ootdiffusion

.gitignore

+*.ckpt
+__pycache__/
+.vscode/
+*.pyc
+.uuid
+.safetensors
+checkpoints/
+*logs*
+train.txt
+datasets
\ No newline at end of file

Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
\ No newline at end of file

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README.md

+# OOTDiffusion
+
+## 论文
+
+**OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on**
+
+* https://arxiv.org/pdf/2403.01779
+
+## 模型结构
+该模型基于`Stable Diffusion`，通过添加`Outfitting Unet`学习衣物特征。
+
+![alt text](readme_imgs/image-1.png)
+
+
+## 算法原理
+
+该算法基于`Stable Diffusion`，通过使用额外的Unet网络学习衣物特征，并使用cross-attention融入主干网络。
+
+![alt text](readme_imgs/image-2.png)
+
+## 环境配置
+
+### Docker（方法一）
+    
+    docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
+
+    docker run --shm-size 10g --network=host --name=ottd --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
+
+    pip install -r requirements.txt
+
+
+### Dockerfile（方法二）
+
+    docker build -t <IMAGE_NAME>:<TAG> .
+
+    docker run --shm-size 10g --network=host --name=ottd --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
+
+    pip install -r requirements.txt
+
+
+### Anaconda (方法三)
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
+https://developer.hpccube.com/tool/
+
+    DTK驱动：dtk24.04
+    python：python3.10
+    torch: 2.1.0
+    torchvision: 0.16.0
+    onnx: 1.15.0
+
+Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应
+
+2、其它非特殊库参照requirements.txt安装
+
+    pip install -r requirements.txt
+
+## 数据集
+
+|名称|链接|
+|:---|:---|
+|VITON-HD|https://openxlab.org.cn/datasets/OpenDataLab/VITON-HD/tree/main/raw <br> https://github.com/shadow2496/VITON-HD  (选择一个即可)|
+|Dress Code|https://github.com/aimagelab/dress-code  (需要填表下载)|
+
+## 训练
+
+    cd train
+    mkdir -p checkpoints/unet_garm checkpoints/unet_vton
+
+    HIP_VISIBLE_DEVICES=0,1,2,3 python main.py
+
+注意：该训练代码为非官方实现，目前仅支持`VITON-HD`类数据集的训练。
+
+## 推理
+
+### 模型下载
+
+https://hf-mirror.com/levihsu/OOTDiffusion/tree/main/checkpoints
+
+https://hf-mirror.com/openai/clip-vit-large-patch14/tree/main
+
+下载链接中的所有模型文件，并放入`checkpoints`文件中。
+
+    checkpoints/
+    ├── clip-vit-large-patch14
+    │   ├── config.json
+    │   ├── merges.txt
+    │   ├── preprocessor_config.json
+    │   ├── pytorch_model.bin
+    │   ├── special_tokens_map.json
+    │   ├── tokenizer_config.json
+    │   ├── tokenizer.json
+    │   └── vocab.json
+    ├── humanparsing
+    │   ├── download.sh
+    │   ├── exp-schp-201908261155-lip.pth
+    │   ├── exp-schp-201908301523-atr.pth
+    │   ├── parsing_atr.onnx
+    │   └── parsing_lip.onnx
+    ├── ootd
+    │   ├── feature_extractor
+    │   │   └── preprocessor_config.json
+    │   ├── model_index.json
+    │   ├── ootd_dc
+    │   │   └── checkpoint-36000
+    │   │       ├── unet_garm
+    │   │       │   ├── config.json
+    │   │       │   └── diffusion_pytorch_model.safetensors
+    │   │       └── unet_vton
+    │   │           ├── config.json
+    │   │           └── diffusion_pytorch_model.safetensors
+    │   ├── ootd_hd
+    │   │   └── checkpoint-36000
+    │   │       ├── unet_garm
+    │   │       │   ├── config.json
+    │   │       │   └── diffusion_pytorch_model.safetensors
+    │   │       └── unet_vton
+    │   │           ├── config.json
+    │   │           └── diffusion_pytorch_model.safetensors
+    │   ├── scheduler
+    │   │   └── scheduler_config.json
+    │   ├── text_encoder
+    │   │   ├── config.json
+    │   │   └── pytorch_model.bin
+    │   ├── tokenizer
+    │   │   ├── merges.txt
+    │   │   ├── special_tokens_map.json
+    │   │   ├── tokenizer_config.json
+    │   │   └── vocab.json
+    │   └── vae
+    │       ├── config.json
+    │       └── diffusion_pytorch_model.bin
+    ├── openpose
+    │   └── ckpts
+    │       └── body_pose_model.pth
+    └── README.txt
+
+### 命令
+
+半身
+
+    # model_path表示任务图片
+    cd OOTDiffusion/run
+    python run_ootd.py --model_path <model-image-path> --cloth_path <cloth-image-path> --scale 2.0 --sample 1
+
+全身
+
+    # category = 0 上半身，1 下半身，2 裙子
+    cd OOTDiffusion/run
+    python run_ootd.py --model_path <model-image-path> --cloth_path <cloth-image-path> --model_type dc --category 2 --scale 2.0 --sample 1
+
+### webui
+
+    cd OOTDiffusion/run
+    python gradio_output.py
+
+
+## result
+
+||人物|衣物|结果|
+|:---|:---:|:---:|:---|
+|hd|![alt text](readme_imgs/input_11.png)|![alt text](readme_imgs/input_12.png)|![alt text](readme_imgs/output1.png)
+|dc|![alt text](readme_imgs/input_21.png)|![alt text](readme_imgs/input_22.png)|![alt text](readme_imgs/output2.png)|
+
+
+### 精度
+
+待补充
+
+## 应用场景
+
+### 算法类别
+
+`AIGC`
+
+### 热点应用行业
+
+`零售,广媒,电商`
+
+## 源码仓库及问题反馈
+
+* https://developer.hpccube.com/codes/modelzoo/ootdiffusion_pytorch
+
+## 参考资料
+
+* https://github.com/levihsu/ootdiffusion
+* https://github.com/lyc0929/OOTDiffusion-train

README_official.md

+# OOTDiffusion
+This repository is the official implementation of OOTDiffusion
+
+🤗 [Try out OOTDiffusion](https://huggingface.co/spaces/levihsu/OOTDiffusion)
+
+(Thanks to [ZeroGPU](https://huggingface.co/zero-gpu-explorers) for providing A100 GPUs)
+
+<!-- Or [try our own demo](https://ootd.ibot.cn/) on RTX 4090 GPUs -->
+
+> **OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on** [[arXiv paper](https://arxiv.org/abs/2403.01779)]<br>
+> [Yuhao Xu](http://levihsu.github.io/), [Tao Gu](https://github.com/T-Gu), [Weifeng Chen](https://github.com/ShineChen1024), [Chengcai Chen](https://www.researchgate.net/profile/Chengcai-Chen)<br>
+> Xiao-i Research
+
+
+Our model checkpoints trained on [VITON-HD](https://github.com/shadow2496/VITON-HD) (half-body) and [Dress Code](https://github.com/aimagelab/dress-code) (full-body) have been released
+
+* 🤗 [Hugging Face link](https://huggingface.co/levihsu/OOTDiffusion) for ***checkpoints*** (ootd, humanparsing, and openpose)
+* 📢📢 We support ONNX for [humanparsing](https://github.com/GoGoDuck912/Self-Correction-Human-Parsing) now. Most environmental issues should have been addressed : )
+* Please also download [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) into ***checkpoints*** folder
+* We've only tested our code and models on Linux (Ubuntu 22.04)
+
+![demo](images/demo.png)&nbsp;
+![workflow](images/workflow.png)&nbsp;
+
+## Installation
+1. Clone the repository
+
+```sh
+git clone https://github.com/levihsu/OOTDiffusion
+```
+
+2. Create a conda environment and install the required packages
+
+```sh
+conda create -n ootd python==3.10
+conda activate ootd
+pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
+pip install -r requirements.txt
+```
+
+## Inference
+1. Half-body model
+
+```sh
+cd OOTDiffusion/run
+python run_ootd.py --model_path <model-image-path> --cloth_path <cloth-image-path> --scale 2.0 --sample 4
+```
+
+2. Full-body model 
+
+> Garment category must be paired: 0 = upperbody; 1 = lowerbody; 2 = dress
+
+```sh
+cd OOTDiffusion/run
+python run_ootd.py --model_path <model-image-path> --cloth_path <cloth-image-path> --model_type dc --category 2 --scale 2.0 --sample 4
+```
+
+## Citation
+```
+@article{xu2024ootdiffusion,
+  title={OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on},
+  author={Xu, Yuhao and Gu, Tao and Chen, Weifeng and Chen, Chengcai},
+  journal={arXiv preprint arXiv:2403.01779},
+  year={2024}
+}
+```
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=levihsu/OOTDiffusion&type=Date)](https://star-history.com/#levihsu/OOTDiffusion&Date)
+
+## TODO List
+- [x] Paper
+- [x] Gradio demo
+- [x] Inference code
+- [x] Model weights
+- [ ] Training code

model.properties

+# 模型编码
+modelCode=640
+# 模型名称
+modelName=ootdiffusion_pytorch
+# 模型描述
+modelDescription=虚拟衣物试穿。
+# 应用场景
+appScenario=推理,训练,AIGC,零售,广媒,电商
+# 框架类型
+frameType=pytorch

ootd/inference_ootd.py

+import pdb
+from pathlib import Path
+import sys
+PROJECT_ROOT = Path(__file__).absolute().parents[0].absolute()
+sys.path.insert(0, str(PROJECT_ROOT))
+import os
+
+import torch
+import numpy as np
+from PIL import Image
+import cv2
+
+import random
+import time
+import pdb
+
+from pipelines_ootd.pipeline_ootd import OotdPipeline
+from pipelines_ootd.unet_garm_2d_condition import UNetGarm2DConditionModel
+from pipelines_ootd.unet_vton_2d_condition import UNetVton2DConditionModel
+from diffusers import UniPCMultistepScheduler
+from diffusers import AutoencoderKL
+
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoProcessor, CLIPVisionModelWithProjection
+from transformers import CLIPTextModel, CLIPTokenizer
+
+VIT_PATH = "../checkpoints/clip-vit-large-patch14"
+VAE_PATH = "../checkpoints/ootd"
+UNET_PATH = "../checkpoints/ootd/ootd_hd/checkpoint-36000"
+MODEL_PATH = "../checkpoints/ootd"
+
+class OOTDiffusion:
+
+    def __init__(self, gpu_id):
+        self.gpu_id = 'cuda:' + str(gpu_id)
+
+        vae = AutoencoderKL.from_pretrained(
+            VAE_PATH,
+            subfolder="vae",
+            torch_dtype=torch.float16,
+        )
+
+        unet_garm = UNetGarm2DConditionModel.from_pretrained(
+            UNET_PATH,
+            subfolder="unet_garm",
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+        )
+        unet_vton = UNetVton2DConditionModel.from_pretrained(
+            UNET_PATH,
+            subfolder="unet_vton",
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+        )
+
+        self.pipe = OotdPipeline.from_pretrained(
+            MODEL_PATH,
+            unet_garm=unet_garm,
+            unet_vton=unet_vton,
+            vae=vae,
+            torch_dtype=torch.float16,
+            variant="fp16",
+            use_safetensors=True,
+            safety_checker=None,
+            requires_safety_checker=False,
+        ).to(self.gpu_id)
+
+        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
+        
+        self.auto_processor = AutoProcessor.from_pretrained(VIT_PATH)
+        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(VIT_PATH).to(self.gpu_id)
+
+        self.tokenizer = CLIPTokenizer.from_pretrained(
+            MODEL_PATH,
+            subfolder="tokenizer",
+        )
+        self.text_encoder = CLIPTextModel.from_pretrained(
+            MODEL_PATH,
+            subfolder="text_encoder",
+        ).to(self.gpu_id)
+
+
+    def tokenize_captions(self, captions, max_length):
+        inputs = self.tokenizer(
+            captions, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt"
+        )
+        return inputs.input_ids
+
+
+    def __call__(self,
+                model_type='hd',
+                category='upperbody',
+                image_garm=None,
+                image_vton=None,
+                mask=None,
+                image_ori=None,
+                num_samples=1,
+                num_steps=20,
+                image_scale=1.0,
+                seed=-1,
+    ):
+        if seed == -1:
+            random.seed(time.time())
+            seed = random.randint(0, 2147483647)
+        print('Initial seed: ' + str(seed))
+        generator = torch.manual_seed(seed)
+
+        with torch.no_grad():
+            prompt_image = self.auto_processor(images=image_garm, return_tensors="pt").to(self.gpu_id)
+            prompt_image = self.image_encoder(prompt_image.data['pixel_values']).image_embeds
+            prompt_image = prompt_image.unsqueeze(1)
+            if model_type == 'hd':
+                prompt_embeds = self.text_encoder(self.tokenize_captions([""], 2).to(self.gpu_id))[0]
+                prompt_embeds[:, 1:] = prompt_image[:]
+            elif model_type == 'dc':
+                prompt_embeds = self.text_encoder(self.tokenize_captions([category], 3).to(self.gpu_id))[0]
+                prompt_embeds = torch.cat([prompt_embeds, prompt_image], dim=1)
+            else:
+                raise ValueError("model_type must be \'hd\' or \'dc\'!")
+
+            images = self.pipe(prompt_embeds=prompt_embeds,
+                        image_garm=image_garm,
+                        image_vton=image_vton, 
+                        mask=mask,
+                        image_ori=image_ori,
+                        num_inference_steps=num_steps,
+                        image_guidance_scale=image_scale,
+                        num_images_per_prompt=num_samples,
+                        generator=generator,
+            ).images
+
+        return images

ootd/inference_ootd_dc.py

+import pdb
+from pathlib import Path
+import sys
+PROJECT_ROOT = Path(__file__).absolute().parents[0].absolute()
+sys.path.insert(0, str(PROJECT_ROOT))
+import os
+import torch
+import numpy as np
+from PIL import Image
+import cv2
+
+import random
+import time
+import pdb
+
+from pipelines_ootd.pipeline_ootd import OotdPipeline
+from pipelines_ootd.unet_garm_2d_condition import UNetGarm2DConditionModel
+from pipelines_ootd.unet_vton_2d_condition import UNetVton2DConditionModel
+from diffusers import UniPCMultistepScheduler
+from diffusers import AutoencoderKL
+
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoProcessor, CLIPVisionModelWithProjection
+from transformers import CLIPTextModel, CLIPTokenizer
+OOTD_ROOT = Path(__file__).resolve().parent.parent
+sys.path.append(str(OOTD_ROOT))
+
+# VIT_PATH = "../checkpoints/clip-vit-large-patch14"
+VIT_PATH = os.path.join(OOTD_ROOT, "checkpoints/clip-vit-large-patch14")
+# VAE_PATH = "../checkpoints/ootd"
+# UNET_PATH = "../checkpoints/ootd/ootd_dc/checkpoint-36000"
+# MODEL_PATH = "../checkpoints/ootd"
+VAE_PATH = os.path.join(OOTD_ROOT, "checkpoints/ootd")
+UNET_PATH = os.path.join(OOTD_ROOT, "checkpoints/ootd/ootd_dc/checkpoint-36000")
+# UNET_PATH = os.path.join(OOTD_ROOT, "train/checkpoints")
+MODEL_PATH = os.path.join(OOTD_ROOT, "checkpoints/ootd")
+
+class OOTDiffusionDC:
+
+    def __init__(self, gpu_id):
+        self.gpu_id = 'cuda:' + str(gpu_id)
+
+        vae = AutoencoderKL.from_pretrained(
+            VAE_PATH,
+            subfolder="vae",
+            torch_dtype=torch.float16,
+        )
+
+        unet_garm = UNetGarm2DConditionModel.from_pretrained(
+            UNET_PATH,
+            subfolder="unet_garm",
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+        )
+        unet_vton = UNetVton2DConditionModel.from_pretrained(
+            UNET_PATH,
+            subfolder="unet_vton",
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+        )
+
+        self.pipe = OotdPipeline.from_pretrained(
+            MODEL_PATH,
+            unet_garm=unet_garm,
+            unet_vton=unet_vton,
+            vae=vae,
+            torch_dtype=torch.float16,
+            variant="fp16",
+            use_safetensors=True,
+            safety_checker=None,
+            requires_safety_checker=False,
+        ).to(self.gpu_id)
+
+        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
+        
+        self.auto_processor = AutoProcessor.from_pretrained(VIT_PATH)
+        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(VIT_PATH).to(self.gpu_id)
+
+        self.tokenizer = CLIPTokenizer.from_pretrained(
+            MODEL_PATH,
+            subfolder="tokenizer",
+        )
+        self.text_encoder = CLIPTextModel.from_pretrained(
+            MODEL_PATH,
+            subfolder="text_encoder",
+        ).to(self.gpu_id)
+
+
+    def tokenize_captions(self, captions, max_length):
+        inputs = self.tokenizer(
+            captions, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt"
+        )
+        return inputs.input_ids
+
+
+    def __call__(self,
+                model_type='hd',
+                category='upperbody',
+                image_garm=None,
+                image_vton=None,
+                mask=None,
+                image_ori=None,
+                num_samples=1,
+                num_steps=20,
+                image_scale=1.0,
+                seed=-1,
+    ):
+        if seed == -1:
+            random.seed(time.time())
+            seed = random.randint(0, 2147483647)
+        print('Initial seed: ' + str(seed))
+        generator = torch.manual_seed(seed)
+
+        with torch.no_grad():
+            prompt_image = self.auto_processor(images=image_garm, return_tensors="pt").to(self.gpu_id)
+            prompt_image = self.image_encoder(prompt_image.data['pixel_values']).image_embeds
+            prompt_image = prompt_image.unsqueeze(1)
+            if model_type == 'hd':
+                prompt_embeds = self.text_encoder(self.tokenize_captions([""], 2).to(self.gpu_id))[0]
+                prompt_embeds[:, 1:] = prompt_image[:]
+            elif model_type == 'dc':
+                prompt_embeds = self.text_encoder(self.tokenize_captions([category], 3).to(self.gpu_id))[0]
+                prompt_embeds = torch.cat([prompt_embeds, prompt_image], dim=1)
+            else:
+                raise ValueError("model_type must be \'hd\' or \'dc\'!")
+
+            images = self.pipe(prompt_embeds=prompt_embeds,
+                        image_garm=image_garm,
+                        image_vton=image_vton, 
+                        mask=mask,
+                        image_ori=image_ori,
+                        num_inference_steps=num_steps,
+                        image_guidance_scale=image_scale,
+                        num_images_per_prompt=num_samples,
+                        generator=generator,
+            ).images
+
+        return images

ootd/inference_ootd_hd.py

+import pdb
+from pathlib import Path
+import sys
+PROJECT_ROOT = Path(__file__).absolute().parents[0].absolute()
+sys.path.insert(0, str(PROJECT_ROOT))
+import os
+import torch
+import numpy as np
+from PIL import Image
+import cv2
+
+import random
+import time
+import pdb
+
+from pipelines_ootd.pipeline_ootd import OotdPipeline
+from pipelines_ootd.unet_garm_2d_condition import UNetGarm2DConditionModel
+from pipelines_ootd.unet_vton_2d_condition import UNetVton2DConditionModel
+from diffusers import UniPCMultistepScheduler
+from diffusers import AutoencoderKL
+
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoProcessor, CLIPVisionModelWithProjection
+from transformers import CLIPTextModel, CLIPTokenizer
+
+OOTD_ROOT = Path(__file__).resolve().parent.parent
+sys.path.append(str(OOTD_ROOT))
+
+# VIT_PATH = "../checkpoints/clip-vit-large-patch14"
+VIT_PATH = os.path.join(OOTD_ROOT, "checkpoints/clip-vit-large-patch14")
+VAE_PATH = "../checkpoints/ootd"
+UNET_PATH = "../checkpoints/ootd/ootd_hd/checkpoint-36000"
+MODEL_PATH = "../checkpoints/ootd"
+
+class OOTDiffusionHD:
+
+    def __init__(self, gpu_id):
+        self.gpu_id = 'cuda:' + str(gpu_id)
+
+        vae = AutoencoderKL.from_pretrained(
+            VAE_PATH,
+            subfolder="vae",
+            torch_dtype=torch.float16,
+        )
+
+        unet_garm = UNetGarm2DConditionModel.from_pretrained(
+            UNET_PATH,
+            subfolder="unet_garm",
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+        )
+        unet_vton = UNetVton2DConditionModel.from_pretrained(
+            UNET_PATH,
+            subfolder="unet_vton",
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+        )
+
+        self.pipe = OotdPipeline.from_pretrained(
+            MODEL_PATH,
+            unet_garm=unet_garm,
+            unet_vton=unet_vton,
+            vae=vae,
+            torch_dtype=torch.float16,
+            variant="fp16",
+            use_safetensors=True,
+            safety_checker=None,
+            requires_safety_checker=False,
+        ).to(self.gpu_id)
+
+        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
+        
+        self.auto_processor = AutoProcessor.from_pretrained(VIT_PATH)
+        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(VIT_PATH).to(self.gpu_id)
+
+        self.tokenizer = CLIPTokenizer.from_pretrained(
+            MODEL_PATH,
+            subfolder="tokenizer",
+        )
+        self.text_encoder = CLIPTextModel.from_pretrained(
+            MODEL_PATH,
+            subfolder="text_encoder",
+        ).to(self.gpu_id)
+
+
+    def tokenize_captions(self, captions, max_length):
+        inputs = self.tokenizer(
+            captions, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt"
+        )
+        return inputs.input_ids
+
+
+    def __call__(self,
+                model_type='hd',
+                category='upperbody',
+                image_garm=None,
+                image_vton=None,
+                mask=None,
+                image_ori=None,
+                num_samples=1,
+                num_steps=20,
+                image_scale=1.0,
+                seed=-1,
+    ):
+        if seed == -1:
+            random.seed(time.time())
+            seed = random.randint(0, 2147483647)
+        print('Initial seed: ' + str(seed))
+        generator = torch.manual_seed(seed)
+
+        with torch.no_grad():
+            prompt_image = self.auto_processor(images=image_garm, return_tensors="pt").to(self.gpu_id)
+            prompt_image = self.image_encoder(prompt_image.data['pixel_values']).image_embeds
+            prompt_image = prompt_image.unsqueeze(1)
+            if model_type == 'hd':
+                prompt_embeds = self.text_encoder(self.tokenize_captions([""], 2).to(self.gpu_id))[0]
+                prompt_embeds[:, 1:] = prompt_image[:]
+            elif model_type == 'dc':
+                prompt_embeds = self.text_encoder(self.tokenize_captions([category], 3).to(self.gpu_id))[0]
+                prompt_embeds = torch.cat([prompt_embeds, prompt_image], dim=1)
+            else:
+                raise ValueError("model_type must be \'hd\' or \'dc\'!")
+
+            images = self.pipe(prompt_embeds=prompt_embeds,
+                        image_garm=image_garm,
+                        image_vton=image_vton, 
+                        mask=mask,
+                        image_ori=image_ori,
+                        num_inference_steps=num_steps,
+                        image_guidance_scale=image_scale,
+                        num_images_per_prompt=num_samples,
+                        generator=generator,
+            ).images
+
+        return images

ootd/pipelines_ootd/attention_garm.py

+# Copyright 2023 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.
+
+# Modified by Yuhao Xu for OOTDiffusion (https://github.com/levihsu/OOTDiffusion)
+from typing import Any, Dict, Optional
+
+import torch
+from torch import nn
+
+from diffusers.utils import USE_PEFT_BACKEND
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.activations import GEGLU, GELU, ApproximateGELU
+from diffusers.models.attention_processor import Attention
+from diffusers.models.embeddings import SinusoidalPositionalEmbedding
+from diffusers.models.lora import LoRACompatibleLinear
+from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormZero
+
+
+@maybe_allow_in_graph
+class GatedSelfAttentionDense(nn.Module):
+    r"""
+    A gated self-attention dense layer that combines visual features and object features.
+
+    Parameters:
+        query_dim (`int`): The number of channels in the query.
+        context_dim (`int`): The number of channels in the context.
+        n_heads (`int`): The number of heads to use for attention.
+        d_head (`int`): The number of channels in each head.
+    """
+
+    def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):
+        super().__init__()
+
+        # we need a linear projection since we need cat visual feature and obj feature
+        self.linear = nn.Linear(context_dim, query_dim)
+
+        self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
+        self.ff = FeedForward(query_dim, activation_fn="geglu")
+
+        self.norm1 = nn.LayerNorm(query_dim)
+        self.norm2 = nn.LayerNorm(query_dim)
+
+        self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0)))
+        self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0)))
+
+        self.enabled = True
+
+    def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:
+        if not self.enabled:
+            return x
+
+        n_visual = x.shape[1]
+        objs = self.linear(objs)
+
+        x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :]
+        x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))
+
+        return x
+
+
+@maybe_allow_in_graph
+class BasicTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm (:
+            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
+            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        attention_type (`str`, *optional*, defaults to `"default"`):
+            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
+        positional_embeddings (`str`, *optional*, defaults to `None`):
+            The type of positional embeddings to apply to.
+        num_positional_embeddings (`int`, *optional*, defaults to `None`):
+            The maximum number of positional embeddings to apply.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single'
+        norm_eps: float = 1e-5,
+        final_dropout: bool = False,
+        attention_type: str = "default",
+        positional_embeddings: Optional[str] = None,
+        num_positional_embeddings: Optional[int] = None,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+
+        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
+        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
+        self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
+        self.use_layer_norm = norm_type == "layer_norm"
+
+        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
+            raise ValueError(
+                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
+                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+            )
+
+        if positional_embeddings and (num_positional_embeddings is None):
+            raise ValueError(
+                "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
+            )
+
+        if positional_embeddings == "sinusoidal":
+            self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
+        else:
+            self.pos_embed = None
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        if self.use_ada_layer_norm:
+            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
+        elif self.use_ada_layer_norm_zero:
+            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
+        else:
+            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )
+
+        # 2. Cross-Attn
+        if cross_attention_dim is not None or double_self_attention:
+            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+            # the second cross attention block.
+            self.norm2 = (
+                AdaLayerNorm(dim, num_embeds_ada_norm)
+                if self.use_ada_layer_norm
+                else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
+            )
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim if not double_self_attention else None,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )  # is self-attn if encoder_hidden_states is none
+        else:
+            self.norm2 = None
+            self.attn2 = None
+
+        # 3. Feed-forward
+        if not self.use_ada_layer_norm_single:
+            self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
+
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
+
+        # 4. Fuser
+        if attention_type == "gated" or attention_type == "gated-text-image":
+            self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)
+
+        # 5. Scale-shift for PixArt-Alpha.
+        if self.use_ada_layer_norm_single:
+            self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        spatial_attn_inputs = [],
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+    ) -> torch.FloatTensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        batch_size = hidden_states.shape[0]
+
+        spatial_attn_input = hidden_states
+        spatial_attn_inputs.append(spatial_attn_input)
+
+        if self.use_ada_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.use_ada_layer_norm_zero:
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        elif self.use_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states)
+        elif self.use_ada_layer_norm_single:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
+            ).chunk(6, dim=1)
+            norm_hidden_states = self.norm1(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
+            norm_hidden_states = norm_hidden_states.squeeze(1)
+        else:
+            raise ValueError("Incorrect norm used")
+
+        if self.pos_embed is not None:
+            norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+        # 1. Retrieve lora scale.
+        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
+
+        # 2. Prepare GLIGEN inputs
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+        if self.use_ada_layer_norm_zero:
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        elif self.use_ada_layer_norm_single:
+            attn_output = gate_msa * attn_output
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 2.5 GLIGEN Control
+        if gligen_kwargs is not None:
+            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            if self.use_ada_layer_norm:
+                norm_hidden_states = self.norm2(hidden_states, timestep)
+            elif self.use_ada_layer_norm_zero or self.use_layer_norm:
+                norm_hidden_states = self.norm2(hidden_states)
+            elif self.use_ada_layer_norm_single:
+                # For PixArt norm2 isn't applied here:
+                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
+                norm_hidden_states = hidden_states
+            else:
+                raise ValueError("Incorrect norm")
+
+            if self.pos_embed is not None and self.use_ada_layer_norm_single is False:
+                norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        if not self.use_ada_layer_norm_single:
+            norm_hidden_states = self.norm3(hidden_states)
+
+        if self.use_ada_layer_norm_zero:
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        if self.use_ada_layer_norm_single:
+            norm_hidden_states = self.norm2(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+                raise ValueError(
+                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+                )
+
+            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+            ff_output = torch.cat(
+                [
+                    self.ff(hid_slice, scale=lora_scale)
+                    for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)
+                ],
+                dim=self._chunk_dim,
+            )
+        else:
+            ff_output = self.ff(norm_hidden_states, scale=lora_scale)
+
+        if self.use_ada_layer_norm_zero:
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+        elif self.use_ada_layer_norm_single:
+            ff_output = gate_mlp * ff_output
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states, spatial_attn_inputs
+
+
+class FeedForward(nn.Module):
+    r"""
+    A feed-forward layer.
+
+    Parameters:
+        dim (`int`): The number of channels in the input.
+        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        dropout: float = 0.0,
+        activation_fn: str = "geglu",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+        linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear
+
+        if activation_fn == "gelu":
+            act_fn = GELU(dim, inner_dim)
+        if activation_fn == "gelu-approximate":
+            act_fn = GELU(dim, inner_dim, approximate="tanh")
+        elif activation_fn == "geglu":
+            act_fn = GEGLU(dim, inner_dim)
+        elif activation_fn == "geglu-approximate":
+            act_fn = ApproximateGELU(dim, inner_dim)
+
+        self.net = nn.ModuleList([])
+        # project in
+        self.net.append(act_fn)
+        # project dropout
+        self.net.append(nn.Dropout(dropout))
+        # project out
+        self.net.append(linear_cls(inner_dim, dim_out))
+        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
+        if final_dropout:
+            self.net.append(nn.Dropout(dropout))
+
+    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
+        compatible_cls = (GEGLU,) if USE_PEFT_BACKEND else (GEGLU, LoRACompatibleLinear)
+        for module in self.net:
+            if isinstance(module, compatible_cls):
+                hidden_states = module(hidden_states, scale)
+            else:
+                hidden_states = module(hidden_states)
+        return hidden_states

ootd/pipelines_ootd/attention_vton.py

+# Copyright 2023 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.
+
+# Modified by Yuhao Xu for OOTDiffusion (https://github.com/levihsu/OOTDiffusion)
+from typing import Any, Dict, Optional
+
+import torch
+from torch import nn
+
+from diffusers.utils import USE_PEFT_BACKEND
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.activations import GEGLU, GELU, ApproximateGELU
+from diffusers.models.attention_processor import Attention
+from diffusers.models.embeddings import SinusoidalPositionalEmbedding
+from diffusers.models.lora import LoRACompatibleLinear
+from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormZero
+
+
+@maybe_allow_in_graph
+class GatedSelfAttentionDense(nn.Module):
+    r"""
+    A gated self-attention dense layer that combines visual features and object features.
+
+    Parameters:
+        query_dim (`int`): The number of channels in the query.
+        context_dim (`int`): The number of channels in the context.
+        n_heads (`int`): The number of heads to use for attention.
+        d_head (`int`): The number of channels in each head.
+    """
+
+    def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):
+        super().__init__()
+
+        # we need a linear projection since we need cat visual feature and obj feature
+        self.linear = nn.Linear(context_dim, query_dim)
+
+        self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
+        self.ff = FeedForward(query_dim, activation_fn="geglu")
+
+        self.norm1 = nn.LayerNorm(query_dim)
+        self.norm2 = nn.LayerNorm(query_dim)
+
+        self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0)))
+        self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0)))
+
+        self.enabled = True
+
+    def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:
+        if not self.enabled:
+            return x
+
+        n_visual = x.shape[1]
+        objs = self.linear(objs)
+
+        x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :]
+        x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))
+
+        return x
+
+
+@maybe_allow_in_graph
+class BasicTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm (:
+            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
+            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        attention_type (`str`, *optional*, defaults to `"default"`):
+            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
+        positional_embeddings (`str`, *optional*, defaults to `None`):
+            The type of positional embeddings to apply to.
+        num_positional_embeddings (`int`, *optional*, defaults to `None`):
+            The maximum number of positional embeddings to apply.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single'
+        norm_eps: float = 1e-5,
+        final_dropout: bool = False,
+        attention_type: str = "default",
+        positional_embeddings: Optional[str] = None,
+        num_positional_embeddings: Optional[int] = None,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+
+        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
+        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
+        self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
+        self.use_layer_norm = norm_type == "layer_norm"
+
+        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
+            raise ValueError(
+                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
+                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+            )
+
+        if positional_embeddings and (num_positional_embeddings is None):
+            raise ValueError(
+                "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
+            )
+
+        if positional_embeddings == "sinusoidal":
+            self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
+        else:
+            self.pos_embed = None
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        if self.use_ada_layer_norm:
+            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
+        elif self.use_ada_layer_norm_zero:
+            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
+        else:
+            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )
+
+        # 2. Cross-Attn
+        if cross_attention_dim is not None or double_self_attention:
+            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+            # the second cross attention block.
+            self.norm2 = (
+                AdaLayerNorm(dim, num_embeds_ada_norm)
+                if self.use_ada_layer_norm
+                else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
+            )
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim if not double_self_attention else None,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )  # is self-attn if encoder_hidden_states is none
+        else:
+            self.norm2 = None
+            self.attn2 = None
+
+        # 3. Feed-forward
+        if not self.use_ada_layer_norm_single:
+            self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
+
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
+
+        # 4. Fuser
+        if attention_type == "gated" or attention_type == "gated-text-image":
+            self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)
+
+        # 5. Scale-shift for PixArt-Alpha.
+        if self.use_ada_layer_norm_single:
+            self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        spatial_attn_inputs = [],
+        spatial_attn_idx = 0,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+    ) -> torch.FloatTensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        batch_size = hidden_states.shape[0]
+
+        spatial_attn_input = spatial_attn_inputs[spatial_attn_idx]
+        spatial_attn_idx += 1
+        hidden_states = torch.cat((hidden_states, spatial_attn_input), dim=1)
+
+        if self.use_ada_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.use_ada_layer_norm_zero:
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        elif self.use_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states)
+        elif self.use_ada_layer_norm_single:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
+            ).chunk(6, dim=1)
+            norm_hidden_states = self.norm1(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
+            norm_hidden_states = norm_hidden_states.squeeze(1)
+        else:
+            raise ValueError("Incorrect norm used")
+
+        if self.pos_embed is not None:
+            norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+        # 1. Retrieve lora scale.
+        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
+
+        # 2. Prepare GLIGEN inputs
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+        if self.use_ada_layer_norm_zero:
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        elif self.use_ada_layer_norm_single:
+            attn_output = gate_msa * attn_output
+
+        
+        hidden_states = attn_output + hidden_states
+        hidden_states, _ = hidden_states.chunk(2, dim=1)
+
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 2.5 GLIGEN Control
+        if gligen_kwargs is not None:
+            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            if self.use_ada_layer_norm:
+                norm_hidden_states = self.norm2(hidden_states, timestep)
+            elif self.use_ada_layer_norm_zero or self.use_layer_norm:
+                norm_hidden_states = self.norm2(hidden_states)
+            elif self.use_ada_layer_norm_single:
+                # For PixArt norm2 isn't applied here:
+                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
+                norm_hidden_states = hidden_states
+            else:
+                raise ValueError("Incorrect norm")
+
+            if self.pos_embed is not None and self.use_ada_layer_norm_single is False:
+                norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        if not self.use_ada_layer_norm_single:
+            norm_hidden_states = self.norm3(hidden_states)
+
+        if self.use_ada_layer_norm_zero:
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        if self.use_ada_layer_norm_single:
+            norm_hidden_states = self.norm2(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+                raise ValueError(
+                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+                )
+
+            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+            ff_output = torch.cat(
+                [
+                    self.ff(hid_slice, scale=lora_scale)
+                    for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)
+                ],
+                dim=self._chunk_dim,
+            )
+        else:
+            ff_output = self.ff(norm_hidden_states, scale=lora_scale)
+
+        if self.use_ada_layer_norm_zero:
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+        elif self.use_ada_layer_norm_single:
+            ff_output = gate_mlp * ff_output
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states, spatial_attn_inputs, spatial_attn_idx
+
+
+class FeedForward(nn.Module):
+    r"""
+    A feed-forward layer.
+
+    Parameters:
+        dim (`int`): The number of channels in the input.
+        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        dropout: float = 0.0,
+        activation_fn: str = "geglu",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+        linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear
+
+        if activation_fn == "gelu":
+            act_fn = GELU(dim, inner_dim)
+        if activation_fn == "gelu-approximate":
+            act_fn = GELU(dim, inner_dim, approximate="tanh")
+        elif activation_fn == "geglu":
+            act_fn = GEGLU(dim, inner_dim)
+        elif activation_fn == "geglu-approximate":
+            act_fn = ApproximateGELU(dim, inner_dim)
+
+        self.net = nn.ModuleList([])
+        # project in
+        self.net.append(act_fn)
+        # project dropout
+        self.net.append(nn.Dropout(dropout))
+        # project out
+        self.net.append(linear_cls(inner_dim, dim_out))
+        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
+        if final_dropout:
+            self.net.append(nn.Dropout(dropout))
+
+    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
+        compatible_cls = (GEGLU,) if USE_PEFT_BACKEND else (GEGLU, LoRACompatibleLinear)
+        for module in self.net:
+            if isinstance(module, compatible_cls):
+                hidden_states = module(hidden_states, scale)
+            else:
+                hidden_states = module(hidden_states)
+        return hidden_states