demo

.gitattributes

@@ -33,3 +33,18 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_053639/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_055109/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_173140/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_181436/input.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_181633/input.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_214810/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_214908/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_215043/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_221300/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_222257/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_222442/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_222522/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_223634/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241126_223719/output.png filter=lfs diff=lfs merge=lfs -text
+saved_results/20241127_025429/output.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,427 @@
+import spaces
+import gradio as gr
+import torch
+from diffusers import StableDiffusionInpaintPipeline, StableDiffusionImg2ImgPipeline
+from PIL import Image
+import random
+import numpy as np
+import torch
+import os
+import json
+from datetime import datetime
+
+from fluxcombined import FluxPipeline
+from scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
+
+# Load the Stable Diffusion Inpainting model
+scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="scheduler")
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16, scheduler=scheduler)
+pipe.to("cuda")  # Comment this line if GPU is not available
+
+# Function to process the image
+@spaces.GPU(duration=120)
+def process_image(
+    mode, image_layers, prompt, edit_prompt, seed, randomize_seed, num_inference_steps,
+    max_steps, learning_rate, max_source_steps, optimization_steps, true_cfg, mask_input
+):
+    image_with_mask = {
+        "image": image_layers["background"],
+        "mask": image_layers["layers"][0] if mask_input is None else mask_input
+    }
+    
+    # Set seed
+    if randomize_seed or seed is None:
+        seed = random.randint(0, 2**32 - 1)
+    generator = torch.Generator("cuda").manual_seed(int(seed))
+
+    # Unpack image and mask
+    if image_with_mask is None:
+        return None, f"❌ Please upload an image and create a mask."
+    image = image_with_mask["image"]
+    mask = image_with_mask["mask"]
+
+    if image is None or mask is None:
+        return None, f"❌ Please ensure both image and mask are provided."
+
+    # Convert images to RGB
+    image = image.convert("RGB")
+    mask = mask.split()[-1]  # Convert mask to grayscale
+
+    if mode == "Inpainting":
+        if not prompt:
+            return None, f"❌ Please provide a prompt for inpainting."
+        with torch.autocast("cuda"):
+            # Placeholder for using advanced parameters in the future
+            # Adjust parameters according to advanced settings if applicable
+            result = pipe.inpaint(
+                prompt=prompt,
+                input_image=image.resize((1024, 1024)),
+                mask_image=mask.resize((1024, 1024)),
+                num_inference_steps=num_inference_steps,
+                guidance_scale=0.5,
+                generator=generator,
+                save_masked_image=False,
+                output_path="",
+                learning_rate=learning_rate,
+                max_steps=max_steps
+            ).images[0]
+            pipe.vae = pipe.vae.to(torch.float16)
+        return result, f"✅ Inpainting completed with seed {seed}."
+    elif mode == "Editing":
+        if not edit_prompt:
+            return None, f"❌ Please provide a prompt for editing."
+        if not prompt:
+            prompt = ""
+        # Resize the mask to match the image
+        # mask = mask.resize(image.size)
+        with torch.autocast("cuda"):
+            # Placeholder for using advanced parameters in the future
+            # Adjust parameters according to advanced settings if applicable
+            result = pipe.edit2(
+                prompt=edit_prompt,
+                input_image=image.resize((1024, 1024)),
+                mask_image=mask.resize((1024, 1024)),
+                num_inference_steps=num_inference_steps,
+                guidance_scale=0.0,
+                generator=generator,
+                save_masked_image=False,
+                output_path="",
+                learning_rate=learning_rate,
+                max_steps=max_steps,
+                optimization_steps=optimization_steps,
+                true_cfg=true_cfg,
+                negative_prompt=prompt,
+                source_steps=max_source_steps,
+            ).images[0]
+        return result, f"✅ Editing completed with seed {seed}."
+    else:
+        return None, f"❌ Invalid mode selected."
+
+# Design the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        <style>
+            body {background-color: #f5f5f5; color: #333333;}
+            h1 {text-align: center; font-family: 'Helvetica', sans-serif; margin-bottom: 10px;}
+            h2 {text-align: center; color: #666666; font-weight: normal; margin-bottom: 30px;}
+            .gradio-container {max-width: 800px; margin: auto;}
+            .footer {text-align: center; margin-top: 20px; color: #999999; font-size: 12px;}
+        </style>
+        """
+    )
+    gr.Markdown("<h1>🍲 FlowChef 🍲</h1>")
+    gr.Markdown("<h2>Inversion/Gradient/Training-free Steering of Flux.1[Dev]</h2>")
+    gr.Markdown("<h2><p><a href='https://flowchef.github.io/'>Project Page</a> | <a href='#'>Paper</a></p> (Steering Rectified Flow Models in the Vector Field for Controlled Image Generation)</h2>")
+    gr.Markdown("<h3>💡 We recommend going through our <a href='#'>tutorial introduction</a> before getting started!</h3>")
+
+    # Store current state
+    current_input_image = None
+    current_mask = None 
+    current_output_image = None
+    current_params = {}
+
+    # Images at the top
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.ImageMask(
+                # source="upload",
+                # tool="sketch",
+                type="pil",
+                label="Input Image and Mask",
+                image_mode="RGBA",
+                height=512,
+                width=512,
+            )
+        with gr.Column():
+            output_image = gr.Image(label="Output Image")
+
+    # All options below
+    with gr.Column():
+        mode = gr.Radio(
+            choices=["Inpainting", "Editing"], label="Select Mode", value="Inpainting"
+        )
+        prompt = gr.Textbox(
+            label="Prompt",
+            placeholder="Describe what should appear in the masked area..."
+        )
+        edit_prompt = gr.Textbox(
+            label="Editing Prompt",
+            placeholder="Describe how you want to edit the image..."
+        )
+        with gr.Row():
+            seed = gr.Number(label="Seed (Optional)", value=None)
+            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+        num_inference_steps = gr.Slider(
+            label="Inference Steps", minimum=1, maximum=50, value=30
+        )
+        # Advanced settings in an accordion
+        with gr.Accordion("Advanced Settings", open=False):
+            max_steps = gr.Slider(label="Max Steps", minimum=1, maximum=30, value=30)
+            learning_rate = gr.Slider(label="Learning Rate", minimum=0.1, maximum=1.0, value=0.5)
+            true_cfg = gr.Slider(label="Guidance Scale (only for editing)", minimum=1, maximum=20, value=2)
+            max_source_steps = gr.Slider(label="Max Source Steps (only for editing)", minimum=1, maximum=30, value=20)
+            optimization_steps = gr.Slider(label="Optimization Steps", minimum=1, maximum=10, value=1)
+            mask_input = gr.Image(
+                type="pil",
+                label="Optional Mask",
+                image_mode="RGBA",
+            )
+        with gr.Row():
+            run_button = gr.Button("Run", variant="primary")
+            save_button = gr.Button("Save Data", variant="secondary")
+
+    def update_visibility(selected_mode):
+        if selected_mode == "Inpainting":
+            return gr.update(visible=True), gr.update(visible=False)
+        else:
+            return gr.update(visible=True), gr.update(visible=True)
+
+    mode.change(
+        update_visibility,
+        inputs=mode,
+        outputs=[prompt, edit_prompt],
+    )
+
+    def run_and_update_status(
+        mode, image_with_mask, prompt, edit_prompt, seed, randomize_seed, num_inference_steps,
+        max_steps, learning_rate, max_source_steps, optimization_steps, true_cfg, mask_input
+    ):
+        result_image, result_status = process_image(
+            mode, image_with_mask, prompt, edit_prompt, seed, randomize_seed, num_inference_steps,
+            max_steps, learning_rate, max_source_steps, optimization_steps, true_cfg, mask_input
+        )
+        
+        # Store current state
+        global current_input_image, current_mask, current_output_image, current_params
+
+        current_input_image = image_with_mask["background"] if image_with_mask else None
+        current_mask = mask_input if mask_input is not None else (image_with_mask["layers"][0] if image_with_mask else None)
+        current_output_image = result_image
+        current_params = {
+            "mode": mode,
+            "prompt": prompt,
+            "edit_prompt": edit_prompt,
+            "seed": seed,
+            "randomize_seed": randomize_seed,
+            "num_inference_steps": num_inference_steps,
+            "max_steps": max_steps,
+            "learning_rate": learning_rate,
+            "max_source_steps": max_source_steps,
+            "optimization_steps": optimization_steps,
+            "true_cfg": true_cfg
+        }
+        
+        return result_image
+
+    def save_data():
+        if not os.path.exists("saved_results"):
+            os.makedirs("saved_results")
+            
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        save_dir = os.path.join("saved_results", timestamp)
+        os.makedirs(save_dir)
+        
+        # Save images
+        if current_input_image:
+            current_input_image.save(os.path.join(save_dir, "input.png"))
+        if current_mask:
+            current_mask.save(os.path.join(save_dir, "mask.png"))
+        if current_output_image:
+            current_output_image.save(os.path.join(save_dir, "output.png"))
+            
+        # Save parameters
+        with open(os.path.join(save_dir, "parameters.json"), "w") as f:
+            json.dump(current_params, f, indent=4)
+            
+        return f"✅ Data saved in {save_dir}"
+
+    run_button.click(
+        fn=run_and_update_status,
+        inputs=[
+            mode,
+            image_input,
+            prompt,
+            edit_prompt,
+            seed,
+            randomize_seed,
+            num_inference_steps,
+            max_steps,
+            learning_rate,
+            max_source_steps,
+            optimization_steps,
+            true_cfg,
+            mask_input
+        ],
+        outputs=output_image,
+    )
+
+    save_button.click(fn=save_data)
+
+    gr.Markdown(
+        "<div class='footer'>Developed with ❤️ using Flux and Gradio by <a href='https://maitreyapatel.com'>Maitreya Patel</a></div>"
+    )
+
+    def load_example_image_with_mask(image_path):
+        # Load the image
+        image = Image.open(image_path)
+        # Create an empty mask of the same size
+        mask = Image.new('L', image.size, 0)
+        return {"background": image, "layers": [mask], "composite": image}
+
+    examples_dir = "assets"
+    volcano_dict = load_example_image_with_mask(os.path.join(examples_dir, "vulcano.jpg"))
+    dog_dict = load_example_image_with_mask(os.path.join(examples_dir, "dog.webp"))
+
+    gr.Examples(
+        examples=[
+            [
+                "Inpainting",  # mode
+                "./saved_results/20241126_053639/input.png",  # image with mask
+                "./saved_results/20241126_053639/mask.png",
+                "./saved_results/20241126_053639/output.png",
+                "a dog",  # prompt
+                " ",  # edit_prompt 
+                0,  # seed
+                True,  # randomize_seed
+                30,  # num_inference_steps
+                30,  # max_steps
+                1.0,  # learning_rate
+                20,  # max_source_steps
+                10,  # optimization_steps
+                2,  # true_cfg
+            ],
+            [
+                "Inpainting",  # mode
+                "./saved_results/20241126_173140/input.png",  # image with mask
+                "./saved_results/20241126_173140/mask.png",
+                "./saved_results/20241126_173140/output.png",
+                "a cat with blue eyes",  # prompt
+                " ",  # edit_prompt 
+                0,  # seed
+                True,  # randomize_seed
+                30,  # num_inference_steps
+                20,  # max_steps
+                1.0,  # learning_rate
+                20,  # max_source_steps
+                10,  # optimization_steps
+                2,  # true_cfg
+            ],
+            [
+                "Editing",  # mode
+                "./saved_results/20241126_181633/input.png",  # image with mask
+                "./saved_results/20241126_181633/mask.png",
+                "./saved_results/20241126_181633/output.png",
+                " ",  # prompt
+                "volcano eruption",  # edit_prompt 
+                0,  # seed
+                True,  # randomize_seed
+                30,  # num_inference_steps
+                20,  # max_steps
+                0.5,  # learning_rate
+                2,  # max_source_steps
+                3,  # optimization_steps
+                4.5,  # true_cfg
+            ],
+            [
+                "Editing",  # mode
+                "./saved_results/20241126_214810/input.png",  # image with mask
+                "./saved_results/20241126_214810/mask.png",
+                "./saved_results/20241126_214810/output.png",
+                " ",  # prompt
+                "a dog with flowers in the mouth",  # edit_prompt 
+                0,  # seed
+                True,  # randomize_seed
+                30,  # num_inference_steps
+                30,  # max_steps
+                1,  # learning_rate
+                5,  # max_source_steps
+                3,  # optimization_steps
+                4.5,  # true_cfg
+            ],
+            [
+                "Inpainting",  # mode
+                "./saved_results/20241127_025429/input.png",  # image with mask
+                "./saved_results/20241127_025429/mask.png",
+                "./saved_results/20241127_025429/output.png",
+                "A building with \"ASU\" written on it.",  # prompt
+                "",  # edit_prompt 
+                52,  # seed
+                False,  # randomize_seed
+                30,  # num_inference_steps
+                30,  # max_steps
+                1,  # learning_rate
+                20,  # max_source_steps
+                10,  # optimization_steps
+                2,  # true_cfg
+            ],
+            [
+                "Inpainting",  # mode
+                "./saved_results/20241126_222257/input.png",  # image with mask
+                "./saved_results/20241126_222257/mask.png",
+                "./saved_results/20241126_222257/output.png",
+                "A cute pig with big eyes",  # prompt
+                "",  # edit_prompt 
+                0,  # seed
+                True,  # randomize_seed
+                30,  # num_inference_steps
+                20,  # max_steps
+                1,  # learning_rate
+                20,  # max_source_steps
+                5,  # optimization_steps
+                2,  # true_cfg
+            ],
+            [
+                "Editing",  # mode
+                "./saved_results/20241126_222522/input.png",  # image with mask
+                "./saved_results/20241126_222522/mask.png",
+                "./saved_results/20241126_222522/output.png",
+                "A cute rabbit with big eyes",  # prompt
+                "A cute pig with big eyes",  # edit_prompt 
+                0,  # seed
+                True,  # randomize_seed
+                30,  # num_inference_steps
+                20,  # max_steps
+                0.4,  # learning_rate
+                5,  # max_source_steps
+                5,  # optimization_steps
+                4.5,  # true_cfg
+            ],
+            [
+                "Editing",  # mode
+                "./saved_results/20241126_223719/input.png",  # image with mask
+                "./saved_results/20241126_223719/mask.png",
+                "./saved_results/20241126_223719/output.png",
+                "a cat",  # prompt
+                "a tiger",  # edit_prompt 
+                0,  # seed
+                True,  # randomize_seed
+                30,  # num_inference_steps
+                30,  # max_steps
+                0.6,  # learning_rate
+                10,  # max_source_steps
+                5,  # optimization_steps
+                4.5,  # true_cfg
+            ],
+        ],
+        inputs=[
+            mode,
+            image_input,
+            mask_input,
+            output_image,
+            prompt,
+            edit_prompt,
+            seed,
+            randomize_seed,
+            num_inference_steps,
+            max_steps,
+            learning_rate,
+            max_source_steps,
+            optimization_steps,
+            true_cfg,
+        ],
+        # outputs=[output_image],
+        # fn=run_and_update_status,
+        # cache_examples=True,
+    )
+demo.launch()

fluxcombined.py

@@ -0,0 +1,1607 @@
+# Copyright 2024 Black Forest Labs and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import FluxLoraLoaderMixin, FromSingleFileMixin
+from diffusers.models.autoencoders import AutoencoderKL
+from diffusers.models.transformers import FluxTransformer2DModel
+from scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+
+import os
+import torch
+import torch.nn as nn
+from os.path import expanduser  # pylint: disable=import-outside-toplevel
+from urllib.request import urlretrieve  # pylint: disable=import-outside-toplevel
+from torchvision import transforms as TF
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import FluxPipeline
+
+        >>> pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+        >>> prompt = "A cat holding a sign that says hello world"
+        >>> # Depending on the variant being used, the pipeline call will slightly vary.
+        >>> # Refer to the pipeline documentation for more details.
+        >>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0]
+        >>> image.save("flux.png")
+        ```
+"""
+
+import sys
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class FluxPipeline(DiffusionPipeline, FluxLoraLoaderMixin):
+    r"""
+    The Flux pipeline for text-to-image generation.
+
+    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
+
+    Args:
+        transformer ([`FluxTransformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        text_encoder_2 ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer_2 (`T5TokenizerFast`):
+            Second Tokenizer of class
+            [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
+    _optional_components = []
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        text_encoder_2: T5EncoderModel,
+        tokenizer_2: T5TokenizerFast,
+        transformer: FluxTransformer2DModel,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels)) if hasattr(self, "vae") and self.vae is not None else 16
+        )
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.tokenizer_max_length = (
+            self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
+        )
+        self.default_sample_size = 64
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_images_per_prompt: int = 1,
+        max_sequence_length: int = 512,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer_2(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_length=False,
+            return_overflowing_tokens=False,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because `max_sequence_length` is set to "
+                f" {max_sequence_length} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False)[0]
+
+        dtype = self.text_encoder_2.dtype
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        _, seq_len, _ = prompt_embeds.shape
+
+        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        return prompt_embeds
+
+    def _get_clip_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]],
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+    ):
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer_max_length,
+            truncation=True,
+            return_overflowing_tokens=False,
+            return_length=False,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer_max_length} tokens: {removed_text}"
+            )
+        prompt_embeds = self.text_encoder(text_input_ids.to(device), output_hidden_states=False)
+
+        # Use pooled output of CLIPTextModel
+        prompt_embeds = prompt_embeds.pooler_output
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
+
+        return prompt_embeds
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        prompt_2: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 512,
+        lora_scale: Optional[float] = None,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in all text-encoders
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            lora_scale (`float`, *optional*):
+                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+        """
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None and USE_PEFT_BACKEND:
+                scale_lora_layers(self.text_encoder, lora_scale)
+            if self.text_encoder_2 is not None and USE_PEFT_BACKEND:
+                scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # We only use the pooled prompt output from the CLIPTextModel
+            pooled_prompt_embeds = self._get_clip_prompt_embeds(
+                prompt=prompt,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+            )
+            prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=prompt_2,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
+        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
+        # text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
+
+        return prompt_embeds, pooled_prompt_embeds, text_ids
+
+    def encode_prompt_edit(
+        self,
+        prompt: Union[str, List[str]],
+        prompt_2: Union[str, List[str]],
+        negative_prompt: Union[str, List[str]] = None,
+        negative_prompt_2: Union[str, List[str]] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 512,
+        lora_scale: Optional[float] = None,
+        do_true_cfg: bool = False,
+    ):
+        device = device or self._execution_device
+
+        # Set LoRA scale if applicable
+        if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            if self.text_encoder is not None and USE_PEFT_BACKEND:
+                scale_lora_layers(self.text_encoder, lora_scale)
+            if self.text_encoder_2 is not None and USE_PEFT_BACKEND:
+                scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        if do_true_cfg and negative_prompt is not None:
+            negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_batch_size = len(negative_prompt)
+
+            if negative_batch_size != batch_size:
+                raise ValueError(
+                    f"Negative prompt batch size ({negative_batch_size}) does not match prompt batch size ({batch_size})"
+                )
+
+            # Concatenate prompts
+            prompts = prompt + negative_prompt
+            prompts_2 = (
+                prompt_2 + negative_prompt_2 if prompt_2 is not None and negative_prompt_2 is not None else None
+            )
+        else:
+            prompts = prompt
+            prompts_2 = prompt_2
+
+        if prompt_embeds is None:
+            if prompts_2 is None:
+                prompts_2 = prompts
+
+            # Get pooled prompt embeddings from CLIPTextModel
+            pooled_prompt_embeds = self._get_clip_prompt_embeds(
+                prompt=prompts,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+            )
+            prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=prompts_2,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+            )
+
+            if do_true_cfg and negative_prompt is not None:
+                # Split embeddings back into positive and negative parts
+                total_batch_size = batch_size * num_images_per_prompt
+                positive_indices = slice(0, total_batch_size)
+                negative_indices = slice(total_batch_size, 2 * total_batch_size)
+
+                positive_pooled_prompt_embeds = pooled_prompt_embeds[positive_indices]
+                negative_pooled_prompt_embeds = pooled_prompt_embeds[negative_indices]
+
+                positive_prompt_embeds = prompt_embeds[positive_indices]
+                negative_prompt_embeds = prompt_embeds[negative_indices]
+
+                pooled_prompt_embeds = positive_pooled_prompt_embeds
+                prompt_embeds = positive_prompt_embeds
+
+        # Unscale LoRA layers
+        if self.text_encoder is not None:
+            if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
+        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
+
+        if do_true_cfg and negative_prompt is not None:
+            return (
+                prompt_embeds,
+                pooled_prompt_embeds,
+                text_ids,
+                negative_prompt_embeds,
+                negative_pooled_prompt_embeds,
+            )
+        else:
+            return prompt_embeds, pooled_prompt_embeds, text_ids, None, None
+
+
+    def check_inputs(
+        self,
+        prompt,
+        prompt_2,
+        height,
+        width,
+        prompt_embeds=None,
+        pooled_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        max_sequence_length=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+        if prompt_embeds is not None and pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+
+        if max_sequence_length is not None and max_sequence_length > 512:
+            raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
+
+    @staticmethod
+    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        # latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+
+        return latent_image_ids.to(device=device, dtype=dtype)
+
+    @staticmethod
+    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+        latents = latents.permute(0, 2, 4, 1, 3, 5)
+        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+        return latents
+
+    @staticmethod
+    def _unpack_latents(latents, height, width, vae_scale_factor):
+        batch_size, num_patches, channels = latents.shape
+
+        height = height // vae_scale_factor
+        width = width // vae_scale_factor
+
+        latents = latents.view(batch_size, height, width, channels // 4, 2, 2)
+        latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+        latents = latents.reshape(batch_size, channels // (2 * 2), height * 2, width * 2)
+
+        return latents
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = 2 * (int(height) // self.vae_scale_factor)
+        width = 2 * (int(width) // self.vae_scale_factor)
+
+        shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is not None:
+            latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype)
+            return latents.to(device=device, dtype=dtype), latent_image_ids
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+
+        latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype)
+
+        return latents, latent_image_ids
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def joint_attention_kwargs(self):
+        return self._joint_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    
+    def prepare_mask_latents(
+        self,
+        mask,
+        masked_image,
+        batch_size,
+        num_channels_latents,
+        num_images_per_prompt,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+    ):
+        height = 2 * (int(height) // self.vae_scale_factor)
+        width = 2 * (int(width) // self.vae_scale_factor)
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+        mask = torch.nn.functional.interpolate(mask, size=(height, width))
+        mask = mask.to(device=device, dtype=dtype)
+
+        batch_size = batch_size * num_images_per_prompt
+
+        masked_image = masked_image.to(device=device, dtype=dtype)
+
+        if masked_image.shape[1] == 16:
+            masked_image_latents = masked_image
+        else:
+            masked_image_latents = retrieve_latents(self.vae.encode(masked_image), generator=generator)
+
+        masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
+
+        # aligning device to prevent device errors when concating it with the latent model input
+        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
+
+        masked_image_latents = self._pack_latents(
+            masked_image_latents,
+            batch_size,
+            num_channels_latents,
+            height,
+            width,
+        )
+        mask = self._pack_latents(
+            mask.repeat(1, num_channels_latents, 1, 1),
+            batch_size,
+            num_channels_latents,
+            height,
+            width,
+        )
+
+        return mask, masked_image_latents
+
+    @torch.no_grad()
+    def inpaint(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 7.0,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+        optimization_steps: int = 3,
+        learning_rate: float = 0.8,
+        max_steps: int = 5,
+        input_image = None,
+        save_masked_image = False,
+        output_path="",
+        mask_image = None,
+    ):
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        random_latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = random_latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 4. Preprocess image
+        # Preprocess mask image
+        mask_image = mask_image.convert("L")
+        mask = TF.ToTensor()(mask_image).to(device=device, dtype=self.transformer.dtype)
+        mask = TF.Resize(input_image.size, interpolation=TF.InterpolationMode.NEAREST)(mask)
+        mask = (mask > 0.5)
+        mask = ~mask
+
+        # # Convert input image to tensor and apply mask
+        # input_image = TF.ToTensor()(input_image).to(device=device, dtype=self.transformer.dtype)
+        # input_image = input_image * mask.float().expand_as(input_image)
+        # input_image = TF.ToPILImage()(input_image.cpu())
+
+        image = self.image_processor.preprocess(input_image)
+        image = image.to(device=device, dtype=self.transformer.dtype)
+        latents = retrieve_latents(self.vae.encode(image), generator=generator) * self.vae.config.scaling_factor
+
+
+        h, w = latents.shape[2], latents.shape[3]
+        mask = TF.ToTensor()(mask_image).to(device=device, dtype=self.transformer.dtype)
+        mask = TF.Resize((h, w), interpolation=TF.InterpolationMode.NEAREST)(mask)
+        
+        # Slightly dilate the mask to increase coverage
+        kernel_size = 1  # Decreased from 3 to 2
+        kernel = torch.ones((1, 1, kernel_size, kernel_size), device=device)
+        mask = torch.nn.functional.conv2d(
+            mask.unsqueeze(0),
+            kernel,
+            padding=0
+        ).squeeze(0)
+        mask = torch.clamp(mask, 0, 1)
+
+        mask = (mask > 0.1).float()
+        
+        # Remove extra channel dimension if present
+        if len(mask.shape) == 3 and mask.shape[0] == 1:
+            mask = mask.squeeze(0)
+
+        bool_mask = mask.bool().unsqueeze(0).unsqueeze(0).expand_as(latents)
+        mask=~bool_mask
+
+        print(mask.shape, latents.shape)
+
+        masked_latents = (latents * mask).clone().detach() # apply the mask and get gt_latents
+        masked_latents = self._pack_latents(masked_latents, batch_size, num_channels_latents, 2 * (int(height) // self.vae_scale_factor), 2 * (int(width) // self.vae_scale_factor))
+        
+        mask = self._pack_latents(mask, batch_size, num_channels_latents, 2 * (int(height) // self.vae_scale_factor), 2 * (int(width) // self.vae_scale_factor))
+
+        # Decode and save the masked image
+        if save_masked_image:
+            with torch.no_grad():
+                save_masked_latents = self._unpack_latents(masked_latents, 1024, 1024, self.vae_scale_factor)
+                save_masked_latents = (save_masked_latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+                mask_image = self.vae.decode(save_masked_latents, return_dict=False)[0]
+                mask_image = self.image_processor.postprocess(mask_image, output_type="pil")
+            mask_image_path = output_path.replace(".png", "_masked.png")
+            mask_image[0].save(mask_image_path)
+
+
+        # initialize the random noise for denoising
+        latents = random_latents.clone().detach()
+
+        self.vae = self.vae.to(torch.float32)
+
+        # 9. Denoising loop
+        self.transformer.eval()
+        self.vae.eval()
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+                # handle guidance
+                if self.transformer.config.guidance_embeds:
+                    guidance = torch.tensor([guidance_scale], device=device)
+                    guidance = guidance.expand(latents.shape[0])
+                else:
+                    guidance = None
+
+                noise_pred = self.transformer(
+                    hidden_states=latents,
+                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+
+                # perform CG
+                if i < max_steps:
+                    opt_latents = latents.detach().clone()
+                    with torch.enable_grad():
+                        opt_latents = opt_latents.detach().requires_grad_()
+                        opt_latents = torch.autograd.Variable(opt_latents, requires_grad=True)
+                        # optimizer = torch.optim.Adam([opt_latents], lr=learning_rate)
+
+                        for _ in range(optimization_steps):
+                            latents_p = self.scheduler.step_final(noise_pred, t, opt_latents, return_dict=False)[0]
+                            loss = (1000*torch.nn.functional.mse_loss(latents_p, masked_latents, reduction='none')*mask).mean()
+
+                            grad = torch.autograd.grad(loss, opt_latents)[0]
+                            # grad = torch.clamp(grad, -0.5, 0.5)
+                            opt_latents = opt_latents - learning_rate * grad
+
+                    latents = opt_latents.detach().clone()
+
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+                    
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents.to(torch.float32), return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
+
+    def get_diff_image(self, latents):
+        latents = self._unpack_latents(latents, 1024, 1024, self.vae_scale_factor)
+        latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+        image = self.vae.decode(latents.to(torch.float32), return_dict=False)[0]
+        image = self.image_processor.postprocess(image, output_type="pt")
+        return image
+    
+    def load_and_preprocess_image(self, image_path, custom_image_processor, device):
+        from diffusers.utils import load_image
+        img = load_image(image_path)
+        img = img.resize((512, 512))
+        return custom_image_processor(img).unsqueeze(0).to(device)
+    
+    
+    @torch.no_grad()
+    def edit(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Union[str, List[str]] = None,  #
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        true_cfg: float = 1.0,  #
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+        optimization_steps: int = 3,
+        learning_rate: float = 0.8,
+        max_steps: int = 5,
+        input_image = None,
+        save_masked_image = False,
+        output_path="",
+        mask_image=None,
+        source_steps=1,
+    ):
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            # negative_prompt=negative_prompt,
+            # negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            # negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            # negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        do_true_cfg = true_cfg > 1 and negative_prompt is not None
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+            negative_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt_edit(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+            do_true_cfg=do_true_cfg,
+        )
+        # text_ids = text_ids.repeat(batch_size, 1, 1)
+
+        if do_true_cfg:
+            # Concatenate embeddings
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        random_latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+        # latent_image_ids = latent_image_ids.repeat(batch_size, 1, 1)
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = random_latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 4. Preprocess image
+        image = self.image_processor.preprocess(input_image)
+        image = image.to(device=device, dtype=self.transformer.dtype)
+        latents = retrieve_latents(self.vae.encode(image), generator=generator) * self.vae.config.scaling_factor
+
+
+        # Convert PIL image to tensor
+        if mask_image:
+            from torchvision import transforms as TF
+
+            h, w = latents.shape[2], latents.shape[3]
+            mask = TF.ToTensor()(mask_image).to(device=device, dtype=self.transformer.dtype)
+            mask = TF.Resize((h, w), interpolation=TF.InterpolationMode.NEAREST)(mask)
+            mask = (mask > 0.5).float()
+            mask = mask.squeeze(0)#.squeeze(0)  # Remove the added dimensions
+        else:
+            mask = torch.ones_like(latents).to(device=device)
+
+        print(mask.shape, latents.shape)
+
+        bool_mask = mask.unsqueeze(0).unsqueeze(0).expand_as(latents)
+        mask=(1-bool_mask*1.0).to(latents.dtype)
+        
+        masked_latents = (latents * mask).clone().detach() # apply the mask and get gt_latents
+        masked_latents = self._pack_latents(masked_latents, batch_size, num_channels_latents, 2 * (int(height) // self.vae_scale_factor), 2 * (int(width) // self.vae_scale_factor))
+
+        source_latents = (latents).clone().detach() # apply the mask and get gt_latents
+        source_latents = self._pack_latents(source_latents, batch_size, num_channels_latents, 2 * (int(height) // self.vae_scale_factor), 2 * (int(width) // self.vae_scale_factor))
+
+        mask = self._pack_latents(mask, batch_size, num_channels_latents, 2 * (int(height) // self.vae_scale_factor), 2 * (int(width) // self.vae_scale_factor))
+
+        # initialize the random noise for denoising
+        latents = random_latents.clone().detach()
+
+        self.vae = self.vae.to(torch.float32)
+
+        # 9. Denoising loop
+        self.transformer.eval()
+        self.vae.eval()
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                latent_model_input = torch.cat([latents] * 2) if do_true_cfg else latents
+
+                # handle guidance
+                if self.transformer.config.guidance_embeds:
+                    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+                    guidance = guidance.expand(latent_model_input.shape[0])
+                else:
+                    guidance = None
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0]).to(latent_model_input.dtype)
+
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                if do_true_cfg:
+                    neg_noise_pred, noise_pred = noise_pred.chunk(2)
+                    # noise_pred = neg_noise_pred + true_cfg * (noise_pred - neg_noise_pred)
+                    noise_pred = noise_pred + (1-mask)*(noise_pred - neg_noise_pred) * true_cfg
+                # else:
+                #     neg_noise_pred, noise_pred = noise_pred.chunk(2)
+
+                # perform CG
+                if i < max_steps:
+                    opt_latents = latents.detach().clone()
+                    with torch.enable_grad():
+                        opt_latents = opt_latents.detach().requires_grad_()
+                        opt_latents = torch.autograd.Variable(opt_latents, requires_grad=True)
+                        # optimizer = torch.optim.Adam([opt_latents], lr=learning_rate)
+
+                        for _ in range(optimization_steps):
+                            latents_p = self.scheduler.step_final(noise_pred, t, opt_latents, return_dict=False)[0]
+                            if i < source_steps:
+                                loss = (1000*torch.nn.functional.mse_loss(latents_p, source_latents, reduction='none')).mean()
+                            else:
+                                loss = (1000*torch.nn.functional.mse_loss(latents_p, masked_latents, reduction='none')*mask).mean()
+
+                            grad = torch.autograd.grad(loss, opt_latents)[0]
+                            # grad = torch.clamp(grad, -0.5, 0.5)
+                            opt_latents = opt_latents - learning_rate * grad
+
+                    latents = opt_latents.detach().clone()
+
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents.to(torch.float32), return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
+    
+    @torch.no_grad()
+    def edit2(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Union[str, List[str]] = None,  #
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        true_cfg: float = 1.0,  #
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+        optimization_steps: int = 3,
+        learning_rate: float = 0.8,
+        max_steps: int = 5,
+        input_image = None,
+        save_masked_image = False,
+        output_path="",
+        mask_image=None,
+        source_steps=1,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            # negative_prompt=negative_prompt,
+            # negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            # negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            # negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        do_true_cfg = true_cfg > 1 and negative_prompt is not None
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+            negative_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt_edit(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+            do_true_cfg=do_true_cfg,
+        )
+        # text_ids = text_ids.repeat(batch_size, 1, 1)
+
+        if do_true_cfg:
+            # Concatenate embeddings
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        random_latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+        # latent_image_ids = latent_image_ids.repeat(batch_size, 1, 1)
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = random_latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 4. Preprocess image
+        image = self.image_processor.preprocess(input_image)
+        image = image.to(device=device, dtype=self.transformer.dtype)
+        latents = retrieve_latents(self.vae.encode(image), generator=generator) * self.vae.config.scaling_factor
+
+
+        # Convert PIL image to tensor
+        if mask_image:
+            from torchvision import transforms as TF
+
+            h, w = latents.shape[2], latents.shape[3]
+            mask = TF.ToTensor()(mask_image).to(device=device, dtype=self.transformer.dtype)
+            mask = TF.Resize((h, w), interpolation=TF.InterpolationMode.NEAREST)(mask)
+            mask = (mask > 0.1).float()
+            mask = mask.squeeze(0)#.squeeze(0)  # Remove the added dimensions
+        else:
+            mask = torch.ones_like(latents).to(device=device)
+
+        bool_mask = mask.unsqueeze(0).unsqueeze(0).expand_as(latents)
+        mask=(1-bool_mask*1.0).to(latents.dtype)
+        
+        masked_latents = (latents * mask).clone().detach() # apply the mask and get gt_latents
+        masked_latents = self._pack_latents(masked_latents, batch_size, num_channels_latents, 2 * (int(height) // self.vae_scale_factor), 2 * (int(width) // self.vae_scale_factor))
+
+        source_latents = (latents).clone().detach() # apply the mask and get gt_latents
+        source_latents = self._pack_latents(source_latents, batch_size, num_channels_latents, 2 * (int(height) // self.vae_scale_factor), 2 * (int(width) // self.vae_scale_factor))
+
+        mask = self._pack_latents(mask, batch_size, num_channels_latents, 2 * (int(height) // self.vae_scale_factor), 2 * (int(width) // self.vae_scale_factor))
+
+        # initialize the random noise for denoising
+        latents = random_latents.clone().detach()
+
+        self.vae = self.vae.to(torch.float32)
+
+        # 9. Denoising loop
+        self.transformer.eval()
+        self.vae.eval()
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                latent_model_input = torch.cat([latents] * 2) if do_true_cfg else latents
+
+                # handle guidance
+                if self.transformer.config.guidance_embeds:
+                    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+                    guidance = guidance.expand(latent_model_input.shape[0])
+                else:
+                    guidance = None
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0]).to(latent_model_input.dtype)
+
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                if do_true_cfg and i < max_steps:
+                    neg_noise_pred, noise_pred = noise_pred.chunk(2)
+                    # noise_pred = neg_noise_pred + true_cfg * (noise_pred - neg_noise_pred)
+                    noise_pred = noise_pred + (1-mask)*(noise_pred - neg_noise_pred) * true_cfg
+                else:
+                    neg_noise_pred, noise_pred = noise_pred.chunk(2)
+
+                # perform CG
+                if i < max_steps:
+                    opt_latents = latents.detach().clone()
+                    with torch.enable_grad():
+                        opt_latents = opt_latents.detach().requires_grad_()
+                        opt_latents = torch.autograd.Variable(opt_latents, requires_grad=True)
+                        # optimizer = torch.optim.Adam([opt_latents], lr=learning_rate)
+
+                        for _ in range(optimization_steps):
+                            latents_p = self.scheduler.step_final(noise_pred, t, opt_latents, return_dict=False)[0]
+                            if i < source_steps:
+                                loss = (1000*torch.nn.functional.mse_loss(latents_p, source_latents, reduction='none')).mean()
+                            else:
+                                loss = (1000*torch.nn.functional.mse_loss(latents_p, masked_latents, reduction='none')*mask).mean()
+
+                            grad = torch.autograd.grad(loss, opt_latents)[0]
+                            # grad = torch.clamp(grad, -0.5, 0.5)
+                            opt_latents = opt_latents - learning_rate * grad
+
+                    latents = opt_latents.detach().clone()
+
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents.to(torch.float32), return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
+    

requirements.txt

@@ -0,0 +1,9 @@
+spaces
+diffusers==0.31.0
+gradio==5.6.0
+numpy==2.1.3
+Pillow==11.0.0
+torch==2.1.2
+torch_xla==2.5.1
+torchvision==0.16.2
+transformers==4.45.2

saved_results/20241126_053639/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Inpainting",
+    "prompt": "a dog",
+    "edit_prompt": "",
+    "seed": 0,
+    "randomize_seed": true,
+    "num_inference_steps": 30,
+    "max_steps": 30,
+    "learning_rate": 1,
+    "max_source_steps": 20,
+    "optimization_steps": 10,
+    "true_cfg": 2
+}

saved_results/20241126_055109/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Inpainting",
+    "prompt": "a dog",
+    "edit_prompt": "",
+    "seed": 0,
+    "randomize_seed": true,
+    "num_inference_steps": 30,
+    "max_steps": 30,
+    "learning_rate": 1,
+    "max_source_steps": 20,
+    "optimization_steps": 10,
+    "true_cfg": 2
+}

saved_results/20241126_173140/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Inpainting",
+    "prompt": "a cat with blue eyes",
+    "edit_prompt": "",
+    "seed": 0,
+    "randomize_seed": true,
+    "num_inference_steps": 30,
+    "max_steps": 20,
+    "learning_rate": 1,
+    "max_source_steps": 20,
+    "optimization_steps": 10,
+    "true_cfg": 2
+}

saved_results/20241126_181436/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Editing",
+    "prompt": " ",
+    "edit_prompt": "volcano eruption",
+    "seed": 0,
+    "randomize_seed": true,
+    "num_inference_steps": 30,
+    "max_steps": 20,
+    "learning_rate": 0.5,
+    "max_source_steps": 2,
+    "optimization_steps": 3,
+    "true_cfg": 4.5
+}

saved_results/20241126_181633/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Editing",
+    "prompt": " ",
+    "edit_prompt": "volcano eruption",
+    "seed": 0,
+    "randomize_seed": true,
+    "num_inference_steps": 30,
+    "max_steps": 20,
+    "learning_rate": 0.5,
+    "max_source_steps": 2,
+    "optimization_steps": 3,
+    "true_cfg": 4.5
+}

saved_results/20241126_214810/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Editing",
+    "prompt": " ",
+    "edit_prompt": "a dog with flowers in the mouth",
+    "seed": 0,
+    "randomize_seed": true,
+    "num_inference_steps": 30,
+    "max_steps": 30,
+    "learning_rate": 1,
+    "max_source_steps": 5,
+    "optimization_steps": 3,
+    "true_cfg": 4.5
+}

saved_results/20241126_214908/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Editing",
+    "prompt": " ",
+    "edit_prompt": "a dog with flowers in the mouth",
+    "seed": 0,
+    "randomize_seed": true,
+    "num_inference_steps": 30,
+    "max_steps": 20,
+    "learning_rate": 1,
+    "max_source_steps": 5,
+    "optimization_steps": 3,
+    "true_cfg": 4.5
+}

saved_results/20241126_215043/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Editing",
+    "prompt": " ",
+    "edit_prompt": "a dog with flowers in the mouth",
+    "seed": 52,
+    "randomize_seed": false,
+    "num_inference_steps": 30,
+    "max_steps": 20,
+    "learning_rate": 1,
+    "max_source_steps": 5,
+    "optimization_steps": 3,
+    "true_cfg": 4.5
+}

saved_results/20241126_221300/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Inpainting",
+    "prompt": "A building with \"ASU\" written on it.",
+    "edit_prompt": "",
+    "seed": 0,
+    "randomize_seed": true,
+    "num_inference_steps": 30,
+    "max_steps": 30,
+    "learning_rate": 1,
+    "max_source_steps": 20,
+    "optimization_steps": 5,
+    "true_cfg": 2
+}

saved_results/20241126_222257/parameters.json

@@ -0,0 +1,13 @@
+{
+    "mode": "Inpainting",
+    "prompt": "A cute pig with big eyes",
+    "edit_prompt": "",
+    "seed": 0,
+    "randomize_seed": true,
+    "num_inference_steps": 30,
+    "max_steps": 19.8,
+    "learning_rate": 1,
+    "max_source_steps": 20,
+    "optimization_steps": 5,
+    "true_cfg": 2
+}