import gradio as gr
import torch
import spaces
from PIL import Image, ImageDraw, ImageFont
# from src.condition import Condition
from diffusers.pipelines import FluxPipeline
import numpy as np
import requests
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import torch.multiprocessing as mp
###
import argparse
import logging
import math
import os
import re
import random
import shutil
from contextlib import nullcontext
from pathlib import Path
from PIL import Image
import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import AcceleratorState
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from packaging import version
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from transformers.utils import ContextManagers
from omegaconf import OmegaConf
from copy import deepcopy
import diffusers
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel, compute_dream_and_update_latents, compute_snr
from diffusers.utils import check_min_version, deprecate, make_image_grid
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import is_compiled_module
from einops import rearrange
from src.flux.sampling import denoise, get_noise, get_schedule, prepare, unpack
from src.flux.util import (configs, load_ae, load_clip,
load_flow_model2, load_t5, save_image, tensor_to_pil_image, load_checkpoint)
from src.flux.modules.layers import DoubleStreamBlockLoraProcessor, SingleStreamBlockLoraProcessor, IPDoubleStreamBlockProcessor, IPSingleStreamBlockProcessor, ImageProjModel
from src.flux.xflux_pipeline import XFluxSampler
from image_datasets.dataset import loader, eval_image_pair_loader, image_resize
from safetensors.torch import load_file
import json
# logger = get_logger(__name__, log_level="INFO")
def get_models(name: str, device, offload: bool, is_schnell: bool):
t5 = load_t5(device, max_length=256 if is_schnell else 512)
clip = load_clip(device)
clip.requires_grad_(False)
model = load_flow_model2(name, device="cpu")
vae = load_ae(name, device="cpu" if offload else device)
return model, vae, t5, clip
args = OmegaConf.load("inference_configs/inference.yaml") #OmegaConf.load(parse_args())
is_schnell = args.model_name == "flux-schnell"
set_seed(args.seed)
# logging_dir = os.path.join(args.output_dir, args.logging_dir)
device = "cuda"
dit, vae, t5, clip = get_models(name=args.model_name, device=device, offload=False, is_schnell=is_schnell)
# # load image encoder
# ip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(os.getenv("CLIP_VIT")).to(
# # accelerator.device, dtype=torch.bfloat16
# device, dtype=torch.bfloat16
# )
# ip_clip_image_processor = CLIPImageProcessor()
if args.use_ip:
sampler = XFluxSampler(clip=clip, t5=t5, ae=vae, model=dit, device=device, ip_loaded=True, spatial_condition=False, clip_image_processor=ip_clip_image_processor, image_encoder=ip_image_encoder, improj=ip_improj)
elif args.use_spatial_condition:
sampler = XFluxSampler(clip=clip, t5=t5, ae=vae, model=dit, device=device, ip_loaded=False, spatial_condition=True, clip_image_processor=None, image_encoder=None, improj=None,share_position_embedding=args.share_position_embedding)
else:
sampler = XFluxSampler(clip=clip, t5=t5, ae=vae, model=dit, device=device, ip_loaded=False, spatial_condition=False, clip_image_processor=None, image_encoder=None, improj=None)
# @spaces.GPU
def generate(image, edit_prompt):
print("hello?????????!!!!!")
# accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
# accelerator = Accelerator(
# gradient_accumulation_steps=1,
# mixed_precision=args.mixed_precision,
# log_with=args.report_to,
# project_config=accelerator_project_config,
# )
# Make one log on every process with the configuration for debugging.
# logging.basicConfig(
# format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
# datefmt="%m/%d/%Y %H:%M:%S",
# level=logging.INFO,
# )
# logger.info(accelerator.state, main_process_only=False)
# if accelerator.is_local_main_process:
# datasets.utils.logging.set_verbosity_warning()
# transformers.utils.logging.set_verbosity_warning()
# diffusers.utils.logging.set_verbosity_info()
# else:
# datasets.utils.logging.set_verbosity_error()
# transformers.utils.logging.set_verbosity_error()
# diffusers.utils.logging.set_verbosity_error()
# if accelerator.is_main_process:
# if args.output_dir is not None:
# os.makedirs(args.output_dir, exist_ok=True)
# gpt_eval_path = os.path.join(args.output_dir,"Eval")
# os.makedirs(gpt_eval_path, exist_ok=True)
# dit, vae, t5, clip = get_models(name=args.model_name, device=accelerator.device, offload=False, is_schnell=is_schnell)
# dit, vae, t5, clip = get_models(name=args.model_name, device=device, offload=False, is_schnell=is_schnell)
if args.use_lora:
lora_attn_procs = {}
if args.use_ip:
ip_attn_procs = {}
if args.double_blocks is None:
double_blocks_idx = list(range(19))
else:
double_blocks_idx = [int(idx) for idx in args.double_blocks.split(",")]
if args.single_blocks is None:
single_blocks_idx = list(range(38))
elif args.single_blocks is not None:
single_blocks_idx = [int(idx) for idx in args.single_blocks.split(",")]
if args.use_lora:
for name, attn_processor in dit.attn_processors.items():
match = re.search(r'\.(\d+)\.', name)
if match:
layer_index = int(match.group(1))
if name.startswith("double_blocks") and layer_index in double_blocks_idx:
# if accelerator.is_main_process:
# print("setting LoRA Processor for", name)
lora_attn_procs[name] = DoubleStreamBlockLoraProcessor(
dim=3072, rank=args.rank
)
elif name.startswith("single_blocks") and layer_index in single_blocks_idx:
# if accelerator.is_main_process:
# print("setting LoRA Processor for", name)
lora_attn_procs[name] = SingleStreamBlockLoraProcessor(
dim=3072, rank=args.rank
)
else:
lora_attn_procs[name] = attn_processor
dit.set_attn_processor(lora_attn_procs)
# if args.use_ip:
# # unpack checkpoint
# checkpoint = load_checkpoint(args.ip_local_path, args.ip_repo_id, args.ip_name)
# prefix = "double_blocks."
# # blocks = {}
# proj = {}
# for key, value in checkpoint.items():
# # if key.startswith(prefix):
# # blocks[key[len(prefix):].replace('.processor.', '.')] = value
# if key.startswith("ip_adapter_proj_model"):
# proj[key[len("ip_adapter_proj_model."):]] = value
# # # load image encoder
# # ip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(os.getenv("CLIP_VIT")).to(
# # # accelerator.device, dtype=torch.bfloat16
# # device, dtype=torch.bfloat16
# # )
# # ip_clip_image_processor = CLIPImageProcessor()
# # setup image embedding projection model
# ip_improj = ImageProjModel(4096, 768, 4)
# ip_improj.load_state_dict(proj)
# # ip_improj = ip_improj.to(accelerator.device, dtype=torch.bfloat16)
# ip_improj = ip_improj.to(device, dtype=torch.bfloat16)
# ip_attn_procs = {}
# for name, _ in dit.attn_processors.items():
# ip_state_dict = {}
# for k in checkpoint.keys():
# if name in k:
# ip_state_dict[k.replace(f'{name}.', '')] = checkpoint[k]
# if ip_state_dict:
# ip_attn_procs[name] = IPDoubleStreamBlockProcessor(4096, 3072)
# ip_attn_procs[name].load_state_dict(ip_state_dict)
# ip_attn_procs[name].to(accelerator.device, dtype=torch.bfloat16)
# else:
# ip_attn_procs[name] = dit.attn_processors[name]
# dit.set_attn_processor(ip_attn_procs)
vae.requires_grad_(False)
t5.requires_grad_(False)
clip.requires_grad_(False)
# weight_dtype = torch.float32
# if accelerator.mixed_precision == "fp16":
# weight_dtype = torch.float16
# args.mixed_precision = accelerator.mixed_precision
# elif accelerator.mixed_precision == "bf16":
# weight_dtype = torch.bfloat16
# args.mixed_precision = accelerator.mixed_precision
# print(f"Resuming from checkpoint {args.ckpt_dir}")
# dit_stat_dict = load_file(args.ckpt_dir)
# Get path from Hub
model_path = hf_hub_download(
repo_id="Boese0601/ByteMorpher",
filename="dit.safetensors"
)
state_dict = load_file(model_path)
dit.load_state_dict(state_dict)
dit = dit.to(weight_dtype)
dit.eval()
# test_dataloader = loader(**args.data_config)
test_dataloader = eval_image_pair_loader(**args.data_config)
# from deepspeed import initialize
dit = accelerator.prepare(dit)
# if accelerator.is_main_process:
# accelerator.init_trackers(args.tracker_project_name, {"test": None})
# logger.info("***** Running Evaluation *****")
# logger.info(f" Instantaneous batch size = {args.eval_batch_size}")
# progress_bar = tqdm(
# range(0, len(test_dataloader)),
# initial=0,
# desc="Steps",
# disable=not accelerator.is_local_main_process,
# )
# for step, batch in enumerate(test_dataloader):
# with accelerator.accumulate(dit):
# img, tgt_image, prompt, edit_prompt, img_name, edit_name = batch
img = image_resize(image, 512)
w, h = img.size
new_w = (w // 32) * 32
new_h = (h // 32) * 32
img = img.resize((new_w, new_h))
img = torch.from_numpy((np.array(img) / 127.5) - 1)
img = img.permute(2, 0, 1).unsqueeze(0)
edit_prompt = edit_prompt
# if args.use_ip:
# sampler = XFluxSampler(clip=clip, t5=t5, ae=vae, model=dit, device=accelerator.device, ip_loaded=True, spatial_condition=False, clip_image_processor=ip_clip_image_processor, image_encoder=ip_image_encoder, improj=ip_improj)
# elif args.use_spatial_condition:
# sampler = XFluxSampler(clip=clip, t5=t5, ae=vae, model=dit, device=accelerator.device, ip_loaded=False, spatial_condition=True, clip_image_processor=None, image_encoder=None, improj=None,share_position_embedding=args.share_position_embedding)
# else:
# sampler = XFluxSampler(clip=clip, t5=t5, ae=vae, model=dit, device=accelerator.device, ip_loaded=False, spatial_condition=False, clip_image_processor=None, image_encoder=None, improj=None)
with torch.no_grad():
result = sampler(prompt=edit_prompt,
width=args.sample_width,
height=args.sample_height,
num_steps=args.sample_steps,
image_prompt=None, # ip_adapter
true_gs=args.cfg_scale,
seed=args.seed,
ip_scale=args.ip_scale if args.use_ip else 1.0,
source_image=img if args.use_spatial_condition else None,
)
gen_img = result
# progress_bar.update(1)
# accelerator.wait_for_everyone()
# accelerator.end_training()
return gen_img
def get_samples():
sample_list = [
{
"image": "assets/0_camera_zoom/20486354.png",
"edit_prompt": "Zoom in on the coral and add a small blue fish in the background.",
},
]
return [
[
Image.open(sample["image"]).resize((512, 512)),
sample["edit_prompt"],
]
for sample in sample_list
]
header = """
# ByteMoprh
"""
def create_app():
with gr.Blocks() as app:
gr.Markdown(header, elem_id="header")
with gr.Row(equal_height=False):
with gr.Column(variant="panel", elem_classes="inputPanel"):
original_image = gr.Image(
type="pil", label="Condition Image", width=300, elem_id="input"
)
edit_prompt = gr.Textbox(lines=2, label="Edit Prompt", elem_id="edit_prompt")
submit_btn = gr.Button("Run", elem_id="submit_btn")
with gr.Column(variant="panel", elem_classes="outputPanel"):
output_image = gr.Image(type="pil", elem_id="output")
with gr.Row():
examples = gr.Examples(
examples=get_samples(),
inputs=[original_image, edit_prompt],
label="Examples",
)
submit_btn.click(
fn=generate,
inputs=[original_image, edit_prompt],
outputs=output_image,
)
gr.HTML(
"""
"""
)
return app
if __name__ == "__main__":
print("CUDA available:", torch.cuda.is_available())
print("CUDA version:", torch.version.cuda)
print("GPU device name:", torch.cuda.get_device_name(0) if torch.cuda.is_available() else "None")
# mp.set_start_method("spawn", force=True)
create_app().launch(debug=False, share=True, ssr_mode=False)
