generative_models/stable-diffusion-finetuning
+```
+
+2. Install the required packages using pip:
+```bash
+pip install -r requirements.txt
+pip install .
+```
+
+## Training
+### Model Checkpoint
+
+The fine-tuning script internally will download checkpoints from https://huggingface.co/stabilityai/stable-diffusion-2-1-base .
+
+Users acknowledge and understand that by downloading the checkpoint referenced herein they will be required to comply
+with third party licenses and rights pertaining to the checkpoint, and users will be solely liable and responsible
+for complying with any applicable licenses. Habana Labs disclaims any warranty or liability with respect to users' use
+or compliance with such third party licenses.
+
+### Dataset Preparation
+For the finetuning we have used synthetic dataset.
+
+1. In the docker container, go to the model directory:
+```bash
+cd Model-References/PyTorch/generative_models/stable-diffusion-finetuning
+```
+
+2. Generate synthetic dataset:
+```bash
+python data/scripts/gen_synth_data.py
+```
+
+### 1. Fine-tuning Stable diffusion with LoRA CLI
+
+#### Single Card Training Examples
+**Run training on 1 HPU:**
+
+```bash
+export MODEL_NAME="stabilityai/stable-diffusion-2-1-base"
+export INSTANCE_DIR=<path-to-instances>
+export OUTPUT_DIR=<path-to-output>
+
+lora_pti \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --use_face_segmentation_condition \
+  --resolution=512 \
+  --train_batch_size=7 \
+  --gradient_accumulation_steps=1 \
+  --learning_rate_unet=5e-5 \
+  --learning_rate_ti=2e-3 \
+  --color_jitter \
+  --lr_scheduler="linear" --lr_scheduler_lora="linear"\
+  --lr_warmup_steps=0 \
+  --placeholder_tokens="<s1>|<s2>" \
+  --use_template="object"\
+  --save_steps=50 \
+  --max_train_steps_ti=500 \
+  --max_train_steps_tuning=1000 \
+  --perform_inversion=True \
+  --clip_ti_decay \
+  --weight_decay_ti=0.000 \
+  --weight_decay_lora=0.001\
+  --continue_inversion \
+  --continue_inversion_lr=1e-3 \
+  --device="hpu" \
+  --lora_rank=16 \
+  --use_lazy_mode=True \
+  --use_fused_adamw=True \
+  --print_freq=50 \
+  --use_fused_clip_norm=True \
+```
+
+[Refer to reference model to see what these parameters mean](https://github.com/cloneofsimo/lora/discussions/121).
+
+
+## Supported Configuration
+| Validated on  | SynapseAI Version | PyTorch Version | Mode |
+|---------|-------------------|-----------------|------------|
+| Gaudi   | 1.11.0             | 2.0.1          | Training |
+| Gaudi2  | 1.14.0             | 2.1.1          | Training |
+
+## Changelog
+
+### Script Modifications
+### 1.13.0
+* Modified training script to support diffusers version 0.21.4.
+
+### 1.12.0
+* Dynamic Shapes will be enabled by default in future releases. It is currently disabled in training script.
+
+### 1.11.0
+* Dynamic Shapes will be enabled by default in future releases. It is currently enabled in training script as a temporary solution.
+
+### 1.10.0
+* Modified README
+* Enabled PyTorch autocast on Gaudi
+* Added additional logging
+* Added support for HPU
+* Added FusedAdamW and FusedClipNorm
+* Added Tensorboard logging
+* Added device trace and memory stats reporting
+* Added print frequency change
+* Enabled HPU graph execution for host optimization

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/data/scripts/gen_synth_data.py

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+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+from PIL import Image, ImageDraw
+import numpy as np
+import os
+import random
+
+def create_image_with_yellow_square_face(idx=0,path='./',dirname='yellow_rect',min_size=400,max_size=2000,min_prop=0.25,max_prop=0.9):
+    # Generate random image size (let's say between 100 and 1000)
+    X,Y = np.random.randint((min_size,min_size),(max_size,max_size))
+
+    # Create a white background image
+    img = Image.new('RGB', (X, Y), (255, 255, 255))
+    # Generate random mask size (let's say between 10 and image size)
+    mask_x = random.randint(int(X*min_prop), int(X*max_prop))
+    mask_y = random.randint(int(Y*min_prop), int(Y*max_prop))
+    # Generate random top-left point for the mask (x and y coordinates)
+    mask_start_x = random.randint(0, X - mask_x)
+    mask_start_y = random.randint(0, Y - mask_y)
+
+    # Create an ImageDraw object
+    draw = ImageDraw.Draw(img)
+    # Draw the mask first
+    mask = Image.new('L', (X, Y), 1)
+    draw_mask=ImageDraw.Draw(mask)
+    draw_mask.rectangle([mask_start_x, mask_start_y, mask_start_x+mask_x, mask_start_y+mask_y], fill=255)
+    data_path=os.path.join(path,dirname)
+
+    if not os.path.exists(data_path):
+        os.makedirs(data_path)
+    mask.save(os.path.join(data_path,f'{idx}.mask.png'))
+
+    # Draw a yellow square (mask) on the image
+    draw.rectangle([mask_start_x, mask_start_y, mask_start_x+mask_x, mask_start_y+mask_y], fill=(255,255,0))
+
+    # Draw two eyes on the square
+    eye_size = min(mask_x // 10,mask_y//10)
+    eye_y = mask_start_y + mask_y // 4
+    for i in range(2):
+        eye_x = mask_start_x + (i+1) * mask_x // 3 - eye_size // 2
+        draw.ellipse([eye_x, eye_y, eye_x+eye_size, eye_y+eye_size], fill=(0,0,0))
+
+    # Draw a smile on the square
+    smile_size = mask_x // 2
+    smile_y = mask_start_y + 3 * mask_y // 5
+    smile_height = mask_y // 5
+    smile_start_x = mask_start_x + mask_x // 4
+    draw.arc([smile_start_x, smile_y, smile_start_x+smile_size, smile_y+smile_height], start=0, end=180, fill=(0,0,0))
+
+    # Save the image to a file
+    img.save(os.path.join(data_path,f'image_{idx}.png'))
+
+    # Return the mask coordinates
+    return mask_start_x, mask_start_y
+
+if __name__ == '__main__':
+    base_path = "./"
+    rel_dir_path = "yellow_rect"
+
+    # Generate dataset of 10 images with corresponding masks
+    for i in range(10):
+        create_image_with_yellow_square_face(i, path=base_path, dirname=rel_dir_path)
+    print("The synthetic dataset is generated at ", os.path.abspath(os.path.join(base_path, rel_dir_path)))

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/__init__.py

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+from .lora import *
+from .dataset import *
+from .utils import *
+from .preprocess_files import *
+from .lora_manager import *

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/cli_lora_add.py

@@ -0,0 +1,187 @@
+from typing import Literal, Union, Dict
+import os
+import shutil
+import fire
+from diffusers import StableDiffusionPipeline
+from safetensors.torch import safe_open, save_file
+
+import torch
+from .lora import (
+    tune_lora_scale,
+    patch_pipe,
+    collapse_lora,
+    monkeypatch_remove_lora,
+)
+from .lora_manager import lora_join
+from .to_ckpt_v2 import convert_to_ckpt
+
+
+def _text_lora_path(path: str) -> str:
+    assert path.endswith(".pt"), "Only .pt files are supported"
+    return ".".join(path.split(".")[:-1] + ["text_encoder", "pt"])
+
+
+def add(
+    path_1: str,
+    path_2: str,
+    output_path: str,
+    alpha_1: float = 0.5,
+    alpha_2: float = 0.5,
+    mode: Literal[
+        "lpl",
+        "upl",
+        "upl-ckpt-v2",
+    ] = "lpl",
+    with_text_lora: bool = False,
+):
+    print("Lora Add, mode " + mode)
+    if mode == "lpl":
+        if path_1.endswith(".pt") and path_2.endswith(".pt"):
+            for _path_1, _path_2, opt in [(path_1, path_2, "unet")] + (
+                [(_text_lora_path(path_1), _text_lora_path(path_2), "text_encoder")]
+                if with_text_lora
+                else []
+            ):
+                print("Loading", _path_1, _path_2)
+                out_list = []
+                if opt == "text_encoder":
+                    if not os.path.exists(_path_1):
+                        print(f"No text encoder found in {_path_1}, skipping...")
+                        continue
+                    if not os.path.exists(_path_2):
+                        print(f"No text encoder found in {_path_1}, skipping...")
+                        continue
+
+                l1 = torch.load(_path_1)
+                l2 = torch.load(_path_2)
+
+                l1pairs = zip(l1[::2], l1[1::2])
+                l2pairs = zip(l2[::2], l2[1::2])
+
+                for (x1, y1), (x2, y2) in zip(l1pairs, l2pairs):
+                    # print("Merging", x1.shape, y1.shape, x2.shape, y2.shape)
+                    x1.data = alpha_1 * x1.data + alpha_2 * x2.data
+                    y1.data = alpha_1 * y1.data + alpha_2 * y2.data
+
+                    out_list.append(x1)
+                    out_list.append(y1)
+
+                if opt == "unet":
+
+                    print("Saving merged UNET to", output_path)
+                    torch.save(out_list, output_path)
+
+                elif opt == "text_encoder":
+                    print("Saving merged text encoder to", _text_lora_path(output_path))
+                    torch.save(
+                        out_list,
+                        _text_lora_path(output_path),
+                    )
+
+        elif path_1.endswith(".safetensors") and path_2.endswith(".safetensors"):
+            safeloras_1 = safe_open(path_1, framework="pt", device="cpu")
+            safeloras_2 = safe_open(path_2, framework="pt", device="cpu")
+
+            metadata = dict(safeloras_1.metadata())
+            metadata.update(dict(safeloras_2.metadata()))
+
+            ret_tensor = {}
+
+            for keys in set(list(safeloras_1.keys()) + list(safeloras_2.keys())):
+                if keys.startswith("text_encoder") or keys.startswith("unet"):
+
+                    tens1 = safeloras_1.get_tensor(keys)
+                    tens2 = safeloras_2.get_tensor(keys)
+
+                    tens = alpha_1 * tens1 + alpha_2 * tens2
+                    ret_tensor[keys] = tens
+                else:
+                    if keys in safeloras_1.keys():
+
+                        tens1 = safeloras_1.get_tensor(keys)
+                    else:
+                        tens1 = safeloras_2.get_tensor(keys)
+
+                    ret_tensor[keys] = tens1
+
+            save_file(ret_tensor, output_path, metadata)
+
+    elif mode == "upl":
+
+        print(
+            f"Merging UNET/CLIP from {path_1} with LoRA from {path_2} to {output_path}. Merging ratio : {alpha_1}."
+        )
+
+        loaded_pipeline = StableDiffusionPipeline.from_pretrained(
+            path_1,
+        ).to("cpu")
+
+        patch_pipe(loaded_pipeline, path_2)
+
+        collapse_lora(loaded_pipeline.unet, alpha_1)
+        collapse_lora(loaded_pipeline.text_encoder, alpha_1)
+
+        monkeypatch_remove_lora(loaded_pipeline.unet)
+        monkeypatch_remove_lora(loaded_pipeline.text_encoder)
+
+        loaded_pipeline.save_pretrained(output_path)
+
+    elif mode == "upl-ckpt-v2":
+
+        assert output_path.endswith(".ckpt"), "Only .ckpt files are supported"
+        name = os.path.basename(output_path)[0:-5]
+
+        print(
+            f"You will be using {name} as the token in A1111 webui. Make sure {name} is unique enough token."
+        )
+
+        loaded_pipeline = StableDiffusionPipeline.from_pretrained(
+            path_1,
+        ).to("cpu")
+
+        tok_dict = patch_pipe(loaded_pipeline, path_2, patch_ti=False)
+
+        collapse_lora(loaded_pipeline.unet, alpha_1)
+        collapse_lora(loaded_pipeline.text_encoder, alpha_1)
+
+        monkeypatch_remove_lora(loaded_pipeline.unet)
+        monkeypatch_remove_lora(loaded_pipeline.text_encoder)
+
+        _tmp_output = output_path + ".tmp"
+
+        loaded_pipeline.save_pretrained(_tmp_output)
+        convert_to_ckpt(_tmp_output, output_path, as_half=True)
+        # remove the tmp_output folder
+        shutil.rmtree(_tmp_output)
+
+        keys = sorted(tok_dict.keys())
+        tok_catted = torch.stack([tok_dict[k] for k in keys])
+        ret = {
+            "string_to_token": {"*": torch.tensor(265)},
+            "string_to_param": {"*": tok_catted},
+            "name": name,
+        }
+
+        torch.save(ret, output_path[:-5] + ".pt")
+        print(
+            f"Textual embedding saved as {output_path[:-5]}.pt, put it in the embedding folder and use it as {name} in A1111 repo, "
+        )
+    elif mode == "ljl":
+        print("Using Join mode : alpha will not have an effect here.")
+        assert path_1.endswith(".safetensors") and path_2.endswith(
+            ".safetensors"
+        ), "Only .safetensors files are supported"
+
+        safeloras_1 = safe_open(path_1, framework="pt", device="cpu")
+        safeloras_2 = safe_open(path_2, framework="pt", device="cpu")
+
+        total_tensor, total_metadata, _, _ = lora_join([safeloras_1, safeloras_2])
+        save_file(total_tensor, output_path, total_metadata)
+
+    else:
+        print("Unknown mode", mode)
+        raise ValueError(f"Unknown mode {mode}")
+
+
+def main():
+    fire.Fire(add)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/cli_lora_pti.py

@@ -0,0 +1,1349 @@
+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+# Bootstrapped from:
+# https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py
+
+import argparse
+import hashlib
+import inspect
+import itertools
+import math
+import os
+import random
+import re
+from pathlib import Path
+from typing import Optional, List, Literal
+import numpy as np
+
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+import torch.utils.checkpoint
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.optimization import get_scheduler
+from huggingface_hub import HfFolder, Repository, whoami
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+from torchvision.transforms.functional import pil_to_tensor
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+import wandb
+import fire
+
+from lora_diffusion import (
+    PivotalTuningDatasetCapation,
+    extract_lora_ups_down,
+    inject_trainable_lora,
+    inject_trainable_lora_extended,
+    inspect_lora,
+    save_lora_weight,
+    save_all,
+    prepare_clip_model_sets,
+    evaluate_pipe,
+    UNET_EXTENDED_TARGET_REPLACE,
+)
+
+import sys
+try:
+  sys.path.append(os.path.realpath(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../../common/")))
+  from tools.synapse_profiler_api import SynapseProfilerApi, TraceType
+except ImportError:
+  pass
+
+def get_vae_encode_output(vae, data, dtype, device):
+        print("use_torch_compile : vae :Model is compiled")
+        def vae_encode(vae, data, dtype, device):
+            return vae.encode(data.to(dtype).to(device)).latent_dist.sample()
+        vae_encode_compiled = torch.compile(vae_encode, backend="aot_hpu_training_backend")
+        return vae_encode_compiled(vae, data, dtype, device)
+
+def get_models(
+    pretrained_model_name_or_path,
+    pretrained_vae_name_or_path,
+    revision,
+    placeholder_tokens: List[str],
+    initializer_tokens: List[str],
+    device="cuda:0",
+):
+
+    tokenizer = CLIPTokenizer.from_pretrained(
+        pretrained_model_name_or_path,
+        subfolder="tokenizer",
+        revision=revision,
+    )
+
+    text_encoder = CLIPTextModel.from_pretrained(
+        pretrained_model_name_or_path,
+        subfolder="text_encoder",
+        revision=revision,
+    )
+
+    placeholder_token_ids = []
+
+    for token, init_tok in zip(placeholder_tokens, initializer_tokens):
+        num_added_tokens = tokenizer.add_tokens(token)
+        if num_added_tokens == 0:
+            raise ValueError(
+                f"The tokenizer already contains the token {token}. Please pass a different"
+                " `placeholder_token` that is not already in the tokenizer."
+            )
+
+        placeholder_token_id = tokenizer.convert_tokens_to_ids(token)
+
+        placeholder_token_ids.append(placeholder_token_id)
+
+        # Load models and create wrapper for stable diffusion
+
+        text_encoder.resize_token_embeddings(len(tokenizer))
+        token_embeds = text_encoder.get_input_embeddings().weight.data
+        if init_tok.startswith("<rand"):
+            # <rand-"sigma">, e.g. <rand-0.5>
+            sigma_val = float(re.findall(r"<rand-(.*)>", init_tok)[0])
+
+            token_embeds[placeholder_token_id] = (
+                torch.randn_like(token_embeds[0]) * sigma_val
+            )
+            print(
+                f"Initialized {token} with random noise (sigma={sigma_val}), empirically {token_embeds[placeholder_token_id].mean().item():.3f} +- {token_embeds[placeholder_token_id].std().item():.3f}"
+            )
+            print(f"Norm : {token_embeds[placeholder_token_id].norm():.4f}")
+
+        elif init_tok == "<zero>":
+            token_embeds[placeholder_token_id] = torch.zeros_like(token_embeds[0])
+        else:
+            token_ids = tokenizer.encode(init_tok, add_special_tokens=False)
+            # Check if initializer_token is a single token or a sequence of tokens
+            if len(token_ids) > 1:
+                raise ValueError("The initializer token must be a single token.")
+
+            initializer_token_id = token_ids[0]
+            token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
+
+    vae = AutoencoderKL.from_pretrained(
+        pretrained_vae_name_or_path or pretrained_model_name_or_path,
+        subfolder=None if pretrained_vae_name_or_path else "vae",
+        revision=None if pretrained_vae_name_or_path else revision,
+    )
+    unet = UNet2DConditionModel.from_pretrained(
+        pretrained_model_name_or_path,
+        subfolder="unet",
+        revision=revision,
+    )
+
+    return (
+        text_encoder.to(device),
+        vae.to(device),
+        unet.to(device),
+        tokenizer,
+        placeholder_token_ids,
+    )
+
+
+@torch.no_grad()
+def text2img_dataloader(
+    train_dataset,
+    train_batch_size,
+    tokenizer,
+    vae,
+    text_encoder,
+    cached_latents: bool = False,
+    use_torch_compile:bool = False,
+):
+
+    if cached_latents:
+        cached_latents_dataset = []
+        for idx in tqdm(range(len(train_dataset))):
+            batch = train_dataset[idx]
+            # rint(batch)
+            latents = None
+            if use_torch_compile:
+                latents = get_vae_encode_output(vae, batch["instance_images"].unsqueeze(0), vae.dtype, vae.device)
+            else:
+                latents = vae.encode(
+                    batch["instance_images"].unsqueeze(0).to(dtype=vae.dtype).to(vae.device)
+                ).latent_dist.sample()
+            latents = latents * 0.18215
+            batch["instance_images"] = latents.squeeze(0)
+            cached_latents_dataset.append(batch)
+
+    def collate_fn(examples):
+        input_ids = [example["instance_prompt_ids"] for example in examples]
+        pixel_values = [example["instance_images"] for example in examples]
+        pixel_values = torch.stack(pixel_values)
+        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
+
+        input_ids = tokenizer.pad(
+            {"input_ids": input_ids},
+            padding="max_length",
+            max_length=tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids
+
+        batch = {
+            "input_ids": input_ids,
+            "pixel_values": pixel_values,
+        }
+
+        if examples[0].get("mask", None) is not None:
+            batch["mask"] = torch.stack([example["mask"] for example in examples])
+
+        return batch
+
+    if cached_latents:
+
+        train_dataloader = torch.utils.data.DataLoader(
+            cached_latents_dataset,
+            batch_size=train_batch_size,
+            shuffle=True,
+            collate_fn=collate_fn,
+        )
+
+        print("PTI : Using cached latent.")
+
+    else:
+        train_dataloader = torch.utils.data.DataLoader(
+            train_dataset,
+            batch_size=train_batch_size,
+            shuffle=True,
+            collate_fn=collate_fn,
+        )
+
+    return train_dataloader
+
+
+def inpainting_dataloader(
+    train_dataset, train_batch_size, tokenizer, vae, text_encoder
+):
+    def collate_fn(examples):
+        input_ids = [example["instance_prompt_ids"] for example in examples]
+        pixel_values = [example["instance_images"] for example in examples]
+        mask_values = [example["instance_masks"] for example in examples]
+        masked_image_values = [
+            example["instance_masked_images"] for example in examples
+        ]
+
+        # Concat class and instance examples for prior preservation.
+        # We do this to avoid doing two forward passes.
+        if examples[0].get("class_prompt_ids", None) is not None:
+            input_ids += [example["class_prompt_ids"] for example in examples]
+            pixel_values += [example["class_images"] for example in examples]
+            mask_values += [example["class_masks"] for example in examples]
+            masked_image_values += [
+                example["class_masked_images"] for example in examples
+            ]
+
+        pixel_values = (
+            torch.stack(pixel_values).to(memory_format=torch.contiguous_format).float()
+        )
+        mask_values = (
+            torch.stack(mask_values).to(memory_format=torch.contiguous_format).float()
+        )
+        masked_image_values = (
+            torch.stack(masked_image_values)
+            .to(memory_format=torch.contiguous_format)
+            .float()
+        )
+
+        input_ids = tokenizer.pad(
+            {"input_ids": input_ids},
+            padding="max_length",
+            max_length=tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids
+
+        batch = {
+            "input_ids": input_ids,
+            "pixel_values": pixel_values,
+            "mask_values": mask_values,
+            "masked_image_values": masked_image_values,
+        }
+
+        if examples[0].get("mask", None) is not None:
+            batch["mask"] = torch.stack([example["mask"] for example in examples])
+
+        return batch
+
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_size=train_batch_size,
+        shuffle=True,
+        collate_fn=collate_fn,
+    )
+
+    return train_dataloader
+
+
+def loss_step(
+    batch,
+    unet,
+    vae,
+    text_encoder,
+    scheduler,
+    train_inpainting=False,
+    t_mutliplier=1.0,
+    mixed_precision=False,
+    mask_temperature=1.0,
+    cached_latents: bool = False,
+    use_torch_compile: bool = False,
+):
+    weight_dtype = torch.float32
+    if not cached_latents:
+        latents = None
+        if use_torch_compile:
+            latents = get_vae_encode_output(vae, batch["pixel_values"], weight_dtype, unet.device)
+        else:
+            latents = vae.encode(
+                batch["pixel_values"].to(dtype=weight_dtype).to(unet.device)
+            ).latent_dist.sample()
+        latents = latents * 0.18215
+
+        if train_inpainting:
+            masked_image_latents = None
+            if use_torch_compile:
+                masked_image_latents = get_vae_encode_output(vae, batch["masked_image_values"], weight_dtype, unet.device)
+            else:
+                masked_image_latents = vae.encode(
+                    batch["masked_image_values"].to(dtype=weight_dtype).to(unet.device)
+                ).latent_dist.sample()
+            masked_image_latents = masked_image_latents * 0.18215
+            mask = F.interpolate(
+                batch["mask_values"].to(dtype=weight_dtype).to(unet.device),
+                scale_factor=1 / 8,
+            )
+    else:
+        latents = batch["pixel_values"]
+
+        if train_inpainting:
+            masked_image_latents = batch["masked_image_latents"]
+            mask = batch["mask_values"]
+
+    noise = torch.randn_like(latents)
+    bsz = latents.shape[0]
+    if use_torch_compile:
+        timesteps = torch.randint(
+            0,
+            int(scheduler.config.num_train_timesteps * t_mutliplier),
+            (bsz,),
+            device="cpu",
+        )
+        timesteps =timesteps.to("hpu")
+    else:
+        timesteps = torch.randint(
+            0,
+            int(scheduler.config.num_train_timesteps * t_mutliplier),
+            (bsz,),
+            device=latents.device,
+        )
+
+    timesteps = timesteps.long()
+
+    noisy_latents = scheduler.add_noise(latents, noise, timesteps)
+
+    if train_inpainting:
+        latent_model_input = torch.cat(
+            [noisy_latents, mask, masked_image_latents], dim=1
+        )
+    else:
+        latent_model_input = noisy_latents
+
+    if mixed_precision:
+        with torch.autocast(device_type=text_encoder.device.type,
+                            dtype=torch.bfloat16 if text_encoder.device.type in ["hpu", "cpu"] else torch.float16,
+                            enabled=mixed_precision):
+
+            encoder_hidden_states = text_encoder(
+                batch["input_ids"].to(text_encoder.device)
+            )[0]
+
+            model_pred = unet(
+                latent_model_input, timesteps, encoder_hidden_states, return_dict=False
+            )[0]
+    else:
+
+        encoder_hidden_states = text_encoder(
+            batch["input_ids"].to(text_encoder.device)
+        )[0]
+
+        model_pred = unet(latent_model_input, timesteps, encoder_hidden_states, return_dict=False)[0]
+
+    if scheduler.config.prediction_type == "epsilon":
+        target = noise
+    elif scheduler.config.prediction_type == "v_prediction":
+        target = scheduler.get_velocity(latents, noise, timesteps)
+    else:
+        raise ValueError(f"Unknown prediction type {scheduler.config.prediction_type}")
+
+    if batch.get("mask", None) is not None:
+
+        mask = (
+            batch["mask"]
+            .to(model_pred.device)
+            .reshape(
+                model_pred.shape[0], 1, model_pred.shape[2] * 8, model_pred.shape[3] * 8
+            )
+        )
+        # resize to match model_pred
+        mask = F.interpolate(
+            mask.float(),
+            size=model_pred.shape[-2:],
+            mode="nearest",
+        )
+
+        mask = (mask + 0.01).pow(mask_temperature)
+
+        mask = mask / mask.max()
+
+        model_pred = model_pred * mask
+
+        target = target * mask
+
+    loss = (
+        F.mse_loss(model_pred.float(), target.float(), reduction="none")
+        .mean([1, 2, 3])
+        .mean()
+    )
+
+    return loss
+
+
+def train_inversion(
+    unet,
+    vae,
+    text_encoder,
+    dataloader,
+    num_steps: int,
+    scheduler,
+    index_no_updates,
+    optimizer,
+    save_steps: int,
+    placeholder_token_ids,
+    placeholder_tokens,
+    save_path: str,
+    tokenizer,
+    lr_scheduler,
+    test_image_path: str,
+    cached_latents: bool,
+    accum_iter: int = 1,
+    log_wandb: bool = False,
+    wandb_log_prompt_cnt: int = 10,
+    class_token: str = "person",
+    train_inpainting: bool = False,
+    mixed_precision: bool = False,
+    clip_ti_decay: bool = True,
+    htcore = None,
+    log_tb: bool = False,
+    writer = None,
+    use_synapse_profiler: bool = False,
+    use_pytorch_profiler: bool = False,
+    profiler_step: int = 3,
+    profile_ti: bool = False,
+    print_freq: int = 50,
+    use_torch_compile:bool = False,
+):
+
+    progress_bar = tqdm(range(num_steps),mininterval=0,miniters=10, smoothing=1)
+    progress_bar.set_description("Steps")
+    global_step = 0
+
+    if not profile_ti:
+        use_synapse_profiler = False
+        use_pytorch_profiler = False
+
+    # Original Emb for TI
+    orig_embeds_params = text_encoder.get_input_embeddings().weight.data.clone()
+
+    if log_wandb or log_tb:
+        preped_clip = prepare_clip_model_sets()
+
+    index_updates = ~index_no_updates
+    loss_sum = 0.0
+    loss_list_ti = []
+    pt_prof = None
+    syn_prof = None
+
+    if use_pytorch_profiler:
+        activities = [torch.profiler.ProfilerActivity.CPU]
+        if htcore:
+            activities.append(torch.profiler.ProfilerActivity.HPU)
+        pt_prof = torch.profiler.profile(
+                activities=activities,
+                schedule=torch.profiler.schedule(wait=0, warmup=0, active=1, repeat=1),
+                on_trace_ready=torch.profiler.tensorboard_trace_handler("./trace-log"),
+                record_shapes=True,
+                with_stack=True)
+        pt_prof.start()
+    if use_synapse_profiler:
+        syn_prof = SynapseProfilerApi()
+    if htcore:
+        torch.hpu.memory.reset_peak_memory_stats()
+    for epoch in range(math.ceil(num_steps / len(dataloader))):
+        unet.eval()
+        text_encoder.train()
+        for batch in dataloader:
+            if syn_prof and global_step == profiler_step:
+                syn_prof.profiler_start(TraceType.TraceDevice,0)
+            lr_scheduler.step()
+            logs={}
+            with torch.set_grad_enabled(True):
+                loss = (
+                    loss_step(
+                        batch,
+                        unet,
+                        vae,
+                        text_encoder,
+                        scheduler,
+                        train_inpainting=train_inpainting,
+                        mixed_precision=mixed_precision,
+                        cached_latents=cached_latents,
+                        use_torch_compile=use_torch_compile,
+                    )
+                    / accum_iter
+                )
+
+                loss.backward()
+                if htcore:
+                    htcore.mark_step()
+                loss_list_ti.append(loss)
+                if global_step % accum_iter == 0:
+                    # print gradient of text encoder embedding
+                    embedding_grad_norm = text_encoder.get_input_embeddings().weight.grad[index_updates, :].detach().norm(dim=-1).mean().item()
+                    #print(embedding_grad_norm)
+                    logs['ti/embedding_grad_norm']=embedding_grad_norm
+                    optimizer.step()
+                    optimizer.zero_grad(set_to_none=True)
+                    if htcore:
+                        htcore.mark_step()
+
+                    with torch.no_grad():
+
+                        # normalize embeddings
+                        if clip_ti_decay:
+                            pre_norm = (
+                                text_encoder.get_input_embeddings()
+                                .weight[index_updates, :]
+                                .norm(dim=-1, keepdim=True)
+                            )
+
+                            lambda_ = min(1.0, 100 * lr_scheduler.get_last_lr()[0])
+                            text_encoder.get_input_embeddings().weight[
+                                index_updates
+                            ] = F.normalize(
+                                text_encoder.get_input_embeddings().weight[
+                                    index_updates, :
+                                ],
+                                dim=-1,
+                            ) * (
+                                pre_norm + lambda_ * (0.4 - pre_norm)
+                            )
+                            #print(pre_norm)
+
+                        current_norm = (
+                            text_encoder.get_input_embeddings()
+                            .weight[index_updates, :]
+                            .norm(dim=-1)
+                        )
+
+                        text_encoder.get_input_embeddings().weight[
+                            index_no_updates
+                        ] = orig_embeds_params[index_no_updates]
+
+                        if htcore:
+                            htcore.mark_step()
+                        #print(f"Current Norm : {current_norm}")
+                        logs["ti/current_norm"]=current_norm.detach().mean().item()
+                        logs['ti/pre_norm']=pre_norm.detach().mean().item()
+
+                if pt_prof and global_step == profiler_step:
+                    pt_prof.step()
+                if syn_prof and global_step == profiler_step:
+                    syn_prof.profiler_sync(0)
+                    syn_prof.profiler_stop(TraceType.TraceDevice, 0)
+                    syn_prof.profiler_get_trace_json(TraceType.TraceDevice, 0)
+                if(global_step % print_freq == 0):
+                    progress_bar.update(print_freq)
+                    logs.update({
+                    "ti/step_loss": loss.detach().item(),
+                    "ti/lr": lr_scheduler.get_last_lr()[0],
+                     })
+                    for loss_t in loss_list_ti:
+                        loss_sum += loss_t.detach().item()
+                    loss_list_ti.clear()
+                global_step += 1
+                #progress_bar.set_postfix(**logs)
+
+            if global_step % save_steps == 0:
+                save_all(
+                    unet=unet,
+                    text_encoder=text_encoder,
+                    placeholder_token_ids=placeholder_token_ids,
+                    placeholder_tokens=placeholder_tokens,
+                    save_path=os.path.join(
+                        save_path, f"step_inv_{global_step}.{'safetensors' if text_encoder.device.type != 'cpu' else 'pt'}"
+                    ),
+                    safe_form=True if text_encoder.device.type != "cpu" else False,
+                    save_lora=False,
+                )
+                if log_wandb or log_tb:
+                    with torch.no_grad():
+                        pipe = StableDiffusionPipeline(
+                            vae=vae,
+                            text_encoder=text_encoder,
+                            tokenizer=tokenizer,
+                            unet=unet,
+                            scheduler=scheduler,
+                            safety_checker=None,
+                            feature_extractor=None,
+                        )
+
+                        # open all images in test_image_path
+                        images = []
+                        for file in os.listdir(test_image_path):
+                            if (
+                                file.lower().endswith(".png")
+                                or file.lower().endswith(".jpg")
+                                or file.lower().endswith(".jpeg")
+                            ):
+                                images.append(
+                                        Image.open(os.path.join(test_image_path, file))
+                                )
+
+                        logs.update({"ti/loss": loss_sum / save_steps})
+                        loss_sum = 0.0
+                        evaluation_metrics,images = evaluate_pipe(
+                                pipe,
+                                target_images=images,
+                                class_token=class_token,
+                                learnt_token="".join(placeholder_tokens),
+                                n_test=wandb_log_prompt_cnt,
+                                n_step=50,
+                                clip_model_sets=preped_clip
+                                )
+                        if log_wandb:
+                            logs.update({ f'{prompt}': wandb.Image(img) for e,(img,prompt) in enumerate(images) })
+                        elif log_tb:
+                            logs.update({"ti/images": images})
+                        logs.update({ f'ti/{k}':v for k,v in evaluation_metrics.items() })
+                        # logs.update({ f'image_{e}': wandb.Image(img,caption=prompt) for e,(img,prompt) in enumerate(v) if k=='images' else  f'ti/{k}':v for k,v in
+                        #     evaluate_pipe(
+                        #         pipe,
+                        #         target_images=images,
+                        #         class_token=class_token,
+                        #         learnt_token="".join(placeholder_tokens),
+                        #         n_test=wandb_log_prompt_cnt,
+                        #         n_step=50,
+                        #         clip_model_sets=preped_clip,
+                        #     ).items()}
+                        # )
+
+            progress_bar.set_postfix(**logs)
+            progress_bar.refresh()
+            if log_wandb:
+                wandb.log(logs)
+            elif log_tb:
+                for key, value in logs.items():
+                    if key == "ti/images":
+                        for img, prompt in images:
+                            writer.add_image(prompt, pil_to_tensor(img), global_step)
+                    elif key == "ti/image_alignment_all" or key == "ti/text_alignment_all":
+                        for i in range(len(value)):
+                            writer.add_scalar(key+str(i), value[i], global_step)
+                    else:
+                        writer.add_scalar(key, value, global_step)
+            if global_step >= num_steps:
+                break
+    if pt_prof:
+        pt_prof.stop()
+
+    if htcore:
+        max_memory = torch.hpu.memory.max_memory_allocated() / 2 ** 30
+        print(f"Inversion Training Peak memory {(max_memory):.2f} GiB")
+
+def perform_tuning(
+    unet,
+    vae,
+    text_encoder,
+    dataloader,
+    num_steps,
+    scheduler,
+    optimizer,
+    save_steps: int,
+    placeholder_token_ids,
+    placeholder_tokens,
+    save_path,
+    lr_scheduler_lora,
+    lora_unet_target_modules,
+    lora_clip_target_modules,
+    mask_temperature,
+    out_name: str,
+    tokenizer,
+    test_image_path: str,
+    cached_latents: bool,
+    log_wandb: bool = False,
+    wandb_log_prompt_cnt: int = 10,
+    class_token: str = "person",
+    train_inpainting: bool = False,
+    htcore = None,
+    log_tb: bool = False,
+    writer = None,
+    use_synapse_profiler: bool = False,
+    use_pytorch_profiler: bool = False,
+    profiler_step: int = 3,
+    profile_tuning: bool = False,
+    print_freq: int =50,
+    use_fused_clip_norm: bool = True,
+    use_torch_compile:bool = False,
+):
+
+    progress_bar = tqdm(range(num_steps),mininterval=0,miniters=10, smoothing=1)
+    progress_bar.set_description("Steps")
+    global_step = 0
+
+    if not profile_tuning:
+        use_synapse_profiler = False
+        use_pytorch_profiler = False
+
+    weight_dtype = torch.float16
+    if htcore and not use_torch_compile:
+        htcore.hpu.ModuleCacher(max_graphs=5)(model=text_encoder,
+                                        inplace=True,
+                                        use_lfu=False,
+                                        verbose=False)
+    unet.train()
+    text_encoder.train()
+
+    if log_wandb or log_tb:
+        preped_clip = prepare_clip_model_sets()
+
+    loss_sum = 0.0
+    loss_list = []
+
+    pt_prof = None
+    syn_prof = None
+    if use_pytorch_profiler:
+        activities = [torch.profiler.ProfilerActivity.CPU]
+        if htcore:
+            activities.append(torch.profiler.ProfilerActivity.HPU)
+        pt_prof = torch.profiler.profile(
+                activities=activities,
+                schedule=torch.profiler.schedule(wait=0, warmup=0, active=1, repeat=1),
+                on_trace_ready=torch.profiler.tensorboard_trace_handler("./trace-log"),
+                record_shapes=True,
+                with_stack=True)
+        pt_prof.start()
+    if use_synapse_profiler:
+        syn_prof = SynapseProfilerApi()
+
+    if use_fused_clip_norm and htcore:
+        from habana_frameworks.torch.hpex.normalization import FusedClipNorm
+        fused_clip_norm = FusedClipNorm(itertools.chain(unet.parameters(), text_encoder.parameters()), 1.0)
+    if htcore:
+        torch.hpu.memory.reset_peak_memory_stats()
+    for epoch in range(math.ceil(num_steps / len(dataloader))):
+        for batch in dataloader:
+            if syn_prof and global_step == profiler_step:
+                syn_prof.profiler_start(TraceType.TraceDevice,0)
+            lr_scheduler_lora.step()
+
+            optimizer.zero_grad(set_to_none=True)
+
+            loss = loss_step(
+                batch,
+                unet,
+                vae,
+                text_encoder,
+                scheduler,
+                train_inpainting=train_inpainting,
+                t_mutliplier=0.8,
+                mixed_precision=True,
+                mask_temperature=mask_temperature,
+                cached_latents=cached_latents,
+                use_torch_compile=use_torch_compile,
+            )
+            loss_list.append(loss)
+
+            loss.backward()
+            if htcore:
+                htcore.mark_step()
+            if use_fused_clip_norm:
+                fused_clip_norm.clip_norm(itertools.chain(unet.parameters(), text_encoder.parameters()))
+            else:
+                torch.nn.utils.clip_grad_norm_(
+                    itertools.chain(unet.parameters(), text_encoder.parameters()), 1.0
+                )
+            optimizer.step()
+            if htcore:
+                htcore.mark_step()
+            if(global_step % print_freq == 0):
+                progress_bar.update(print_freq)
+                logs = {
+                    "tuning/step_loss": loss.detach().item(),
+                    "tuning/lr": lr_scheduler_lora.get_last_lr()[0],
+                 }
+                for loss_t in loss_list:
+                    loss_sum += loss_t.detach().item()
+                loss_list.clear()
+
+            if pt_prof and global_step == profiler_step:
+                pt_prof.step()
+            if syn_prof and global_step == profiler_step:
+                syn_prof.profiler_sync(0)
+                syn_prof.profiler_stop(TraceType.TraceDevice, 0)
+                syn_prof.profiler_get_trace_json(TraceType.TraceDevice, 0)
+            #progress_bar.set_postfix(**logs)
+            global_step += 1
+
+            if global_step % save_steps == 0:
+                save_all(
+                    unet,
+                    text_encoder,
+                    placeholder_token_ids=placeholder_token_ids,
+                    placeholder_tokens=placeholder_tokens,
+                    save_path=os.path.join(
+                        save_path, f"step_{global_step}.{'safetensors' if text_encoder.device.type != 'cpu' else 'pt'}"
+                    ),
+                    safe_form=True if text_encoder.device.type != "cpu" else False,
+                    target_replace_module_text=lora_clip_target_modules,
+                    target_replace_module_unet=lora_unet_target_modules,
+                )
+                unet_moved = (
+                    torch.tensor(list(itertools.chain(*inspect_lora(unet).values())))
+                    .mean()
+                    .item()
+                )
+
+                #print("LORA Unet Moved", unet_moved)
+                clip_moved = (
+                    torch.tensor(
+                        list(itertools.chain(*inspect_lora(text_encoder).values()))
+                    )
+                    .mean()
+                    .item()
+                )
+
+                #print("LORA CLIP Moved", clip_moved)
+
+                if log_wandb or log_tb:
+                    with torch.no_grad():
+                        pipe = StableDiffusionPipeline(
+                            vae=vae,
+                            text_encoder=text_encoder,
+                            tokenizer=tokenizer,
+                            unet=unet,
+                            scheduler=scheduler,
+                            safety_checker=None,
+                            feature_extractor=None,
+                        )
+
+                        # open all images in test_image_path
+                        images = []
+                        for file in os.listdir(test_image_path):
+                            if file.endswith(".png") or file.endswith(".jpg"):
+                                images.append(
+                                    Image.open(os.path.join(test_image_path, file))
+                                )
+
+                        #wandb.log({"loss": loss_sum / save_steps, "unet_lora_step":unet_moved,"clip_lora_moved":clip_moved})
+                        logs.update({"tuning/loss": loss_sum / save_steps, "tuning/unet_lora_step":unet_moved,"tuning/clip_lora_moved":clip_moved})
+                        loss_sum = 0.0
+                        #wandb.log(
+                        evaluation_metrics,images = evaluate_pipe(
+                                pipe,
+                                target_images=images,
+                                class_token=class_token,
+                                learnt_token="".join(placeholder_tokens),
+                                n_test=wandb_log_prompt_cnt,
+                                n_step=50,
+                                clip_model_sets=preped_clip
+                                )
+                        if log_wandb:
+                            logs.update({ f'{prompt}': wandb.Image(img) for e,(img,prompt) in enumerate(images) } )
+                        elif log_tb:
+                            logs.update({"tuning/images": images})
+                        logs.update({ f'tuning/{k}':v for k,v in evaluation_metrics.items() })
+
+                        # logs.update({ ( f'image_{e}': wandb.Image(img,caption=prompt) for e,(img,prompt) in enumerate(v) ) if k=='images' else f'tuning/{k}':v for k,v in evaluate_pipe(
+                        #         pipe,
+                        #         target_images=images,
+                        #         class_token=class_token,
+                        #         learnt_token="".join(placeholder_tokens),
+                        #         n_test=wandb_log_prompt_cnt,
+                        #         n_step=50,
+                        #         clip_model_sets=preped_clip,
+                        #     ).items()}
+                        # )
+
+            progress_bar.set_postfix(**logs)
+            progress_bar.refresh()
+
+            if log_wandb:
+                wandb.log(logs)
+            elif log_tb:
+                for key, value in logs.items():
+                    if key == "tuning/images":
+                        for img, prompt in images:
+                            writer.add_image(prompt, pil_to_tensor(img), global_step)
+                    elif key == "tuning/image_alignment_all" or key == "tuning/text_alignment_all":
+                        for i in range(len(value)):
+                            writer.add_scalar(key+str(i), value[i], global_step)
+                    else:
+                        writer.add_scalar(key, value, global_step)
+
+            if global_step >= num_steps:
+                break
+
+    if htcore:
+        max_memory = torch.hpu.memory.max_memory_allocated() / 2 ** 30
+        print(f"Average Peak memory {(max_memory):.2f} GiB")
+    if pt_prof:
+        pt_prof.stop()
+    save_all(
+        unet,
+        text_encoder,
+        placeholder_token_ids=placeholder_token_ids,
+        placeholder_tokens=placeholder_tokens,
+        save_path=os.path.join(save_path, f"{out_name}.{'safetensors' if text_encoder.device.type != 'cpu' else 'pt'}"),
+        safe_form=True if text_encoder.device.type != "cpu" else False,
+        target_replace_module_text=lora_clip_target_modules,
+        target_replace_module_unet=lora_unet_target_modules,
+    )
+
+
+def train(
+    instance_data_dir: str,
+    pretrained_model_name_or_path: str,
+    output_dir: str,
+    train_text_encoder: bool = True,
+    pretrained_vae_name_or_path: str = None,
+    revision: Optional[str] = None,
+    perform_inversion: bool = True,
+    use_template: Literal[None, "object", "style"] = None,
+    train_inpainting: bool = False,
+    placeholder_tokens: str = "",
+    placeholder_token_at_data: Optional[str] = None,
+    initializer_tokens: Optional[str] = None,
+    seed: int = 42,
+    resolution: int = 512,
+    color_jitter: bool = True,
+    train_batch_size: int = 1,
+    sample_batch_size: int = 1,
+    max_train_steps_tuning: int = 1000,
+    max_train_steps_ti: int = 1000,
+    save_steps: int = 100,
+    gradient_accumulation_steps: int = 4,
+    gradient_checkpointing: bool = False,
+    lora_rank: int = 4,
+    lora_unet_target_modules={"CrossAttention", "Attention", "GEGLU"},
+    lora_clip_target_modules={"CLIPAttention"},
+    lora_dropout_p: float = 0.0,
+    lora_scale: float = 1.0,
+    use_extended_lora: bool = False,
+    clip_ti_decay: bool = True,
+    learning_rate_unet: float = 1e-4,
+    learning_rate_text: float = 1e-5,
+    learning_rate_ti: float = 5e-4,
+    continue_inversion: bool = False,
+    continue_inversion_lr: Optional[float] = None,
+    use_face_segmentation_condition: bool = False,
+    cached_latents: bool = True,
+    use_mask_captioned_data: bool = False,
+    mask_temperature: float = 1.0,
+    scale_lr: bool = False,
+    lr_scheduler: str = "linear",
+    lr_warmup_steps: int = 0,
+    lr_scheduler_lora: str = "linear",
+    lr_warmup_steps_lora: int = 0,
+    weight_decay_ti: float = 0.00,
+    weight_decay_lora: float = 0.001,
+    use_8bit_adam: bool = False,
+    device="cuda:0",
+    extra_args: Optional[dict] = None,
+    log_wandb: bool = False,
+    wandb_log_prompt_cnt: int = 10,
+    wandb_project_name: str = "new_pti_project",
+    wandb_entity: str = "new_pti_entity",
+    proxy_token: str = "person",
+    enable_xformers_memory_efficient_attention: bool = False,
+    out_name: str = "final_lora",
+    use_lazy_mode: bool = True,
+    use_fused_adamw: bool = True,
+    log_tb: bool = False,
+    log_dir: str = "/tmp/runs/",
+    use_synapse_profiler: bool = False,
+    use_pytorch_profiler: bool = False,
+    profiler_step: int = 3,
+    profile_ti: bool = False,
+    profile_tuning: bool = False,
+    print_freq: int = 50,
+    use_fused_clip_norm: bool = True,
+    use_torch_compile: bool = False,
+):
+    htcore = None
+    writer = None
+    if use_torch_compile:
+        if device != "hpu":
+            use_lazy_mode = False
+        assert not use_lazy_mode, f"use_torch_compile and use_lazy_mode both can't be used"
+
+    if device == "hpu":
+        if use_lazy_mode:
+            assert os.getenv('PT_HPU_LAZY_MODE') == '1' or os.getenv('PT_HPU_LAZY_MODE') == None, f"use_lazy_mode == True, but PT_HPU_LAZY_MODE={os.getenv('PT_HPU_LAZY_MODE')}. For run lazy mode, set PT_HPU_LAZY_MODE to 1"
+            import habana_frameworks.torch.core as htcore
+        elif use_torch_compile:
+            assert os.getenv('PT_HPU_LAZY_MODE')== '0', f"args.use_torch_compile == True, but PT_HPU_LAZY_MODE={os.getenv('PT_HPU_LAZY_MODE')}. For torch.compile mode, set PT_HPU_LAZY_MODE to 0"
+            import habana_frameworks.torch.core as htcore
+        else:
+            import habana_frameworks.torch.core
+        # Disable hpu dynamic shape
+        try:
+            import habana_frameworks.torch.hpu as hthpu
+            hthpu.disable_dynamic_shape()
+        except ImportError:
+            print("habana_frameworks could not be loaded")
+    else:
+        use_lazy_mode = False
+        use_fused_adamw = False
+        use_fused_clip_norm = False
+
+    torch.manual_seed(seed)
+
+    if log_wandb:
+        wandb.init(
+            project=wandb_project_name,
+            entity=wandb_entity,
+            name=f"steps_{max_train_steps_ti}_lr_{learning_rate_ti}_{instance_data_dir.split('/')[-1]}",
+            reinit=True,
+            config={
+                **(extra_args if extra_args is not None else {}),
+            },
+        )
+    elif log_tb:
+        from torch.utils.tensorboard import SummaryWriter
+        from datetime import datetime
+        writer = SummaryWriter(log_dir=(log_dir+datetime.now().strftime("%b%d_%H-%M-%S")))
+
+    if output_dir is not None:
+        os.makedirs(output_dir, exist_ok=True)
+    # print(placeholder_tokens, initializer_tokens)
+    if len(placeholder_tokens) == 0:
+        placeholder_tokens = []
+        print("PTI : Placeholder Tokens not given, using null token")
+    else:
+        placeholder_tokens = placeholder_tokens.split("|")
+
+        assert (
+            sorted(placeholder_tokens) == placeholder_tokens
+        ), f"Placeholder tokens should be sorted. Use something like {'|'.join(sorted(placeholder_tokens))}'"
+
+    if initializer_tokens is None:
+        print("PTI : Initializer Tokens not given, doing random inits")
+        initializer_tokens = ["<rand-0.017>"] * len(placeholder_tokens)
+    else:
+        initializer_tokens = initializer_tokens.split("|")
+
+    assert len(initializer_tokens) == len(
+        placeholder_tokens
+    ), "Unequal Initializer token for Placeholder tokens."
+
+    if proxy_token is not None:
+        class_token = proxy_token
+    class_token = "".join(initializer_tokens)
+
+    if placeholder_token_at_data is not None:
+        tok, pat = placeholder_token_at_data.split("|")
+        token_map = {tok: pat}
+
+    else:
+        token_map = {"DUMMY": "".join(placeholder_tokens)}
+
+    print("PTI : Placeholder Tokens", placeholder_tokens)
+    print("PTI : Initializer Tokens", initializer_tokens)
+
+    # get the models
+    text_encoder, vae, unet, tokenizer, placeholder_token_ids = get_models(
+        pretrained_model_name_or_path,
+        pretrained_vae_name_or_path,
+        revision,
+        placeholder_tokens,
+        initializer_tokens,
+        device=device,
+    )
+
+    if use_torch_compile:
+        if device == "hpu":
+            print("use_torch_compile : text_encoder :Model is compiled")
+            text_encoder = torch.compile(text_encoder, backend="aot_hpu_training_backend")
+            print("use_torch_compile : unet :Model is compiled")
+            unet = torch.compile(unet, backend="aot_hpu_training_backend")
+
+    noise_scheduler = DDPMScheduler.from_config(
+        pretrained_model_name_or_path, subfolder="scheduler"
+    )
+
+    if gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+
+    if enable_xformers_memory_efficient_attention:
+        from diffusers.utils.import_utils import is_xformers_available
+
+        if is_xformers_available():
+            unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError(
+                "xformers is not available. Make sure it is installed correctly"
+            )
+
+    if scale_lr:
+        unet_lr = learning_rate_unet * gradient_accumulation_steps * train_batch_size
+        text_encoder_lr = (
+            learning_rate_text * gradient_accumulation_steps * train_batch_size
+        )
+        ti_lr = learning_rate_ti * gradient_accumulation_steps * train_batch_size
+    else:
+        unet_lr = learning_rate_unet
+        text_encoder_lr = learning_rate_text
+        ti_lr = learning_rate_ti
+
+    train_dataset = PivotalTuningDatasetCapation(
+        instance_data_root=instance_data_dir,
+        token_map=token_map,
+        use_template=use_template,
+        tokenizer=tokenizer,
+        size=resolution,
+        color_jitter=color_jitter,
+        use_face_segmentation_condition=use_face_segmentation_condition,
+        use_mask_captioned_data=use_mask_captioned_data,
+        train_inpainting=train_inpainting,
+    )
+
+    train_dataset.blur_amount = 200
+
+    if train_inpainting:
+        assert not cached_latents, "Cached latents not supported for inpainting"
+
+        train_dataloader = inpainting_dataloader(
+            train_dataset, train_batch_size, tokenizer, vae, text_encoder
+        )
+    else:
+        train_dataloader = text2img_dataloader(
+            train_dataset,
+            train_batch_size,
+            tokenizer,
+            vae,
+            text_encoder,
+            cached_latents=cached_latents,
+            use_torch_compile=use_torch_compile
+        )
+
+    index_no_updates = torch.arange(len(tokenizer)) != -1
+    if htcore and not use_torch_compile:
+        htcore.hpu.ModuleCacher(max_graphs=5)(model=unet,
+                                        inplace=True,
+                                        use_lfu=False,
+                                        verbose=False)
+
+    for tok_id in placeholder_token_ids:
+        index_no_updates[tok_id] = False
+
+    unet.requires_grad_(False)
+    vae.requires_grad_(False)
+
+    params_to_freeze = itertools.chain(
+        text_encoder.text_model.encoder.parameters(),
+        text_encoder.text_model.final_layer_norm.parameters(),
+        text_encoder.text_model.embeddings.position_embedding.parameters(),
+    )
+    for param in params_to_freeze:
+        param.requires_grad = False
+
+    #if cached_latents:
+        #vae = None
+    # STEP 1 : Perform Inversion
+    if perform_inversion:
+        ti_optimizer = None
+        if use_fused_adamw and htcore:
+            from habana_frameworks.torch.hpex.optimizers import FusedAdamW
+            ti_optimizer = FusedAdamW(text_encoder.get_input_embeddings().parameters(), lr=ti_lr, eps=1e-08, weight_decay=weight_decay_ti)
+        else:
+            ti_optimizer = optim.AdamW(
+                text_encoder.get_input_embeddings().parameters(), #weight[~index_no_updates]],
+                lr=ti_lr,
+                betas=(0.9, 0.999),
+                eps=1e-08,
+                weight_decay=weight_decay_ti,
+            )
+
+        lr_scheduler = get_scheduler(
+            lr_scheduler,
+            optimizer=ti_optimizer,
+            num_warmup_steps=lr_warmup_steps,
+            num_training_steps=max_train_steps_ti,
+        )
+
+        train_inversion(
+            unet,
+            vae,
+            text_encoder,
+            train_dataloader,
+            max_train_steps_ti,
+            cached_latents=cached_latents,
+            accum_iter=gradient_accumulation_steps,
+            scheduler=noise_scheduler,
+            index_no_updates=index_no_updates,
+            optimizer=ti_optimizer,
+            lr_scheduler=lr_scheduler,
+            save_steps=save_steps,
+            placeholder_tokens=placeholder_tokens,
+            placeholder_token_ids=placeholder_token_ids,
+            save_path=output_dir,
+            test_image_path=instance_data_dir,
+            log_wandb=log_wandb,
+            wandb_log_prompt_cnt=wandb_log_prompt_cnt,
+            class_token=class_token,
+            train_inpainting=train_inpainting,
+            mixed_precision=False,
+            tokenizer=tokenizer,
+            clip_ti_decay=clip_ti_decay,
+            htcore=htcore,
+            log_tb=log_tb,
+            writer=writer,
+            use_synapse_profiler=use_synapse_profiler,
+            use_pytorch_profiler=use_pytorch_profiler,
+            profiler_step=profiler_step,
+            profile_ti=profile_ti,
+            print_freq=print_freq,
+            use_torch_compile=use_torch_compile,
+        )
+
+        del ti_optimizer
+
+    # Next perform Tuning with LoRA:
+    if not use_extended_lora:
+        unet_lora_params, _ = inject_trainable_lora(
+            unet,
+            r=lora_rank,
+            target_replace_module=lora_unet_target_modules,
+            dropout_p=lora_dropout_p,
+            scale=lora_scale,
+        )
+    else:
+        print("PTI : USING EXTENDED UNET!!!")
+        lora_unet_target_modules = (
+            lora_unet_target_modules | UNET_EXTENDED_TARGET_REPLACE
+        )
+        print("PTI : Will replace modules: ", lora_unet_target_modules)
+
+        unet_lora_params, _ = inject_trainable_lora_extended(
+            unet, r=lora_rank, target_replace_module=lora_unet_target_modules
+        )
+    print(f"PTI : has {len(unet_lora_params)} lora")
+
+    print("PTI : Before training:")
+    inspect_lora(unet)
+
+    params_to_optimize = [
+        {"params": itertools.chain(*unet_lora_params), "lr": unet_lr},
+    ]
+
+    text_encoder.requires_grad_(False)
+
+    if continue_inversion:
+        params_to_optimize += [
+            {
+                "params": text_encoder.get_input_embeddings().parameters(), #.weight[~index_no_updates]],
+                "lr": continue_inversion_lr
+                if continue_inversion_lr is not None
+                else ti_lr,
+            }
+        ]
+        text_encoder.requires_grad_(True)
+        params_to_freeze = itertools.chain(
+            text_encoder.text_model.encoder.parameters(),
+            text_encoder.text_model.final_layer_norm.parameters(),
+            text_encoder.text_model.embeddings.position_embedding.parameters(),
+        )
+        for param in params_to_freeze:
+            param.requires_grad = False
+    else:
+        text_encoder.requires_grad_(False)
+    if train_text_encoder:
+        text_encoder_lora_params, _ = inject_trainable_lora(
+            text_encoder,
+            target_replace_module=lora_clip_target_modules,
+            r=lora_rank,
+        )
+        params_to_optimize += [
+            {
+                "params": itertools.chain(*text_encoder_lora_params),
+                "lr": text_encoder_lr,
+            }
+        ]
+        inspect_lora(text_encoder)
+
+    lora_optimizers = None
+    if use_fused_adamw and htcore:
+        from habana_frameworks.torch.hpex.optimizers import FusedAdamW
+        lora_optimizers = FusedAdamW(params_to_optimize, eps=1e-08, weight_decay=weight_decay_lora)
+    else:
+        lora_optimizers = optim.AdamW(params_to_optimize, weight_decay=weight_decay_lora)
+
+    unet.train()
+    if train_text_encoder:
+        text_encoder.train()
+
+    train_dataset.blur_amount = 70
+
+    lr_scheduler_lora = get_scheduler(
+        lr_scheduler_lora,
+        optimizer=lora_optimizers,
+        num_warmup_steps=lr_warmup_steps_lora,
+        num_training_steps=max_train_steps_tuning,
+    )
+
+    perform_tuning(
+        unet,
+        vae,
+        text_encoder,
+        train_dataloader,
+        max_train_steps_tuning,
+        cached_latents=cached_latents,
+        scheduler=noise_scheduler,
+        optimizer=lora_optimizers,
+        save_steps=save_steps,
+        placeholder_tokens=placeholder_tokens,
+        placeholder_token_ids=placeholder_token_ids,
+        save_path=output_dir,
+        lr_scheduler_lora=lr_scheduler_lora,
+        lora_unet_target_modules=lora_unet_target_modules,
+        lora_clip_target_modules=lora_clip_target_modules,
+        mask_temperature=mask_temperature,
+        tokenizer=tokenizer,
+        out_name=out_name,
+        test_image_path=instance_data_dir,
+        log_wandb=log_wandb,
+        wandb_log_prompt_cnt=wandb_log_prompt_cnt,
+        class_token=class_token,
+        train_inpainting=train_inpainting,
+        htcore=htcore,
+        log_tb=log_tb,
+        writer=writer,
+        use_synapse_profiler=use_synapse_profiler,
+        use_pytorch_profiler=use_pytorch_profiler,
+        profiler_step=profiler_step,
+        profile_tuning=profile_tuning,
+        print_freq=print_freq,
+        use_fused_clip_norm=use_fused_clip_norm,
+        use_torch_compile=use_torch_compile,
+    )
+    if writer:
+        writer.close()
+
+
+def main():
+    fire.Fire(train)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/cli_pt_to_safetensors.py

@@ -0,0 +1,85 @@
+import os
+
+import fire
+import torch
+from lora_diffusion import (
+    DEFAULT_TARGET_REPLACE,
+    TEXT_ENCODER_DEFAULT_TARGET_REPLACE,
+    UNET_DEFAULT_TARGET_REPLACE,
+    convert_loras_to_safeloras_with_embeds,
+    safetensors_available,
+)
+
+_target_by_name = {
+    "unet": UNET_DEFAULT_TARGET_REPLACE,
+    "text_encoder": TEXT_ENCODER_DEFAULT_TARGET_REPLACE,
+}
+
+
+def convert(*paths, outpath, overwrite=False, **settings):
+    """
+    Converts one or more pytorch Lora and/or Textual Embedding pytorch files
+    into a safetensor file.
+
+    Pass all the input paths as arguments. Whether they are Textual Embedding
+    or Lora models will be auto-detected.
+
+    For Lora models, their name will be taken from the path, i.e.
+        "lora_weight.pt" => unet
+        "lora_weight.text_encoder.pt" => text_encoder
+
+    You can also set target_modules and/or rank by providing an argument prefixed
+    by the name.
+
+    So a complete example might be something like:
+
+    ```
+    python -m lora_diffusion.cli_pt_to_safetensors lora_weight.* --outpath lora_weight.safetensor --unet.rank 8
+    ```
+    """
+    modelmap = {}
+    embeds = {}
+
+    if os.path.exists(outpath) and not overwrite:
+        raise ValueError(
+            f"Output path {outpath} already exists, and overwrite is not True"
+        )
+
+    for path in paths:
+        data = torch.load(path)
+
+        if isinstance(data, dict):
+            print(f"Loading textual inversion embeds {data.keys()} from {path}")
+            embeds.update(data)
+
+        else:
+            name_parts = os.path.split(path)[1].split(".")
+            name = name_parts[-2] if len(name_parts) > 2 else "unet"
+
+            model_settings = {
+                "target_modules": _target_by_name.get(name, DEFAULT_TARGET_REPLACE),
+                "rank": 4,
+            }
+
+            prefix = f"{name}."
+            
+            arg_settings = { k[len(prefix) :]: v for k, v in settings.items() if k.startswith(prefix) }
+            model_settings = { **model_settings, **arg_settings }
+
+            print(f"Loading Lora for {name} from {path} with settings {model_settings}")
+
+            modelmap[name] = (
+                path,
+                model_settings["target_modules"],
+                model_settings["rank"],
+            )
+
+    convert_loras_to_safeloras_with_embeds(modelmap, embeds, outpath)
+
+
+def main():
+    fire.Fire(convert)
+
+
+if __name__ == "__main__":
+    main()

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/cli_svd.py

@@ -0,0 +1,146 @@
+import fire
+from diffusers import StableDiffusionPipeline
+import torch
+import torch.nn as nn
+
+from .lora import (
+    save_all,
+    _find_modules,
+    LoraInjectedConv2d,
+    LoraInjectedLinear,
+    inject_trainable_lora,
+    inject_trainable_lora_extended,
+)
+
+
+def _iter_lora(model):
+    for module in model.modules():
+        if isinstance(module, LoraInjectedConv2d) or isinstance(
+            module, LoraInjectedLinear
+        ):
+            yield module
+
+
+def overwrite_base(base_model, tuned_model, rank, clamp_quantile):
+    device = base_model.device
+    dtype = base_model.dtype
+
+    for lor_base, lor_tune in zip(_iter_lora(base_model), _iter_lora(tuned_model)):
+
+        if isinstance(lor_base, LoraInjectedLinear):
+            residual = lor_tune.linear.weight.data - lor_base.linear.weight.data
+            # SVD on residual
+            print("Distill Linear shape ", residual.shape)
+            residual = residual.float()
+            U, S, Vh = torch.linalg.svd(residual)
+            U = U[:, :rank]
+            S = S[:rank]
+            U = U @ torch.diag(S)
+
+            Vh = Vh[:rank, :]
+
+            dist = torch.cat([U.flatten(), Vh.flatten()])
+            hi_val = torch.quantile(dist, clamp_quantile)
+            low_val = -hi_val
+
+            U = U.clamp(low_val, hi_val)
+            Vh = Vh.clamp(low_val, hi_val)
+
+            assert lor_base.lora_up.weight.shape == U.shape
+            assert lor_base.lora_down.weight.shape == Vh.shape
+
+            lor_base.lora_up.weight.data = U.to(device=device, dtype=dtype)
+            lor_base.lora_down.weight.data = Vh.to(device=device, dtype=dtype)
+
+        if isinstance(lor_base, LoraInjectedConv2d):
+            residual = lor_tune.conv.weight.data - lor_base.conv.weight.data
+            print("Distill Conv shape ", residual.shape)
+
+            residual = residual.float()
+            residual = residual.flatten(start_dim=1)
+
+            # SVD on residual
+            U, S, Vh = torch.linalg.svd(residual)
+            U = U[:, :rank]
+            S = S[:rank]
+            U = U @ torch.diag(S)
+
+            Vh = Vh[:rank, :]
+
+            dist = torch.cat([U.flatten(), Vh.flatten()])
+            hi_val = torch.quantile(dist, clamp_quantile)
+            low_val = -hi_val
+
+            U = U.clamp(low_val, hi_val)
+            Vh = Vh.clamp(low_val, hi_val)
+
+            # U is (out_channels, rank) with 1x1 conv. So,
+            U = U.reshape(U.shape[0], U.shape[1], 1, 1)
+            # V is (rank, in_channels * kernel_size1 * kernel_size2)
+            # now reshape:
+            Vh = Vh.reshape(
+                Vh.shape[0],
+                lor_base.conv.in_channels,
+                lor_base.conv.kernel_size[0],
+                lor_base.conv.kernel_size[1],
+            )
+
+            assert lor_base.lora_up.weight.shape == U.shape
+            assert lor_base.lora_down.weight.shape == Vh.shape
+
+            lor_base.lora_up.weight.data = U.to(device=device, dtype=dtype)
+            lor_base.lora_down.weight.data = Vh.to(device=device, dtype=dtype)
+
+
+def svd_distill(
+    target_model: str,
+    base_model: str,
+    rank: int = 4,
+    clamp_quantile: float = 0.99,
+    device: str = "cuda:0",
+    save_path: str = "svd_distill.safetensors",
+):
+    pipe_base = StableDiffusionPipeline.from_pretrained(
+        base_model, torch_dtype=torch.float16
+    ).to(device)
+
+    pipe_tuned = StableDiffusionPipeline.from_pretrained(
+        target_model, torch_dtype=torch.float16
+    ).to(device)
+
+    # Inject unet
+    _ = inject_trainable_lora_extended(pipe_base.unet, r=rank)
+    _ = inject_trainable_lora_extended(pipe_tuned.unet, r=rank)
+
+    overwrite_base(
+        pipe_base.unet, pipe_tuned.unet, rank=rank, clamp_quantile=clamp_quantile
+    )
+
+    # Inject text encoder
+    _ = inject_trainable_lora(
+        pipe_base.text_encoder, r=rank, target_replace_module={"CLIPAttention"}
+    )
+    _ = inject_trainable_lora(
+        pipe_tuned.text_encoder, r=rank, target_replace_module={"CLIPAttention"}
+    )
+
+    overwrite_base(
+        pipe_base.text_encoder,
+        pipe_tuned.text_encoder,
+        rank=rank,
+        clamp_quantile=clamp_quantile,
+    )
+
+    save_all(
+        unet=pipe_base.unet,
+        text_encoder=pipe_base.text_encoder,
+        placeholder_token_ids=None,
+        placeholder_tokens=None,
+        save_path=save_path,
+        save_lora=True,
+        save_ti=False,
+    )
+
+
+def main():
+    fire.Fire(svd_distill)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/dataset.py

@@ -0,0 +1,311 @@
+import random
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Union
+
+from PIL import Image
+from torch import zeros_like
+from torch.utils.data import Dataset
+from torchvision import transforms
+import glob
+from .preprocess_files import face_mask_google_mediapipe
+
+OBJECT_TEMPLATE = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+STYLE_TEMPLATE = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+NULL_TEMPLATE = ["{}"]
+
+TEMPLATE_MAP = {
+    "object": OBJECT_TEMPLATE,
+    "style": STYLE_TEMPLATE,
+    "null": NULL_TEMPLATE,
+}
+
+
+def _randomset(lis):
+    ret = []
+    for i in range(len(lis)):
+        if random.random() < 0.5:
+            ret.append(lis[i])
+    return ret
+
+
+def _shuffle(lis):
+
+    return random.sample(lis, len(lis))
+
+
+def _get_cutout_holes(
+    height,
+    width,
+    min_holes=8,
+    max_holes=32,
+    min_height=16,
+    max_height=128,
+    min_width=16,
+    max_width=128,
+):
+    holes = []
+    for _n in range(random.randint(min_holes, max_holes)):
+        hole_height = random.randint(min_height, max_height)
+        hole_width = random.randint(min_width, max_width)
+        y1 = random.randint(0, height - hole_height)
+        x1 = random.randint(0, width - hole_width)
+        y2 = y1 + hole_height
+        x2 = x1 + hole_width
+        holes.append((x1, y1, x2, y2))
+    return holes
+
+
+def _generate_random_mask(image):
+    mask = zeros_like(image[:1])
+    holes = _get_cutout_holes(mask.shape[1], mask.shape[2])
+    for (x1, y1, x2, y2) in holes:
+        mask[:, y1:y2, x1:x2] = 1.0
+    if random.uniform(0, 1) < 0.25:
+        mask.fill_(1.0)
+    masked_image = image * (mask < 0.5)
+    return mask, masked_image
+
+
+class PivotalTuningDatasetCapation(Dataset):
+    """
+    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
+    It pre-processes the images and the tokenizes prompts.
+    """
+
+    def __init__(
+        self,
+        instance_data_root,
+        tokenizer,
+        token_map: Optional[dict] = None,
+        use_template: Optional[str] = None,
+        size=512,
+        h_flip=True,
+        color_jitter=False,
+        resize=True,
+        use_mask_captioned_data=False,
+        use_face_segmentation_condition=False,
+        train_inpainting=False,
+        blur_amount: int = 70,
+    ):
+        self.size = size
+        self.tokenizer = tokenizer
+        self.resize = resize
+        self.train_inpainting = train_inpainting
+
+        instance_data_root = Path(instance_data_root)
+        if not instance_data_root.exists():
+            raise ValueError("Instance images root doesn't exists.")
+
+        self.instance_images_path = []
+        self.mask_path = []
+
+        assert not (
+            use_mask_captioned_data and use_template
+        ), "Can't use both mask caption data and template."
+
+        # Prepare the instance images
+        if use_mask_captioned_data:
+            src_imgs = glob.glob(str(instance_data_root) + "/*src.jpg")
+            for f in src_imgs:
+                idx = int(str(Path(f).stem).split(".")[0])
+                mask_path = f"{instance_data_root}/{idx}.mask.png"
+
+                if Path(mask_path).exists():
+                    self.instance_images_path.append(f)
+                    self.mask_path.append(mask_path)
+                else:
+                    print(f"Mask not found for {f}")
+
+            self.captions = open(f"{instance_data_root}/caption.txt").readlines()
+
+        else:
+            possibily_src_images = (
+                glob.glob(str(instance_data_root) + "/*.jpg")
+                + glob.glob(str(instance_data_root) + "/*.png")
+                + glob.glob(str(instance_data_root) + "/*.jpeg")
+            )
+            possibily_src_images = (
+                set(possibily_src_images)
+                - set(glob.glob(str(instance_data_root) + "/*mask.png"))
+                - set([str(instance_data_root) + "/caption.txt"])
+            )
+
+            self.instance_images_path = list(set(possibily_src_images))
+            self.captions = [
+                x.split("/")[-1].split(".")[0] for x in self.instance_images_path
+            ]
+
+        assert (
+            len(self.instance_images_path) > 0
+        ), "No images found in the instance data root."
+
+        self.instance_images_path = sorted(self.instance_images_path)
+
+        self.use_mask = use_face_segmentation_condition or use_mask_captioned_data
+        self.use_mask_captioned_data = use_mask_captioned_data
+
+        if use_face_segmentation_condition:
+
+            for idx in range(len(self.instance_images_path)):
+                targ = f"{instance_data_root}/{idx}.mask.png"
+                # see if the mask exists
+                if not Path(targ).exists():
+                    print(f"Mask not found for {targ}")
+
+                    print(
+                        "Warning : this will pre-process all the images in the instance data root."
+                    )
+
+                    if len(self.mask_path) > 0:
+                        print(
+                            "Warning : masks already exists, but will be overwritten."
+                        )
+
+                    masks = face_mask_google_mediapipe(
+                        [
+                            Image.open(f).convert("RGB")
+                            for f in self.instance_images_path
+                        ]
+                    )
+                    for idx, mask in enumerate(masks):
+                        mask.save(f"{instance_data_root}/{idx}.mask.png")
+
+                    break
+
+            for idx in range(len(self.instance_images_path)):
+                self.mask_path.append(f"{instance_data_root}/{idx}.mask.png")
+
+        self.num_instance_images = len(self.instance_images_path)
+        self.token_map = token_map
+
+        self.use_template = use_template
+        if use_template is not None:
+            self.templates = TEMPLATE_MAP[use_template]
+
+        self._length = self.num_instance_images
+
+        self.h_flip = h_flip
+        self.image_transforms = transforms.Compose(
+            [
+                transforms.Resize(
+                    size, interpolation=transforms.InterpolationMode.BILINEAR
+                )
+                if resize
+                else transforms.Lambda(lambda x: x),
+                transforms.ColorJitter(0.1, 0.1)
+                if color_jitter
+                else transforms.Lambda(lambda x: x),
+                transforms.CenterCrop(size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+        self.blur_amount = blur_amount
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, index):
+        example = {}
+        instance_image = Image.open(
+            self.instance_images_path[index % self.num_instance_images]
+        )
+        if not instance_image.mode == "RGB":
+            instance_image = instance_image.convert("RGB")
+        example["instance_images"] = self.image_transforms(instance_image)
+
+        if self.train_inpainting:
+            (
+                example["instance_masks"],
+                example["instance_masked_images"],
+            ) = _generate_random_mask(example["instance_images"])
+
+        if self.use_template:
+            assert self.token_map is not None
+            input_tok = list(self.token_map.values())[0]
+
+            text = random.choice(self.templates).format(input_tok)
+        else:
+            text = self.captions[index % self.num_instance_images].strip()
+
+            if self.token_map is not None:
+                for token, value in self.token_map.items():
+                    text = text.replace(token, value)
+
+        print(text)
+
+        if self.use_mask:
+            example["mask"] = (
+                self.image_transforms(
+                    Image.open(self.mask_path[index % self.num_instance_images])
+                )
+                * 0.5
+                + 1.0
+            )
+
+        if self.h_flip and random.random() > 0.5:
+            hflip = transforms.RandomHorizontalFlip(p=1)
+
+            example["instance_images"] = hflip(example["instance_images"])
+            if self.use_mask:
+                example["mask"] = hflip(example["mask"])
+
+        example["instance_prompt_ids"] = self.tokenizer(
+            text,
+            padding="do_not_pad",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+        ).input_ids
+
+        return example

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/lora.py

@@ -0,0 +1,1119 @@
+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+import json
+import math
+from itertools import groupby
+from typing import Callable, Dict, List, Optional, Set, Tuple, Type, Union
+
+import numpy as np
+import PIL
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+    from safetensors.torch import safe_open
+    from safetensors.torch import save_file as safe_save
+
+    safetensors_available = True
+except ImportError:
+    from .safe_open import safe_open
+
+    def safe_save(
+        tensors: Dict[str, torch.Tensor],
+        filename: str,
+        metadata: Optional[Dict[str, str]] = None,
+    ) -> None:
+        raise EnvironmentError(
+            "Saving safetensors requires the safetensors library. Please install with pip or similar."
+        )
+
+    safetensors_available = False
+
+
+class LoraInjectedLinear(nn.Module):
+    def __init__(
+        self, in_features, out_features, bias=False, r=4, dropout_p=0.1, scale=1.0
+    ):
+        super().__init__()
+
+        if r > min(in_features, out_features):
+            raise ValueError(
+                f"LoRA rank {r} must be less or equal than {min(in_features, out_features)}"
+            )
+        self.r = r
+        self.linear = nn.Linear(in_features, out_features, bias)
+        self.lora_down = nn.Linear(in_features, r, bias=False)
+        self.dropout = nn.Dropout(dropout_p)
+        self.lora_up = nn.Linear(r, out_features, bias=False)
+        self.scale = scale
+        self.selector = nn.Identity()
+
+        nn.init.normal_(self.lora_down.weight, std=1 / r)
+        nn.init.zeros_(self.lora_up.weight)
+
+    def forward(self, input, scale=None):
+        # Below change is needed to support diffusers>=0.21.0
+        if scale is not None:
+            self.scale = scale
+        return (
+            self.linear(input)
+            + self.dropout(self.lora_up(self.selector(self.lora_down(input))))
+            * self.scale
+        )
+
+    def realize_as_lora(self):
+        return self.lora_up.weight.data * self.scale, self.lora_down.weight.data
+
+    def set_selector_from_diag(self, diag: torch.Tensor):
+        # diag is a 1D tensor of size (r,)
+        assert diag.shape == (self.r,)
+        self.selector = nn.Linear(self.r, self.r, bias=False)
+        self.selector.weight.data = torch.diag(diag)
+        self.selector.weight.data = self.selector.weight.data.to(
+            self.lora_up.weight.device
+        ).to(self.lora_up.weight.dtype)
+
+
+class LoraInjectedConv2d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups: int = 1,
+        bias: bool = True,
+        r: int = 4,
+        dropout_p: float = 0.1,
+        scale: float = 1.0,
+    ):
+        super().__init__()
+        if r > min(in_channels, out_channels):
+            raise ValueError(
+                f"LoRA rank {r} must be less or equal than {min(in_channels, out_channels)}"
+            )
+        self.r = r
+        self.conv = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+
+        self.lora_down = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=r,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=False,
+        )
+        self.dropout = nn.Dropout(dropout_p)
+        self.lora_up = nn.Conv2d(
+            in_channels=r,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=False,
+        )
+        self.selector = nn.Identity()
+        self.scale = scale
+
+        nn.init.normal_(self.lora_down.weight, std=1 / r)
+        nn.init.zeros_(self.lora_up.weight)
+
+    def forward(self, input):
+        return (
+            self.conv(input)
+            + self.dropout(self.lora_up(self.selector(self.lora_down(input))))
+            * self.scale
+        )
+
+    def realize_as_lora(self):
+        return self.lora_up.weight.data * self.scale, self.lora_down.weight.data
+
+    def set_selector_from_diag(self, diag: torch.Tensor):
+        # diag is a 1D tensor of size (r,)
+        assert diag.shape == (self.r,)
+        self.selector = nn.Conv2d(
+            in_channels=self.r,
+            out_channels=self.r,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=False,
+        )
+        self.selector.weight.data = torch.diag(diag)
+
+        # same device + dtype as lora_up
+        self.selector.weight.data = self.selector.weight.data.to(
+            self.lora_up.weight.device
+        ).to(self.lora_up.weight.dtype)
+
+
+UNET_DEFAULT_TARGET_REPLACE = {"CrossAttention", "Attention", "GEGLU"}
+
+UNET_EXTENDED_TARGET_REPLACE = {"ResnetBlock2D", "CrossAttention", "Attention", "GEGLU"}
+
+TEXT_ENCODER_DEFAULT_TARGET_REPLACE = {"CLIPAttention"}
+
+TEXT_ENCODER_EXTENDED_TARGET_REPLACE = {"CLIPAttention"}
+
+DEFAULT_TARGET_REPLACE = UNET_DEFAULT_TARGET_REPLACE
+
+EMBED_FLAG = "<embed>"
+
+
+def _find_children(
+    model,
+    search_class: List[Type[nn.Module]] = [nn.Linear],
+):
+    """
+    Find all modules of a certain class (or union of classes).
+
+    Returns all matching modules, along with the parent of those moduless and the
+    names they are referenced by.
+    """
+    # For each target find every linear_class module that isn't a child of a LoraInjectedLinear
+    for parent in model.modules():
+        for name, module in parent.named_children():
+            if any([isinstance(module, _class) for _class in search_class]):
+                yield parent, name, module
+
+
+def _find_modules_v2(
+    model,
+    ancestor_class: Optional[Set[str]] = None,
+    search_class: List[Type[nn.Module]] = [nn.Linear],
+    exclude_children_of: Optional[List[Type[nn.Module]]] = [
+        LoraInjectedLinear,
+        LoraInjectedConv2d,
+    ],
+):
+    """
+    Find all modules of a certain class (or union of classes) that are direct or
+    indirect descendants of other modules of a certain class (or union of classes).
+
+    Returns all matching modules, along with the parent of those moduless and the
+    names they are referenced by.
+    """
+
+    # Get the targets we should replace all linears under
+    if ancestor_class is not None:
+        ancestors = (
+            module
+            for module in model.modules()
+            if module.__class__.__name__ in ancestor_class
+        )
+    else:
+        # this, incase you want to naively iterate over all modules.
+        ancestors = [module for module in model.modules()]
+
+    # For each target find every linear_class module that isn't a child of a LoraInjectedLinear
+    for ancestor in ancestors:
+        for fullname, module in ancestor.named_modules():
+            if any([isinstance(module, _class) for _class in search_class]):
+                # Find the direct parent if this is a descendant, not a child, of target
+                *path, name = fullname.split(".")
+                parent = ancestor
+                while path:
+                    parent = parent.get_submodule(path.pop(0))
+                # Skip this linear if it's a child of a LoraInjectedLinear
+                if exclude_children_of and any(
+                    [isinstance(parent, _class) for _class in exclude_children_of]
+                ):
+                    continue
+                # Otherwise, yield it
+                yield parent, name, module
+
+
+def _find_modules_old(
+    model,
+    ancestor_class: Set[str] = DEFAULT_TARGET_REPLACE,
+    search_class: List[Type[nn.Module]] = [nn.Linear],
+    exclude_children_of: Optional[List[Type[nn.Module]]] = [LoraInjectedLinear],
+):
+    ret = []
+    for _module in model.modules():
+        if _module.__class__.__name__ in ancestor_class:
+
+            for name, _child_module in _module.named_modules():
+                if _child_module.__class__ in search_class:
+                    ret.append((_module, name, _child_module))
+    print(ret)
+    return ret
+
+
+_find_modules = _find_modules_v2
+
+
+def inject_trainable_lora(
+    model: nn.Module,
+    target_replace_module: Set[str] = DEFAULT_TARGET_REPLACE,
+    r: int = 4,
+    loras=None,  # path to lora .pt
+    verbose: bool = False,
+    dropout_p: float = 0.0,
+    scale: float = 1.0,
+):
+    """
+    inject lora into model, and returns lora parameter groups.
+    """
+
+    require_grad_params = []
+    names = []
+
+    if loras != None:
+        loras = torch.load(loras)
+
+    for _module, name, _child_module in _find_modules(
+        model, target_replace_module, search_class=[nn.Linear]
+    ):
+        weight = _child_module.weight
+        bias = _child_module.bias
+        if verbose:
+            print("LoRA Injection : injecting lora into ", name)
+            print("LoRA Injection : weight shape", weight.shape)
+        _tmp = LoraInjectedLinear(
+            _child_module.in_features,
+            _child_module.out_features,
+            _child_module.bias is not None,
+            r=r,
+            dropout_p=dropout_p,
+            scale=scale,
+        )
+        _tmp.linear.weight = weight
+        if bias is not None:
+            _tmp.linear.bias = bias
+
+        # switch the module
+        _tmp.to(_child_module.weight.device).to(_child_module.weight.dtype)
+        _module._modules[name] = _tmp
+
+        require_grad_params.append(_module._modules[name].lora_up.parameters())
+        require_grad_params.append(_module._modules[name].lora_down.parameters())
+
+        if loras != None:
+            _module._modules[name].lora_up.weight = loras.pop(0)
+            _module._modules[name].lora_down.weight = loras.pop(0)
+
+        _module._modules[name].lora_up.weight.requires_grad = True
+        _module._modules[name].lora_down.weight.requires_grad = True
+        names.append(name)
+
+    return require_grad_params, names
+
+
+def inject_trainable_lora_extended(
+    model: nn.Module,
+    target_replace_module: Set[str] = UNET_EXTENDED_TARGET_REPLACE,
+    r: int = 4,
+    loras=None,  # path to lora .pt
+):
+    """
+    inject lora into model, and returns lora parameter groups.
+    """
+
+    require_grad_params = []
+    names = []
+
+    if loras != None:
+        loras = torch.load(loras)
+
+    for _module, name, _child_module in _find_modules(
+        model, target_replace_module, search_class=[nn.Linear, nn.Conv2d]
+    ):
+        if _child_module.__class__ == nn.Linear:
+            weight = _child_module.weight
+            bias = _child_module.bias
+            _tmp = LoraInjectedLinear(
+                _child_module.in_features,
+                _child_module.out_features,
+                _child_module.bias is not None,
+                r=r,
+            )
+            _tmp.linear.weight = weight
+            if bias is not None:
+                _tmp.linear.bias = bias
+        elif _child_module.__class__ == nn.Conv2d:
+            weight = _child_module.weight
+            bias = _child_module.bias
+            _tmp = LoraInjectedConv2d(
+                _child_module.in_channels,
+                _child_module.out_channels,
+                _child_module.kernel_size,
+                _child_module.stride,
+                _child_module.padding,
+                _child_module.dilation,
+                _child_module.groups,
+                _child_module.bias is not None,
+                r=r,
+            )
+
+            _tmp.conv.weight = weight
+            if bias is not None:
+                _tmp.conv.bias = bias
+
+        # switch the module
+        _tmp.to(_child_module.weight.device).to(_child_module.weight.dtype)
+        if bias is not None:
+            _tmp.to(_child_module.bias.device).to(_child_module.bias.dtype)
+
+        _module._modules[name] = _tmp
+
+        require_grad_params.append(_module._modules[name].lora_up.parameters())
+        require_grad_params.append(_module._modules[name].lora_down.parameters())
+
+        if loras != None:
+            _module._modules[name].lora_up.weight = loras.pop(0)
+            _module._modules[name].lora_down.weight = loras.pop(0)
+
+        _module._modules[name].lora_up.weight.requires_grad = True
+        _module._modules[name].lora_down.weight.requires_grad = True
+        names.append(name)
+
+    return require_grad_params, names
+
+
+def extract_lora_ups_down(model, target_replace_module=DEFAULT_TARGET_REPLACE):
+
+    loras = []
+
+    for _m, _n, _child_module in _find_modules(
+        model,
+        target_replace_module,
+        search_class=[LoraInjectedLinear, LoraInjectedConv2d],
+    ):
+        loras.append((_child_module.lora_up, _child_module.lora_down))
+
+    if len(loras) == 0:
+        raise ValueError("No lora injected.")
+
+    return loras
+
+
+def extract_lora_as_tensor(
+    model, target_replace_module=DEFAULT_TARGET_REPLACE, as_fp16=True, as_bf16=True,
+):
+
+    loras = []
+
+    for _m, _n, _child_module in _find_modules(
+        model,
+        target_replace_module,
+        search_class=[LoraInjectedLinear, LoraInjectedConv2d],
+    ):
+        up, down = _child_module.realize_as_lora()
+        if model.device.type == 'hpu' and as_bf16:
+            up = up.to(torch.bfloat16)
+            down = down.to(torch.bfloat16)
+        elif as_fp16:
+            up = up.to(torch.float16)
+            down = down.to(torch.float16)
+
+        loras.append((up, down))
+
+    if len(loras) == 0:
+        raise ValueError("No lora injected.")
+
+    return loras
+
+
+def save_lora_weight(
+    model,
+    path="./lora.pt",
+    target_replace_module=DEFAULT_TARGET_REPLACE,
+):
+    weights = []
+    for _up, _down in extract_lora_ups_down(
+        model, target_replace_module=target_replace_module
+    ):
+        weights.append(_up.weight.to("cpu").to(torch.float16))
+        weights.append(_down.weight.to("cpu").to(torch.float16))
+
+    torch.save(weights, path)
+
+
+def save_lora_as_json(model, path="./lora.json"):
+    weights = []
+    for _up, _down in extract_lora_ups_down(model):
+        weights.append(_up.weight.detach().cpu().numpy().tolist())
+        weights.append(_down.weight.detach().cpu().numpy().tolist())
+
+    import json
+
+    with open(path, "w") as f:
+        json.dump(weights, f)
+
+
+def save_safeloras_with_embeds(
+    modelmap: Dict[str, Tuple[nn.Module, Set[str]]] = {},
+    embeds: Dict[str, torch.Tensor] = {},
+    outpath="./lora.safetensors",
+):
+    """
+    Saves the Lora from multiple modules in a single safetensor file.
+
+    modelmap is a dictionary of {
+        "module name": (module, target_replace_module)
+    }
+    """
+    weights = {}
+    metadata = {}
+
+    for name, (model, target_replace_module) in modelmap.items():
+        metadata[name] = json.dumps(list(target_replace_module))
+
+        for i, (_up, _down) in enumerate(
+            extract_lora_as_tensor(model, target_replace_module)
+        ):
+            rank = _down.shape[0]
+
+            metadata[f"{name}:{i}:rank"] = str(rank)
+            weights[f"{name}:{i}:up"] = _up
+            weights[f"{name}:{i}:down"] = _down
+
+    for token, tensor in embeds.items():
+        metadata[token] = EMBED_FLAG
+        weights[token] = tensor
+
+    print(f"Saving weights to {outpath}")
+    safe_save(weights, outpath, metadata)
+
+
+def save_safeloras(
+    modelmap: Dict[str, Tuple[nn.Module, Set[str]]] = {},
+    outpath="./lora.safetensors",
+):
+    return save_safeloras_with_embeds(modelmap=modelmap, outpath=outpath)
+
+
+def convert_loras_to_safeloras_with_embeds(
+    modelmap: Dict[str, Tuple[str, Set[str], int]] = {},
+    embeds: Dict[str, torch.Tensor] = {},
+    outpath="./lora.safetensors",
+):
+    """
+    Converts the Lora from multiple pytorch .pt files into a single safetensor file.
+
+    modelmap is a dictionary of {
+        "module name": (pytorch_model_path, target_replace_module, rank)
+    }
+    """
+
+    weights = {}
+    metadata = {}
+
+    for name, (path, target_replace_module, r) in modelmap.items():
+        metadata[name] = json.dumps(list(target_replace_module))
+
+        lora = torch.load(path)
+        for i, weight in enumerate(lora):
+            is_up = i % 2 == 0
+            i = i // 2
+
+            if is_up:
+                metadata[f"{name}:{i}:rank"] = str(r)
+                weights[f"{name}:{i}:up"] = weight
+            else:
+                weights[f"{name}:{i}:down"] = weight
+
+    for token, tensor in embeds.items():
+        metadata[token] = EMBED_FLAG
+        weights[token] = tensor
+
+    print(f"Saving weights to {outpath}")
+    safe_save(weights, outpath, metadata)
+
+
+def convert_loras_to_safeloras(
+    modelmap: Dict[str, Tuple[str, Set[str], int]] = {},
+    outpath="./lora.safetensors",
+):
+    convert_loras_to_safeloras_with_embeds(modelmap=modelmap, outpath=outpath)
+
+
+def parse_safeloras(
+    safeloras,
+) -> Dict[str, Tuple[List[nn.parameter.Parameter], List[int], List[str]]]:
+    """
+    Converts a loaded safetensor file that contains a set of module Loras
+    into Parameters and other information
+
+    Output is a dictionary of {
+        "module name": (
+            [list of weights],
+            [list of ranks],
+            target_replacement_modules
+        )
+    }
+    """
+    loras = {}
+    metadata = safeloras.metadata()
+
+    get_name = lambda k: k.split(":")[0]
+
+    keys = list(safeloras.keys())
+    keys.sort(key=get_name)
+
+    for name, module_keys in groupby(keys, get_name):
+        info = metadata.get(name)
+
+        if not info:
+            raise ValueError(
+                f"Tensor {name} has no metadata - is this a Lora safetensor?"
+            )
+
+        # Skip Textual Inversion embeds
+        if info == EMBED_FLAG:
+            continue
+
+        # Handle Loras
+        # Extract the targets
+        target = json.loads(info)
+
+        # Build the result lists - Python needs us to preallocate lists to insert into them
+        module_keys = list(module_keys)
+        ranks = [4] * (len(module_keys) // 2)
+        weights = [None] * len(module_keys)
+
+        for key in module_keys:
+            # Split the model name and index out of the key
+            _, idx, direction = key.split(":")
+            idx = int(idx)
+
+            # Add the rank
+            ranks[idx] = int(metadata[f"{name}:{idx}:rank"])
+
+            # Insert the weight into the list
+            idx = idx * 2 + (1 if direction == "down" else 0)
+            weights[idx] = nn.parameter.Parameter(safeloras.get_tensor(key))
+
+        loras[name] = (weights, ranks, target)
+
+    return loras
+
+
+def parse_safeloras_embeds(
+    safeloras,
+) -> Dict[str, torch.Tensor]:
+    """
+    Converts a loaded safetensor file that contains Textual Inversion embeds into
+    a dictionary of embed_token: Tensor
+    """
+    embeds = {}
+    metadata = safeloras.metadata()
+
+    for key in safeloras.keys():
+        # Only handle Textual Inversion embeds
+        meta = metadata.get(key)
+        if not meta or meta != EMBED_FLAG:
+            continue
+
+        embeds[key] = safeloras.get_tensor(key)
+
+    return embeds
+
+
+def load_safeloras(path, device="cpu"):
+    safeloras = safe_open(path, framework="pt", device=device)
+    return parse_safeloras(safeloras)
+
+
+def load_safeloras_embeds(path, device="cpu"):
+    safeloras = safe_open(path, framework="pt", device=device)
+    return parse_safeloras_embeds(safeloras)
+
+
+def load_safeloras_both(path, device="cpu"):
+    safeloras = safe_open(path, framework="pt", device=device)
+    return parse_safeloras(safeloras), parse_safeloras_embeds(safeloras)
+
+
+def collapse_lora(model, alpha=1.0):
+
+    for _module, name, _child_module in _find_modules(
+        model,
+        UNET_EXTENDED_TARGET_REPLACE | TEXT_ENCODER_EXTENDED_TARGET_REPLACE,
+        search_class=[LoraInjectedLinear, LoraInjectedConv2d],
+    ):
+
+        if isinstance(_child_module, LoraInjectedLinear):
+            print("Collapsing Lin Lora in", name)
+
+            _child_module.linear.weight = nn.Parameter(
+                _child_module.linear.weight.data
+                + alpha
+                * (
+                    _child_module.lora_up.weight.data
+                    @ _child_module.lora_down.weight.data
+                )
+                .type(_child_module.linear.weight.dtype)
+                .to(_child_module.linear.weight.device)
+            )
+
+        else:
+            print("Collapsing Conv Lora in", name)
+            _child_module.conv.weight = nn.Parameter(
+                _child_module.conv.weight.data
+                + alpha
+                * (
+                    _child_module.lora_up.weight.data.flatten(start_dim=1)
+                    @ _child_module.lora_down.weight.data.flatten(start_dim=1)
+                )
+                .reshape(_child_module.conv.weight.data.shape)
+                .type(_child_module.conv.weight.dtype)
+                .to(_child_module.conv.weight.device)
+            )
+
+
+def monkeypatch_or_replace_lora(
+    model,
+    loras,
+    target_replace_module=DEFAULT_TARGET_REPLACE,
+    r: Union[int, List[int]] = 4,
+):
+    for _module, name, _child_module in _find_modules(
+        model, target_replace_module, search_class=[nn.Linear, LoraInjectedLinear]
+    ):
+        _source = (
+            _child_module.linear
+            if isinstance(_child_module, LoraInjectedLinear)
+            else _child_module
+        )
+
+        weight = _source.weight
+        bias = _source.bias
+        _tmp = LoraInjectedLinear(
+            _source.in_features,
+            _source.out_features,
+            _source.bias is not None,
+            r=r.pop(0) if isinstance(r, list) else r,
+        )
+        _tmp.linear.weight = weight
+
+        if bias is not None:
+            _tmp.linear.bias = bias
+
+        # switch the module
+        _module._modules[name] = _tmp
+
+        up_weight = loras.pop(0)
+        down_weight = loras.pop(0)
+
+        _module._modules[name].lora_up.weight = nn.Parameter(
+            up_weight.type(weight.dtype)
+        )
+        _module._modules[name].lora_down.weight = nn.Parameter(
+            down_weight.type(weight.dtype)
+        )
+
+        _module._modules[name].to(weight.device)
+
+
+def monkeypatch_or_replace_lora_extended(
+    model,
+    loras,
+    target_replace_module=DEFAULT_TARGET_REPLACE,
+    r: Union[int, List[int]] = 4,
+):
+    for _module, name, _child_module in _find_modules(
+        model,
+        target_replace_module,
+        search_class=[nn.Linear, LoraInjectedLinear, nn.Conv2d, LoraInjectedConv2d],
+    ):
+
+        if (_child_module.__class__ == nn.Linear) or (
+            _child_module.__class__ == LoraInjectedLinear
+        ):
+            if len(loras[0].shape) != 2:
+                continue
+
+            _source = (
+                _child_module.linear
+                if isinstance(_child_module, LoraInjectedLinear)
+                else _child_module
+            )
+
+            weight = _source.weight
+            bias = _source.bias
+            _tmp = LoraInjectedLinear(
+                _source.in_features,
+                _source.out_features,
+                _source.bias is not None,
+                r=r.pop(0) if isinstance(r, list) else r,
+            )
+            _tmp.linear.weight = weight
+
+            if bias is not None:
+                _tmp.linear.bias = bias
+
+        elif (_child_module.__class__ == nn.Conv2d) or (
+            _child_module.__class__ == LoraInjectedConv2d
+        ):
+            if len(loras[0].shape) != 4:
+                continue
+            _source = (
+                _child_module.conv
+                if isinstance(_child_module, LoraInjectedConv2d)
+                else _child_module
+            )
+
+            weight = _source.weight
+            bias = _source.bias
+            _tmp = LoraInjectedConv2d(
+                _source.in_channels,
+                _source.out_channels,
+                _source.kernel_size,
+                _source.stride,
+                _source.padding,
+                _source.dilation,
+                _source.groups,
+                _source.bias is not None,
+                r=r.pop(0) if isinstance(r, list) else r,
+            )
+
+            _tmp.conv.weight = weight
+
+            if bias is not None:
+                _tmp.conv.bias = bias
+
+        # switch the module
+        _module._modules[name] = _tmp
+
+        up_weight = loras.pop(0)
+        down_weight = loras.pop(0)
+
+        _module._modules[name].lora_up.weight = nn.Parameter(
+            up_weight.type(weight.dtype)
+        )
+        _module._modules[name].lora_down.weight = nn.Parameter(
+            down_weight.type(weight.dtype)
+        )
+
+        _module._modules[name].to(weight.device)
+
+
+def monkeypatch_or_replace_safeloras(models, safeloras):
+    loras = parse_safeloras(safeloras)
+
+    for name, (lora, ranks, target) in loras.items():
+        model = getattr(models, name, None)
+
+        if not model:
+            print(f"No model provided for {name}, contained in Lora")
+            continue
+
+        monkeypatch_or_replace_lora_extended(model, lora, target, ranks)
+
+
+def monkeypatch_remove_lora(model):
+    for _module, name, _child_module in _find_modules(
+        model, search_class=[LoraInjectedLinear, LoraInjectedConv2d]
+    ):
+        if isinstance(_child_module, LoraInjectedLinear):
+            _source = _child_module.linear
+            weight, bias = _source.weight, _source.bias
+
+            _tmp = nn.Linear(
+                _source.in_features, _source.out_features, bias is not None
+            )
+
+            _tmp.weight = weight
+            if bias is not None:
+                _tmp.bias = bias
+
+        else:
+            _source = _child_module.conv
+            weight, bias = _source.weight, _source.bias
+
+            _tmp = nn.Conv2d(
+                in_channels=_source.in_channels,
+                out_channels=_source.out_channels,
+                kernel_size=_source.kernel_size,
+                stride=_source.stride,
+                padding=_source.padding,
+                dilation=_source.dilation,
+                groups=_source.groups,
+                bias=bias is not None,
+            )
+
+            _tmp.weight = weight
+            if bias is not None:
+                _tmp.bias = bias
+
+        _module._modules[name] = _tmp
+
+
+def monkeypatch_add_lora(
+    model,
+    loras,
+    target_replace_module=DEFAULT_TARGET_REPLACE,
+    alpha: float = 1.0,
+    beta: float = 1.0,
+):
+    for _module, name, _child_module in _find_modules(
+        model, target_replace_module, search_class=[LoraInjectedLinear]
+    ):
+        weight = _child_module.linear.weight
+
+        up_weight = loras.pop(0)
+        down_weight = loras.pop(0)
+
+        _module._modules[name].lora_up.weight = nn.Parameter(
+            up_weight.type(weight.dtype).to(weight.device) * alpha
+            + _module._modules[name].lora_up.weight.to(weight.device) * beta
+        )
+        _module._modules[name].lora_down.weight = nn.Parameter(
+            down_weight.type(weight.dtype).to(weight.device) * alpha
+            + _module._modules[name].lora_down.weight.to(weight.device) * beta
+        )
+
+        _module._modules[name].to(weight.device)
+
+
+def tune_lora_scale(model, alpha: float = 1.0):
+    for _module in model.modules():
+        if _module.__class__.__name__ in ["LoraInjectedLinear", "LoraInjectedConv2d"]:
+            _module.scale = alpha
+
+
+def set_lora_diag(model, diag: torch.Tensor):
+    for _module in model.modules():
+        if _module.__class__.__name__ in ["LoraInjectedLinear", "LoraInjectedConv2d"]:
+            _module.set_selector_from_diag(diag)
+
+
+def _text_lora_path(path: str) -> str:
+    assert path.endswith(".pt"), "Only .pt files are supported"
+    return ".".join(path.split(".")[:-1] + ["text_encoder", "pt"])
+
+
+def _ti_lora_path(path: str) -> str:
+    assert path.endswith(".pt"), "Only .pt files are supported"
+    return ".".join(path.split(".")[:-1] + ["ti", "pt"])
+
+
+def apply_learned_embed_in_clip(
+    learned_embeds,
+    text_encoder,
+    tokenizer,
+    token: Optional[Union[str, List[str]]] = None,
+    idempotent=False,
+):
+    if isinstance(token, str):
+        trained_tokens = [token]
+    elif isinstance(token, list):
+        assert len(learned_embeds.keys()) == len(
+            token
+        ), "The number of tokens and the number of embeds should be the same"
+        trained_tokens = token
+    else:
+        trained_tokens = list(learned_embeds.keys())
+
+    for token in trained_tokens:
+        print(token)
+        embeds = learned_embeds[token]
+
+        # cast to dtype of text_encoder
+        dtype = text_encoder.get_input_embeddings().weight.dtype
+        num_added_tokens = tokenizer.add_tokens(token)
+
+        i = 1
+        if not idempotent:
+            while num_added_tokens == 0:
+                print(f"The tokenizer already contains the token {token}.")
+                token = f"{token[:-1]}-{i}>"
+                print(f"Attempting to add the token {token}.")
+                num_added_tokens = tokenizer.add_tokens(token)
+                i += 1
+        elif num_added_tokens == 0 and idempotent:
+            print(f"The tokenizer already contains the token {token}.")
+            print(f"Replacing {token} embedding.")
+
+        # resize the token embeddings
+        text_encoder.resize_token_embeddings(len(tokenizer))
+
+        # get the id for the token and assign the embeds
+        token_id = tokenizer.convert_tokens_to_ids(token)
+        text_encoder.get_input_embeddings().weight.data[token_id] = embeds
+    return token
+
+
+def load_learned_embed_in_clip(
+    learned_embeds_path,
+    text_encoder,
+    tokenizer,
+    token: Optional[Union[str, List[str]]] = None,
+    idempotent=False,
+):
+    learned_embeds = torch.load(learned_embeds_path)
+    apply_learned_embed_in_clip(
+        learned_embeds, text_encoder, tokenizer, token, idempotent
+    )
+
+
+def patch_pipe(
+    pipe,
+    maybe_unet_path,
+    token: Optional[str] = None,
+    r: int = 4,
+    patch_unet=True,
+    patch_text=True,
+    patch_ti=True,
+    idempotent_token=True,
+    unet_target_replace_module=DEFAULT_TARGET_REPLACE,
+    text_target_replace_module=TEXT_ENCODER_DEFAULT_TARGET_REPLACE,
+):
+    if maybe_unet_path.endswith(".pt"):
+        # torch format
+
+        if maybe_unet_path.endswith(".ti.pt"):
+            unet_path = maybe_unet_path[:-6] + ".pt"
+        elif maybe_unet_path.endswith(".text_encoder.pt"):
+            unet_path = maybe_unet_path[:-16] + ".pt"
+        else:
+            unet_path = maybe_unet_path
+
+        ti_path = _ti_lora_path(unet_path)
+        text_path = _text_lora_path(unet_path)
+
+        if patch_unet:
+            print("LoRA : Patching Unet")
+            monkeypatch_or_replace_lora(
+                pipe.unet,
+                torch.load(unet_path),
+                r=r,
+                target_replace_module=unet_target_replace_module,
+            )
+
+        if patch_text:
+            print("LoRA : Patching text encoder")
+            monkeypatch_or_replace_lora(
+                pipe.text_encoder,
+                torch.load(text_path),
+                target_replace_module=text_target_replace_module,
+                r=r,
+            )
+        if patch_ti:
+            print("LoRA : Patching token input")
+            token = load_learned_embed_in_clip(
+                ti_path,
+                pipe.text_encoder,
+                pipe.tokenizer,
+                token=token,
+                idempotent=idempotent_token,
+            )
+
+    elif maybe_unet_path.endswith(".safetensors"):
+        safeloras = safe_open(maybe_unet_path, framework="pt", device="cpu")
+        monkeypatch_or_replace_safeloras(pipe, safeloras)
+        tok_dict = parse_safeloras_embeds(safeloras)
+        if patch_ti:
+            apply_learned_embed_in_clip(
+                tok_dict,
+                pipe.text_encoder,
+                pipe.tokenizer,
+                token=token,
+                idempotent=idempotent_token,
+            )
+        return tok_dict
+
+
+@torch.no_grad()
+def inspect_lora(model):
+    moved = {}
+
+    for name, _module in model.named_modules():
+        if _module.__class__.__name__ in ["LoraInjectedLinear", "LoraInjectedConv2d"]:
+            ups = _module.lora_up.weight.data.clone()
+            downs = _module.lora_down.weight.data.clone()
+
+            wght: torch.Tensor = ups.flatten(1) @ downs.flatten(1)
+
+            dist = wght.flatten().abs().mean().item()
+            if name in moved:
+                moved[name].append(dist)
+            else:
+                moved[name] = [dist]
+
+    return moved
+
+
+def save_all(
+    unet,
+    text_encoder,
+    save_path,
+    placeholder_token_ids=None,
+    placeholder_tokens=None,
+    save_lora=True,
+    save_ti=True,
+    target_replace_module_text=TEXT_ENCODER_DEFAULT_TARGET_REPLACE,
+    target_replace_module_unet=DEFAULT_TARGET_REPLACE,
+    safe_form=True,
+):
+    if not safe_form:
+        # save ti
+        if save_ti:
+            ti_path = _ti_lora_path(save_path)
+            learned_embeds_dict = {}
+            for tok, tok_id in zip(placeholder_tokens, placeholder_token_ids):
+                learned_embeds = text_encoder.get_input_embeddings().weight[tok_id]
+                print(
+                    f"Current Learned Embeddings for {tok}:, id {tok_id} ",
+                    learned_embeds[:4],
+                )
+                learned_embeds_dict[tok] = learned_embeds.detach().cpu()
+
+            torch.save(learned_embeds_dict, ti_path)
+            print("Ti saved to ", ti_path)
+
+        # save text encoder
+        if save_lora:
+
+            save_lora_weight(
+                unet, save_path, target_replace_module=target_replace_module_unet
+            )
+            print("Unet saved to ", save_path)
+
+            save_lora_weight(
+                text_encoder,
+                _text_lora_path(save_path),
+                target_replace_module=target_replace_module_text,
+            )
+            print("Text Encoder saved to ", _text_lora_path(save_path))
+
+    else:
+        assert save_path.endswith(
+            ".safetensors"
+        ), f"Save path : {save_path} should end with .safetensors"
+
+        loras = {}
+        embeds = {}
+
+        if save_lora:
+
+            loras["unet"] = (unet, target_replace_module_unet)
+            loras["text_encoder"] = (text_encoder, target_replace_module_text)
+
+        if save_ti:
+            for tok, tok_id in zip(placeholder_tokens, placeholder_token_ids):
+                learned_embeds = text_encoder.get_input_embeddings().weight[tok_id]
+                print(
+                    f"Current Learned Embeddings for {tok}:, id {tok_id} ",
+                    learned_embeds[:4],
+                )
+                embeds[tok] = learned_embeds.detach().cpu()
+
+        save_safeloras_with_embeds(loras, embeds, save_path)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/lora_manager.py

@@ -0,0 +1,144 @@
+from typing import List
+import torch
+from safetensors import safe_open
+from diffusers import StableDiffusionPipeline
+from .lora import (
+    monkeypatch_or_replace_safeloras,
+    apply_learned_embed_in_clip,
+    set_lora_diag,
+    parse_safeloras_embeds,
+)
+
+
+def lora_join(lora_safetenors: list):
+    metadatas = [dict(safelora.metadata()) for safelora in lora_safetenors]
+    _total_metadata = {}
+    total_metadata = {}
+    total_tensor = {}
+    total_rank = 0
+    ranklist = []
+    for _metadata in metadatas:
+        rankset = []
+        for k, v in _metadata.items():
+            if k.endswith("rank"):
+                rankset.append(int(v))
+
+        assert len(set(rankset)) <= 1, "Rank should be the same per model"
+        if len(rankset) == 0:
+            rankset = [0]
+
+        total_rank += rankset[0]
+        _total_metadata.update(_metadata)
+        ranklist.append(rankset[0])
+
+    # remove metadata about tokens
+    for k, v in _total_metadata.items():
+        if v != "<embed>":
+            total_metadata[k] = v
+
+    tensorkeys = set()
+    for safelora in lora_safetenors:
+        tensorkeys.update(safelora.keys())
+
+    for keys in tensorkeys:
+        if keys.startswith("text_encoder") or keys.startswith("unet"):
+            tensorset = [safelora.get_tensor(keys) for safelora in lora_safetenors]
+
+            is_down = keys.endswith("down")
+
+            if is_down:
+                _tensor = torch.cat(tensorset, dim=0)
+                assert _tensor.shape[0] == total_rank
+            else:
+                _tensor = torch.cat(tensorset, dim=1)
+                assert _tensor.shape[1] == total_rank
+
+            total_tensor[keys] = _tensor
+            keys_rank = ":".join(keys.split(":")[:-1]) + ":rank"
+            total_metadata[keys_rank] = str(total_rank)
+    token_size_list = []
+    for idx, safelora in enumerate(lora_safetenors):
+        tokens = [k for k, v in safelora.metadata().items() if v == "<embed>"]
+        for jdx, token in enumerate(sorted(tokens)):
+
+            total_tensor[f"<s{idx}-{jdx}>"] = safelora.get_tensor(token)
+            total_metadata[f"<s{idx}-{jdx}>"] = "<embed>"
+
+            print(f"Embedding {token} replaced to <s{idx}-{jdx}>")
+
+        token_size_list.append(len(tokens))
+
+    return total_tensor, total_metadata, ranklist, token_size_list
+
+
+class DummySafeTensorObject:
+    def __init__(self, tensor: dict, metadata):
+        self.tensor = tensor
+        self._metadata = metadata
+
+    def keys(self):
+        return self.tensor.keys()
+
+    def metadata(self):
+        return self._metadata
+
+    def get_tensor(self, key):
+        return self.tensor[key]
+
+
+class LoRAManager:
+    def __init__(self, lora_paths_list: List[str], pipe: StableDiffusionPipeline):
+
+        self.lora_paths_list = lora_paths_list
+        self.pipe = pipe
+        self._setup()
+
+    def _setup(self):
+
+        self._lora_safetenors = [
+            safe_open(path, framework="pt", device="cpu")
+            for path in self.lora_paths_list
+        ]
+
+        (
+            total_tensor,
+            total_metadata,
+            self.ranklist,
+            self.token_size_list,
+        ) = lora_join(self._lora_safetenors)
+
+        self.total_safelora = DummySafeTensorObject(total_tensor, total_metadata)
+
+        monkeypatch_or_replace_safeloras(self.pipe, self.total_safelora)
+        tok_dict = parse_safeloras_embeds(self.total_safelora)
+
+        apply_learned_embed_in_clip(
+            tok_dict,
+            self.pipe.text_encoder,
+            self.pipe.tokenizer,
+            token=None,
+            idempotent=True,
+        )
+
+    def tune(self, scales):
+
+        assert len(scales) == len(
+            self.ranklist
+        ), "Scale list should be the same length as ranklist"
+
+        diags = []
+        for scale, rank in zip(scales, self.ranklist):
+            diags = diags + [scale] * rank
+
+        set_lora_diag(self.pipe.unet, torch.tensor(diags))
+
+    def prompt(self, prompt):
+        if prompt is not None:
+            for idx, tok_size in enumerate(self.token_size_list):
+                prompt = prompt.replace(
+                    f"<{idx + 1}>",
+                    "".join([f"<s{idx}-{jdx}>" for jdx in range(tok_size)]),
+                )
+        # TODO : Rescale LoRA + Text inputs based on prompt scale params
+
+        return prompt

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/preprocess_files.py

@@ -0,0 +1,327 @@
+# Have SwinIR upsample
+# Have BLIP auto caption
+# Have CLIPSeg auto mask concept
+
+from typing import List, Literal, Union, Optional, Tuple
+import os
+from PIL import Image, ImageFilter
+import torch
+import numpy as np
+import fire
+from tqdm import tqdm
+import glob
+from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+
+
+@torch.no_grad()
+def swin_ir_sr(
+    images: List[Image.Image],
+    model_id: Literal[
+        "caidas/swin2SR-classical-sr-x2-64", "caidas/swin2SR-classical-sr-x4-48"
+    ] = "caidas/swin2SR-classical-sr-x2-64",
+    target_size: Optional[Tuple[int, int]] = None,
+    device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
+    **kwargs,
+) -> List[Image.Image]:
+    """
+    Upscales images using SwinIR. Returns a list of PIL images.
+    """
+    # So this is currently in main branch, so this can be used in the future I guess?
+    from transformers import Swin2SRForImageSuperResolution, Swin2SRImageProcessor
+
+    model = Swin2SRForImageSuperResolution.from_pretrained(
+        model_id,
+    ).to(device)
+    processor = Swin2SRImageProcessor()
+
+    out_images = []
+
+    for image in tqdm(images):
+
+        ori_w, ori_h = image.size
+        if target_size is not None:
+            if ori_w >= target_size[0] and ori_h >= target_size[1]:
+                out_images.append(image)
+                continue
+
+        inputs = processor(image, return_tensors="pt").to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        output = (
+            outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+        )
+        output = np.moveaxis(output, source=0, destination=-1)
+        output = (output * 255.0).round().astype(np.uint8)
+        output = Image.fromarray(output)
+
+        out_images.append(output)
+
+    return out_images
+
+
+@torch.no_grad()
+def clipseg_mask_generator(
+    images: List[Image.Image],
+    target_prompts: Union[List[str], str],
+    model_id: Literal[
+        "CIDAS/clipseg-rd64-refined", "CIDAS/clipseg-rd16"
+    ] = "CIDAS/clipseg-rd64-refined",
+    device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
+    bias: float = 0.01,
+    temp: float = 1.0,
+    **kwargs,
+) -> List[Image.Image]:
+    """
+    Returns a greyscale mask for each image, where the mask is the probability of the target prompt being present in the image
+    """
+
+    if isinstance(target_prompts, str):
+        print(
+            f'Warning: only one target prompt "{target_prompts}" was given, so it will be used for all images'
+        )
+
+        target_prompts = [target_prompts] * len(images)
+
+    processor = CLIPSegProcessor.from_pretrained(model_id)
+    model = CLIPSegForImageSegmentation.from_pretrained(model_id).to(device)
+
+    masks = []
+
+    for image, prompt in tqdm(zip(images, target_prompts)):
+
+        original_size = image.size
+
+        inputs = processor(
+            text=[prompt, ""],
+            images=[image] * 2,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+        ).to(device)
+
+        outputs = model(**inputs)
+
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits / temp, dim=0)[0]
+        probs = (probs + bias).clamp_(0, 1)
+        probs = 255 * probs / probs.max()
+
+        # make mask greyscale
+        mask = Image.fromarray(probs.cpu().numpy()).convert("L")
+
+        # resize mask to original size
+        mask = mask.resize(original_size)
+
+        masks.append(mask)
+
+    return masks
+
+
+@torch.no_grad()
+def blip_captioning_dataset(
+    images: List[Image.Image],
+    text: Optional[str] = None,
+    model_id: Literal[
+        "Salesforce/blip-image-captioning-large",
+        "Salesforce/blip-image-captioning-base",
+    ] = "Salesforce/blip-image-captioning-large",
+    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
+    **kwargs,
+) -> List[str]:
+    """
+    Returns a list of captions for the given images
+    """
+
+    from transformers import BlipProcessor, BlipForConditionalGeneration
+
+    processor = BlipProcessor.from_pretrained(model_id)
+    model = BlipForConditionalGeneration.from_pretrained(model_id).to(device)
+    captions = []
+
+    for image in tqdm(images):
+        inputs = processor(image, text=text, return_tensors="pt").to("cuda")
+        out = model.generate(
+            **inputs, max_length=150, do_sample=True, top_k=50, temperature=0.7
+        )
+        caption = processor.decode(out[0], skip_special_tokens=True)
+
+        captions.append(caption)
+
+    return captions
+
+
+def face_mask_google_mediapipe(
+    images: List[Image.Image], blur_amount: float = 80.0, bias: float = 0.05
+) -> List[Image.Image]:
+    """
+    Returns a list of images with mask on the face parts.
+    """
+    import mediapipe as mp
+
+    mp_face_detection = mp.solutions.face_detection
+
+    face_detection = mp_face_detection.FaceDetection(
+        model_selection=1, min_detection_confidence=0.5
+    )
+
+    masks = []
+    for image in tqdm(images):
+
+        image = np.array(image)
+
+        results = face_detection.process(image)
+        black_image = np.ones((image.shape[0], image.shape[1]), dtype=np.uint8)
+
+        if results.detections:
+
+            for detection in results.detections:
+
+                x_min = int(
+                    detection.location_data.relative_bounding_box.xmin * image.shape[1]
+                )
+                y_min = int(
+                    detection.location_data.relative_bounding_box.ymin * image.shape[0]
+                )
+                width = int(
+                    detection.location_data.relative_bounding_box.width * image.shape[1]
+                )
+                height = int(
+                    detection.location_data.relative_bounding_box.height
+                    * image.shape[0]
+                )
+
+                # draw the colored rectangle
+                black_image[y_min : y_min + height, x_min : x_min + width] = 255
+
+        black_image = Image.fromarray(black_image)
+        masks.append(black_image)
+
+    return masks
+
+
+def _crop_to_square(
+    image: Image.Image, com: List[Tuple[int, int]], resize_to: Optional[int] = None
+):
+    cx, cy = com
+    width, height = image.size
+    if width > height:
+        left_possible = max(cx - height / 2, 0)
+        left = min(left_possible, width - height)
+        right = left + height
+        top = 0
+        bottom = height
+    else:
+        left = 0
+        right = width
+        top_possible = max(cy - width / 2, 0)
+        top = min(top_possible, height - width)
+        bottom = top + width
+
+    image = image.crop((left, top, right, bottom))
+
+    if resize_to:
+        image = image.resize((resize_to, resize_to), Image.Resampling.LANCZOS)
+
+    return image
+
+
+def _center_of_mass(mask: Image.Image):
+    """
+    Returns the center of mass of the mask
+    """
+    x, y = np.meshgrid(np.arange(mask.size[0]), np.arange(mask.size[1]))
+
+    x_ = x * np.array(mask)
+    y_ = y * np.array(mask)
+
+    x = np.sum(x_) / np.sum(mask)
+    y = np.sum(y_) / np.sum(mask)
+
+    return x, y
+
+
+def load_and_save_masks_and_captions(
+    files: Union[str, List[str]],
+    output_dir: str,
+    caption_text: Optional[str] = None,
+    target_prompts: Optional[Union[List[str], str]] = None,
+    target_size: int = 512,
+    crop_based_on_salience: bool = True,
+    use_face_detection_instead: bool = False,
+    temp: float = 1.0,
+    n_length: int = -1,
+):
+    """
+    Loads images from the given files, generates masks for them, and saves the masks and captions and upscale images
+    to output dir.
+    """
+    os.makedirs(output_dir, exist_ok=True)
+
+    # load images
+    if isinstance(files, str):
+        # check if it is a directory
+        if os.path.isdir(files):
+            # get all the .png .jpg in the directory
+            files = glob.glob(os.path.join(files, "*.png")) + glob.glob(
+                os.path.join(files, "*.jpg")
+            )
+
+        if len(files) == 0:
+            raise Exception(
+                f"No files found in {files}. Either {files} is not a directory or it does not contain any .png or .jpg files."
+            )
+        if n_length == -1:
+            n_length = len(files)
+        files = sorted(files)[:n_length]
+
+    images = [Image.open(file) for file in files]
+
+    # captions
+    print(f"Generating {len(images)} captions...")
+    captions = blip_captioning_dataset(images, text=caption_text)
+
+    if target_prompts is None:
+        target_prompts = captions
+
+    print(f"Generating {len(images)} masks...")
+    if not use_face_detection_instead:
+        seg_masks = clipseg_mask_generator(
+            images=images, target_prompts=target_prompts, temp=temp
+        )
+    else:
+        seg_masks = face_mask_google_mediapipe(images=images)
+
+    # find the center of mass of the mask
+    if crop_based_on_salience:
+        coms = [_center_of_mass(mask) for mask in seg_masks]
+    else:
+        coms = [(image.size[0] / 2, image.size[1] / 2) for image in images]
+    # based on the center of mass, crop the image to a square
+    images = [
+        _crop_to_square(image, com, resize_to=None) for image, com in zip(images, coms)
+    ]
+
+    print(f"Upscaling {len(images)} images...")
+    # upscale images anyways
+    images = swin_ir_sr(images, target_size=(target_size, target_size))
+    images = [
+        image.resize((target_size, target_size), Image.Resampling.LANCZOS)
+        for image in images
+    ]
+
+    seg_masks = [
+        _crop_to_square(mask, com, resize_to=target_size)
+        for mask, com in zip(seg_masks, coms)
+    ]
+    with open(os.path.join(output_dir, "caption.txt"), "w") as f:
+        # save images and masks
+        for idx, (image, mask, caption) in enumerate(zip(images, seg_masks, captions)):
+            image.save(os.path.join(output_dir, f"{idx}.src.jpg"), quality=99)
+            mask.save(os.path.join(output_dir, f"{idx}.mask.png"))
+
+            f.write(caption + "\n")
+
+
+def main():
+    fire.Fire(load_and_save_masks_and_captions)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/safe_open.py

@@ -0,0 +1,68 @@
+"""
+Pure python version of Safetensors safe_open
+From https://gist.github.com/Narsil/3edeec2669a5e94e4707aa0f901d2282
+"""
+
+import json
+import mmap
+import os
+
+import torch
+
+
+class SafetensorsWrapper:
+    def __init__(self, metadata, tensors):
+        self._metadata = metadata
+        self._tensors = tensors
+
+    def metadata(self):
+        return self._metadata
+
+    def keys(self):
+        return self._tensors.keys()
+
+    def get_tensor(self, k):
+        return self._tensors[k]
+
+
+DTYPES = {
+    "F32": torch.float32,
+    "F16": torch.float16,
+    "BF16": torch.bfloat16,
+}
+
+
+def create_tensor(storage, info, offset):
+    dtype = DTYPES[info["dtype"]]
+    shape = info["shape"]
+    start, stop = info["data_offsets"]
+    return (
+        torch.asarray(storage[start + offset : stop + offset], dtype=torch.uint8)
+        .view(dtype=dtype)
+        .reshape(shape)
+    )
+
+
+def safe_open(filename, framework="pt", device="cpu"):
+    if framework != "pt":
+        raise ValueError("`framework` must be 'pt'")
+
+    with open(filename, mode="r", encoding="utf8") as file_obj:
+        with mmap.mmap(file_obj.fileno(), length=0, access=mmap.ACCESS_READ) as m:
+            header = m.read(8)
+            n = int.from_bytes(header, "little")
+            metadata_bytes = m.read(n)
+            metadata = json.loads(metadata_bytes)
+
+    size = os.stat(filename).st_size
+    storage = torch.ByteStorage.from_file(filename, shared=False, size=size).untyped()
+    offset = n + 8
+
+    return SafetensorsWrapper(
+        metadata=metadata.get("__metadata__", {}),
+        tensors={
+            name: create_tensor(storage, info, offset).to(device)
+            for name, info in metadata.items()
+            if name != "__metadata__"
+        },
+    )

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/to_ckpt_v2.py

@@ -0,0 +1,232 @@
+# from https://gist.github.com/jachiam/8a5c0b607e38fcc585168b90c686eb05
+# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
+# *Only* converts the UNet, VAE, and Text Encoder.
+# Does not convert optimizer state or any other thing.
+# Written by jachiam
+import argparse
+import os.path as osp
+
+import torch
+
+
+# =================#
+# UNet Conversion #
+# =================#
+
+unet_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
+    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
+    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
+    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
+    ("input_blocks.0.0.weight", "conv_in.weight"),
+    ("input_blocks.0.0.bias", "conv_in.bias"),
+    ("out.0.weight", "conv_norm_out.weight"),
+    ("out.0.bias", "conv_norm_out.bias"),
+    ("out.2.weight", "conv_out.weight"),
+    ("out.2.bias", "conv_out.bias"),
+]
+
+unet_conversion_map_resnet = [
+    # (stable-diffusion, HF Diffusers)
+    ("in_layers.0", "norm1"),
+    ("in_layers.2", "conv1"),
+    ("out_layers.0", "norm2"),
+    ("out_layers.3", "conv2"),
+    ("emb_layers.1", "time_emb_proj"),
+    ("skip_connection", "conv_shortcut"),
+]
+
+unet_conversion_map_layer = []
+# hardcoded number of downblocks and resnets/attentions...
+# would need smarter logic for other networks.
+for i in range(4):
+    # loop over downblocks/upblocks
+
+    for j in range(2):
+        # loop over resnets/attentions for downblocks
+        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
+        sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
+        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
+
+        if i < 3:
+            # no attention layers in down_blocks.3
+            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
+            sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
+            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
+
+    for j in range(3):
+        # loop over resnets/attentions for upblocks
+        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
+        sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
+        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
+
+        if i > 0:
+            # no attention layers in up_blocks.0
+            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
+            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
+            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
+
+    if i < 3:
+        # no downsample in down_blocks.3
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
+        sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
+        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        # no upsample in up_blocks.3
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
+        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
+
+hf_mid_atn_prefix = "mid_block.attentions.0."
+sd_mid_atn_prefix = "middle_block.1."
+unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
+
+for j in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{j}."
+    sd_mid_res_prefix = f"middle_block.{2*j}."
+    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+def convert_unet_state_dict(unet_state_dict):
+    # buyer beware: this is a *brittle* function,
+    # and correct output requires that all of these pieces interact in
+    # the exact order in which I have arranged them.
+    mapping = {k: k for k in unet_state_dict.keys()}
+    for sd_name, hf_name in unet_conversion_map:
+        mapping[hf_name] = sd_name
+    for k, v in mapping.items():
+        if "resnets" in k:
+            for sd_part, hf_part in unet_conversion_map_resnet:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    for k, v in mapping.items():
+        for sd_part, hf_part in unet_conversion_map_layer:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
+    return new_state_dict
+
+
+# ================#
+# VAE Conversion #
+# ================#
+
+vae_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("nin_shortcut", "conv_shortcut"),
+    ("norm_out", "conv_norm_out"),
+    ("mid.attn_1.", "mid_block.attentions.0."),
+]
+
+for i in range(4):
+    # down_blocks have two resnets
+    for j in range(2):
+        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
+        sd_down_prefix = f"encoder.down.{i}.block.{j}."
+        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
+
+    if i < 3:
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
+        sd_downsample_prefix = f"down.{i}.downsample."
+        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"up.{3-i}.upsample."
+        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
+
+    # up_blocks have three resnets
+    # also, up blocks in hf are numbered in reverse from sd
+    for j in range(3):
+        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
+        sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
+        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
+
+# this part accounts for mid blocks in both the encoder and the decoder
+for i in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{i}."
+    sd_mid_res_prefix = f"mid.block_{i+1}."
+    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+vae_conversion_map_attn = [
+    # (stable-diffusion, HF Diffusers)
+    ("norm.", "group_norm."),
+    ("q.", "query."),
+    ("k.", "key."),
+    ("v.", "value."),
+    ("proj_out.", "proj_attn."),
+]
+
+
+def reshape_weight_for_sd(w):
+    # convert HF linear weights to SD conv2d weights
+    return w.reshape(*w.shape, 1, 1)
+
+
+def convert_vae_state_dict(vae_state_dict):
+    mapping = {k: k for k in vae_state_dict.keys()}
+    for k, v in mapping.items():
+        for sd_part, hf_part in vae_conversion_map:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    for k, v in mapping.items():
+        if "attentions" in k:
+            for sd_part, hf_part in vae_conversion_map_attn:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
+    weights_to_convert = ["q", "k", "v", "proj_out"]
+    for k, v in new_state_dict.items():
+        for weight_name in weights_to_convert:
+            if f"mid.attn_1.{weight_name}.weight" in k:
+                print(f"Reshaping {k} for SD format")
+                new_state_dict[k] = reshape_weight_for_sd(v)
+    return new_state_dict
+
+
+# =========================#
+# Text Encoder Conversion #
+# =========================#
+# pretty much a no-op
+
+
+def convert_text_enc_state_dict(text_enc_dict):
+    return text_enc_dict
+
+
+def convert_to_ckpt(model_path, checkpoint_path, as_half):
+
+    assert model_path is not None, "Must provide a model path!"
+
+    assert checkpoint_path is not None, "Must provide a checkpoint path!"
+
+    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
+    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
+    text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
+
+    # Convert the UNet model
+    unet_state_dict = torch.load(unet_path, map_location="cpu")
+    unet_state_dict = convert_unet_state_dict(unet_state_dict)
+    unet_state_dict = {
+        "model.diffusion_model." + k: v for k, v in unet_state_dict.items()
+    }
+
+    # Convert the VAE model
+    vae_state_dict = torch.load(vae_path, map_location="cpu")
+    vae_state_dict = convert_vae_state_dict(vae_state_dict)
+    vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
+
+    # Convert the text encoder model
+    text_enc_dict = torch.load(text_enc_path, map_location="cpu")
+    text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
+    text_enc_dict = {
+        "cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()
+    }
+
+    # Put together new checkpoint
+    state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
+    if as_half:
+        state_dict = {k: v.half() for k, v in state_dict.items()}
+    state_dict = {"state_dict": state_dict}
+    torch.save(state_dict, checkpoint_path)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/utils.py

@@ -0,0 +1,218 @@
+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+from typing import List, Union
+
+import torch
+from PIL import Image
+from transformers import (
+    CLIPProcessor,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers import StableDiffusionPipeline
+from .lora import patch_pipe, tune_lora_scale, _text_lora_path, _ti_lora_path
+import os
+import glob
+import math
+
+EXAMPLE_PROMPTS = [
+    "<obj> swimming in a pool",
+    "<obj> at a beach with a view of seashore",
+    "<obj> in times square",
+    "<obj> wearing sunglasses",
+    "<obj> in a construction outfit",
+    "<obj> playing with a ball",
+    "<obj> wearing headphones",
+    "<obj> oil painting ghibli inspired",
+    "<obj> working on the laptop",
+    "<obj> with mountains and sunset in background",
+    "Painting of <obj> at a beach by artist claude monet",
+    "<obj> digital painting 3d render geometric style",
+    "A screaming <obj>",
+    "A depressed <obj>",
+    "A sleeping <obj>",
+    "A sad <obj>",
+    "A joyous <obj>",
+    "A frowning <obj>",
+    "A sculpture of <obj>",
+    "<obj> near a pool",
+    "<obj> at a beach with a view of seashore",
+    "<obj> in a garden",
+    "<obj> in grand canyon",
+    "<obj> floating in ocean",
+    "<obj> and an armchair",
+    "A maple tree on the side of <obj>",
+    "<obj> and an orange sofa",
+    "<obj> with chocolate cake on it",
+    "<obj> with a vase of rose flowers on it",
+    "A digital illustration of <obj>",
+    "Georgia O'Keeffe style <obj> painting",
+    "A watercolor painting of <obj> on a beach",
+]
+
+
+def image_grid(_imgs, rows=None, cols=None):
+
+    if rows is None and cols is None:
+        rows = cols = math.ceil(len(_imgs) ** 0.5)
+
+    if rows is None:
+        rows = math.ceil(len(_imgs) / cols)
+    if cols is None:
+        cols = math.ceil(len(_imgs) / rows)
+
+    w, h = _imgs[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+    grid_w, grid_h = grid.size
+
+    for i, img in enumerate(_imgs):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+def text_img_alignment(img_embeds, text_embeds, target_img_embeds):
+    # evaluation inspired from textual inversion paper
+    # https://arxiv.org/abs/2208.01618
+
+    # text alignment
+    assert img_embeds.shape[0] == text_embeds.shape[0]
+    text_img_sim = (img_embeds * text_embeds).sum(dim=-1) / (
+        img_embeds.norm(dim=-1) * text_embeds.norm(dim=-1)
+    )
+
+    # image alignment
+    img_embed_normalized = img_embeds / img_embeds.norm(dim=-1, keepdim=True)
+
+    avg_target_img_embed = (
+        (target_img_embeds / target_img_embeds.norm(dim=-1, keepdim=True))
+        .mean(dim=0)
+        .unsqueeze(0)
+        .repeat(img_embeds.shape[0], 1)
+    )
+
+    img_img_sim = (img_embed_normalized * avg_target_img_embed).sum(dim=-1)
+
+    return {
+        "text_alignment_avg": text_img_sim.mean().item(),
+        "image_alignment_avg": img_img_sim.mean().item(),
+        "text_alignment_all": text_img_sim.tolist(),
+        "image_alignment_all": img_img_sim.tolist(),
+    }
+
+
+def prepare_clip_model_sets(eval_clip_id: str = "openai/clip-vit-large-patch14"):
+    text_model = CLIPTextModelWithProjection.from_pretrained(eval_clip_id)
+    tokenizer = CLIPTokenizer.from_pretrained(eval_clip_id)
+    vis_model = CLIPVisionModelWithProjection.from_pretrained(eval_clip_id)
+    processor = CLIPProcessor.from_pretrained(eval_clip_id)
+
+    return text_model, tokenizer, vis_model, processor
+
+
+def evaluate_pipe(
+    pipe,
+    target_images: List[Image.Image],
+    class_token: str = "",
+    learnt_token: str = "",
+    guidance_scale: float = 5.0,
+    seed=0,
+    clip_model_sets=None,
+    eval_clip_id: str = "openai/clip-vit-large-patch14",
+    n_test: int = 10,
+    n_step: int = 50,
+):
+
+    if clip_model_sets is not None:
+        text_model, tokenizer, vis_model, processor = clip_model_sets
+    else:
+        text_model, tokenizer, vis_model, processor = prepare_clip_model_sets(
+            eval_clip_id
+        )
+
+    images = []
+    prompts = []
+    img_embeds = []
+    text_embeds = []
+    for prompt in EXAMPLE_PROMPTS[:n_test]:
+        prompt = prompt.replace("<obj>", learnt_token)
+        torch.manual_seed(seed)
+        with torch.autocast("cuda"):
+            img = pipe(
+                prompt, num_inference_steps=n_step, guidance_scale=guidance_scale
+            ).images[0]
+        images.append(img)
+        prompts.append(prompt)
+        # image
+        inputs = processor(images=img, return_tensors="pt")
+        img_embed = vis_model(**inputs).image_embeds
+        img_embeds.append(img_embed)
+
+        prompt = prompt.replace(learnt_token, class_token)
+        # prompts
+        inputs = tokenizer([prompt], padding=True, return_tensors="pt")
+        outputs = text_model(**inputs)
+        text_embed = outputs.text_embeds
+        text_embeds.append(text_embed)
+
+    # target images
+    inputs = processor(images=target_images, return_tensors="pt")
+    target_img_embeds = vis_model(**inputs).image_embeds
+
+    img_embeds = torch.cat(img_embeds, dim=0)
+    text_embeds = torch.cat(text_embeds, dim=0)
+
+    return text_img_alignment(img_embeds, text_embeds, target_img_embeds),list(zip(images,prompts))
+
+
+def visualize_progress(
+    path_alls: Union[str, List[str]],
+    prompt: str,
+    model_id: str = "runwayml/stable-diffusion-v1-5",
+    device="cuda:0",
+    patch_unet=True,
+    patch_text=True,
+    patch_ti=True,
+    unet_scale=1.0,
+    text_sclae=1.0,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+    offset: int = 0,
+    limit: int = 10,
+    seed: int = 0,
+):
+
+    imgs = []
+    if isinstance(path_alls, str):
+        alls = list(set(glob.glob(path_alls)))
+
+        alls.sort(key=os.path.getmtime)
+    else:
+        alls = path_alls
+
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id, torch_dtype=torch.float16
+    ).to(device)
+
+    print(f"Found {len(alls)} checkpoints")
+    for path in alls[offset:limit]:
+        print(path)
+
+        patch_pipe(
+            pipe, path, patch_unet=patch_unet, patch_text=patch_text, patch_ti=patch_ti
+        )
+
+        tune_lora_scale(pipe.unet, unet_scale)
+        tune_lora_scale(pipe.text_encoder, text_sclae)
+
+        torch.manual_seed(seed)
+        image = pipe(
+            prompt,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+        ).images[0]
+        imgs.append(image)
+
+    return imgs

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/lora_diffusion/xformers_utils.py

@@ -0,0 +1,70 @@
+import functools
+
+import torch
+from diffusers.models.attention import BasicTransformerBlock
+from diffusers.utils.import_utils import is_xformers_available
+
+from .lora import LoraInjectedLinear
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+
+@functools.cache
+def test_xformers_backwards(size):
+    @torch.enable_grad()
+    def _grad(size):
+        q = torch.randn((1, 4, size), device="cuda")
+        k = torch.randn((1, 4, size), device="cuda")
+        v = torch.randn((1, 4, size), device="cuda")
+
+        q = q.detach().requires_grad_()
+        k = k.detach().requires_grad_()
+        v = v.detach().requires_grad_()
+
+        out = xformers.ops.memory_efficient_attention(q, k, v)
+        loss = out.sum(2).mean(0).sum()
+
+        return torch.autograd.grad(loss, v)
+
+    try:
+        _grad(size)
+        print(size, "pass")
+        return True
+    except Exception as e:
+        print(size, "fail")
+        return False
+
+
+def set_use_memory_efficient_attention_xformers(
+    module: torch.nn.Module, valid: bool
+) -> None:
+    def fn_test_dim_head(module: torch.nn.Module):
+        if isinstance(module, BasicTransformerBlock):
+            # dim_head isn't stored anywhere, so back-calculate
+            source = module.attn1.to_v
+            if isinstance(source, LoraInjectedLinear):
+                source = source.linear
+
+            dim_head = source.out_features // module.attn1.heads
+
+            result = test_xformers_backwards(dim_head)
+
+            # If dim_head > dim_head_max, turn xformers off
+            if not result:
+                module.set_use_memory_efficient_attention_xformers(False)
+
+        for child in module.children():
+            fn_test_dim_head(child)
+
+    if not is_xformers_available() and valid:
+        print("XFormers is not available. Skipping.")
+        return
+
+    module.set_use_memory_efficient_attention_xformers(valid)
+
+    if valid:
+        fn_test_dim_head(module)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/requirements.txt

@@ -0,0 +1,15 @@
+lightning==2.1.2
+lightning-habana==1.3.0
+diffusers>=0.11.0,<0.21.0
+transformers>=4.25.1
+scipy
+ftfy
+fire
+wandb
+safetensors
+opencv-python
+torchvision
+mediapipe
+triton
+tensorboard
+accelerate

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/setup.py

@@ -0,0 +1,28 @@
+import os
+
+import pkg_resources
+from setuptools import find_packages, setup
+
+setup(
+    name="lora_diffusion",
+    py_modules=["lora_diffusion"],
+    version="0.1.7",
+    description="Low Rank Adaptation for Diffusion Models. Works with Stable Diffusion out-of-the-box.",
+    author="Simo Ryu",
+    packages=find_packages(),
+    entry_points={
+        "console_scripts": [
+            "lora_add = lora_diffusion.cli_lora_add:main",
+            "lora_pti = lora_diffusion.cli_lora_pti:main",
+            "lora_distill = lora_diffusion.cli_svd:main",
+            "lora_ppim = lora_diffusion.preprocess_files:main",
+        ],
+    },
+    install_requires=[
+        str(r)
+        for r in pkg_resources.parse_requirements(
+            open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
+        )
+    ],
+    include_package_data=True,
+)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/stability_ft.sh

@@ -0,0 +1,49 @@
+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+#!/bin/bash
+export PATH=$PATH:~/.local/bin
+#export WANDB_API_KEY=
+export MODEL_NAME="stabilityai/stable-diffusion-2-1-base"
+export INSTANCE_DIR='data/bo'
+export OUTPUT_DIR='results/bo'
+#export MASK_DIR=$SM_CHANNEL_MASK
+export TRAIN_STEPS_TUNING=1000
+export SM_MODEL_DIR=${OUTPUT_DIR}
+mkdir -p $OUTPUT_DIR
+
+lora_pti \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --use_face_segmentation_condition \
+  --resolution=512 \
+  --train_batch_size=7 \
+  --gradient_accumulation_steps=1 \
+  --learning_rate_unet=5e-5 \
+  --learning_rate_ti=2e-3 \
+  --color_jitter \
+  --lr_scheduler="linear" --lr_scheduler_lora="linear"\
+  --lr_warmup_steps=0 \
+  --placeholder_tokens="<s1>|<s2>" \
+  --use_template="object"\
+  --save_steps=50 \
+  --max_train_steps_ti=500 \
+  --max_train_steps_tuning=$TRAIN_STEPS_TUNING \
+  --perform_inversion=True \
+  --clip_ti_decay \
+  --weight_decay_ti=0.000 \
+  --weight_decay_lora=0.001\
+  --continue_inversion \
+  --continue_inversion_lr=1e-3 \
+  --device="hpu" \
+  --lora_rank=16 \
+  --use_lazy_mode=True \
+  --use_fused_adamw=True \
+  --print_freq=50 \
+  --use_fused_clip_norm=True \
+  2>&1 |tee log_1x_ft.txt
+
+
+
+cp $OUTPUT_DIR/step_$TRAIN_STEPS_TUNING.safetensors $SM_MODEL_DIR/lora.safetensors

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/stability_ft_cpu.sh

@@ -0,0 +1,45 @@
+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+#!/bin/bash
+export PATH=$PATH:~/.local/bin
+#export WANDB_API_KEY=
+export MODEL_NAME="stabilityai/stable-diffusion-2-1-base"
+export INSTANCE_DIR='data/bo'
+export OUTPUT_DIR='results/bo'
+#export MASK_DIR=$SM_CHANNEL_MASK
+export TRAIN_STEPS_TI=5
+export TRAIN_STEPS_TUNING=10
+export SM_MODEL_DIR=${OUTPUT_DIR}
+mkdir -p $OUTPUT_DIR
+
+lora_pti \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --use_face_segmentation_condition \
+  --resolution=512 \
+  --train_batch_size=2 \
+  --gradient_accumulation_steps=1 \
+  --learning_rate_unet=5e-5 \
+  --learning_rate_ti=2e-3 \
+  --color_jitter \
+  --lr_scheduler="linear" --lr_scheduler_lora="linear"\
+  --lr_warmup_steps=0 \
+  --placeholder_tokens="<s1>|<s2>" \
+  --use_template="object"\
+  --save_steps=3 \
+  --max_train_steps_ti=$TRAIN_STEPS_TI \
+  --max_train_steps_tuning=$TRAIN_STEPS_TUNING \
+  --perform_inversion=True \
+  --clip_ti_decay \
+  --weight_decay_ti=0.000 \
+  --weight_decay_lora=0.001\
+  --continue_inversion \
+  --continue_inversion_lr=1e-3 \
+  --device="cpu" \
+  --lora_rank=16 \
+  --print_freq=2 \
+  2>&1 |tee log_1x_ft_cpu.txt
+
+

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-finetuning/stability_ft_tc.sh

@@ -0,0 +1,51 @@
+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+#!/bin/bash
+export PATH=$PATH:~/.local/bin
+#export WANDB_API_KEY=
+export MODEL_NAME="stabilityai/stable-diffusion-2-1-base"
+export INSTANCE_DIR='data/bo'
+export OUTPUT_DIR='results/bo'
+#export MASK_DIR=$SM_CHANNEL_MASK
+export TRAIN_STEPS_TUNING=1000
+export SM_MODEL_DIR=${OUTPUT_DIR}
+mkdir -p $OUTPUT_DIR
+
+PT_HPU_LAZY_MODE=0 \
+lora_pti \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --use_face_segmentation_condition \
+  --resolution=512 \
+  --train_batch_size=7 \
+  --gradient_accumulation_steps=1 \
+  --learning_rate_unet=5e-5 \
+  --learning_rate_ti=2e-3 \
+  --color_jitter \
+  --lr_scheduler="linear" --lr_scheduler_lora="linear"\
+  --lr_warmup_steps=0 \
+  --placeholder_tokens="<s1>|<s2>" \
+  --use_template="object"\
+  --save_steps=50 \
+  --max_train_steps_ti=500 \
+  --max_train_steps_tuning=$TRAIN_STEPS_TUNING \
+  --perform_inversion=True \
+  --clip_ti_decay \
+  --weight_decay_ti=0.000 \
+  --weight_decay_lora=0.001\
+  --continue_inversion \
+  --continue_inversion_lr=1e-3 \
+  --device="hpu" \
+  --lora_rank=16 \
+  --use_fused_adamw=True \
+  --use_lazy_mode=False \
+  --print_freq=1 \
+  --use_fused_clip_norm=True \
+  --use_torch_compile=True \
+  2>&1 |tee log_1x_ft_hpu_compile.txt
+
+
+
+cp $OUTPUT_DIR/step_$TRAIN_STEPS_TUNING.safetensors $SM_MODEL_DIR/lora.safetensors

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/configs/stable-diffusion/v2-inference.yaml

@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: False
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
+        device: "hpu"

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/configs/stable-diffusion/v2-inpainting-inference.yaml

@@ -0,0 +1,158 @@
+model:
+  base_learning_rate: 5.0e-05
+  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: hybrid
+    scale_factor: 0.18215
+    monitor: val/loss_simple_ema
+    finetune_keys: null
+    use_ema: False
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        image_size: 32 # unused
+        in_channels: 9
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+            - 1
+            - 2
+            - 4
+            - 4
+          num_res_blocks: 2
+          attn_resolutions: [ ]
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
+
+
+data:
+  target: ldm.data.laion.WebDataModuleFromConfig
+  params:
+    tar_base: null  # for concat as in LAION-A
+    p_unsafe_threshold: 0.1
+    filter_word_list: "data/filters.yaml"
+    max_pwatermark: 0.45
+    batch_size: 8
+    num_workers: 6
+    multinode: True
+    min_size: 512
+    train:
+      shards:
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -"
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -"
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -"
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -"
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -"  #{00000-94333}.tar"
+      shuffle: 10000
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 512
+          interpolation: 3
+      - target: torchvision.transforms.RandomCrop
+        params:
+          size: 512
+      postprocess:
+        target: ldm.data.laion.AddMask
+        params:
+          mode: "512train-large"
+          p_drop: 0.25
+    # NOTE use enough shards to avoid empty validation loops in workers
+    validation:
+      shards:
+        - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - "
+      shuffle: 0
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 512
+          interpolation: 3
+      - target: torchvision.transforms.CenterCrop
+        params:
+          size: 512
+      postprocess:
+        target: ldm.data.laion.AddMask
+        params:
+          mode: "512train-large"
+          p_drop: 0.25
+
+lightning:
+  find_unused_parameters: True
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 10000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        enable_autocast: False
+        disabled: False
+        batch_frequency: 1000
+        max_images: 4
+        increase_log_steps: False
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          inpaint: False
+          plot_progressive_rows: False
+          plot_diffusion_rows: False
+          N: 4
+          unconditional_guidance_scale: 5.0
+          unconditional_guidance_label: [""]
+          ddim_steps: 50  # todo check these out for depth2img,
+          ddim_eta: 0.0   # todo check these out for depth2img,
+
+  trainer:
+    benchmark: True
+    val_check_interval: 5000000
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/configs/stable-diffusion/v2-midas-inference.yaml

@@ -0,0 +1,74 @@
+model:
+  base_learning_rate: 5.0e-07
+  target: ldm.models.diffusion.ddpm.LatentDepth2ImageDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: hybrid
+    scale_factor: 0.18215
+    monitor: val/loss_simple_ema
+    finetune_keys: null
+    use_ema: False
+
+    depth_stage_config:
+      target: ldm.modules.midas.api.MiDaSInference
+      params:
+        model_type: "dpt_hybrid"
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        image_size: 32 # unused
+        in_channels: 5
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+            - 1
+            - 2
+            - 4
+            - 4
+          num_res_blocks: 2
+          attn_resolutions: [ ]
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
+
+

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/configs/stable-diffusion/x4-upscaling.yaml

@@ -0,0 +1,76 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
+  params:
+    parameterization: "v"
+    low_scale_key: "lr"
+    linear_start: 0.0001
+    linear_end: 0.02
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 128
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: "hybrid-adm"
+    monitor: val/loss_simple_ema
+    scale_factor: 0.08333
+    use_ema: False
+
+    low_scale_config:
+      target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation
+      params:
+        noise_schedule_config: # image space
+          linear_start: 0.0001
+          linear_end: 0.02
+        max_noise_level: 350
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        num_classes: 1000  # timesteps for noise conditioning (here constant, just need one)
+        image_size: 128
+        in_channels: 7
+        out_channels: 4
+        model_channels: 256
+        attention_resolutions: [ 2,4,8]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 2, 4]
+        disable_self_attentions: [True, True, True, False]
+        disable_middle_self_attn: False
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+        use_linear_in_transformer: True
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        ddconfig:
+          # attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though)
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [ 1,2,4 ]  # num_down = len(ch_mult)-1
+          num_res_blocks: 2
+          attn_resolutions: [ ]
+          dropout: 0.0
+
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
+

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/data/util.py

@@ -0,0 +1,24 @@
+import torch
+
+from ldm.modules.midas.api import load_midas_transform
+
+
+class AddMiDaS(object):
+    def __init__(self, model_type):
+        super().__init__()
+        self.transform = load_midas_transform(model_type)
+
+    def pt2np(self, x):
+        x = ((x + 1.0) * .5).detach().cpu().numpy()
+        return x
+
+    def np2pt(self, x):
+        x = torch.from_numpy(x) * 2 - 1.
+        return x
+
+    def __call__(self, sample):
+        # sample['jpg'] is tensor hwc in [-1, 1] at this point
+        x = self.pt2np(sample['jpg'])
+        x = self.transform({"image": x})["image"]
+        sample['midas_in'] = x
+        return sample

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/autoencoder.py

@@ -0,0 +1,219 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
+from ldm.util import instantiate_from_config
+from ldm.modules.ema import LitEma
+
+
+class AutoencoderKL(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 ema_decay=None,
+                 learn_logvar=False
+                 ):
+        super().__init__()
+        self.learn_logvar = learn_logvar
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        assert ddconfig["double_z"]
+        self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.embed_dim = embed_dim
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+
+        self.use_ema = ema_decay is not None
+        if self.use_ema:
+            self.ema_decay = ema_decay
+            assert 0. < ema_decay < 1.
+            self.model_ema = LitEma(self, decay=ema_decay)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path}")
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self)
+
+    def encode(self, x):
+        h = self.encoder(x)
+        moments = self.quant_conv(h)
+        posterior = DiagonalGaussianDistribution(moments)
+        return posterior
+
+    def decode(self, z):
+        z = self.post_quant_conv(z)
+        dec = self.decoder(z)
+        return dec
+
+    def forward(self, input, sample_posterior=True):
+        posterior = self.encode(input)
+        if sample_posterior:
+            z = posterior.sample()
+        else:
+            z = posterior.mode()
+        dec = self.decode(z)
+        return dec, posterior
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        inputs = self.get_input(batch, self.image_key)
+        reconstructions, posterior = self(inputs)
+
+        if optimizer_idx == 0:
+            # train encoder+decoder+logvar
+            aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # train the discriminator
+            discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+                                                last_layer=self.get_last_layer(), split="train")
+
+            self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
+        return log_dict
+
+    def _validation_step(self, batch, batch_idx, postfix=""):
+        inputs = self.get_input(batch, self.image_key)
+        reconstructions, posterior = self(inputs)
+        aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
+                                        last_layer=self.get_last_layer(), split="val"+postfix)
+
+        discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
+                                            last_layer=self.get_last_layer(), split="val"+postfix)
+
+        self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list(
+            self.quant_conv.parameters()) + list(self.post_quant_conv.parameters())
+        if self.learn_logvar:
+            print(f"{self.__class__.__name__}: Learning logvar")
+            ae_params_list.append(self.loss.logvar)
+        opt_ae = torch.optim.Adam(ae_params_list,
+                                  lr=lr, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr, betas=(0.5, 0.9))
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    @torch.no_grad()
+    def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if not only_inputs:
+            xrec, posterior = self(x)
+            if x.shape[1] > 3:
+                # colorize with random projection
+                assert xrec.shape[1] > 3
+                x = self.to_rgb(x)
+                xrec = self.to_rgb(xrec)
+            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+            log["reconstructions"] = xrec
+            if log_ema or self.use_ema:
+                with self.ema_scope():
+                    xrec_ema, posterior_ema = self(x)
+                    if x.shape[1] > 3:
+                        # colorize with random projection
+                        assert xrec_ema.shape[1] > 3
+                        xrec_ema = self.to_rgb(xrec_ema)
+                    log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample()))
+                    log["reconstructions_ema"] = xrec_ema
+        log["inputs"] = x
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class IdentityFirstStage(torch.nn.Module):
+    def __init__(self, *args, vq_interface=False, **kwargs):
+        self.vq_interface = vq_interface
+        super().__init__()
+
+    def encode(self, x, *args, **kwargs):
+        return x
+
+    def decode(self, x, *args, **kwargs):
+        return x
+
+    def quantize(self, x, *args, **kwargs):
+        if self.vq_interface:
+            return x, None, [None, None, None]
+        return x
+
+    def forward(self, x, *args, **kwargs):
+        return x
+

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/diffusion/ddim.py

@@ -0,0 +1,158 @@
+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+import torch
+import numpy as np
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+from ldm.models.diffusion.sampler import Sampler
+
+
+class DDIMSampler(Sampler):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.model_wrap = model.apply_model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+        self.rand_scale = 1.0
+
+    def register_buffer(self, name, attr):
+        if self.model.device == "cuda":
+            if type(attr) == torch.Tensor:
+                if attr.device != torch.device("cuda"):
+                    attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+
+        def to_torch(x): return x.clone().detach().to(
+            torch.float32).to(self.model.device)
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(
+            self.model.alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod',
+                             to_torch(np.sqrt(alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(
+            np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod',
+                             to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod',
+                             to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(
+            np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta, verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas',
+                             np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps',
+                             sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def compile(self,
+                S,
+                shape,
+                batch_size=1,
+                eta=0.,
+                temperature=1.,
+                verbose=False,
+                unconditional_guidance_scale=1.,
+                use_original_steps=False,
+                **kwargs
+                ):
+
+        self.steps = S
+        self.batch_size = batch_size
+        self.shape = shape
+        self.eta = eta
+        self.temperature = temperature
+        self.cond_scale = unconditional_guidance_scale
+        self.x_shape = (self.batch_size,
+                        self.shape[0], self.shape[1], self.shape[2])
+
+        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+        self.ts_list = torch.Tensor(
+            np.expand_dims(self.ddim_timesteps, axis=0))
+        self.ts_list = self.ts_list.fliplr().to(torch.int32).to(self.model.device)
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        alphas_prev = torch.Tensor(alphas_prev)
+
+        self.params_init = [
+            ('alpha', alphas),
+            ('alpha_prev', alphas_prev),
+            ('rsqrt(alpha)', alphas.rsqrt()),
+            ('sqrt(alpha_prev)', alphas_prev.sqrt()),
+            ('sqrt(1-alpha)', sqrt_one_minus_alphas),
+            ('sigma', torch.Tensor(sigmas)),
+            ('dir', torch.sqrt(1. - alphas_prev - sigmas**2))
+        ]
+
+        self.params = torch.stack(list(map(lambda x: x[1], self.params_init)))
+        self.params = self.params.fliplr().to(
+            self.model.betas.dtype).to(self.model.device)
+
+    def one_step(self, x, c_in, ts_t, param_t):
+        ts = ts_t[0].broadcast_to((self.batch_size)).contiguous()
+
+        param = {}
+        for idx, val in enumerate(self.params_init):
+            param[val[0]] = param_t[idx].broadcast_to(
+                (self.batch_size, 1, 1, 1)).contiguous()
+
+        model_output = self.run_model(x, c_in, ts)
+
+        if self.model.parameterization == "v":
+            e_t = self.model.predict_eps_from_z_and_v(x, ts, model_output)
+        else:
+            e_t = model_output
+
+        # current prediction for x_0
+        if self.model.parameterization != "v":
+            pred_x0 = (x - param['sqrt(1-alpha)'] *
+                       e_t) * param['rsqrt(alpha)']
+        else:
+            pred_x0 = self.model.predict_start_from_z_and_v(
+                x, ts, model_output)
+
+        # direction pointing to x_t
+        dir_xt = param['dir'] * e_t
+        noise = param['sigma'] * \
+            noise_like(x.shape, self.model.device, False) * self.temperature
+        x = param['sqrt(alpha_prev)'] * pred_x0 + dir_xt + noise
+        return x
+
+    def sampler_step(self, arg):
+        x, c_in, ts, params = arg
+        x = self.one_step(x, c_in, ts[:, 0], params[:, 0])
+        ts = torch.roll(ts, shifts=-1, dims=1)
+        params = torch.roll(params, shifts=-1, dims=1)
+        return [x, c_in, ts, params]
+
+    def init_loop(self, x, c_in):
+        return [x, c_in, self.ts_list.clone(), self.params.clone()]

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/diffusion/ddpm.py

@@ -0,0 +1,1795 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager, nullcontext
+from functools import partial
+import itertools
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.rank_zero import rank_zero_only
+from omegaconf import ListConfig
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+                         'crossattn': 'c_crossattn',
+                         'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+    return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPM(pl.LightningModule):
+    # classic DDPM with Gaussian diffusion, in image space
+    def __init__(self,
+                 unet_config,
+                 timesteps=1000,
+                 beta_schedule="linear",
+                 loss_type="l2",
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 load_only_unet=False,
+                 monitor="val/loss",
+                 use_ema=True,
+                 first_stage_key="image",
+                 image_size=256,
+                 channels=3,
+                 log_every_t=100,
+                 clip_denoised=True,
+                 linear_start=1e-4,
+                 linear_end=2e-2,
+                 cosine_s=8e-3,
+                 given_betas=None,
+                 original_elbo_weight=0.,
+                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+                 l_simple_weight=1.,
+                 conditioning_key=None,
+                 parameterization="eps",  # all assuming fixed variance schedules
+                 scheduler_config=None,
+                 use_positional_encodings=False,
+                 learn_logvar=False,
+                 logvar_init=0.,
+                 make_it_fit=False,
+                 ucg_training=None,
+                 reset_ema=False,
+                 reset_num_ema_updates=False,
+                 ):
+        super().__init__()
+        assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
+        self.parameterization = parameterization
+        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+        self.cond_stage_model = None
+        self.clip_denoised = clip_denoised
+        self.log_every_t = log_every_t
+        self.first_stage_key = first_stage_key
+        self.image_size = image_size  # try conv?
+        self.channels = channels
+        self.use_positional_encodings = use_positional_encodings
+        self.model = DiffusionWrapper(unet_config, conditioning_key)
+        count_params(self.model, verbose=True)
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self.model)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.use_scheduler = scheduler_config is not None
+        if self.use_scheduler:
+            self.scheduler_config = scheduler_config
+
+        self.v_posterior = v_posterior
+        self.original_elbo_weight = original_elbo_weight
+        self.l_simple_weight = l_simple_weight
+
+        if monitor is not None:
+            self.monitor = monitor
+        self.make_it_fit = make_it_fit
+        if reset_ema: assert exists(ckpt_path)
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+            if reset_ema:
+                assert self.use_ema
+                print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+                self.model_ema = LitEma(self.model)
+        if reset_num_ema_updates:
+            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+            assert self.use_ema
+            self.model_ema.reset_num_updates()
+
+        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+        self.loss_type = loss_type
+
+        self.learn_logvar = learn_logvar
+        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+        if self.learn_logvar:
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+        self.ucg_training = ucg_training or dict()
+        if self.ucg_training:
+            self.ucg_prng = np.random.RandomState()
+
+    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        if exists(given_betas):
+            betas = given_betas
+        else:
+            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+                                       cosine_s=cosine_s)
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+                1. - alphas_cumprod) + self.v_posterior * betas
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        if self.parameterization == "eps":
+            lvlb_weights = self.betas ** 2 / (
+                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+        elif self.parameterization == "x0":
+            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+        elif self.parameterization == "v":
+            lvlb_weights = torch.ones_like(self.betas ** 2 / (
+                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
+        else:
+            raise NotImplementedError("mu not supported")
+        lvlb_weights[0] = lvlb_weights[1]
+        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+        assert not torch.isnan(self.lvlb_weights).all()
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    @torch.no_grad()
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        if self.make_it_fit:
+            n_params = len([name for name, _ in
+                            itertools.chain(self.named_parameters(),
+                                            self.named_buffers())])
+            for name, param in tqdm(
+                    itertools.chain(self.named_parameters(),
+                                    self.named_buffers()),
+                    desc="Fitting old weights to new weights",
+                    total=n_params
+            ):
+                if not name in sd:
+                    continue
+                old_shape = sd[name].shape
+                new_shape = param.shape
+                assert len(old_shape) == len(new_shape)
+                if len(new_shape) > 2:
+                    # we only modify first two axes
+                    assert new_shape[2:] == old_shape[2:]
+                # assumes first axis corresponds to output dim
+                if not new_shape == old_shape:
+                    new_param = param.clone()
+                    old_param = sd[name]
+                    if len(new_shape) == 1:
+                        for i in range(new_param.shape[0]):
+                            new_param[i] = old_param[i % old_shape[0]]
+                    elif len(new_shape) >= 2:
+                        for i in range(new_param.shape[0]):
+                            for j in range(new_param.shape[1]):
+                                new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
+
+                        n_used_old = torch.ones(old_shape[1])
+                        for j in range(new_param.shape[1]):
+                            n_used_old[j % old_shape[1]] += 1
+                        n_used_new = torch.zeros(new_shape[1])
+                        for j in range(new_param.shape[1]):
+                            n_used_new[j] = n_used_old[j % old_shape[1]]
+
+                        n_used_new = n_used_new[None, :]
+                        while len(n_used_new.shape) < len(new_shape):
+                            n_used_new = n_used_new.unsqueeze(-1)
+                        new_param /= n_used_new
+
+                    sd[name] = new_param
+
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys:\n {missing}")
+        if len(unexpected) > 0:
+            print(f"\nUnexpected Keys:\n {unexpected}")
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def predict_start_from_z_and_v(self, x_t, t, v):
+        # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        return (
+                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+        )
+
+    def predict_eps_from_z_and_v(self, x_t, t, v):
+        return (
+                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, clip_denoised: bool):
+        model_out = self.model(x, t)
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape, return_intermediates=False):
+        device = self.betas.device
+        b = shape[0]
+        img = torch.randn(shape, device=device)
+        intermediates = [img]
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+                                clip_denoised=self.clip_denoised)
+            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+                intermediates.append(img)
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, batch_size=16, return_intermediates=False):
+        image_size = self.image_size
+        channels = self.channels
+        return self.p_sample_loop((batch_size, channels, image_size, image_size),
+                                  return_intermediates=return_intermediates)
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+    def get_v(self, x, noise, t):
+        return (
+                extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
+        )
+
+    def get_loss(self, pred, target, mean=True):
+        if self.loss_type == 'l1':
+            loss = (target - pred).abs()
+            if mean:
+                loss = loss.mean()
+        elif self.loss_type == 'l2':
+            if mean:
+                loss = torch.nn.functional.mse_loss(target, pred)
+            else:
+                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+        else:
+            raise NotImplementedError("unknown loss type '{loss_type}'")
+
+        return loss
+
+    def p_losses(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_out = self.model(x_noisy, t)
+
+        loss_dict = {}
+        if self.parameterization == "eps":
+            target = noise
+        elif self.parameterization == "x0":
+            target = x_start
+        elif self.parameterization == "v":
+            target = self.get_v(x_start, noise, t)
+        else:
+            raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
+
+        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+        log_prefix = 'train' if self.training else 'val'
+
+        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+        loss_simple = loss.mean() * self.l_simple_weight
+
+        loss_vlb = (self.lvlb_weights[t] * loss).mean()
+        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+        loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+        loss_dict.update({f'{log_prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def forward(self, x, *args, **kwargs):
+        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        return self.p_losses(x, t, *args, **kwargs)
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = rearrange(x, 'b h w c -> b c h w')
+        x = x.to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def shared_step(self, batch):
+        x = self.get_input(batch, self.first_stage_key)
+        loss, loss_dict = self(x)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        for k in self.ucg_training:
+            p = self.ucg_training[k]["p"]
+            val = self.ucg_training[k]["val"]
+            if val is None:
+                val = ""
+            for i in range(len(batch[k])):
+                if self.ucg_prng.choice(2, p=[1 - p, p]):
+                    batch[k][i] = val
+
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(loss_dict, prog_bar=True,
+                      logger=True, on_step=True, on_epoch=True)
+
+        self.log("global_step", self.global_step,
+                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        if self.use_scheduler:
+            lr = self.optimizers().param_groups[0]['lr']
+            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        return loss
+
+    @torch.no_grad()
+    def validation_step(self, batch, batch_idx):
+        _, loss_dict_no_ema = self.shared_step(batch)
+        with self.ema_scope():
+            _, loss_dict_ema = self.shared_step(batch)
+            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    def _get_rows_from_list(self, samples):
+        n_imgs_per_row = len(samples)
+        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.first_stage_key)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+
+        # get diffusion row
+        diffusion_row = list()
+        x_start = x[:n_row]
+
+        for t in range(self.num_timesteps):
+            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                t = t.to(self.device).long()
+                noise = torch.randn_like(x_start)
+                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+                diffusion_row.append(x_noisy)
+
+        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+            log["samples"] = samples
+            log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.learn_logvar:
+            params = params + [self.logvar]
+        opt = torch.optim.AdamW(params, lr=lr)
+        return opt
+
+
+class LatentDiffusion(DDPM):
+    """main class"""
+
+    def __init__(self,
+                 first_stage_config,
+                 cond_stage_config,
+                 num_timesteps_cond=None,
+                 cond_stage_key="image",
+                 cond_stage_trainable=False,
+                 concat_mode=True,
+                 cond_stage_forward=None,
+                 conditioning_key=None,
+                 scale_factor=1.0,
+                 scale_by_std=False,
+                 force_null_conditioning=False,
+                 *args, **kwargs):
+        self.force_null_conditioning = force_null_conditioning
+        self.num_timesteps_cond = default(num_timesteps_cond, 1)
+        self.scale_by_std = scale_by_std
+        assert self.num_timesteps_cond <= kwargs['timesteps']
+        # for backwards compatibility after implementation of DiffusionWrapper
+        if conditioning_key is None:
+            conditioning_key = 'concat' if concat_mode else 'crossattn'
+        if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
+            conditioning_key = None
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        reset_ema = kwargs.pop("reset_ema", False)
+        reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
+        ignore_keys = kwargs.pop("ignore_keys", [])
+        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+        self.concat_mode = concat_mode
+        self.cond_stage_trainable = cond_stage_trainable
+        self.cond_stage_key = cond_stage_key
+        try:
+            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+        except:
+            self.num_downs = 0
+        if not scale_by_std:
+            self.scale_factor = scale_factor
+        else:
+            self.register_buffer('scale_factor', torch.tensor(scale_factor))
+        self.instantiate_first_stage(first_stage_config)
+        self.instantiate_cond_stage(cond_stage_config)
+        self.cond_stage_forward = cond_stage_forward
+        self.clip_denoised = False
+        self.bbox_tokenizer = None
+
+        self.restarted_from_ckpt = False
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys)
+            self.restarted_from_ckpt = True
+            if reset_ema:
+                assert self.use_ema
+                print(
+                    f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+                self.model_ema = LitEma(self.model)
+        if reset_num_ema_updates:
+            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+            assert self.use_ema
+            self.model_ema.reset_num_updates()
+
+    def make_cond_schedule(self, ):
+        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+        self.cond_ids[:self.num_timesteps_cond] = ids
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+            x = super().get_input(batch, self.first_stage_key)
+            x = x.to(self.device)
+            encoder_posterior = self.encode_first_stage(x)
+            z = self.get_first_stage_encoding(encoder_posterior).detach()
+            del self.scale_factor
+            self.register_buffer('scale_factor', 1. / z.flatten().std())
+            print(f"setting self.scale_factor to {self.scale_factor}")
+            print("### USING STD-RESCALING ###")
+
+    def register_schedule(self,
+                          given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+        self.shorten_cond_schedule = self.num_timesteps_cond > 1
+        if self.shorten_cond_schedule:
+            self.make_cond_schedule()
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+    def instantiate_cond_stage(self, config):
+        if not self.cond_stage_trainable:
+            if config == "__is_first_stage__":
+                print("Using first stage also as cond stage.")
+                self.cond_stage_model = self.first_stage_model
+            elif config == "__is_unconditional__":
+                print(f"Training {self.__class__.__name__} as an unconditional model.")
+                self.cond_stage_model = None
+                # self.be_unconditional = True
+            else:
+                model = instantiate_from_config(config)
+                self.cond_stage_model = model.eval()
+                self.cond_stage_model.train = disabled_train
+                for param in self.cond_stage_model.parameters():
+                    param.requires_grad = False
+        else:
+            assert config != '__is_first_stage__'
+            assert config != '__is_unconditional__'
+            model = instantiate_from_config(config)
+            self.cond_stage_model = model
+
+    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+        denoise_row = []
+        for zd in tqdm(samples, desc=desc):
+            denoise_row.append(self.decode_first_stage(zd.to(self.device),
+                                                       force_not_quantize=force_no_decoder_quantization))
+        n_imgs_per_row = len(denoise_row)
+        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
+        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    def get_first_stage_encoding(self, encoder_posterior):
+        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+            z = encoder_posterior.sample()
+        elif isinstance(encoder_posterior, torch.Tensor):
+            z = encoder_posterior
+        else:
+            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+        return self.scale_factor * z
+
+    def get_learned_conditioning(self, c):
+        if self.cond_stage_forward is None:
+            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+                c = self.cond_stage_model.encode(c)
+                if isinstance(c, DiagonalGaussianDistribution):
+                    c = c.mode()
+            else:
+                c = self.cond_stage_model(c)
+        else:
+            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+        return c
+
+    def meshgrid(self, h, w):
+        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+        arr = torch.cat([y, x], dim=-1)
+        return arr
+
+    def delta_border(self, h, w):
+        """
+        :param h: height
+        :param w: width
+        :return: normalized distance to image border,
+         wtith min distance = 0 at border and max dist = 0.5 at image center
+        """
+        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+        arr = self.meshgrid(h, w) / lower_right_corner
+        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+        return edge_dist
+
+    def get_weighting(self, h, w, Ly, Lx, device):
+        weighting = self.delta_border(h, w)
+        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+                               self.split_input_params["clip_max_weight"], )
+        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+        if self.split_input_params["tie_braker"]:
+            L_weighting = self.delta_border(Ly, Lx)
+            L_weighting = torch.clip(L_weighting,
+                                     self.split_input_params["clip_min_tie_weight"],
+                                     self.split_input_params["clip_max_tie_weight"])
+
+            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+            weighting = weighting * L_weighting
+        return weighting
+
+    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
+        """
+        :param x: img of size (bs, c, h, w)
+        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+        """
+        bs, nc, h, w = x.shape
+
+        # number of crops in image
+        Ly = (h - kernel_size[0]) // stride[0] + 1
+        Lx = (w - kernel_size[1]) // stride[1] + 1
+
+        if uf == 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+        elif uf > 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+                                dilation=1, padding=0,
+                                stride=(stride[0] * uf, stride[1] * uf))
+            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+        elif df > 1 and uf == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+                                dilation=1, padding=0,
+                                stride=(stride[0] // df, stride[1] // df))
+            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+        else:
+            raise NotImplementedError
+
+        return fold, unfold, normalization, weighting
+
+    @torch.no_grad()
+    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+                  cond_key=None, return_original_cond=False, bs=None, return_x=False):
+        x = super().get_input(batch, k)
+        if bs is not None:
+            x = x[:bs]
+        x = x.to(self.device)
+        encoder_posterior = self.encode_first_stage(x)
+        z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+        if self.model.conditioning_key is not None and not self.force_null_conditioning:
+            if cond_key is None:
+                cond_key = self.cond_stage_key
+            if cond_key != self.first_stage_key:
+                if cond_key in ['caption', 'coordinates_bbox', "txt"]:
+                    xc = batch[cond_key]
+                elif cond_key in ['class_label', 'cls']:
+                    xc = batch
+                else:
+                    xc = super().get_input(batch, cond_key).to(self.device)
+            else:
+                xc = x
+            if not self.cond_stage_trainable or force_c_encode:
+                if isinstance(xc, dict) or isinstance(xc, list):
+                    c = self.get_learned_conditioning(xc)
+                else:
+                    c = self.get_learned_conditioning(xc.to(self.device))
+            else:
+                c = xc
+            if bs is not None:
+                c = c[:bs]
+
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                ckey = __conditioning_keys__[self.model.conditioning_key]
+                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+        else:
+            c = None
+            xc = None
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                c = {'pos_x': pos_x, 'pos_y': pos_y}
+        out = [z, c]
+        if return_first_stage_outputs:
+            xrec = self.decode_first_stage(z)
+            out.extend([x, xrec])
+        if return_x:
+            out.extend([x])
+        if return_original_cond:
+            out.append(xc)
+        return out
+
+    @torch.no_grad()
+    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+        return self.first_stage_model.decode(z)
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        return self.first_stage_model.encode(x)
+
+    def shared_step(self, batch, **kwargs):
+        x, c = self.get_input(batch, self.first_stage_key)
+        loss = self(x, c)
+        return loss
+
+    def forward(self, x, c, *args, **kwargs):
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        if self.model.conditioning_key is not None:
+            assert c is not None
+            if self.cond_stage_trainable:
+                c = self.get_learned_conditioning(c)
+            if self.shorten_cond_schedule:  # TODO: drop this option
+                tc = self.cond_ids[t].to(self.device)
+                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+        return self.p_losses(x, c, t, *args, **kwargs)
+
+    def apply_model(self, x_noisy, t, cond, return_ids=False):
+        if isinstance(cond, dict):
+            # hybrid case, cond is expected to be a dict
+            pass
+        else:
+            if not isinstance(cond, list):
+                cond = [cond]
+            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+            cond = {key: cond}
+
+        x_recon = self.model(x_noisy, t, **cond)
+
+        if isinstance(x_recon, tuple) and not return_ids:
+            return x_recon[0]
+        else:
+            return x_recon
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def p_losses(self, x_start, cond, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_output = self.apply_model(x_noisy, t, cond)
+
+        loss_dict = {}
+        prefix = 'train' if self.training else 'val'
+
+        if self.parameterization == "x0":
+            target = x_start
+        elif self.parameterization == "eps":
+            target = noise
+        elif self.parameterization == "v":
+            target = self.get_v(x_start, noise, t)
+        else:
+            raise NotImplementedError()
+
+        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+        logvar_t = self.logvar[t].to(self.device)
+        loss = loss_simple / torch.exp(logvar_t) + logvar_t
+        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+        if self.learn_logvar:
+            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+            loss_dict.update({'logvar': self.logvar.data.mean()})
+
+        loss = self.l_simple_weight * loss.mean()
+
+        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+        loss += (self.original_elbo_weight * loss_vlb)
+        loss_dict.update({f'{prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+                        return_x0=False, score_corrector=None, corrector_kwargs=None):
+        t_in = t
+        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+        if score_corrector is not None:
+            assert self.parameterization == "eps"
+            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+        if return_codebook_ids:
+            model_out, logits = model_out
+
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        else:
+            raise NotImplementedError()
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+        if quantize_denoised:
+            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        if return_codebook_ids:
+            return model_mean, posterior_variance, posterior_log_variance, logits
+        elif return_x0:
+            return model_mean, posterior_variance, posterior_log_variance, x_recon
+        else:
+            return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+        b, *_, device = *x.shape, x.device
+        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+                                       return_codebook_ids=return_codebook_ids,
+                                       quantize_denoised=quantize_denoised,
+                                       return_x0=return_x0,
+                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+        if return_codebook_ids:
+            raise DeprecationWarning("Support dropped.")
+            model_mean, _, model_log_variance, logits = outputs
+        elif return_x0:
+            model_mean, _, model_log_variance, x0 = outputs
+        else:
+            model_mean, _, model_log_variance = outputs
+
+        noise = noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+        if return_codebook_ids:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+        if return_x0:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+        else:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+                              log_every_t=None):
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        timesteps = self.num_timesteps
+        if batch_size is not None:
+            b = batch_size if batch_size is not None else shape[0]
+            shape = [batch_size] + list(shape)
+        else:
+            b = batch_size = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=self.device)
+        else:
+            img = x_T
+        intermediates = []
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+                        total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+        if type(temperature) == float:
+            temperature = [temperature] * timesteps
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img, x0_partial = self.p_sample(img, cond, ts,
+                                            clip_denoised=self.clip_denoised,
+                                            quantize_denoised=quantize_denoised, return_x0=True,
+                                            temperature=temperature[i], noise_dropout=noise_dropout,
+                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(x0_partial)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_loop(self, cond, shape, return_intermediates=False,
+                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, start_T=None,
+                      log_every_t=None):
+
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        device = self.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        intermediates = [img]
+        if timesteps is None:
+            timesteps = self.num_timesteps
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+
+        if mask is not None:
+            assert x0 is not None
+            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img = self.p_sample(img, cond, ts,
+                                clip_denoised=self.clip_denoised,
+                                quantize_denoised=quantize_denoised)
+            if mask is not None:
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(img)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+               verbose=True, timesteps=None, quantize_denoised=False,
+               mask=None, x0=None, shape=None, **kwargs):
+        if shape is None:
+            shape = (batch_size, self.channels, self.image_size, self.image_size)
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+        return self.p_sample_loop(cond,
+                                  shape,
+                                  return_intermediates=return_intermediates, x_T=x_T,
+                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+                                  mask=mask, x0=x0)
+
+    @torch.no_grad()
+    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
+        if ddim:
+            ddim_sampler = DDIMSampler(self)
+            shape = (self.channels, self.image_size, self.image_size)
+            samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
+                                                         shape, cond, verbose=False, **kwargs)
+
+        else:
+            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+                                                 return_intermediates=True, **kwargs)
+
+        return samples, intermediates
+
+    @torch.no_grad()
+    def get_unconditional_conditioning(self, batch_size, null_label=None):
+        if null_label is not None:
+            xc = null_label
+            if isinstance(xc, ListConfig):
+                xc = list(xc)
+            if isinstance(xc, dict) or isinstance(xc, list):
+                c = self.get_learned_conditioning(xc)
+            else:
+                if hasattr(xc, "to"):
+                    xc = xc.to(self.device)
+                c = self.get_learned_conditioning(xc)
+        else:
+            if self.cond_stage_key in ["class_label", "cls"]:
+                xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
+                return self.get_learned_conditioning(xc)
+            else:
+                raise NotImplementedError("todo")
+        if isinstance(c, list):  # in case the encoder gives us a list
+            for i in range(len(c)):
+                c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
+        else:
+            c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
+        return c
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
+                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+                   use_ema_scope=True,
+                   **kwargs):
+        ema_scope = self.ema_scope if use_ema_scope else nullcontext
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+                                           return_first_stage_outputs=True,
+                                           force_c_encode=True,
+                                           return_original_cond=True,
+                                           bs=N)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption", "txt"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ['class_label', "cls"]:
+                try:
+                    xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+                    log['conditioning'] = xc
+                except KeyError:
+                    # probably no "human_label" in batch
+                    pass
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with ema_scope("Sampling"):
+                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                         ddim_steps=ddim_steps, eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+                    self.first_stage_model, IdentityFirstStage):
+                # also display when quantizing x0 while sampling
+                with ema_scope("Plotting Quantized Denoised"):
+                    samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                             ddim_steps=ddim_steps, eta=ddim_eta,
+                                                             quantize_denoised=True)
+                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+                    #                                      quantize_denoised=True)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_x0_quantized"] = x_samples
+
+        if unconditional_guidance_scale > 1.0:
+            uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+            if self.model.conditioning_key == "crossattn-adm":
+                uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
+            with ema_scope("Sampling with classifier-free guidance"):
+                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                 ddim_steps=ddim_steps, eta=ddim_eta,
+                                                 unconditional_guidance_scale=unconditional_guidance_scale,
+                                                 unconditional_conditioning=uc,
+                                                 )
+                x_samples_cfg = self.decode_first_stage(samples_cfg)
+                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+        if inpaint:
+            # make a simple center square
+            b, h, w = z.shape[0], z.shape[2], z.shape[3]
+            mask = torch.ones(N, h, w).to(self.device)
+            # zeros will be filled in
+            mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+            mask = mask[:, None, ...]
+            with ema_scope("Plotting Inpaint"):
+                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+            x_samples = self.decode_first_stage(samples.to(self.device))
+            log["samples_inpainting"] = x_samples
+            log["mask"] = mask
+
+            # outpaint
+            mask = 1. - mask
+            with ema_scope("Plotting Outpaint"):
+                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+            x_samples = self.decode_first_stage(samples.to(self.device))
+            log["samples_outpainting"] = x_samples
+
+        if plot_progressive_rows:
+            with ema_scope("Plotting Progressives"):
+                img, progressives = self.progressive_denoising(c,
+                                                               shape=(self.channels, self.image_size, self.image_size),
+                                                               batch_size=N)
+            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+            log["progressive_row"] = prog_row
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.cond_stage_trainable:
+            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+            params = params + list(self.cond_stage_model.parameters())
+        if self.learn_logvar:
+            print('Diffusion model optimizing logvar')
+            params.append(self.logvar)
+        opt = torch.optim.AdamW(params, lr=lr)
+        if self.use_scheduler:
+            assert 'target' in self.scheduler_config
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                }]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def to_rgb(self, x):
+        x = x.float()
+        if not hasattr(self, "colorize"):
+            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+        x = nn.functional.conv2d(x, weight=self.colorize)
+        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+        return x
+
+
+class DiffusionWrapper(pl.LightningModule):
+    def __init__(self, diff_model_config, conditioning_key):
+        super().__init__()
+        self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
+        self.diffusion_model = instantiate_from_config(diff_model_config)
+        self.conditioning_key = conditioning_key
+        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
+
+    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None):
+        if self.conditioning_key is None:
+            out = self.diffusion_model(x, t)
+        elif self.conditioning_key == 'concat':
+            xc = torch.cat([x] + c_concat, dim=1)
+            out = self.diffusion_model(xc, t)
+        elif self.conditioning_key == 'crossattn':
+            if not self.sequential_cross_attn:
+                cc = torch.cat(c_crossattn, 1)
+            else:
+                cc = c_crossattn
+            out = self.diffusion_model(x, t, context=cc)
+        elif self.conditioning_key == 'hybrid':
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc)
+        elif self.conditioning_key == 'hybrid-adm':
+            assert c_adm is not None
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc, y=c_adm)
+        elif self.conditioning_key == 'crossattn-adm':
+            assert c_adm is not None
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(x, t, context=cc, y=c_adm)
+        elif self.conditioning_key == 'adm':
+            cc = c_crossattn[0]
+            out = self.diffusion_model(x, t, y=cc)
+        else:
+            raise NotImplementedError()
+
+        return out
+
+
+class LatentUpscaleDiffusion(LatentDiffusion):
+    def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        # assumes that neither the cond_stage nor the low_scale_model contain trainable params
+        assert not self.cond_stage_trainable
+        self.instantiate_low_stage(low_scale_config)
+        self.low_scale_key = low_scale_key
+        self.noise_level_key = noise_level_key
+
+    def instantiate_low_stage(self, config):
+        model = instantiate_from_config(config)
+        self.low_scale_model = model.eval()
+        self.low_scale_model.train = disabled_train
+        for param in self.low_scale_model.parameters():
+            param.requires_grad = False
+
+    @torch.no_grad()
+    def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
+        if not log_mode:
+            z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
+        else:
+            z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+                                                  force_c_encode=True, return_original_cond=True, bs=bs)
+        x_low = batch[self.low_scale_key][:bs]
+        x_low = rearrange(x_low, 'b h w c -> b c h w')
+        x_low = x_low.to(memory_format=torch.contiguous_format).float()
+        zx, noise_level = self.low_scale_model(x_low)
+        if self.noise_level_key is not None:
+            # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
+            raise NotImplementedError('TODO')
+
+        all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
+        if log_mode:
+            # TODO: maybe disable if too expensive
+            x_low_rec = self.low_scale_model.decode(zx)
+            return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
+        return z, all_conds
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True,
+                   unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True,
+                   **kwargs):
+        ema_scope = self.ema_scope if use_ema_scope else nullcontext
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N,
+                                                                          log_mode=True)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        log["x_lr"] = x_low
+        log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption", "txt"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ['class_label', 'cls']:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with ema_scope("Sampling"):
+                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                         ddim_steps=ddim_steps, eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+        if unconditional_guidance_scale > 1.0:
+            uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+            # TODO explore better "unconditional" choices for the other keys
+            # maybe guide away from empty text label and highest noise level and maximally degraded zx?
+            uc = dict()
+            for k in c:
+                if k == "c_crossattn":
+                    assert isinstance(c[k], list) and len(c[k]) == 1
+                    uc[k] = [uc_tmp]
+                elif k == "c_adm":  # todo: only run with text-based guidance?
+                    assert isinstance(c[k], torch.Tensor)
+                    #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
+                    uc[k] = c[k]
+                elif isinstance(c[k], list):
+                    uc[k] = [c[k][i] for i in range(len(c[k]))]
+                else:
+                    uc[k] = c[k]
+
+            with ema_scope("Sampling with classifier-free guidance"):
+                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+                                                 ddim_steps=ddim_steps, eta=ddim_eta,
+                                                 unconditional_guidance_scale=unconditional_guidance_scale,
+                                                 unconditional_conditioning=uc,
+                                                 )
+                x_samples_cfg = self.decode_first_stage(samples_cfg)
+                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+        if plot_progressive_rows:
+            with ema_scope("Plotting Progressives"):
+                img, progressives = self.progressive_denoising(c,
+                                                               shape=(self.channels, self.image_size, self.image_size),
+                                                               batch_size=N)
+            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+            log["progressive_row"] = prog_row
+
+        return log
+
+
+class LatentFinetuneDiffusion(LatentDiffusion):
+    """
+         Basis for different finetunas, such as inpainting or depth2image
+         To disable finetuning mode, set finetune_keys to None
+    """
+
+    def __init__(self,
+                 concat_keys: tuple,
+                 finetune_keys=("model.diffusion_model.input_blocks.0.0.weight",
+                                "model_ema.diffusion_modelinput_blocks00weight"
+                                ),
+                 keep_finetune_dims=4,
+                 # if model was trained without concat mode before and we would like to keep these channels
+                 c_concat_log_start=None,  # to log reconstruction of c_concat codes
+                 c_concat_log_end=None,
+                 *args, **kwargs
+                 ):
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", list())
+        super().__init__(*args, **kwargs)
+        self.finetune_keys = finetune_keys
+        self.concat_keys = concat_keys
+        self.keep_dims = keep_finetune_dims
+        self.c_concat_log_start = c_concat_log_start
+        self.c_concat_log_end = c_concat_log_end
+        if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
+        if exists(ckpt_path):
+            self.init_from_ckpt(ckpt_path, ignore_keys)
+
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+
+            # make it explicit, finetune by including extra input channels
+            if exists(self.finetune_keys) and k in self.finetune_keys:
+                new_entry = None
+                for name, param in self.named_parameters():
+                    if name in self.finetune_keys:
+                        print(
+                            f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only")
+                        new_entry = torch.zeros_like(param)  # zero init
+                assert exists(new_entry), 'did not find matching parameter to modify'
+                new_entry[:, :self.keep_dims, ...] = sd[k]
+                sd[k] = new_entry
+
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+                   use_ema_scope=True,
+                   **kwargs):
+        ema_scope = self.ema_scope if use_ema_scope else nullcontext
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
+        c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption", "txt"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ['class_label', 'cls']:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
+            log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end])
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with ema_scope("Sampling"):
+                samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+                                                         batch_size=N, ddim=use_ddim,
+                                                         ddim_steps=ddim_steps, eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+        if unconditional_guidance_scale > 1.0:
+            uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+            uc_cat = c_cat
+            uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
+            with ema_scope("Sampling with classifier-free guidance"):
+                samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+                                                 batch_size=N, ddim=use_ddim,
+                                                 ddim_steps=ddim_steps, eta=ddim_eta,
+                                                 unconditional_guidance_scale=unconditional_guidance_scale,
+                                                 unconditional_conditioning=uc_full,
+                                                 )
+                x_samples_cfg = self.decode_first_stage(samples_cfg)
+                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+        return log
+
+
+class LatentInpaintDiffusion(LatentFinetuneDiffusion):
+    """
+    can either run as pure inpainting model (only concat mode) or with mixed conditionings,
+    e.g. mask as concat and text via cross-attn.
+    To disable finetuning mode, set finetune_keys to None
+     """
+
+    def __init__(self,
+                 concat_keys=("mask", "masked_image"),
+                 masked_image_key="masked_image",
+                 *args, **kwargs
+                 ):
+        super().__init__(concat_keys, *args, **kwargs)
+        self.masked_image_key = masked_image_key
+        assert self.masked_image_key in concat_keys
+
+    @torch.no_grad()
+    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+        # note: restricted to non-trainable encoders currently
+        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
+        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+                                              force_c_encode=True, return_original_cond=True, bs=bs)
+
+        assert exists(self.concat_keys)
+        c_cat = list()
+        for ck in self.concat_keys:
+            cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+            if bs is not None:
+                cc = cc[:bs]
+                cc = cc.to(self.device)
+            bchw = z.shape
+            if ck != self.masked_image_key:
+                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
+            else:
+                cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
+            c_cat.append(cc)
+        c_cat = torch.cat(c_cat, dim=1)
+        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+        if return_first_stage_outputs:
+            return z, all_conds, x, xrec, xc
+        return z, all_conds
+
+    @torch.no_grad()
+    def log_images(self, *args, **kwargs):
+        log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
+        log["masked_image"] = rearrange(args[0]["masked_image"],
+                                        'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+        return log
+
+
+class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
+    """
+    condition on monocular depth estimation
+    """
+
+    def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs):
+        super().__init__(concat_keys=concat_keys, *args, **kwargs)
+        self.depth_model = instantiate_from_config(depth_stage_config)
+        self.depth_stage_key = concat_keys[0]
+
+    @torch.no_grad()
+    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+        # note: restricted to non-trainable encoders currently
+        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img'
+        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+                                              force_c_encode=True, return_original_cond=True, bs=bs)
+
+        assert exists(self.concat_keys)
+        assert len(self.concat_keys) == 1
+        c_cat = list()
+        for ck in self.concat_keys:
+            cc = batch[ck]
+            if bs is not None:
+                cc = cc[:bs]
+                cc = cc.to(self.device)
+            cc = self.depth_model(cc)
+            cc = torch.nn.functional.interpolate(
+                cc,
+                size=z.shape[2:],
+                mode="bicubic",
+                align_corners=False,
+            )
+
+            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
+                                                                                           keepdim=True)
+            cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.
+            c_cat.append(cc)
+        c_cat = torch.cat(c_cat, dim=1)
+        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+        if return_first_stage_outputs:
+            return z, all_conds, x, xrec, xc
+        return z, all_conds
+
+    @torch.no_grad()
+    def log_images(self, *args, **kwargs):
+        log = super().log_images(*args, **kwargs)
+        depth = self.depth_model(args[0][self.depth_stage_key])
+        depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \
+                               torch.amax(depth, dim=[1, 2, 3], keepdim=True)
+        log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1.
+        return log
+
+
+class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
+    """
+        condition on low-res image (and optionally on some spatial noise augmentation)
+    """
+    def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None,
+                 low_scale_config=None, low_scale_key=None, *args, **kwargs):
+        super().__init__(concat_keys=concat_keys, *args, **kwargs)
+        self.reshuffle_patch_size = reshuffle_patch_size
+        self.low_scale_model = None
+        if low_scale_config is not None:
+            print("Initializing a low-scale model")
+            assert exists(low_scale_key)
+            self.instantiate_low_stage(low_scale_config)
+            self.low_scale_key = low_scale_key
+
+    def instantiate_low_stage(self, config):
+        model = instantiate_from_config(config)
+        self.low_scale_model = model.eval()
+        self.low_scale_model.train = disabled_train
+        for param in self.low_scale_model.parameters():
+            param.requires_grad = False
+
+    @torch.no_grad()
+    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+        # note: restricted to non-trainable encoders currently
+        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft'
+        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+                                              force_c_encode=True, return_original_cond=True, bs=bs)
+
+        assert exists(self.concat_keys)
+        assert len(self.concat_keys) == 1
+        # optionally make spatial noise_level here
+        c_cat = list()
+        noise_level = None
+        for ck in self.concat_keys:
+            cc = batch[ck]
+            cc = rearrange(cc, 'b h w c -> b c h w')
+            if exists(self.reshuffle_patch_size):
+                assert isinstance(self.reshuffle_patch_size, int)
+                cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
+                               p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size)
+            if bs is not None:
+                cc = cc[:bs]
+                cc = cc.to(self.device)
+            if exists(self.low_scale_model) and ck == self.low_scale_key:
+                cc, noise_level = self.low_scale_model(cc)
+            c_cat.append(cc)
+        c_cat = torch.cat(c_cat, dim=1)
+        if exists(noise_level):
+            all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level}
+        else:
+            all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+        if return_first_stage_outputs:
+            return z, all_conds, x, xrec, xc
+        return z, all_conds
+
+    @torch.no_grad()
+    def log_images(self, *args, **kwargs):
+        log = super().log_images(*args, **kwargs)
+        log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w')
+        return log

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/diffusion/dpm_solver/__init__.py

@@ -0,0 +1 @@
+from .sampler import DPMSolverSampler

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/diffusion/dpm_solver/dpm_solver.py

@@ -0,0 +1,1154 @@
+import torch
+import torch.nn.functional as F
+import math
+from tqdm import tqdm
+
+
+class NoiseScheduleVP:
+    def __init__(
+            self,
+            schedule='discrete',
+            betas=None,
+            alphas_cumprod=None,
+            continuous_beta_0=0.1,
+            continuous_beta_1=20.,
+    ):
+        """Create a wrapper class for the forward SDE (VP type).
+        ***
+        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
+                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
+        ***
+        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
+        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
+        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
+            log_alpha_t = self.marginal_log_mean_coeff(t)
+            sigma_t = self.marginal_std(t)
+            lambda_t = self.marginal_lambda(t)
+        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
+            t = self.inverse_lambda(lambda_t)
+        ===============================================================
+        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
+        1. For discrete-time DPMs:
+            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
+                t_i = (i + 1) / N
+            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
+            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
+            Args:
+                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
+                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
+            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
+            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
+                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
+                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
+                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
+                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
+                and
+                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
+        2. For continuous-time DPMs:
+            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
+            schedule are the default settings in DDPM and improved-DDPM:
+            Args:
+                beta_min: A `float` number. The smallest beta for the linear schedule.
+                beta_max: A `float` number. The largest beta for the linear schedule.
+                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
+                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
+                T: A `float` number. The ending time of the forward process.
+        ===============================================================
+        Args:
+            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
+                    'linear' or 'cosine' for continuous-time DPMs.
+        Returns:
+            A wrapper object of the forward SDE (VP type).
+
+        ===============================================================
+        Example:
+        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', betas=betas)
+        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+        # For continuous-time DPMs (VPSDE), linear schedule:
+        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
+        """
+
+        if schedule not in ['discrete', 'linear', 'cosine']:
+            raise ValueError(
+                "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
+                    schedule))
+
+        self.schedule = schedule
+        if schedule == 'discrete':
+            if betas is not None:
+                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
+            else:
+                assert alphas_cumprod is not None
+                log_alphas = 0.5 * torch.log(alphas_cumprod)
+            self.total_N = len(log_alphas)
+            self.T = 1.
+            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
+            self.log_alpha_array = log_alphas.reshape((1, -1,))
+        else:
+            self.total_N = 1000
+            self.beta_0 = continuous_beta_0
+            self.beta_1 = continuous_beta_1
+            self.cosine_s = 0.008
+            self.cosine_beta_max = 999.
+            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
+                        1. + self.cosine_s) / math.pi - self.cosine_s
+            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
+            self.schedule = schedule
+            if schedule == 'cosine':
+                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
+                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
+                self.T = 0.9946
+            else:
+                self.T = 1.
+
+    def marginal_log_mean_coeff(self, t):
+        """
+        Compute log(alpha_t) of a given continuous-time label t in [0, T].
+        """
+        if self.schedule == 'discrete':
+            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
+                                  self.log_alpha_array.to(t.device)).reshape((-1))
+        elif self.schedule == 'linear':
+            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+        elif self.schedule == 'cosine':
+            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
+            log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
+            return log_alpha_t
+
+    def marginal_alpha(self, t):
+        """
+        Compute alpha_t of a given continuous-time label t in [0, T].
+        """
+        return torch.exp(self.marginal_log_mean_coeff(t))
+
+    def marginal_std(self, t):
+        """
+        Compute sigma_t of a given continuous-time label t in [0, T].
+        """
+        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+    def marginal_lambda(self, t):
+        """
+        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+        """
+        log_mean_coeff = self.marginal_log_mean_coeff(t)
+        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+        return log_mean_coeff - log_std
+
+    def inverse_lambda(self, lamb):
+        """
+        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
+        """
+        if self.schedule == 'linear':
+            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            Delta = self.beta_0 ** 2 + tmp
+            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
+        elif self.schedule == 'discrete':
+            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
+            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
+                               torch.flip(self.t_array.to(lamb.device), [1]))
+            return t.reshape((-1,))
+        else:
+            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
+                        1. + self.cosine_s) / math.pi - self.cosine_s
+            t = t_fn(log_alpha)
+            return t
+
+
+def model_wrapper(
+        model,
+        noise_schedule,
+        model_type="noise",
+        model_kwargs={},
+        guidance_type="uncond",
+        condition=None,
+        unconditional_condition=None,
+        guidance_scale=1.,
+        classifier_fn=None,
+        classifier_kwargs={},
+):
+    """Create a wrapper function for the noise prediction model.
+    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
+    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
+    We support four types of the diffusion model by setting `model_type`:
+        1. "noise": noise prediction model. (Trained by predicting noise).
+        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
+        3. "v": velocity prediction model. (Trained by predicting the velocity).
+            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
+            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
+                arXiv preprint arXiv:2202.00512 (2022).
+            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
+                arXiv preprint arXiv:2210.02303 (2022).
+
+        4. "score": marginal score function. (Trained by denoising score matching).
+            Note that the score function and the noise prediction model follows a simple relationship:
+            ```
+                noise(x_t, t) = -sigma_t * score(x_t, t)
+            ```
+    We support three types of guided sampling by DPMs by setting `guidance_type`:
+        1. "uncond": unconditional sampling by DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            ``
+        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            ``
+            The input `classifier_fn` has the following format:
+            ``
+                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
+            ``
+            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
+                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
+        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
+            ``
+            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
+            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
+                arXiv preprint arXiv:2207.12598 (2022).
+
+    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
+    or continuous-time labels (i.e. epsilon to T).
+    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
+    ``
+        def model_fn(x, t_continuous) -> noise:
+            t_input = get_model_input_time(t_continuous)
+            return noise_pred(model, x, t_input, **model_kwargs)
+    ``
+    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
+    ===============================================================
+    Args:
+        model: A diffusion model with the corresponding format described above.
+        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+        model_type: A `str`. The parameterization type of the diffusion model.
+                    "noise" or "x_start" or "v" or "score".
+        model_kwargs: A `dict`. A dict for the other inputs of the model function.
+        guidance_type: A `str`. The type of the guidance for sampling.
+                    "uncond" or "classifier" or "classifier-free".
+        condition: A pytorch tensor. The condition for the guided sampling.
+                    Only used for "classifier" or "classifier-free" guidance type.
+        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
+                    Only used for "classifier-free" guidance type.
+        guidance_scale: A `float`. The scale for the guided sampling.
+        classifier_fn: A classifier function. Only used for the classifier guidance.
+        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
+    Returns:
+        A noise prediction model that accepts the noised data and the continuous time as the inputs.
+    """
+
+    def get_model_input_time(t_continuous):
+        """
+        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
+        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
+        For continuous-time DPMs, we just use `t_continuous`.
+        """
+        if noise_schedule.schedule == 'discrete':
+            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+        else:
+            return t_continuous
+
+    def noise_pred_fn(x, t_continuous, cond=None):
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        t_input = get_model_input_time(t_continuous)
+        if cond is None:
+            output = model(x, t_input, **model_kwargs)
+        else:
+            output = model(x, t_input, cond, **model_kwargs)
+        if model_type == "noise":
+            return output
+        elif model_type == "x_start":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
+        elif model_type == "v":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+        elif model_type == "score":
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return -expand_dims(sigma_t, dims) * output
+
+    def cond_grad_fn(x, t_input):
+        """
+        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
+        """
+        with torch.enable_grad():
+            x_in = x.detach().requires_grad_(True)
+            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
+            return torch.autograd.grad(log_prob.sum(), x_in)[0]
+
+    def model_fn(x, t_continuous):
+        """
+        The noise predicition model function that is used for DPM-Solver.
+        """
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        if guidance_type == "uncond":
+            return noise_pred_fn(x, t_continuous)
+        elif guidance_type == "classifier":
+            assert classifier_fn is not None
+            t_input = get_model_input_time(t_continuous)
+            cond_grad = cond_grad_fn(x, t_input)
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            noise = noise_pred_fn(x, t_continuous)
+            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
+        elif guidance_type == "classifier-free":
+            if guidance_scale == 1. or unconditional_condition is None:
+                return noise_pred_fn(x, t_continuous, cond=condition)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t_continuous] * 2)
+                c_in = torch.cat([unconditional_condition, condition])
+                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+                return noise_uncond + guidance_scale * (noise - noise_uncond)
+
+    assert model_type in ["noise", "x_start", "v"]
+    assert guidance_type in ["uncond", "classifier", "classifier-free"]
+    return model_fn
+
+
+class DPM_Solver:
+    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
+        """Construct a DPM-Solver.
+        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
+        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
+        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
+            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
+            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
+        Args:
+            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
+                ``
+                def model_fn(x, t_continuous):
+                    return noise
+                ``
+            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
+            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
+            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
+
+        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
+        """
+        self.model = model_fn
+        self.noise_schedule = noise_schedule
+        self.predict_x0 = predict_x0
+        self.thresholding = thresholding
+        self.max_val = max_val
+
+    def noise_prediction_fn(self, x, t):
+        """
+        Return the noise prediction model.
+        """
+        return self.model(x, t)
+
+    def data_prediction_fn(self, x, t):
+        """
+        Return the data prediction model (with thresholding).
+        """
+        noise = self.noise_prediction_fn(x, t)
+        dims = x.dim()
+        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
+        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
+        if self.thresholding:
+            p = 0.995  # A hyperparameter in the paper of "Imagen" [1].
+            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
+            x0 = torch.clamp(x0, -s, s) / s
+        return x0
+
+    def model_fn(self, x, t):
+        """
+        Convert the model to the noise prediction model or the data prediction model.
+        """
+        if self.predict_x0:
+            return self.data_prediction_fn(x, t)
+        else:
+            return self.noise_prediction_fn(x, t)
+
+    def get_time_steps(self, skip_type, t_T, t_0, N, device):
+        """Compute the intermediate time steps for sampling.
+        Args:
+            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+                - 'logSNR': uniform logSNR for the time steps.
+                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+            t_T: A `float`. The starting time of the sampling (default is T).
+            t_0: A `float`. The ending time of the sampling (default is epsilon).
+            N: A `int`. The total number of the spacing of the time steps.
+            device: A torch device.
+        Returns:
+            A pytorch tensor of the time steps, with the shape (N + 1,).
+        """
+        if skip_type == 'logSNR':
+            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
+            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
+            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
+            return self.noise_schedule.inverse_lambda(logSNR_steps)
+        elif skip_type == 'time_uniform':
+            return torch.linspace(t_T, t_0, N + 1).to(device)
+        elif skip_type == 'time_quadratic':
+            t_order = 2
+            t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
+            return t
+        else:
+            raise ValueError(
+                "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+
+    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
+        """
+        Get the order of each step for sampling by the singlestep DPM-Solver.
+        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
+        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
+            - If order == 1:
+                We take `steps` of DPM-Solver-1 (i.e. DDIM).
+            - If order == 2:
+                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
+                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
+                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
+            - If order == 3:
+                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
+                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
+                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
+        ============================================
+        Args:
+            order: A `int`. The max order for the solver (2 or 3).
+            steps: A `int`. The total number of function evaluations (NFE).
+            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+                - 'logSNR': uniform logSNR for the time steps.
+                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+            t_T: A `float`. The starting time of the sampling (default is T).
+            t_0: A `float`. The ending time of the sampling (default is epsilon).
+            device: A torch device.
+        Returns:
+            orders: A list of the solver order of each step.
+        """
+        if order == 3:
+            K = steps // 3 + 1
+            if steps % 3 == 0:
+                orders = [3, ] * (K - 2) + [2, 1]
+            elif steps % 3 == 1:
+                orders = [3, ] * (K - 1) + [1]
+            else:
+                orders = [3, ] * (K - 1) + [2]
+        elif order == 2:
+            if steps % 2 == 0:
+                K = steps // 2
+                orders = [2, ] * K
+            else:
+                K = steps // 2 + 1
+                orders = [2, ] * (K - 1) + [1]
+        elif order == 1:
+            K = 1
+            orders = [1, ] * steps
+        else:
+            raise ValueError("'order' must be '1' or '2' or '3'.")
+        if skip_type == 'logSNR':
+            # To reproduce the results in DPM-Solver paper
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
+        else:
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
+                torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
+        return timesteps_outer, orders
+
+    def denoise_to_zero_fn(self, x, s):
+        """
+        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
+        """
+        return self.data_prediction_fn(x, s)
+
+    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
+        """
+        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            model_s: A pytorch tensor. The model function evaluated at time `s`.
+                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+            return_intermediate: A `bool`. If true, also return the model value at time `s`.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        ns = self.noise_schedule
+        dims = x.dim()
+        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+        h = lambda_t - lambda_s
+        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
+        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        if self.predict_x0:
+            phi_1 = torch.expm1(-h)
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_t = (
+                    expand_dims(sigma_t / sigma_s, dims) * x
+                    - expand_dims(alpha_t * phi_1, dims) * model_s
+            )
+            if return_intermediate:
+                return x_t, {'model_s': model_s}
+            else:
+                return x_t
+        else:
+            phi_1 = torch.expm1(h)
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                    - expand_dims(sigma_t * phi_1, dims) * model_s
+            )
+            if return_intermediate:
+                return x_t, {'model_s': model_s}
+            else:
+                return x_t
+
+    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
+                                            solver_type='dpm_solver'):
+        """
+        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            r1: A `float`. The hyperparameter of the second-order solver.
+            model_s: A pytorch tensor. The model function evaluated at time `s`.
+                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if solver_type not in ['dpm_solver', 'taylor']:
+            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+        if r1 is None:
+            r1 = 0.5
+        ns = self.noise_schedule
+        dims = x.dim()
+        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+        h = lambda_t - lambda_s
+        lambda_s1 = lambda_s + r1 * h
+        s1 = ns.inverse_lambda(lambda_s1)
+        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
+            s1), ns.marginal_log_mean_coeff(t)
+        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
+        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
+
+        if self.predict_x0:
+            phi_11 = torch.expm1(-r1 * h)
+            phi_1 = torch.expm1(-h)
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_s1 = (
+                    expand_dims(sigma_s1 / sigma_s, dims) * x
+                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
+            )
+            model_s1 = self.model_fn(x_s1, s1)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(sigma_t / sigma_s, dims) * x
+                        - expand_dims(alpha_t * phi_1, dims) * model_s
+                        - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                        expand_dims(sigma_t / sigma_s, dims) * x
+                        - expand_dims(alpha_t * phi_1, dims) * model_s
+                        + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
+                                    model_s1 - model_s)
+                )
+        else:
+            phi_11 = torch.expm1(r1 * h)
+            phi_1 = torch.expm1(h)
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_s1 = (
+                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
+            )
+            model_s1 = self.model_fn(x_s1, s1)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                        - expand_dims(sigma_t * phi_1, dims) * model_s
+                        - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                        - expand_dims(sigma_t * phi_1, dims) * model_s
+                        - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
+                )
+        if return_intermediate:
+            return x_t, {'model_s': model_s, 'model_s1': model_s1}
+        else:
+            return x_t
+
+    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
+                                           return_intermediate=False, solver_type='dpm_solver'):
+        """
+        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            r1: A `float`. The hyperparameter of the third-order solver.
+            r2: A `float`. The hyperparameter of the third-order solver.
+            model_s: A pytorch tensor. The model function evaluated at time `s`.
+                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
+                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
+            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if solver_type not in ['dpm_solver', 'taylor']:
+            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+        if r1 is None:
+            r1 = 1. / 3.
+        if r2 is None:
+            r2 = 2. / 3.
+        ns = self.noise_schedule
+        dims = x.dim()
+        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+        h = lambda_t - lambda_s
+        lambda_s1 = lambda_s + r1 * h
+        lambda_s2 = lambda_s + r2 * h
+        s1 = ns.inverse_lambda(lambda_s1)
+        s2 = ns.inverse_lambda(lambda_s2)
+        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
+            s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
+        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
+            s2), ns.marginal_std(t)
+        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
+
+        if self.predict_x0:
+            phi_11 = torch.expm1(-r1 * h)
+            phi_12 = torch.expm1(-r2 * h)
+            phi_1 = torch.expm1(-h)
+            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
+            phi_2 = phi_1 / h + 1.
+            phi_3 = phi_2 / h - 0.5
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            if model_s1 is None:
+                x_s1 = (
+                        expand_dims(sigma_s1 / sigma_s, dims) * x
+                        - expand_dims(alpha_s1 * phi_11, dims) * model_s
+                )
+                model_s1 = self.model_fn(x_s1, s1)
+            x_s2 = (
+                    expand_dims(sigma_s2 / sigma_s, dims) * x
+                    - expand_dims(alpha_s2 * phi_12, dims) * model_s
+                    + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
+            )
+            model_s2 = self.model_fn(x_s2, s2)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(sigma_t / sigma_s, dims) * x
+                        - expand_dims(alpha_t * phi_1, dims) * model_s
+                        + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
+                )
+            elif solver_type == 'taylor':
+                D1_0 = (1. / r1) * (model_s1 - model_s)
+                D1_1 = (1. / r2) * (model_s2 - model_s)
+                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+                x_t = (
+                        expand_dims(sigma_t / sigma_s, dims) * x
+                        - expand_dims(alpha_t * phi_1, dims) * model_s
+                        + expand_dims(alpha_t * phi_2, dims) * D1
+                        - expand_dims(alpha_t * phi_3, dims) * D2
+                )
+        else:
+            phi_11 = torch.expm1(r1 * h)
+            phi_12 = torch.expm1(r2 * h)
+            phi_1 = torch.expm1(h)
+            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
+            phi_2 = phi_1 / h - 1.
+            phi_3 = phi_2 / h - 0.5
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            if model_s1 is None:
+                x_s1 = (
+                        expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+                        - expand_dims(sigma_s1 * phi_11, dims) * model_s
+                )
+                model_s1 = self.model_fn(x_s1, s1)
+            x_s2 = (
+                    expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
+                    - expand_dims(sigma_s2 * phi_12, dims) * model_s
+                    - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
+            )
+            model_s2 = self.model_fn(x_s2, s2)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                        - expand_dims(sigma_t * phi_1, dims) * model_s
+                        - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
+                )
+            elif solver_type == 'taylor':
+                D1_0 = (1. / r1) * (model_s1 - model_s)
+                D1_1 = (1. / r2) * (model_s2 - model_s)
+                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                        - expand_dims(sigma_t * phi_1, dims) * model_s
+                        - expand_dims(sigma_t * phi_2, dims) * D1
+                        - expand_dims(sigma_t * phi_3, dims) * D2
+                )
+
+        if return_intermediate:
+            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
+        else:
+            return x_t
+
+    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
+        """
+        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            model_prev_list: A list of pytorch tensor. The previous computed model values.
+            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if solver_type not in ['dpm_solver', 'taylor']:
+            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+        ns = self.noise_schedule
+        dims = x.dim()
+        model_prev_1, model_prev_0 = model_prev_list
+        t_prev_1, t_prev_0 = t_prev_list
+        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
+            t_prev_0), ns.marginal_lambda(t)
+        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h_0 = lambda_prev_0 - lambda_prev_1
+        h = lambda_t - lambda_prev_0
+        r0 = h_0 / h
+        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+        if self.predict_x0:
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(sigma_t / sigma_prev_0, dims) * x
+                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+                        - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                        expand_dims(sigma_t / sigma_prev_0, dims) * x
+                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+                        + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
+                )
+        else:
+            if solver_type == 'dpm_solver':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+                        - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+                        - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
+                )
+        return x_t
+
+    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
+        """
+        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            model_prev_list: A list of pytorch tensor. The previous computed model values.
+            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        ns = self.noise_schedule
+        dims = x.dim()
+        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
+        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
+        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
+            t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
+        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h_1 = lambda_prev_1 - lambda_prev_2
+        h_0 = lambda_prev_0 - lambda_prev_1
+        h = lambda_t - lambda_prev_0
+        r0, r1 = h_0 / h, h_1 / h
+        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
+        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
+        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
+        if self.predict_x0:
+            x_t = (
+                    expand_dims(sigma_t / sigma_prev_0, dims) * x
+                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
+                    - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
+            )
+        else:
+            x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
+                    - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
+            )
+        return x_t
+
+    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
+                                     r2=None):
+        """
+        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+            r1: A `float`. The hyperparameter of the second-order or third-order solver.
+            r2: A `float`. The hyperparameter of the third-order solver.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if order == 1:
+            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
+        elif order == 2:
+            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
+                                                            solver_type=solver_type, r1=r1)
+        elif order == 3:
+            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
+                                                           solver_type=solver_type, r1=r1, r2=r2)
+        else:
+            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
+        """
+        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            model_prev_list: A list of pytorch tensor. The previous computed model values.
+            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if order == 1:
+            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
+        elif order == 2:
+            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+        elif order == 3:
+            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+        else:
+            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
+                            solver_type='dpm_solver'):
+        """
+        The adaptive step size solver based on singlestep DPM-Solver.
+        Args:
+            x: A pytorch tensor. The initial value at time `t_T`.
+            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
+            t_T: A `float`. The starting time of the sampling (default is T).
+            t_0: A `float`. The ending time of the sampling (default is epsilon).
+            h_init: A `float`. The initial step size (for logSNR).
+            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
+            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
+            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
+            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
+                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_0: A pytorch tensor. The approximated solution at time `t_0`.
+        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
+        """
+        ns = self.noise_schedule
+        s = t_T * torch.ones((x.shape[0],)).to(x)
+        lambda_s = ns.marginal_lambda(s)
+        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
+        h = h_init * torch.ones_like(s).to(x)
+        x_prev = x
+        nfe = 0
+        if order == 2:
+            r1 = 0.5
+            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
+            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
+                                                                                               solver_type=solver_type,
+                                                                                               **kwargs)
+        elif order == 3:
+            r1, r2 = 1. / 3., 2. / 3.
+            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
+                                                                                    return_intermediate=True,
+                                                                                    solver_type=solver_type)
+            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
+                                                                                              solver_type=solver_type,
+                                                                                              **kwargs)
+        else:
+            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
+        while torch.abs((s - t_0)).mean() > t_err:
+            t = ns.inverse_lambda(lambda_s + h)
+            x_lower, lower_noise_kwargs = lower_update(x, s, t)
+            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
+            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
+            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
+            E = norm_fn((x_higher - x_lower) / delta).max()
+            if torch.all(E <= 1.):
+                x = x_higher
+                s = t
+                x_prev = x_lower
+                lambda_s = ns.marginal_lambda(s)
+            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
+            nfe += order
+        print('adaptive solver nfe', nfe)
+        return x
+
+    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
+               method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
+               atol=0.0078, rtol=0.05,
+               ):
+        """
+        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
+        =====================================================
+        We support the following algorithms for both noise prediction model and data prediction model:
+            - 'singlestep':
+                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
+                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
+                The total number of function evaluations (NFE) == `steps`.
+                Given a fixed NFE == `steps`, the sampling procedure is:
+                    - If `order` == 1:
+                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
+                    - If `order` == 2:
+                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
+                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
+                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+                    - If `order` == 3:
+                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
+                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
+            - 'multistep':
+                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
+                We initialize the first `order` values by lower order multistep solvers.
+                Given a fixed NFE == `steps`, the sampling procedure is:
+                    Denote K = steps.
+                    - If `order` == 1:
+                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
+                    - If `order` == 2:
+                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
+                    - If `order` == 3:
+                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
+            - 'singlestep_fixed':
+                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
+                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
+            - 'adaptive':
+                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
+                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
+                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
+                (NFE) and the sample quality.
+                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
+                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
+        =====================================================
+        Some advices for choosing the algorithm:
+            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
+                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
+                e.g.
+                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
+                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
+                            skip_type='time_uniform', method='singlestep')
+            - For **guided sampling with large guidance scale** by DPMs:
+                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
+                e.g.
+                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
+                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
+                            skip_type='time_uniform', method='multistep')
+        We support three types of `skip_type`:
+            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
+            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
+            - 'time_quadratic': quadratic time for the time steps.
+        =====================================================
+        Args:
+            x: A pytorch tensor. The initial value at time `t_start`
+                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
+            steps: A `int`. The total number of function evaluations (NFE).
+            t_start: A `float`. The starting time of the sampling.
+                If `T` is None, we use self.noise_schedule.T (default is 1.0).
+            t_end: A `float`. The ending time of the sampling.
+                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
+                e.g. if total_N == 1000, we have `t_end` == 1e-3.
+                For discrete-time DPMs:
+                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
+                For continuous-time DPMs:
+                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
+            order: A `int`. The order of DPM-Solver.
+            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
+            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
+            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
+                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
+                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
+                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
+                for diffusion models sampling by diffusion SDEs for low-resolutional images
+                (such as CIFAR-10). However, we observed that such trick does not matter for
+                high-resolutional images. As it needs an additional NFE, we do not recommend
+                it for high-resolutional images.
+            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
+                Only valid for `method=multistep` and `steps < 15`. We empirically find that
+                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
+                (especially for steps <= 10). So we recommend to set it to be `True`.
+            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
+            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+        Returns:
+            x_end: A pytorch tensor. The approximated solution at time `t_end`.
+        """
+        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
+        t_T = self.noise_schedule.T if t_start is None else t_start
+        device = x.device
+        if method == 'adaptive':
+            with torch.no_grad():
+                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
+                                             solver_type=solver_type)
+        elif method == 'multistep':
+            assert steps >= order
+            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+            assert timesteps.shape[0] - 1 == steps
+            with torch.no_grad():
+                vec_t = timesteps[0].expand((x.shape[0]))
+                model_prev_list = [self.model_fn(x, vec_t)]
+                t_prev_list = [vec_t]
+                # Init the first `order` values by lower order multistep DPM-Solver.
+                for init_order in tqdm(range(1, order), desc="DPM init order"):
+                    vec_t = timesteps[init_order].expand(x.shape[0])
+                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
+                                                         solver_type=solver_type)
+                    model_prev_list.append(self.model_fn(x, vec_t))
+                    t_prev_list.append(vec_t)
+                # Compute the remaining values by `order`-th order multistep DPM-Solver.
+                for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
+                    vec_t = timesteps[step].expand(x.shape[0])
+                    if lower_order_final and steps < 15:
+                        step_order = min(order, steps + 1 - step)
+                    else:
+                        step_order = order
+                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
+                                                         solver_type=solver_type)
+                    for i in range(order - 1):
+                        t_prev_list[i] = t_prev_list[i + 1]
+                        model_prev_list[i] = model_prev_list[i + 1]
+                    t_prev_list[-1] = vec_t
+                    # We do not need to evaluate the final model value.
+                    if step < steps:
+                        model_prev_list[-1] = self.model_fn(x, vec_t)
+        elif method in ['singlestep', 'singlestep_fixed']:
+            if method == 'singlestep':
+                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
+                                                                                              skip_type=skip_type,
+                                                                                              t_T=t_T, t_0=t_0,
+                                                                                              device=device)
+            elif method == 'singlestep_fixed':
+                K = steps // order
+                orders = [order, ] * K
+                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
+            for i, order in enumerate(orders):
+                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
+                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
+                                                      N=order, device=device)
+                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
+                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
+                h = lambda_inner[-1] - lambda_inner[0]
+                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
+                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
+                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
+        if denoise_to_zero:
+            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+        return x
+
+
+#############################################################
+# other utility functions
+#############################################################
+
+def interpolate_fn(x, xp, yp):
+    """
+    A piecewise linear function y = f(x), using xp and yp as keypoints.
+    We implement f(x) in a differentiable way (i.e. applicable for autograd).
+    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
+    Args:
+        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
+        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
+        yp: PyTorch tensor with shape [C, K].
+    Returns:
+        The function values f(x), with shape [N, C].
+    """
+    N, K = x.shape[0], xp.shape[1]
+    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
+    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
+    x_idx = torch.argmin(x_indices, dim=2)
+    cand_start_idx = x_idx - 1
+    start_idx = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(1, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
+    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
+    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
+    start_idx2 = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(0, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
+    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
+    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
+    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
+    return cand
+
+
+def expand_dims(v, dims):
+    """
+    Expand the tensor `v` to the dim `dims`.
+    Args:
+        `v`: a PyTorch tensor with shape [N].
+        `dim`: a `int`.
+    Returns:
+        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
+    """
+    return v[(...,) + (None,) * (dims - 1)]

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/diffusion/dpm_solver/sampler.py

@@ -0,0 +1,87 @@
+"""SAMPLING ONLY."""
+import torch
+
+from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
+
+
+MODEL_TYPES = {
+    "eps": "noise",
+    "v": "v"
+}
+
+
+class DPMSolverSampler(object):
+    def __init__(self, model, **kwargs):
+        super().__init__()
+        self.model = model
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
+        self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+
+        print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
+
+        device = self.model.betas.device
+        if x_T is None:
+            img = torch.randn(size, device=device)
+        else:
+            img = x_T
+
+        ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
+
+        model_fn = model_wrapper(
+            lambda x, t, c: self.model.apply_model(x, t, c),
+            ns,
+            model_type=MODEL_TYPES[self.model.parameterization],
+            guidance_type="classifier-free",
+            condition=conditioning,
+            unconditional_condition=unconditional_conditioning,
+            guidance_scale=unconditional_guidance_scale,
+        )
+
+        dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
+        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
+
+        return x.to(device), None

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/diffusion/dpmpp_2m.py

@@ -0,0 +1,78 @@
+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+import torch
+import k_diffusion as K
+import numpy as np
+
+from ldm.models.diffusion.sampler import Sampler
+
+
+class DPMPP2M_Sampler(Sampler):
+    def __init__(self, model, v_mode, **kwargs):
+        super().__init__()
+        self.model = model
+        if v_mode:
+            self.model_wrap = K.external.CompVisVDenoiser(model)
+        else:
+            self.model_wrap = K.external.CompVisDenoiser(model)
+
+    def generate_params(self, sigmas):
+        """DPM-Solver++(2M)."""
+        # Based on https://github.com/crowsonkb/k-diffusion/blob/v0.0.14/k_diffusion/sampling.py#L585
+        device = sigmas.device
+        sigmas = sigmas.cpu()
+        def sigma_fn(t): return t.neg().exp()
+        def t_fn(sigma): return sigma.log().neg()
+        params = []
+        for i in range(len(sigmas) - 1):
+            sigma = sigmas[i]
+            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+            h = t_next - t
+            a = sigma_fn(t_next) / sigma_fn(t)
+            if i == 0 or sigmas[i + 1] == 0:
+                b = 1.0
+                c = 0.0
+            else:
+                h_last = t - t_fn(sigmas[i - 1])
+                r = h_last / h
+                b = 1 + 1 / (2 * r)
+                c = 1 / (2 * r)
+            b *= - (-h).expm1()
+            c *= (-h).expm1()
+            p = np.array([a.numpy(), b.numpy(), c.numpy(), sigma.numpy()])
+            params.append(p)
+        params = torch.Tensor(np.stack(params, axis=0)
+                              ).transpose(0, 1).to(device)
+        return params
+
+    @torch.no_grad()
+    def compile(self,
+                S,
+                shape,
+                unconditional_guidance_scale=1.,
+                batch_size=1,
+                **kwargs
+                ):
+        self.sigmas = self.model_wrap.get_sigmas(S)
+        self.params = self.generate_params(self.sigmas)
+        self.cond_scale = unconditional_guidance_scale
+        self.old_denoised_zeros = self.sigmas.new_zeros([batch_size] + shape)
+        self.rand_scale = self.params[3, 0].to(torch.float32).cpu()
+        self.batch_size = batch_size
+
+    def one_step(self, x, c_in, old_denoised, param_t):
+        a, b, c, sigma = param_t.chunk(4)
+        sigma = sigma.broadcast_to((self.batch_size)).contiguous()
+        denoised = self.run_model(x, c_in, sigma)
+        x = a * x + b * denoised + c * old_denoised
+        return x, denoised
+
+    def sampler_step(self, arg):
+        x, c_in, params, old_denoised = arg
+        x, denoised = self.one_step(x, c_in, old_denoised, params[:, 0])
+        params = torch.roll(params, shifts=-1, dims=1)
+        return [x, c_in, params, denoised]
+
+    def init_loop(self, x, c_in):
+        return [x, c_in, self.params.clone(), self.old_denoised_zeros.clone()]

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/diffusion/plms.py

@@ -0,0 +1,244 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+from ldm.models.diffusion.sampling_util import norm_thresholding
+
+
+class PLMSSampler(object):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        if ddim_eta != 0:
+            raise ValueError('ddim_eta must be 0 for PLMS')
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta,verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               dynamic_threshold=None,
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        print(f'Data shape for PLMS sampling is {size}')
+
+        samples, intermediates = self.plms_sampling(conditioning, size,
+                                                    callback=callback,
+                                                    img_callback=img_callback,
+                                                    quantize_denoised=quantize_x0,
+                                                    mask=mask, x0=x0,
+                                                    ddim_use_original_steps=False,
+                                                    noise_dropout=noise_dropout,
+                                                    temperature=temperature,
+                                                    score_corrector=score_corrector,
+                                                    corrector_kwargs=corrector_kwargs,
+                                                    x_T=x_T,
+                                                    log_every_t=log_every_t,
+                                                    unconditional_guidance_scale=unconditional_guidance_scale,
+                                                    unconditional_conditioning=unconditional_conditioning,
+                                                    dynamic_threshold=dynamic_threshold,
+                                                    )
+        return samples, intermediates
+
+    @torch.no_grad()
+    def plms_sampling(self, cond, shape,
+                      x_T=None, ddim_use_original_steps=False,
+                      callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, log_every_t=100,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,
+                      dynamic_threshold=None):
+        device = self.model.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        if timesteps is None:
+            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        elif timesteps is not None and not ddim_use_original_steps:
+            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            timesteps = self.ddim_timesteps[:subset_end]
+
+        intermediates = {'x_inter': [img], 'pred_x0': [img]}
+        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        print(f"Running PLMS Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
+        old_eps = []
+
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
+
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1. - mask) * img
+
+            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+                                      quantize_denoised=quantize_denoised, temperature=temperature,
+                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
+                                      corrector_kwargs=corrector_kwargs,
+                                      unconditional_guidance_scale=unconditional_guidance_scale,
+                                      unconditional_conditioning=unconditional_conditioning,
+                                      old_eps=old_eps, t_next=ts_next,
+                                      dynamic_threshold=dynamic_threshold)
+            img, pred_x0, e_t = outs
+            old_eps.append(e_t)
+            if len(old_eps) >= 4:
+                old_eps.pop(0)
+            if callback: callback(i)
+            if img_callback: img_callback(pred_x0, i)
+
+            if index % log_every_t == 0 or index == total_steps - 1:
+                intermediates['x_inter'].append(img)
+                intermediates['pred_x0'].append(pred_x0)
+
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
+                      dynamic_threshold=None):
+        b, *_, device = *x.shape, x.device
+
+        def get_model_output(x, t):
+            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+                e_t = self.model.apply_model(x, t, c)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t] * 2)
+                c_in = torch.cat([unconditional_conditioning, c])
+                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+            if score_corrector is not None:
+                assert self.model.parameterization == "eps"
+                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+            return e_t
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+        def get_x_prev_and_pred_x0(e_t, index):
+            # select parameters corresponding to the currently considered timestep
+            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+            # current prediction for x_0
+            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+            if quantize_denoised:
+                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+            if dynamic_threshold is not None:
+                pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
+            # direction pointing to x_t
+            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+            if noise_dropout > 0.:
+                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+            return x_prev, pred_x0
+
+        e_t = get_model_output(x, t)
+        if len(old_eps) == 0:
+            # Pseudo Improved Euler (2nd order)
+            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+            e_t_next = get_model_output(x_prev, t_next)
+            e_t_prime = (e_t + e_t_next) / 2
+        elif len(old_eps) == 1:
+            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (3 * e_t - old_eps[-1]) / 2
+        elif len(old_eps) == 2:
+            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+        elif len(old_eps) >= 3:
+            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+        return x_prev, pred_x0, e_t

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/diffusion/sampler.py

@@ -0,0 +1,19 @@
+###############################################################################
+# Copyright (C) 2023 Habana Labs, Ltd. an Intel Company
+###############################################################################
+import torch
+
+
+class Sampler(object):
+    def __init__(self, **kwargs):
+        super().__init__()
+
+    @torch.no_grad()
+    def compile(self, S, shape, **kwargs):
+        pass
+
+    def run_model(self, x, c_in, sigma):
+        x_in = torch.cat([x] * 2)
+        sigma_in = torch.cat([sigma] * 2)
+        uncond, cond = self.model_wrap(x_in, sigma_in, cond=c_in).chunk(2)
+        return uncond + (cond - uncond) * self.cond_scale

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/models/diffusion/sampling_util.py

@@ -0,0 +1,22 @@
+import torch
+import numpy as np
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions.
+    From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def norm_thresholding(x0, value):
+    s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
+    return x0 * (value / s)
+
+
+def spatial_norm_thresholding(x0, value):
+    # b c h w
+    s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
+    return x0 * (value / s)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/modules/attention.py

@@ -0,0 +1,352 @@
+from inspect import isfunction
+import math
+import os
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from typing import Optional, Any
+
+from ldm.modules.diffusionmodules.util import checkpoint
+
+
+try:
+    import xformers
+    import xformers.ops
+    XFORMERS_IS_AVAILBLE = True
+except:
+    XFORMERS_IS_AVAILBLE = False
+
+# CrossAttn precision handling
+import os
+_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
+
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
+    return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def Normalize(in_channels):
+    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class SpatialSelfAttention(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.k = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.v = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.proj_out = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=1,
+                                        stride=1,
+                                        padding=0)
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b,c,h,w = q.shape
+        q = rearrange(q, 'b c h w -> b (h w) c')
+        k = rearrange(k, 'b c h w -> b c (h w)')
+        w_ = torch.einsum('bij,bjk->bik', q, k)
+
+        w_ = w_ * (int(c)**(-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = rearrange(v, 'b c h w -> b c (h w)')
+        w_ = rearrange(w_, 'b i j -> b j i')
+        h_ = torch.einsum('bij,bjk->bik', v, w_)
+        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
+        h_ = self.proj_out(h_)
+
+        return x+h_
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.scale = nn.Parameter(torch.Tensor([dim_head ** -0.5]))
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x, context=None, mask=None):
+        h = self.heads
+
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+        k *= self.scale
+
+        # force cast to fp32 to avoid overflowing
+        if _ATTN_PRECISION =="fp32":
+            with torch.autocast(enabled=False, device_type = 'cuda'):
+                q, k = q.float(), k.float()
+                sim = einsum('b i d, b j d -> b i j', q, k)
+        else:
+            sim = einsum('b i d, b j d -> b i j', q, k)
+        
+        del q, k
+    
+        if exists(mask):
+            mask = rearrange(mask, 'b ... -> b (...)')
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        if os.environ["CUSTOM_SOFTMAX_FLAVOR"] != "0":
+            try:
+                import habana_frameworks.torch.hpex.kernels as hpu_kernels
+                sim = hpu_kernels.CustomSoftmax.apply(sim, int(os.environ["CUSTOM_SOFTMAX_FLAVOR"]))
+            except:
+                sim = sim.softmax(dim=-1)
+                print("Not using custom softmax!")
+        else:
+            sim = sim.softmax(dim=-1)
+
+        out = einsum('b i j, b j d -> b i d', sim, v)
+        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+        return self.to_out(out)
+
+
+class MemoryEfficientCrossAttention(nn.Module):
+    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
+        super().__init__()
+        print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
+              f"{heads} heads.")
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+        self.attention_op: Optional[Any] = None
+
+    def forward(self, x, context=None, mask=None):
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        b, _, _ = q.shape
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, t.shape[1], self.heads, self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * self.heads, t.shape[1], self.dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+        # actually compute the attention, what we cannot get enough of
+        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+        if exists(mask):
+            raise NotImplementedError
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, self.heads, out.shape[1], self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, out.shape[1], self.heads * self.dim_head)
+        )
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    ATTENTION_MODES = {
+        "softmax": CrossAttention,  # vanilla attention
+        "softmax-xformers": MemoryEfficientCrossAttention
+    }
+    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
+                 disable_self_attn=False):
+        super().__init__()
+        attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
+        assert attn_mode in self.ATTENTION_MODES
+        attn_cls = self.ATTENTION_MODES[attn_mode]
+        self.disable_self_attn = disable_self_attn
+        self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
+                              context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
+                              heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.norm3 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+
+    def forward(self, x, context=None):
+        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+
+    def _forward(self, x, context=None):
+        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
+        x = self.attn2(self.norm2(x), context=context) + x
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class SpatialTransformer(nn.Module):
+    """
+    Transformer block for image-like data.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    NEW: use_linear for more efficiency instead of the 1x1 convs
+    """
+    def __init__(self, in_channels, n_heads, d_head,
+                 depth=1, dropout=0., context_dim=None,
+                 disable_self_attn=False, use_linear=False,
+                 use_checkpoint=True):
+        super().__init__()
+        if exists(context_dim) and not isinstance(context_dim, list):
+            context_dim = [context_dim]
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = Normalize(in_channels)
+        if not use_linear:
+            self.proj_in = nn.Conv2d(in_channels,
+                                     inner_dim,
+                                     kernel_size=1,
+                                     stride=1,
+                                     padding=0)
+        else:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
+                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
+                for d in range(depth)]
+        )
+        if not use_linear:
+            self.proj_out = zero_module(nn.Conv2d(inner_dim,
+                                                  in_channels,
+                                                  kernel_size=1,
+                                                  stride=1,
+                                                  padding=0))
+        else:
+            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
+        self.use_linear = use_linear
+
+    def forward(self, x, context=None):
+        # note: if no context is given, cross-attention defaults to self-attention
+        if not isinstance(context, list):
+            context = [context]
+        b, c, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+        if self.use_linear:
+            x = self.proj_in(x)
+        for i, block in enumerate(self.transformer_blocks):
+            x = block(x, context=context[i])
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+        if not self.use_linear:
+            x = self.proj_out(x)
+        return x + x_in
+

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/modules/diffusionmodules/openaimodel.py

@@ -0,0 +1,786 @@
+from abc import abstractmethod
+import math
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ldm.modules.diffusionmodules.util import (
+    checkpoint,
+    conv_nd,
+    linear,
+    avg_pool_nd,
+    zero_module,
+    normalization,
+    timestep_embedding,
+)
+from ldm.modules.attention import SpatialTransformer
+from ldm.util import exists
+
+
+# dummy replace
+def convert_module_to_f16(x):
+    pass
+
+def convert_module_to_f32(x):
+    pass
+
+
+## go
+class AttentionPool2d(nn.Module):
+    """
+    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+    """
+
+    def __init__(
+        self,
+        spacial_dim: int,
+        embed_dim: int,
+        num_heads_channels: int,
+        output_dim: int = None,
+    ):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
+        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+        self.num_heads = embed_dim // num_heads_channels
+        self.attention = QKVAttention(self.num_heads)
+
+    def forward(self, x):
+        b, c, *_spatial = x.shape
+        x = x.reshape(b, c, -1)  # NC(HW)
+        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
+        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
+        x = self.qkv_proj(x)
+        x = self.attention(x)
+        x = self.c_proj(x)
+        return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(self, x, emb, context=None):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb)
+            elif isinstance(layer, SpatialTransformer):
+                x = layer(x, context)
+            else:
+                x = layer(x)
+        return x
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(
+                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+            )
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+class TransposedUpsample(nn.Module):
+    'Learned 2x upsampling without padding'
+    def __init__(self, channels, out_channels=None, ks=5):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+
+        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
+
+    def forward(self,x):
+        return self.up(x)
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(
+                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, 3, padding=1
+            )
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        return checkpoint(
+            self._forward, (x, emb), self.parameters(), self.use_checkpoint
+        )
+
+
+    def _forward(self, x, emb):
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = th.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        if use_new_attention_order:
+            # split qkv before split heads
+            self.attention = QKVAttention(self.num_heads)
+        else:
+            # split heads before split qkv
+            self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x):
+        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+        #return pt_checkpoint(self._forward, x)  # pytorch
+
+    def _forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+    """
+    A counter for the `thop` package to count the operations in an
+    attention operation.
+    Meant to be used like:
+        macs, params = thop.profile(
+            model,
+            inputs=(inputs, timestamps),
+            custom_ops={QKVAttention: QKVAttention.count_flops},
+        )
+    """
+    b, c, *spatial = y[0].shape
+    num_spatial = int(np.prod(spatial))
+    # We perform two matmuls with the same number of ops.
+    # The first computes the weight matrix, the second computes
+    # the combination of the value vectors.
+    matmul_ops = 2 * b * (num_spatial ** 2) * c
+    model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        num_classes=None,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=-1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        use_spatial_transformer=False,    # custom transformer support
+        transformer_depth=1,              # custom transformer support
+        context_dim=None,                 # custom transformer support
+        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
+        legacy=True,
+        disable_self_attentions=None,
+        num_attention_blocks=None,
+        disable_middle_self_attn=False,
+        use_linear_in_transformer=False,
+    ):
+        super().__init__()
+        if use_spatial_transformer:
+            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+        if context_dim is not None:
+            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            from omegaconf.listconfig import ListConfig
+            if type(context_dim) == ListConfig:
+                context_dim = list(context_dim)
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        if isinstance(num_res_blocks, int):
+            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+        else:
+            if len(num_res_blocks) != len(channel_mult):
+                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+                                 "as a list/tuple (per-level) with the same length as channel_mult")
+            self.num_res_blocks = num_res_blocks
+        if disable_self_attentions is not None:
+            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+            assert len(disable_self_attentions) == len(channel_mult)
+        if num_attention_blocks is not None:
+            assert len(num_attention_blocks) == len(self.num_res_blocks)
+            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+                  f"attention will still not be set.")
+
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.predict_codebook_ids = n_embed is not None
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            if isinstance(self.num_classes, int):
+                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+            elif self.num_classes == "continuous":
+                print("setting up linear c_adm embedding layer")
+                self.label_emb = nn.Linear(1, time_embed_dim)
+            else:
+                raise ValueError()
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for nr in range(self.num_res_blocks[level]):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                use_checkpoint=use_checkpoint,
+                                num_heads=num_heads,
+                                num_head_channels=dim_head,
+                                use_new_attention_order=use_new_attention_order,
+                            ) if not use_spatial_transformer else SpatialTransformer(
+                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint
+                            )
+                        )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            #num_heads = 1
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=dim_head,
+                use_new_attention_order=use_new_attention_order,
+            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
+                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+                            use_checkpoint=use_checkpoint
+                        ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(self.num_res_blocks[level] + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=model_channels * mult,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                use_checkpoint=use_checkpoint,
+                                num_heads=num_heads_upsample,
+                                num_head_channels=dim_head,
+                                use_new_attention_order=use_new_attention_order,
+                            ) if not use_spatial_transformer else SpatialTransformer(
+                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint
+                            )
+                        )
+                if level and i == self.num_res_blocks[level]:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+        if self.predict_codebook_ids:
+            self.id_predictor = nn.Sequential(
+            normalization(ch),
+            conv_nd(dims, model_channels, n_embed, 1),
+            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
+        )
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+        self.output_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+        self.output_blocks.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param context: conditioning plugged in via crossattn
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+        hs = []
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        if self.num_classes is not None:
+            assert y.shape[0] == x.shape[0]
+            emb = emb + self.label_emb(y)
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb, context)
+            hs.append(h)
+        h = self.middle_block(h, emb, context)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context)
+        h = h.type(x.dtype)
+        if self.predict_codebook_ids:
+            return self.id_predictor(h)
+        else:
+            return self.out(h)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/modules/diffusionmodules/util.py

@@ -0,0 +1,270 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+
+import os
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import repeat
+
+from ldm.util import instantiate_from_config
+
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+    if schedule == "linear":
+        betas = (
+                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+        )
+
+    elif schedule == "cosine":
+        timesteps = (
+                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+        )
+        alphas = timesteps / (1 + cosine_s) * np.pi / 2
+        alphas = torch.cos(alphas).pow(2)
+        alphas = alphas / alphas[0]
+        betas = 1 - alphas[1:] / alphas[:-1]
+        betas = np.clip(betas, a_min=0, a_max=0.999)
+
+    elif schedule == "sqrt_linear":
+        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+    elif schedule == "sqrt":
+        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+    else:
+        raise ValueError(f"schedule '{schedule}' unknown.")
+    return betas.numpy()
+
+
+def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+    if ddim_discr_method == 'uniform':
+        c = num_ddpm_timesteps // num_ddim_timesteps
+        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+    elif ddim_discr_method == 'quad':
+        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+    else:
+        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+
+    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+    # add one to get the final alpha values right (the ones from first scale to data during sampling)
+    steps_out = ddim_timesteps + 1
+    if verbose:
+        print(f'Selected timesteps for ddim sampler: {steps_out}')
+    return steps_out
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+    # select alphas for computing the variance schedule
+    alphas = alphacums[ddim_timesteps]
+    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+    # according the the formula provided in https://arxiv.org/abs/2010.02502
+    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+    if verbose:
+        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+        print(f'For the chosen value of eta, which is {eta}, '
+              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+    return sigmas, alphas, alphas_prev
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def checkpoint(func, inputs, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass.
+    :param func: the function to evaluate.
+    :param inputs: the argument sequence to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        args = tuple(inputs) + tuple(params)
+        return CheckpointFunction.apply(func, len(inputs), *args)
+    else:
+        return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, run_function, length, *args):
+        ctx.run_function = run_function
+        ctx.input_tensors = list(args[:length])
+        ctx.input_params = list(args[length:])
+        ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
+                                   "dtype": torch.get_autocast_gpu_dtype(),
+                                   "cache_enabled": torch.is_autocast_cache_enabled()}
+        with torch.no_grad():
+            output_tensors = ctx.run_function(*ctx.input_tensors)
+        return output_tensors
+
+    @staticmethod
+    def backward(ctx, *output_grads):
+        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+        with torch.enable_grad(), \
+                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+            # Fixes a bug where the first op in run_function modifies the
+            # Tensor storage in place, which is not allowed for detach()'d
+            # Tensors.
+            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+            output_tensors = ctx.run_function(*shallow_copies)
+        input_grads = torch.autograd.grad(
+            output_tensors,
+            ctx.input_tensors + ctx.input_params,
+            output_grads,
+            allow_unused=True,
+        )
+        del ctx.input_tensors
+        del ctx.input_params
+        del output_tensors
+        return (None, None) + input_grads
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+    """
+    Create sinusoidal timestep embeddings.
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    if not repeat_only:
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=timesteps.device)
+        args = timesteps[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    else:
+        embedding = repeat(timesteps, 'b -> b d', d=dim)
+    return embedding
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels):
+    """
+    Make a standard normalization layer.
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(32, channels)
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+    def forward(self, x):
+        return x * torch.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class HybridConditioner(nn.Module):
+
+    def __init__(self, c_concat_config, c_crossattn_config):
+        super().__init__()
+        self.concat_conditioner = instantiate_from_config(c_concat_config)
+        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+    def forward(self, c_concat, c_crossattn):
+        c_concat = self.concat_conditioner(c_concat)
+        c_crossattn = self.crossattn_conditioner(c_crossattn)
+        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/modules/ema.py

@@ -0,0 +1,80 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+    def __init__(self, model, decay=0.9999, use_num_upates=True):
+        super().__init__()
+        if decay < 0.0 or decay > 1.0:
+            raise ValueError('Decay must be between 0 and 1')
+
+        self.m_name2s_name = {}
+        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
+        else torch.tensor(-1, dtype=torch.int))
+
+        for name, p in model.named_parameters():
+            if p.requires_grad:
+                # remove as '.'-character is not allowed in buffers
+                s_name = name.replace('.', '')
+                self.m_name2s_name.update({name: s_name})
+                self.register_buffer(s_name, p.clone().detach().data)
+
+        self.collected_params = []
+
+    def reset_num_updates(self):
+        del self.num_updates
+        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
+
+    def forward(self, model):
+        decay = self.decay
+
+        if self.num_updates >= 0:
+            self.num_updates += 1
+            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
+
+        one_minus_decay = 1.0 - decay
+
+        with torch.no_grad():
+            m_param = dict(model.named_parameters())
+            shadow_params = dict(self.named_buffers())
+
+            for key in m_param:
+                if m_param[key].requires_grad:
+                    sname = self.m_name2s_name[key]
+                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+                else:
+                    assert not key in self.m_name2s_name
+
+    def copy_to(self, model):
+        m_param = dict(model.named_parameters())
+        shadow_params = dict(self.named_buffers())
+        for key in m_param:
+            if m_param[key].requires_grad:
+                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+            else:
+                assert not key in self.m_name2s_name
+
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, parameters):
+        """
+        Restore the parameters stored with the `store` method.
+        Useful to validate the model with EMA parameters without affecting the
+        original optimization process. Store the parameters before the
+        `copy_to` method. After validation (or model saving), use this to
+        restore the former parameters.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+        """
+        for c_param, param in zip(self.collected_params, parameters):
+            param.data.copy_(c_param.data)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/modules/encoders/modules.py

@@ -0,0 +1,213 @@
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+
+from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
+
+import open_clip
+from ldm.util import default, count_params
+
+
+class AbstractEncoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def encode(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class IdentityEncoder(AbstractEncoder):
+
+    def encode(self, x):
+        return x
+
+
+class ClassEmbedder(nn.Module):
+    def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
+        super().__init__()
+        self.key = key
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+        self.n_classes = n_classes
+        self.ucg_rate = ucg_rate
+
+    def forward(self, batch, key=None, disable_dropout=False):
+        if key is None:
+            key = self.key
+        # this is for use in crossattn
+        c = batch[key][:, None]
+        if self.ucg_rate > 0. and not disable_dropout:
+            mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
+            c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1)
+            c = c.long()
+        c = self.embedding(c)
+        return c
+
+    def get_unconditional_conditioning(self, bs, device="cuda"):
+        uc_class = self.n_classes - 1  # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
+        uc = torch.ones((bs,), device=device) * uc_class
+        uc = {self.key: uc}
+        return uc
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class FrozenT5Embedder(AbstractEncoder):
+    """Uses the T5 transformer encoder for text"""
+    def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+        super().__init__()
+        self.tokenizer = T5Tokenizer.from_pretrained(version)
+        self.transformer = T5EncoderModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length   # TODO: typical value?
+        if freeze:
+            self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        #self.train = disabled_train
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from huggingface)"""
+    LAYERS = [
+        "last",
+        "pooled",
+        "hidden"
+    ]
+    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
+                 freeze=True, layer="last", layer_idx=None):  # clip-vit-base-patch32
+        super().__init__()
+        assert layer in self.LAYERS
+        self.tokenizer = CLIPTokenizer.from_pretrained(version)
+        self.transformer = CLIPTextModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        self.layer_idx = layer_idx
+        if layer == "hidden":
+            assert layer_idx is not None
+            assert 0 <= abs(layer_idx) <= 12
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        #self.train = disabled_train
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
+        if self.layer == "last":
+            z = outputs.last_hidden_state
+        elif self.layer == "pooled":
+            z = outputs.pooler_output[:, None, :]
+        else:
+            z = outputs.hidden_states[self.layer_idx]
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenOpenCLIPEmbedder(AbstractEncoder):
+    """
+    Uses the OpenCLIP transformer encoder for text
+    """
+    LAYERS = [
+        #"pooled",
+        "last",
+        "penultimate"
+    ]
+    def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
+                 freeze=True, layer="last"):
+        super().__init__()
+        assert layer in self.LAYERS
+        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
+        del model.visual
+        self.model = model
+
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        if self.layer == "last":
+            self.layer_idx = 0
+        elif self.layer == "penultimate":
+            self.layer_idx = 1
+        else:
+            raise NotImplementedError()
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        tokens = open_clip.tokenize(text)
+        z = self.encode_with_transformer(tokens.to(self.device))
+        return z
+
+    def encode_with_transformer(self, text):
+        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
+        x = x + self.model.positional_embedding
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        x = self.model.ln_final(x)
+        return x
+
+    def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
+        for i, r in enumerate(self.model.transformer.resblocks):
+            if i == len(self.model.transformer.resblocks) - self.layer_idx:
+                break
+            if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
+                x = checkpoint(r, x, attn_mask)
+            else:
+                x = r(x, attn_mask=attn_mask)
+        return x
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPT5Encoder(AbstractEncoder):
+    def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
+                 clip_max_length=77, t5_max_length=77):
+        super().__init__()
+        self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
+        self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
+        print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
+              f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
+
+    def encode(self, text):
+        return self(text)
+
+    def forward(self, text):
+        clip_z = self.clip_encoder.encode(text)
+        t5_z = self.t5_encoder.encode(text)
+        return [clip_z, t5_z]
+
+

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/modules/midas/api.py

@@ -0,0 +1,170 @@
+# based on https://github.com/isl-org/MiDaS
+
+import cv2
+import torch
+import torch.nn as nn
+from torchvision.transforms import Compose
+
+from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
+from ldm.modules.midas.midas.midas_net import MidasNet
+from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
+from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
+
+
+ISL_PATHS = {
+    "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
+    "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
+    "midas_v21": "",
+    "midas_v21_small": "",
+}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def load_midas_transform(model_type):
+    # https://github.com/isl-org/MiDaS/blob/master/run.py
+    # load transform only
+    if model_type == "dpt_large":  # DPT-Large
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "dpt_hybrid":  # DPT-Hybrid
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "midas_v21":
+        net_w, net_h = 384, 384
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    elif model_type == "midas_v21_small":
+        net_w, net_h = 256, 256
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    else:
+        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
+
+    transform = Compose(
+        [
+            Resize(
+                net_w,
+                net_h,
+                resize_target=None,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=32,
+                resize_method=resize_mode,
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            normalization,
+            PrepareForNet(),
+        ]
+    )
+
+    return transform
+
+
+def load_model(model_type):
+    # https://github.com/isl-org/MiDaS/blob/master/run.py
+    # load network
+    model_path = ISL_PATHS[model_type]
+    if model_type == "dpt_large":  # DPT-Large
+        model = DPTDepthModel(
+            path=model_path,
+            backbone="vitl16_384",
+            non_negative=True,
+        )
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "dpt_hybrid":  # DPT-Hybrid
+        model = DPTDepthModel(
+            path=model_path,
+            backbone="vitb_rn50_384",
+            non_negative=True,
+        )
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "midas_v21":
+        model = MidasNet(model_path, non_negative=True)
+        net_w, net_h = 384, 384
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
+
+    elif model_type == "midas_v21_small":
+        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
+                               non_negative=True, blocks={'expand': True})
+        net_w, net_h = 256, 256
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
+
+    else:
+        print(f"model_type '{model_type}' not implemented, use: --model_type large")
+        assert False
+
+    transform = Compose(
+        [
+            Resize(
+                net_w,
+                net_h,
+                resize_target=None,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=32,
+                resize_method=resize_mode,
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            normalization,
+            PrepareForNet(),
+        ]
+    )
+
+    return model.eval(), transform
+
+
+class MiDaSInference(nn.Module):
+    MODEL_TYPES_TORCH_HUB = [
+        "DPT_Large",
+        "DPT_Hybrid",
+        "MiDaS_small"
+    ]
+    MODEL_TYPES_ISL = [
+        "dpt_large",
+        "dpt_hybrid",
+        "midas_v21",
+        "midas_v21_small",
+    ]
+
+    def __init__(self, model_type):
+        super().__init__()
+        assert (model_type in self.MODEL_TYPES_ISL)
+        model, _ = load_model(model_type)
+        self.model = model
+        self.model.train = disabled_train
+
+    def forward(self, x):
+        # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
+        # NOTE: we expect that the correct transform has been called during dataloading.
+        with torch.no_grad():
+            prediction = self.model(x)
+            prediction = torch.nn.functional.interpolate(
+                prediction.unsqueeze(1),
+                size=x.shape[2:],
+                mode="bicubic",
+                align_corners=False,
+            )
+        assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
+        return prediction
+

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/modules/midas/midas/base_model.py

@@ -0,0 +1,16 @@
+import torch
+
+
+class BaseModel(torch.nn.Module):
+    def load(self, path):
+        """Load model from file.
+
+        Args:
+            path (str): file path
+        """
+        parameters = torch.load(path, map_location=torch.device('cpu'))
+
+        if "optimizer" in parameters:
+            parameters = parameters["model"]
+
+        self.load_state_dict(parameters)

docker/bloom13b/Model-References/PyTorch/generative_models/stable-diffusion-v-2-1/ldm/modules/midas/midas/blocks.py

@@ -0,0 +1,342 @@
+import torch
+import torch.nn as nn
+
+from .vit import (
+    _make_pretrained_vitb_rn50_384,
+    _make_pretrained_vitl16_384,
+    _make_pretrained_vitb16_384,
+    forward_vit,
+)
+
+def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
+    if backbone == "vitl16_384":
+        pretrained = _make_pretrained_vitl16_384(
+            use_pretrained, hooks=hooks, use_readout=use_readout
+        )
+        scratch = _make_scratch(
+            [256, 512, 1024, 1024], features, groups=groups, expand=expand
+        )  # ViT-L/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb_rn50_384":
+        pretrained = _make_pretrained_vitb_rn50_384(
+            use_pretrained,
+            hooks=hooks,
+            use_vit_only=use_vit_only,
+            use_readout=use_readout,
+        )
+        scratch = _make_scratch(
+            [256, 512, 768, 768], features, groups=groups, expand=expand
+        )  # ViT-H/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb16_384":
+        pretrained = _make_pretrained_vitb16_384(
+            use_pretrained, hooks=hooks, use_readout=use_readout
+        )
+        scratch = _make_scratch(
+            [96, 192, 384, 768], features, groups=groups, expand=expand
+        )  # ViT-B/16 - 84.6% Top1 (backbone)
+    elif backbone == "resnext101_wsl":
+        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
+        scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand)     # efficientnet_lite3  
+    elif backbone == "efficientnet_lite3":
+        pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
+        scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand)  # efficientnet_lite3     
+    else:
+        print(f"Backbone '{backbone}' not implemented")
+        assert False
+        
+    return pretrained, scratch
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    out_shape4 = out_shape
+    if expand==True:
+        out_shape1 = out_shape
+        out_shape2 = out_shape*2
+        out_shape3 = out_shape*4
+        out_shape4 = out_shape*8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer4_rn = nn.Conv2d(
+        in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+
+    return scratch
+
+
+def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
+    efficientnet = torch.hub.load(
+        "rwightman/gen-efficientnet-pytorch",
+        "tf_efficientnet_lite3",
+        pretrained=use_pretrained,
+        exportable=exportable
+    )
+    return _make_efficientnet_backbone(efficientnet)
+
+
+def _make_efficientnet_backbone(effnet):
+    pretrained = nn.Module()
+
+    pretrained.layer1 = nn.Sequential(
+        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
+    )
+    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
+    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
+    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
+
+    return pretrained
+    
+
+def _make_resnet_backbone(resnet):
+    pretrained = nn.Module()
+    pretrained.layer1 = nn.Sequential(
+        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
+    )
+
+    pretrained.layer2 = resnet.layer2
+    pretrained.layer3 = resnet.layer3
+    pretrained.layer4 = resnet.layer4
+
+    return pretrained
+
+
+def _make_pretrained_resnext101_wsl(use_pretrained):
+    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
+    return _make_resnet_backbone(resnet)
+
+
+
+class Interpolate(nn.Module):
+    """Interpolation module.
+    """
+
+    def __init__(self, scale_factor, mode, align_corners=False):
+        """Init.
+
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
+        )
+
+        return x
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        out = self.relu(x)
+        out = self.conv1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+
+        return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.resConfUnit1 = ResidualConvUnit(features)
+        self.resConfUnit2 = ResidualConvUnit(features)
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            output += self.resConfUnit1(xs[1])
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=True
+        )
+
+        return output
+
+
+
+
+class ResidualConvUnit_custom(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups=1
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+        
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+
+        if self.bn==True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn==True:
+            out = self.bn1(out)
+       
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn==True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+        # return out + x
+
+
+class FeatureFusionBlock_custom(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock_custom, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups=1
+
+        self.expand = expand
+        out_features = features
+        if self.expand==True:
+            out_features = features//2
+        
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+        
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+            # output += res
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
+