Merge changes

docs/source/en/api/models/controlnet.md

@@ -39,12 +39,12 @@ pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=contro
 
 ## ControlNetOutput
 
-[[autodoc]] models.controlnet.ControlNetOutput
+[[autodoc]] models.controlnets.controlnet.ControlNetOutput
 
 ## FlaxControlNetModel
 
 [[autodoc]] FlaxControlNetModel
 
 ## FlaxControlNetOutput
 
-[[autodoc]] models.controlnet_flax.FlaxControlNetOutput
+[[autodoc]] models.controlnets.controlnet_flax.FlaxControlNetOutput

docs/source/en/api/models/controlnet_sd3.md

@@ -38,5 +38,5 @@ pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-di
 
 ## SD3ControlNetOutput
 
-[[autodoc]] models.controlnet_sd3.SD3ControlNetOutput
+[[autodoc]] models.controlnets.controlnet_sd3.SD3ControlNetOutput
 

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -39,7 +39,7 @@
 from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
 from huggingface_hub import create_repo, upload_folder
 from packaging import version
-from peft import LoraConfig
+from peft import LoraConfig, set_peft_model_state_dict
 from peft.utils import get_peft_model_state_dict
 from PIL import Image
 from PIL.ImageOps import exif_transpose
@@ -59,12 +59,13 @@
 )
 from diffusers.loaders import StableDiffusionLoraLoaderMixin
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import compute_snr
+from diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr
 from diffusers.utils import (
     check_min_version,
     convert_all_state_dict_to_peft,
     convert_state_dict_to_diffusers,
     convert_state_dict_to_kohya,
+    convert_unet_state_dict_to_peft,
     is_wandb_available,
 )
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
@@ -1319,6 +1320,37 @@ def load_model_hook(models, input_dir):
             else:
                 raise ValueError(f"unexpected save model: {model.__class__}")
 
+        lora_state_dict, network_alphas = StableDiffusionPipeline.lora_state_dict(input_dir)
+
+        unet_state_dict = {f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")}
+        unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)
+        incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name="default")
+        if incompatible_keys is not None:
+            # check only for unexpected keys
+            unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+            if unexpected_keys:
+                logger.warning(
+                    f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                    f" {unexpected_keys}. "
+                )
+
+        if args.train_text_encoder:
+            # Do we need to call `scale_lora_layers()` here?
+            _set_state_dict_into_text_encoder(lora_state_dict, prefix="text_encoder.", text_encoder=text_encoder_one_)
+
+            _set_state_dict_into_text_encoder(
+                lora_state_dict, prefix="text_encoder_2.", text_encoder=text_encoder_one_
+            )
+
+        # Make sure the trainable params are in float32. This is again needed since the base models
+        # are in `weight_dtype`. More details:
+        # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
+        if args.mixed_precision == "fp16":
+            models = [unet_]
+            if args.train_text_encoder:
+                models.extend([text_encoder_one_])
+                # only upcast trainable parameters (LoRA) into fp32
+                cast_training_params(models)
         lora_state_dict, network_alphas = StableDiffusionLoraLoaderMixin.lora_state_dict(input_dir)
         StableDiffusionLoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)
 

examples/community/README.md

@@ -10,6 +10,7 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 
 | Example                                                                                                                               | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | Code Example                                                                              | Colab                                                                                                                                                                                                              |                                                        Author |
 |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
+|Adaptive Mask Inpainting|Adaptive Mask Inpainting algorithm from [Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models](https://github.com/snuvclab/coma) (ECCV '24, Oral) provides a way to insert human inside the scene image without altering the background, by inpainting with adapting mask.|[Adaptive Mask Inpainting](#adaptive-mask-inpainting)|-|[Hyeonwoo Kim](https://sshowbiz.xyz),[Sookwan Han](https://jellyheadandrew.github.io)|
 |Flux with CFG|[Flux with CFG](https://github.com/ToTheBeginning/PuLID/blob/main/docs/pulid_for_flux.md) provides an implementation of using CFG in [Flux](https://blackforestlabs.ai/announcing-black-forest-labs/).|[Flux with CFG](#flux-with-cfg)|NA|[Linoy Tsaban](https://github.com/linoytsaban), [Apolinário](https://github.com/apolinario), and [Sayak Paul](https://github.com/sayakpaul)|
 |Differential Diffusion|[Differential Diffusion](https://github.com/exx8/differential-diffusion) modifies an image according to a text prompt, and according to a map that specifies the amount of change in each region.|[Differential Diffusion](#differential-diffusion)|[![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/exx8/differential-diffusion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/exx8/differential-diffusion/blob/main/examples/SD2.ipynb)|[Eran Levin](https://github.com/exx8) and [Ohad Fried](https://www.ohadf.com/)|
 | HD-Painter                                                                                                                            | [HD-Painter](https://github.com/Picsart-AI-Research/HD-Painter) enables prompt-faithfull and high resolution (up to 2k) image inpainting upon any diffusion-based image inpainting method.                                                                                                                                                                                                                                                                                                               | [HD-Painter](#hd-painter)                                                                 | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/PAIR/HD-Painter)                                                                              | [Manukyan Hayk](https://github.com/haikmanukyan) and [Sargsyan Andranik](https://github.com/AndranikSargsyan) |
@@ -85,6 +86,161 @@ pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion
 
 ## Example usages
 
+### Adaptive Mask Inpainting
+
+**Hyeonwoo Kim\*, Sookwan Han\*, Patrick Kwon, Hanbyul Joo**
+
+**Seoul National University, Naver Webtoon**
+
+Adaptive Mask Inpainting, presented in the ECCV'24 oral paper [*Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models*](https://snuvclab.github.io/coma), is an algorithm designed to insert humans into scene images without altering the background. Traditional inpainting methods often fail to preserve object geometry and details within the masked region, leading to false affordances. Adaptive Mask Inpainting addresses this issue by progressively specifying the inpainting region over diffusion timesteps, ensuring that the inserted human integrates seamlessly with the existing scene.
+
+Here is the demonstration of Adaptive Mask Inpainting:
+
+<video controls>
+  <source src="https://snuvclab.github.io/coma/static/videos/adaptive_mask_inpainting_vis.mp4" type="video/mp4">
+  Your browser does not support the video tag.
+</video>
+
+![teaser-img](https://snuvclab.github.io/coma/static/images/example_result_adaptive_mask_inpainting.png)
+
+
+You can find additional information about Adaptive Mask Inpainting in the [paper](https://arxiv.org/pdf/2401.12978) or in the [project website](https://snuvclab.github.io/coma).
+
+#### Usage example
+First, clone the diffusers github repository, and run the following command to set environment.
+```Shell
+git clone https://github.com/huggingface/diffusers.git
+cd diffusers
+
+conda create --name ami python=3.9 -y
+conda activate ami
+
+conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge -y
+python -m pip install detectron2==0.6 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
+pip install easydict
+pip install diffusers==0.20.2 accelerate safetensors transformers
+pip install setuptools==59.5.0
+pip install opencv-python
+pip install numpy==1.24.1
+```
+Then, run the below code under 'diffusers' directory.
+```python
+import numpy as np
+import torch
+from PIL import Image
+
+from diffusers import DDIMScheduler
+from diffusers import DiffusionPipeline
+from diffusers.utils import load_image
+
+from examples.community.adaptive_mask_inpainting import download_file, AdaptiveMaskInpaintPipeline, AMI_INSTALL_MESSAGE
+
+print(AMI_INSTALL_MESSAGE)
+
+from easydict import EasyDict
+
+
+
+if __name__ == "__main__":    
+    """
+    Download Necessary Files
+    """
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/model_final_edd263.pkl?download=true",
+        output_file = "model_final_edd263.pkl",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/pointrend_rcnn_R_50_FPN_3x_coco.yaml?download=true",
+        output_file = "pointrend_rcnn_R_50_FPN_3x_coco.yaml",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/input_img.png?download=true",
+        output_file = "input_img.png",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/input_mask.png?download=true",
+        output_file = "input_mask.png",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/Base-PointRend-RCNN-FPN.yaml?download=true",
+        output_file = "Base-PointRend-RCNN-FPN.yaml",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/Base-RCNN-FPN.yaml?download=true",
+        output_file = "Base-RCNN-FPN.yaml",
+        exist_ok=True,
+    )
+
+    """ 
+    Prepare Adaptive Mask Inpainting Pipeline
+    """
+    # device
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    num_steps = 50
+
+    # Scheduler
+    scheduler = DDIMScheduler(
+        beta_start=0.00085, 
+        beta_end=0.012, 
+        beta_schedule="scaled_linear", 
+        clip_sample=False, 
+        set_alpha_to_one=False
+    )
+    scheduler.set_timesteps(num_inference_steps=num_steps)
+
+    ## load models as pipelines
+    pipeline = AdaptiveMaskInpaintPipeline.from_pretrained(
+        "Uminosachi/realisticVisionV51_v51VAE-inpainting", 
+        scheduler=scheduler, 
+        torch_dtype=torch.float16, 
+        requires_safety_checker=False
+    ).to(device)
+
+    ## disable safety checker
+    enable_safety_checker = False
+    if not enable_safety_checker:
+        pipeline.safety_checker = None
+
+    """ 
+    Run Adaptive Mask Inpainting 
+    """
+    default_mask_image = Image.open("./input_mask.png").convert("L")
+    init_image = Image.open("./input_img.png").convert("RGB")
+
+
+    seed = 59
+    generator = torch.Generator(device=device)
+    generator.manual_seed(seed)
+
+    image = pipeline(
+        prompt="a man sitting on a couch",
+        negative_prompt="worst quality, normal quality, low quality, bad anatomy, artifacts, blurry, cropped, watermark, greyscale, nsfw",
+        image=init_image,
+        default_mask_image=default_mask_image,
+        guidance_scale=11.0,
+        strength=0.98,
+        use_adaptive_mask=True,
+        generator=generator,
+        enforce_full_mask_ratio=0.0,
+        visualization_save_dir="./ECCV2024_adaptive_mask_inpainting_demo", # DON'T CHANGE THIS!!!
+        human_detection_thres=0.015,
+    ).images[0]
+
+
+    image.save(f'final_img.png')
+```
+#### [Troubleshooting]
+
+If you run into an error `cannot import name 'cached_download' from 'huggingface_hub'` (issue [1851](https://github.com/easydiffusion/easydiffusion/issues/1851)), remove `cached_download` from the import line in the file `diffusers/utils/dynamic_modules_utils.py`. 
+
+For example, change the import line from `.../env/lib/python3.8/site-packages/diffusers/utils/dynamic_modules_utils.py`.
+
+
 ### Flux with CFG
 
 Know more about Flux [here](https://blackforestlabs.ai/announcing-black-forest-labs/). Since Flux doesn't use CFG, this implementation provides one, inspired by the [PuLID Flux adaptation](https://github.com/ToTheBeginning/PuLID/blob/main/docs/pulid_for_flux.md).