Repository: KwaiVGI/SynCamMaster Branch: main Commit: 43ea4d961189 Files: 222 Total size: 32.7 MB Directory structure: gitextract_iigplzbn/ ├── .gitignore ├── README.md ├── diffsynth/ │ ├── __init__.py │ ├── configs/ │ │ ├── __init__.py │ │ └── model_config.py │ ├── controlnets/ │ │ ├── __init__.py │ │ ├── controlnet_unit.py │ │ └── processors.py │ ├── data/ │ │ ├── __init__.py │ │ ├── simple_text_image.py │ │ └── video.py │ ├── extensions/ │ │ ├── ESRGAN/ │ │ │ └── __init__.py │ │ ├── FastBlend/ │ │ │ ├── __init__.py │ │ │ ├── api.py │ │ │ ├── cupy_kernels.py │ │ │ ├── data.py │ │ │ ├── patch_match.py │ │ │ └── runners/ │ │ │ ├── __init__.py │ │ │ ├── accurate.py │ │ │ ├── balanced.py │ │ │ ├── fast.py │ │ │ └── interpolation.py │ │ ├── ImageQualityMetric/ │ │ │ ├── BLIP/ │ │ │ │ ├── __init__.py │ │ │ │ ├── blip.py │ │ │ │ ├── blip_pretrain.py │ │ │ │ ├── med.py │ │ │ │ └── vit.py │ │ │ ├── __init__.py │ │ │ ├── aesthetic.py │ │ │ ├── clip.py │ │ │ ├── config.py │ │ │ ├── hps.py │ │ │ ├── imagereward.py │ │ │ ├── mps.py │ │ │ ├── open_clip/ │ │ │ │ ├── __init__.py │ │ │ │ ├── coca_model.py │ │ │ │ ├── constants.py │ │ │ │ ├── factory.py │ │ │ │ ├── generation_utils.py │ │ │ │ ├── hf_configs.py │ │ │ │ ├── hf_model.py │ │ │ │ ├── loss.py │ │ │ │ ├── model.py │ │ │ │ ├── model_configs/ │ │ │ │ │ └── ViT-H-14.json │ │ │ │ ├── modified_resnet.py │ │ │ │ ├── openai.py │ │ │ │ ├── pretrained.py │ │ │ │ ├── push_to_hf_hub.py │ │ │ │ ├── timm_model.py │ │ │ │ ├── tokenizer.py │ │ │ │ ├── transform.py │ │ │ │ ├── transformer.py │ │ │ │ ├── utils.py │ │ │ │ └── version.py │ │ │ ├── pickscore.py │ │ │ └── trainer/ │ │ │ ├── __init__.py │ │ │ └── models/ │ │ │ ├── __init__.py │ │ │ ├── base_model.py │ │ │ ├── clip_model.py │ │ │ └── cross_modeling.py │ │ ├── RIFE/ │ │ │ └── __init__.py │ │ └── __init__.py │ ├── models/ │ │ ├── __init__.py │ │ ├── attention.py │ │ ├── cog_dit.py │ │ ├── cog_vae.py │ │ ├── downloader.py │ │ ├── flux_controlnet.py │ │ ├── flux_dit.py │ │ ├── flux_ipadapter.py │ │ ├── flux_text_encoder.py │ │ ├── flux_vae.py │ │ ├── hunyuan_dit.py │ │ ├── hunyuan_dit_text_encoder.py │ │ ├── hunyuan_video_dit.py │ │ ├── hunyuan_video_text_encoder.py │ │ ├── hunyuan_video_vae_decoder.py │ │ ├── hunyuan_video_vae_encoder.py │ │ ├── kolors_text_encoder.py │ │ ├── lora.py │ │ ├── model_manager.py │ │ ├── omnigen.py │ │ ├── sd3_dit.py │ │ ├── sd3_text_encoder.py │ │ ├── sd3_vae_decoder.py │ │ ├── sd3_vae_encoder.py │ │ ├── sd_controlnet.py │ │ ├── sd_ipadapter.py │ │ ├── sd_motion.py │ │ ├── sd_text_encoder.py │ │ ├── sd_unet.py │ │ ├── sd_vae_decoder.py │ │ ├── sd_vae_encoder.py │ │ ├── sdxl_controlnet.py │ │ ├── sdxl_ipadapter.py │ │ ├── sdxl_motion.py │ │ ├── sdxl_text_encoder.py │ │ ├── sdxl_unet.py │ │ ├── sdxl_vae_decoder.py │ │ ├── sdxl_vae_encoder.py │ │ ├── stepvideo_dit.py │ │ ├── stepvideo_text_encoder.py │ │ ├── stepvideo_vae.py │ │ ├── svd_image_encoder.py │ │ ├── svd_unet.py │ │ ├── svd_vae_decoder.py │ │ ├── svd_vae_encoder.py │ │ ├── tiler.py │ │ ├── utils.py │ │ ├── wan_video_dit.py │ │ ├── wan_video_image_encoder.py │ │ ├── wan_video_text_encoder.py │ │ └── wan_video_vae.py │ ├── pipelines/ │ │ ├── __init__.py │ │ ├── base.py │ │ ├── cog_video.py │ │ ├── dancer.py │ │ ├── flux_image.py │ │ ├── hunyuan_image.py │ │ ├── hunyuan_video.py │ │ ├── omnigen_image.py │ │ ├── pipeline_runner.py │ │ ├── sd3_image.py │ │ ├── sd_image.py │ │ ├── sd_video.py │ │ ├── sdxl_image.py │ │ ├── sdxl_video.py │ │ ├── step_video.py │ │ ├── svd_video.py │ │ ├── wan_video.py │ │ └── wan_video_syncammaster.py │ ├── processors/ │ │ ├── FastBlend.py │ │ ├── PILEditor.py │ │ ├── RIFE.py │ │ ├── __init__.py │ │ ├── base.py │ │ └── sequencial_processor.py │ ├── prompters/ │ │ ├── __init__.py │ │ ├── base_prompter.py │ │ ├── cog_prompter.py │ │ ├── flux_prompter.py │ │ ├── hunyuan_dit_prompter.py │ │ ├── hunyuan_video_prompter.py │ │ ├── kolors_prompter.py │ │ ├── omnigen_prompter.py │ │ ├── omost.py │ │ ├── prompt_refiners.py │ │ ├── sd3_prompter.py │ │ ├── sd_prompter.py │ │ ├── sdxl_prompter.py │ │ ├── stepvideo_prompter.py │ │ └── wan_prompter.py │ ├── schedulers/ │ │ ├── __init__.py │ │ ├── continuous_ode.py │ │ ├── ddim.py │ │ └── flow_match.py │ ├── tokenizer_configs/ │ │ ├── __init__.py │ │ ├── cog/ │ │ │ └── tokenizer/ │ │ │ ├── added_tokens.json │ │ │ ├── special_tokens_map.json │ │ │ ├── spiece.model │ │ │ └── tokenizer_config.json │ │ ├── flux/ │ │ │ ├── tokenizer_1/ │ │ │ │ ├── merges.txt │ │ │ │ ├── special_tokens_map.json │ │ │ │ ├── tokenizer_config.json │ │ │ │ └── vocab.json │ │ │ └── tokenizer_2/ │ │ │ ├── special_tokens_map.json │ │ │ ├── spiece.model │ │ │ ├── tokenizer.json │ │ │ └── tokenizer_config.json │ │ ├── hunyuan_dit/ │ │ │ ├── tokenizer/ │ │ │ │ ├── special_tokens_map.json │ │ │ │ ├── tokenizer_config.json │ │ │ │ ├── vocab.txt │ │ │ │ └── vocab_org.txt │ │ │ └── tokenizer_t5/ │ │ │ ├── config.json │ │ │ ├── special_tokens_map.json │ │ │ ├── spiece.model │ │ │ └── tokenizer_config.json │ │ ├── hunyuan_video/ │ │ │ ├── tokenizer_1/ │ │ │ │ ├── merges.txt │ │ │ │ ├── special_tokens_map.json │ │ │ │ ├── tokenizer_config.json │ │ │ │ └── vocab.json │ │ │ └── tokenizer_2/ │ │ │ ├── preprocessor_config.json │ │ │ ├── special_tokens_map.json │ │ │ ├── tokenizer.json │ │ │ └── tokenizer_config.json │ │ ├── kolors/ │ │ │ └── tokenizer/ │ │ │ ├── tokenizer.model │ │ │ ├── tokenizer_config.json │ │ │ └── vocab.txt │ │ ├── stable_diffusion/ │ │ │ └── tokenizer/ │ │ │ ├── merges.txt │ │ │ ├── special_tokens_map.json │ │ │ ├── tokenizer_config.json │ │ │ └── vocab.json │ │ ├── stable_diffusion_3/ │ │ │ ├── tokenizer_1/ │ │ │ │ ├── merges.txt │ │ │ │ ├── special_tokens_map.json │ │ │ │ ├── tokenizer_config.json │ │ │ │ └── vocab.json │ │ │ ├── tokenizer_2/ │ │ │ │ ├── merges.txt │ │ │ │ ├── special_tokens_map.json │ │ │ │ ├── tokenizer_config.json │ │ │ │ └── vocab.json │ │ │ └── tokenizer_3/ │ │ │ ├── special_tokens_map.json │ │ │ ├── spiece.model │ │ │ ├── tokenizer.json │ │ │ └── tokenizer_config.json │ │ └── stable_diffusion_xl/ │ │ └── tokenizer_2/ │ │ ├── merges.txt │ │ ├── special_tokens_map.json │ │ ├── tokenizer_config.json │ │ └── vocab.json │ ├── trainers/ │ │ ├── __init__.py │ │ └── text_to_image.py │ └── vram_management/ │ ├── __init__.py │ └── layers.py ├── download_wan2.1.py ├── example_test_data/ │ ├── cameras/ │ │ └── camera_extrinsics.json │ └── metadata.csv ├── generate_sample_list.py ├── inference_syncammaster.py ├── models/ │ └── SynCamMaster/ │ └── checkpoints/ │ └── Put SynCamMaster ckpt file here.txt ├── requirements.txt ├── setup.py ├── train_syncammaster.py └── vis_cam.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ *__pycache__ *.ckpt Wan-AI ================================================ FILE: README.md ================================================ # SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints
### [arXiv] [Project Page] [Dataset] _**[Jianhong Bai1*](https://jianhongbai.github.io/), [Menghan Xia2†](https://menghanxia.github.io/), [Xintao Wang2](https://xinntao.github.io/), [Ziyang Yuan3](https://scholar.google.ru/citations?user=fWxWEzsAAAAJ&hl=en), [Xiao Fu4](https://fuxiao0719.github.io/),
[Zuozhu Liu1](https://person.zju.edu.cn/en/lzz), [Haoji Hu1](https://person.zju.edu.cn/en/huhaoji), [Pengfei Wan2](https://scholar.google.com/citations?user=P6MraaYAAAAJ&hl=en), [Di Zhang2](https://openreview.net/profile?id=~Di_ZHANG3)**_
(*Work done during an internship at KwaiVGI, Kuaishou Technology †corresponding author) 1Zhejiang University, 2Kuaishou Technology, 3Tsinghua University, 4CUHK. **ICLR 2025**
**Important Note:** This open-source repository is intended to provide a reference implementation. Due to the difference in the underlying T2V model's performance, the open-source version may not achieve the same performance as the model in our paper. ## 🔥 Updates - __[2025.04.15]__: Please feel free to explore our subsequent work, [ReCamMaster](https://github.com/KwaiVGI/ReCamMaster). - __[2025.04.15]__: Update a new version of the [SynCamVideo Dataset](https://huggingface.co/datasets/KwaiVGI/SynCamVideo-Dataset). - __[2025.04.15]__: Release the [training and inference code](https://github.com/KwaiVGI/SynCamMaster?tab=readme-ov-file#%EF%B8%8F-code-syncammaster--wan21-inference--training), [model checkpoint](https://huggingface.co/KwaiVGI/SynCamMaster-Wan2.1/blob/main/step20000.ckpt). - __[2024.12.10]__: Release the [project page](https://jianhongbai.github.io/SynCamMaster/) and the [SynCamVideo Dataset](https://huggingface.co/datasets/KwaiVGI/SynCamVideo-Dataset/). ## 📖 Introduction **TL;DR:** We propose SynCamMaster, an efficient method to lift pre-trained text-to-video models for open-domain multi-camera video generation from diverse viewpoints. We also release a multi-camera synchronized video [dataset](https://huggingface.co/datasets/KwaiVGI/SynCamVideo-Dataset) rendered with Unreal Engine 5.
https://github.com/user-attachments/assets/1ecfaea8-5d87-4bb5-94fc-062f84bd67a1 ## ⚙️ Code: SynCamMaster + Wan2.1 (Inference & Training) The model utilized in our paper is an internally developed T2V model, not [Wan2.1](https://github.com/Wan-Video/Wan2.1). Due to company policy restrictions, we are unable to open-source the model used in the paper. Consequently, we migrated SynCamMaster to Wan2.1 to validate the effectiveness of our method. Due to differences in the underlying T2V model, you may not achieve the same results as demonstrated in the demo. ### Inference Step 1: Set up the environment [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) requires Rust and Cargo to compile extensions. You can install them using the following command: ```shell curl --proto '=https' --tlsv1.2 -sSf [https://sh.rustup.rs](https://sh.rustup.rs/) | sh . "$HOME/.cargo/env" ``` Install [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): ```shell git clone https://github.com/KwaiVGI/SynCamMaster.git cd SynCamMaster pip install -e . ``` Step 2: Download the pretrained checkpoints 1. Download the pre-trained Wan2.1 models ```shell cd SynCamMaster python download_wan2.1.py ``` 2. Download the pre-trained SynCamMaster checkpoint Please download from [huggingface](https://huggingface.co/KwaiVGI/SynCamMaster-Wan2.1/blob/main/step20000.ckpt) and place it in ```models/SynCamMaster/checkpoints```. Step 3: Test the example videos ```shell python inference_syncammaster.py --cam_type "az" ``` We provide several preset camera types. Additionally, you can generate new camera poses for testing. ### Training Step 1: Set up the environment ```shell pip install lightning pandas websockets ``` Step 2: Prepare the training dataset 1. Download the [SynCamVideo dataset](https://huggingface.co/datasets/KwaiVGI/SynCamVideo-Dataset). 2. Extract VAE features ```shell CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python train_syncammaster.py --task data_process --dataset_path path/to/the/SynCamVideo/Dataset --output_path ./models --text_encoder_path "models/Wan-AI/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth" --vae_path "models/Wan-AI/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth" --tiled --num_frames 81 --height 480 --width 832 --dataloader_num_workers 2 ``` 3. Generate Captions for Each Video You can use video caption tools like [LLaVA](https://github.com/haotian-liu/LLaVA) to generate captions for each video and store them in the ```metadata.csv``` file. 4. Calculate the availble sample list ```shell python generate_sample_list.py ``` Step 3: Training ```shell CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python train_syncammaster.py --task train --output_path ./models/train --dit_path "models/Wan-AI/Wan2.1-T2V-1.3B/diffusion_pytorch_model.safetensors" --steps_per_epoch 8000 --max_epochs 100 --learning_rate 1e-4 --accumulate_grad_batches 1 --use_gradient_checkpointing --dataloader_num_workers 4 ``` We do not explore the optimal set of hyper-parameters and train with a batch size of 1 on each GPU. You may achieve better model performance by adjusting hyper-parameters such as the learning rate and increasing the batch size. Step 4: Test the model ```shell python inference_syncammaster.py --cam_type "az" --ckpt_path path/to/the/checkpoint ``` ## 📷 Dataset: SynCamVideo Dataset ### 1. Dataset Introduction **TL;DR:** The SynCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera poses. The SynCamVideo Dataset can be valuable in fields such as camera-controlled video generation, synchronized video production, and 3D/4D reconstruction. The camera is stationary in the SynCamVideo Dataset. If you require footage with moving cameras rather than stationary ones, please explore our [MultiCamVideo](https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset) Dataset. https://github.com/user-attachments/assets/b49fc632-d1df-49fd-93d2-8513fbdb9377 The SynCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera poses. It consists of 3.4K different dynamic scenes, each captured by 10 cameras, resulting in a total of 34K videos. Each dynamic scene is composed of four elements: {3D environment, character, animation, camera}. Specifically, we use animation to drive the character, and position the animated character within the 3D environment. Then, Time-synchronized cameras are set up to render the multi-camera video data.

Example Image

**3D Environment:** We collect 37 high-quality 3D environments assets from [Fab](https://www.fab.com). To minimize the domain gap between rendered data and real-world videos, we primarily select visually realistic 3D scenes, while choosing a few stylized or surreal 3D scenes as a supplement. To ensure data diversity, the selected scenes cover a variety of indoor and outdoor settings, such as city streets, shopping malls, cafes, office rooms, and the countryside. **Character:** We collect 66 different human 3D models as characters from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com). **Animation:** We collect 93 different animations from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com), including common actions such as waving, dancing, and cheering. We use these animations to drive the collected characters and create diverse datasets through various combinations. **Camera:** To enhance the diversity of the dataset, each camera is randomly sampled on a hemispherical surface centered around the character. ### 2. Statistics and Configurations Dataset Statistics: | Number of Dynamic Scenes | Camera per Scene | Total Videos | |:------------------------:|:----------------:|:------------:| | 3400 | 10 | 34,000 | Video Configurations: | Resolution | Frame Number | FPS | |:-----------:|:------------:|:------------------------:| | 1280x1280 | 81 | 15 | Note: You can use 'center crop' to adjust the video's aspect ratio to fit your video generation model, such as 16:9, 9:16, 4:3, or 3:4. Camera Configurations: | Focal Length | Aperture | Sensor Height | Sensor Width | |:-----------------------:|:------------------:|:-------------:|:------------:| | 24mm | 5.0 | 23.76mm | 23.76mm | ### 3. File Structure ``` SynCamVideo-Dataset ├── train │ └── f24_aperture5 │ ├── scene1 # one dynamic scene │ │ ├── videos │ │ │ ├── cam01.mp4 # synchronized 81-frame videos at 1280x1280 resolution │ │ │ ├── cam02.mp4 │ │ │ ├── ... │ │ │ └── cam10.mp4 │ │ └── cameras │ │ └── camera_extrinsics.json # 81-frame camera extrinsics of the 10 cameras │ ├── ... │ └── scene3400 └── val └── basic ├── videos │ ├── cam01.mp4 # example videos corresponding to the validation cameras │ ├── cam02.mp4 │ ├── ... │ └── cam10.mp4 └── cameras └── camera_extrinsics.json # 10 cameras for validation ``` ### 3. Useful scripts - Data Extraction ```bash tar -xzvf SynCamVideo-Dataset.tar.gz ``` - Camera Visualization ```python python vis_cam.py ``` The visualization script is modified from [CameraCtrl](https://github.com/hehao13/CameraCtrl/blob/main/tools/visualize_trajectory.py), thanks to their inspiring work.

Example Image

## 🤗 Awesome Related Works Feel free to explore these outstanding related works, including but not limited to: [GCD](https://gcd.cs.columbia.edu/): synthesize large-angle novel viewpoints of 4D dynamic scenes from a monocular video. [CVD](https://collaborativevideodiffusion.github.io): multi-view video generation with multiple camera trajectories. [SV4D](https://sv4d.github.io): multi-view consistent dynamic 3D content generation. Additionally, check out our "MasterFamily" projects: [ReCamMaster](https://jianhongbai.github.io/ReCamMaster/): re-capture in-the-wild videos with novel camera trajectories. [3DTrajMaster](http://fuxiao0719.github.io/projects/3dtrajmaster): control multiple entity motions in 3D space (6DoF) for text-to-video generation. [StyleMaster](https://zixuan-ye.github.io/stylemaster/): enable artistic video generation and translation with reference style image. ## Acknowledgments We thank Jinwen Cao, Yisong Guo, Haowen Ji, Jichao Wang, and Yi Wang from Kuaishou Technology for their invaluable help in constructing the SynCamVideo-Dataset. We thank [Guanjun Wu](https://guanjunwu.github.io/) and Jiangnan Ye for their help on running 4DGS. ## 🌟 Citation Please leave us a star 🌟 and cite our paper if you find our work helpful. ``` @article{bai2024syncammaster, title={SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints}, author={Bai, Jianhong and Xia, Menghan and Wang, Xintao and Yuan, Ziyang and Fu, Xiao and Liu, Zuozhu and Hu, Haoji and Wan, Pengfei and Zhang, Di}, journal={arXiv preprint arXiv:2412.07760}, year={2024} } ``` ================================================ FILE: diffsynth/__init__.py ================================================ from .data import * from .models import * from .prompters import * from .schedulers import * from .pipelines import * from .controlnets import * ================================================ FILE: diffsynth/configs/__init__.py ================================================ ================================================ FILE: diffsynth/configs/model_config.py ================================================ from typing_extensions import Literal, TypeAlias from ..models.sd_text_encoder import SDTextEncoder from ..models.sd_unet import SDUNet from ..models.sd_vae_encoder import SDVAEEncoder from ..models.sd_vae_decoder import SDVAEDecoder from ..models.sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 from ..models.sdxl_unet import SDXLUNet from ..models.sdxl_vae_decoder import SDXLVAEDecoder from ..models.sdxl_vae_encoder import SDXLVAEEncoder from ..models.sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 from ..models.sd3_dit import SD3DiT from ..models.sd3_vae_decoder import SD3VAEDecoder from ..models.sd3_vae_encoder import SD3VAEEncoder from ..models.sd_controlnet import SDControlNet from ..models.sdxl_controlnet import SDXLControlNetUnion from ..models.sd_motion import SDMotionModel from ..models.sdxl_motion import SDXLMotionModel from ..models.svd_image_encoder import SVDImageEncoder from ..models.svd_unet import SVDUNet from ..models.svd_vae_decoder import SVDVAEDecoder from ..models.svd_vae_encoder import SVDVAEEncoder from ..models.sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder from ..models.sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder from ..models.hunyuan_dit import HunyuanDiT from ..models.flux_dit import FluxDiT from ..models.flux_text_encoder import FluxTextEncoder2 from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder from ..models.flux_controlnet import FluxControlNet from ..models.flux_ipadapter import FluxIpAdapter from ..models.cog_vae import CogVAEEncoder, CogVAEDecoder from ..models.cog_dit import CogDiT from ..models.omnigen import OmniGenTransformer from ..models.hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder from ..models.hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder from ..extensions.RIFE import IFNet from ..extensions.ESRGAN import RRDBNet from ..models.hunyuan_video_dit import HunyuanVideoDiT from ..models.stepvideo_vae import StepVideoVAE from ..models.stepvideo_dit import StepVideoModel from ..models.wan_video_dit import WanModel from ..models.wan_video_text_encoder import WanTextEncoder from ..models.wan_video_image_encoder import WanImageEncoder from ..models.wan_video_vae import WanVideoVAE model_loader_configs = [ # These configs are provided for detecting model type automatically. # The format is (state_dict_keys_hash, state_dict_keys_hash_with_shape, model_names, model_classes, model_resource) (None, "091b0e30e77c76626b3ba62acdf95343", ["sd_controlnet"], [SDControlNet], "civitai"), (None, "4a6c8306a27d916dea81263c8c88f450", ["hunyuan_dit_clip_text_encoder"], [HunyuanDiTCLIPTextEncoder], "civitai"), (None, "f4aec400fe394297961218c768004521", ["hunyuan_dit"], [HunyuanDiT], "civitai"), (None, "9e6e58043a5a2e332803ed42f6ee7181", ["hunyuan_dit_t5_text_encoder"], [HunyuanDiTT5TextEncoder], "civitai"), (None, "13115dd45a6e1c39860f91ab073b8a78", ["sdxl_vae_encoder", "sdxl_vae_decoder"], [SDXLVAEEncoder, SDXLVAEDecoder], "diffusers"), (None, "d78aa6797382a6d455362358a3295ea9", ["sd_ipadapter_clip_image_encoder"], [IpAdapterCLIPImageEmbedder], "diffusers"), (None, "e291636cc15e803186b47404262ef812", ["sd_ipadapter"], [SDIpAdapter], "civitai"), (None, "399c81f2f8de8d1843d0127a00f3c224", ["sdxl_ipadapter_clip_image_encoder"], [IpAdapterXLCLIPImageEmbedder], "diffusers"), (None, "a64eac9aa0db4b9602213bc0131281c7", ["sdxl_ipadapter"], [SDXLIpAdapter], "civitai"), (None, "52817e4fdd89df154f02749ca6f692ac", ["sdxl_unet"], [SDXLUNet], "diffusers"), (None, "03343c606f16d834d6411d0902b53636", ["sd_text_encoder", "sd_unet", "sd_vae_decoder", "sd_vae_encoder"], [SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder], "civitai"), (None, "d4ba77a7ece070679b4a987f58f201e9", ["sd_text_encoder"], [SDTextEncoder], "civitai"), (None, "d0c89e55c5a57cf3981def0cb1c9e65a", ["sd_vae_decoder", "sd_vae_encoder"], [SDVAEDecoder, SDVAEEncoder], "civitai"), (None, "3926bf373b39a67eeafd7901478a47a7", ["sd_unet"], [SDUNet], "civitai"), (None, "1e0c39ec176b9007c05f76d52b554a4d", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), (None, "d9e0290829ba8d98e28e1a2b1407db4a", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_text_encoder_3", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), (None, "5072d0b24e406b49507abe861cf97691", ["sd3_text_encoder_3"], [SD3TextEncoder3], "civitai"), (None, "4cf64a799d04260df438c6f33c9a047e", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), (None, "d9b008a867c498ab12ad24042eff8e3f", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), # SDXL-Turbo (None, "025bb7452e531a3853d951d77c63f032", ["sdxl_text_encoder", "sdxl_text_encoder_2"], [SDXLTextEncoder, SDXLTextEncoder2], "civitai"), (None, "298997b403a4245c04102c9f36aac348", ["sdxl_unet"], [SDXLUNet], "civitai"), (None, "2a07abce74b4bdc696b76254ab474da6", ["svd_image_encoder", "svd_unet", "svd_vae_decoder", "svd_vae_encoder"], [SVDImageEncoder, SVDUNet, SVDVAEDecoder, SVDVAEEncoder], "civitai"), (None, "c96a285a6888465f87de22a984d049fb", ["sd_motion_modules"], [SDMotionModel], "civitai"), (None, "72907b92caed19bdb2adb89aa4063fe2", ["sdxl_motion_modules"], [SDXLMotionModel], "civitai"), (None, "31d2d9614fba60511fc9bf2604aa01f7", ["sdxl_controlnet"], [SDXLControlNetUnion], "diffusers"), (None, "94eefa3dac9cec93cb1ebaf1747d7b78", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"), (None, "1aafa3cc91716fb6b300cc1cd51b85a3", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "diffusers"), (None, "21ea55f476dfc4fd135587abb59dfe5d", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "civitai"), (None, "a29710fea6dddb0314663ee823598e50", ["flux_dit"], [FluxDiT], "civitai"), (None, "57b02550baab820169365b3ee3afa2c9", ["flux_dit"], [FluxDiT], "civitai"), (None, "3394f306c4cbf04334b712bf5aaed95f", ["flux_dit"], [FluxDiT], "civitai"), (None, "023f054d918a84ccf503481fd1e3379e", ["flux_dit"], [FluxDiT], "civitai"), (None, "605c56eab23e9e2af863ad8f0813a25d", ["flux_dit"], [FluxDiT], "diffusers"), (None, "280189ee084bca10f70907bf6ce1649d", ["cog_vae_encoder", "cog_vae_decoder"], [CogVAEEncoder, CogVAEDecoder], "diffusers"), (None, "9b9313d104ac4df27991352fec013fd4", ["rife"], [IFNet], "civitai"), (None, "6b7116078c4170bfbeaedc8fe71f6649", ["esrgan"], [RRDBNet], "civitai"), (None, "61cbcbc7ac11f169c5949223efa960d1", ["omnigen_transformer"], [OmniGenTransformer], "diffusers"), (None, "78d18b9101345ff695f312e7e62538c0", ["flux_controlnet"], [FluxControlNet], "diffusers"), (None, "b001c89139b5f053c715fe772362dd2a", ["flux_controlnet"], [FluxControlNet], "diffusers"), (None, "52357cb26250681367488a8954c271e8", ["flux_controlnet"], [FluxControlNet], "diffusers"), (None, "0cfd1740758423a2a854d67c136d1e8c", ["flux_controlnet"], [FluxControlNet], "diffusers"), (None, "4daaa66cc656a8fe369908693dad0a35", ["flux_ipadapter"], [FluxIpAdapter], "diffusers"), (None, "51aed3d27d482fceb5e0739b03060e8f", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), (None, "98cc34ccc5b54ae0e56bdea8688dcd5a", ["sd3_text_encoder_2"], [SD3TextEncoder2], "civitai"), (None, "77ff18050dbc23f50382e45d51a779fe", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), (None, "5da81baee73198a7c19e6d2fe8b5148e", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"), (None, "aeb82dce778a03dcb4d726cb03f3c43f", ["hunyuan_video_vae_decoder", "hunyuan_video_vae_encoder"], [HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder], "diffusers"), (None, "b9588f02e78f5ccafc9d7c0294e46308", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"), (None, "84ef4bd4757f60e906b54aa6a7815dc6", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"), (None, "68beaf8429b7c11aa8ca05b1bd0058bd", ["stepvideo_vae"], [StepVideoVAE], "civitai"), (None, "5c0216a2132b082c10cb7a0e0377e681", ["stepvideo_dit"], [StepVideoModel], "civitai"), (None, "9269f8db9040a9d860eaca435be61814", ["wan_video_dit"], [WanModel], "civitai"), (None, "aafcfd9672c3a2456dc46e1cb6e52c70", ["wan_video_dit"], [WanModel], "civitai"), (None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"), (None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"), (None, "9c8818c2cbea55eca56c7b447df170da", ["wan_video_text_encoder"], [WanTextEncoder], "civitai"), (None, "5941c53e207d62f20f9025686193c40b", ["wan_video_image_encoder"], [WanImageEncoder], "civitai"), (None, "1378ea763357eea97acdef78e65d6d96", ["wan_video_vae"], [WanVideoVAE], "civitai"), (None, "ccc42284ea13e1ad04693284c7a09be6", ["wan_video_vae"], [WanVideoVAE], "civitai"), ] huggingface_model_loader_configs = [ # These configs are provided for detecting model type automatically. # The format is (architecture_in_huggingface_config, huggingface_lib, model_name, redirected_architecture) ("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder", None), ("MarianMTModel", "transformers.models.marian.modeling_marian", "translator", None), ("BloomForCausalLM", "transformers.models.bloom.modeling_bloom", "beautiful_prompt", None), ("Qwen2ForCausalLM", "transformers.models.qwen2.modeling_qwen2", "qwen_prompt", None), # ("LlamaForCausalLM", "transformers.models.llama.modeling_llama", "omost_prompt", None), ("T5EncoderModel", "diffsynth.models.flux_text_encoder", "flux_text_encoder_2", "FluxTextEncoder2"), ("CogVideoXTransformer3DModel", "diffsynth.models.cog_dit", "cog_dit", "CogDiT"), ("SiglipModel", "transformers.models.siglip.modeling_siglip", "siglip_vision_model", "SiglipVisionModel"), ("LlamaForCausalLM", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoLLMEncoder"), ("LlavaForConditionalGeneration", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoMLLMEncoder"), ("Step1Model", "diffsynth.models.stepvideo_text_encoder", "stepvideo_text_encoder_2", "STEP1TextEncoder"), ] patch_model_loader_configs = [ # These configs are provided for detecting model type automatically. # The format is (state_dict_keys_hash_with_shape, model_name, model_class, extra_kwargs) ("9a4ab6869ac9b7d6e31f9854e397c867", ["svd_unet"], [SVDUNet], {"add_positional_conv": 128}), ] preset_models_on_huggingface = { "HunyuanDiT": [ ("Tencent-Hunyuan/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"), ("Tencent-Hunyuan/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"), ("Tencent-Hunyuan/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"), ("Tencent-Hunyuan/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"), ], "stable-video-diffusion-img2vid-xt": [ ("stabilityai/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"), ], "ExVideo-SVD-128f-v1": [ ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"), ], # Stable Diffusion "StableDiffusion_v15": [ ("benjamin-paine/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"), ], "DreamShaper_8": [ ("Yntec/Dreamshaper8", "dreamshaper_8.safetensors", "models/stable_diffusion"), ], # Textual Inversion "TextualInversion_VeryBadImageNegative_v1.3": [ ("gemasai/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"), ], # Stable Diffusion XL "StableDiffusionXL_v1": [ ("stabilityai/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"), ], "BluePencilXL_v200": [ ("frankjoshua/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"), ], "StableDiffusionXL_Turbo": [ ("stabilityai/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"), ], # Stable Diffusion 3 "StableDiffusion3": [ ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"), ], "StableDiffusion3_without_T5": [ ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"), ], # ControlNet "ControlNet_v11f1p_sd15_depth": [ ("lllyasviel/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"), ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") ], "ControlNet_v11p_sd15_softedge": [ ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"), ("lllyasviel/Annotators", "ControlNetHED.pth", "models/Annotators") ], "ControlNet_v11f1e_sd15_tile": [ ("lllyasviel/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet") ], "ControlNet_v11p_sd15_lineart": [ ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"), ("lllyasviel/Annotators", "sk_model.pth", "models/Annotators"), ("lllyasviel/Annotators", "sk_model2.pth", "models/Annotators") ], "ControlNet_union_sdxl_promax": [ ("xinsir/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"), ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") ], # AnimateDiff "AnimateDiff_v2": [ ("guoyww/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"), ], "AnimateDiff_xl_beta": [ ("guoyww/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"), ], # Qwen Prompt "QwenPrompt": [ ("Qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("Qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("Qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"), ("Qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("Qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("Qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("Qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"), ("Qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ], # Beautiful Prompt "BeautifulPrompt": [ ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ], # Omost prompt "OmostPrompt":[ ("lllyasviel/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("lllyasviel/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("lllyasviel/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("lllyasviel/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("lllyasviel/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("lllyasviel/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ], # Translator "opus-mt-zh-en": [ ("Helsinki-NLP/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"), ("Helsinki-NLP/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"), ("Helsinki-NLP/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"), ("Helsinki-NLP/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"), ("Helsinki-NLP/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"), ("Helsinki-NLP/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"), ("Helsinki-NLP/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"), ("Helsinki-NLP/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"), ], # IP-Adapter "IP-Adapter-SD": [ ("h94/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"), ("h94/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"), ], "IP-Adapter-SDXL": [ ("h94/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"), ("h94/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"), ], "SDXL-vae-fp16-fix": [ ("madebyollin/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix") ], # Kolors "Kolors": [ ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"), ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"), ], # FLUX "FLUX.1-dev": [ ("black-forest-labs/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"), ("black-forest-labs/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("black-forest-labs/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"), ("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"), ], "InstantX/FLUX.1-dev-IP-Adapter": { "file_list": [ ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"), ("google/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), ("google/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), ], "load_path": [ "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder", ], }, # RIFE "RIFE": [ ("AlexWortega/RIFE", "flownet.pkl", "models/RIFE"), ], # CogVideo "CogVideoX-5B": [ ("THUDM/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"), ("THUDM/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"), ("THUDM/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), ("THUDM/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), ("THUDM/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"), ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"), ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), ("THUDM/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"), ], # Stable Diffusion 3.5 "StableDiffusion3.5-large": [ ("stabilityai/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"), ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), ("stabilityai/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), ], } preset_models_on_modelscope = { # Hunyuan DiT "HunyuanDiT": [ ("modelscope/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"), ("modelscope/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"), ("modelscope/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"), ("modelscope/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"), ], # Stable Video Diffusion "stable-video-diffusion-img2vid-xt": [ ("AI-ModelScope/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"), ], # ExVideo "ExVideo-SVD-128f-v1": [ ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"), ], "ExVideo-CogVideoX-LoRA-129f-v1": [ ("ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1", "ExVideo-CogVideoX-LoRA-129f-v1.safetensors", "models/lora"), ], # Stable Diffusion "StableDiffusion_v15": [ ("AI-ModelScope/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"), ], "DreamShaper_8": [ ("sd_lora/dreamshaper_8", "dreamshaper_8.safetensors", "models/stable_diffusion"), ], "AingDiffusion_v12": [ ("sd_lora/aingdiffusion_v12", "aingdiffusion_v12.safetensors", "models/stable_diffusion"), ], "Flat2DAnimerge_v45Sharp": [ ("sd_lora/Flat-2D-Animerge", "flat2DAnimerge_v45Sharp.safetensors", "models/stable_diffusion"), ], # Textual Inversion "TextualInversion_VeryBadImageNegative_v1.3": [ ("sd_lora/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"), ], # Stable Diffusion XL "StableDiffusionXL_v1": [ ("AI-ModelScope/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"), ], "BluePencilXL_v200": [ ("sd_lora/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"), ], "StableDiffusionXL_Turbo": [ ("AI-ModelScope/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"), ], "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0": [ ("sd_lora/zyd232_ChineseInkStyle_SDXL_v1_0", "zyd232_ChineseInkStyle_SDXL_v1_0.safetensors", "models/lora"), ], # Stable Diffusion 3 "StableDiffusion3": [ ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"), ], "StableDiffusion3_without_T5": [ ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"), ], # ControlNet "ControlNet_v11f1p_sd15_depth": [ ("AI-ModelScope/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"), ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") ], "ControlNet_v11p_sd15_softedge": [ ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"), ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators") ], "ControlNet_v11f1e_sd15_tile": [ ("AI-ModelScope/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet") ], "ControlNet_v11p_sd15_lineart": [ ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"), ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"), ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators") ], "ControlNet_union_sdxl_promax": [ ("AI-ModelScope/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"), ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") ], "Annotators:Depth": [ ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators"), ], "Annotators:Softedge": [ ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators"), ], "Annotators:Lineart": [ ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"), ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators"), ], "Annotators:Normal": [ ("sd_lora/Annotators", "scannet.pt", "models/Annotators"), ], "Annotators:Openpose": [ ("sd_lora/Annotators", "body_pose_model.pth", "models/Annotators"), ("sd_lora/Annotators", "facenet.pth", "models/Annotators"), ("sd_lora/Annotators", "hand_pose_model.pth", "models/Annotators"), ], # AnimateDiff "AnimateDiff_v2": [ ("Shanghai_AI_Laboratory/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"), ], "AnimateDiff_xl_beta": [ ("Shanghai_AI_Laboratory/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"), ], # RIFE "RIFE": [ ("Damo_XR_Lab/cv_rife_video-frame-interpolation", "flownet.pkl", "models/RIFE"), ], # Qwen Prompt "QwenPrompt": { "file_list": [ ("qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"), ("qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ("qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"), ("qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"), ], "load_path": [ "models/QwenPrompt/qwen2-1.5b-instruct", ], }, # Beautiful Prompt "BeautifulPrompt": { "file_list": [ ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), ], "load_path": [ "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd", ], }, # Omost prompt "OmostPrompt": { "file_list": [ ("Omost/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("Omost/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("Omost/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("Omost/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("Omost/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("Omost/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("Omost/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ("Omost/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), ], "load_path": [ "models/OmostPrompt/omost-llama-3-8b-4bits", ], }, # Translator "opus-mt-zh-en": { "file_list": [ ("moxying/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"), ("moxying/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"), ("moxying/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"), ("moxying/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"), ("moxying/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"), ("moxying/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"), ("moxying/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"), ("moxying/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"), ], "load_path": [ "models/translator/opus-mt-zh-en", ], }, # IP-Adapter "IP-Adapter-SD": [ ("AI-ModelScope/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"), ("AI-ModelScope/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"), ], "IP-Adapter-SDXL": [ ("AI-ModelScope/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"), ("AI-ModelScope/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"), ], # Kolors "Kolors": { "file_list": [ ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"), ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"), ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"), ], "load_path": [ "models/kolors/Kolors/text_encoder", "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors", ], }, "SDXL-vae-fp16-fix": [ ("AI-ModelScope/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix") ], # FLUX "FLUX.1-dev": { "file_list": [ ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"), ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"), ("AI-ModelScope/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"), ], "load_path": [ "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2", "models/FLUX/FLUX.1-dev/ae.safetensors", "models/FLUX/FLUX.1-dev/flux1-dev.safetensors" ], }, "FLUX.1-schnell": { "file_list": [ ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"), ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"), ("AI-ModelScope/FLUX.1-schnell", "flux1-schnell.safetensors", "models/FLUX/FLUX.1-schnell"), ], "load_path": [ "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2", "models/FLUX/FLUX.1-dev/ae.safetensors", "models/FLUX/FLUX.1-schnell/flux1-schnell.safetensors" ], }, "InstantX/FLUX.1-dev-Controlnet-Union-alpha": [ ("InstantX/FLUX.1-dev-Controlnet-Union-alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha"), ], "jasperai/Flux.1-dev-Controlnet-Depth": [ ("jasperai/Flux.1-dev-Controlnet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Depth"), ], "jasperai/Flux.1-dev-Controlnet-Surface-Normals": [ ("jasperai/Flux.1-dev-Controlnet-Surface-Normals", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals"), ], "jasperai/Flux.1-dev-Controlnet-Upscaler": [ ("jasperai/Flux.1-dev-Controlnet-Upscaler", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler"), ], "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha": [ ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha"), ], "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta": [ ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"), ], "Shakker-Labs/FLUX.1-dev-ControlNet-Depth": [ ("Shakker-Labs/FLUX.1-dev-ControlNet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Depth"), ], "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro": [ ("Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"), ], "InstantX/FLUX.1-dev-IP-Adapter": { "file_list": [ ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"), ("AI-ModelScope/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), ("AI-ModelScope/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), ], "load_path": [ "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder", ], }, # ESRGAN "ESRGAN_x4": [ ("AI-ModelScope/Real-ESRGAN", "RealESRGAN_x4.pth", "models/ESRGAN"), ], # RIFE "RIFE": [ ("AI-ModelScope/RIFE", "flownet.pkl", "models/RIFE"), ], # Omnigen "OmniGen-v1": { "file_list": [ ("BAAI/OmniGen-v1", "vae/diffusion_pytorch_model.safetensors", "models/OmniGen/OmniGen-v1/vae"), ("BAAI/OmniGen-v1", "model.safetensors", "models/OmniGen/OmniGen-v1"), ("BAAI/OmniGen-v1", "config.json", "models/OmniGen/OmniGen-v1"), ("BAAI/OmniGen-v1", "special_tokens_map.json", "models/OmniGen/OmniGen-v1"), ("BAAI/OmniGen-v1", "tokenizer_config.json", "models/OmniGen/OmniGen-v1"), ("BAAI/OmniGen-v1", "tokenizer.json", "models/OmniGen/OmniGen-v1"), ], "load_path": [ "models/OmniGen/OmniGen-v1/vae/diffusion_pytorch_model.safetensors", "models/OmniGen/OmniGen-v1/model.safetensors", ] }, # CogVideo "CogVideoX-5B": { "file_list": [ ("ZhipuAI/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"), ("ZhipuAI/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"), ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), ("ZhipuAI/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"), ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"), ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), ("ZhipuAI/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"), ], "load_path": [ "models/CogVideo/CogVideoX-5b/text_encoder", "models/CogVideo/CogVideoX-5b/transformer", "models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors", ], }, # Stable Diffusion 3.5 "StableDiffusion3.5-large": [ ("AI-ModelScope/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"), ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), ], "StableDiffusion3.5-medium": [ ("AI-ModelScope/stable-diffusion-3.5-medium", "sd3.5_medium.safetensors", "models/stable_diffusion_3"), ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), ], "StableDiffusion3.5-large-turbo": [ ("AI-ModelScope/stable-diffusion-3.5-large-turbo", "sd3.5_large_turbo.safetensors", "models/stable_diffusion_3"), ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), ], "HunyuanVideo":{ "file_list": [ ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"), ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"), ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideo/transformers") ], "load_path": [ "models/HunyuanVideo/text_encoder/model.safetensors", "models/HunyuanVideo/text_encoder_2", "models/HunyuanVideo/vae/pytorch_model.pt", "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt" ], }, "HunyuanVideoI2V":{ "file_list": [ ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideoI2V/text_encoder"), ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00001-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00002-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00003-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00004-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "config.json", "models/HunyuanVideoI2V/text_encoder_2"), ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model.safetensors.index.json", "models/HunyuanVideoI2V/text_encoder_2"), ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/vae/pytorch_model.pt", "models/HunyuanVideoI2V/vae"), ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideoI2V/transformers") ], "load_path": [ "models/HunyuanVideoI2V/text_encoder/model.safetensors", "models/HunyuanVideoI2V/text_encoder_2", "models/HunyuanVideoI2V/vae/pytorch_model.pt", "models/HunyuanVideoI2V/transformers/mp_rank_00_model_states.pt" ], }, "HunyuanVideo-fp8":{ "file_list": [ ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"), ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"), ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"), ("DiffSynth-Studio/HunyuanVideo-safetensors", "model.fp8.safetensors", "models/HunyuanVideo/transformers") ], "load_path": [ "models/HunyuanVideo/text_encoder/model.safetensors", "models/HunyuanVideo/text_encoder_2", "models/HunyuanVideo/vae/pytorch_model.pt", "models/HunyuanVideo/transformers/model.fp8.safetensors" ], }, } Preset_model_id: TypeAlias = Literal[ "HunyuanDiT", "stable-video-diffusion-img2vid-xt", "ExVideo-SVD-128f-v1", "ExVideo-CogVideoX-LoRA-129f-v1", "StableDiffusion_v15", "DreamShaper_8", "AingDiffusion_v12", "Flat2DAnimerge_v45Sharp", "TextualInversion_VeryBadImageNegative_v1.3", "StableDiffusionXL_v1", "BluePencilXL_v200", "StableDiffusionXL_Turbo", "ControlNet_v11f1p_sd15_depth", "ControlNet_v11p_sd15_softedge", "ControlNet_v11f1e_sd15_tile", "ControlNet_v11p_sd15_lineart", "AnimateDiff_v2", "AnimateDiff_xl_beta", "RIFE", "BeautifulPrompt", "opus-mt-zh-en", "IP-Adapter-SD", "IP-Adapter-SDXL", "StableDiffusion3", "StableDiffusion3_without_T5", "Kolors", "SDXL-vae-fp16-fix", "ControlNet_union_sdxl_promax", "FLUX.1-dev", "FLUX.1-schnell", "InstantX/FLUX.1-dev-Controlnet-Union-alpha", "jasperai/Flux.1-dev-Controlnet-Depth", "jasperai/Flux.1-dev-Controlnet-Surface-Normals", "jasperai/Flux.1-dev-Controlnet-Upscaler", "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", "Shakker-Labs/FLUX.1-dev-ControlNet-Depth", "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "InstantX/FLUX.1-dev-IP-Adapter", "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0", "QwenPrompt", "OmostPrompt", "ESRGAN_x4", "RIFE", "OmniGen-v1", "CogVideoX-5B", "Annotators:Depth", "Annotators:Softedge", "Annotators:Lineart", "Annotators:Normal", "Annotators:Openpose", "StableDiffusion3.5-large", "StableDiffusion3.5-medium", "HunyuanVideo", "HunyuanVideo-fp8", "HunyuanVideoI2V", ] ================================================ FILE: diffsynth/controlnets/__init__.py ================================================ from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager from .processors import Annotator ================================================ FILE: diffsynth/controlnets/controlnet_unit.py ================================================ import torch import numpy as np from .processors import Processor_id class ControlNetConfigUnit: def __init__(self, processor_id: Processor_id, model_path, scale=1.0, skip_processor=False): self.processor_id = processor_id self.model_path = model_path self.scale = scale self.skip_processor = skip_processor class ControlNetUnit: def __init__(self, processor, model, scale=1.0): self.processor = processor self.model = model self.scale = scale class MultiControlNetManager: def __init__(self, controlnet_units=[]): self.processors = [unit.processor for unit in controlnet_units] self.models = [unit.model for unit in controlnet_units] self.scales = [unit.scale for unit in controlnet_units] def cpu(self): for model in self.models: model.cpu() def to(self, device): for model in self.models: model.to(device) for processor in self.processors: processor.to(device) def process_image(self, image, processor_id=None): if processor_id is None: processed_image = [processor(image) for processor in self.processors] else: processed_image = [self.processors[processor_id](image)] processed_image = torch.concat([ torch.Tensor(np.array(image_, dtype=np.float32) / 255).permute(2, 0, 1).unsqueeze(0) for image_ in processed_image ], dim=0) return processed_image def __call__( self, sample, timestep, encoder_hidden_states, conditionings, tiled=False, tile_size=64, tile_stride=32, **kwargs ): res_stack = None for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales): res_stack_ = model( sample, timestep, encoder_hidden_states, conditioning, **kwargs, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, processor_id=processor.processor_id ) res_stack_ = [res * scale for res in res_stack_] if res_stack is None: res_stack = res_stack_ else: res_stack = [i + j for i, j in zip(res_stack, res_stack_)] return res_stack class FluxMultiControlNetManager(MultiControlNetManager): def __init__(self, controlnet_units=[]): super().__init__(controlnet_units=controlnet_units) def process_image(self, image, processor_id=None): if processor_id is None: processed_image = [processor(image) for processor in self.processors] else: processed_image = [self.processors[processor_id](image)] return processed_image def __call__(self, conditionings, **kwargs): res_stack, single_res_stack = None, None for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales): res_stack_, single_res_stack_ = model(controlnet_conditioning=conditioning, processor_id=processor.processor_id, **kwargs) res_stack_ = [res * scale for res in res_stack_] single_res_stack_ = [res * scale for res in single_res_stack_] if res_stack is None: res_stack = res_stack_ single_res_stack = single_res_stack_ else: res_stack = [i + j for i, j in zip(res_stack, res_stack_)] single_res_stack = [i + j for i, j in zip(single_res_stack, single_res_stack_)] return res_stack, single_res_stack ================================================ FILE: diffsynth/controlnets/processors.py ================================================ from typing_extensions import Literal, TypeAlias Processor_id: TypeAlias = Literal[ "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "normal", "tile", "none", "inpaint" ] class Annotator: def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda', skip_processor=False): if not skip_processor: if processor_id == "canny": from controlnet_aux.processor import CannyDetector self.processor = CannyDetector() elif processor_id == "depth": from controlnet_aux.processor import MidasDetector self.processor = MidasDetector.from_pretrained(model_path).to(device) elif processor_id == "softedge": from controlnet_aux.processor import HEDdetector self.processor = HEDdetector.from_pretrained(model_path).to(device) elif processor_id == "lineart": from controlnet_aux.processor import LineartDetector self.processor = LineartDetector.from_pretrained(model_path).to(device) elif processor_id == "lineart_anime": from controlnet_aux.processor import LineartAnimeDetector self.processor = LineartAnimeDetector.from_pretrained(model_path).to(device) elif processor_id == "openpose": from controlnet_aux.processor import OpenposeDetector self.processor = OpenposeDetector.from_pretrained(model_path).to(device) elif processor_id == "normal": from controlnet_aux.processor import NormalBaeDetector self.processor = NormalBaeDetector.from_pretrained(model_path).to(device) elif processor_id == "tile" or processor_id == "none" or processor_id == "inpaint": self.processor = None else: raise ValueError(f"Unsupported processor_id: {processor_id}") else: self.processor = None self.processor_id = processor_id self.detect_resolution = detect_resolution def to(self,device): if hasattr(self.processor,"model") and hasattr(self.processor.model,"to"): self.processor.model.to(device) def __call__(self, image, mask=None): width, height = image.size if self.processor_id == "openpose": kwargs = { "include_body": True, "include_hand": True, "include_face": True } else: kwargs = {} if self.processor is not None: detect_resolution = self.detect_resolution if self.detect_resolution is not None else min(width, height) image = self.processor(image, detect_resolution=detect_resolution, image_resolution=min(width, height), **kwargs) image = image.resize((width, height)) return image ================================================ FILE: diffsynth/data/__init__.py ================================================ from .video import VideoData, save_video, save_frames ================================================ FILE: diffsynth/data/simple_text_image.py ================================================ import torch, os, torchvision from torchvision import transforms import pandas as pd from PIL import Image class TextImageDataset(torch.utils.data.Dataset): def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False): self.steps_per_epoch = steps_per_epoch metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv")) self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]] self.text = metadata["text"].to_list() self.height = height self.width = width self.image_processor = transforms.Compose( [ transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)), transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) def __getitem__(self, index): data_id = torch.randint(0, len(self.path), (1,))[0] data_id = (data_id + index) % len(self.path) # For fixed seed. text = self.text[data_id] image = Image.open(self.path[data_id]).convert("RGB") target_height, target_width = self.height, self.width width, height = image.size scale = max(target_width / width, target_height / height) shape = [round(height*scale),round(width*scale)] image = torchvision.transforms.functional.resize(image,shape,interpolation=transforms.InterpolationMode.BILINEAR) image = self.image_processor(image) return {"text": text, "image": image} def __len__(self): return self.steps_per_epoch ================================================ FILE: diffsynth/data/video.py ================================================ import imageio, os import numpy as np from PIL import Image from tqdm import tqdm class LowMemoryVideo: def __init__(self, file_name): self.reader = imageio.get_reader(file_name) def __len__(self): return self.reader.count_frames() def __getitem__(self, item): return Image.fromarray(np.array(self.reader.get_data(item))).convert("RGB") def __del__(self): self.reader.close() def split_file_name(file_name): result = [] number = -1 for i in file_name: if ord(i)>=ord("0") and ord(i)<=ord("9"): if number == -1: number = 0 number = number*10 + ord(i) - ord("0") else: if number != -1: result.append(number) number = -1 result.append(i) if number != -1: result.append(number) result = tuple(result) return result def search_for_images(folder): file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")] file_list = [(split_file_name(file_name), file_name) for file_name in file_list] file_list = [i[1] for i in sorted(file_list)] file_list = [os.path.join(folder, i) for i in file_list] return file_list class LowMemoryImageFolder: def __init__(self, folder, file_list=None): if file_list is None: self.file_list = search_for_images(folder) else: self.file_list = [os.path.join(folder, file_name) for file_name in file_list] def __len__(self): return len(self.file_list) def __getitem__(self, item): return Image.open(self.file_list[item]).convert("RGB") def __del__(self): pass def crop_and_resize(image, height, width): image = np.array(image) image_height, image_width, _ = image.shape if image_height / image_width < height / width: croped_width = int(image_height / height * width) left = (image_width - croped_width) // 2 image = image[:, left: left+croped_width] image = Image.fromarray(image).resize((width, height)) else: croped_height = int(image_width / width * height) left = (image_height - croped_height) // 2 image = image[left: left+croped_height, :] image = Image.fromarray(image).resize((width, height)) return image class VideoData: def __init__(self, video_file=None, image_folder=None, height=None, width=None, **kwargs): if video_file is not None: self.data_type = "video" self.data = LowMemoryVideo(video_file, **kwargs) elif image_folder is not None: self.data_type = "images" self.data = LowMemoryImageFolder(image_folder, **kwargs) else: raise ValueError("Cannot open video or image folder") self.length = None self.set_shape(height, width) def raw_data(self): frames = [] for i in range(self.__len__()): frames.append(self.__getitem__(i)) return frames def set_length(self, length): self.length = length def set_shape(self, height, width): self.height = height self.width = width def __len__(self): if self.length is None: return len(self.data) else: return self.length def shape(self): if self.height is not None and self.width is not None: return self.height, self.width else: height, width, _ = self.__getitem__(0).shape return height, width def __getitem__(self, item): frame = self.data.__getitem__(item) width, height = frame.size if self.height is not None and self.width is not None: if self.height != height or self.width != width: frame = crop_and_resize(frame, self.height, self.width) return frame def __del__(self): pass def save_images(self, folder): os.makedirs(folder, exist_ok=True) for i in tqdm(range(self.__len__()), desc="Saving images"): frame = self.__getitem__(i) frame.save(os.path.join(folder, f"{i}.png")) def save_video(frames, save_path, fps, quality=9, ffmpeg_params=None): writer = imageio.get_writer(save_path, fps=fps, quality=quality, ffmpeg_params=ffmpeg_params) for frame in tqdm(frames, desc="Saving video"): frame = np.array(frame) writer.append_data(frame) writer.close() def save_frames(frames, save_path): os.makedirs(save_path, exist_ok=True) for i, frame in enumerate(tqdm(frames, desc="Saving images")): frame.save(os.path.join(save_path, f"{i}.png")) ================================================ FILE: diffsynth/extensions/ESRGAN/__init__.py ================================================ import torch from einops import repeat from PIL import Image import numpy as np class ResidualDenseBlock(torch.nn.Module): def __init__(self, num_feat=64, num_grow_ch=32): super(ResidualDenseBlock, self).__init__() self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x): x1 = self.lrelu(self.conv1(x)) x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) return x5 * 0.2 + x class RRDB(torch.nn.Module): def __init__(self, num_feat, num_grow_ch=32): super(RRDB, self).__init__() self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) def forward(self, x): out = self.rdb1(x) out = self.rdb2(out) out = self.rdb3(out) return out * 0.2 + x class RRDBNet(torch.nn.Module): def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, **kwargs): super(RRDBNet, self).__init__() self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)]) self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) # upsample self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x): feat = x feat = self.conv_first(feat) body_feat = self.conv_body(self.body(feat)) feat = feat + body_feat # upsample feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") feat = self.lrelu(self.conv_up1(feat)) feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") feat = self.lrelu(self.conv_up2(feat)) out = self.conv_last(self.lrelu(self.conv_hr(feat))) return out @staticmethod def state_dict_converter(): return RRDBNetStateDictConverter() class RRDBNetStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): return state_dict, {"upcast_to_float32": True} def from_civitai(self, state_dict): return state_dict, {"upcast_to_float32": True} class ESRGAN(torch.nn.Module): def __init__(self, model): super().__init__() self.model = model @staticmethod def from_model_manager(model_manager): return ESRGAN(model_manager.fetch_model("esrgan")) def process_image(self, image): image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1) return image def process_images(self, images): images = [self.process_image(image) for image in images] images = torch.stack(images) return images def decode_images(self, images): images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) images = [Image.fromarray(image) for image in images] return images @torch.no_grad() def upscale(self, images, batch_size=4, progress_bar=lambda x:x): if not isinstance(images, list): images = [images] is_single_image = True else: is_single_image = False # Preprocess input_tensor = self.process_images(images) # Interpolate output_tensor = [] for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) batch_input_tensor = input_tensor[batch_id: batch_id_] batch_input_tensor = batch_input_tensor.to( device=self.model.conv_first.weight.device, dtype=self.model.conv_first.weight.dtype) batch_output_tensor = self.model(batch_input_tensor) output_tensor.append(batch_output_tensor.cpu()) # Output output_tensor = torch.concat(output_tensor, dim=0) # To images output_images = self.decode_images(output_tensor) if is_single_image: output_images = output_images[0] return output_images ================================================ FILE: diffsynth/extensions/FastBlend/__init__.py ================================================ from .runners.fast import TableManager, PyramidPatchMatcher from PIL import Image import numpy as np import cupy as cp class FastBlendSmoother: def __init__(self): self.batch_size = 8 self.window_size = 64 self.ebsynth_config = { "minimum_patch_size": 5, "threads_per_block": 8, "num_iter": 5, "gpu_id": 0, "guide_weight": 10.0, "initialize": "identity", "tracking_window_size": 0, } @staticmethod def from_model_manager(model_manager): # TODO: fetch GPU ID from model_manager return FastBlendSmoother() def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config): frames_guide = [np.array(frame) for frame in frames_guide] frames_style = [np.array(frame) for frame in frames_style] table_manager = TableManager() patch_match_engine = PyramidPatchMatcher( image_height=frames_style[0].shape[0], image_width=frames_style[0].shape[1], channel=3, **ebsynth_config ) # left part table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, batch_size, desc="FastBlend Step 1/4") table_l = table_manager.remapping_table_to_blending_table(table_l) table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, window_size, batch_size, desc="FastBlend Step 2/4") # right part table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, batch_size, desc="FastBlend Step 3/4") table_r = table_manager.remapping_table_to_blending_table(table_r) table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, window_size, batch_size, desc="FastBlend Step 4/4")[::-1] # merge frames = [] for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r): weight_m = -1 weight = weight_l + weight_m + weight_r frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight) frames.append(frame) frames = [Image.fromarray(frame.clip(0, 255).astype("uint8")) for frame in frames] return frames def __call__(self, rendered_frames, original_frames=None, **kwargs): frames = self.run( original_frames, rendered_frames, self.batch_size, self.window_size, self.ebsynth_config ) mempool = cp.get_default_memory_pool() pinned_mempool = cp.get_default_pinned_memory_pool() mempool.free_all_blocks() pinned_mempool.free_all_blocks() return frames ================================================ FILE: diffsynth/extensions/FastBlend/api.py ================================================ from .runners import AccurateModeRunner, FastModeRunner, BalancedModeRunner, InterpolationModeRunner, InterpolationModeSingleFrameRunner from .data import VideoData, get_video_fps, save_video, search_for_images import os import gradio as gr def check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder): frames_guide = VideoData(video_guide, video_guide_folder) frames_style = VideoData(video_style, video_style_folder) message = "" if len(frames_guide) < len(frames_style): message += f"The number of frames mismatches. Only the first {len(frames_guide)} frames of style video will be used.\n" frames_style.set_length(len(frames_guide)) elif len(frames_guide) > len(frames_style): message += f"The number of frames mismatches. Only the first {len(frames_style)} frames of guide video will be used.\n" frames_guide.set_length(len(frames_style)) height_guide, width_guide = frames_guide.shape() height_style, width_style = frames_style.shape() if height_guide != height_style or width_guide != width_style: message += f"The shape of frames mismatches. The frames in style video will be resized to (height: {height_guide}, width: {width_guide})\n" frames_style.set_shape(height_guide, width_guide) return frames_guide, frames_style, message def smooth_video( video_guide, video_guide_folder, video_style, video_style_folder, mode, window_size, batch_size, tracking_window_size, output_path, fps, minimum_patch_size, num_iter, guide_weight, initialize, progress = None, ): # input frames_guide, frames_style, message = check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder) if len(message) > 0: print(message) # output if output_path == "": if video_style is None: output_path = os.path.join(video_style_folder, "output") else: output_path = os.path.join(os.path.split(video_style)[0], "output") os.makedirs(output_path, exist_ok=True) print("No valid output_path. Your video will be saved here:", output_path) elif not os.path.exists(output_path): os.makedirs(output_path, exist_ok=True) print("Your video will be saved here:", output_path) frames_path = os.path.join(output_path, "frames") video_path = os.path.join(output_path, "video.mp4") os.makedirs(frames_path, exist_ok=True) # process if mode == "Fast" or mode == "Balanced": tracking_window_size = 0 ebsynth_config = { "minimum_patch_size": minimum_patch_size, "threads_per_block": 8, "num_iter": num_iter, "gpu_id": 0, "guide_weight": guide_weight, "initialize": initialize, "tracking_window_size": tracking_window_size, } if mode == "Fast": FastModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) elif mode == "Balanced": BalancedModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) elif mode == "Accurate": AccurateModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) # output try: fps = int(fps) except: fps = get_video_fps(video_style) if video_style is not None else 30 print("Fps:", fps) print("Saving video...") video_path = save_video(frames_path, video_path, num_frames=len(frames_style), fps=fps) print("Success!") print("Your frames are here:", frames_path) print("Your video is here:", video_path) return output_path, fps, video_path class KeyFrameMatcher: def __init__(self): pass def extract_number_from_filename(self, file_name): result = [] number = -1 for i in file_name: if ord(i)>=ord("0") and ord(i)<=ord("9"): if number == -1: number = 0 number = number*10 + ord(i) - ord("0") else: if number != -1: result.append(number) number = -1 if number != -1: result.append(number) result = tuple(result) return result def extract_number_from_filenames(self, file_names): numbers = [self.extract_number_from_filename(file_name) for file_name in file_names] min_length = min(len(i) for i in numbers) for i in range(min_length-1, -1, -1): if len(set(number[i] for number in numbers))==len(file_names): return [number[i] for number in numbers] return list(range(len(file_names))) def match_using_filename(self, file_names_a, file_names_b): file_names_b_set = set(file_names_b) matched_file_name = [] for file_name in file_names_a: if file_name not in file_names_b_set: matched_file_name.append(None) else: matched_file_name.append(file_name) return matched_file_name def match_using_numbers(self, file_names_a, file_names_b): numbers_a = self.extract_number_from_filenames(file_names_a) numbers_b = self.extract_number_from_filenames(file_names_b) numbers_b_dict = {number: file_name for number, file_name in zip(numbers_b, file_names_b)} matched_file_name = [] for number in numbers_a: if number in numbers_b_dict: matched_file_name.append(numbers_b_dict[number]) else: matched_file_name.append(None) return matched_file_name def match_filenames(self, file_names_a, file_names_b): matched_file_name = self.match_using_filename(file_names_a, file_names_b) if sum([i is not None for i in matched_file_name]) > 0: return matched_file_name matched_file_name = self.match_using_numbers(file_names_a, file_names_b) return matched_file_name def detect_frames(frames_path, keyframes_path): if not os.path.exists(frames_path) and not os.path.exists(keyframes_path): return "Please input the directory of guide video and rendered frames" elif not os.path.exists(frames_path): return "Please input the directory of guide video" elif not os.path.exists(keyframes_path): return "Please input the directory of rendered frames" frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)] keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)] if len(frames)==0: return f"No images detected in {frames_path}" if len(keyframes)==0: return f"No images detected in {keyframes_path}" matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes) max_filename_length = max([len(i) for i in frames]) if sum([i is not None for i in matched_keyframes])==0: message = "" for frame, matched_keyframe in zip(frames, matched_keyframes): message += frame + " " * (max_filename_length - len(frame) + 1) message += "--> No matched keyframes\n" else: message = "" for frame, matched_keyframe in zip(frames, matched_keyframes): message += frame + " " * (max_filename_length - len(frame) + 1) if matched_keyframe is None: message += "--> [to be rendered]\n" else: message += f"--> {matched_keyframe}\n" return message def check_input_for_interpolating(frames_path, keyframes_path): # search for images frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)] keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)] # match frames matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes) file_list = [file_name for file_name in matched_keyframes if file_name is not None] index_style = [i for i, file_name in enumerate(matched_keyframes) if file_name is not None] frames_guide = VideoData(None, frames_path) frames_style = VideoData(None, keyframes_path, file_list=file_list) # match shape message = "" height_guide, width_guide = frames_guide.shape() height_style, width_style = frames_style.shape() if height_guide != height_style or width_guide != width_style: message += f"The shape of frames mismatches. The rendered keyframes will be resized to (height: {height_guide}, width: {width_guide})\n" frames_style.set_shape(height_guide, width_guide) return frames_guide, frames_style, index_style, message def interpolate_video( frames_path, keyframes_path, output_path, fps, batch_size, tracking_window_size, minimum_patch_size, num_iter, guide_weight, initialize, progress = None, ): # input frames_guide, frames_style, index_style, message = check_input_for_interpolating(frames_path, keyframes_path) if len(message) > 0: print(message) # output if output_path == "": output_path = os.path.join(keyframes_path, "output") os.makedirs(output_path, exist_ok=True) print("No valid output_path. Your video will be saved here:", output_path) elif not os.path.exists(output_path): os.makedirs(output_path, exist_ok=True) print("Your video will be saved here:", output_path) output_frames_path = os.path.join(output_path, "frames") output_video_path = os.path.join(output_path, "video.mp4") os.makedirs(output_frames_path, exist_ok=True) # process ebsynth_config = { "minimum_patch_size": minimum_patch_size, "threads_per_block": 8, "num_iter": num_iter, "gpu_id": 0, "guide_weight": guide_weight, "initialize": initialize, "tracking_window_size": tracking_window_size } if len(index_style)==1: InterpolationModeSingleFrameRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path) else: InterpolationModeRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path) try: fps = int(fps) except: fps = 30 print("Fps:", fps) print("Saving video...") video_path = save_video(output_frames_path, output_video_path, num_frames=len(frames_guide), fps=fps) print("Success!") print("Your frames are here:", output_frames_path) print("Your video is here:", video_path) return output_path, fps, video_path def on_ui_tabs(): with gr.Blocks(analytics_enabled=False) as ui_component: with gr.Tab("Blend"): gr.Markdown(""" # Blend Given a guide video and a style video, this algorithm will make the style video fluent according to the motion features of the guide video. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/208d902d-6aba-48d7-b7d5-cd120ebd306d) to see the example. Note that this extension doesn't support long videos. Please use short videos (e.g., several seconds). The algorithm is mainly designed for 512*512 resolution. Please use a larger `Minimum patch size` for higher resolution. """) with gr.Row(): with gr.Column(): with gr.Tab("Guide video"): video_guide = gr.Video(label="Guide video") with gr.Tab("Guide video (images format)"): video_guide_folder = gr.Textbox(label="Guide video (images format)", value="") with gr.Column(): with gr.Tab("Style video"): video_style = gr.Video(label="Style video") with gr.Tab("Style video (images format)"): video_style_folder = gr.Textbox(label="Style video (images format)", value="") with gr.Column(): output_path = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of style video") fps = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps") video_output = gr.Video(label="Output video", interactive=False, show_share_button=True) btn = gr.Button(value="Blend") with gr.Row(): with gr.Column(): gr.Markdown("# Settings") mode = gr.Radio(["Fast", "Balanced", "Accurate"], label="Inference mode", value="Fast", interactive=True) window_size = gr.Slider(label="Sliding window size", value=15, minimum=1, maximum=1000, step=1, interactive=True) batch_size = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True) tracking_window_size = gr.Slider(label="Tracking window size (only for accurate mode)", value=0, minimum=0, maximum=10, step=1, interactive=True) gr.Markdown("## Advanced Settings") minimum_patch_size = gr.Slider(label="Minimum patch size (odd number)", value=5, minimum=5, maximum=99, step=2, interactive=True) num_iter = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True) guide_weight = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True) initialize = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True) with gr.Column(): gr.Markdown(""" # Reference * Output directory: the directory to save the video. * Inference mode |Mode|Time|Memory|Quality|Frame by frame output|Description| |-|-|-|-|-|-| |Fast|■|■■■|■■|No|Blend the frames using a tree-like data structure, which requires much RAM but is fast.| |Balanced|■■|■|■■|Yes|Blend the frames naively.| |Accurate|■■■|■|■■■|Yes|Blend the frames and align them together for higher video quality. When [batch size] >= [sliding window size] * 2 + 1, the performance is the best.| * Sliding window size: our algorithm will blend the frames in a sliding windows. If the size is n, each frame will be blended with the last n frames and the next n frames. A large sliding window can make the video fluent but sometimes smoggy. * Batch size: a larger batch size makes the program faster but requires more VRAM. * Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough. * Advanced settings * Minimum patch size (odd number): the minimum patch size used for patch matching. (Default: 5) * Number of iterations: the number of iterations of patch matching. (Default: 5) * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10) * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity) """) btn.click( smooth_video, inputs=[ video_guide, video_guide_folder, video_style, video_style_folder, mode, window_size, batch_size, tracking_window_size, output_path, fps, minimum_patch_size, num_iter, guide_weight, initialize ], outputs=[output_path, fps, video_output] ) with gr.Tab("Interpolate"): gr.Markdown(""" # Interpolate Given a guide video and some rendered keyframes, this algorithm will render the remaining frames. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/3490c5b4-8f67-478f-86de-f9adc2ace16a) to see the example. The algorithm is experimental and is only tested for 512*512 resolution. """) with gr.Row(): with gr.Column(): with gr.Row(): with gr.Column(): video_guide_folder_ = gr.Textbox(label="Guide video (images format)", value="") with gr.Column(): rendered_keyframes_ = gr.Textbox(label="Rendered keyframes (images format)", value="") with gr.Row(): detected_frames = gr.Textbox(label="Detected frames", value="Please input the directory of guide video and rendered frames", lines=9, max_lines=9, interactive=False) video_guide_folder_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames) rendered_keyframes_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames) with gr.Column(): output_path_ = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of rendered keyframes") fps_ = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps") video_output_ = gr.Video(label="Output video", interactive=False, show_share_button=True) btn_ = gr.Button(value="Interpolate") with gr.Row(): with gr.Column(): gr.Markdown("# Settings") batch_size_ = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True) tracking_window_size_ = gr.Slider(label="Tracking window size", value=0, minimum=0, maximum=10, step=1, interactive=True) gr.Markdown("## Advanced Settings") minimum_patch_size_ = gr.Slider(label="Minimum patch size (odd number, larger is better)", value=15, minimum=5, maximum=99, step=2, interactive=True) num_iter_ = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True) guide_weight_ = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True) initialize_ = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True) with gr.Column(): gr.Markdown(""" # Reference * Output directory: the directory to save the video. * Batch size: a larger batch size makes the program faster but requires more VRAM. * Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough. * Advanced settings * Minimum patch size (odd number): the minimum patch size used for patch matching. **This parameter should be larger than that in blending. (Default: 15)** * Number of iterations: the number of iterations of patch matching. (Default: 5) * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10) * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity) """) btn_.click( interpolate_video, inputs=[ video_guide_folder_, rendered_keyframes_, output_path_, fps_, batch_size_, tracking_window_size_, minimum_patch_size_, num_iter_, guide_weight_, initialize_, ], outputs=[output_path_, fps_, video_output_] ) return [(ui_component, "FastBlend", "FastBlend_ui")] ================================================ FILE: diffsynth/extensions/FastBlend/cupy_kernels.py ================================================ import cupy as cp remapping_kernel = cp.RawKernel(r''' extern "C" __global__ void remap( const int height, const int width, const int channel, const int patch_size, const int pad_size, const float* source_style, const int* nnf, float* target_style ) { const int r = (patch_size - 1) / 2; const int x = blockDim.x * blockIdx.x + threadIdx.x; const int y = blockDim.y * blockIdx.y + threadIdx.y; if (x >= height or y >= width) return; const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; const int pid = (x + pad_size) * (width + pad_size * 2) + (y + pad_size); const int min_px = x < r ? -x : -r; const int max_px = x + r > height - 1 ? height - 1 - x : r; const int min_py = y < r ? -y : -r; const int max_py = y + r > width - 1 ? width - 1 - y : r; int num = 0; for (int px = min_px; px <= max_px; px++){ for (int py = min_py; py <= max_py; py++){ const int nid = (x + px) * width + y + py; const int x_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 0] - px; const int y_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 1] - py; if (x_ < 0 or y_ < 0 or x_ >= height or y_ >= width)continue; const int pid_ = (x_ + pad_size) * (width + pad_size * 2) + (y_ + pad_size); num++; for (int c = 0; c < channel; c++){ target_style[z + pid * channel + c] += source_style[z + pid_ * channel + c]; } } } for (int c = 0; c < channel; c++){ target_style[z + pid * channel + c] /= num; } } ''', 'remap') patch_error_kernel = cp.RawKernel(r''' extern "C" __global__ void patch_error( const int height, const int width, const int channel, const int patch_size, const int pad_size, const float* source, const int* nnf, const float* target, float* error ) { const int r = (patch_size - 1) / 2; const int x = blockDim.x * blockIdx.x + threadIdx.x; const int y = blockDim.y * blockIdx.y + threadIdx.y; const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; if (x >= height or y >= width) return; const int x_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 0]; const int y_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 1]; float e = 0; for (int px = -r; px <= r; px++){ for (int py = -r; py <= r; py++){ const int pid = (x + pad_size + px) * (width + pad_size * 2) + y + pad_size + py; const int pid_ = (x_ + pad_size + px) * (width + pad_size * 2) + y_ + pad_size + py; for (int c = 0; c < channel; c++){ const float diff = target[z + pid * channel + c] - source[z + pid_ * channel + c]; e += diff * diff; } } } error[blockIdx.z * height * width + x * width + y] = e; } ''', 'patch_error') pairwise_patch_error_kernel = cp.RawKernel(r''' extern "C" __global__ void pairwise_patch_error( const int height, const int width, const int channel, const int patch_size, const int pad_size, const float* source_a, const int* nnf_a, const float* source_b, const int* nnf_b, float* error ) { const int r = (patch_size - 1) / 2; const int x = blockDim.x * blockIdx.x + threadIdx.x; const int y = blockDim.y * blockIdx.y + threadIdx.y; const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; if (x >= height or y >= width) return; const int z_nnf = blockIdx.z * height * width * 2 + (x * width + y) * 2; const int x_a = nnf_a[z_nnf + 0]; const int y_a = nnf_a[z_nnf + 1]; const int x_b = nnf_b[z_nnf + 0]; const int y_b = nnf_b[z_nnf + 1]; float e = 0; for (int px = -r; px <= r; px++){ for (int py = -r; py <= r; py++){ const int pid_a = (x_a + pad_size + px) * (width + pad_size * 2) + y_a + pad_size + py; const int pid_b = (x_b + pad_size + px) * (width + pad_size * 2) + y_b + pad_size + py; for (int c = 0; c < channel; c++){ const float diff = source_a[z + pid_a * channel + c] - source_b[z + pid_b * channel + c]; e += diff * diff; } } } error[blockIdx.z * height * width + x * width + y] = e; } ''', 'pairwise_patch_error') ================================================ FILE: diffsynth/extensions/FastBlend/data.py ================================================ import imageio, os import numpy as np from PIL import Image def read_video(file_name): reader = imageio.get_reader(file_name) video = [] for frame in reader: frame = np.array(frame) video.append(frame) reader.close() return video def get_video_fps(file_name): reader = imageio.get_reader(file_name) fps = reader.get_meta_data()["fps"] reader.close() return fps def save_video(frames_path, video_path, num_frames, fps): writer = imageio.get_writer(video_path, fps=fps, quality=9) for i in range(num_frames): frame = np.array(Image.open(os.path.join(frames_path, "%05d.png" % i))) writer.append_data(frame) writer.close() return video_path class LowMemoryVideo: def __init__(self, file_name): self.reader = imageio.get_reader(file_name) def __len__(self): return self.reader.count_frames() def __getitem__(self, item): return np.array(self.reader.get_data(item)) def __del__(self): self.reader.close() def split_file_name(file_name): result = [] number = -1 for i in file_name: if ord(i)>=ord("0") and ord(i)<=ord("9"): if number == -1: number = 0 number = number*10 + ord(i) - ord("0") else: if number != -1: result.append(number) number = -1 result.append(i) if number != -1: result.append(number) result = tuple(result) return result def search_for_images(folder): file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")] file_list = [(split_file_name(file_name), file_name) for file_name in file_list] file_list = [i[1] for i in sorted(file_list)] file_list = [os.path.join(folder, i) for i in file_list] return file_list def read_images(folder): file_list = search_for_images(folder) frames = [np.array(Image.open(i)) for i in file_list] return frames class LowMemoryImageFolder: def __init__(self, folder, file_list=None): if file_list is None: self.file_list = search_for_images(folder) else: self.file_list = [os.path.join(folder, file_name) for file_name in file_list] def __len__(self): return len(self.file_list) def __getitem__(self, item): return np.array(Image.open(self.file_list[item])) def __del__(self): pass class VideoData: def __init__(self, video_file, image_folder, **kwargs): if video_file is not None: self.data_type = "video" self.data = LowMemoryVideo(video_file, **kwargs) elif image_folder is not None: self.data_type = "images" self.data = LowMemoryImageFolder(image_folder, **kwargs) else: raise ValueError("Cannot open video or image folder") self.length = None self.height = None self.width = None def raw_data(self): frames = [] for i in range(self.__len__()): frames.append(self.__getitem__(i)) return frames def set_length(self, length): self.length = length def set_shape(self, height, width): self.height = height self.width = width def __len__(self): if self.length is None: return len(self.data) else: return self.length def shape(self): if self.height is not None and self.width is not None: return self.height, self.width else: height, width, _ = self.__getitem__(0).shape return height, width def __getitem__(self, item): frame = self.data.__getitem__(item) height, width, _ = frame.shape if self.height is not None and self.width is not None: if self.height != height or self.width != width: frame = Image.fromarray(frame).resize((self.width, self.height)) frame = np.array(frame) return frame def __del__(self): pass ================================================ FILE: diffsynth/extensions/FastBlend/patch_match.py ================================================ from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_error_kernel import numpy as np import cupy as cp import cv2 class PatchMatcher: def __init__( self, height, width, channel, minimum_patch_size, threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, random_search_steps=3, random_search_range=4, use_mean_target_style=False, use_pairwise_patch_error=False, tracking_window_size=0 ): self.height = height self.width = width self.channel = channel self.minimum_patch_size = minimum_patch_size self.threads_per_block = threads_per_block self.num_iter = num_iter self.gpu_id = gpu_id self.guide_weight = guide_weight self.random_search_steps = random_search_steps self.random_search_range = random_search_range self.use_mean_target_style = use_mean_target_style self.use_pairwise_patch_error = use_pairwise_patch_error self.tracking_window_size = tracking_window_size self.patch_size_list = [minimum_patch_size + i*2 for i in range(num_iter)][::-1] self.pad_size = self.patch_size_list[0] // 2 self.grid = ( (height + threads_per_block - 1) // threads_per_block, (width + threads_per_block - 1) // threads_per_block ) self.block = (threads_per_block, threads_per_block) def pad_image(self, image): return cp.pad(image, ((0, 0), (self.pad_size, self.pad_size), (self.pad_size, self.pad_size), (0, 0))) def unpad_image(self, image): return image[:, self.pad_size: -self.pad_size, self.pad_size: -self.pad_size, :] def apply_nnf_to_image(self, nnf, source): batch_size = source.shape[0] target = cp.zeros((batch_size, self.height + self.pad_size * 2, self.width + self.pad_size * 2, self.channel), dtype=cp.float32) remapping_kernel( self.grid + (batch_size,), self.block, (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target) ) return target def get_patch_error(self, source, nnf, target): batch_size = source.shape[0] error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32) patch_error_kernel( self.grid + (batch_size,), self.block, (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target, error) ) return error def get_pairwise_patch_error(self, source, nnf): batch_size = source.shape[0]//2 error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32) source_a, nnf_a = source[0::2].copy(), nnf[0::2].copy() source_b, nnf_b = source[1::2].copy(), nnf[1::2].copy() pairwise_patch_error_kernel( self.grid + (batch_size,), self.block, (self.height, self.width, self.channel, self.patch_size, self.pad_size, source_a, nnf_a, source_b, nnf_b, error) ) error = error.repeat(2, axis=0) return error def get_error(self, source_guide, target_guide, source_style, target_style, nnf): error_guide = self.get_patch_error(source_guide, nnf, target_guide) if self.use_mean_target_style: target_style = self.apply_nnf_to_image(nnf, source_style) target_style = target_style.mean(axis=0, keepdims=True) target_style = target_style.repeat(source_guide.shape[0], axis=0) if self.use_pairwise_patch_error: error_style = self.get_pairwise_patch_error(source_style, nnf) else: error_style = self.get_patch_error(source_style, nnf, target_style) error = error_guide * self.guide_weight + error_style return error def clamp_bound(self, nnf): nnf[:,:,:,0] = cp.clip(nnf[:,:,:,0], 0, self.height-1) nnf[:,:,:,1] = cp.clip(nnf[:,:,:,1], 0, self.width-1) return nnf def random_step(self, nnf, r): batch_size = nnf.shape[0] step = cp.random.randint(-r, r+1, size=(batch_size, self.height, self.width, 2), dtype=cp.int32) upd_nnf = self.clamp_bound(nnf + step) return upd_nnf def neighboor_step(self, nnf, d): if d==0: upd_nnf = cp.concatenate([nnf[:, :1, :], nnf[:, :-1, :]], axis=1) upd_nnf[:, :, :, 0] += 1 elif d==1: upd_nnf = cp.concatenate([nnf[:, :, :1], nnf[:, :, :-1]], axis=2) upd_nnf[:, :, :, 1] += 1 elif d==2: upd_nnf = cp.concatenate([nnf[:, 1:, :], nnf[:, -1:, :]], axis=1) upd_nnf[:, :, :, 0] -= 1 elif d==3: upd_nnf = cp.concatenate([nnf[:, :, 1:], nnf[:, :, -1:]], axis=2) upd_nnf[:, :, :, 1] -= 1 upd_nnf = self.clamp_bound(upd_nnf) return upd_nnf def shift_nnf(self, nnf, d): if d>0: d = min(nnf.shape[0], d) upd_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0) else: d = max(-nnf.shape[0], d) upd_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0) return upd_nnf def track_step(self, nnf, d): if self.use_pairwise_patch_error: upd_nnf = cp.zeros_like(nnf) upd_nnf[0::2] = self.shift_nnf(nnf[0::2], d) upd_nnf[1::2] = self.shift_nnf(nnf[1::2], d) else: upd_nnf = self.shift_nnf(nnf, d) return upd_nnf def C(self, n, m): # not used c = 1 for i in range(1, n+1): c *= i for i in range(1, m+1): c //= i for i in range(1, n-m+1): c //= i return c def bezier_step(self, nnf, r): # not used n = r * 2 - 1 upd_nnf = cp.zeros(shape=nnf.shape, dtype=cp.float32) for i, d in enumerate(list(range(-r, 0)) + list(range(1, r+1))): if d>0: ctl_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0) elif d<0: ctl_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0) upd_nnf += ctl_nnf * (self.C(n, i) / 2**n) upd_nnf = self.clamp_bound(upd_nnf).astype(nnf.dtype) return upd_nnf def update(self, source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf): upd_err = self.get_error(source_guide, target_guide, source_style, target_style, upd_nnf) upd_idx = (upd_err < err) nnf[upd_idx] = upd_nnf[upd_idx] err[upd_idx] = upd_err[upd_idx] return nnf, err def propagation(self, source_guide, target_guide, source_style, target_style, nnf, err): for d in cp.random.permutation(4): upd_nnf = self.neighboor_step(nnf, d) nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) return nnf, err def random_search(self, source_guide, target_guide, source_style, target_style, nnf, err): for i in range(self.random_search_steps): upd_nnf = self.random_step(nnf, self.random_search_range) nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) return nnf, err def track(self, source_guide, target_guide, source_style, target_style, nnf, err): for d in range(1, self.tracking_window_size + 1): upd_nnf = self.track_step(nnf, d) nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) upd_nnf = self.track_step(nnf, -d) nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) return nnf, err def iteration(self, source_guide, target_guide, source_style, target_style, nnf, err): nnf, err = self.propagation(source_guide, target_guide, source_style, target_style, nnf, err) nnf, err = self.random_search(source_guide, target_guide, source_style, target_style, nnf, err) nnf, err = self.track(source_guide, target_guide, source_style, target_style, nnf, err) return nnf, err def estimate_nnf(self, source_guide, target_guide, source_style, nnf): with cp.cuda.Device(self.gpu_id): source_guide = self.pad_image(source_guide) target_guide = self.pad_image(target_guide) source_style = self.pad_image(source_style) for it in range(self.num_iter): self.patch_size = self.patch_size_list[it] target_style = self.apply_nnf_to_image(nnf, source_style) err = self.get_error(source_guide, target_guide, source_style, target_style, nnf) nnf, err = self.iteration(source_guide, target_guide, source_style, target_style, nnf, err) target_style = self.unpad_image(self.apply_nnf_to_image(nnf, source_style)) return nnf, target_style class PyramidPatchMatcher: def __init__( self, image_height, image_width, channel, minimum_patch_size, threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, use_mean_target_style=False, use_pairwise_patch_error=False, tracking_window_size=0, initialize="identity" ): maximum_patch_size = minimum_patch_size + (num_iter - 1) * 2 self.pyramid_level = int(np.log2(min(image_height, image_width) / maximum_patch_size)) self.pyramid_heights = [] self.pyramid_widths = [] self.patch_matchers = [] self.minimum_patch_size = minimum_patch_size self.num_iter = num_iter self.gpu_id = gpu_id self.initialize = initialize for level in range(self.pyramid_level): height = image_height//(2**(self.pyramid_level - 1 - level)) width = image_width//(2**(self.pyramid_level - 1 - level)) self.pyramid_heights.append(height) self.pyramid_widths.append(width) self.patch_matchers.append(PatchMatcher( height, width, channel, minimum_patch_size=minimum_patch_size, threads_per_block=threads_per_block, num_iter=num_iter, gpu_id=gpu_id, guide_weight=guide_weight, use_mean_target_style=use_mean_target_style, use_pairwise_patch_error=use_pairwise_patch_error, tracking_window_size=tracking_window_size )) def resample_image(self, images, level): height, width = self.pyramid_heights[level], self.pyramid_widths[level] images = images.get() images_resample = [] for image in images: image_resample = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA) images_resample.append(image_resample) images_resample = cp.array(np.stack(images_resample), dtype=cp.float32) return images_resample def initialize_nnf(self, batch_size): if self.initialize == "random": height, width = self.pyramid_heights[0], self.pyramid_widths[0] nnf = cp.stack([ cp.random.randint(0, height, (batch_size, height, width), dtype=cp.int32), cp.random.randint(0, width, (batch_size, height, width), dtype=cp.int32) ], axis=3) elif self.initialize == "identity": height, width = self.pyramid_heights[0], self.pyramid_widths[0] nnf = cp.stack([ cp.repeat(cp.arange(height), width).reshape(height, width), cp.tile(cp.arange(width), height).reshape(height, width) ], axis=2) nnf = cp.stack([nnf] * batch_size) else: raise NotImplementedError() return nnf def update_nnf(self, nnf, level): # upscale nnf = nnf.repeat(2, axis=1).repeat(2, axis=2) * 2 nnf[:,[i for i in range(nnf.shape[0]) if i&1],:,0] += 1 nnf[:,:,[i for i in range(nnf.shape[0]) if i&1],1] += 1 # check if scale is 2 height, width = self.pyramid_heights[level], self.pyramid_widths[level] if height != nnf.shape[0] * 2 or width != nnf.shape[1] * 2: nnf = nnf.get().astype(np.float32) nnf = [cv2.resize(n, (width, height), interpolation=cv2.INTER_LINEAR) for n in nnf] nnf = cp.array(np.stack(nnf), dtype=cp.int32) nnf = self.patch_matchers[level].clamp_bound(nnf) return nnf def apply_nnf_to_image(self, nnf, image): with cp.cuda.Device(self.gpu_id): image = self.patch_matchers[-1].pad_image(image) image = self.patch_matchers[-1].apply_nnf_to_image(nnf, image) return image def estimate_nnf(self, source_guide, target_guide, source_style): with cp.cuda.Device(self.gpu_id): if not isinstance(source_guide, cp.ndarray): source_guide = cp.array(source_guide, dtype=cp.float32) if not isinstance(target_guide, cp.ndarray): target_guide = cp.array(target_guide, dtype=cp.float32) if not isinstance(source_style, cp.ndarray): source_style = cp.array(source_style, dtype=cp.float32) for level in range(self.pyramid_level): nnf = self.initialize_nnf(source_guide.shape[0]) if level==0 else self.update_nnf(nnf, level) source_guide_ = self.resample_image(source_guide, level) target_guide_ = self.resample_image(target_guide, level) source_style_ = self.resample_image(source_style, level) nnf, target_style = self.patch_matchers[level].estimate_nnf( source_guide_, target_guide_, source_style_, nnf ) return nnf.get(), target_style.get() ================================================ FILE: diffsynth/extensions/FastBlend/runners/__init__.py ================================================ from .accurate import AccurateModeRunner from .fast import FastModeRunner from .balanced import BalancedModeRunner from .interpolation import InterpolationModeRunner, InterpolationModeSingleFrameRunner ================================================ FILE: diffsynth/extensions/FastBlend/runners/accurate.py ================================================ from ..patch_match import PyramidPatchMatcher import os import numpy as np from PIL import Image from tqdm import tqdm class AccurateModeRunner: def __init__(self): pass def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Accurate Mode", save_path=None): patch_match_engine = PyramidPatchMatcher( image_height=frames_style[0].shape[0], image_width=frames_style[0].shape[1], channel=3, use_mean_target_style=True, **ebsynth_config ) # run n = len(frames_style) for target in tqdm(range(n), desc=desc): l, r = max(target - window_size, 0), min(target + window_size + 1, n) remapped_frames = [] for i in range(l, r, batch_size): j = min(i + batch_size, r) source_guide = np.stack([frames_guide[source] for source in range(i, j)]) target_guide = np.stack([frames_guide[target]] * (j - i)) source_style = np.stack([frames_style[source] for source in range(i, j)]) _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) remapped_frames.append(target_style) frame = np.concatenate(remapped_frames, axis=0).mean(axis=0) frame = frame.clip(0, 255).astype("uint8") if save_path is not None: Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target)) ================================================ FILE: diffsynth/extensions/FastBlend/runners/balanced.py ================================================ from ..patch_match import PyramidPatchMatcher import os import numpy as np from PIL import Image from tqdm import tqdm class BalancedModeRunner: def __init__(self): pass def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Balanced Mode", save_path=None): patch_match_engine = PyramidPatchMatcher( image_height=frames_style[0].shape[0], image_width=frames_style[0].shape[1], channel=3, **ebsynth_config ) # tasks n = len(frames_style) tasks = [] for target in range(n): for source in range(target - window_size, target + window_size + 1): if source >= 0 and source < n and source != target: tasks.append((source, target)) # run frames = [(None, 1) for i in range(n)] for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc): tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] source_guide = np.stack([frames_guide[source] for source, target in tasks_batch]) target_guide = np.stack([frames_guide[target] for source, target in tasks_batch]) source_style = np.stack([frames_style[source] for source, target in tasks_batch]) _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) for (source, target), result in zip(tasks_batch, target_style): frame, weight = frames[target] if frame is None: frame = frames_style[target] frames[target] = ( frame * (weight / (weight + 1)) + result / (weight + 1), weight + 1 ) if weight + 1 == min(n, target + window_size + 1) - max(0, target - window_size): frame = frame.clip(0, 255).astype("uint8") if save_path is not None: Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target)) frames[target] = (None, 1) ================================================ FILE: diffsynth/extensions/FastBlend/runners/fast.py ================================================ from ..patch_match import PyramidPatchMatcher import functools, os import numpy as np from PIL import Image from tqdm import tqdm class TableManager: def __init__(self): pass def task_list(self, n): tasks = [] max_level = 1 while (1<=n: break meta_data = { "source": i, "target": j, "level": level + 1 } tasks.append(meta_data) tasks.sort(key=functools.cmp_to_key(lambda u, v: u["level"]-v["level"])) return tasks def build_remapping_table(self, frames_guide, frames_style, patch_match_engine, batch_size, desc=""): n = len(frames_guide) tasks = self.task_list(n) remapping_table = [[(frames_style[i], 1)] for i in range(n)] for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc): tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] source_guide = np.stack([frames_guide[task["source"]] for task in tasks_batch]) target_guide = np.stack([frames_guide[task["target"]] for task in tasks_batch]) source_style = np.stack([frames_style[task["source"]] for task in tasks_batch]) _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) for task, result in zip(tasks_batch, target_style): target, level = task["target"], task["level"] if len(remapping_table[target])==level: remapping_table[target].append((result, 1)) else: frame, weight = remapping_table[target][level] remapping_table[target][level] = ( frame * (weight / (weight + 1)) + result / (weight + 1), weight + 1 ) return remapping_table def remapping_table_to_blending_table(self, table): for i in range(len(table)): for j in range(1, len(table[i])): frame_1, weight_1 = table[i][j-1] frame_2, weight_2 = table[i][j] frame = (frame_1 + frame_2) / 2 weight = weight_1 + weight_2 table[i][j] = (frame, weight) return table def tree_query(self, leftbound, rightbound): node_list = [] node_index = rightbound while node_index>=leftbound: node_level = 0 while (1<=leftbound: node_level += 1 node_list.append((node_index, node_level)) node_index -= 1<0: tasks = [] for m in range(index_style[0]): tasks.append((index_style[0], m, index_style[0])) task_group.append(tasks) # middle frames for l, r in zip(index_style[:-1], index_style[1:]): tasks = [] for m in range(l, r): tasks.append((l, m, r)) task_group.append(tasks) # last frame tasks = [] for m in range(index_style[-1], n): tasks.append((index_style[-1], m, index_style[-1])) task_group.append(tasks) return task_group def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None): patch_match_engine = PyramidPatchMatcher( image_height=frames_style[0].shape[0], image_width=frames_style[0].shape[1], channel=3, use_mean_target_style=False, use_pairwise_patch_error=True, **ebsynth_config ) # task index_dict = self.get_index_dict(index_style) task_group = self.get_task_group(index_style, len(frames_guide)) # run for tasks in task_group: index_start, index_end = min([i[1] for i in tasks]), max([i[1] for i in tasks]) for batch_id in tqdm(range(0, len(tasks), batch_size), desc=f"Rendering frames {index_start}...{index_end}"): tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] source_guide, target_guide, source_style = [], [], [] for l, m, r in tasks_batch: # l -> m source_guide.append(frames_guide[l]) target_guide.append(frames_guide[m]) source_style.append(frames_style[index_dict[l]]) # r -> m source_guide.append(frames_guide[r]) target_guide.append(frames_guide[m]) source_style.append(frames_style[index_dict[r]]) source_guide = np.stack(source_guide) target_guide = np.stack(target_guide) source_style = np.stack(source_style) _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) if save_path is not None: for frame_l, frame_r, (l, m, r) in zip(target_style[0::2], target_style[1::2], tasks_batch): weight_l, weight_r = self.get_weight(l, m, r) frame = frame_l * weight_l + frame_r * weight_r frame = frame.clip(0, 255).astype("uint8") Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % m)) class InterpolationModeSingleFrameRunner: def __init__(self): pass def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None): # check input tracking_window_size = ebsynth_config["tracking_window_size"] if tracking_window_size * 2 >= batch_size: raise ValueError("batch_size should be larger than track_window_size * 2") frame_style = frames_style[0] frame_guide = frames_guide[index_style[0]] patch_match_engine = PyramidPatchMatcher( image_height=frame_style.shape[0], image_width=frame_style.shape[1], channel=3, **ebsynth_config ) # run frame_id, n = 0, len(frames_guide) for i in tqdm(range(0, n, batch_size - tracking_window_size * 2), desc=f"Rendering frames 0...{n}"): if i + batch_size > n: l, r = max(n - batch_size, 0), n else: l, r = i, i + batch_size source_guide = np.stack([frame_guide] * (r-l)) target_guide = np.stack([frames_guide[i] for i in range(l, r)]) source_style = np.stack([frame_style] * (r-l)) _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) for i, frame in zip(range(l, r), target_style): if i==frame_id: frame = frame.clip(0, 255).astype("uint8") Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % frame_id)) frame_id += 1 if r < n and r-frame_id <= tracking_window_size: break ================================================ FILE: diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py ================================================ from .blip_pretrain import * ================================================ FILE: diffsynth/extensions/ImageQualityMetric/BLIP/blip.py ================================================ ''' * Adapted from BLIP (https://github.com/salesforce/BLIP) ''' import warnings warnings.filterwarnings("ignore") import torch import os from urllib.parse import urlparse from timm.models.hub import download_cached_file from transformers import BertTokenizer from .vit import VisionTransformer, interpolate_pos_embed def default_bert(): current_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.abspath(os.path.join(current_dir, '../../../../')) model_path = os.path.join(project_root, 'models', 'QualityMetric') return os.path.join(model_path, "bert-base-uncased") def init_tokenizer(bert_model_path): tokenizer = BertTokenizer.from_pretrained(bert_model_path) tokenizer.add_special_tokens({'bos_token':'[DEC]'}) tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] return tokenizer def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): assert vit in ['base', 'large'], "vit parameter must be base or large" if vit=='base': vision_width = 768 visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, drop_path_rate=0 or drop_path_rate ) elif vit=='large': vision_width = 1024 visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, drop_path_rate=0.1 or drop_path_rate ) return visual_encoder, vision_width def is_url(url_or_filename): parsed = urlparse(url_or_filename) return parsed.scheme in ("http", "https") def load_checkpoint(model,url_or_filename): if is_url(url_or_filename): cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) checkpoint = torch.load(cached_file, map_location='cpu') elif os.path.isfile(url_or_filename): checkpoint = torch.load(url_or_filename, map_location='cpu') else: raise RuntimeError('checkpoint url or path is invalid') state_dict = checkpoint['model'] state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], model.visual_encoder_m) for key in model.state_dict().keys(): if key in state_dict.keys(): if state_dict[key].shape!=model.state_dict()[key].shape: print(key, ": ", state_dict[key].shape, ', ', model.state_dict()[key].shape) del state_dict[key] msg = model.load_state_dict(state_dict,strict=False) print('load checkpoint from %s'%url_or_filename) return model,msg ================================================ FILE: diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py ================================================ ''' * Adapted from BLIP (https://github.com/salesforce/BLIP) ''' import transformers transformers.logging.set_verbosity_error() from torch import nn import os from .med import BertConfig, BertModel from .blip import create_vit, init_tokenizer class BLIP_Pretrain(nn.Module): def __init__(self, med_config = "med_config.json", image_size = 224, vit = 'base', vit_grad_ckpt = False, vit_ckpt_layer = 0, embed_dim = 256, queue_size = 57600, momentum = 0.995, bert_model_path = "" ): """ Args: med_config (str): path for the mixture of encoder-decoder model's configuration file image_size (int): input image size vit (str): model size of vision transformer """ super().__init__() self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0) self.tokenizer = init_tokenizer(bert_model_path) encoder_config = BertConfig.from_json_file(med_config) encoder_config.encoder_width = vision_width self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) text_width = self.text_encoder.config.hidden_size self.vision_proj = nn.Linear(vision_width, embed_dim) self.text_proj = nn.Linear(text_width, embed_dim) ================================================ FILE: diffsynth/extensions/ImageQualityMetric/BLIP/med.py ================================================ ''' * Adapted from BLIP (https://github.com/salesforce/BLIP) * Based on huggingface code base * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert ''' import math from typing import Tuple import torch from torch import Tensor, device, nn import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from transformers.activations import ACT2FN from transformers.file_utils import ( ModelOutput, ) from transformers.modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from transformers.modeling_utils import ( PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer, ) from transformers.utils import logging from transformers.models.bert.configuration_bert import BertConfig logger = logging.get_logger(__name__) class BertEmbeddings(nn.Module): """Construct the embeddings from word and position embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.config = config def forward( self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) embeddings = inputs_embeds if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class BertSelfAttention(nn.Module): def __init__(self, config, is_cross_attention): super().__init__() self.config = config if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.hidden_size, config.num_attention_heads) ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) if is_cross_attention: self.key = nn.Linear(config.encoder_width, self.all_head_size) self.value = nn.Linear(config.encoder_width, self.all_head_size) else: self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.save_attention = False def save_attn_gradients(self, attn_gradients): self.attn_gradients = attn_gradients def get_attn_gradients(self): return self.attn_gradients def save_attention_map(self, attention_map): self.attention_map = attention_map def get_attention_map(self): return self.attention_map def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": seq_length = hidden_states.size()[1] position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) if is_cross_attention and self.save_attention: self.save_attention_map(attention_probs) attention_probs.register_hook(self.save_attn_gradients) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs_dropped = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs_dropped = attention_probs_dropped * head_mask context_layer = torch.matmul(attention_probs_dropped, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) outputs = outputs + (past_key_value,) return outputs class BertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertAttention(nn.Module): def __init__(self, config, is_cross_attention=False): super().__init__() self.self = BertSelfAttention(config, is_cross_attention) self.output = BertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class BertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class BertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertLayer(nn.Module): def __init__(self, config, layer_num): super().__init__() self.config = config self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = BertAttention(config) self.layer_num = layer_num if self.config.add_cross_attention: self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) self.intermediate = BertIntermediate(config) self.output = BertOutput(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, mode=None, ): # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] if mode=='multimodal': assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions=output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class BertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, mode='multimodal', ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i in range(self.config.num_hidden_layers): layer_module = self.layer[i] if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warn( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, mode=mode, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, mode=mode, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) class BertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class BertPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class BertLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = BertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class BertOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = BertLMPredictionHead(config) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores class BertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = BertConfig base_model_prefix = "bert" _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """ Initialize the weights """ if isinstance(module, (nn.Linear, nn.Embedding)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() class BertModel(BertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in `Attention is all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an input to the forward pass. """ def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = BertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) if add_pooling_layer else None self.init_weights() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: """ Makes broadcastable attention and causal masks so that future and masked tokens are ignored. Arguments: attention_mask (:obj:`torch.Tensor`): Mask with ones indicating tokens to attend to, zeros for tokens to ignore. input_shape (:obj:`Tuple[int]`): The shape of the input to the model. device: (:obj:`torch.device`): The device of the input to the model. Returns: :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. """ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. if attention_mask.dim() == 3: extended_attention_mask = attention_mask[:, None, :, :] elif attention_mask.dim() == 2: # Provided a padding mask of dimensions [batch_size, seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] if is_decoder: batch_size, seq_length = input_shape seq_ids = torch.arange(seq_length, device=device) causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] # in case past_key_values are used we need to add a prefix ones mask to the causal mask # causal and attention masks must have same type with pytorch version < 1.3 causal_mask = causal_mask.to(attention_mask.dtype) if causal_mask.shape[1] < attention_mask.shape[1]: prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] causal_mask = torch.cat( [ torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), causal_mask, ], axis=-1, ) extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] else: extended_attention_mask = attention_mask[:, None, None, :] else: raise ValueError( "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( input_shape, attention_mask.shape ) ) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 return extended_attention_mask def forward( self, input_ids=None, attention_mask=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, is_decoder=False, mode='multimodal', ): r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. use_cache (:obj:`bool`, `optional`): If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up decoding (see :obj:`past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() batch_size, seq_length = input_shape device = input_ids.device elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size, seq_length = input_shape device = inputs_embeds.device elif encoder_embeds is not None: input_shape = encoder_embeds.size()[:-1] batch_size, seq_length = input_shape device = encoder_embeds.device else: raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device, is_decoder) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if encoder_hidden_states is not None: if type(encoder_hidden_states) == list: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() else: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if type(encoder_attention_mask) == list: encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] elif encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) if encoder_embeds is None: embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) else: embedding_output = encoder_embeds encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, mode=mode, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) class BertLMHeadModel(BertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] def __init__(self, config): super().__init__(config) self.bert = BertModel(config, add_pooling_layer=False) self.cls = BertOnlyMLMHead(config) self.init_weights() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings def forward( self, input_ids=None, attention_mask=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, return_logits=False, is_decoder=True, reduction='mean', mode='multimodal', ): r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. use_cache (:obj:`bool`, `optional`): If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up decoding (see :obj:`past_key_values`). Returns: Example:: >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig >>> import torch >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> config = BertConfig.from_pretrained("bert-base-cased") >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.bert( input_ids, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, is_decoder=is_decoder, mode=mode, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) if return_logits: return prediction_scores[:, :-1, :].contiguous() lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if reduction=='none': lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return { "input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past, "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), "is_decoder": True, } def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past ================================================ FILE: diffsynth/extensions/ImageQualityMetric/BLIP/vit.py ================================================ ''' * Adapted from BLIP (https://github.com/salesforce/BLIP) * Based on timm code base * https://github.com/rwightman/pytorch-image-models/tree/master/timm ''' import torch import torch.nn as nn import torch.nn.functional as F from functools import partial from timm.models.vision_transformer import _cfg, PatchEmbed from timm.models.registry import register_model from timm.models.layers import trunc_normal_, DropPath from timm.models.helpers import named_apply, adapt_input_conv # from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper class Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights self.scale = qk_scale or head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.attn_gradients = None self.attention_map = None def save_attn_gradients(self, attn_gradients): self.attn_gradients = attn_gradients def get_attn_gradients(self): return self.attn_gradients def save_attention_map(self, attention_map): self.attention_map = attention_map def get_attention_map(self): return self.attention_map def forward(self, x, register_hook=False): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) if register_hook: self.save_attention_map(attn) attn.register_hook(self.save_attn_gradients) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # if use_grad_checkpointing: # self.attn = checkpoint_wrapper(self.attn) # self.mlp = checkpoint_wrapper(self.mlp) def forward(self, x, register_hook=False): x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class VisionTransformer(nn.Module): """ Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, use_grad_checkpointing=False, ckpt_layer=0): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True qk_scale (float): override default qk scale of head_dim ** -0.5 if set representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate norm_layer: (nn.Module): normalization layer """ super().__init__() self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.ModuleList([ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) ) for i in range(depth)]) self.norm = norm_layer(embed_dim) trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token'} def forward(self, x, register_blk=-1): B = x.shape[0] x = self.patch_embed(x) cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks x = torch.cat((cls_tokens, x), dim=1) x = x + self.pos_embed[:,:x.size(1),:] x = self.pos_drop(x) for i,blk in enumerate(self.blocks): x = blk(x, register_blk==i) x = self.norm(x) return x @torch.jit.ignore() def load_pretrained(self, checkpoint_path, prefix=''): _load_weights(self, checkpoint_path, prefix) @torch.no_grad() def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): """ Load weights from .npz checkpoints for official Google Brain Flax implementation """ import numpy as np def _n2p(w, t=True): if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: w = w.flatten() if t: if w.ndim == 4: w = w.transpose([3, 2, 0, 1]) elif w.ndim == 3: w = w.transpose([2, 0, 1]) elif w.ndim == 2: w = w.transpose([1, 0]) return torch.from_numpy(w) w = np.load(checkpoint_path) if not prefix and 'opt/target/embedding/kernel' in w: prefix = 'opt/target/' if hasattr(model.patch_embed, 'backbone'): # hybrid backbone = model.patch_embed.backbone stem_only = not hasattr(backbone, 'stem') stem = backbone if stem_only else backbone.stem stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) if not stem_only: for i, stage in enumerate(backbone.stages): for j, block in enumerate(stage.blocks): bp = f'{prefix}block{i + 1}/unit{j + 1}/' for r in range(3): getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) if block.downsample is not None: block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) else: embed_conv_w = adapt_input_conv( model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) model.patch_embed.proj.weight.copy_(embed_conv_w) model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) if pos_embed_w.shape != model.pos_embed.shape: pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) model.pos_embed.copy_(pos_embed_w) model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) # if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: # model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) # model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) # if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: # model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) # model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) for i, block in enumerate(model.blocks.children()): block_prefix = f'{prefix}Transformer/encoderblock_{i}/' mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) block.attn.qkv.weight.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) block.attn.qkv.bias.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) for r in range(2): getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): # interpolate position embedding embedding_size = pos_embed_checkpoint.shape[-1] num_patches = visual_encoder.patch_embed.num_patches num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches # height (== width) for the checkpoint position embedding orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) # height (== width) for the new position embedding new_size = int(num_patches ** 0.5) if orig_size!=new_size: # class_token and dist_token are kept unchanged extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] # only the position tokens are interpolated pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) pos_tokens = torch.nn.functional.interpolate( pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) return new_pos_embed else: return pos_embed_checkpoint ================================================ FILE: diffsynth/extensions/ImageQualityMetric/__init__.py ================================================ from modelscope import snapshot_download from typing_extensions import Literal, TypeAlias import os from diffsynth.extensions.ImageQualityMetric.aesthetic import AestheticScore from diffsynth.extensions.ImageQualityMetric.imagereward import ImageRewardScore from diffsynth.extensions.ImageQualityMetric.pickscore import PickScore from diffsynth.extensions.ImageQualityMetric.clip import CLIPScore from diffsynth.extensions.ImageQualityMetric.hps import HPScore_v2 from diffsynth.extensions.ImageQualityMetric.mps import MPScore preference_model_id: TypeAlias = Literal[ "ImageReward", "Aesthetic", "PickScore", "CLIP", "HPSv2", "HPSv2.1", "MPS", ] model_dict = { "ImageReward": { "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", "allow_file_pattern": [ "ImageReward/ImageReward.safetensors", "ImageReward/med_config.json", "bert-base-uncased/config.json", "bert-base-uncased/model.safetensors", "bert-base-uncased/tokenizer.json", "bert-base-uncased/tokenizer_config.json", "bert-base-uncased/vocab.txt", ], "load_path": { "imagereward": "ImageReward/ImageReward.safetensors", "med_config": "ImageReward/med_config.json", "bert_model_path": "bert-base-uncased", }, "model_class": ImageRewardScore }, "Aesthetic": { "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", "allow_file_pattern": [ "aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors", "clip-vit-large-patch14/config.json", "clip-vit-large-patch14/merges.txt", "clip-vit-large-patch14/model.safetensors", "clip-vit-large-patch14/preprocessor_config.json", "clip-vit-large-patch14/special_tokens_map.json", "clip-vit-large-patch14/tokenizer.json", "clip-vit-large-patch14/tokenizer_config.json", "clip-vit-large-patch14/vocab.json", ], "load_path": { "aesthetic_predictor": "aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors", "clip-large": "clip-vit-large-patch14", }, "model_class": AestheticScore }, "PickScore": { "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", "allow_file_pattern": [ "PickScore_v1/*", "CLIP-ViT-H-14-laion2B-s32B-b79K/config.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt", "CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json", ], "load_path": { "pickscore": "PickScore_v1", "clip": "CLIP-ViT-H-14-laion2B-s32B-b79K", }, "model_class": PickScore }, "CLIP": { "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", "allow_file_pattern": [ "CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin", "bpe_simple_vocab_16e6.txt.gz", ], "load_path": { "open_clip": "CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin", "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz", }, "model_class": CLIPScore }, "HPSv2": { "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", "allow_file_pattern": [ "HPS_v2/HPS_v2_compressed.safetensors", "bpe_simple_vocab_16e6.txt.gz", ], "load_path": { "hpsv2": "HPS_v2/HPS_v2_compressed.safetensors", "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz", }, "model_class": HPScore_v2, "extra_kwargs": {"model_version": "v2"} }, "HPSv2.1": { "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", "allow_file_pattern": [ "HPS_v2/HPS_v2.1_compressed.safetensors", "bpe_simple_vocab_16e6.txt.gz", ], "load_path": { "hpsv2.1": "HPS_v2/HPS_v2.1_compressed.safetensors", "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz", }, "model_class": HPScore_v2, "extra_kwargs": {"model_version": "v21"} }, "MPS": { "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", "allow_file_pattern": [ "MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors", "CLIP-ViT-H-14-laion2B-s32B-b79K/config.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt", "CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json", "CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json", ], "load_path": { "mps": "MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors", "clip": "CLIP-ViT-H-14-laion2B-s32B-b79K", }, "model_class": MPScore }, } def download_preference_model(model_name: preference_model_id, cache_dir="models"): metadata = model_dict[model_name] snapshot_download(model_id=metadata["model_id"], allow_file_pattern=metadata["allow_file_pattern"], cache_dir=cache_dir) load_path = metadata["load_path"] load_path = {key: os.path.join(cache_dir, metadata["model_id"], path) for key, path in load_path.items()} return load_path def load_preference_model(model_name: preference_model_id, device = "cuda", path = None): model_class = model_dict[model_name]["model_class"] extra_kwargs = model_dict[model_name].get("extra_kwargs", {}) preference_model = model_class(device=device, path=path, **extra_kwargs) return preference_model ================================================ FILE: diffsynth/extensions/ImageQualityMetric/aesthetic.py ================================================ from typing import List, Optional from PIL import Image import torch from transformers import AutoProcessor, AutoModel from safetensors.torch import load_file import os from typing import Union, List from .config import MODEL_PATHS class MLP(torch.nn.Module): def __init__(self, input_size: int, xcol: str = "emb", ycol: str = "avg_rating"): super().__init__() self.input_size = input_size self.xcol = xcol self.ycol = ycol self.layers = torch.nn.Sequential( torch.nn.Linear(self.input_size, 1024), #torch.nn.ReLU(), torch.nn.Dropout(0.2), torch.nn.Linear(1024, 128), #torch.nn.ReLU(), torch.nn.Dropout(0.2), torch.nn.Linear(128, 64), #torch.nn.ReLU(), torch.nn.Dropout(0.1), torch.nn.Linear(64, 16), #torch.nn.ReLU(), torch.nn.Linear(16, 1), ) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.layers(x) def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: x = batch[self.xcol] y = batch[self.ycol].reshape(-1, 1) x_hat = self.layers(x) loss = torch.nn.functional.mse_loss(x_hat, y) return loss def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor: x = batch[self.xcol] y = batch[self.ycol].reshape(-1, 1) x_hat = self.layers(x) loss = torch.nn.functional.mse_loss(x_hat, y) return loss def configure_optimizers(self) -> torch.optim.Optimizer: return torch.optim.Adam(self.parameters(), lr=1e-3) class AestheticScore(torch.nn.Module): def __init__(self, device: torch.device, path: str = MODEL_PATHS): super().__init__() self.device = device self.aes_model_path = path.get("aesthetic_predictor") # Load the MLP model self.model = MLP(768) try: if self.aes_model_path.endswith(".safetensors"): state_dict = load_file(self.aes_model_path) else: state_dict = torch.load(self.aes_model_path) self.model.load_state_dict(state_dict) except Exception as e: raise ValueError(f"Error loading model weights from {self.aes_model_path}: {e}") self.model.to(device) self.model.eval() # Load the CLIP model and processor clip_model_name = path.get('clip-large') self.model2 = AutoModel.from_pretrained(clip_model_name).eval().to(device) self.processor = AutoProcessor.from_pretrained(clip_model_name) def _calculate_score(self, image: torch.Tensor) -> float: """Calculate the aesthetic score for a single image. Args: image (torch.Tensor): The processed image tensor. Returns: float: The aesthetic score. """ with torch.no_grad(): # Get image embeddings image_embs = self.model2.get_image_features(image) image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True) # Compute score score = self.model(image_embs).cpu().flatten().item() return score @torch.no_grad() def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]: """Score the images based on their aesthetic quality. Args: images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). Returns: List[float]: List of scores for the images. """ try: if isinstance(images, (str, Image.Image)): # Single image if isinstance(images, str): pil_image = Image.open(images) else: pil_image = images # Prepare image inputs image_inputs = self.processor( images=pil_image, padding=True, truncation=True, max_length=77, return_tensors="pt", ).to(self.device) return [self._calculate_score(image_inputs["pixel_values"])] elif isinstance(images, list): # Multiple images scores = [] for one_image in images: if isinstance(one_image, str): pil_image = Image.open(one_image) elif isinstance(one_image, Image.Image): pil_image = one_image else: raise TypeError("The type of parameter images is illegal.") # Prepare image inputs image_inputs = self.processor( images=pil_image, padding=True, truncation=True, max_length=77, return_tensors="pt", ).to(self.device) scores.append(self._calculate_score(image_inputs["pixel_values"])) return scores else: raise TypeError("The type of parameter images is illegal.") except Exception as e: raise RuntimeError(f"Error in scoring images: {e}") ================================================ FILE: diffsynth/extensions/ImageQualityMetric/clip.py ================================================ from typing import List, Union from PIL import Image import torch from .open_clip import create_model_and_transforms, get_tokenizer from .config import MODEL_PATHS class CLIPScore(torch.nn.Module): def __init__(self, device: torch.device, path: str = MODEL_PATHS): super().__init__() """Initialize the CLIPScore with a model and tokenizer. Args: device (torch.device): The device to load the model on. """ self.device = device # Create model and transforms self.model, _, self.preprocess_val = create_model_and_transforms( "ViT-H-14", # "laion2B-s32B-b79K", pretrained=path.get("open_clip"), precision="amp", device=device, jit=False, force_quick_gelu=False, force_custom_text=False, force_patch_dropout=False, force_image_size=None, pretrained_image=False, image_mean=None, image_std=None, light_augmentation=True, aug_cfg={}, output_dict=True, with_score_predictor=False, with_region_predictor=False, ) # Initialize tokenizer self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"]) self.model = self.model.to(device) self.model.eval() def _calculate_score(self, image: torch.Tensor, prompt: str) -> float: """Calculate the CLIP score for a single image and prompt. Args: image (torch.Tensor): The processed image tensor. prompt (str): The prompt text. Returns: float: The CLIP score. """ with torch.no_grad(): # Process the prompt text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True) # Calculate the CLIP score outputs = self.model(image, text) image_features, text_features = outputs["image_features"], outputs["text_features"] logits_per_image = image_features @ text_features.T clip_score = torch.diagonal(logits_per_image).cpu().numpy() return clip_score[0].item() @torch.no_grad() def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: """Score the images based on the prompt. Args: images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). prompt (str): The prompt text. Returns: List[float]: List of CLIP scores for the images. """ if isinstance(images, (str, Image.Image)): # Single image if isinstance(images, str): image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True) else: image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True) return [self._calculate_score(image, prompt)] elif isinstance(images, list): # Multiple images scores = [] for one_images in images: if isinstance(one_images, str): image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True) elif isinstance(one_images, Image.Image): image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True) else: raise TypeError("The type of parameter images is illegal.") scores.append(self._calculate_score(image, prompt)) return scores else: raise TypeError("The type of parameter images is illegal.") ================================================ FILE: diffsynth/extensions/ImageQualityMetric/config.py ================================================ import os current_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.abspath(os.path.join(current_dir, '../../../')) model_path = os.path.join(project_root, 'models', 'QualityMetric') def get_model_path(model_name): return os.path.join(model_path, model_name) MODEL_PATHS = { "aesthetic_predictor": get_model_path("aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors"), "open_clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin"), "hpsv2": get_model_path("HPS_v2/HPS_v2_compressed.safetensors"), "hpsv2.1": get_model_path("HPS_v2/HPS_v2.1_compressed.safetensors"), "imagereward": get_model_path("ImageReward/ImageReward.safetensors"), "med_config": get_model_path("ImageReward/med_config.json"), "clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K"), "clip-large": get_model_path("clip-vit-large-patch14"), "mps": get_model_path("MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors"), "pickscore": get_model_path("PickScore_v1") } ================================================ FILE: diffsynth/extensions/ImageQualityMetric/hps.py ================================================ from typing import List, Union from PIL import Image import torch from .open_clip import create_model_and_transforms, get_tokenizer from safetensors.torch import load_file import os from .config import MODEL_PATHS class HPScore_v2(torch.nn.Module): def __init__(self, device: torch.device, path: str = MODEL_PATHS, model_version: str = "v2"): super().__init__() """Initialize the Selector with a model and tokenizer. Args: device (torch.device): The device to load the model on. model_version (str): The version of the model to load. Supports "v2" or "v21". Default is "v2". """ self.device = device if model_version == "v2": safetensors_path = path.get("hpsv2") elif model_version == "v21": safetensors_path = path.get("hpsv2.1") else: raise ValueError(f"Unsupported model version: {model_version}. Choose 'v2' or 'v21'.") # Create model and transforms model, _, self.preprocess_val = create_model_and_transforms( "ViT-H-14", # "laion2B-s32B-b79K", pretrained=path.get("open_clip"), precision="amp", device=device, jit=False, force_quick_gelu=False, force_custom_text=False, force_patch_dropout=False, force_image_size=None, pretrained_image=False, image_mean=None, image_std=None, light_augmentation=True, aug_cfg={}, output_dict=True, with_score_predictor=False, with_region_predictor=False, ) # Load model weights try: state_dict = load_file(safetensors_path) model.load_state_dict(state_dict) except Exception as e: raise ValueError(f"Error loading model weights from {safetensors_path}: {e}") # Initialize tokenizer and model self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"]) model = model.to(device) model.eval() self.model = model def _calculate_score(self, image: torch.Tensor, prompt: str) -> float: """Calculate the HPS score for a single image and prompt. Args: image (torch.Tensor): The processed image tensor. prompt (str): The prompt text. Returns: float: The HPS score. """ with torch.no_grad(): # Process the prompt text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True) # Calculate the HPS score outputs = self.model(image, text) image_features, text_features = outputs["image_features"], outputs["text_features"] logits_per_image = image_features @ text_features.T hps_score = torch.diagonal(logits_per_image).cpu().numpy() return hps_score[0].item() @torch.no_grad() def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: """Score the images based on the prompt. Args: images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). prompt (str): The prompt text. Returns: List[float]: List of HPS scores for the images. """ try: if isinstance(images, (str, Image.Image)): # Single image if isinstance(images, str): image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True) else: image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True) return [self._calculate_score(image, prompt)] elif isinstance(images, list): # Multiple images scores = [] for one_images in images: if isinstance(one_images, str): image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True) elif isinstance(one_images, Image.Image): image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True) else: raise TypeError("The type of parameter images is illegal.") scores.append(self._calculate_score(image, prompt)) return scores else: raise TypeError("The type of parameter images is illegal.") except Exception as e: raise RuntimeError(f"Error in scoring images: {e}") ================================================ FILE: diffsynth/extensions/ImageQualityMetric/imagereward.py ================================================ import os import torch from PIL import Image from typing import List, Union from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize from .BLIP.blip_pretrain import BLIP_Pretrain from torchvision.transforms import InterpolationMode from safetensors.torch import load_file from .config import MODEL_PATHS BICUBIC = InterpolationMode.BICUBIC def _convert_image_to_rgb(image): return image.convert("RGB") def _transform(n_px): return Compose([ Resize(n_px, interpolation=BICUBIC), CenterCrop(n_px), _convert_image_to_rgb, ToTensor(), Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), ]) class MLP(torch.nn.Module): def __init__(self, input_size): super().__init__() self.input_size = input_size self.layers = torch.nn.Sequential( torch.nn.Linear(self.input_size, 1024), #nn.ReLU(), torch.nn.Dropout(0.2), torch.nn.Linear(1024, 128), #nn.ReLU(), torch.nn.Dropout(0.2), torch.nn.Linear(128, 64), #nn.ReLU(), torch.nn.Dropout(0.1), torch.nn.Linear(64, 16), #nn.ReLU(), torch.nn.Linear(16, 1) ) # initial MLP param for name, param in self.layers.named_parameters(): if 'weight' in name: torch.nn.init.normal_(param, mean=0.0, std=1.0/(self.input_size+1)) if 'bias' in name: torch.nn.init.constant_(param, val=0) def forward(self, input): return self.layers(input) class ImageReward(torch.nn.Module): def __init__(self, med_config, device='cpu', bert_model_path=""): super().__init__() self.device = device self.blip = BLIP_Pretrain(image_size=224, vit='large', med_config=med_config, bert_model_path=bert_model_path) self.preprocess = _transform(224) self.mlp = MLP(768) self.mean = 0.16717362830052426 self.std = 1.0333394966054072 def score_grad(self, prompt_ids, prompt_attention_mask, image): """Calculate the score with gradient for a single image and prompt. Args: prompt_ids (torch.Tensor): Tokenized prompt IDs. prompt_attention_mask (torch.Tensor): Attention mask for the prompt. image (torch.Tensor): The processed image tensor. Returns: torch.Tensor: The reward score. """ image_embeds = self.blip.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device) text_output = self.blip.text_encoder( prompt_ids, attention_mask=prompt_attention_mask, encoder_hidden_states=image_embeds, encoder_attention_mask=image_atts, return_dict=True, ) txt_features = text_output.last_hidden_state[:, 0, :] rewards = self.mlp(txt_features) rewards = (rewards - self.mean) / self.std return rewards def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]: """Score the images based on the prompt. Args: prompt (str): The prompt text. images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). Returns: List[float]: List of scores for the images. """ if isinstance(images, (str, Image.Image)): # Single image if isinstance(images, str): pil_image = Image.open(images) else: pil_image = images image = self.preprocess(pil_image).unsqueeze(0).to(self.device) return [self._calculate_score(prompt, image).item()] elif isinstance(images, list): # Multiple images scores = [] for one_image in images: if isinstance(one_image, str): pil_image = Image.open(one_image) elif isinstance(one_image, Image.Image): pil_image = one_image else: raise TypeError("The type of parameter images is illegal.") image = self.preprocess(pil_image).unsqueeze(0).to(self.device) scores.append(self._calculate_score(prompt, image).item()) return scores else: raise TypeError("The type of parameter images is illegal.") def _calculate_score(self, prompt: str, image: torch.Tensor) -> torch.Tensor: """Calculate the score for a single image and prompt. Args: prompt (str): The prompt text. image (torch.Tensor): The processed image tensor. Returns: torch.Tensor: The reward score. """ text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device) image_embeds = self.blip.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device) text_output = self.blip.text_encoder( text_input.input_ids, attention_mask=text_input.attention_mask, encoder_hidden_states=image_embeds, encoder_attention_mask=image_atts, return_dict=True, ) txt_features = text_output.last_hidden_state[:, 0, :].float() rewards = self.mlp(txt_features) rewards = (rewards - self.mean) / self.std return rewards def inference_rank(self, prompt: str, generations_list: List[Union[str, Image.Image]]) -> tuple: """Rank the images based on the prompt. Args: prompt (str): The prompt text. generations_list (List[Union[str, Image.Image]]): List of image paths or PIL images. Returns: tuple: (indices, rewards) where indices are the ranks and rewards are the scores. """ text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device) txt_set = [] for generation in generations_list: if isinstance(generation, str): pil_image = Image.open(generation) elif isinstance(generation, Image.Image): pil_image = generation else: raise TypeError("The type of parameter generations_list is illegal.") image = self.preprocess(pil_image).unsqueeze(0).to(self.device) image_embeds = self.blip.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device) text_output = self.blip.text_encoder( text_input.input_ids, attention_mask=text_input.attention_mask, encoder_hidden_states=image_embeds, encoder_attention_mask=image_atts, return_dict=True, ) txt_set.append(text_output.last_hidden_state[:, 0, :]) txt_features = torch.cat(txt_set, 0).float() rewards = self.mlp(txt_features) rewards = (rewards - self.mean) / self.std rewards = torch.squeeze(rewards) _, rank = torch.sort(rewards, dim=0, descending=True) _, indices = torch.sort(rank, dim=0) indices = indices + 1 return indices.detach().cpu().numpy().tolist(), rewards.detach().cpu().numpy().tolist() class ImageRewardScore(torch.nn.Module): def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS): super().__init__() self.device = device if isinstance(device, torch.device) else torch.device(device) model_path = path.get("imagereward") med_config = path.get("med_config") state_dict = load_file(model_path) self.model = ImageReward(device=self.device, med_config=med_config, bert_model_path=path.get("bert_model_path")).to(self.device) self.model.load_state_dict(state_dict, strict=False) self.model.eval() @torch.no_grad() def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: """Score the images based on the prompt. Args: images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). prompt (str): The prompt text. Returns: List[float]: List of scores for the images. """ return self.model.score(images, prompt) ================================================ FILE: diffsynth/extensions/ImageQualityMetric/mps.py ================================================ import numpy as np import torch from PIL import Image from io import BytesIO from tqdm.auto import tqdm from transformers import CLIPFeatureExtractor, CLIPImageProcessor from transformers import CLIPConfig from dataclasses import dataclass from transformers import CLIPModel as HFCLIPModel from safetensors.torch import load_file from torch import nn, einsum from .trainer.models.base_model import BaseModelConfig from transformers import CLIPConfig from transformers import AutoProcessor, AutoModel, AutoTokenizer from typing import Any, Optional, Tuple, Union, List import torch from .trainer.models.cross_modeling import Cross_model from .trainer.models import clip_model import torch.nn.functional as F import gc import json from .config import MODEL_PATHS class MPScore(torch.nn.Module): def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS, condition: str = 'overall'): super().__init__() """Initialize the MPSModel with a processor, tokenizer, and model. Args: device (Union[str, torch.device]): The device to load the model on. """ self.device = device processor_name_or_path = path.get("clip") self.image_processor = CLIPImageProcessor.from_pretrained(processor_name_or_path) self.tokenizer = AutoTokenizer.from_pretrained(processor_name_or_path, trust_remote_code=True) self.model = clip_model.CLIPModel(processor_name_or_path, config_file=True) state_dict = load_file(path.get("mps")) self.model.load_state_dict(state_dict, strict=False) self.model.to(device) self.condition = condition def _calculate_score(self, image: torch.Tensor, prompt: str) -> float: """Calculate the reward score for a single image and prompt. Args: image (torch.Tensor): The processed image tensor. prompt (str): The prompt text. Returns: float: The reward score. """ def _tokenize(caption): input_ids = self.tokenizer( caption, max_length=self.tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" ).input_ids return input_ids text_input = _tokenize(prompt).to(self.device) if self.condition == 'overall': condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry, shape, face, hair, hands, limbs, structure, instance, texture, quantity, attributes, position, number, location, word, things' elif self.condition == 'aesthetics': condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry' elif self.condition == 'quality': condition_prompt = 'shape, face, hair, hands, limbs, structure, instance, texture' elif self.condition == 'semantic': condition_prompt = 'quantity, attributes, position, number, location' else: raise ValueError( f"Unsupported condition: {self.condition}. Choose 'overall', 'aesthetics', 'quality', or 'semantic'.") condition_batch = _tokenize(condition_prompt).repeat(text_input.shape[0], 1).to(self.device) with torch.no_grad(): text_f, text_features = self.model.model.get_text_features(text_input) image_f = self.model.model.get_image_features(image.half()) condition_f, _ = self.model.model.get_text_features(condition_batch) sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f) sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0] sim_text_condition = sim_text_condition / sim_text_condition.max() mask = torch.where(sim_text_condition > 0.3, 0, float('-inf')) mask = mask.repeat(1, image_f.shape[1], 1) image_features = self.model.cross_model(image_f, text_f, mask.half())[:, 0, :] image_features = image_features / image_features.norm(dim=-1, keepdim=True) text_features = text_features / text_features.norm(dim=-1, keepdim=True) image_score = self.model.logit_scale.exp() * text_features @ image_features.T return image_score[0].cpu().numpy().item() @torch.no_grad() def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: """Score the images based on the prompt. Args: images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). prompt (str): The prompt text. Returns: List[float]: List of reward scores for the images. """ if isinstance(images, (str, Image.Image)): # Single image if isinstance(images, str): image = self.image_processor(Image.open(images), return_tensors="pt")["pixel_values"].to(self.device) else: image = self.image_processor(images, return_tensors="pt")["pixel_values"].to(self.device) return [self._calculate_score(image, prompt)] elif isinstance(images, list): # Multiple images scores = [] for one_images in images: if isinstance(one_images, str): image = self.image_processor(Image.open(one_images), return_tensors="pt")["pixel_values"].to(self.device) elif isinstance(one_images, Image.Image): image = self.image_processor(one_images, return_tensors="pt")["pixel_values"].to(self.device) else: raise TypeError("The type of parameter images is illegal.") scores.append(self._calculate_score(image, prompt)) return scores else: raise TypeError("The type of parameter images is illegal.") ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py ================================================ from .coca_model import CoCa from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_loss from .factory import list_models, add_model_config, get_model_config, load_checkpoint from .loss import ClipLoss, DistillClipLoss, CoCaLoss from .model import CLIP, CustomTextCLIP, CLIPTextCfg, CLIPVisionCfg, \ convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype from .openai import load_openai_model, list_openai_models from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model, \ get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained from .push_to_hf_hub import push_pretrained_to_hf_hub, push_to_hf_hub from .tokenizer import SimpleTokenizer from .transform import image_transform, AugmentationCfg from .utils import freeze_batch_norm_2d ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py ================================================ from typing import Optional import torch from torch import nn from torch.nn import functional as F import numpy as np from dataclasses import dataclass from .transformer import ( LayerNormFp32, LayerNorm, QuickGELU, MultimodalTransformer, ) from .model import CLIPTextCfg, CLIPVisionCfg, _build_vision_tower, _build_text_tower try: from transformers import ( BeamSearchScorer, LogitsProcessorList, TopPLogitsWarper, TopKLogitsWarper, RepetitionPenaltyLogitsProcessor, MinLengthLogitsProcessor, MaxLengthCriteria, StoppingCriteriaList ) GENERATION_TYPES = { "top_k": TopKLogitsWarper, "top_p": TopPLogitsWarper, "beam_search": "beam_search" } _has_transformers = True except ImportError as e: GENERATION_TYPES = { "top_k": None, "top_p": None, "beam_search": "beam_search" } _has_transformers = False @dataclass class MultimodalCfg(CLIPTextCfg): mlp_ratio: int = 4 dim_head: int = 64 heads: int = 8 n_queries: int = 256 attn_pooler_heads: int = 8 def _build_text_decoder_tower( embed_dim, multimodal_cfg, quick_gelu: bool = False, cast_dtype: Optional[torch.dtype] = None, ): multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg act_layer = QuickGELU if quick_gelu else nn.GELU norm_layer = ( LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm ) decoder = MultimodalTransformer( context_length=multimodal_cfg.context_length, width=multimodal_cfg.width, heads=multimodal_cfg.heads, layers=multimodal_cfg.layers, ls_init_value=multimodal_cfg.ls_init_value, output_dim=embed_dim, act_layer=act_layer, norm_layer=norm_layer, ) return decoder class CoCa(nn.Module): def __init__( self, embed_dim, multimodal_cfg: MultimodalCfg, text_cfg: CLIPTextCfg, vision_cfg: CLIPVisionCfg, quick_gelu: bool = False, cast_dtype: Optional[torch.dtype] = None, pad_id: int = 0, ): super().__init__() multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg text_cfg = CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg vision_cfg = CLIPVisionCfg(**vision_cfg) if isinstance(vision_cfg, dict) else vision_cfg self.text = _build_text_tower( embed_dim=embed_dim, text_cfg=text_cfg, quick_gelu=quick_gelu, cast_dtype=cast_dtype, ) vocab_size = ( text_cfg.vocab_size # for hf models if hasattr(text_cfg, "hf_model_name") and text_cfg.hf_model_name is not None else text_cfg.vocab_size ) self.visual = _build_vision_tower( embed_dim=embed_dim, vision_cfg=vision_cfg, quick_gelu=quick_gelu, cast_dtype=cast_dtype, ) self.text_decoder = _build_text_decoder_tower( vocab_size, multimodal_cfg=multimodal_cfg, quick_gelu=quick_gelu, cast_dtype=cast_dtype, ) self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) self.pad_id = pad_id @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.visual.set_grad_checkpointing(enable) self.text.set_grad_checkpointing(enable) self.text_decoder.set_grad_checkpointing(enable) def _encode_image(self, images, normalize=True): image_latent, tokens_embs = self.visual(images) image_latent = F.normalize(image_latent, dim=-1) if normalize else image_latent return image_latent, tokens_embs def _encode_text(self, text, normalize=True, embed_cls=True): text = text[:, :-1] if embed_cls else text # make space for CLS token text_latent, token_emb = self.text(text) text_latent = F.normalize(text_latent, dim=-1) if normalize else text_latent return text_latent, token_emb def encode_image(self, images, normalize=True): image_latent, _ = self._encode_image(images, normalize=normalize) return image_latent def encode_text(self, text, normalize=True, embed_cls=True): text_latent, _ = self._encode_text(text, normalize=normalize, embed_cls=embed_cls) return text_latent def forward(self, image, text, embed_cls=True, image_latent=None, image_embs=None): text_latent, token_embs = self._encode_text(text, embed_cls=embed_cls) if image_latent is None or image_embs is None: image_latent, image_embs = self._encode_image(image) # TODO: add assertion to avoid bugs? labels = text[:, -token_embs.shape[1]:] logits = self.text_decoder(image_embs, token_embs) return { "image_features": image_latent, "text_features": text_latent, "logits": logits, "labels": labels, "logit_scale": self.logit_scale.exp() } def generate( self, image, text=None, seq_len=30, max_seq_len=77, temperature=1., generation_type="beam_search", top_p=0.1, # keep tokens in the 1 - top_p quantile top_k=1, # keeps the top_k most probable tokens pad_token_id=None, eos_token_id=None, sot_token_id=None, num_beams=6, num_beam_groups=3, min_seq_len=5, stopping_criteria=None, repetition_penalty=1.0, fixed_output_length=False # if True output.shape == (batch_size, seq_len) ): # taking many ideas and components from HuggingFace GenerationMixin # https://huggingface.co/docs/transformers/main/en/main_classes/text_generation assert _has_transformers, "Please install transformers for generate functionality. `pip install transformers`." assert seq_len > min_seq_len, "seq_len must be larger than min_seq_len" with torch.no_grad(): sot_token_id = 49406 if sot_token_id is None else sot_token_id eos_token_id = 49407 if eos_token_id is None else eos_token_id pad_token_id = self.pad_id if pad_token_id is None else pad_token_id logit_processor = LogitsProcessorList( [ MinLengthLogitsProcessor(min_seq_len, eos_token_id), RepetitionPenaltyLogitsProcessor(repetition_penalty), ] ) if stopping_criteria is None: stopping_criteria = [MaxLengthCriteria(max_length=seq_len)] stopping_criteria = StoppingCriteriaList( stopping_criteria ) device = image.device if generation_type == "beam_search": output = self._generate_beamsearch( image_inputs = image, pad_token_id=pad_token_id, eos_token_id=eos_token_id, sot_token_id=sot_token_id, num_beams=num_beams, num_beam_groups=num_beam_groups, min_seq_len=min_seq_len, stopping_criteria=stopping_criteria, logit_processor=logit_processor, ) if fixed_output_length and output.shape[1] < seq_len: return torch.cat( (output, torch.ones(output.shape[0], seq_len-output.shape[1], device=device, dtype=output.dtype) * self.pad_id), dim=1 ) return output elif generation_type == "top_p": logit_warper = GENERATION_TYPES[generation_type](top_p) elif generation_type == "top_k": logit_warper = GENERATION_TYPES[generation_type](top_k) else: raise ValueError( f"generation_type has to be one of " f"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}." ) image_latent, image_embs = self._encode_image(image) if text is None: text = torch.ones((image.shape[0], 1), device=device, dtype=torch.long) * sot_token_id was_training = self.training num_dims = len(text.shape) if num_dims == 1: text = text[None, :] cur_len = text.shape[1] self.eval() out = text while True: x = out[:, -max_seq_len:] cur_len = x.shape[1] logits = self(image, x, image_latent=image_latent, image_embs=image_embs, embed_cls=False)["logits"][:, -1] mask = (out[:, -1] == eos_token_id) | (out[:, -1] == pad_token_id) sample = torch.ones((out.shape[0], 1), device=device, dtype=torch.long) * pad_token_id if mask.all(): if not fixed_output_length: break else: logits = logits[~mask, :] filtered_logits = logit_processor(x[~mask, :], logits) filtered_logits = logit_warper(x[~mask, :], filtered_logits) probs = F.softmax(filtered_logits / temperature, dim=-1) if (cur_len + 1 == seq_len): sample[~mask, :] = torch.ones((sum(~mask), 1), device=device, dtype=torch.long) * eos_token_id else: sample[~mask, :] = torch.multinomial(probs, 1) out = torch.cat((out, sample), dim=-1) cur_len += 1 if stopping_criteria(out, None): break if num_dims == 1: out = out.squeeze(0) self.train(was_training) return out def _generate_beamsearch( self, image_inputs, pad_token_id=None, eos_token_id=None, sot_token_id=None, num_beams=6, num_beam_groups=3, min_seq_len=5, stopping_criteria=None, logit_processor=None, logit_warper=None, ): device = image_inputs.device batch_size = image_inputs.shape[0] image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0) image_latent, image_embs = self._encode_image(image_inputs) input_ids = torch.ones((batch_size * num_beams, 1), device=device, dtype=torch.long) input_ids = input_ids * sot_token_id beam_scorer = BeamSearchScorer( batch_size=batch_size, num_beams=num_beams, device=device, num_beam_groups=num_beam_groups, ) # instantiate logits processors logits_processor = ( LogitsProcessorList([MinLengthLogitsProcessor(min_seq_len, eos_token_id=eos_token_id)]) if logit_processor is None else logit_processor ) batch_size = len(beam_scorer._beam_hyps) num_beams = beam_scorer.num_beams num_beam_groups = beam_scorer.num_beam_groups num_sub_beams = num_beams // num_beam_groups batch_beam_size, cur_len = input_ids.shape beam_indices = None if num_beams * batch_size != batch_beam_size: raise ValueError( f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}." ) beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device) # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in # the same group don't produce same tokens everytime. beam_scores[:, ::num_sub_beams] = 0 beam_scores = beam_scores.view((batch_size * num_beams,)) while True: # predicted tokens in cur_len step current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device) # indices which will form the beams in the next time step reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device) # do one decoder step on all beams of all sentences in batch model_inputs = prepare_inputs_for_generation(input_ids=input_ids, image_inputs=image_inputs) outputs = self( model_inputs['images'], model_inputs['text'], embed_cls=False, image_latent=image_latent, image_embs=image_embs ) for beam_group_idx in range(num_beam_groups): group_start_idx = beam_group_idx * num_sub_beams group_end_idx = min(group_start_idx + num_sub_beams, num_beams) group_size = group_end_idx - group_start_idx # indices of beams of current group among all sentences in batch batch_group_indices = [] for batch_idx in range(batch_size): batch_group_indices.extend( [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)] ) group_input_ids = input_ids[batch_group_indices] # select outputs of beams of currentg group only next_token_logits = outputs['logits'][batch_group_indices, -1, :] vocab_size = next_token_logits.shape[-1] next_token_scores_processed = logits_processor( group_input_ids, next_token_logits, current_tokens=current_tokens, beam_group_idx=beam_group_idx ) next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1) next_token_scores = next_token_scores.expand_as(next_token_scores_processed) # reshape for beam search next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size) next_token_scores, next_tokens = torch.topk( next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True ) next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor") next_tokens = next_tokens % vocab_size # stateless process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None beam_outputs = beam_scorer.process( group_input_ids, next_token_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id, beam_indices=process_beam_indices, ) beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"] beam_next_tokens = beam_outputs["next_beam_tokens"] beam_idx = beam_outputs["next_beam_indices"] input_ids[batch_group_indices] = group_input_ids[beam_idx] group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1) current_tokens[batch_group_indices] = group_input_ids[:, -1] # (beam_idx // group_size) -> batch_idx # (beam_idx % group_size) -> offset of idx inside the group reordering_indices[batch_group_indices] = ( num_beams * torch.div(beam_idx, group_size, rounding_mode="floor") + group_start_idx + (beam_idx % group_size) ) input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1) # increase cur_len cur_len = cur_len + 1 if beam_scorer.is_done or stopping_criteria(input_ids, None): break final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None sequence_outputs = beam_scorer.finalize( input_ids, beam_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id, max_length=stopping_criteria.max_length, beam_indices=final_beam_indices, ) return sequence_outputs['sequences'] def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **kwargs): if past: input_ids = input_ids[:, -1].unsqueeze(-1) attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) else: position_ids = None return { "text": input_ids, "images": image_inputs, "past_key_values": past, "position_ids": position_ids, "attention_mask": attention_mask, } ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/constants.py ================================================ OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/factory.py ================================================ import json import logging import os import pathlib import re from copy import deepcopy from pathlib import Path # from turtle import forward from typing import Any, Dict, Optional, Tuple, Union import torch from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\ resize_pos_embed, get_cast_dtype from .coca_model import CoCa from .loss import ClipLoss, DistillClipLoss, CoCaLoss from .openai import load_openai_model from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model, download_pretrained_from_hf from .transform import image_transform, AugmentationCfg from .tokenizer import HFTokenizer, SimpleTokenizer HF_HUB_PREFIX = 'hf-hub:' _MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] _MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs def _natural_key(string_): return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] def _rescan_model_configs(): global _MODEL_CONFIGS config_ext = ('.json',) config_files = [] for config_path in _MODEL_CONFIG_PATHS: if config_path.is_file() and config_path.suffix in config_ext: config_files.append(config_path) elif config_path.is_dir(): for ext in config_ext: config_files.extend(config_path.glob(f'*{ext}')) for cf in config_files: with open(cf, 'r') as f: model_cfg = json.load(f) if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')): _MODEL_CONFIGS[cf.stem] = model_cfg _MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))} _rescan_model_configs() # initial populate of model config registry def list_models(): """ enumerate available model architectures based on config files """ return list(_MODEL_CONFIGS.keys()) def add_model_config(path): """ add model config path or file and update registry """ if not isinstance(path, Path): path = Path(path) _MODEL_CONFIG_PATHS.append(path) _rescan_model_configs() def get_model_config(model_name): if model_name in _MODEL_CONFIGS: return deepcopy(_MODEL_CONFIGS[model_name]) else: return None def get_tokenizer(model_name, open_clip_bpe_path=None): if model_name.startswith(HF_HUB_PREFIX): tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):]) else: config = get_model_config(model_name) tokenizer = HFTokenizer( config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else SimpleTokenizer(open_clip_bpe_path) return tokenizer def load_state_dict(checkpoint_path: str, map_location='cpu'): checkpoint = torch.load(checkpoint_path, map_location=map_location) if isinstance(checkpoint, dict) and 'state_dict' in checkpoint: state_dict = checkpoint['state_dict'] else: state_dict = checkpoint if next(iter(state_dict.items()))[0].startswith('module'): state_dict = {k[7:]: v for k, v in state_dict.items()} return state_dict def load_checkpoint(model, checkpoint_path, strict=True): state_dict = load_state_dict(checkpoint_path) # detect old format and make compatible with new format if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'): state_dict = convert_to_custom_text_state_dict(state_dict) resize_pos_embed(state_dict, model) incompatible_keys = model.load_state_dict(state_dict, strict=strict) return incompatible_keys def create_model( model_name: str, pretrained: Optional[str] = None, precision: str = 'fp32', device: Union[str, torch.device] = 'cpu', jit: bool = False, force_quick_gelu: bool = False, force_custom_text: bool = False, force_patch_dropout: Optional[float] = None, force_image_size: Optional[Union[int, Tuple[int, int]]] = None, pretrained_image: bool = False, pretrained_hf: bool = True, cache_dir: Optional[str] = None, output_dict: Optional[bool] = None, require_pretrained: bool = False, ): has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX) if has_hf_hub_prefix: model_id = model_name[len(HF_HUB_PREFIX):] checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir) config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir) with open(config_path, 'r', encoding='utf-8') as f: config = json.load(f) pretrained_cfg = config['preprocess_cfg'] model_cfg = config['model_cfg'] else: model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names checkpoint_path = None pretrained_cfg = {} model_cfg = None if isinstance(device, str): device = torch.device(device) if pretrained and pretrained.lower() == 'openai': logging.info(f'Loading pretrained {model_name} from OpenAI.') model = load_openai_model( model_name, precision=precision, device=device, jit=jit, cache_dir=cache_dir, ) # to always output dict even if it is clip if output_dict and hasattr(model, "output_dict"): model.output_dict = True else: model_cfg = model_cfg or get_model_config(model_name) if model_cfg is not None: logging.info(f'Loaded {model_name} model config.') else: logging.error(f'Model config for {model_name} not found; available models {list_models()}.') raise RuntimeError(f'Model config for {model_name} not found.') if force_quick_gelu: # override for use of QuickGELU on non-OpenAI transformer models model_cfg["quick_gelu"] = True if force_patch_dropout is not None: # override the default patch dropout value model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout if force_image_size is not None: # override model config's image size model_cfg["vision_cfg"]["image_size"] = force_image_size if pretrained_image: if 'timm_model_name' in model_cfg.get('vision_cfg', {}): # pretrained weight loading for timm models set via vision_cfg model_cfg['vision_cfg']['timm_model_pretrained'] = True else: assert False, 'pretrained image towers currently only supported for timm models' cast_dtype = get_cast_dtype(precision) is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {}) custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model if custom_text: if is_hf_model: model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf if "coca" in model_name: model = CoCa(**model_cfg, cast_dtype=cast_dtype) else: model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype) else: model = CLIP(**model_cfg, cast_dtype=cast_dtype) pretrained_loaded = False if pretrained: checkpoint_path = '' pretrained_cfg = get_pretrained_cfg(model_name, pretrained) if pretrained_cfg: checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir) elif os.path.exists(pretrained): checkpoint_path = pretrained if checkpoint_path: logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') load_checkpoint(model, checkpoint_path) else: error_str = ( f'Pretrained weights ({pretrained}) not found for model {model_name}.' f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.') logging.warning(error_str) raise RuntimeError(error_str) pretrained_loaded = True elif has_hf_hub_prefix: logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') load_checkpoint(model, checkpoint_path) pretrained_loaded = True if require_pretrained and not pretrained_loaded: # callers of create_model_from_pretrained always expect pretrained weights raise RuntimeError( f'Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded.') model.to(device=device) if precision in ("fp16", "bf16"): convert_weights_to_lp(model, dtype=torch.bfloat16 if precision == 'bf16' else torch.float16) # set image / mean metadata from pretrained_cfg if available, or use default model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD # to always output dict even if it is clip if output_dict and hasattr(model, "output_dict"): model.output_dict = True if jit: model = torch.jit.script(model) return model def create_loss(args): if args.distill: return DistillClipLoss( local_loss=args.local_loss, gather_with_grad=args.gather_with_grad, cache_labels=True, rank=args.rank, world_size=args.world_size, use_horovod=args.horovod, ) elif "coca" in args.model.lower(): return CoCaLoss( caption_loss_weight=args.coca_caption_loss_weight, clip_loss_weight=args.coca_contrastive_loss_weight, local_loss=args.local_loss, gather_with_grad=args.gather_with_grad, cache_labels=True, rank=args.rank, world_size=args.world_size, use_horovod=args.horovod, ) return ClipLoss( local_loss=args.local_loss, gather_with_grad=args.gather_with_grad, cache_labels=True, rank=args.rank, world_size=args.world_size, use_horovod=args.horovod, ) class MLP(torch.nn.Module): def __init__(self, input_size): super().__init__() self.input_size = input_size self.layers = torch.nn.Sequential( torch.nn.Linear(self.input_size, 1024), torch.nn.Dropout(0.2), torch.nn.Linear(1024, 128), torch.nn.Dropout(0.2), torch.nn.Linear(128, 64), torch.nn.Dropout(0.1), torch.nn.Linear(64, 16), torch.nn.Linear(16, 1) ) def forward(self, x): return self.layers(x) # class semantic_head(torch.nn.Module): # def __init__(self, input_size): # super().__init__() # self.input_size = input_size # for ViT-L-14 is 1024 # self.seg_head = torch.nn.Sequential( # torch.nn.Linear(input_size, 128), # torch.nn.Dropout(0.2), # torch.nn.Linear(128, 64), # torch.nn.Dropout(0.1), # torch.nn.Linear(64, 16), # torch.nn.Linear(16, 1), # ) # self.sigmoid = torch.nn.Sigmoid() # def forward(self, x): # return self.sigmoid(self.seg_head(x)) def create_model_and_transforms( model_name: str, pretrained: Optional[str] = None, precision: str = 'fp32', device: Union[str, torch.device] = 'cpu', jit: bool = False, force_quick_gelu: bool = False, force_custom_text: bool = False, force_patch_dropout: Optional[float] = None, force_image_size: Optional[Union[int, Tuple[int, int]]] = None, pretrained_image: bool = False, pretrained_hf: bool = True, image_mean: Optional[Tuple[float, ...]] = None, image_std: Optional[Tuple[float, ...]] = None, aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, cache_dir: Optional[str] = None, light_augmentation = False, output_dict: Optional[bool] = None, with_score_predictor: bool = False, with_region_predictor: bool = False ): model = create_model( model_name, pretrained, precision=precision, device=device, jit=jit, force_quick_gelu=force_quick_gelu, force_custom_text=force_custom_text, force_patch_dropout=force_patch_dropout, force_image_size=force_image_size, pretrained_image=pretrained_image, pretrained_hf=pretrained_hf, cache_dir=cache_dir, output_dict=output_dict, ) image_mean = image_mean or getattr(model.visual, 'image_mean', None) image_std = image_std or getattr(model.visual, 'image_std', None) if with_score_predictor: model.score_predictor = MLP(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype) if with_region_predictor: # model.region_predictor = semantic_head(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype) model.region_predictor = torch.nn.Linear(model.visual.proj.size(0), 1).to(device=device, dtype=model.visual.proj.dtype) # preprocess_train = image_transform_region( # model.visual.image_size, # is_train=True, # mean=image_mean, # std=image_std # ) # preprocess_val = image_transform_region( # model.visual.image_size, # is_train=False, # mean=image_mean, # std=image_std # ) if light_augmentation: preprocess_val = image_transform( model.visual.image_size, is_train=False, mean=image_mean, std=image_std, resize_longest_max=True, ) preprocess_train = preprocess_val else: preprocess_train = image_transform( model.visual.image_size, is_train=True, mean=image_mean, std=image_std ) preprocess_val = image_transform( model.visual.image_size, is_train=False, mean=image_mean, std=image_std ) return model, preprocess_train, preprocess_val def create_model_from_pretrained( model_name: str, pretrained: Optional[str] = None, precision: str = 'fp32', device: Union[str, torch.device] = 'cpu', jit: bool = False, force_quick_gelu: bool = False, force_custom_text: bool = False, force_image_size: Optional[Union[int, Tuple[int, int]]] = None, return_transform: bool = True, image_mean: Optional[Tuple[float, ...]] = None, image_std: Optional[Tuple[float, ...]] = None, cache_dir: Optional[str] = None, ): model = create_model( model_name, pretrained, precision=precision, device=device, jit=jit, force_quick_gelu=force_quick_gelu, force_custom_text=force_custom_text, force_image_size=force_image_size, cache_dir=cache_dir, require_pretrained=True, ) if not return_transform: return model image_mean = image_mean or getattr(model.visual, 'image_mean', None) image_std = image_std or getattr(model.visual, 'image_std', None) preprocess = image_transform( model.visual.image_size, is_train=False, mean=image_mean, std=image_std, ) return model, preprocess ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py ================================================ ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py ================================================ # HF architecture dict: arch_dict = { # https://huggingface.co/docs/transformers/model_doc/roberta#roberta "roberta": { "config_names": { "context_length": "max_position_embeddings", "vocab_size": "vocab_size", "width": "hidden_size", "heads": "num_attention_heads", "layers": "num_hidden_layers", "layer_attr": "layer", "token_embeddings_attr": "embeddings" }, "pooler": "mean_pooler", }, # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig "xlm-roberta": { "config_names": { "context_length": "max_position_embeddings", "vocab_size": "vocab_size", "width": "hidden_size", "heads": "num_attention_heads", "layers": "num_hidden_layers", "layer_attr": "layer", "token_embeddings_attr": "embeddings" }, "pooler": "mean_pooler", }, # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 "mt5": { "config_names": { # unlimited seqlen # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 "context_length": "", "vocab_size": "vocab_size", "width": "d_model", "heads": "num_heads", "layers": "num_layers", "layer_attr": "block", "token_embeddings_attr": "embed_tokens" }, "pooler": "mean_pooler", }, } ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py ================================================ """ huggingface model adapter Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. """ import re import torch import torch.nn as nn from torch import TensorType try: import transformers from transformers import AutoModel, AutoTokenizer, AutoConfig, PretrainedConfig from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \ BaseModelOutputWithPoolingAndCrossAttentions except ImportError as e: transformers = None class BaseModelOutput: pass class PretrainedConfig: pass from .hf_configs import arch_dict # utils def _camel2snake(s): return re.sub(r'(? torch.Tensor: # calculated ground-truth and cache if enabled if self.prev_num_logits != num_logits or device not in self.labels: labels = torch.arange(num_logits, device=device, dtype=torch.long) if self.world_size > 1 and self.local_loss: labels = labels + num_logits * self.rank if self.cache_labels: self.labels[device] = labels self.prev_num_logits = num_logits else: labels = self.labels[device] return labels def get_logits(self, image_features, text_features, logit_scale): if self.world_size > 1: all_image_features, all_text_features = gather_features( image_features, text_features, self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) if self.local_loss: logits_per_image = logit_scale * image_features @ all_text_features.T logits_per_text = logit_scale * text_features @ all_image_features.T else: logits_per_image = logit_scale * all_image_features @ all_text_features.T logits_per_text = logits_per_image.T else: logits_per_image = logit_scale * image_features @ text_features.T logits_per_text = logit_scale * text_features @ image_features.T return logits_per_image, logits_per_text def forward(self, image_features, text_features, logit_scale, output_dict=False): device = image_features.device logits_per_image, logits_per_text = self.get_logits(image_features, text_features, logit_scale) labels = self.get_ground_truth(device, logits_per_image.shape[0]) total_loss = ( F.cross_entropy(logits_per_image, labels) + F.cross_entropy(logits_per_text, labels) ) / 2 return total_loss class PreferenceLoss(nn.Module): def forward(self, logits_per_image, num_images, labels): paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))] paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-999) ce_loss = F.cross_entropy(paired_logits, labels) return ce_loss class HPSLoss(nn.Module): def forward(self, text_logits, labels): device = text_logits.device text_0_logits, text_1_logits = text_logits.chunk(2, dim=-1) label_0, label_1 = labels.chunk(2, dim=-1) index = torch.arange(text_0_logits.shape[0], device=device, dtype=torch.long) text_0_logits = text_0_logits[index, index] text_1_logits = text_1_logits[index, index] text_logits = torch.stack([text_0_logits, text_1_logits], dim=-1) text_0_labels = torch.zeros(text_logits.shape[0], device=device, dtype=torch.long) text_1_labels = text_0_labels + 1 text_0_loss = torch.nn.functional.cross_entropy(text_logits, text_0_labels, reduction="none") text_1_loss = torch.nn.functional.cross_entropy(text_logits, text_1_labels, reduction="none") text_loss = label_0 * text_0_loss + label_1 * text_1_loss # absolute_example_weight = 1 / num_per_prompt # denominator = absolute_example_weight.sum() # weight_per_example = absolute_example_weight / denominator # text_loss *= weight_per_example text_loss = text_loss.sum() return text_loss class RankingLoss(nn.Module): def forward(self, logits_per_image, num_images, labels, margin = 1.0): paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))] label_list = [label for label in labels.split(num_images.tolist())] # ranked_logits = [torch.index_select(paired_logits_list[i], 0, rank) for i, rank in enumerate(label_list)] paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-1) padded_labels = pad_sequence(label_list, batch_first=True, padding_value=10) # regulized_logits = torch.log(torch.sigmoid(paired_logits)) diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2) # diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2) # diff_label = torch.clamp(padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2), min=-1, max=1) diff_label = - (padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2)) mask = torch.triu(torch.ones(diff.shape[1], diff.shape[1]), diagonal=1).bool().detach() loss = torch.clamp(margin - torch.mul(diff[:, ~mask],diff_label[:,~mask]), min=0).mean() return loss class CoCaLoss(ClipLoss): def __init__( self, caption_loss_weight, clip_loss_weight, pad_id=0, # pad_token for open_clip custom tokenizer local_loss=False, gather_with_grad=False, cache_labels=False, rank=0, world_size=1, use_horovod=False, ): super().__init__( local_loss=local_loss, gather_with_grad=gather_with_grad, cache_labels=cache_labels, rank=rank, world_size=world_size, use_horovod=use_horovod ) self.clip_loss_weight = clip_loss_weight self.caption_loss_weight = caption_loss_weight self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id) def forward(self, image_features, text_features, logits, labels, logit_scale, output_dict=False): clip_loss = super().forward(image_features, text_features, logit_scale) clip_loss = self.clip_loss_weight * clip_loss caption_loss = self.caption_loss( logits.permute(0, 2, 1), labels, ) caption_loss = caption_loss * self.caption_loss_weight if output_dict: return {"contrastive_loss": clip_loss, "caption_loss": caption_loss} return clip_loss, caption_loss class DistillClipLoss(ClipLoss): def dist_loss(self, teacher_logits, student_logits): return -(teacher_logits.softmax(dim=1) * student_logits.log_softmax(dim=1)).sum(dim=1).mean(dim=0) def forward( self, image_features, text_features, logit_scale, dist_image_features, dist_text_features, dist_logit_scale, output_dict=False, ): logits_per_image, logits_per_text = \ self.get_logits(image_features, text_features, logit_scale) dist_logits_per_image, dist_logits_per_text = \ self.get_logits(dist_image_features, dist_text_features, dist_logit_scale) labels = self.get_ground_truth(image_features.device, logits_per_image.shape[0]) contrastive_loss = ( F.cross_entropy(logits_per_image, labels) + F.cross_entropy(logits_per_text, labels) ) / 2 distill_loss = ( self.dist_loss(dist_logits_per_image, logits_per_image) + self.dist_loss(dist_logits_per_text, logits_per_text) ) / 2 if output_dict: return {"contrastive_loss": contrastive_loss, "distill_loss": distill_loss} return contrastive_loss, distill_loss ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/model.py ================================================ """ CLIP Model Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. """ from dataclasses import dataclass import logging import math from typing import Optional, Tuple, Union import numpy as np import torch import torch.nn.functional as F from torch import nn from torch.utils.checkpoint import checkpoint from .hf_model import HFTextEncoder from .modified_resnet import ModifiedResNet from .timm_model import TimmModel from .transformer import LayerNormFp32, LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer from .utils import to_2tuple @dataclass class CLIPVisionCfg: layers: Union[Tuple[int, int, int, int], int] = 12 width: int = 768 head_width: int = 64 mlp_ratio: float = 4.0 patch_size: int = 16 image_size: Union[Tuple[int, int], int] = 224 ls_init_value: Optional[float] = None # layer scale initial value patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580) attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer n_queries: int = 256 # n_queries for attentional pooler attn_pooler_heads: int = 8 # n heads for attentional_pooling timm_model_name: str = None # a valid model name overrides layers, width, patch_size timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') timm_proj_bias: bool = False # enable bias final projection timm_drop: float = 0. # head dropout timm_drop_path: Optional[float] = None # backbone stochastic depth output_tokens: bool = False @dataclass class CLIPTextCfg: context_length: int = 77 vocab_size: int = 49408 width: int = 512 heads: int = 8 layers: int = 12 ls_init_value: Optional[float] = None # layer scale initial value hf_model_name: str = None hf_tokenizer_name: str = None hf_model_pretrained: bool = True proj: str = 'mlp' pooler_type: str = 'mean_pooler' embed_cls: bool = False pad_id: int = 0 output_tokens: bool = False def get_cast_dtype(precision: str): cast_dtype = None if precision == 'bf16': cast_dtype = torch.bfloat16 elif precision == 'fp16': cast_dtype = torch.float16 return cast_dtype def _build_vision_tower( embed_dim: int, vision_cfg: CLIPVisionCfg, quick_gelu: bool = False, cast_dtype: Optional[torch.dtype] = None ): if isinstance(vision_cfg, dict): vision_cfg = CLIPVisionCfg(**vision_cfg) # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more # memory efficient in recent PyTorch releases (>= 1.10). # NOTE: timm models always use native GELU regardless of quick_gelu flag. act_layer = QuickGELU if quick_gelu else nn.GELU if vision_cfg.timm_model_name: visual = TimmModel( vision_cfg.timm_model_name, pretrained=vision_cfg.timm_model_pretrained, pool=vision_cfg.timm_pool, proj=vision_cfg.timm_proj, proj_bias=vision_cfg.timm_proj_bias, drop=vision_cfg.timm_drop, drop_path=vision_cfg.timm_drop_path, embed_dim=embed_dim, image_size=vision_cfg.image_size, ) act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models elif isinstance(vision_cfg.layers, (tuple, list)): vision_heads = vision_cfg.width * 32 // vision_cfg.head_width visual = ModifiedResNet( layers=vision_cfg.layers, output_dim=embed_dim, heads=vision_heads, image_size=vision_cfg.image_size, width=vision_cfg.width, ) else: vision_heads = vision_cfg.width // vision_cfg.head_width norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm visual = VisionTransformer( image_size=vision_cfg.image_size, patch_size=vision_cfg.patch_size, width=vision_cfg.width, layers=vision_cfg.layers, heads=vision_heads, mlp_ratio=vision_cfg.mlp_ratio, ls_init_value=vision_cfg.ls_init_value, patch_dropout=vision_cfg.patch_dropout, input_patchnorm=vision_cfg.input_patchnorm, global_average_pool=vision_cfg.global_average_pool, attentional_pool=vision_cfg.attentional_pool, n_queries=vision_cfg.n_queries, attn_pooler_heads=vision_cfg.attn_pooler_heads, output_tokens=vision_cfg.output_tokens, output_dim=embed_dim, act_layer=act_layer, norm_layer=norm_layer, ) return visual def _build_text_tower( embed_dim: int, text_cfg: CLIPTextCfg, quick_gelu: bool = False, cast_dtype: Optional[torch.dtype] = None, ): if isinstance(text_cfg, dict): text_cfg = CLIPTextCfg(**text_cfg) if text_cfg.hf_model_name: text = HFTextEncoder( text_cfg.hf_model_name, output_dim=embed_dim, proj=text_cfg.proj, pooler_type=text_cfg.pooler_type, pretrained=text_cfg.hf_model_pretrained, output_tokens=text_cfg.output_tokens, ) else: act_layer = QuickGELU if quick_gelu else nn.GELU norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm text = TextTransformer( context_length=text_cfg.context_length, vocab_size=text_cfg.vocab_size, width=text_cfg.width, heads=text_cfg.heads, layers=text_cfg.layers, ls_init_value=text_cfg.ls_init_value, output_dim=embed_dim, embed_cls=text_cfg.embed_cls, output_tokens=text_cfg.output_tokens, pad_id=text_cfg.pad_id, act_layer=act_layer, norm_layer=norm_layer, ) return text class CLIP(nn.Module): output_dict: torch.jit.Final[bool] def __init__( self, embed_dim: int, vision_cfg: CLIPVisionCfg, text_cfg: CLIPTextCfg, quick_gelu: bool = False, cast_dtype: Optional[torch.dtype] = None, output_dict: bool = False, ): super().__init__() self.output_dict = output_dict self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) self.transformer = text.transformer self.vocab_size = text.vocab_size self.token_embedding = text.token_embedding self.positional_embedding = text.positional_embedding self.ln_final = text.ln_final self.text_projection = text.text_projection self.register_buffer('attn_mask', text.attn_mask, persistent=False) self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True): locked_layers = [] locked_layers.append(self.token_embedding) self.positional_embedding.requires_grad = False if unlocked_layers > 0: locked_layers.append(self.transformer.resblocks[:-unlocked_layers]) else: locked_layers.append(self.transformer) locked_layers.append(self.ln_final) self.text_projection.requires_grad = False # freeze layers for module in locked_layers: for n, p in module.named_parameters(): p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.visual.set_grad_checkpointing(enable) self.transformer.grad_checkpointing = enable def encode_image(self, image, normalize: bool = False): features = self.visual(image) return F.normalize(features, dim=-1) if normalize else features def encode_text(self, text, normalize: bool = False): cast_dtype = self.transformer.get_cast_dtype() x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] x = x + self.positional_embedding.to(cast_dtype) x = x.permute(1, 0, 2) # NLD -> LND x = self.transformer(x, attn_mask=self.attn_mask) x = x.permute(1, 0, 2) # LND -> NLD x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] # take features from the eot embedding (eot_token is the highest number in each sequence) x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection return F.normalize(x, dim=-1) if normalize else x def forward(self, image, text): image_features = self.encode_image(image, normalize=True) text_features = self.encode_text(text, normalize=True) if self.output_dict: return { "image_features": image_features, "text_features": text_features, "logit_scale": self.logit_scale.exp() } return image_features, text_features, self.logit_scale.exp() class CustomTextCLIP(nn.Module): output_dict: torch.jit.Final[bool] def __init__( self, embed_dim: int, vision_cfg: CLIPVisionCfg, text_cfg: CLIPTextCfg, quick_gelu: bool = False, cast_dtype: Optional[torch.dtype] = None, output_dict: bool = False, ): super().__init__() self.output_dict = output_dict self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True): self.text.lock(unlocked_layers, freeze_layer_norm) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.visual.set_grad_checkpointing(enable) self.text.set_grad_checkpointing(enable) def encode_image(self, image, normalize: bool = False): features = self.visual(image) return F.normalize(features, dim=-1) if normalize else features def encode_text(self, text, normalize: bool = False): features = self.text(text) return F.normalize(features, dim=-1) if normalize else features def forward(self, image, text): image_features = self.encode_image(image, normalize=True) text_features = self.encode_text(text, normalize=True) if self.output_dict: return { "image_features": image_features, "text_features": text_features, "logit_scale": self.logit_scale.exp() } return image_features, text_features, self.logit_scale.exp() def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): """Convert applicable model parameters to low-precision (bf16 or fp16)""" def _convert_weights(l): if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): l.weight.data = l.weight.data.to(dtype) if l.bias is not None: l.bias.data = l.bias.data.to(dtype) if isinstance(l, (nn.MultiheadAttention, Attention)): for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: tensor = getattr(l, attr) if tensor is not None: tensor.data = tensor.data.to(dtype) for name in ["text_projection", "proj"]: if hasattr(l, name): attr = getattr(l, name) if attr is not None: attr.data = attr.data.to(dtype) model.apply(_convert_weights) convert_weights_to_fp16 = convert_weights_to_lp # backwards compat # used to maintain checkpoint compatibility def convert_to_custom_text_state_dict(state_dict: dict): if 'text_projection' in state_dict: # old format state_dict, move text tower -> .text new_state_dict = {} for k, v in state_dict.items(): if any(k.startswith(p) for p in ( 'text_projection', 'positional_embedding', 'token_embedding', 'transformer', 'ln_final', )): k = 'text.' + k new_state_dict[k] = v return new_state_dict return state_dict def build_model_from_openai_state_dict( state_dict: dict, quick_gelu=True, cast_dtype=torch.float16, ): vit = "visual.proj" in state_dict if vit: vision_width = state_dict["visual.conv1.weight"].shape[0] vision_layers = len( [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) image_size = vision_patch_size * grid_size else: counts: list = [ len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] vision_layers = tuple(counts) vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) vision_patch_size = None assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] image_size = output_width * 32 embed_dim = state_dict["text_projection"].shape[1] context_length = state_dict["positional_embedding"].shape[0] vocab_size = state_dict["token_embedding.weight"].shape[0] transformer_width = state_dict["ln_final.weight"].shape[0] transformer_heads = transformer_width // 64 transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) vision_cfg = CLIPVisionCfg( layers=vision_layers, width=vision_width, patch_size=vision_patch_size, image_size=image_size, ) text_cfg = CLIPTextCfg( context_length=context_length, vocab_size=vocab_size, width=transformer_width, heads=transformer_heads, layers=transformer_layers, ) model = CLIP( embed_dim, vision_cfg=vision_cfg, text_cfg=text_cfg, quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU cast_dtype=cast_dtype, ) for key in ["input_resolution", "context_length", "vocab_size"]: state_dict.pop(key, None) convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16 model.load_state_dict(state_dict) return model.eval() def trace_model(model, batch_size=256, device=torch.device('cpu')): model.eval() image_size = model.visual.image_size example_images = torch.ones((batch_size, 3, image_size, image_size), device=device) example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device) model = torch.jit.trace_module( model, inputs=dict( forward=(example_images, example_text), encode_text=(example_text,), encode_image=(example_images,) )) model.visual.image_size = image_size return model def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', antialias: bool = True): # Rescale the grid of position embeddings when loading from state_dict old_pos_embed = state_dict.get('visual.positional_embedding', None) if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): return grid_size = to_2tuple(model.visual.grid_size) extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) new_seq_len = grid_size[0] * grid_size[1] + extra_tokens if new_seq_len == old_pos_embed.shape[0]: return if extra_tokens: pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] else: pos_emb_tok, pos_emb_img = None, old_pos_embed old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) pos_emb_img = F.interpolate( pos_emb_img, size=grid_size, mode=interpolation, antialias=antialias, align_corners=False, ) pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] if pos_emb_tok is not None: new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) else: new_pos_embed = pos_emb_img state_dict['visual.positional_embedding'] = new_pos_embed ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json ================================================ { "embed_dim": 1024, "vision_cfg": { "image_size": 224, "layers": 32, "width": 1280, "head_width": 80, "patch_size": 14 }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 1024, "heads": 16, "layers": 24 } } ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py ================================================ from collections import OrderedDict import torch from torch import nn from torch.nn import functional as F from .utils import freeze_batch_norm_2d class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1): super().__init__() # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.act1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.act2 = nn.ReLU(inplace=True) self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) self.bn3 = nn.BatchNorm2d(planes * self.expansion) self.act3 = nn.ReLU(inplace=True) self.downsample = None self.stride = stride if stride > 1 or inplanes != planes * Bottleneck.expansion: # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 self.downsample = nn.Sequential(OrderedDict([ ("-1", nn.AvgPool2d(stride)), ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), ("1", nn.BatchNorm2d(planes * self.expansion)) ])) def forward(self, x: torch.Tensor): identity = x out = self.act1(self.bn1(self.conv1(x))) out = self.act2(self.bn2(self.conv2(out))) out = self.avgpool(out) out = self.bn3(self.conv3(out)) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.act3(out) return out class AttentionPool2d(nn.Module): def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): super().__init__() self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) self.k_proj = nn.Linear(embed_dim, embed_dim) self.q_proj = nn.Linear(embed_dim, embed_dim) self.v_proj = nn.Linear(embed_dim, embed_dim) self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) self.num_heads = num_heads def forward(self, x): x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC x, _ = F.multi_head_attention_forward( query=x, key=x, value=x, embed_dim_to_check=x.shape[-1], num_heads=self.num_heads, q_proj_weight=self.q_proj.weight, k_proj_weight=self.k_proj.weight, v_proj_weight=self.v_proj.weight, in_proj_weight=None, in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), bias_k=None, bias_v=None, add_zero_attn=False, dropout_p=0., out_proj_weight=self.c_proj.weight, out_proj_bias=self.c_proj.bias, use_separate_proj_weight=True, training=self.training, need_weights=False ) return x[0] class ModifiedResNet(nn.Module): """ A ResNet class that is similar to torchvision's but contains the following changes: - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 - The final pooling layer is a QKV attention instead of an average pool """ def __init__(self, layers, output_dim, heads, image_size=224, width=64): super().__init__() self.output_dim = output_dim self.image_size = image_size # the 3-layer stem self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(width // 2) self.act1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(width // 2) self.act2 = nn.ReLU(inplace=True) self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) self.bn3 = nn.BatchNorm2d(width) self.act3 = nn.ReLU(inplace=True) self.avgpool = nn.AvgPool2d(2) # residual layers self._inplanes = width # this is a *mutable* variable used during construction self.layer1 = self._make_layer(width, layers[0]) self.layer2 = self._make_layer(width * 2, layers[1], stride=2) self.layer3 = self._make_layer(width * 4, layers[2], stride=2) self.layer4 = self._make_layer(width * 8, layers[3], stride=2) embed_dim = width * 32 # the ResNet feature dimension self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) self.init_parameters() def _make_layer(self, planes, blocks, stride=1): layers = [Bottleneck(self._inplanes, planes, stride)] self._inplanes = planes * Bottleneck.expansion for _ in range(1, blocks): layers.append(Bottleneck(self._inplanes, planes)) return nn.Sequential(*layers) def init_parameters(self): if self.attnpool is not None: std = self.attnpool.c_proj.in_features ** -0.5 nn.init.normal_(self.attnpool.q_proj.weight, std=std) nn.init.normal_(self.attnpool.k_proj.weight, std=std) nn.init.normal_(self.attnpool.v_proj.weight, std=std) nn.init.normal_(self.attnpool.c_proj.weight, std=std) for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: for name, param in resnet_block.named_parameters(): if name.endswith("bn3.weight"): nn.init.zeros_(param) def lock(self, unlocked_groups=0, freeze_bn_stats=False): assert unlocked_groups == 0, 'partial locking not currently supported for this model' for param in self.parameters(): param.requires_grad = False if freeze_bn_stats: freeze_batch_norm_2d(self) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): # FIXME support for non-transformer pass def stem(self, x): x = self.act1(self.bn1(self.conv1(x))) x = self.act2(self.bn2(self.conv2(x))) x = self.act3(self.bn3(self.conv3(x))) x = self.avgpool(x) return x def forward(self, x): x = self.stem(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.attnpool(x) return x ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/openai.py ================================================ """ OpenAI pretrained model functions Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. """ import os import warnings from typing import List, Optional, Union import torch from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url __all__ = ["list_openai_models", "load_openai_model"] def list_openai_models() -> List[str]: """Returns the names of available CLIP models""" return list_pretrained_models_by_tag('openai') def load_openai_model( name: str, precision: Optional[str] = None, device: Optional[Union[str, torch.device]] = None, jit: bool = True, cache_dir: Optional[str] = None, ): """Load a CLIP model Parameters ---------- name : str A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict precision: str Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. device : Union[str, torch.device] The device to put the loaded model jit : bool Whether to load the optimized JIT model (default) or more hackable non-JIT model. cache_dir : Optional[str] The directory to cache the downloaded model weights Returns ------- model : torch.nn.Module The CLIP model preprocess : Callable[[PIL.Image], torch.Tensor] A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input """ if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" if precision is None: precision = 'fp32' if device == 'cpu' else 'fp16' if get_pretrained_url(name, 'openai'): model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir) elif os.path.isfile(name): model_path = name else: raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") try: # loading JIT archive model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() state_dict = None except RuntimeError: # loading saved state dict if jit: warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") jit = False state_dict = torch.load(model_path, map_location="cpu") if not jit: # Build a non-jit model from the OpenAI jitted model state dict cast_dtype = get_cast_dtype(precision) try: model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype) except KeyError: sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use model = model.to(device) if precision.startswith('amp') or precision == 'fp32': model.float() elif precision == 'bf16': convert_weights_to_lp(model, dtype=torch.bfloat16) return model # patch the device names device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] def patch_device(module): try: graphs = [module.graph] if hasattr(module, "graph") else [] except RuntimeError: graphs = [] if hasattr(module, "forward1"): graphs.append(module.forward1.graph) for graph in graphs: for node in graph.findAllNodes("prim::Constant"): if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): node.copyAttributes(device_node) model.apply(patch_device) patch_device(model.encode_image) patch_device(model.encode_text) # patch dtype to float32 (typically for CPU) if precision == 'fp32': float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] float_node = float_input.node() def patch_float(module): try: graphs = [module.graph] if hasattr(module, "graph") else [] except RuntimeError: graphs = [] if hasattr(module, "forward1"): graphs.append(module.forward1.graph) for graph in graphs: for node in graph.findAllNodes("aten::to"): inputs = list(node.inputs()) for i in [1, 2]: # dtype can be the second or third argument to aten::to() if inputs[i].node()["value"] == 5: inputs[i].node().copyAttributes(float_node) model.apply(patch_float) patch_float(model.encode_image) patch_float(model.encode_text) model.float() # ensure image_size attr available at consistent location for both jit and non-jit model.visual.image_size = model.input_resolution.item() return model ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py ================================================ import hashlib import os import urllib import warnings from functools import partial from typing import Dict, Union from tqdm import tqdm from .version import __version__ try: from huggingface_hub import hf_hub_download hf_hub_download = partial(hf_hub_download, library_name="open_clip", library_version=__version__) _has_hf_hub = True except ImportError: hf_hub_download = None _has_hf_hub = False def _pcfg(url='', hf_hub='', mean=None, std=None): return dict( url=url, hf_hub=hf_hub, mean=mean, std=std, ) _RN50 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"), yfcc15m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"), cc12m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"), ) _RN50_quickgelu = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"), yfcc15m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"), cc12m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"), ) _RN101 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"), yfcc15m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"), ) _RN101_quickgelu = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"), yfcc15m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"), ) _RN50x4 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt"), ) _RN50x16 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt"), ) _RN50x64 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt"), ) _VITB32 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), laion2b_e16=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') ) _VITB32_quickgelu = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), ) _VITB16 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), # laion400m_32k=_pcfg( # url="", # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), # laion400m_64k=_pcfg( # url="", # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), ) _VITB16_PLUS_240 = dict( laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), ) _VITL14 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), laion2b_s32b_b82k=_pcfg( hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), ) _VITL14_336 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), ) _VITH14 = dict( laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), ) _VITg14 = dict( laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), ) _VITbigG14 = dict( laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), ) _robertaViTB32 = dict( laion2b_s12b_b32k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/'), ) _xlmRobertaBaseViTB32 = dict( laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/'), ) _xlmRobertaLargeFrozenViTH14 = dict( frozen_laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/'), ) _convnext_base = dict( laion400m_s13b_b51k=_pcfg(hf_hub='laion/CLIP-convnext_base-laion400M-s13B-b51K/'), ) _convnext_base_w = dict( laion2b_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K/'), laion2b_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg/'), laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K/'), ) _convnext_base_w_320 = dict( laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/'), laion_aesthetic_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg/'), ) _convnext_large_d = dict( laion2b_s26b_b102k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg/'), ) _convnext_large_d_320 = dict( laion2b_s29b_b131k_ft=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft/'), laion2b_s29b_b131k_ft_soup=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup/'), ) _convnext_xxlarge = dict( laion2b_s34b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg/'), laion2b_s34b_b82k_augreg_rewind=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind/'), laion2b_s34b_b82k_augreg_soup=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup/'), ) _coca_VITB32 = dict( laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-B-32-laion2B-s13B-b90k/'), mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-B-32-laion2B-s13B-b90k/') ) _coca_VITL14 = dict( laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-L-14-laion2B-s13B-b90k/'), mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-L-14-laion2B-s13B-b90k/') ) _PRETRAINED = { "RN50": _RN50, "RN50-quickgelu": _RN50_quickgelu, "RN101": _RN101, "RN101-quickgelu": _RN101_quickgelu, "RN50x4": _RN50x4, "RN50x16": _RN50x16, "RN50x64": _RN50x64, "ViT-B-32": _VITB32, "ViT-B-32-quickgelu": _VITB32_quickgelu, "ViT-B-16": _VITB16, "ViT-B-16-plus-240": _VITB16_PLUS_240, "ViT-L-14": _VITL14, "ViT-L-14-336": _VITL14_336, "ViT-H-14": _VITH14, "ViT-g-14": _VITg14, "ViT-bigG-14": _VITbigG14, "roberta-ViT-B-32": _robertaViTB32, "xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32, "xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14, "convnext_base": _convnext_base, "convnext_base_w": _convnext_base_w, "convnext_base_w_320": _convnext_base_w_320, "convnext_large_d": _convnext_large_d, "convnext_large_d_320": _convnext_large_d_320, "convnext_xxlarge": _convnext_xxlarge, "coca_ViT-B-32": _coca_VITB32, "coca_ViT-L-14": _coca_VITL14, } def _clean_tag(tag: str): # normalize pretrained tags return tag.lower().replace('-', '_') def list_pretrained(as_str: bool = False): """ returns list of pretrained models Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True """ return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] def list_pretrained_models_by_tag(tag: str): """ return all models having the specified pretrain tag """ models = [] tag = _clean_tag(tag) for k in _PRETRAINED.keys(): if tag in _PRETRAINED[k]: models.append(k) return models def list_pretrained_tags_by_model(model: str): """ return all pretrain tags for the specified model architecture """ tags = [] if model in _PRETRAINED: tags.extend(_PRETRAINED[model].keys()) return tags def is_pretrained_cfg(model: str, tag: str): if model not in _PRETRAINED: return False return _clean_tag(tag) in _PRETRAINED[model] def get_pretrained_cfg(model: str, tag: str): if model not in _PRETRAINED: return {} model_pretrained = _PRETRAINED[model] return model_pretrained.get(_clean_tag(tag), {}) def get_pretrained_url(model: str, tag: str): cfg = get_pretrained_cfg(model, _clean_tag(tag)) return cfg.get('url', '') def download_pretrained_from_url( url: str, cache_dir: Union[str, None] = None, ): if not cache_dir: cache_dir = os.path.expanduser("~/.cache/clip") os.makedirs(cache_dir, exist_ok=True) filename = os.path.basename(url) if 'openaipublic' in url: expected_sha256 = url.split("/")[-2] elif 'mlfoundations' in url: expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] else: expected_sha256 = '' download_target = os.path.join(cache_dir, filename) if os.path.exists(download_target) and not os.path.isfile(download_target): raise RuntimeError(f"{download_target} exists and is not a regular file") if os.path.isfile(download_target): if expected_sha256: if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): return download_target else: warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") else: return download_target with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: while True: buffer = source.read(8192) if not buffer: break output.write(buffer) loop.update(len(buffer)) if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") return download_target def has_hf_hub(necessary=False): if not _has_hf_hub and necessary: # if no HF Hub module installed, and it is necessary to continue, raise error raise RuntimeError( 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') return _has_hf_hub def download_pretrained_from_hf( model_id: str, filename: str = 'open_clip_pytorch_model.bin', revision=None, cache_dir: Union[str, None] = None, ): has_hf_hub(True) cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir) return cached_file def download_pretrained( cfg: Dict, force_hf_hub: bool = False, cache_dir: Union[str, None] = None, ): target = '' if not cfg: return target download_url = cfg.get('url', '') download_hf_hub = cfg.get('hf_hub', '') if download_hf_hub and force_hf_hub: # use HF hub even if url exists download_url = '' if download_url: target = download_pretrained_from_url(download_url, cache_dir=cache_dir) elif download_hf_hub: has_hf_hub(True) # we assume the hf_hub entries in pretrained config combine model_id + filename in # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. model_id, filename = os.path.split(download_hf_hub) if filename: target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir) else: target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) return target ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py ================================================ import argparse import json from pathlib import Path from tempfile import TemporaryDirectory from typing import Optional, Tuple import torch try: from huggingface_hub import ( create_repo, get_hf_file_metadata, hf_hub_download, hf_hub_url, repo_type_and_id_from_hf_id, upload_folder, ) from huggingface_hub.utils import EntryNotFoundError _has_hf_hub = True except ImportError: _has_hf_hub = False from .factory import create_model_from_pretrained, get_model_config, get_tokenizer from .tokenizer import HFTokenizer def save_config_for_hf( model, config_path: str, model_config: Optional[dict] ): preprocess_cfg = { 'mean': model.visual.image_mean, 'std': model.visual.image_std, } hf_config = { 'model_cfg': model_config, 'preprocess_cfg': preprocess_cfg, } with config_path.open('w') as f: json.dump(hf_config, f, indent=2) def save_for_hf( model, tokenizer: HFTokenizer, model_config: dict, save_directory: str, weights_filename='open_clip_pytorch_model.bin', config_filename='open_clip_config.json', ): save_directory = Path(save_directory) save_directory.mkdir(exist_ok=True, parents=True) weights_path = save_directory / weights_filename torch.save(model.state_dict(), weights_path) tokenizer.save_pretrained(save_directory) config_path = save_directory / config_filename save_config_for_hf(model, config_path, model_config=model_config) def push_to_hf_hub( model, tokenizer, model_config: Optional[dict], repo_id: str, commit_message: str = 'Add model', token: Optional[str] = None, revision: Optional[str] = None, private: bool = False, create_pr: bool = False, model_card: Optional[dict] = None, ): if not isinstance(tokenizer, HFTokenizer): # default CLIP tokenizers use https://huggingface.co/openai/clip-vit-large-patch14 tokenizer = HFTokenizer('openai/clip-vit-large-patch14') # Create repo if it doesn't exist yet repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True) # Infer complete repo_id from repo_url # Can be different from the input `repo_id` if repo_owner was implicit _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url) repo_id = f"{repo_owner}/{repo_name}" # Check if README file already exist in repo try: get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision)) has_readme = True except EntryNotFoundError: has_readme = False # Dump model and push to Hub with TemporaryDirectory() as tmpdir: # Save model weights and config. save_for_hf( model, tokenizer=tokenizer, model_config=model_config, save_directory=tmpdir, ) # Add readme if it does not exist if not has_readme: model_card = model_card or {} model_name = repo_id.split('/')[-1] readme_path = Path(tmpdir) / "README.md" readme_text = generate_readme(model_card, model_name) readme_path.write_text(readme_text) # Upload model and return return upload_folder( repo_id=repo_id, folder_path=tmpdir, revision=revision, create_pr=create_pr, commit_message=commit_message, ) def push_pretrained_to_hf_hub( model_name, pretrained: str, repo_id: str, image_mean: Optional[Tuple[float, ...]] = None, image_std: Optional[Tuple[float, ...]] = None, commit_message: str = 'Add model', token: Optional[str] = None, revision: Optional[str] = None, private: bool = False, create_pr: bool = False, model_card: Optional[dict] = None, ): model, preprocess_eval = create_model_from_pretrained( model_name, pretrained=pretrained, image_mean=image_mean, image_std=image_std, ) model_config = get_model_config(model_name) assert model_config tokenizer = get_tokenizer(model_name) push_to_hf_hub( model=model, tokenizer=tokenizer, model_config=model_config, repo_id=repo_id, commit_message=commit_message, token=token, revision=revision, private=private, create_pr=create_pr, model_card=model_card, ) def generate_readme(model_card: dict, model_name: str): readme_text = "---\n" readme_text += "tags:\n- zero-shot-image-classification\n- clip\n" readme_text += "library_tag: open_clip\n" readme_text += f"license: {model_card.get('license', 'mit')}\n" if 'details' in model_card and 'Dataset' in model_card['details']: readme_text += 'datasets:\n' readme_text += f"- {model_card['details']['Dataset'].lower()}\n" readme_text += "---\n" readme_text += f"# Model card for {model_name}\n" if 'description' in model_card: readme_text += f"\n{model_card['description']}\n" if 'details' in model_card: readme_text += f"\n## Model Details\n" for k, v in model_card['details'].items(): if isinstance(v, (list, tuple)): readme_text += f"- **{k}:**\n" for vi in v: readme_text += f" - {vi}\n" elif isinstance(v, dict): readme_text += f"- **{k}:**\n" for ki, vi in v.items(): readme_text += f" - {ki}: {vi}\n" else: readme_text += f"- **{k}:** {v}\n" if 'usage' in model_card: readme_text += f"\n## Model Usage\n" readme_text += model_card['usage'] readme_text += '\n' if 'comparison' in model_card: readme_text += f"\n## Model Comparison\n" readme_text += model_card['comparison'] readme_text += '\n' if 'citation' in model_card: readme_text += f"\n## Citation\n" if not isinstance(model_card['citation'], (list, tuple)): citations = [model_card['citation']] else: citations = model_card['citation'] for c in citations: readme_text += f"```bibtex\n{c}\n```\n" return readme_text if __name__ == "__main__": parser = argparse.ArgumentParser(description="Push to Hugging Face Hub") parser.add_argument( "--model", type=str, help="Name of the model to use.", ) parser.add_argument( "--pretrained", type=str, help="Use a pretrained CLIP model weights with the specified tag or file path.", ) parser.add_argument( "--repo-id", type=str, help="Destination HF Hub repo-id ie 'organization/model_id'.", ) parser.add_argument( '--image-mean', type=float, nargs='+', default=None, metavar='MEAN', help='Override default image mean value of dataset') parser.add_argument( '--image-std', type=float, nargs='+', default=None, metavar='STD', help='Override default image std deviation of of dataset') args = parser.parse_args() print(f'Saving model {args.model} with pretrained weights {args.pretrained} to Hugging Face Hub at {args.repo_id}') # FIXME add support to pass model_card json / template from file via cmd line push_pretrained_to_hf_hub( args.model, args.pretrained, args.repo_id, image_mean=args.image_mean, # override image mean/std if trained w/ non defaults image_std=args.image_std, ) print(f'{args.model} saved.') ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py ================================================ """ timm model adapter Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. """ import logging from collections import OrderedDict import torch import torch.nn as nn try: import timm from timm.models.layers import Mlp, to_2tuple try: # old timm imports < 0.8.1 from timm.models.layers.attention_pool2d import RotAttentionPool2d from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d except ImportError: # new timm imports >= 0.8.1 from timm.layers import RotAttentionPool2d from timm.layers import AttentionPool2d as AbsAttentionPool2d except ImportError: timm = None from .utils import freeze_batch_norm_2d class TimmModel(nn.Module): """ timm model adapter # FIXME this adapter is a work in progress, may change in ways that break weight compat """ def __init__( self, model_name, embed_dim, image_size=224, pool='avg', proj='linear', proj_bias=False, drop=0., drop_path=None, pretrained=False, ): super().__init__() if timm is None: raise RuntimeError("Please `pip install timm` to use timm models.") self.image_size = to_2tuple(image_size) timm_kwargs = {} if drop_path is not None: timm_kwargs['drop_path_rate'] = drop_path self.trunk = timm.create_model(model_name, pretrained=pretrained, **timm_kwargs) feat_size = self.trunk.default_cfg.get('pool_size', None) feature_ndim = 1 if not feat_size else 2 if pool in ('abs_attn', 'rot_attn'): assert feature_ndim == 2 # if attn pooling used, remove both classifier and default pool self.trunk.reset_classifier(0, global_pool='') else: # reset global pool if pool config set, otherwise leave as network default reset_kwargs = dict(global_pool=pool) if pool else {} self.trunk.reset_classifier(0, **reset_kwargs) prev_chs = self.trunk.num_features head_layers = OrderedDict() if pool == 'abs_attn': head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) prev_chs = embed_dim elif pool == 'rot_attn': head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) prev_chs = embed_dim else: assert proj, 'projection layer needed if non-attention pooling is used.' # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used if proj == 'linear': head_layers['drop'] = nn.Dropout(drop) head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias) elif proj == 'mlp': head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=(drop, 0), bias=(True, proj_bias)) self.head = nn.Sequential(head_layers) def lock(self, unlocked_groups=0, freeze_bn_stats=False): """ lock modules Args: unlocked_groups (int): leave last n layer groups unlocked (default: 0) """ if not unlocked_groups: # lock full model for param in self.trunk.parameters(): param.requires_grad = False if freeze_bn_stats: freeze_batch_norm_2d(self.trunk) else: # NOTE: partial freeze requires latest timm (master) branch and is subject to change try: # FIXME import here until API stable and in an official release from timm.models.helpers import group_parameters, group_modules except ImportError: raise RuntimeError( 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') matcher = self.trunk.group_matcher() gparams = group_parameters(self.trunk, matcher) max_layer_id = max(gparams.keys()) max_layer_id = max_layer_id - unlocked_groups for group_idx in range(max_layer_id + 1): group = gparams[group_idx] for param in group: self.trunk.get_parameter(param).requires_grad = False if freeze_bn_stats: gmodules = group_modules(self.trunk, matcher, reverse=True) gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} freeze_batch_norm_2d(self.trunk, gmodules) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): try: self.trunk.set_grad_checkpointing(enable) except Exception as e: logging.warning('grad checkpointing not supported for this timm image tower, continuing without...') def forward(self, x): x = self.trunk(x) x = self.head(x) return x ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py ================================================ """ CLIP tokenizer Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. """ import gzip import html import os from functools import lru_cache from typing import Union, List import ftfy import regex as re import torch # https://stackoverflow.com/q/62691279 import os os.environ["TOKENIZERS_PARALLELISM"] = "false" @lru_cache() def default_bpe(): current_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.abspath(os.path.join(current_dir, '../../../../')) quality_metric_path = os.path.join(project_root, 'models', 'QualityMetric') return os.path.join(quality_metric_path, "bpe_simple_vocab_16e6.txt.gz") @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. """ bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8+n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs def basic_clean(text): text = ftfy.fix_text(text) text = html.unescape(html.unescape(text)) return text.strip() def whitespace_clean(text): text = re.sub(r'\s+', ' ', text) text = text.strip() return text class SimpleTokenizer(object): def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') merges = merges[1:49152-256-2+1] merges = [tuple(merge.split()) for merge in merges] vocab = list(bytes_to_unicode().values()) vocab = vocab + [v+'' for v in vocab] for merge in merges: vocab.append(''.join(merge)) if not special_tokens: special_tokens = ['', ''] else: special_tokens = ['', ''] + special_tokens vocab.extend(special_tokens) self.encoder = dict(zip(vocab, range(len(vocab)))) self.decoder = {v: k for k, v in self.encoder.items()} self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {t:t for t in special_tokens} special = "|".join(special_tokens) self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) self.vocab_size = len(self.encoder) self.all_special_ids = [self.encoder[t] for t in special_tokens] def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token[:-1]) + ( token[-1] + '',) pairs = get_pairs(word) if not pairs: return token+'' while True: bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) new_word.extend(word[i:j]) i = j except: new_word.extend(word[i:]) break if word[i] == first and i < len(word)-1 and word[i+1] == second: new_word.append(first+second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = ' '.join(word) self.cache[token] = word return word def encode(self, text): bpe_tokens = [] text = whitespace_clean(basic_clean(text)).lower() for token in re.findall(self.pat, text): token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) return bpe_tokens def decode(self, tokens): text = ''.join([self.decoder[token] for token in tokens]) text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') return text def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: """ Returns the tokenized representation of given input string(s) Parameters ---------- texts : Union[str, List[str]] An input string or a list of input strings to tokenize context_length : int The context length to use; all CLIP models use 77 as the context length Returns ------- A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] """ if isinstance(texts, str): texts = [texts] sot_token = self.encoder[""] eot_token = self.encoder[""] all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts] result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) for i, tokens in enumerate(all_tokens): if len(tokens) > context_length: tokens = tokens[:context_length] # Truncate tokens[-1] = eot_token result[i, :len(tokens)] = torch.tensor(tokens) return result class HFTokenizer: """HuggingFace tokenizer wrapper""" def __init__(self, tokenizer_name: str): from transformers import AutoTokenizer self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) def save_pretrained(self, dest): self.tokenizer.save_pretrained(dest) def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.Tensor: # same cleaning as for default tokenizer, except lowercasing # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance if isinstance(texts, str): texts = [texts] texts = [whitespace_clean(basic_clean(text)) for text in texts] input_ids = self.tokenizer( texts, return_tensors='pt', max_length=context_length, padding='max_length', truncation=True, ).input_ids return input_ids ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/transform.py ================================================ import warnings from dataclasses import dataclass, asdict from typing import Any, Dict, Optional, Sequence, Tuple, Union import torch import torch.nn as nn import torchvision.transforms.functional as F from functools import partial from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ CenterCrop from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD @dataclass class AugmentationCfg: scale: Tuple[float, float] = (0.9, 1.0) ratio: Optional[Tuple[float, float]] = None color_jitter: Optional[Union[float, Tuple[float, float, float]]] = None interpolation: Optional[str] = None re_prob: Optional[float] = None re_count: Optional[int] = None use_timm: bool = False class ResizeMaxSize(nn.Module): def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0): super().__init__() if not isinstance(max_size, int): raise TypeError(f"Size should be int. Got {type(max_size)}") self.max_size = max_size self.interpolation = interpolation self.fn = min if fn == 'min' else min self.fill = fill def forward(self, img): if isinstance(img, torch.Tensor): height, width = img.shape[1:] else: width, height = img.size scale = self.max_size / float(max(height, width)) if scale != 1.0: new_size = tuple(round(dim * scale) for dim in (height, width)) img = F.resize(img, new_size, self.interpolation) pad_h = self.max_size - new_size[0] pad_w = self.max_size - new_size[1] img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill) return img def _convert_to_rgb_or_rgba(image): if image.mode == 'RGBA': return image else: return image.convert('RGB') # def transform_and_split(merged, transform_fn, normalize_fn): # transformed = transform_fn(merged) # crop_img, crop_label = torch.split(transformed, [3,1], dim=0) # # crop_img = _convert_to_rgb(crop_img) # crop_img = normalize_fn(ToTensor()(crop_img)) # return crop_img, crop_label class MaskAwareNormalize(nn.Module): def __init__(self, mean, std): super().__init__() self.normalize = Normalize(mean=mean, std=std) def forward(self, tensor): if tensor.shape[0] == 4: return torch.cat([self.normalize(tensor[:3]), tensor[3:]], dim=0) else: return self.normalize(tensor) def image_transform( image_size: int, is_train: bool, mean: Optional[Tuple[float, ...]] = None, std: Optional[Tuple[float, ...]] = None, resize_longest_max: bool = False, fill_color: int = 0, aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, ): mean = mean or OPENAI_DATASET_MEAN if not isinstance(mean, (list, tuple)): mean = (mean,) * 3 std = std or OPENAI_DATASET_STD if not isinstance(std, (list, tuple)): std = (std,) * 3 if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: # for square size, pass size as int so that Resize() uses aspect preserving shortest edge image_size = image_size[0] if isinstance(aug_cfg, dict): aug_cfg = AugmentationCfg(**aug_cfg) else: aug_cfg = aug_cfg or AugmentationCfg() normalize = MaskAwareNormalize(mean=mean, std=std) if is_train: aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None} use_timm = aug_cfg_dict.pop('use_timm', False) if use_timm: assert False, "not tested for augmentation with mask" from timm.data import create_transform # timm can still be optional if isinstance(image_size, (tuple, list)): assert len(image_size) >= 2 input_size = (3,) + image_size[-2:] else: input_size = (3, image_size, image_size) # by default, timm aug randomly alternates bicubic & bilinear for better robustness at inference time aug_cfg_dict.setdefault('interpolation', 'random') aug_cfg_dict.setdefault('color_jitter', None) # disable by default train_transform = create_transform( input_size=input_size, is_training=True, hflip=0., mean=mean, std=std, re_mode='pixel', **aug_cfg_dict, ) else: train_transform = Compose([ _convert_to_rgb_or_rgba, ToTensor(), RandomResizedCrop( image_size, scale=aug_cfg_dict.pop('scale'), interpolation=InterpolationMode.BICUBIC, ), normalize, ]) if aug_cfg_dict: warnings.warn(f'Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())}).') return train_transform else: transforms = [ _convert_to_rgb_or_rgba, ToTensor(), ] if resize_longest_max: transforms.extend([ ResizeMaxSize(image_size, fill=fill_color) ]) else: transforms.extend([ Resize(image_size, interpolation=InterpolationMode.BICUBIC), CenterCrop(image_size), ]) transforms.extend([ normalize, ]) return Compose(transforms) # def image_transform_region( # image_size: int, # is_train: bool, # mean: Optional[Tuple[float, ...]] = None, # std: Optional[Tuple[float, ...]] = None, # resize_longest_max: bool = False, # fill_color: int = 0, # aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, # ): # mean = mean or OPENAI_DATASET_MEAN # if not isinstance(mean, (list, tuple)): # mean = (mean,) * 3 # std = std or OPENAI_DATASET_STD # if not isinstance(std, (list, tuple)): # std = (std,) * 3 # if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: # # for square size, pass size as int so that Resize() uses aspect preserving shortest edge # image_size = image_size[0] # if isinstance(aug_cfg, dict): # aug_cfg = AugmentationCfg(**aug_cfg) # else: # aug_cfg = aug_cfg or AugmentationCfg() # normalize = Normalize(mean=mean, std=std) # if is_train: # aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None} # transform = Compose([ # RandomResizedCrop( # image_size, # scale=aug_cfg_dict.pop('scale'), # interpolation=InterpolationMode.BICUBIC, # ), # ]) # train_transform = Compose([ # partial(transform_and_split, transform_fn=transform,normalize_fn=normalize) # ]) # return train_transform # else: # if resize_longest_max: # transform = [ # ResizeMaxSize(image_size, fill=fill_color) # ] # val_transform = Compose([ # partial(transform_and_split, transform_fn=transform,normalize_fn=normalize), # ]) # else: # transform = [ # Resize(image_size, interpolation=InterpolationMode.BICUBIC), # CenterCrop(image_size), # ] # val_transform = Compose([ # partial(transform_and_split, transform_fn=transform,normalize_fn=normalize), # ]) # return val_transform ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py ================================================ from collections import OrderedDict import math from typing import Callable, Optional, Sequence, Tuple import torch from torch import nn from torch.nn import functional as F from torch.utils.checkpoint import checkpoint from .utils import to_2tuple class LayerNormFp32(nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" def forward(self, x: torch.Tensor): orig_type = x.dtype x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps) return x.to(orig_type) class LayerNorm(nn.LayerNorm): """Subclass torch's LayerNorm (with cast back to input dtype).""" def forward(self, x: torch.Tensor): orig_type = x.dtype x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) return x.to(orig_type) class QuickGELU(nn.Module): # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class LayerScale(nn.Module): def __init__(self, dim, init_values=1e-5, inplace=False): super().__init__() self.inplace = inplace self.gamma = nn.Parameter(init_values * torch.ones(dim)) def forward(self, x): return x.mul_(self.gamma) if self.inplace else x * self.gamma class PatchDropout(nn.Module): """ https://arxiv.org/abs/2212.00794 """ def __init__(self, prob, exclude_first_token=True): super().__init__() assert 0 <= prob < 1. self.prob = prob self.exclude_first_token = exclude_first_token # exclude CLS token def forward(self, x): if not self.training or self.prob == 0.: return x if self.exclude_first_token: cls_tokens, x = x[:, :1], x[:, 1:] else: cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) batch = x.size()[0] num_tokens = x.size()[1] batch_indices = torch.arange(batch) batch_indices = batch_indices[..., None] keep_prob = 1 - self.prob num_patches_keep = max(1, int(num_tokens * keep_prob)) rand = torch.randn(batch, num_tokens) patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices x = x[batch_indices, patch_indices_keep] if self.exclude_first_token: x = torch.cat((cls_tokens, x), dim=1) return x class Attention(nn.Module): def __init__( self, dim, num_heads=8, qkv_bias=True, scaled_cosine=False, scale_heads=False, logit_scale_max=math.log(1. / 0.01), attn_drop=0., proj_drop=0. ): super().__init__() self.scaled_cosine = scaled_cosine self.scale_heads = scale_heads assert dim % num_heads == 0, 'dim should be divisible by num_heads' self.num_heads = num_heads self.head_dim = dim // num_heads self.scale = self.head_dim ** -0.5 self.logit_scale_max = logit_scale_max # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) if qkv_bias: self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) else: self.in_proj_bias = None if self.scaled_cosine: self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) else: self.logit_scale = None self.attn_drop = nn.Dropout(attn_drop) if self.scale_heads: self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) else: self.head_scale = None self.out_proj = nn.Linear(dim, dim) self.out_drop = nn.Dropout(proj_drop) def forward(self, x, attn_mask: Optional[torch.Tensor] = None): L, N, C = x.shape q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) if self.logit_scale is not None: attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() attn = attn.view(N, self.num_heads, L, L) * logit_scale attn = attn.view(-1, L, L) else: q = q * self.scale attn = torch.bmm(q, k.transpose(-1, -2)) if attn_mask is not None: if attn_mask.dtype == torch.bool: new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) new_attn_mask.masked_fill_(attn_mask, float("-inf")) attn_mask = new_attn_mask attn += attn_mask attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = torch.bmm(attn, v) if self.head_scale is not None: x = x.view(N, self.num_heads, L, C) * self.head_scale x = x.view(-1, L, C) x = x.transpose(0, 1).reshape(L, N, C) x = self.out_proj(x) x = self.out_drop(x) return x class AttentionalPooler(nn.Module): def __init__( self, d_model: int, context_dim: int, n_head: int = 8, n_queries: int = 256, norm_layer: Callable = LayerNorm ): super().__init__() self.query = nn.Parameter(torch.randn(n_queries, d_model)) self.attn = nn.MultiheadAttention(d_model, n_head, kdim=context_dim, vdim=context_dim) self.ln_q = norm_layer(d_model) self.ln_k = norm_layer(context_dim) def forward(self, x: torch.Tensor): x = self.ln_k(x).permute(1, 0, 2) # NLD -> LND N = x.shape[1] q = self.ln_q(self.query) out = self.attn(self._repeat(q, N), x, x, need_weights=False)[0] return out.permute(1, 0, 2) # LND -> NLD def _repeat(self, query, N: int): return query.unsqueeze(1).repeat(1, N, 1) class ResidualAttentionBlock(nn.Module): def __init__( self, d_model: int, n_head: int, mlp_ratio: float = 4.0, ls_init_value: float = None, act_layer: Callable = nn.GELU, norm_layer: Callable = LayerNorm, is_cross_attention: bool = False, ): super().__init__() self.ln_1 = norm_layer(d_model) self.attn = nn.MultiheadAttention(d_model, n_head) self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() if is_cross_attention: self.ln_1_kv = norm_layer(d_model) self.ln_2 = norm_layer(d_model) mlp_width = int(d_model * mlp_ratio) self.mlp = nn.Sequential(OrderedDict([ ("c_fc", nn.Linear(d_model, mlp_width)), ("gelu", act_layer()), ("c_proj", nn.Linear(mlp_width, d_model)) ])) self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() def attention( self, q_x: torch.Tensor, k_x: Optional[torch.Tensor] = None, v_x: Optional[torch.Tensor] = None, attn_mask: Optional[torch.Tensor] = None, ): k_x = k_x if k_x is not None else q_x v_x = v_x if v_x is not None else q_x attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None return self.attn( q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask )[0] def forward( self, q_x: torch.Tensor, k_x: Optional[torch.Tensor] = None, v_x: Optional[torch.Tensor] = None, attn_mask: Optional[torch.Tensor] = None, ): k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask)) x = x + self.ls_2(self.mlp(self.ln_2(x))) return x class CustomResidualAttentionBlock(nn.Module): def __init__( self, d_model: int, n_head: int, mlp_ratio: float = 4.0, ls_init_value: float = None, act_layer: Callable = nn.GELU, norm_layer: Callable = LayerNorm, scale_cosine_attn: bool = False, scale_heads: bool = False, scale_attn: bool = False, scale_fc: bool = False, ): super().__init__() self.ln_1 = norm_layer(d_model) self.attn = Attention( d_model, n_head, scaled_cosine=scale_cosine_attn, scale_heads=scale_heads, ) self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() self.ln_2 = norm_layer(d_model) mlp_width = int(d_model * mlp_ratio) self.mlp = nn.Sequential(OrderedDict([ ("c_fc", nn.Linear(d_model, mlp_width)), ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()), ("gelu", act_layer()), ("c_proj", nn.Linear(mlp_width, d_model)) ])) self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): x = x + self.ls_1(self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask))) x = x + self.ls_2(self.mlp(self.ln_2(x))) return x class Transformer(nn.Module): def __init__( self, width: int, layers: int, heads: int, mlp_ratio: float = 4.0, ls_init_value: float = None, act_layer: Callable = nn.GELU, norm_layer: Callable = LayerNorm, ): super().__init__() self.width = width self.layers = layers self.grad_checkpointing = False self.resblocks = nn.ModuleList([ ResidualAttentionBlock( width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer) for _ in range(layers) ]) def get_cast_dtype(self) -> torch.dtype: return self.resblocks[0].mlp.c_fc.weight.dtype def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): for r in self.resblocks: if self.grad_checkpointing and not torch.jit.is_scripting(): # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 x = checkpoint(r, x, None, None, attn_mask) else: x = r(x, attn_mask=attn_mask) return x class VisionTransformer(nn.Module): output_tokens: torch.jit.Final[bool] def __init__( self, image_size: int, patch_size: int, width: int, layers: int, heads: int, mlp_ratio: float, ls_init_value: float = None, global_average_pool: bool = False, attentional_pool: bool = False, n_queries: int = 256, attn_pooler_heads: int = 8, output_dim: int = 512, patch_dropout: float = 0., input_patchnorm: bool = False, act_layer: Callable = nn.GELU, norm_layer: Callable = LayerNorm, output_tokens: bool = False ): super().__init__() self.output_tokens = output_tokens image_height, image_width = self.image_size = to_2tuple(image_size) patch_height, patch_width = self.patch_size = to_2tuple(patch_size) self.grid_size = (image_height // patch_height, image_width // patch_width) self.output_dim = output_dim # whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1 self.input_patchnorm = input_patchnorm if input_patchnorm: patch_input_dim = patch_height * patch_width * 3 self.patchnorm_pre_ln = LayerNorm(patch_input_dim) self.conv1 = nn.Linear(patch_input_dim, width) else: self.patchnorm_pre_ln = nn.Identity() self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) # class embeddings and positional embeddings scale = width ** -0.5 self.class_embedding = nn.Parameter(scale * torch.randn(width)) self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() self.ln_pre = norm_layer(width) self.transformer = Transformer( width, layers, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, ) self.global_average_pool = global_average_pool if attentional_pool: self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries) self.ln_post = norm_layer(output_dim) self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim)) else: self.attn_pool = None self.ln_post = norm_layer(width) self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) self.init_parameters() def lock(self, unlocked_groups=0, freeze_bn_stats=False): for param in self.parameters(): param.requires_grad = False if unlocked_groups != 0: groups = [ [ self.conv1, self.class_embedding, self.positional_embedding, self.ln_pre, ], *self.transformer.resblocks[:-1], [ self.transformer.resblocks[-1], self.ln_post, ], self.proj, ] def _unlock(x): if isinstance(x, Sequence): for g in x: _unlock(g) else: if isinstance(x, torch.nn.Parameter): x.requires_grad = True else: for p in x.parameters(): p.requires_grad = True _unlock(groups[-unlocked_groups:]) def init_parameters(self): # FIXME OpenAI CLIP did not define an init for the VisualTransformer # TODO experiment if default PyTorch init, below, or alternate init is best. # nn.init.normal_(self.class_embedding, std=self.scale) # nn.init.normal_(self.positional_embedding, std=self.scale) # # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) # attn_std = self.transformer.width ** -0.5 # fc_std = (2 * self.transformer.width) ** -0.5 # for block in self.transformer.resblocks: # nn.init.normal_(block.attn.in_proj_weight, std=attn_std) # nn.init.normal_(block.attn.out_proj.weight, std=proj_std) # nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) # nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) # # if self.text_projection is not None: # nn.init.normal_(self.text_projection, std=self.scale) pass @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.transformer.grad_checkpointing = enable def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: if self.global_average_pool: return x.mean(dim=1), x else: return x[:, 0], x[:, 1:] def forward(self, x: torch.Tensor, skip_pool: bool = False): # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1 if self.input_patchnorm: # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)') x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1]) x = x.permute(0, 2, 4, 1, 3, 5) x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1) x = self.patchnorm_pre_ln(x) x = self.conv1(x) else: x = self.conv1(x) # shape = [*, width, grid, grid] x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] # class embeddings and positional embeddings x = torch.cat( [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] x = x + self.positional_embedding.to(x.dtype) # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in x = self.patch_dropout(x) x = self.ln_pre(x) x = x.permute(1, 0, 2) # NLD -> LND x = self.transformer(x) x = x.permute(1, 0, 2) # LND -> NLD if skip_pool: return x if self.attn_pool is not None: x = self.attn_pool(x) x = self.ln_post(x) pooled, tokens = self._global_pool(x) else: pooled, tokens = self._global_pool(x) pooled = self.ln_post(pooled) if self.proj is not None: pooled = pooled @ self.proj if self.output_tokens: return pooled, tokens return pooled class TextTransformer(nn.Module): output_tokens: torch.jit.Final[bool] def __init__( self, context_length: int = 77, vocab_size: int = 49408, width: int = 512, heads: int = 8, layers: int = 12, ls_init_value: float = None, output_dim: int = 512, act_layer: Callable = nn.GELU, norm_layer: Callable = LayerNorm, embed_cls: bool = False, pad_id: int = 0, output_tokens: bool = False, ): super().__init__() self.output_tokens = output_tokens self.num_pos = self.context_length = context_length self.vocab_size = vocab_size self.width = width self.output_dim = output_dim self.heads = heads self.pad_id = pad_id self.text_projection = nn.Parameter(torch.empty(width, output_dim)) if embed_cls: self.cls_emb = nn.Parameter(torch.empty(width)) self.num_pos += 1 else: self.cls_emb = None self.token_embedding = nn.Embedding(vocab_size, width) self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width)) self.transformer = Transformer( width=width, layers=layers, heads=heads, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, ) self.ln_final = norm_layer(width) self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) self.init_parameters() def init_parameters(self): nn.init.normal_(self.token_embedding.weight, std=0.02) nn.init.normal_(self.positional_embedding, std=0.01) if self.cls_emb is not None: nn.init.normal_(self.cls_emb, std=0.01) proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) attn_std = self.transformer.width ** -0.5 fc_std = (2 * self.transformer.width) ** -0.5 for block in self.transformer.resblocks: nn.init.normal_(block.attn.in_proj_weight, std=attn_std) nn.init.normal_(block.attn.out_proj.weight, std=proj_std) nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) if self.text_projection is not None: nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.transformer.grad_checkpointing = enable def build_attention_mask(self): # lazily create causal attention mask, with full attention between the tokens # pytorch uses additive attention mask; fill with -inf mask = torch.empty(self.num_pos, self.num_pos) mask.fill_(float("-inf")) mask.triu_(1) # zero out the lower diagonal return mask def build_cls_mask(self, text, cast_dtype: torch.dtype): cls_mask = (text != self.pad_id).unsqueeze(1) cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0) additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device) additive_mask.fill_(0) additive_mask.masked_fill_(~cls_mask, float("-inf")) additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0) return additive_mask def _repeat(self, t, N: int): return t.reshape(1, 1, -1).repeat(N, 1, 1) def forward(self, text): cast_dtype = self.transformer.get_cast_dtype() seq_len = text.shape[1] x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] attn_mask = self.attn_mask if self.cls_emb is not None: seq_len += 1 x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1) cls_mask = self.build_cls_mask(text, cast_dtype) attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len] x = x + self.positional_embedding[:seq_len].to(cast_dtype) x = x.permute(1, 0, 2) # NLD -> LND x = self.transformer(x, attn_mask=attn_mask) x = x.permute(1, 0, 2) # LND -> NLD # x.shape = [batch_size, n_ctx, transformer.width] # take features from the eot embedding (eot_token is the highest number in each sequence) if self.cls_emb is not None: pooled, tokens = x[:, -1], x[:, :-1] pooled = self.ln_final(pooled) else: x = self.ln_final(x) pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x if self.text_projection is not None: pooled = pooled @ self.text_projection if self.output_tokens: return pooled, tokens return pooled class MultimodalTransformer(Transformer): def __init__( self, width: int, layers: int, heads: int, context_length: int = 77, mlp_ratio: float = 4.0, ls_init_value: float = None, act_layer: Callable = nn.GELU, norm_layer: Callable = LayerNorm, output_dim: int = 512, ): super().__init__( width=width, layers=layers, heads=heads, mlp_ratio=mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, ) self.context_length = context_length self.cross_attn = nn.ModuleList([ ResidualAttentionBlock( width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, is_cross_attention=True, ) for _ in range(layers) ]) self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) self.ln_final = norm_layer(width) self.text_projection = nn.Parameter(torch.empty(width, output_dim)) def init_parameters(self): proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) attn_std = self.transformer.width ** -0.5 fc_std = (2 * self.transformer.width) ** -0.5 for block in self.transformer.resblocks: nn.init.normal_(block.attn.in_proj_weight, std=attn_std) nn.init.normal_(block.attn.out_proj.weight, std=proj_std) nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) for block in self.transformer.cross_attn: nn.init.normal_(block.attn.in_proj_weight, std=attn_std) nn.init.normal_(block.attn.out_proj.weight, std=proj_std) nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) if self.text_projection is not None: nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) def build_attention_mask(self): # lazily create causal attention mask, with full attention between the tokens # pytorch uses additive attention mask; fill with -inf mask = torch.empty(self.context_length, self.context_length) mask.fill_(float("-inf")) mask.triu_(1) # zero out the lower diagonal return mask def forward(self, image_embs, text_embs): text_embs = text_embs.permute(1, 0, 2) # NLD -> LNDsq image_embs = image_embs.permute(1, 0, 2) # NLD -> LND seq_len = text_embs.shape[0] for resblock, cross_attn in zip(self.resblocks, self.cross_attn): if self.grad_checkpointing and not torch.jit.is_scripting(): # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 text_embs = checkpoint(resblock, text_embs, None, None, self.attn_mask[:seq_len, :seq_len]) text_embs = checkpoint(cross_attn, text_embs, image_embs, image_embs, None) else: text_embs = resblock(text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len]) text_embs = cross_attn(text_embs, k_x=image_embs, v_x=image_embs) x = text_embs.permute(1, 0, 2) # LND -> NLD x = self.ln_final(x) if self.text_projection is not None: x = x @ self.text_projection return x @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.grad_checkpointing = enable ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/utils.py ================================================ from itertools import repeat import collections.abc from torch import nn as nn from torchvision.ops.misc import FrozenBatchNorm2d def freeze_batch_norm_2d(module, module_match={}, name=''): """ Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and returned. Otherwise, the module is walked recursively and submodules are converted in place. Args: module (torch.nn.Module): Any PyTorch module. module_match (dict): Dictionary of full module names to freeze (all if empty) name (str): Full module name (prefix) Returns: torch.nn.Module: Resulting module Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 """ res = module is_match = True if module_match: is_match = name in module_match if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): res = FrozenBatchNorm2d(module.num_features) res.num_features = module.num_features res.affine = module.affine if module.affine: res.weight.data = module.weight.data.clone().detach() res.bias.data = module.bias.data.clone().detach() res.running_mean.data = module.running_mean.data res.running_var.data = module.running_var.data res.eps = module.eps else: for child_name, child in module.named_children(): full_child_name = '.'.join([name, child_name]) if name else child_name new_child = freeze_batch_norm_2d(child, module_match, full_child_name) if new_child is not child: res.add_module(child_name, new_child) return res # From PyTorch internals def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable): return x return tuple(repeat(x, n)) return parse to_1tuple = _ntuple(1) to_2tuple = _ntuple(2) to_3tuple = _ntuple(3) to_4tuple = _ntuple(4) to_ntuple = lambda n, x: _ntuple(n)(x) ================================================ FILE: diffsynth/extensions/ImageQualityMetric/open_clip/version.py ================================================ __version__ = '2.16.0' ================================================ FILE: diffsynth/extensions/ImageQualityMetric/pickscore.py ================================================ import torch from PIL import Image from transformers import AutoProcessor, AutoModel from typing import List, Union import os from .config import MODEL_PATHS class PickScore(torch.nn.Module): def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS): super().__init__() """Initialize the Selector with a processor and model. Args: device (Union[str, torch.device]): The device to load the model on. """ self.device = device if isinstance(device, torch.device) else torch.device(device) processor_name_or_path = path.get("clip") model_pretrained_name_or_path = path.get("pickscore") self.processor = AutoProcessor.from_pretrained(processor_name_or_path) self.model = AutoModel.from_pretrained(model_pretrained_name_or_path).eval().to(self.device) def _calculate_score(self, image: torch.Tensor, prompt: str, softmax: bool = False) -> float: """Calculate the score for a single image and prompt. Args: image (torch.Tensor): The processed image tensor. prompt (str): The prompt text. softmax (bool): Whether to apply softmax to the scores. Returns: float: The score for the image. """ with torch.no_grad(): # Prepare text inputs text_inputs = self.processor( text=prompt, padding=True, truncation=True, max_length=77, return_tensors="pt", ).to(self.device) # Embed images and text image_embs = self.model.get_image_features(pixel_values=image) image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True) text_embs = self.model.get_text_features(**text_inputs) text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True) # Compute score score = (text_embs @ image_embs.T)[0] if softmax: # Apply logit scale and softmax score = torch.softmax(self.model.logit_scale.exp() * score, dim=-1) return score.cpu().item() @torch.no_grad() def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str, softmax: bool = False) -> List[float]: """Score the images based on the prompt. Args: images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). prompt (str): The prompt text. softmax (bool): Whether to apply softmax to the scores. Returns: List[float]: List of scores for the images. """ try: if isinstance(images, (str, Image.Image)): # Single image if isinstance(images, str): pil_image = Image.open(images) else: pil_image = images # Prepare image inputs image_inputs = self.processor( images=pil_image, padding=True, truncation=True, max_length=77, return_tensors="pt", ).to(self.device) return [self._calculate_score(image_inputs["pixel_values"], prompt, softmax)] elif isinstance(images, list): # Multiple images scores = [] for one_image in images: if isinstance(one_image, str): pil_image = Image.open(one_image) elif isinstance(one_image, Image.Image): pil_image = one_image else: raise TypeError("The type of parameter images is illegal.") # Prepare image inputs image_inputs = self.processor( images=pil_image, padding=True, truncation=True, max_length=77, return_tensors="pt", ).to(self.device) scores.append(self._calculate_score(image_inputs["pixel_values"], prompt, softmax)) return scores else: raise TypeError("The type of parameter images is illegal.") except Exception as e: raise RuntimeError(f"Error in scoring images: {e}") ================================================ FILE: diffsynth/extensions/ImageQualityMetric/trainer/__init__.py ================================================ from .models import * ================================================ FILE: diffsynth/extensions/ImageQualityMetric/trainer/models/__init__.py ================================================ from .base_model import * from .clip_model import * from .cross_modeling import * ================================================ FILE: diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py ================================================ from dataclasses import dataclass @dataclass class BaseModelConfig: pass ================================================ FILE: diffsynth/extensions/ImageQualityMetric/trainer/models/clip_model.py ================================================ from dataclasses import dataclass from transformers import CLIPModel as HFCLIPModel from transformers import AutoTokenizer from torch import nn, einsum from .base_model import BaseModelConfig from transformers import CLIPConfig from typing import Any, Optional, Tuple, Union import torch from .cross_modeling import Cross_model import json, os class XCLIPModel(HFCLIPModel): def __init__(self, config: CLIPConfig): super().__init__(config) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: # Use CLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # pooled_output = text_outputs[1] # text_features = self.text_projection(pooled_output) last_hidden_state = text_outputs[0] text_features = self.text_projection(last_hidden_state) pooled_output = text_outputs[1] text_features_EOS = self.text_projection(pooled_output) # del last_hidden_state, text_outputs # gc.collect() return text_features, text_features_EOS def get_image_features( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: # Use CLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # pooled_output = vision_outputs[1] # pooled_output # image_features = self.visual_projection(pooled_output) last_hidden_state = vision_outputs[0] image_features = self.visual_projection(last_hidden_state) return image_features @dataclass class ClipModelConfig(BaseModelConfig): _target_: str = "diffsynth.extensions.QualityMetric.trainer.models.clip_model.CLIPModel" pretrained_model_name_or_path: str ="checkpoints/clip-vit-base-patch32" class CLIPModel(nn.Module): def __init__(self, ckpt, config_file=False): super().__init__() if config_file is None: self.model = XCLIPModel.from_pretrained(ckpt) else: with open(os.path.join(ckpt, "config.json"), "r", encoding="utf-8") as f: config = json.load(f) config = CLIPConfig(**config) self.model = XCLIPModel._from_config(config) self.cross_model = Cross_model(dim=1024, layer_num=4, heads=16) def get_text_features(self, *args, **kwargs): return self.model.get_text_features(*args, **kwargs) def get_image_features(self, *args, **kwargs): return self.model.get_image_features(*args, **kwargs) def forward(self, text_inputs=None, image_inputs=None, condition_inputs=None): outputs = () text_f, text_EOS = self.model.get_text_features(text_inputs) # B*77*1024 outputs += text_EOS, image_f = self.model.get_image_features(image_inputs.half()) # 2B*257*1024 condition_f, _ = self.model.get_text_features(condition_inputs) # B*5*1024 sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f) sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0] sim_text_condition = sim_text_condition / sim_text_condition.max() mask = torch.where(sim_text_condition > 0.01, 0, float('-inf')) # B*1*77 mask = mask.repeat(1,image_f.shape[1],1) # B*257*77 bc = int(image_f.shape[0]/2) sim0 = self.cross_model(image_f[:bc,:,:], text_f,mask.half()) sim1 = self.cross_model(image_f[bc:,:,:], text_f,mask.half()) outputs += sim0[:,0,:], outputs += sim1[:,0,:], return outputs @property def logit_scale(self): return self.model.logit_scale def save(self, path): self.model.save_pretrained(path) ================================================ FILE: diffsynth/extensions/ImageQualityMetric/trainer/models/cross_modeling.py ================================================ import torch from torch import einsum, nn import torch.nn.functional as F from einops import rearrange, repeat # helper functions def exists(val): return val is not None def default(val, d): return val if exists(val) else d # normalization # they use layernorm without bias, something that pytorch does not offer class LayerNorm(nn.Module): def __init__(self, dim): super().__init__() self.weight = nn.Parameter(torch.ones(dim)) self.register_buffer("bias", torch.zeros(dim)) def forward(self, x): return F.layer_norm(x, x.shape[-1:], self.weight, self.bias) # residual class Residual(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn def forward(self, x, *args, **kwargs): return self.fn(x, *args, **kwargs) + x # rotary positional embedding # https://arxiv.org/abs/2104.09864 class RotaryEmbedding(nn.Module): def __init__(self, dim): super().__init__() inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer("inv_freq", inv_freq) def forward(self, max_seq_len, *, device): seq = torch.arange(max_seq_len, device=device, dtype=self.inv_freq.dtype) freqs = einsum("i , j -> i j", seq, self.inv_freq) return torch.cat((freqs, freqs), dim=-1) def rotate_half(x): x = rearrange(x, "... (j d) -> ... j d", j=2) x1, x2 = x.unbind(dim=-2) return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(pos, t): return (t * pos.cos()) + (rotate_half(t) * pos.sin()) # classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU for gating the feedforward # https://arxiv.org/abs/2002.05202 class SwiGLU(nn.Module): def forward(self, x): x, gate = x.chunk(2, dim=-1) return F.silu(gate) * x # parallel attention and feedforward with residual # discovered by Wang et al + EleutherAI from GPT-J fame class ParallelTransformerBlock(nn.Module): def __init__(self, dim, dim_head=64, heads=8, ff_mult=4): super().__init__() self.norm = LayerNorm(dim) attn_inner_dim = dim_head * heads ff_inner_dim = dim * ff_mult self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2)) self.heads = heads self.scale = dim_head**-0.5 self.rotary_emb = RotaryEmbedding(dim_head) self.fused_attn_ff_proj = nn.Linear(dim, sum(self.fused_dims), bias=False) self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False) self.ff_out = nn.Sequential( SwiGLU(), nn.Linear(ff_inner_dim, dim, bias=False) ) self.register_buffer("pos_emb", None, persistent=False) def get_rotary_embedding(self, n, device): if self.pos_emb is not None and self.pos_emb.shape[-2] >= n: return self.pos_emb[:n] pos_emb = self.rotary_emb(n, device=device) self.register_buffer("pos_emb", pos_emb, persistent=False) return pos_emb def forward(self, x, attn_mask=None): """ einstein notation b - batch h - heads n, i, j - sequence length (base sequence length, source, target) d - feature dimension """ n, device, h = x.shape[1], x.device, self.heads # pre layernorm x = self.norm(x) # attention queries, keys, values, and feedforward inner q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1) # split heads # they use multi-query single-key-value attention, yet another Noam Shazeer paper # they found no performance loss past a certain scale, and more efficient decoding obviously # https://arxiv.org/abs/1911.02150 q = rearrange(q, "b n (h d) -> b h n d", h=h) # rotary embeddings positions = self.get_rotary_embedding(n, device) q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k)) # scale q = q * self.scale # similarity sim = einsum("b h i d, b j d -> b h i j", q, k) # extra attention mask - for masking out attention from text CLS token to padding if exists(attn_mask): attn_mask = rearrange(attn_mask, 'b i j -> b 1 i j') sim = sim.masked_fill(~attn_mask, -torch.finfo(sim.dtype).max) # attention sim = sim - sim.amax(dim=-1, keepdim=True).detach() attn = sim.softmax(dim=-1) # aggregate values out = einsum("b h i j, b j d -> b h i d", attn, v) # merge heads out = rearrange(out, "b h n d -> b n (h d)") return self.attn_out(out) + self.ff_out(ff) # cross attention - using multi-query + one-headed key / values as in PaLM w/ optional parallel feedforward class CrossAttention(nn.Module): def __init__( self, dim, *, context_dim=None, dim_head=64, heads=12, parallel_ff=False, ff_mult=4, norm_context=False ): super().__init__() self.heads = heads self.scale = dim_head ** -0.5 inner_dim = heads * dim_head context_dim = default(context_dim, dim) self.norm = LayerNorm(dim) self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity() self.to_q = nn.Linear(dim, inner_dim, bias=False) self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False) self.to_out = nn.Linear(inner_dim, dim, bias=False) # whether to have parallel feedforward ff_inner_dim = ff_mult * dim self.ff = nn.Sequential( nn.Linear(dim, ff_inner_dim * 2, bias=False), SwiGLU(), nn.Linear(ff_inner_dim, dim, bias=False) ) if parallel_ff else None def forward(self, x, context, mask): """ einstein notation b - batch h - heads n, i, j - sequence length (base sequence length, source, target) d - feature dimension """ # pre-layernorm, for queries and context x = self.norm(x) context = self.context_norm(context) # get queries q = self.to_q(x) q = rearrange(q, 'b n (h d) -> b h n d', h = self.heads) # scale q = q * self.scale # get key / values k, v = self.to_kv(context).chunk(2, dim=-1) # query / key similarity sim = einsum('b h i d, b j d -> b h i j', q, k) # attention mask = mask.unsqueeze(1).repeat(1,self.heads,1,1) sim = sim + mask # context mask sim = sim - sim.amax(dim=-1, keepdim=True) attn = sim.softmax(dim=-1) # aggregate out = einsum('b h i j, b j d -> b h i d', attn, v) # merge and combine heads out = rearrange(out, 'b h n d -> b n (h d)') out = self.to_out(out) # add parallel feedforward (for multimodal layers) if exists(self.ff): out = out + self.ff(x) return out class Cross_model(nn.Module): def __init__( self, dim=512, layer_num=4, dim_head=64, heads=8, ff_mult=4 ): super().__init__() self.layers = nn.ModuleList([]) for ind in range(layer_num): self.layers.append(nn.ModuleList([ Residual(CrossAttention(dim=dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult)), Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult)) ])) def forward( self, query_tokens, context_tokens, mask ): for cross_attn, self_attn_ff in self.layers: query_tokens = cross_attn(query_tokens, context_tokens,mask) query_tokens = self_attn_ff(query_tokens) return query_tokens ================================================ FILE: diffsynth/extensions/RIFE/__init__.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from PIL import Image def warp(tenInput, tenFlow, device): backwarp_tenGrid = {} k = (str(tenFlow.device), str(tenFlow.size())) if k not in backwarp_tenGrid: tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view( 1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1) tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view( 1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3]) backwarp_tenGrid[k] = torch.cat( [tenHorizontal, tenVertical], 1).to(device) tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1) g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1) return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True) def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.PReLU(out_planes) ) class IFBlock(nn.Module): def __init__(self, in_planes, c=64): super(IFBlock, self).__init__() self.conv0 = nn.Sequential(conv(in_planes, c//2, 3, 2, 1), conv(c//2, c, 3, 2, 1),) self.convblock0 = nn.Sequential(conv(c, c), conv(c, c)) self.convblock1 = nn.Sequential(conv(c, c), conv(c, c)) self.convblock2 = nn.Sequential(conv(c, c), conv(c, c)) self.convblock3 = nn.Sequential(conv(c, c), conv(c, c)) self.conv1 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 4, 4, 2, 1)) self.conv2 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 1, 4, 2, 1)) def forward(self, x, flow, scale=1): x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale feat = self.conv0(torch.cat((x, flow), 1)) feat = self.convblock0(feat) + feat feat = self.convblock1(feat) + feat feat = self.convblock2(feat) + feat feat = self.convblock3(feat) + feat flow = self.conv1(feat) mask = self.conv2(feat) flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) return flow, mask class IFNet(nn.Module): def __init__(self, **kwargs): super(IFNet, self).__init__() self.block0 = IFBlock(7+4, c=90) self.block1 = IFBlock(7+4, c=90) self.block2 = IFBlock(7+4, c=90) self.block_tea = IFBlock(10+4, c=90) def forward(self, x, scale_list=[4, 2, 1], training=False): if training == False: channel = x.shape[1] // 2 img0 = x[:, :channel] img1 = x[:, channel:] flow_list = [] merged = [] mask_list = [] warped_img0 = img0 warped_img1 = img1 flow = (x[:, :4]).detach() * 0 mask = (x[:, :1]).detach() * 0 block = [self.block0, self.block1, self.block2] for i in range(3): f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i]) f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i]) flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 mask = mask + (m0 + (-m1)) / 2 mask_list.append(mask) flow_list.append(flow) warped_img0 = warp(img0, flow[:, :2], device=x.device) warped_img1 = warp(img1, flow[:, 2:4], device=x.device) merged.append((warped_img0, warped_img1)) ''' c0 = self.contextnet(img0, flow[:, :2]) c1 = self.contextnet(img1, flow[:, 2:4]) tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) res = tmp[:, 1:4] * 2 - 1 ''' for i in range(3): mask_list[i] = torch.sigmoid(mask_list[i]) merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i]) return flow_list, mask_list[2], merged @staticmethod def state_dict_converter(): return IFNetStateDictConverter() class IFNetStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = {k.replace("module.", ""): v for k, v in state_dict.items()} return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict), {"upcast_to_float32": True} class RIFEInterpolater: def __init__(self, model, device="cuda"): self.model = model self.device = device # IFNet only does not support float16 self.torch_dtype = torch.float32 @staticmethod def from_model_manager(model_manager): return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device) def process_image(self, image): width, height = image.size if width % 32 != 0 or height % 32 != 0: width = (width + 31) // 32 height = (height + 31) // 32 image = image.resize((width, height)) image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1) return image def process_images(self, images): images = [self.process_image(image) for image in images] images = torch.stack(images) return images def decode_images(self, images): images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) images = [Image.fromarray(image) for image in images] return images def add_interpolated_images(self, images, interpolated_images): output_images = [] for image, interpolated_image in zip(images, interpolated_images): output_images.append(image) output_images.append(interpolated_image) output_images.append(images[-1]) return output_images @torch.no_grad() def interpolate_(self, images, scale=1.0): input_tensor = self.process_images(images) input_tensor = torch.cat((input_tensor[:-1], input_tensor[1:]), dim=1) input_tensor = input_tensor.to(device=self.device, dtype=self.torch_dtype) flow, mask, merged = self.model(input_tensor, [4/scale, 2/scale, 1/scale]) output_images = self.decode_images(merged[2].cpu()) if output_images[0].size != images[0].size: output_images = [image.resize(images[0].size) for image in output_images] return output_images @torch.no_grad() def interpolate(self, images, scale=1.0, batch_size=4, num_iter=1, progress_bar=lambda x:x): # Preprocess processed_images = self.process_images(images) for iter in range(num_iter): # Input input_tensor = torch.cat((processed_images[:-1], processed_images[1:]), dim=1) # Interpolate output_tensor = [] for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) batch_input_tensor = input_tensor[batch_id: batch_id_] batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) output_tensor.append(merged[2].cpu()) # Output output_tensor = torch.concat(output_tensor, dim=0).clip(0, 1) processed_images = self.add_interpolated_images(processed_images, output_tensor) processed_images = torch.stack(processed_images) # To images output_images = self.decode_images(processed_images) if output_images[0].size != images[0].size: output_images = [image.resize(images[0].size) for image in output_images] return output_images class RIFESmoother(RIFEInterpolater): def __init__(self, model, device="cuda"): super(RIFESmoother, self).__init__(model, device=device) @staticmethod def from_model_manager(model_manager): return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device) def process_tensors(self, input_tensor, scale=1.0, batch_size=4): output_tensor = [] for batch_id in range(0, input_tensor.shape[0], batch_size): batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) batch_input_tensor = input_tensor[batch_id: batch_id_] batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) output_tensor.append(merged[2].cpu()) output_tensor = torch.concat(output_tensor, dim=0) return output_tensor @torch.no_grad() def __call__(self, rendered_frames, scale=1.0, batch_size=4, num_iter=1, **kwargs): # Preprocess processed_images = self.process_images(rendered_frames) for iter in range(num_iter): # Input input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1) # Interpolate output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) # Blend input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1) output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) # Add to frames processed_images[1:-1] = output_tensor # To images output_images = self.decode_images(processed_images) if output_images[0].size != rendered_frames[0].size: output_images = [image.resize(rendered_frames[0].size) for image in output_images] return output_images ================================================ FILE: diffsynth/extensions/__init__.py ================================================ ================================================ FILE: diffsynth/models/__init__.py ================================================ from .model_manager import * ================================================ FILE: diffsynth/models/attention.py ================================================ import torch from einops import rearrange def low_version_attention(query, key, value, attn_bias=None): scale = 1 / query.shape[-1] ** 0.5 query = query * scale attn = torch.matmul(query, key.transpose(-2, -1)) if attn_bias is not None: attn = attn + attn_bias attn = attn.softmax(-1) return attn @ value class Attention(torch.nn.Module): def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False): super().__init__() dim_inner = head_dim * num_heads kv_dim = kv_dim if kv_dim is not None else q_dim self.num_heads = num_heads self.head_dim = head_dim self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q) self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out) def interact_with_ipadapter(self, hidden_states, q, ip_k, ip_v, scale=1.0): batch_size = q.shape[0] ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v) hidden_states = hidden_states + scale * ip_hidden_states return hidden_states def torch_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None): if encoder_hidden_states is None: encoder_hidden_states = hidden_states batch_size = encoder_hidden_states.shape[0] q = self.to_q(hidden_states) k = self.to_k(encoder_hidden_states) v = self.to_v(encoder_hidden_states) q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) if qkv_preprocessor is not None: q, k, v = qkv_preprocessor(q, k, v) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) if ipadapter_kwargs is not None: hidden_states = self.interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) hidden_states = self.to_out(hidden_states) return hidden_states def xformers_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None): if encoder_hidden_states is None: encoder_hidden_states = hidden_states q = self.to_q(hidden_states) k = self.to_k(encoder_hidden_states) v = self.to_v(encoder_hidden_states) q = rearrange(q, "b f (n d) -> (b n) f d", n=self.num_heads) k = rearrange(k, "b f (n d) -> (b n) f d", n=self.num_heads) v = rearrange(v, "b f (n d) -> (b n) f d", n=self.num_heads) if attn_mask is not None: hidden_states = low_version_attention(q, k, v, attn_bias=attn_mask) else: import xformers.ops as xops hidden_states = xops.memory_efficient_attention(q, k, v) hidden_states = rearrange(hidden_states, "(b n) f d -> b f (n d)", n=self.num_heads) hidden_states = hidden_states.to(q.dtype) hidden_states = self.to_out(hidden_states) return hidden_states def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None): return self.torch_forward(hidden_states, encoder_hidden_states=encoder_hidden_states, attn_mask=attn_mask, ipadapter_kwargs=ipadapter_kwargs, qkv_preprocessor=qkv_preprocessor) ================================================ FILE: diffsynth/models/cog_dit.py ================================================ import torch from einops import rearrange, repeat from .sd3_dit import TimestepEmbeddings from .attention import Attention from .utils import load_state_dict_from_folder from .tiler import TileWorker2Dto3D import numpy as np class CogPatchify(torch.nn.Module): def __init__(self, dim_in, dim_out, patch_size) -> None: super().__init__() self.proj = torch.nn.Conv3d(dim_in, dim_out, kernel_size=(1, patch_size, patch_size), stride=(1, patch_size, patch_size)) def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states = rearrange(hidden_states, "B C T H W -> B (T H W) C") return hidden_states class CogAdaLayerNorm(torch.nn.Module): def __init__(self, dim, dim_cond, single=False): super().__init__() self.single = single self.linear = torch.nn.Linear(dim_cond, dim * (2 if single else 6)) self.norm = torch.nn.LayerNorm(dim, elementwise_affine=True, eps=1e-5) def forward(self, hidden_states, prompt_emb, emb): emb = self.linear(torch.nn.functional.silu(emb)) if self.single: shift, scale = emb.unsqueeze(1).chunk(2, dim=2) hidden_states = self.norm(hidden_states) * (1 + scale) + shift return hidden_states else: shift_a, scale_a, gate_a, shift_b, scale_b, gate_b = emb.unsqueeze(1).chunk(6, dim=2) hidden_states = self.norm(hidden_states) * (1 + scale_a) + shift_a prompt_emb = self.norm(prompt_emb) * (1 + scale_b) + shift_b return hidden_states, prompt_emb, gate_a, gate_b class CogDiTBlock(torch.nn.Module): def __init__(self, dim, dim_cond, num_heads): super().__init__() self.norm1 = CogAdaLayerNorm(dim, dim_cond) self.attn1 = Attention(q_dim=dim, num_heads=48, head_dim=dim//num_heads, bias_q=True, bias_kv=True, bias_out=True) self.norm_q = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True) self.norm_k = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True) self.norm2 = CogAdaLayerNorm(dim, dim_cond) self.ff = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) def apply_rotary_emb(self, x, freqs_cis): cos, sin = freqs_cis # [S, D] cos = cos[None, None] sin = sin[None, None] cos, sin = cos.to(x.device), sin.to(x.device) x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) return out def process_qkv(self, q, k, v, image_rotary_emb, text_seq_length): q = self.norm_q(q) k = self.norm_k(k) q[:, :, text_seq_length:] = self.apply_rotary_emb(q[:, :, text_seq_length:], image_rotary_emb) k[:, :, text_seq_length:] = self.apply_rotary_emb(k[:, :, text_seq_length:], image_rotary_emb) return q, k, v def forward(self, hidden_states, prompt_emb, time_emb, image_rotary_emb): # Attention norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm1( hidden_states, prompt_emb, time_emb ) attention_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1) attention_io = self.attn1( attention_io, qkv_preprocessor=lambda q, k, v: self.process_qkv(q, k, v, image_rotary_emb, prompt_emb.shape[1]) ) hidden_states = hidden_states + gate_a * attention_io[:, prompt_emb.shape[1]:] prompt_emb = prompt_emb + gate_b * attention_io[:, :prompt_emb.shape[1]] # Feed forward norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm2( hidden_states, prompt_emb, time_emb ) ff_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1) ff_io = self.ff(ff_io) hidden_states = hidden_states + gate_a * ff_io[:, prompt_emb.shape[1]:] prompt_emb = prompt_emb + gate_b * ff_io[:, :prompt_emb.shape[1]] return hidden_states, prompt_emb class CogDiT(torch.nn.Module): def __init__(self): super().__init__() self.patchify = CogPatchify(16, 3072, 2) self.time_embedder = TimestepEmbeddings(3072, 512) self.context_embedder = torch.nn.Linear(4096, 3072) self.blocks = torch.nn.ModuleList([CogDiTBlock(3072, 512, 48) for _ in range(42)]) self.norm_final = torch.nn.LayerNorm((3072,), eps=1e-05, elementwise_affine=True) self.norm_out = CogAdaLayerNorm(3072, 512, single=True) self.proj_out = torch.nn.Linear(3072, 64, bias=True) def get_resize_crop_region_for_grid(self, src, tgt_width, tgt_height): tw = tgt_width th = tgt_height h, w = src r = h / w if r > (th / tw): resize_height = th resize_width = int(round(th / h * w)) else: resize_width = tw resize_height = int(round(tw / w * h)) crop_top = int(round((th - resize_height) / 2.0)) crop_left = int(round((tw - resize_width) / 2.0)) return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) def get_3d_rotary_pos_embed( self, embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True ): start, stop = crops_coords grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32) grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32) grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32) # Compute dimensions for each axis dim_t = embed_dim // 4 dim_h = embed_dim // 8 * 3 dim_w = embed_dim // 8 * 3 # Temporal frequencies freqs_t = 1.0 / (theta ** (torch.arange(0, dim_t, 2).float() / dim_t)) grid_t = torch.from_numpy(grid_t).float() freqs_t = torch.einsum("n , f -> n f", grid_t, freqs_t) freqs_t = freqs_t.repeat_interleave(2, dim=-1) # Spatial frequencies for height and width freqs_h = 1.0 / (theta ** (torch.arange(0, dim_h, 2).float() / dim_h)) freqs_w = 1.0 / (theta ** (torch.arange(0, dim_w, 2).float() / dim_w)) grid_h = torch.from_numpy(grid_h).float() grid_w = torch.from_numpy(grid_w).float() freqs_h = torch.einsum("n , f -> n f", grid_h, freqs_h) freqs_w = torch.einsum("n , f -> n f", grid_w, freqs_w) freqs_h = freqs_h.repeat_interleave(2, dim=-1) freqs_w = freqs_w.repeat_interleave(2, dim=-1) # Broadcast and concatenate tensors along specified dimension def broadcast(tensors, dim=-1): num_tensors = len(tensors) shape_lens = {len(t.shape) for t in tensors} assert len(shape_lens) == 1, "tensors must all have the same number of dimensions" shape_len = list(shape_lens)[0] dim = (dim + shape_len) if dim < 0 else dim dims = list(zip(*(list(t.shape) for t in tensors))) expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] assert all( [*(len(set(t[1])) <= 2 for t in expandable_dims)] ), "invalid dimensions for broadcastable concatenation" max_dims = [(t[0], max(t[1])) for t in expandable_dims] expanded_dims = [(t[0], (t[1],) * num_tensors) for t in max_dims] expanded_dims.insert(dim, (dim, dims[dim])) expandable_shapes = list(zip(*(t[1] for t in expanded_dims))) tensors = [t[0].expand(*t[1]) for t in zip(tensors, expandable_shapes)] return torch.cat(tensors, dim=dim) freqs = broadcast((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1) t, h, w, d = freqs.shape freqs = freqs.view(t * h * w, d) # Generate sine and cosine components sin = freqs.sin() cos = freqs.cos() if use_real: return cos, sin else: freqs_cis = torch.polar(torch.ones_like(freqs), freqs) return freqs_cis def prepare_rotary_positional_embeddings( self, height: int, width: int, num_frames: int, device: torch.device, ): grid_height = height // 2 grid_width = width // 2 base_size_width = 720 // (8 * 2) base_size_height = 480 // (8 * 2) grid_crops_coords = self.get_resize_crop_region_for_grid( (grid_height, grid_width), base_size_width, base_size_height ) freqs_cos, freqs_sin = self.get_3d_rotary_pos_embed( embed_dim=64, crops_coords=grid_crops_coords, grid_size=(grid_height, grid_width), temporal_size=num_frames, use_real=True, ) freqs_cos = freqs_cos.to(device=device) freqs_sin = freqs_sin.to(device=device) return freqs_cos, freqs_sin def unpatchify(self, hidden_states, height, width): hidden_states = rearrange(hidden_states, "B (T H W) (C P Q) -> B C T (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2) return hidden_states def build_mask(self, T, H, W, dtype, device, is_bound): t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) border_width = (H + W) // 4 pad = torch.ones_like(h) * border_width mask = torch.stack([ pad if is_bound[0] else t + 1, pad if is_bound[1] else T - t, pad if is_bound[2] else h + 1, pad if is_bound[3] else H - h, pad if is_bound[4] else w + 1, pad if is_bound[5] else W - w ]).min(dim=0).values mask = mask.clip(1, border_width) mask = (mask / border_width).to(dtype=dtype, device=device) mask = rearrange(mask, "T H W -> 1 1 T H W") return mask def tiled_forward(self, hidden_states, timestep, prompt_emb, tile_size=(60, 90), tile_stride=(30, 45)): B, C, T, H, W = hidden_states.shape value = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.device) weight = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.device) # Split tasks tasks = [] for h in range(0, H, tile_stride): for w in range(0, W, tile_stride): if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W): continue h_, w_ = h + tile_size, w + tile_size if h_ > H: h, h_ = max(H - tile_size, 0), H if w_ > W: w, w_ = max(W - tile_size, 0), W tasks.append((h, h_, w, w_)) # Run for hl, hr, wl, wr in tasks: mask = self.build_mask( value.shape[2], (hr-hl), (wr-wl), hidden_states.dtype, hidden_states.device, is_bound=(True, True, hl==0, hr>=H, wl==0, wr>=W) ) model_output = self.forward(hidden_states[:, :, :, hl:hr, wl:wr], timestep, prompt_emb) value[:, :, :, hl:hr, wl:wr] += model_output * mask weight[:, :, :, hl:hr, wl:wr] += mask value = value / weight return value def forward(self, hidden_states, timestep, prompt_emb, image_rotary_emb=None, tiled=False, tile_size=90, tile_stride=30, use_gradient_checkpointing=False): if tiled: return TileWorker2Dto3D().tiled_forward( forward_fn=lambda x: self.forward(x, timestep, prompt_emb), model_input=hidden_states, tile_size=tile_size, tile_stride=tile_stride, tile_device=hidden_states.device, tile_dtype=hidden_states.dtype, computation_device=self.context_embedder.weight.device, computation_dtype=self.context_embedder.weight.dtype ) num_frames, height, width = hidden_states.shape[-3:] if image_rotary_emb is None: image_rotary_emb = self.prepare_rotary_positional_embeddings(height, width, num_frames, device=self.context_embedder.weight.device) hidden_states = self.patchify(hidden_states) time_emb = self.time_embedder(timestep, dtype=hidden_states.dtype) prompt_emb = self.context_embedder(prompt_emb) def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward for block in self.blocks: if self.training and use_gradient_checkpointing: hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, prompt_emb, time_emb, image_rotary_emb, use_reentrant=False, ) else: hidden_states, prompt_emb = block(hidden_states, prompt_emb, time_emb, image_rotary_emb) hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) hidden_states = self.norm_final(hidden_states) hidden_states = hidden_states[:, prompt_emb.shape[1]:] hidden_states = self.norm_out(hidden_states, prompt_emb, time_emb) hidden_states = self.proj_out(hidden_states) hidden_states = self.unpatchify(hidden_states, height, width) return hidden_states @staticmethod def state_dict_converter(): return CogDiTStateDictConverter() @staticmethod def from_pretrained(file_path, torch_dtype=torch.bfloat16): model = CogDiT().to(torch_dtype) state_dict = load_state_dict_from_folder(file_path, torch_dtype=torch_dtype) state_dict = CogDiT.state_dict_converter().from_diffusers(state_dict) model.load_state_dict(state_dict) return model class CogDiTStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "patch_embed.proj.weight": "patchify.proj.weight", "patch_embed.proj.bias": "patchify.proj.bias", "patch_embed.text_proj.weight": "context_embedder.weight", "patch_embed.text_proj.bias": "context_embedder.bias", "time_embedding.linear_1.weight": "time_embedder.timestep_embedder.0.weight", "time_embedding.linear_1.bias": "time_embedder.timestep_embedder.0.bias", "time_embedding.linear_2.weight": "time_embedder.timestep_embedder.2.weight", "time_embedding.linear_2.bias": "time_embedder.timestep_embedder.2.bias", "norm_final.weight": "norm_final.weight", "norm_final.bias": "norm_final.bias", "norm_out.linear.weight": "norm_out.linear.weight", "norm_out.linear.bias": "norm_out.linear.bias", "norm_out.norm.weight": "norm_out.norm.weight", "norm_out.norm.bias": "norm_out.norm.bias", "proj_out.weight": "proj_out.weight", "proj_out.bias": "proj_out.bias", } suffix_dict = { "norm1.linear.weight": "norm1.linear.weight", "norm1.linear.bias": "norm1.linear.bias", "norm1.norm.weight": "norm1.norm.weight", "norm1.norm.bias": "norm1.norm.bias", "attn1.norm_q.weight": "norm_q.weight", "attn1.norm_q.bias": "norm_q.bias", "attn1.norm_k.weight": "norm_k.weight", "attn1.norm_k.bias": "norm_k.bias", "attn1.to_q.weight": "attn1.to_q.weight", "attn1.to_q.bias": "attn1.to_q.bias", "attn1.to_k.weight": "attn1.to_k.weight", "attn1.to_k.bias": "attn1.to_k.bias", "attn1.to_v.weight": "attn1.to_v.weight", "attn1.to_v.bias": "attn1.to_v.bias", "attn1.to_out.0.weight": "attn1.to_out.weight", "attn1.to_out.0.bias": "attn1.to_out.bias", "norm2.linear.weight": "norm2.linear.weight", "norm2.linear.bias": "norm2.linear.bias", "norm2.norm.weight": "norm2.norm.weight", "norm2.norm.bias": "norm2.norm.bias", "ff.net.0.proj.weight": "ff.0.weight", "ff.net.0.proj.bias": "ff.0.bias", "ff.net.2.weight": "ff.2.weight", "ff.net.2.bias": "ff.2.bias", } state_dict_ = {} for name, param in state_dict.items(): if name in rename_dict: if name == "patch_embed.proj.weight": param = param.unsqueeze(2) state_dict_[rename_dict[name]] = param else: names = name.split(".") if names[0] == "transformer_blocks": suffix = ".".join(names[2:]) state_dict_[f"blocks.{names[1]}." + suffix_dict[suffix]] = param return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/cog_vae.py ================================================ import torch from einops import rearrange, repeat from .tiler import TileWorker2Dto3D class Downsample3D(torch.nn.Module): def __init__( self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 2, padding: int = 0, compress_time: bool = False, ): super().__init__() self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) self.compress_time = compress_time def forward(self, x: torch.Tensor, xq: torch.Tensor) -> torch.Tensor: if self.compress_time: batch_size, channels, frames, height, width = x.shape # (batch_size, channels, frames, height, width) -> (batch_size, height, width, channels, frames) -> (batch_size * height * width, channels, frames) x = x.permute(0, 3, 4, 1, 2).reshape(batch_size * height * width, channels, frames) if x.shape[-1] % 2 == 1: x_first, x_rest = x[..., 0], x[..., 1:] if x_rest.shape[-1] > 0: # (batch_size * height * width, channels, frames - 1) -> (batch_size * height * width, channels, (frames - 1) // 2) x_rest = torch.nn.functional.avg_pool1d(x_rest, kernel_size=2, stride=2) x = torch.cat([x_first[..., None], x_rest], dim=-1) # (batch_size * height * width, channels, (frames // 2) + 1) -> (batch_size, height, width, channels, (frames // 2) + 1) -> (batch_size, channels, (frames // 2) + 1, height, width) x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2) else: # (batch_size * height * width, channels, frames) -> (batch_size * height * width, channels, frames // 2) x = torch.nn.functional.avg_pool1d(x, kernel_size=2, stride=2) # (batch_size * height * width, channels, frames // 2) -> (batch_size, height, width, channels, frames // 2) -> (batch_size, channels, frames // 2, height, width) x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2) # Pad the tensor pad = (0, 1, 0, 1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) batch_size, channels, frames, height, width = x.shape # (batch_size, channels, frames, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size * frames, channels, height, width) x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channels, height, width) x = self.conv(x) # (batch_size * frames, channels, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size, channels, frames, height, width) x = x.reshape(batch_size, frames, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4) return x class Upsample3D(torch.nn.Module): def __init__( self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, padding: int = 1, compress_time: bool = False, ) -> None: super().__init__() self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) self.compress_time = compress_time def forward(self, inputs: torch.Tensor, xq: torch.Tensor) -> torch.Tensor: if self.compress_time: if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1: # split first frame x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:] x_first = torch.nn.functional.interpolate(x_first, scale_factor=2.0) x_rest = torch.nn.functional.interpolate(x_rest, scale_factor=2.0) x_first = x_first[:, :, None, :, :] inputs = torch.cat([x_first, x_rest], dim=2) elif inputs.shape[2] > 1: inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0) else: inputs = inputs.squeeze(2) inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0) inputs = inputs[:, :, None, :, :] else: # only interpolate 2D b, c, t, h, w = inputs.shape inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w) inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0) inputs = inputs.reshape(b, t, c, *inputs.shape[2:]).permute(0, 2, 1, 3, 4) b, c, t, h, w = inputs.shape inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w) inputs = self.conv(inputs) inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3, 4) return inputs class CogVideoXSpatialNorm3D(torch.nn.Module): def __init__(self, f_channels, zq_channels, groups): super().__init__() self.norm_layer = torch.nn.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True) self.conv_y = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1) self.conv_b = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1) def forward(self, f: torch.Tensor, zq: torch.Tensor) -> torch.Tensor: if f.shape[2] > 1 and f.shape[2] % 2 == 1: f_first, f_rest = f[:, :, :1], f[:, :, 1:] f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:] z_first, z_rest = zq[:, :, :1], zq[:, :, 1:] z_first = torch.nn.functional.interpolate(z_first, size=f_first_size) z_rest = torch.nn.functional.interpolate(z_rest, size=f_rest_size) zq = torch.cat([z_first, z_rest], dim=2) else: zq = torch.nn.functional.interpolate(zq, size=f.shape[-3:]) norm_f = self.norm_layer(f) new_f = norm_f * self.conv_y(zq) + self.conv_b(zq) return new_f class Resnet3DBlock(torch.nn.Module): def __init__(self, in_channels, out_channels, spatial_norm_dim, groups, eps=1e-6, use_conv_shortcut=False): super().__init__() self.nonlinearity = torch.nn.SiLU() if spatial_norm_dim is None: self.norm1 = torch.nn.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps) self.norm2 = torch.nn.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps) else: self.norm1 = CogVideoXSpatialNorm3D(in_channels, spatial_norm_dim, groups) self.norm2 = CogVideoXSpatialNorm3D(out_channels, spatial_norm_dim, groups) self.conv1 = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1)) self.conv2 = CachedConv3d(out_channels, out_channels, kernel_size=3, padding=(0, 1, 1)) if in_channels != out_channels: if use_conv_shortcut: self.conv_shortcut = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1)) else: self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1) else: self.conv_shortcut = lambda x: x def forward(self, hidden_states, zq): residual = hidden_states hidden_states = self.norm1(hidden_states, zq) if isinstance(self.norm1, CogVideoXSpatialNorm3D) else self.norm1(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.conv1(hidden_states) hidden_states = self.norm2(hidden_states, zq) if isinstance(self.norm2, CogVideoXSpatialNorm3D) else self.norm2(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.conv2(hidden_states) hidden_states = hidden_states + self.conv_shortcut(residual) return hidden_states class CachedConv3d(torch.nn.Conv3d): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0): super().__init__(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) self.cached_tensor = None def clear_cache(self): self.cached_tensor = None def forward(self, input: torch.Tensor, use_cache = True) -> torch.Tensor: if use_cache: if self.cached_tensor is None: self.cached_tensor = torch.concat([input[:, :, :1]] * 2, dim=2) input = torch.concat([self.cached_tensor, input], dim=2) self.cached_tensor = input[:, :, -2:] return super().forward(input) class CogVAEDecoder(torch.nn.Module): def __init__(self): super().__init__() self.scaling_factor = 0.7 self.conv_in = CachedConv3d(16, 512, kernel_size=3, stride=1, padding=(0, 1, 1)) self.blocks = torch.nn.ModuleList([ Resnet3DBlock(512, 512, 16, 32), Resnet3DBlock(512, 512, 16, 32), Resnet3DBlock(512, 512, 16, 32), Resnet3DBlock(512, 512, 16, 32), Resnet3DBlock(512, 512, 16, 32), Resnet3DBlock(512, 512, 16, 32), Upsample3D(512, 512, compress_time=True), Resnet3DBlock(512, 256, 16, 32), Resnet3DBlock(256, 256, 16, 32), Resnet3DBlock(256, 256, 16, 32), Resnet3DBlock(256, 256, 16, 32), Upsample3D(256, 256, compress_time=True), Resnet3DBlock(256, 256, 16, 32), Resnet3DBlock(256, 256, 16, 32), Resnet3DBlock(256, 256, 16, 32), Resnet3DBlock(256, 256, 16, 32), Upsample3D(256, 256, compress_time=False), Resnet3DBlock(256, 128, 16, 32), Resnet3DBlock(128, 128, 16, 32), Resnet3DBlock(128, 128, 16, 32), Resnet3DBlock(128, 128, 16, 32), ]) self.norm_out = CogVideoXSpatialNorm3D(128, 16, 32) self.conv_act = torch.nn.SiLU() self.conv_out = CachedConv3d(128, 3, kernel_size=3, stride=1, padding=(0, 1, 1)) def forward(self, sample): sample = sample / self.scaling_factor hidden_states = self.conv_in(sample) for block in self.blocks: hidden_states = block(hidden_states, sample) hidden_states = self.norm_out(hidden_states, sample) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states def decode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x): if tiled: B, C, T, H, W = sample.shape return TileWorker2Dto3D().tiled_forward( forward_fn=lambda x: self.decode_small_video(x), model_input=sample, tile_size=tile_size, tile_stride=tile_stride, tile_device=sample.device, tile_dtype=sample.dtype, computation_device=sample.device, computation_dtype=sample.dtype, scales=(3/16, (T//2*8+T%2)/T, 8, 8), progress_bar=progress_bar ) else: return self.decode_small_video(sample) def decode_small_video(self, sample): B, C, T, H, W = sample.shape computation_device = self.conv_in.weight.device computation_dtype = self.conv_in.weight.dtype value = [] for i in range(T//2): tl = i*2 + T%2 - (T%2 and i==0) tr = i*2 + 2 + T%2 model_input = sample[:, :, tl: tr, :, :].to(dtype=computation_dtype, device=computation_device) model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device) value.append(model_output) value = torch.concat(value, dim=2) for name, module in self.named_modules(): if isinstance(module, CachedConv3d): module.clear_cache() return value @staticmethod def state_dict_converter(): return CogVAEDecoderStateDictConverter() class CogVAEEncoder(torch.nn.Module): def __init__(self): super().__init__() self.scaling_factor = 0.7 self.conv_in = CachedConv3d(3, 128, kernel_size=3, stride=1, padding=(0, 1, 1)) self.blocks = torch.nn.ModuleList([ Resnet3DBlock(128, 128, None, 32), Resnet3DBlock(128, 128, None, 32), Resnet3DBlock(128, 128, None, 32), Downsample3D(128, 128, compress_time=True), Resnet3DBlock(128, 256, None, 32), Resnet3DBlock(256, 256, None, 32), Resnet3DBlock(256, 256, None, 32), Downsample3D(256, 256, compress_time=True), Resnet3DBlock(256, 256, None, 32), Resnet3DBlock(256, 256, None, 32), Resnet3DBlock(256, 256, None, 32), Downsample3D(256, 256, compress_time=False), Resnet3DBlock(256, 512, None, 32), Resnet3DBlock(512, 512, None, 32), Resnet3DBlock(512, 512, None, 32), Resnet3DBlock(512, 512, None, 32), Resnet3DBlock(512, 512, None, 32), ]) self.norm_out = torch.nn.GroupNorm(32, 512, eps=1e-06, affine=True) self.conv_act = torch.nn.SiLU() self.conv_out = CachedConv3d(512, 32, kernel_size=3, stride=1, padding=(0, 1, 1)) def forward(self, sample): hidden_states = self.conv_in(sample) for block in self.blocks: hidden_states = block(hidden_states, sample) hidden_states = self.norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states)[:, :16] hidden_states = hidden_states * self.scaling_factor return hidden_states def encode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x): if tiled: B, C, T, H, W = sample.shape return TileWorker2Dto3D().tiled_forward( forward_fn=lambda x: self.encode_small_video(x), model_input=sample, tile_size=(i * 8 for i in tile_size), tile_stride=(i * 8 for i in tile_stride), tile_device=sample.device, tile_dtype=sample.dtype, computation_device=sample.device, computation_dtype=sample.dtype, scales=(16/3, (T//4+T%2)/T, 1/8, 1/8), progress_bar=progress_bar ) else: return self.encode_small_video(sample) def encode_small_video(self, sample): B, C, T, H, W = sample.shape computation_device = self.conv_in.weight.device computation_dtype = self.conv_in.weight.dtype value = [] for i in range(T//8): t = i*8 + T%2 - (T%2 and i==0) t_ = i*8 + 8 + T%2 model_input = sample[:, :, t: t_, :, :].to(dtype=computation_dtype, device=computation_device) model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device) value.append(model_output) value = torch.concat(value, dim=2) for name, module in self.named_modules(): if isinstance(module, CachedConv3d): module.clear_cache() return value @staticmethod def state_dict_converter(): return CogVAEEncoderStateDictConverter() class CogVAEEncoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "encoder.conv_in.conv.weight": "conv_in.weight", "encoder.conv_in.conv.bias": "conv_in.bias", "encoder.down_blocks.0.downsamplers.0.conv.weight": "blocks.3.conv.weight", "encoder.down_blocks.0.downsamplers.0.conv.bias": "blocks.3.conv.bias", "encoder.down_blocks.1.downsamplers.0.conv.weight": "blocks.7.conv.weight", "encoder.down_blocks.1.downsamplers.0.conv.bias": "blocks.7.conv.bias", "encoder.down_blocks.2.downsamplers.0.conv.weight": "blocks.11.conv.weight", "encoder.down_blocks.2.downsamplers.0.conv.bias": "blocks.11.conv.bias", "encoder.norm_out.weight": "norm_out.weight", "encoder.norm_out.bias": "norm_out.bias", "encoder.conv_out.conv.weight": "conv_out.weight", "encoder.conv_out.conv.bias": "conv_out.bias", } prefix_dict = { "encoder.down_blocks.0.resnets.0.": "blocks.0.", "encoder.down_blocks.0.resnets.1.": "blocks.1.", "encoder.down_blocks.0.resnets.2.": "blocks.2.", "encoder.down_blocks.1.resnets.0.": "blocks.4.", "encoder.down_blocks.1.resnets.1.": "blocks.5.", "encoder.down_blocks.1.resnets.2.": "blocks.6.", "encoder.down_blocks.2.resnets.0.": "blocks.8.", "encoder.down_blocks.2.resnets.1.": "blocks.9.", "encoder.down_blocks.2.resnets.2.": "blocks.10.", "encoder.down_blocks.3.resnets.0.": "blocks.12.", "encoder.down_blocks.3.resnets.1.": "blocks.13.", "encoder.down_blocks.3.resnets.2.": "blocks.14.", "encoder.mid_block.resnets.0.": "blocks.15.", "encoder.mid_block.resnets.1.": "blocks.16.", } suffix_dict = { "norm1.norm_layer.weight": "norm1.norm_layer.weight", "norm1.norm_layer.bias": "norm1.norm_layer.bias", "norm1.conv_y.conv.weight": "norm1.conv_y.weight", "norm1.conv_y.conv.bias": "norm1.conv_y.bias", "norm1.conv_b.conv.weight": "norm1.conv_b.weight", "norm1.conv_b.conv.bias": "norm1.conv_b.bias", "norm2.norm_layer.weight": "norm2.norm_layer.weight", "norm2.norm_layer.bias": "norm2.norm_layer.bias", "norm2.conv_y.conv.weight": "norm2.conv_y.weight", "norm2.conv_y.conv.bias": "norm2.conv_y.bias", "norm2.conv_b.conv.weight": "norm2.conv_b.weight", "norm2.conv_b.conv.bias": "norm2.conv_b.bias", "conv1.conv.weight": "conv1.weight", "conv1.conv.bias": "conv1.bias", "conv2.conv.weight": "conv2.weight", "conv2.conv.bias": "conv2.bias", "conv_shortcut.weight": "conv_shortcut.weight", "conv_shortcut.bias": "conv_shortcut.bias", "norm1.weight": "norm1.weight", "norm1.bias": "norm1.bias", "norm2.weight": "norm2.weight", "norm2.bias": "norm2.bias", } state_dict_ = {} for name, param in state_dict.items(): if name in rename_dict: state_dict_[rename_dict[name]] = param else: for prefix in prefix_dict: if name.startswith(prefix): suffix = name[len(prefix):] state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) class CogVAEDecoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "decoder.conv_in.conv.weight": "conv_in.weight", "decoder.conv_in.conv.bias": "conv_in.bias", "decoder.up_blocks.0.upsamplers.0.conv.weight": "blocks.6.conv.weight", "decoder.up_blocks.0.upsamplers.0.conv.bias": "blocks.6.conv.bias", "decoder.up_blocks.1.upsamplers.0.conv.weight": "blocks.11.conv.weight", "decoder.up_blocks.1.upsamplers.0.conv.bias": "blocks.11.conv.bias", "decoder.up_blocks.2.upsamplers.0.conv.weight": "blocks.16.conv.weight", "decoder.up_blocks.2.upsamplers.0.conv.bias": "blocks.16.conv.bias", "decoder.norm_out.norm_layer.weight": "norm_out.norm_layer.weight", "decoder.norm_out.norm_layer.bias": "norm_out.norm_layer.bias", "decoder.norm_out.conv_y.conv.weight": "norm_out.conv_y.weight", "decoder.norm_out.conv_y.conv.bias": "norm_out.conv_y.bias", "decoder.norm_out.conv_b.conv.weight": "norm_out.conv_b.weight", "decoder.norm_out.conv_b.conv.bias": "norm_out.conv_b.bias", "decoder.conv_out.conv.weight": "conv_out.weight", "decoder.conv_out.conv.bias": "conv_out.bias" } prefix_dict = { "decoder.mid_block.resnets.0.": "blocks.0.", "decoder.mid_block.resnets.1.": "blocks.1.", "decoder.up_blocks.0.resnets.0.": "blocks.2.", "decoder.up_blocks.0.resnets.1.": "blocks.3.", "decoder.up_blocks.0.resnets.2.": "blocks.4.", "decoder.up_blocks.0.resnets.3.": "blocks.5.", "decoder.up_blocks.1.resnets.0.": "blocks.7.", "decoder.up_blocks.1.resnets.1.": "blocks.8.", "decoder.up_blocks.1.resnets.2.": "blocks.9.", "decoder.up_blocks.1.resnets.3.": "blocks.10.", "decoder.up_blocks.2.resnets.0.": "blocks.12.", "decoder.up_blocks.2.resnets.1.": "blocks.13.", "decoder.up_blocks.2.resnets.2.": "blocks.14.", "decoder.up_blocks.2.resnets.3.": "blocks.15.", "decoder.up_blocks.3.resnets.0.": "blocks.17.", "decoder.up_blocks.3.resnets.1.": "blocks.18.", "decoder.up_blocks.3.resnets.2.": "blocks.19.", "decoder.up_blocks.3.resnets.3.": "blocks.20.", } suffix_dict = { "norm1.norm_layer.weight": "norm1.norm_layer.weight", "norm1.norm_layer.bias": "norm1.norm_layer.bias", "norm1.conv_y.conv.weight": "norm1.conv_y.weight", "norm1.conv_y.conv.bias": "norm1.conv_y.bias", "norm1.conv_b.conv.weight": "norm1.conv_b.weight", "norm1.conv_b.conv.bias": "norm1.conv_b.bias", "norm2.norm_layer.weight": "norm2.norm_layer.weight", "norm2.norm_layer.bias": "norm2.norm_layer.bias", "norm2.conv_y.conv.weight": "norm2.conv_y.weight", "norm2.conv_y.conv.bias": "norm2.conv_y.bias", "norm2.conv_b.conv.weight": "norm2.conv_b.weight", "norm2.conv_b.conv.bias": "norm2.conv_b.bias", "conv1.conv.weight": "conv1.weight", "conv1.conv.bias": "conv1.bias", "conv2.conv.weight": "conv2.weight", "conv2.conv.bias": "conv2.bias", "conv_shortcut.weight": "conv_shortcut.weight", "conv_shortcut.bias": "conv_shortcut.bias", } state_dict_ = {} for name, param in state_dict.items(): if name in rename_dict: state_dict_[rename_dict[name]] = param else: for prefix in prefix_dict: if name.startswith(prefix): suffix = name[len(prefix):] state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/downloader.py ================================================ from huggingface_hub import hf_hub_download from modelscope import snapshot_download import os, shutil from typing_extensions import Literal, TypeAlias from typing import List from ..configs.model_config import preset_models_on_huggingface, preset_models_on_modelscope, Preset_model_id def download_from_modelscope(model_id, origin_file_path, local_dir): os.makedirs(local_dir, exist_ok=True) file_name = os.path.basename(origin_file_path) if file_name in os.listdir(local_dir): print(f" {file_name} has been already in {local_dir}.") else: print(f" Start downloading {os.path.join(local_dir, file_name)}") snapshot_download(model_id, allow_file_pattern=origin_file_path, local_dir=local_dir) downloaded_file_path = os.path.join(local_dir, origin_file_path) target_file_path = os.path.join(local_dir, os.path.split(origin_file_path)[-1]) if downloaded_file_path != target_file_path: shutil.move(downloaded_file_path, target_file_path) shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0])) def download_from_huggingface(model_id, origin_file_path, local_dir): os.makedirs(local_dir, exist_ok=True) file_name = os.path.basename(origin_file_path) if file_name in os.listdir(local_dir): print(f" {file_name} has been already in {local_dir}.") else: print(f" Start downloading {os.path.join(local_dir, file_name)}") hf_hub_download(model_id, origin_file_path, local_dir=local_dir) downloaded_file_path = os.path.join(local_dir, origin_file_path) target_file_path = os.path.join(local_dir, file_name) if downloaded_file_path != target_file_path: shutil.move(downloaded_file_path, target_file_path) shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0])) Preset_model_website: TypeAlias = Literal[ "HuggingFace", "ModelScope", ] website_to_preset_models = { "HuggingFace": preset_models_on_huggingface, "ModelScope": preset_models_on_modelscope, } website_to_download_fn = { "HuggingFace": download_from_huggingface, "ModelScope": download_from_modelscope, } def download_customized_models( model_id, origin_file_path, local_dir, downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"], ): downloaded_files = [] for website in downloading_priority: # Check if the file is downloaded. file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path)) if file_to_download in downloaded_files: continue # Download website_to_download_fn[website](model_id, origin_file_path, local_dir) if os.path.basename(origin_file_path) in os.listdir(local_dir): downloaded_files.append(file_to_download) return downloaded_files def download_models( model_id_list: List[Preset_model_id] = [], downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"], ): print(f"Downloading models: {model_id_list}") downloaded_files = [] load_files = [] for model_id in model_id_list: for website in downloading_priority: if model_id in website_to_preset_models[website]: # Parse model metadata model_metadata = website_to_preset_models[website][model_id] if isinstance(model_metadata, list): file_data = model_metadata else: file_data = model_metadata.get("file_list", []) # Try downloading the model from this website. model_files = [] for model_id, origin_file_path, local_dir in file_data: # Check if the file is downloaded. file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path)) if file_to_download in downloaded_files: continue # Download website_to_download_fn[website](model_id, origin_file_path, local_dir) if os.path.basename(origin_file_path) in os.listdir(local_dir): downloaded_files.append(file_to_download) model_files.append(file_to_download) # If the model is successfully downloaded, break. if len(model_files) > 0: if isinstance(model_metadata, dict) and "load_path" in model_metadata: model_files = model_metadata["load_path"] load_files.extend(model_files) break return load_files ================================================ FILE: diffsynth/models/flux_controlnet.py ================================================ import torch from einops import rearrange, repeat from .flux_dit import RoPEEmbedding, TimestepEmbeddings, FluxJointTransformerBlock, FluxSingleTransformerBlock, RMSNorm from .utils import hash_state_dict_keys, init_weights_on_device class FluxControlNet(torch.nn.Module): def __init__(self, disable_guidance_embedder=False, num_joint_blocks=5, num_single_blocks=10, num_mode=0, mode_dict={}, additional_input_dim=0): super().__init__() self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56]) self.time_embedder = TimestepEmbeddings(256, 3072) self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072) self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072)) self.context_embedder = torch.nn.Linear(4096, 3072) self.x_embedder = torch.nn.Linear(64, 3072) self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_joint_blocks)]) self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(num_single_blocks)]) self.controlnet_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_joint_blocks)]) self.controlnet_single_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_single_blocks)]) self.mode_dict = mode_dict self.controlnet_mode_embedder = torch.nn.Embedding(num_mode, 3072) if len(mode_dict) > 0 else None self.controlnet_x_embedder = torch.nn.Linear(64 + additional_input_dim, 3072) def prepare_image_ids(self, latents): batch_size, _, height, width = latents.shape 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( batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels ) latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype) return latent_image_ids def patchify(self, hidden_states): hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2) return hidden_states def align_res_stack_to_original_blocks(self, res_stack, num_blocks, hidden_states): if len(res_stack) == 0: return [torch.zeros_like(hidden_states)] * num_blocks interval = (num_blocks + len(res_stack) - 1) // len(res_stack) aligned_res_stack = [res_stack[block_id // interval] for block_id in range(num_blocks)] return aligned_res_stack def forward( self, hidden_states, controlnet_conditioning, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None, processor_id=None, tiled=False, tile_size=128, tile_stride=64, **kwargs ): if image_ids is None: image_ids = self.prepare_image_ids(hidden_states) conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb) if self.guidance_embedder is not None: guidance = guidance * 1000 conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype) prompt_emb = self.context_embedder(prompt_emb) if self.controlnet_mode_embedder is not None: # Different from FluxDiT processor_id = torch.tensor([self.mode_dict[processor_id]], dtype=torch.int) processor_id = repeat(processor_id, "D -> B D", B=1).to(text_ids.device) prompt_emb = torch.concat([self.controlnet_mode_embedder(processor_id), prompt_emb], dim=1) text_ids = torch.cat([text_ids[:, :1], text_ids], dim=1) image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) hidden_states = self.patchify(hidden_states) hidden_states = self.x_embedder(hidden_states) controlnet_conditioning = self.patchify(controlnet_conditioning) # Different from FluxDiT hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_conditioning) # Different from FluxDiT controlnet_res_stack = [] for block, controlnet_block in zip(self.blocks, self.controlnet_blocks): hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb) controlnet_res_stack.append(controlnet_block(hidden_states)) controlnet_single_res_stack = [] hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) for block, controlnet_block in zip(self.single_blocks, self.controlnet_single_blocks): hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb) controlnet_single_res_stack.append(controlnet_block(hidden_states[:, prompt_emb.shape[1]:])) controlnet_res_stack = self.align_res_stack_to_original_blocks(controlnet_res_stack, 19, hidden_states[:, prompt_emb.shape[1]:]) controlnet_single_res_stack = self.align_res_stack_to_original_blocks(controlnet_single_res_stack, 38, hidden_states[:, prompt_emb.shape[1]:]) return controlnet_res_stack, controlnet_single_res_stack @staticmethod def state_dict_converter(): return FluxControlNetStateDictConverter() def quantize(self): def cast_to(weight, dtype=None, device=None, copy=False): if device is None or weight.device == device: if not copy: if dtype is None or weight.dtype == dtype: return weight return weight.to(dtype=dtype, copy=copy) r = torch.empty_like(weight, dtype=dtype, device=device) r.copy_(weight) return r def cast_weight(s, input=None, dtype=None, device=None): if input is not None: if dtype is None: dtype = input.dtype if device is None: device = input.device weight = cast_to(s.weight, dtype, device) return weight def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): if input is not None: if dtype is None: dtype = input.dtype if bias_dtype is None: bias_dtype = dtype if device is None: device = input.device bias = None weight = cast_to(s.weight, dtype, device) bias = cast_to(s.bias, bias_dtype, device) return weight, bias class quantized_layer: class QLinear(torch.nn.Linear): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def forward(self,input,**kwargs): weight,bias= cast_bias_weight(self,input) return torch.nn.functional.linear(input,weight,bias) class QRMSNorm(torch.nn.Module): def __init__(self, module): super().__init__() self.module = module def forward(self,hidden_states,**kwargs): weight= cast_weight(self.module,hidden_states) input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps) hidden_states = hidden_states.to(input_dtype) * weight return hidden_states class QEmbedding(torch.nn.Embedding): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def forward(self,input,**kwargs): weight= cast_weight(self,input) return torch.nn.functional.embedding( input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse) def replace_layer(model): for name, module in model.named_children(): if isinstance(module,quantized_layer.QRMSNorm): continue if isinstance(module, torch.nn.Linear): with init_weights_on_device(): new_layer = quantized_layer.QLinear(module.in_features,module.out_features) new_layer.weight = module.weight if module.bias is not None: new_layer.bias = module.bias setattr(model, name, new_layer) elif isinstance(module, RMSNorm): if hasattr(module,"quantized"): continue module.quantized= True new_layer = quantized_layer.QRMSNorm(module) setattr(model, name, new_layer) elif isinstance(module,torch.nn.Embedding): rows, cols = module.weight.shape new_layer = quantized_layer.QEmbedding( num_embeddings=rows, embedding_dim=cols, _weight=module.weight, # _freeze=module.freeze, padding_idx=module.padding_idx, max_norm=module.max_norm, norm_type=module.norm_type, scale_grad_by_freq=module.scale_grad_by_freq, sparse=module.sparse) setattr(model, name, new_layer) else: replace_layer(module) replace_layer(self) class FluxControlNetStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): hash_value = hash_state_dict_keys(state_dict) global_rename_dict = { "context_embedder": "context_embedder", "x_embedder": "x_embedder", "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0", "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2", "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0", "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2", "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0", "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2", "norm_out.linear": "final_norm_out.linear", "proj_out": "final_proj_out", } rename_dict = { "proj_out": "proj_out", "norm1.linear": "norm1_a.linear", "norm1_context.linear": "norm1_b.linear", "attn.to_q": "attn.a_to_q", "attn.to_k": "attn.a_to_k", "attn.to_v": "attn.a_to_v", "attn.to_out.0": "attn.a_to_out", "attn.add_q_proj": "attn.b_to_q", "attn.add_k_proj": "attn.b_to_k", "attn.add_v_proj": "attn.b_to_v", "attn.to_add_out": "attn.b_to_out", "ff.net.0.proj": "ff_a.0", "ff.net.2": "ff_a.2", "ff_context.net.0.proj": "ff_b.0", "ff_context.net.2": "ff_b.2", "attn.norm_q": "attn.norm_q_a", "attn.norm_k": "attn.norm_k_a", "attn.norm_added_q": "attn.norm_q_b", "attn.norm_added_k": "attn.norm_k_b", } rename_dict_single = { "attn.to_q": "a_to_q", "attn.to_k": "a_to_k", "attn.to_v": "a_to_v", "attn.norm_q": "norm_q_a", "attn.norm_k": "norm_k_a", "norm.linear": "norm.linear", "proj_mlp": "proj_in_besides_attn", "proj_out": "proj_out", } state_dict_ = {} for name, param in state_dict.items(): if name.endswith(".weight") or name.endswith(".bias"): suffix = ".weight" if name.endswith(".weight") else ".bias" prefix = name[:-len(suffix)] if prefix in global_rename_dict: state_dict_[global_rename_dict[prefix] + suffix] = param elif prefix.startswith("transformer_blocks."): names = prefix.split(".") names[0] = "blocks" middle = ".".join(names[2:]) if middle in rename_dict: name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]]) state_dict_[name_] = param elif prefix.startswith("single_transformer_blocks."): names = prefix.split(".") names[0] = "single_blocks" middle = ".".join(names[2:]) if middle in rename_dict_single: name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]]) state_dict_[name_] = param else: state_dict_[name] = param else: state_dict_[name] = param for name in list(state_dict_.keys()): if ".proj_in_besides_attn." in name: name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.") param = torch.concat([ state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")], state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")], state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")], state_dict_[name], ], dim=0) state_dict_[name_] = param state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q.")) state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k.")) state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v.")) state_dict_.pop(name) for name in list(state_dict_.keys()): for component in ["a", "b"]: if f".{component}_to_q." in name: name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.") param = torch.concat([ state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")], state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")], state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")], ], dim=0) state_dict_[name_] = param state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q.")) state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k.")) state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v.")) if hash_value == "78d18b9101345ff695f312e7e62538c0": extra_kwargs = {"num_mode": 10, "mode_dict": {"canny": 0, "tile": 1, "depth": 2, "blur": 3, "pose": 4, "gray": 5, "lq": 6}} elif hash_value == "b001c89139b5f053c715fe772362dd2a": extra_kwargs = {"num_single_blocks": 0} elif hash_value == "52357cb26250681367488a8954c271e8": extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0, "additional_input_dim": 4} elif hash_value == "0cfd1740758423a2a854d67c136d1e8c": extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 1} else: extra_kwargs = {} return state_dict_, extra_kwargs def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/flux_dit.py ================================================ import torch from .sd3_dit import TimestepEmbeddings, AdaLayerNorm, RMSNorm from einops import rearrange from .tiler import TileWorker from .utils import init_weights_on_device def interact_with_ipadapter(hidden_states, q, ip_k, ip_v, scale=1.0): batch_size, num_tokens = hidden_states.shape[0:2] ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v) ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, num_tokens, -1) hidden_states = hidden_states + scale * ip_hidden_states return hidden_states class RoPEEmbedding(torch.nn.Module): def __init__(self, dim, theta, axes_dim): super().__init__() self.dim = dim self.theta = theta self.axes_dim = axes_dim def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: assert dim % 2 == 0, "The dimension must be even." scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim omega = 1.0 / (theta**scale) batch_size, seq_length = pos.shape out = torch.einsum("...n,d->...nd", pos, omega) cos_out = torch.cos(out) sin_out = torch.sin(out) stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1) out = stacked_out.view(batch_size, -1, dim // 2, 2, 2) return out.float() def forward(self, ids): n_axes = ids.shape[-1] emb = torch.cat([self.rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3) return emb.unsqueeze(1) class FluxJointAttention(torch.nn.Module): def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False): super().__init__() self.num_heads = num_heads self.head_dim = head_dim self.only_out_a = only_out_a self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3) self.norm_q_a = RMSNorm(head_dim, eps=1e-6) self.norm_k_a = RMSNorm(head_dim, eps=1e-6) self.norm_q_b = RMSNorm(head_dim, eps=1e-6) self.norm_k_b = RMSNorm(head_dim, eps=1e-6) self.a_to_out = torch.nn.Linear(dim_a, dim_a) if not only_out_a: self.b_to_out = torch.nn.Linear(dim_b, dim_b) def apply_rope(self, xq, xk, freqs_cis): xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None): batch_size = hidden_states_a.shape[0] # Part A qkv_a = self.a_to_qkv(hidden_states_a) qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q_a, k_a, v_a = qkv_a.chunk(3, dim=1) q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a) # Part B qkv_b = self.b_to_qkv(hidden_states_b) qkv_b = qkv_b.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q_b, k_b, v_b = qkv_b.chunk(3, dim=1) q_b, k_b = self.norm_q_b(q_b), self.norm_k_b(k_b) q = torch.concat([q_b, q_a], dim=2) k = torch.concat([k_b, k_a], dim=2) v = torch.concat([v_b, v_a], dim=2) q, k = self.apply_rope(q, k, image_rotary_emb) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) hidden_states_b, hidden_states_a = hidden_states[:, :hidden_states_b.shape[1]], hidden_states[:, hidden_states_b.shape[1]:] if ipadapter_kwargs_list is not None: hidden_states_a = interact_with_ipadapter(hidden_states_a, q_a, **ipadapter_kwargs_list) hidden_states_a = self.a_to_out(hidden_states_a) if self.only_out_a: return hidden_states_a else: hidden_states_b = self.b_to_out(hidden_states_b) return hidden_states_a, hidden_states_b class FluxJointTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads): super().__init__() self.norm1_a = AdaLayerNorm(dim) self.norm1_b = AdaLayerNorm(dim) self.attn = FluxJointAttention(dim, dim, num_attention_heads, dim // num_attention_heads) self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_a = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_b = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None): norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) # Attention attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb, attn_mask, ipadapter_kwargs_list) # Part A hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) # Part B hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) return hidden_states_a, hidden_states_b class FluxSingleAttention(torch.nn.Module): def __init__(self, dim_a, dim_b, num_heads, head_dim): super().__init__() self.num_heads = num_heads self.head_dim = head_dim self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) self.norm_q_a = RMSNorm(head_dim, eps=1e-6) self.norm_k_a = RMSNorm(head_dim, eps=1e-6) def apply_rope(self, xq, xk, freqs_cis): xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) def forward(self, hidden_states, image_rotary_emb): batch_size = hidden_states.shape[0] qkv_a = self.a_to_qkv(hidden_states) qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q_a, k_a, v = qkv_a.chunk(3, dim=1) q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a) q, k = self.apply_rope(q_a, k_a, image_rotary_emb) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) return hidden_states class AdaLayerNormSingle(torch.nn.Module): def __init__(self, dim): super().__init__() self.silu = torch.nn.SiLU() self.linear = torch.nn.Linear(dim, 3 * dim, bias=True) self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) def forward(self, x, emb): emb = self.linear(self.silu(emb)) shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1) x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] return x, gate_msa class FluxSingleTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads): super().__init__() self.num_heads = num_attention_heads self.head_dim = dim // num_attention_heads self.dim = dim self.norm = AdaLayerNormSingle(dim) self.to_qkv_mlp = torch.nn.Linear(dim, dim * (3 + 4)) self.norm_q_a = RMSNorm(self.head_dim, eps=1e-6) self.norm_k_a = RMSNorm(self.head_dim, eps=1e-6) self.proj_out = torch.nn.Linear(dim * 5, dim) def apply_rope(self, xq, xk, freqs_cis): xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) def process_attention(self, hidden_states, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None): batch_size = hidden_states.shape[0] qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q, k, v = qkv.chunk(3, dim=1) q, k = self.norm_q_a(q), self.norm_k_a(k) q, k = self.apply_rope(q, k, image_rotary_emb) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) if ipadapter_kwargs_list is not None: hidden_states = interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs_list) return hidden_states def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None): residual = hidden_states_a norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb) hidden_states_a = self.to_qkv_mlp(norm_hidden_states) attn_output, mlp_hidden_states = hidden_states_a[:, :, :self.dim * 3], hidden_states_a[:, :, self.dim * 3:] attn_output = self.process_attention(attn_output, image_rotary_emb, attn_mask, ipadapter_kwargs_list) mlp_hidden_states = torch.nn.functional.gelu(mlp_hidden_states, approximate="tanh") hidden_states_a = torch.cat([attn_output, mlp_hidden_states], dim=2) hidden_states_a = gate.unsqueeze(1) * self.proj_out(hidden_states_a) hidden_states_a = residual + hidden_states_a return hidden_states_a, hidden_states_b class AdaLayerNormContinuous(torch.nn.Module): def __init__(self, dim): super().__init__() self.silu = torch.nn.SiLU() self.linear = torch.nn.Linear(dim, dim * 2, bias=True) self.norm = torch.nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False) def forward(self, x, conditioning): emb = self.linear(self.silu(conditioning)) scale, shift = torch.chunk(emb, 2, dim=1) x = self.norm(x) * (1 + scale)[:, None] + shift[:, None] return x class FluxDiT(torch.nn.Module): def __init__(self, disable_guidance_embedder=False): super().__init__() self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56]) self.time_embedder = TimestepEmbeddings(256, 3072) self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072) self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072)) self.context_embedder = torch.nn.Linear(4096, 3072) self.x_embedder = torch.nn.Linear(64, 3072) self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(19)]) self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(38)]) self.final_norm_out = AdaLayerNormContinuous(3072) self.final_proj_out = torch.nn.Linear(3072, 64) def patchify(self, hidden_states): hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2) return hidden_states def unpatchify(self, hidden_states, height, width): hidden_states = rearrange(hidden_states, "B (H W) (C P Q) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2) return hidden_states def prepare_image_ids(self, latents): batch_size, _, height, width = latents.shape 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( batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels ) latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype) return latent_image_ids def tiled_forward( self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, tile_size=128, tile_stride=64, **kwargs ): # Due to the global positional embedding, we cannot implement layer-wise tiled forward. hidden_states = TileWorker().tiled_forward( lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None), hidden_states, tile_size, tile_stride, tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) return hidden_states def construct_mask(self, entity_masks, prompt_seq_len, image_seq_len): N = len(entity_masks) batch_size = entity_masks[0].shape[0] total_seq_len = N * prompt_seq_len + image_seq_len patched_masks = [self.patchify(entity_masks[i]) for i in range(N)] attention_mask = torch.ones((batch_size, total_seq_len, total_seq_len), dtype=torch.bool).to(device=entity_masks[0].device) image_start = N * prompt_seq_len image_end = N * prompt_seq_len + image_seq_len # prompt-image mask for i in range(N): prompt_start = i * prompt_seq_len prompt_end = (i + 1) * prompt_seq_len image_mask = torch.sum(patched_masks[i], dim=-1) > 0 image_mask = image_mask.unsqueeze(1).repeat(1, prompt_seq_len, 1) # prompt update with image attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask # image update with prompt attention_mask[:, image_start:image_end, prompt_start:prompt_end] = image_mask.transpose(1, 2) # prompt-prompt mask for i in range(N): for j in range(N): if i != j: prompt_start_i = i * prompt_seq_len prompt_end_i = (i + 1) * prompt_seq_len prompt_start_j = j * prompt_seq_len prompt_end_j = (j + 1) * prompt_seq_len attention_mask[:, prompt_start_i:prompt_end_i, prompt_start_j:prompt_end_j] = False attention_mask = attention_mask.float() attention_mask[attention_mask == 0] = float('-inf') attention_mask[attention_mask == 1] = 0 return attention_mask def process_entity_masks(self, hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids): repeat_dim = hidden_states.shape[1] max_masks = 0 attention_mask = None prompt_embs = [prompt_emb] if entity_masks is not None: # entity_masks batch_size, max_masks = entity_masks.shape[0], entity_masks.shape[1] entity_masks = entity_masks.repeat(1, 1, repeat_dim, 1, 1) entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)] # global mask global_mask = torch.ones_like(entity_masks[0]).to(device=hidden_states.device, dtype=hidden_states.dtype) entity_masks = entity_masks + [global_mask] # append global to last # attention mask attention_mask = self.construct_mask(entity_masks, prompt_emb.shape[1], hidden_states.shape[1]) attention_mask = attention_mask.to(device=hidden_states.device, dtype=hidden_states.dtype) attention_mask = attention_mask.unsqueeze(1) # embds: n_masks * b * seq * d local_embs = [entity_prompt_emb[:, i, None].squeeze(1) for i in range(max_masks)] prompt_embs = local_embs + prompt_embs # append global to last prompt_embs = [self.context_embedder(prompt_emb) for prompt_emb in prompt_embs] prompt_emb = torch.cat(prompt_embs, dim=1) # positional embedding text_ids = torch.cat([text_ids] * (max_masks + 1), dim=1) image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) return prompt_emb, image_rotary_emb, attention_mask def forward( self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None, tiled=False, tile_size=128, tile_stride=64, entity_prompt_emb=None, entity_masks=None, use_gradient_checkpointing=False, **kwargs ): if tiled: return self.tiled_forward( hidden_states, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, tile_size=tile_size, tile_stride=tile_stride, **kwargs ) if image_ids is None: image_ids = self.prepare_image_ids(hidden_states) conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb) if self.guidance_embedder is not None: guidance = guidance * 1000 conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype) height, width = hidden_states.shape[-2:] hidden_states = self.patchify(hidden_states) hidden_states = self.x_embedder(hidden_states) if entity_prompt_emb is not None and entity_masks is not None: prompt_emb, image_rotary_emb, attention_mask = self.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids) else: prompt_emb = self.context_embedder(prompt_emb) image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) attention_mask = None def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward for block in self.blocks: if self.training and use_gradient_checkpointing: hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, use_reentrant=False, ) else: hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask) hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) for block in self.single_blocks: if self.training and use_gradient_checkpointing: hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, use_reentrant=False, ) else: hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask) hidden_states = hidden_states[:, prompt_emb.shape[1]:] hidden_states = self.final_norm_out(hidden_states, conditioning) hidden_states = self.final_proj_out(hidden_states) hidden_states = self.unpatchify(hidden_states, height, width) return hidden_states def quantize(self): def cast_to(weight, dtype=None, device=None, copy=False): if device is None or weight.device == device: if not copy: if dtype is None or weight.dtype == dtype: return weight return weight.to(dtype=dtype, copy=copy) r = torch.empty_like(weight, dtype=dtype, device=device) r.copy_(weight) return r def cast_weight(s, input=None, dtype=None, device=None): if input is not None: if dtype is None: dtype = input.dtype if device is None: device = input.device weight = cast_to(s.weight, dtype, device) return weight def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): if input is not None: if dtype is None: dtype = input.dtype if bias_dtype is None: bias_dtype = dtype if device is None: device = input.device bias = None weight = cast_to(s.weight, dtype, device) bias = cast_to(s.bias, bias_dtype, device) return weight, bias class quantized_layer: class Linear(torch.nn.Linear): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def forward(self,input,**kwargs): weight,bias= cast_bias_weight(self,input) return torch.nn.functional.linear(input,weight,bias) class RMSNorm(torch.nn.Module): def __init__(self, module): super().__init__() self.module = module def forward(self,hidden_states,**kwargs): weight= cast_weight(self.module,hidden_states) input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps) hidden_states = hidden_states.to(input_dtype) * weight return hidden_states def replace_layer(model): for name, module in model.named_children(): if isinstance(module, torch.nn.Linear): with init_weights_on_device(): new_layer = quantized_layer.Linear(module.in_features,module.out_features) new_layer.weight = module.weight if module.bias is not None: new_layer.bias = module.bias # del module setattr(model, name, new_layer) elif isinstance(module, RMSNorm): if hasattr(module,"quantized"): continue module.quantized= True new_layer = quantized_layer.RMSNorm(module) setattr(model, name, new_layer) else: replace_layer(module) replace_layer(self) @staticmethod def state_dict_converter(): return FluxDiTStateDictConverter() class FluxDiTStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): global_rename_dict = { "context_embedder": "context_embedder", "x_embedder": "x_embedder", "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0", "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2", "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0", "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2", "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0", "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2", "norm_out.linear": "final_norm_out.linear", "proj_out": "final_proj_out", } rename_dict = { "proj_out": "proj_out", "norm1.linear": "norm1_a.linear", "norm1_context.linear": "norm1_b.linear", "attn.to_q": "attn.a_to_q", "attn.to_k": "attn.a_to_k", "attn.to_v": "attn.a_to_v", "attn.to_out.0": "attn.a_to_out", "attn.add_q_proj": "attn.b_to_q", "attn.add_k_proj": "attn.b_to_k", "attn.add_v_proj": "attn.b_to_v", "attn.to_add_out": "attn.b_to_out", "ff.net.0.proj": "ff_a.0", "ff.net.2": "ff_a.2", "ff_context.net.0.proj": "ff_b.0", "ff_context.net.2": "ff_b.2", "attn.norm_q": "attn.norm_q_a", "attn.norm_k": "attn.norm_k_a", "attn.norm_added_q": "attn.norm_q_b", "attn.norm_added_k": "attn.norm_k_b", } rename_dict_single = { "attn.to_q": "a_to_q", "attn.to_k": "a_to_k", "attn.to_v": "a_to_v", "attn.norm_q": "norm_q_a", "attn.norm_k": "norm_k_a", "norm.linear": "norm.linear", "proj_mlp": "proj_in_besides_attn", "proj_out": "proj_out", } state_dict_ = {} for name, param in state_dict.items(): if name.endswith(".weight") or name.endswith(".bias"): suffix = ".weight" if name.endswith(".weight") else ".bias" prefix = name[:-len(suffix)] if prefix in global_rename_dict: state_dict_[global_rename_dict[prefix] + suffix] = param elif prefix.startswith("transformer_blocks."): names = prefix.split(".") names[0] = "blocks" middle = ".".join(names[2:]) if middle in rename_dict: name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]]) state_dict_[name_] = param elif prefix.startswith("single_transformer_blocks."): names = prefix.split(".") names[0] = "single_blocks" middle = ".".join(names[2:]) if middle in rename_dict_single: name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]]) state_dict_[name_] = param else: pass else: pass for name in list(state_dict_.keys()): if "single_blocks." in name and ".a_to_q." in name: mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None) if mlp is None: mlp = torch.zeros(4 * state_dict_[name].shape[0], *state_dict_[name].shape[1:], dtype=state_dict_[name].dtype) else: state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn.")) param = torch.concat([ state_dict_.pop(name), state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")), state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")), mlp, ], dim=0) name_ = name.replace(".a_to_q.", ".to_qkv_mlp.") state_dict_[name_] = param for name in list(state_dict_.keys()): for component in ["a", "b"]: if f".{component}_to_q." in name: name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.") param = torch.concat([ state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")], state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")], state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")], ], dim=0) state_dict_[name_] = param state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q.")) state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k.")) state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v.")) return state_dict_ def from_civitai(self, state_dict): rename_dict = { "time_in.in_layer.bias": "time_embedder.timestep_embedder.0.bias", "time_in.in_layer.weight": "time_embedder.timestep_embedder.0.weight", "time_in.out_layer.bias": "time_embedder.timestep_embedder.2.bias", "time_in.out_layer.weight": "time_embedder.timestep_embedder.2.weight", "txt_in.bias": "context_embedder.bias", "txt_in.weight": "context_embedder.weight", "vector_in.in_layer.bias": "pooled_text_embedder.0.bias", "vector_in.in_layer.weight": "pooled_text_embedder.0.weight", "vector_in.out_layer.bias": "pooled_text_embedder.2.bias", "vector_in.out_layer.weight": "pooled_text_embedder.2.weight", "final_layer.linear.bias": "final_proj_out.bias", "final_layer.linear.weight": "final_proj_out.weight", "guidance_in.in_layer.bias": "guidance_embedder.timestep_embedder.0.bias", "guidance_in.in_layer.weight": "guidance_embedder.timestep_embedder.0.weight", "guidance_in.out_layer.bias": "guidance_embedder.timestep_embedder.2.bias", "guidance_in.out_layer.weight": "guidance_embedder.timestep_embedder.2.weight", "img_in.bias": "x_embedder.bias", "img_in.weight": "x_embedder.weight", "final_layer.adaLN_modulation.1.weight": "final_norm_out.linear.weight", "final_layer.adaLN_modulation.1.bias": "final_norm_out.linear.bias", } suffix_rename_dict = { "img_attn.norm.key_norm.scale": "attn.norm_k_a.weight", "img_attn.norm.query_norm.scale": "attn.norm_q_a.weight", "img_attn.proj.bias": "attn.a_to_out.bias", "img_attn.proj.weight": "attn.a_to_out.weight", "img_attn.qkv.bias": "attn.a_to_qkv.bias", "img_attn.qkv.weight": "attn.a_to_qkv.weight", "img_mlp.0.bias": "ff_a.0.bias", "img_mlp.0.weight": "ff_a.0.weight", "img_mlp.2.bias": "ff_a.2.bias", "img_mlp.2.weight": "ff_a.2.weight", "img_mod.lin.bias": "norm1_a.linear.bias", "img_mod.lin.weight": "norm1_a.linear.weight", "txt_attn.norm.key_norm.scale": "attn.norm_k_b.weight", "txt_attn.norm.query_norm.scale": "attn.norm_q_b.weight", "txt_attn.proj.bias": "attn.b_to_out.bias", "txt_attn.proj.weight": "attn.b_to_out.weight", "txt_attn.qkv.bias": "attn.b_to_qkv.bias", "txt_attn.qkv.weight": "attn.b_to_qkv.weight", "txt_mlp.0.bias": "ff_b.0.bias", "txt_mlp.0.weight": "ff_b.0.weight", "txt_mlp.2.bias": "ff_b.2.bias", "txt_mlp.2.weight": "ff_b.2.weight", "txt_mod.lin.bias": "norm1_b.linear.bias", "txt_mod.lin.weight": "norm1_b.linear.weight", "linear1.bias": "to_qkv_mlp.bias", "linear1.weight": "to_qkv_mlp.weight", "linear2.bias": "proj_out.bias", "linear2.weight": "proj_out.weight", "modulation.lin.bias": "norm.linear.bias", "modulation.lin.weight": "norm.linear.weight", "norm.key_norm.scale": "norm_k_a.weight", "norm.query_norm.scale": "norm_q_a.weight", } state_dict_ = {} for name, param in state_dict.items(): if name.startswith("model.diffusion_model."): name = name[len("model.diffusion_model."):] names = name.split(".") if name in rename_dict: rename = rename_dict[name] if name.startswith("final_layer.adaLN_modulation.1."): param = torch.concat([param[3072:], param[:3072]], dim=0) state_dict_[rename] = param elif names[0] == "double_blocks": rename = f"blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])] state_dict_[rename] = param elif names[0] == "single_blocks": if ".".join(names[2:]) in suffix_rename_dict: rename = f"single_blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])] state_dict_[rename] = param else: pass if "guidance_embedder.timestep_embedder.0.weight" not in state_dict_: return state_dict_, {"disable_guidance_embedder": True} else: return state_dict_ ================================================ FILE: diffsynth/models/flux_ipadapter.py ================================================ from .svd_image_encoder import SVDImageEncoder from .sd3_dit import RMSNorm from transformers import CLIPImageProcessor import torch class MLPProjModel(torch.nn.Module): def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4): super().__init__() self.cross_attention_dim = cross_attention_dim self.num_tokens = num_tokens self.proj = torch.nn.Sequential( torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2), torch.nn.GELU(), torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens), ) self.norm = torch.nn.LayerNorm(cross_attention_dim) def forward(self, id_embeds): x = self.proj(id_embeds) x = x.reshape(-1, self.num_tokens, self.cross_attention_dim) x = self.norm(x) return x class IpAdapterModule(torch.nn.Module): def __init__(self, num_attention_heads, attention_head_dim, input_dim): super().__init__() self.num_heads = num_attention_heads self.head_dim = attention_head_dim output_dim = num_attention_heads * attention_head_dim self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False) self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False) self.norm_added_k = RMSNorm(attention_head_dim, eps=1e-5, elementwise_affine=False) def forward(self, hidden_states): batch_size = hidden_states.shape[0] # ip_k ip_k = self.to_k_ip(hidden_states) ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) ip_k = self.norm_added_k(ip_k) # ip_v ip_v = self.to_v_ip(hidden_states) ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) return ip_k, ip_v class FluxIpAdapter(torch.nn.Module): def __init__(self, num_attention_heads=24, attention_head_dim=128, cross_attention_dim=4096, num_tokens=128, num_blocks=57): super().__init__() self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(num_attention_heads, attention_head_dim, cross_attention_dim) for _ in range(num_blocks)]) self.image_proj = MLPProjModel(cross_attention_dim=cross_attention_dim, id_embeddings_dim=1152, num_tokens=num_tokens) self.set_adapter() def set_adapter(self): self.call_block_id = {i:i for i in range(len(self.ipadapter_modules))} def forward(self, hidden_states, scale=1.0): hidden_states = self.image_proj(hidden_states) hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1]) ip_kv_dict = {} for block_id in self.call_block_id: ipadapter_id = self.call_block_id[block_id] ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states) ip_kv_dict[block_id] = { "ip_k": ip_k, "ip_v": ip_v, "scale": scale } return ip_kv_dict @staticmethod def state_dict_converter(): return FluxIpAdapterStateDictConverter() class FluxIpAdapterStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = {} for name in state_dict["ip_adapter"]: name_ = 'ipadapter_modules.' + name state_dict_[name_] = state_dict["ip_adapter"][name] for name in state_dict["image_proj"]: name_ = "image_proj." + name state_dict_[name_] = state_dict["image_proj"][name] return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/flux_text_encoder.py ================================================ import torch from transformers import T5EncoderModel, T5Config from .sd_text_encoder import SDTextEncoder class FluxTextEncoder2(T5EncoderModel): def __init__(self, config): super().__init__(config) self.eval() def forward(self, input_ids): outputs = super().forward(input_ids=input_ids) prompt_emb = outputs.last_hidden_state return prompt_emb @staticmethod def state_dict_converter(): return FluxTextEncoder2StateDictConverter() class FluxTextEncoder2StateDictConverter(): def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = state_dict return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/flux_vae.py ================================================ from .sd3_vae_encoder import SD3VAEEncoder, SDVAEEncoderStateDictConverter from .sd3_vae_decoder import SD3VAEDecoder, SDVAEDecoderStateDictConverter class FluxVAEEncoder(SD3VAEEncoder): def __init__(self): super().__init__() self.scaling_factor = 0.3611 self.shift_factor = 0.1159 @staticmethod def state_dict_converter(): return FluxVAEEncoderStateDictConverter() class FluxVAEDecoder(SD3VAEDecoder): def __init__(self): super().__init__() self.scaling_factor = 0.3611 self.shift_factor = 0.1159 @staticmethod def state_dict_converter(): return FluxVAEDecoderStateDictConverter() class FluxVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter): def __init__(self): pass def from_civitai(self, state_dict): rename_dict = { "encoder.conv_in.bias": "conv_in.bias", "encoder.conv_in.weight": "conv_in.weight", "encoder.conv_out.bias": "conv_out.bias", "encoder.conv_out.weight": "conv_out.weight", "encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", "encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", "encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", "encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", "encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", "encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight", "encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias", "encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight", "encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias", "encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight", "encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias", "encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight", "encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias", "encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight", "encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias", "encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight", "encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias", "encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight", "encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias", "encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight", "encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias", "encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight", "encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias", "encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight", "encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias", "encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight", "encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias", "encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight", "encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias", "encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight", "encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias", "encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight", "encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias", "encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight", "encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias", "encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight", "encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias", "encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight", "encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias", "encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight", "encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias", "encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight", "encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias", "encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight", "encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias", "encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight", "encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias", "encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight", "encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias", "encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight", "encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias", "encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight", "encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias", "encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight", "encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias", "encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight", "encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias", "encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight", "encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias", "encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight", "encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias", "encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight", "encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias", "encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight", "encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias", "encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight", "encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias", "encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight", "encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias", "encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight", "encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias", "encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight", "encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias", "encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight", "encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias", "encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight", "encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias", "encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight", "encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias", "encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight", "encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias", "encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight", "encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias", "encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight", "encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", "encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", "encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", "encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", "encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", "encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", "encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", "encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", "encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", "encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", "encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", "encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", "encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", "encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", "encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", "encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", "encoder.norm_out.bias": "conv_norm_out.bias", "encoder.norm_out.weight": "conv_norm_out.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if "transformer_blocks" in rename_dict[name]: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ class FluxVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter): def __init__(self): pass def from_civitai(self, state_dict): rename_dict = { "decoder.conv_in.bias": "conv_in.bias", "decoder.conv_in.weight": "conv_in.weight", "decoder.conv_out.bias": "conv_out.bias", "decoder.conv_out.weight": "conv_out.weight", "decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias", "decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight", "decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias", "decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight", "decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias", "decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight", "decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias", "decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight", "decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias", "decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight", "decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias", "decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight", "decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias", "decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight", "decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias", "decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight", "decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias", "decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight", "decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias", "decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight", "decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias", "decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight", "decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias", "decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight", "decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias", "decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight", "decoder.norm_out.bias": "conv_norm_out.bias", "decoder.norm_out.weight": "conv_norm_out.weight", "decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias", "decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight", "decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias", "decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight", "decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias", "decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight", "decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias", "decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight", "decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias", "decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight", "decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias", "decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight", "decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias", "decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight", "decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias", "decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight", "decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias", "decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight", "decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias", "decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight", "decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias", "decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight", "decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias", "decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight", "decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias", "decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight", "decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias", "decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight", "decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias", "decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight", "decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias", "decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight", "decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias", "decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight", "decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias", "decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight", "decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias", "decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight", "decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias", "decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight", "decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias", "decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight", "decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias", "decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight", "decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias", "decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight", "decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias", "decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight", "decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias", "decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight", "decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias", "decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight", "decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias", "decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight", "decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias", "decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight", "decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias", "decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight", "decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias", "decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight", "decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias", "decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight", "decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias", "decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight", "decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias", "decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight", "decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias", "decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight", "decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias", "decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight", "decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias", "decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight", "decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias", "decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight", "decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias", "decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight", "decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias", "decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight", "decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias", "decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight", "decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias", "decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight", "decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias", "decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight", "decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias", "decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight", "decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias", "decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight", "decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias", "decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight", "decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias", "decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight", "decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias", "decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight", "decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias", "decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight", "decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias", "decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight", "decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias", "decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight", "decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias", "decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight", "decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias", "decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight", "decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias", "decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if "transformer_blocks" in rename_dict[name]: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ ================================================ FILE: diffsynth/models/hunyuan_dit.py ================================================ from .attention import Attention from einops import repeat, rearrange import math import torch class HunyuanDiTRotaryEmbedding(torch.nn.Module): def __init__(self, q_norm_shape=88, k_norm_shape=88, rotary_emb_on_k=True): super().__init__() self.q_norm = torch.nn.LayerNorm((q_norm_shape,), elementwise_affine=True, eps=1e-06) self.k_norm = torch.nn.LayerNorm((k_norm_shape,), elementwise_affine=True, eps=1e-06) self.rotary_emb_on_k = rotary_emb_on_k self.k_cache, self.v_cache = [], [] def reshape_for_broadcast(self, freqs_cis, x): ndim = x.ndim shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) def rotate_half(self, x): x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) return torch.stack([-x_imag, x_real], dim=-1).flatten(3) def apply_rotary_emb(self, xq, xk, freqs_cis): xk_out = None cos, sin = self.reshape_for_broadcast(freqs_cis, xq) cos, sin = cos.to(xq.device), sin.to(xq.device) xq_out = (xq.float() * cos + self.rotate_half(xq.float()) * sin).type_as(xq) if xk is not None: xk_out = (xk.float() * cos + self.rotate_half(xk.float()) * sin).type_as(xk) return xq_out, xk_out def forward(self, q, k, v, freqs_cis_img, to_cache=False): # norm q = self.q_norm(q) k = self.k_norm(k) # RoPE if self.rotary_emb_on_k: q, k = self.apply_rotary_emb(q, k, freqs_cis_img) else: q, _ = self.apply_rotary_emb(q, None, freqs_cis_img) if to_cache: self.k_cache.append(k) self.v_cache.append(v) elif len(self.k_cache) > 0 and len(self.v_cache) > 0: k = torch.concat([k] + self.k_cache, dim=2) v = torch.concat([v] + self.v_cache, dim=2) self.k_cache, self.v_cache = [], [] return q, k, v class FP32_Layernorm(torch.nn.LayerNorm): def forward(self, inputs): origin_dtype = inputs.dtype return torch.nn.functional.layer_norm(inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps).to(origin_dtype) class FP32_SiLU(torch.nn.SiLU): def forward(self, inputs): origin_dtype = inputs.dtype return torch.nn.functional.silu(inputs.float(), inplace=False).to(origin_dtype) class HunyuanDiTFinalLayer(torch.nn.Module): def __init__(self, final_hidden_size=1408, condition_dim=1408, patch_size=2, out_channels=8): super().__init__() self.norm_final = torch.nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6) self.linear = torch.nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True) self.adaLN_modulation = torch.nn.Sequential( FP32_SiLU(), torch.nn.Linear(condition_dim, 2 * final_hidden_size, bias=True) ) def modulate(self, x, shift, scale): return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) def forward(self, hidden_states, condition_emb): shift, scale = self.adaLN_modulation(condition_emb).chunk(2, dim=1) hidden_states = self.modulate(self.norm_final(hidden_states), shift, scale) hidden_states = self.linear(hidden_states) return hidden_states class HunyuanDiTBlock(torch.nn.Module): def __init__( self, hidden_dim=1408, condition_dim=1408, num_heads=16, mlp_ratio=4.3637, text_dim=1024, skip_connection=False ): super().__init__() self.norm1 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True) self.rota1 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads) self.attn1 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, bias_q=True, bias_kv=True, bias_out=True) self.norm2 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True) self.rota2 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads, rotary_emb_on_k=False) self.attn2 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, kv_dim=text_dim, bias_q=True, bias_kv=True, bias_out=True) self.norm3 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True) self.modulation = torch.nn.Sequential(FP32_SiLU(), torch.nn.Linear(condition_dim, hidden_dim, bias=True)) self.mlp = torch.nn.Sequential( torch.nn.Linear(hidden_dim, int(hidden_dim*mlp_ratio), bias=True), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(int(hidden_dim*mlp_ratio), hidden_dim, bias=True) ) if skip_connection: self.skip_norm = FP32_Layernorm((hidden_dim * 2,), eps=1e-6, elementwise_affine=True) self.skip_linear = torch.nn.Linear(hidden_dim * 2, hidden_dim, bias=True) else: self.skip_norm, self.skip_linear = None, None def forward(self, hidden_states, condition_emb, text_emb, freq_cis_img, residual=None, to_cache=False): # Long Skip Connection if self.skip_norm is not None and self.skip_linear is not None: hidden_states = torch.cat([hidden_states, residual], dim=-1) hidden_states = self.skip_norm(hidden_states) hidden_states = self.skip_linear(hidden_states) # Self-Attention shift_msa = self.modulation(condition_emb).unsqueeze(dim=1) attn_input = self.norm1(hidden_states) + shift_msa hidden_states = hidden_states + self.attn1(attn_input, qkv_preprocessor=lambda q, k, v: self.rota1(q, k, v, freq_cis_img, to_cache=to_cache)) # Cross-Attention attn_input = self.norm3(hidden_states) hidden_states = hidden_states + self.attn2(attn_input, text_emb, qkv_preprocessor=lambda q, k, v: self.rota2(q, k, v, freq_cis_img)) # FFN Layer mlp_input = self.norm2(hidden_states) hidden_states = hidden_states + self.mlp(mlp_input) return hidden_states class AttentionPool(torch.nn.Module): def __init__(self, spacial_dim, embed_dim, num_heads, output_dim = None): super().__init__() self.positional_embedding = torch.nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5) self.k_proj = torch.nn.Linear(embed_dim, embed_dim) self.q_proj = torch.nn.Linear(embed_dim, embed_dim) self.v_proj = torch.nn.Linear(embed_dim, embed_dim) self.c_proj = torch.nn.Linear(embed_dim, output_dim or embed_dim) self.num_heads = num_heads def forward(self, x): x = x.permute(1, 0, 2) # NLC -> LNC x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC x = x + self.positional_embedding[:, None, :].to(x.dtype) # (L+1)NC x, _ = torch.nn.functional.multi_head_attention_forward( query=x[:1], key=x, value=x, embed_dim_to_check=x.shape[-1], num_heads=self.num_heads, q_proj_weight=self.q_proj.weight, k_proj_weight=self.k_proj.weight, v_proj_weight=self.v_proj.weight, in_proj_weight=None, in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), bias_k=None, bias_v=None, add_zero_attn=False, dropout_p=0, out_proj_weight=self.c_proj.weight, out_proj_bias=self.c_proj.bias, use_separate_proj_weight=True, training=self.training, need_weights=False ) return x.squeeze(0) class PatchEmbed(torch.nn.Module): def __init__( self, patch_size=(2, 2), in_chans=4, embed_dim=1408, bias=True, ): super().__init__() self.proj = torch.nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) def forward(self, x): x = self.proj(x) x = x.flatten(2).transpose(1, 2) # BCHW -> BNC return x def timestep_embedding(t, dim, max_period=10000, repeat_only=False): # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py if not repeat_only: half = dim // 2 freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half ).to(device=t.device) # size: [dim/2], 一个指数衰减的曲线 args = t[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat( [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 ) else: embedding = repeat(t, "b -> b d", d=dim) return embedding class TimestepEmbedder(torch.nn.Module): def __init__(self, hidden_size=1408, frequency_embedding_size=256): super().__init__() self.mlp = torch.nn.Sequential( torch.nn.Linear(frequency_embedding_size, hidden_size, bias=True), torch.nn.SiLU(), torch.nn.Linear(hidden_size, hidden_size, bias=True), ) self.frequency_embedding_size = frequency_embedding_size def forward(self, t): t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype) t_emb = self.mlp(t_freq) return t_emb class HunyuanDiT(torch.nn.Module): def __init__(self, num_layers_down=21, num_layers_up=19, in_channels=4, out_channels=8, hidden_dim=1408, text_dim=1024, t5_dim=2048, text_length=77, t5_length=256): super().__init__() # Embedders self.text_emb_padding = torch.nn.Parameter(torch.randn(text_length + t5_length, text_dim, dtype=torch.float32)) self.t5_embedder = torch.nn.Sequential( torch.nn.Linear(t5_dim, t5_dim * 4, bias=True), FP32_SiLU(), torch.nn.Linear(t5_dim * 4, text_dim, bias=True), ) self.t5_pooler = AttentionPool(t5_length, t5_dim, num_heads=8, output_dim=1024) self.style_embedder = torch.nn.Parameter(torch.randn(hidden_dim)) self.patch_embedder = PatchEmbed(in_chans=in_channels) self.timestep_embedder = TimestepEmbedder() self.extra_embedder = torch.nn.Sequential( torch.nn.Linear(256 * 6 + 1024 + hidden_dim, hidden_dim * 4), FP32_SiLU(), torch.nn.Linear(hidden_dim * 4, hidden_dim), ) # Transformer blocks self.num_layers_down = num_layers_down self.num_layers_up = num_layers_up self.blocks = torch.nn.ModuleList( [HunyuanDiTBlock(skip_connection=False) for _ in range(num_layers_down)] + \ [HunyuanDiTBlock(skip_connection=True) for _ in range(num_layers_up)] ) # Output layers self.final_layer = HunyuanDiTFinalLayer() self.out_channels = out_channels def prepare_text_emb(self, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5): text_emb_mask = text_emb_mask.bool() text_emb_mask_t5 = text_emb_mask_t5.bool() text_emb_t5 = self.t5_embedder(text_emb_t5) text_emb = torch.cat([text_emb, text_emb_t5], dim=1) text_emb_mask = torch.cat([text_emb_mask, text_emb_mask_t5], dim=-1) text_emb = torch.where(text_emb_mask.unsqueeze(2), text_emb, self.text_emb_padding.to(text_emb)) return text_emb def prepare_extra_emb(self, text_emb_t5, timestep, size_emb, dtype, batch_size): # Text embedding pooled_text_emb_t5 = self.t5_pooler(text_emb_t5) # Timestep embedding timestep_emb = self.timestep_embedder(timestep) # Size embedding size_emb = timestep_embedding(size_emb.view(-1), 256).to(dtype) size_emb = size_emb.view(-1, 6 * 256) # Style embedding style_emb = repeat(self.style_embedder, "D -> B D", B=batch_size) # Concatenate all extra vectors extra_emb = torch.cat([pooled_text_emb_t5, size_emb, style_emb], dim=1) condition_emb = timestep_emb + self.extra_embedder(extra_emb) return condition_emb def unpatchify(self, x, h, w): return rearrange(x, "B (H W) (P Q C) -> B C (H P) (W Q)", H=h, W=w, P=2, Q=2) def build_mask(self, data, is_bound): _, _, H, W = data.shape h = repeat(torch.arange(H), "H -> H W", H=H, W=W) w = repeat(torch.arange(W), "W -> H W", H=H, W=W) border_width = (H + W) // 4 pad = torch.ones_like(h) * border_width mask = torch.stack([ pad if is_bound[0] else h + 1, pad if is_bound[1] else H - h, pad if is_bound[2] else w + 1, pad if is_bound[3] else W - w ]).min(dim=0).values mask = mask.clip(1, border_width) mask = (mask / border_width).to(dtype=data.dtype, device=data.device) mask = rearrange(mask, "H W -> 1 H W") return mask def tiled_block_forward(self, block, hidden_states, condition_emb, text_emb, freq_cis_img, residual, torch_dtype, data_device, computation_device, tile_size, tile_stride): B, C, H, W = hidden_states.shape weight = torch.zeros((1, 1, H, W), dtype=torch_dtype, device=data_device) values = torch.zeros((B, C, H, W), dtype=torch_dtype, device=data_device) # Split tasks tasks = [] for h in range(0, H, tile_stride): for w in range(0, W, tile_stride): if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W): continue h_, w_ = h + tile_size, w + tile_size if h_ > H: h, h_ = H - tile_size, H if w_ > W: w, w_ = W - tile_size, W tasks.append((h, h_, w, w_)) # Run for hl, hr, wl, wr in tasks: hidden_states_batch = hidden_states[:, :, hl:hr, wl:wr].to(computation_device) hidden_states_batch = rearrange(hidden_states_batch, "B C H W -> B (H W) C") if residual is not None: residual_batch = residual[:, :, hl:hr, wl:wr].to(computation_device) residual_batch = rearrange(residual_batch, "B C H W -> B (H W) C") else: residual_batch = None # Forward hidden_states_batch = block(hidden_states_batch, condition_emb, text_emb, freq_cis_img, residual_batch).to(data_device) hidden_states_batch = rearrange(hidden_states_batch, "B (H W) C -> B C H W", H=hr-hl) mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W)) values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask weight[:, :, hl:hr, wl:wr] += mask values /= weight return values def forward( self, hidden_states, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5, timestep, size_emb, freq_cis_img, tiled=False, tile_size=64, tile_stride=32, to_cache=False, use_gradient_checkpointing=False, ): # Embeddings text_emb = self.prepare_text_emb(text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5) condition_emb = self.prepare_extra_emb(text_emb_t5, timestep, size_emb, hidden_states.dtype, hidden_states.shape[0]) # Input height, width = hidden_states.shape[-2], hidden_states.shape[-1] hidden_states = self.patch_embedder(hidden_states) # Blocks def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if tiled: hidden_states = rearrange(hidden_states, "B (H W) C -> B C H W", H=height//2) residuals = [] for block_id, block in enumerate(self.blocks): residual = residuals.pop() if block_id >= self.num_layers_down else None hidden_states = self.tiled_block_forward( block, hidden_states, condition_emb, text_emb, freq_cis_img, residual, torch_dtype=hidden_states.dtype, data_device=hidden_states.device, computation_device=hidden_states.device, tile_size=tile_size, tile_stride=tile_stride ) if block_id < self.num_layers_down - 2: residuals.append(hidden_states) hidden_states = rearrange(hidden_states, "B C H W -> B (H W) C") else: residuals = [] for block_id, block in enumerate(self.blocks): residual = residuals.pop() if block_id >= self.num_layers_down else None if self.training and use_gradient_checkpointing: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, condition_emb, text_emb, freq_cis_img, residual, use_reentrant=False, ) else: hidden_states = block(hidden_states, condition_emb, text_emb, freq_cis_img, residual, to_cache=to_cache) if block_id < self.num_layers_down - 2: residuals.append(hidden_states) # Output hidden_states = self.final_layer(hidden_states, condition_emb) hidden_states = self.unpatchify(hidden_states, height//2, width//2) hidden_states, _ = hidden_states.chunk(2, dim=1) return hidden_states @staticmethod def state_dict_converter(): return HunyuanDiTStateDictConverter() class HunyuanDiTStateDictConverter(): def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = {} for name, param in state_dict.items(): name_ = name name_ = name_.replace(".default_modulation.", ".modulation.") name_ = name_.replace(".mlp.fc1.", ".mlp.0.") name_ = name_.replace(".mlp.fc2.", ".mlp.2.") name_ = name_.replace(".attn1.q_norm.", ".rota1.q_norm.") name_ = name_.replace(".attn2.q_norm.", ".rota2.q_norm.") name_ = name_.replace(".attn1.k_norm.", ".rota1.k_norm.") name_ = name_.replace(".attn2.k_norm.", ".rota2.k_norm.") name_ = name_.replace(".q_proj.", ".to_q.") name_ = name_.replace(".out_proj.", ".to_out.") name_ = name_.replace("text_embedding_padding", "text_emb_padding") name_ = name_.replace("mlp_t5.0.", "t5_embedder.0.") name_ = name_.replace("mlp_t5.2.", "t5_embedder.2.") name_ = name_.replace("pooler.", "t5_pooler.") name_ = name_.replace("x_embedder.", "patch_embedder.") name_ = name_.replace("t_embedder.", "timestep_embedder.") name_ = name_.replace("t5_pooler.to_q.", "t5_pooler.q_proj.") name_ = name_.replace("style_embedder.weight", "style_embedder") if ".kv_proj." in name_: param_k = param[:param.shape[0]//2] param_v = param[param.shape[0]//2:] state_dict_[name_.replace(".kv_proj.", ".to_k.")] = param_k state_dict_[name_.replace(".kv_proj.", ".to_v.")] = param_v elif ".Wqkv." in name_: param_q = param[:param.shape[0]//3] param_k = param[param.shape[0]//3:param.shape[0]//3*2] param_v = param[param.shape[0]//3*2:] state_dict_[name_.replace(".Wqkv.", ".to_q.")] = param_q state_dict_[name_.replace(".Wqkv.", ".to_k.")] = param_k state_dict_[name_.replace(".Wqkv.", ".to_v.")] = param_v elif "style_embedder" in name_: state_dict_[name_] = param.squeeze() else: state_dict_[name_] = param return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/hunyuan_dit_text_encoder.py ================================================ from transformers import BertModel, BertConfig, T5EncoderModel, T5Config import torch class HunyuanDiTCLIPTextEncoder(BertModel): def __init__(self): config = BertConfig( _name_or_path = "", architectures = ["BertModel"], attention_probs_dropout_prob = 0.1, bos_token_id = 0, classifier_dropout = None, directionality = "bidi", eos_token_id = 2, hidden_act = "gelu", hidden_dropout_prob = 0.1, hidden_size = 1024, initializer_range = 0.02, intermediate_size = 4096, layer_norm_eps = 1e-12, max_position_embeddings = 512, model_type = "bert", num_attention_heads = 16, num_hidden_layers = 24, output_past = True, pad_token_id = 0, pooler_fc_size = 768, pooler_num_attention_heads = 12, pooler_num_fc_layers = 3, pooler_size_per_head = 128, pooler_type = "first_token_transform", position_embedding_type = "absolute", torch_dtype = "float32", transformers_version = "4.37.2", type_vocab_size = 2, use_cache = True, vocab_size = 47020 ) super().__init__(config, add_pooling_layer=False) self.eval() def forward(self, input_ids, attention_mask, clip_skip=1): input_shape = input_ids.size() batch_size, seq_length = input_shape device = input_ids.device past_key_values_length = 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) embedding_output = self.embeddings( input_ids=input_ids, position_ids=None, token_type_ids=None, inputs_embeds=None, past_key_values_length=0, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=False, output_attentions=False, output_hidden_states=True, return_dict=True, ) all_hidden_states = encoder_outputs.hidden_states prompt_emb = all_hidden_states[-clip_skip] if clip_skip > 1: mean, std = all_hidden_states[-1].mean(), all_hidden_states[-1].std() prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean return prompt_emb @staticmethod def state_dict_converter(): return HunyuanDiTCLIPTextEncoderStateDictConverter() class HunyuanDiTT5TextEncoder(T5EncoderModel): def __init__(self): config = T5Config( _name_or_path = "../HunyuanDiT/t2i/mt5", architectures = ["MT5ForConditionalGeneration"], classifier_dropout = 0.0, d_ff = 5120, d_kv = 64, d_model = 2048, decoder_start_token_id = 0, dense_act_fn = "gelu_new", dropout_rate = 0.1, eos_token_id = 1, feed_forward_proj = "gated-gelu", initializer_factor = 1.0, is_encoder_decoder = True, is_gated_act = True, layer_norm_epsilon = 1e-06, model_type = "t5", num_decoder_layers = 24, num_heads = 32, num_layers = 24, output_past = True, pad_token_id = 0, relative_attention_max_distance = 128, relative_attention_num_buckets = 32, tie_word_embeddings = False, tokenizer_class = "T5Tokenizer", transformers_version = "4.37.2", use_cache = True, vocab_size = 250112 ) super().__init__(config) self.eval() def forward(self, input_ids, attention_mask, clip_skip=1): outputs = super().forward( input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=True, ) prompt_emb = outputs.hidden_states[-clip_skip] if clip_skip > 1: mean, std = outputs.hidden_states[-1].mean(), outputs.hidden_states[-1].std() prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean return prompt_emb @staticmethod def state_dict_converter(): return HunyuanDiTT5TextEncoderStateDictConverter() class HunyuanDiTCLIPTextEncoderStateDictConverter(): def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = {name[5:]: param for name, param in state_dict.items() if name.startswith("bert.")} return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) class HunyuanDiTT5TextEncoderStateDictConverter(): def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("encoder.")} state_dict_["shared.weight"] = state_dict["shared.weight"] return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/hunyuan_video_dit.py ================================================ import torch from .sd3_dit import TimestepEmbeddings, RMSNorm from .utils import init_weights_on_device from einops import rearrange, repeat from tqdm import tqdm from typing import Union, Tuple, List from .utils import hash_state_dict_keys def HunyuanVideoRope(latents): def _to_tuple(x, dim=2): if isinstance(x, int): return (x,) * dim elif len(x) == dim: return x else: raise ValueError(f"Expected length {dim} or int, but got {x}") def get_meshgrid_nd(start, *args, dim=2): """ Get n-D meshgrid with start, stop and num. Args: start (int or tuple): If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. For n-dim, start/stop/num should be int or n-tuple. If n-tuple is provided, the meshgrid will be stacked following the dim order in n-tuples. *args: See above. dim (int): Dimension of the meshgrid. Defaults to 2. Returns: grid (np.ndarray): [dim, ...] """ if len(args) == 0: # start is grid_size num = _to_tuple(start, dim=dim) start = (0,) * dim stop = num elif len(args) == 1: # start is start, args[0] is stop, step is 1 start = _to_tuple(start, dim=dim) stop = _to_tuple(args[0], dim=dim) num = [stop[i] - start[i] for i in range(dim)] elif len(args) == 2: # start is start, args[0] is stop, args[1] is num start = _to_tuple(start, dim=dim) # Left-Top eg: 12,0 stop = _to_tuple(args[0], dim=dim) # Right-Bottom eg: 20,32 num = _to_tuple(args[1], dim=dim) # Target Size eg: 32,124 else: raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") # PyTorch implement of np.linspace(start[i], stop[i], num[i], endpoint=False) axis_grid = [] for i in range(dim): a, b, n = start[i], stop[i], num[i] g = torch.linspace(a, b, n + 1, dtype=torch.float32)[:n] axis_grid.append(g) grid = torch.meshgrid(*axis_grid, indexing="ij") # dim x [W, H, D] grid = torch.stack(grid, dim=0) # [dim, W, H, D] return grid def get_1d_rotary_pos_embed( dim: int, pos: Union[torch.FloatTensor, int], theta: float = 10000.0, use_real: bool = False, theta_rescale_factor: float = 1.0, interpolation_factor: float = 1.0, ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: """ Precompute the frequency tensor for complex exponential (cis) with given dimensions. (Note: `cis` means `cos + i * sin`, where i is the imaginary unit.) This function calculates a frequency tensor with complex exponential using the given dimension 'dim' and the end index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64 data type. Args: dim (int): Dimension of the frequency tensor. pos (int or torch.FloatTensor): Position indices for the frequency tensor. [S] or scalar theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0. use_real (bool, optional): If True, return real part and imaginary part separately. Otherwise, return complex numbers. theta_rescale_factor (float, optional): Rescale factor for theta. Defaults to 1.0. Returns: freqs_cis: Precomputed frequency tensor with complex exponential. [S, D/2] freqs_cos, freqs_sin: Precomputed frequency tensor with real and imaginary parts separately. [S, D] """ if isinstance(pos, int): pos = torch.arange(pos).float() # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning # has some connection to NTK literature if theta_rescale_factor != 1.0: theta *= theta_rescale_factor ** (dim / (dim - 2)) freqs = 1.0 / ( theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim) ) # [D/2] # assert interpolation_factor == 1.0, f"interpolation_factor: {interpolation_factor}" freqs = torch.outer(pos * interpolation_factor, freqs) # [S, D/2] if use_real: freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] return freqs_cos, freqs_sin else: freqs_cis = torch.polar( torch.ones_like(freqs), freqs ) # complex64 # [S, D/2] return freqs_cis def get_nd_rotary_pos_embed( rope_dim_list, start, *args, theta=10000.0, use_real=False, theta_rescale_factor: Union[float, List[float]] = 1.0, interpolation_factor: Union[float, List[float]] = 1.0, ): """ This is a n-d version of precompute_freqs_cis, which is a RoPE for tokens with n-d structure. Args: rope_dim_list (list of int): Dimension of each rope. len(rope_dim_list) should equal to n. sum(rope_dim_list) should equal to head_dim of attention layer. start (int | tuple of int | list of int): If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. *args: See above. theta (float): Scaling factor for frequency computation. Defaults to 10000.0. use_real (bool): If True, return real part and imaginary part separately. Otherwise, return complex numbers. Some libraries such as TensorRT does not support complex64 data type. So it is useful to provide a real part and an imaginary part separately. theta_rescale_factor (float): Rescale factor for theta. Defaults to 1.0. Returns: pos_embed (torch.Tensor): [HW, D/2] """ grid = get_meshgrid_nd( start, *args, dim=len(rope_dim_list) ) # [3, W, H, D] / [2, W, H] if isinstance(theta_rescale_factor, int) or isinstance(theta_rescale_factor, float): theta_rescale_factor = [theta_rescale_factor] * len(rope_dim_list) elif isinstance(theta_rescale_factor, list) and len(theta_rescale_factor) == 1: theta_rescale_factor = [theta_rescale_factor[0]] * len(rope_dim_list) assert len(theta_rescale_factor) == len( rope_dim_list ), "len(theta_rescale_factor) should equal to len(rope_dim_list)" if isinstance(interpolation_factor, int) or isinstance(interpolation_factor, float): interpolation_factor = [interpolation_factor] * len(rope_dim_list) elif isinstance(interpolation_factor, list) and len(interpolation_factor) == 1: interpolation_factor = [interpolation_factor[0]] * len(rope_dim_list) assert len(interpolation_factor) == len( rope_dim_list ), "len(interpolation_factor) should equal to len(rope_dim_list)" # use 1/ndim of dimensions to encode grid_axis embs = [] for i in range(len(rope_dim_list)): emb = get_1d_rotary_pos_embed( rope_dim_list[i], grid[i].reshape(-1), theta, use_real=use_real, theta_rescale_factor=theta_rescale_factor[i], interpolation_factor=interpolation_factor[i], ) # 2 x [WHD, rope_dim_list[i]] embs.append(emb) if use_real: cos = torch.cat([emb[0] for emb in embs], dim=1) # (WHD, D/2) sin = torch.cat([emb[1] for emb in embs], dim=1) # (WHD, D/2) return cos, sin else: emb = torch.cat(embs, dim=1) # (WHD, D/2) return emb freqs_cos, freqs_sin = get_nd_rotary_pos_embed( [16, 56, 56], [latents.shape[2], latents.shape[3] // 2, latents.shape[4] // 2], theta=256, use_real=True, theta_rescale_factor=1, ) return freqs_cos, freqs_sin class PatchEmbed(torch.nn.Module): def __init__(self, patch_size=(1, 2, 2), in_channels=16, embed_dim=3072): super().__init__() self.proj = torch.nn.Conv3d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): x = self.proj(x) x = x.flatten(2).transpose(1, 2) return x class IndividualTokenRefinerBlock(torch.nn.Module): def __init__(self, hidden_size=3072, num_heads=24): super().__init__() self.num_heads = num_heads self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6) self.self_attn_qkv = torch.nn.Linear(hidden_size, hidden_size * 3) self.self_attn_proj = torch.nn.Linear(hidden_size, hidden_size) self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6) self.mlp = torch.nn.Sequential( torch.nn.Linear(hidden_size, hidden_size * 4), torch.nn.SiLU(), torch.nn.Linear(hidden_size * 4, hidden_size) ) self.adaLN_modulation = torch.nn.Sequential( torch.nn.SiLU(), torch.nn.Linear(hidden_size, hidden_size * 2, device="cuda", dtype=torch.bfloat16), ) def forward(self, x, c, attn_mask=None): gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1) norm_x = self.norm1(x) qkv = self.self_attn_qkv(norm_x) q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) attn = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) attn = rearrange(attn, "B H L D -> B L (H D)") x = x + self.self_attn_proj(attn) * gate_msa.unsqueeze(1) x = x + self.mlp(self.norm2(x)) * gate_mlp.unsqueeze(1) return x class SingleTokenRefiner(torch.nn.Module): def __init__(self, in_channels=4096, hidden_size=3072, depth=2): super().__init__() self.input_embedder = torch.nn.Linear(in_channels, hidden_size, bias=True) self.t_embedder = TimestepEmbeddings(256, hidden_size, computation_device="cpu") self.c_embedder = torch.nn.Sequential( torch.nn.Linear(in_channels, hidden_size), torch.nn.SiLU(), torch.nn.Linear(hidden_size, hidden_size) ) self.blocks = torch.nn.ModuleList([IndividualTokenRefinerBlock(hidden_size=hidden_size) for _ in range(depth)]) def forward(self, x, t, mask=None): timestep_aware_representations = self.t_embedder(t, dtype=torch.float32) mask_float = mask.float().unsqueeze(-1) context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1) context_aware_representations = self.c_embedder(context_aware_representations) c = timestep_aware_representations + context_aware_representations x = self.input_embedder(x) mask = mask.to(device=x.device, dtype=torch.bool) mask = repeat(mask, "B L -> B 1 D L", D=mask.shape[-1]) mask = mask & mask.transpose(2, 3) mask[:, :, :, 0] = True for block in self.blocks: x = block(x, c, mask) return x class ModulateDiT(torch.nn.Module): def __init__(self, hidden_size, factor=6): super().__init__() self.act = torch.nn.SiLU() self.linear = torch.nn.Linear(hidden_size, factor * hidden_size) def forward(self, x): return self.linear(self.act(x)) def modulate(x, shift=None, scale=None, tr_shift=None, tr_scale=None, tr_token=None): if tr_shift is not None: x_zero = x[:, :tr_token] * (1 + tr_scale.unsqueeze(1)) + tr_shift.unsqueeze(1) x_orig = x[:, tr_token:] * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) x = torch.concat((x_zero, x_orig), dim=1) return x if scale is None and shift is None: return x elif shift is None: return x * (1 + scale.unsqueeze(1)) elif scale is None: return x + shift.unsqueeze(1) else: return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) def reshape_for_broadcast( freqs_cis, x: torch.Tensor, head_first=False, ): ndim = x.ndim assert 0 <= 1 < ndim if isinstance(freqs_cis, tuple): # freqs_cis: (cos, sin) in real space if head_first: assert freqs_cis[0].shape == ( x.shape[-2], x.shape[-1], ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}" shape = [ d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape) ] else: assert freqs_cis[0].shape == ( x.shape[1], x.shape[-1], ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}" shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) else: # freqs_cis: values in complex space if head_first: assert freqs_cis.shape == ( x.shape[-2], x.shape[-1], ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}" shape = [ d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape) ] else: assert freqs_cis.shape == ( x.shape[1], x.shape[-1], ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}" shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis.view(*shape) def rotate_half(x): x_real, x_imag = ( x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) ) # [B, S, H, D//2] return torch.stack([-x_imag, x_real], dim=-1).flatten(3) def apply_rotary_emb( xq: torch.Tensor, xk: torch.Tensor, freqs_cis, head_first: bool = False, ): xk_out = None if isinstance(freqs_cis, tuple): cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] cos, sin = cos.to(xq.device), sin.to(xq.device) # real * cos - imag * sin # imag * cos + real * sin xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq) xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk) else: # view_as_complex will pack [..., D/2, 2](real) to [..., D/2](complex) xq_ = torch.view_as_complex( xq.float().reshape(*xq.shape[:-1], -1, 2) ) # [B, S, H, D//2] freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to( xq.device ) # [S, D//2] --> [1, S, 1, D//2] # (real, imag) * (cos, sin) = (real * cos - imag * sin, imag * cos + real * sin) # view_as_real will expand [..., D/2](complex) to [..., D/2, 2](real) xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) xk_ = torch.view_as_complex( xk.float().reshape(*xk.shape[:-1], -1, 2) ) # [B, S, H, D//2] xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) return xq_out, xk_out def attention(q, k, v): q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) x = torch.nn.functional.scaled_dot_product_attention(q, k, v) x = x.transpose(1, 2).flatten(2, 3) return x def apply_gate(x, gate, tr_gate=None, tr_token=None): if tr_gate is not None: x_zero = x[:, :tr_token] * tr_gate.unsqueeze(1) x_orig = x[:, tr_token:] * gate.unsqueeze(1) return torch.concat((x_zero, x_orig), dim=1) else: return x * gate.unsqueeze(1) class MMDoubleStreamBlockComponent(torch.nn.Module): def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4): super().__init__() self.heads_num = heads_num self.mod = ModulateDiT(hidden_size) self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3) self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) self.to_out = torch.nn.Linear(hidden_size, hidden_size) self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.ff = torch.nn.Sequential( torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size) ) def forward(self, hidden_states, conditioning, freqs_cis=None, token_replace_vec=None, tr_token=None): mod1_shift, mod1_scale, mod1_gate, mod2_shift, mod2_scale, mod2_gate = self.mod(conditioning).chunk(6, dim=-1) if token_replace_vec is not None: assert tr_token is not None tr_mod1_shift, tr_mod1_scale, tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = self.mod(token_replace_vec).chunk(6, dim=-1) else: tr_mod1_shift, tr_mod1_scale, tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = None, None, None, None, None, None norm_hidden_states = self.norm1(hidden_states) norm_hidden_states = modulate(norm_hidden_states, shift=mod1_shift, scale=mod1_scale, tr_shift=tr_mod1_shift, tr_scale=tr_mod1_scale, tr_token=tr_token) qkv = self.to_qkv(norm_hidden_states) q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) q = self.norm_q(q) k = self.norm_k(k) if freqs_cis is not None: q, k = apply_rotary_emb(q, k, freqs_cis, head_first=False) return (q, k, v), (mod1_gate, mod2_shift, mod2_scale, mod2_gate), (tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate) def process_ff(self, hidden_states, attn_output, mod, mod_tr=None, tr_token=None): mod1_gate, mod2_shift, mod2_scale, mod2_gate = mod if mod_tr is not None: tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = mod_tr else: tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = None, None, None, None hidden_states = hidden_states + apply_gate(self.to_out(attn_output), mod1_gate, tr_mod1_gate, tr_token) x = self.ff(modulate(self.norm2(hidden_states), shift=mod2_shift, scale=mod2_scale, tr_shift=tr_mod2_shift, tr_scale=tr_mod2_scale, tr_token=tr_token)) hidden_states = hidden_states + apply_gate(x, mod2_gate, tr_mod2_gate, tr_token) return hidden_states class MMDoubleStreamBlock(torch.nn.Module): def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4): super().__init__() self.component_a = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio) self.component_b = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio) def forward(self, hidden_states_a, hidden_states_b, conditioning, freqs_cis, token_replace_vec=None, tr_token=None, split_token=71): (q_a, k_a, v_a), mod_a, mod_tr = self.component_a(hidden_states_a, conditioning, freqs_cis, token_replace_vec, tr_token) (q_b, k_b, v_b), mod_b, _ = self.component_b(hidden_states_b, conditioning, freqs_cis=None) q_a, q_b = torch.concat([q_a, q_b[:, :split_token]], dim=1), q_b[:, split_token:].contiguous() k_a, k_b = torch.concat([k_a, k_b[:, :split_token]], dim=1), k_b[:, split_token:].contiguous() v_a, v_b = torch.concat([v_a, v_b[:, :split_token]], dim=1), v_b[:, split_token:].contiguous() attn_output_a = attention(q_a, k_a, v_a) attn_output_b = attention(q_b, k_b, v_b) attn_output_a, attn_output_b = attn_output_a[:, :-split_token].contiguous(), torch.concat([attn_output_a[:, -split_token:], attn_output_b], dim=1) hidden_states_a = self.component_a.process_ff(hidden_states_a, attn_output_a, mod_a, mod_tr, tr_token) hidden_states_b = self.component_b.process_ff(hidden_states_b, attn_output_b, mod_b) return hidden_states_a, hidden_states_b class MMSingleStreamBlockOriginal(torch.nn.Module): def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4): super().__init__() self.hidden_size = hidden_size self.heads_num = heads_num self.mlp_hidden_dim = hidden_size * mlp_width_ratio self.linear1 = torch.nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) self.linear2 = torch.nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) self.q_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) self.k_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) self.pre_norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.mlp_act = torch.nn.GELU(approximate="tanh") self.modulation = ModulateDiT(hidden_size, factor=3) def forward(self, x, vec, freqs_cis=None, txt_len=256): mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1) x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale) qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) q = self.q_norm(q) k = self.k_norm(k) q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :] k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :] q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False) q = torch.cat((q_a, q_b), dim=1) k = torch.cat((k_a, k_b), dim=1) attn_output_a = attention(q[:, :-185].contiguous(), k[:, :-185].contiguous(), v[:, :-185].contiguous()) attn_output_b = attention(q[:, -185:].contiguous(), k[:, -185:].contiguous(), v[:, -185:].contiguous()) attn_output = torch.concat([attn_output_a, attn_output_b], dim=1) output = self.linear2(torch.cat((attn_output, self.mlp_act(mlp)), 2)) return x + output * mod_gate.unsqueeze(1) class MMSingleStreamBlock(torch.nn.Module): def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4): super().__init__() self.heads_num = heads_num self.mod = ModulateDiT(hidden_size, factor=3) self.norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3) self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6) self.to_out = torch.nn.Linear(hidden_size, hidden_size) self.ff = torch.nn.Sequential( torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size, bias=False) ) def forward(self, hidden_states, conditioning, freqs_cis=None, txt_len=256, token_replace_vec=None, tr_token=None, split_token=71): mod_shift, mod_scale, mod_gate = self.mod(conditioning).chunk(3, dim=-1) if token_replace_vec is not None: assert tr_token is not None tr_mod_shift, tr_mod_scale, tr_mod_gate = self.mod(token_replace_vec).chunk(3, dim=-1) else: tr_mod_shift, tr_mod_scale, tr_mod_gate = None, None, None norm_hidden_states = self.norm(hidden_states) norm_hidden_states = modulate(norm_hidden_states, shift=mod_shift, scale=mod_scale, tr_shift=tr_mod_shift, tr_scale=tr_mod_scale, tr_token=tr_token) qkv = self.to_qkv(norm_hidden_states) q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) q = self.norm_q(q) k = self.norm_k(k) q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :] k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :] q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False) v_len = txt_len - split_token q_a, q_b = torch.concat([q_a, q_b[:, :split_token]], dim=1), q_b[:, split_token:].contiguous() k_a, k_b = torch.concat([k_a, k_b[:, :split_token]], dim=1), k_b[:, split_token:].contiguous() v_a, v_b = v[:, :-v_len].contiguous(), v[:, -v_len:].contiguous() attn_output_a = attention(q_a, k_a, v_a) attn_output_b = attention(q_b, k_b, v_b) attn_output = torch.concat([attn_output_a, attn_output_b], dim=1) hidden_states = hidden_states + apply_gate(self.to_out(attn_output), mod_gate, tr_mod_gate, tr_token) hidden_states = hidden_states + apply_gate(self.ff(norm_hidden_states), mod_gate, tr_mod_gate, tr_token) return hidden_states class FinalLayer(torch.nn.Module): def __init__(self, hidden_size=3072, patch_size=(1, 2, 2), out_channels=16): super().__init__() self.norm_final = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.linear = torch.nn.Linear(hidden_size, patch_size[0] * patch_size[1] * patch_size[2] * out_channels) self.adaLN_modulation = torch.nn.Sequential(torch.nn.SiLU(), torch.nn.Linear(hidden_size, 2 * hidden_size)) def forward(self, x, c): shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) x = modulate(self.norm_final(x), shift=shift, scale=scale) x = self.linear(x) return x class HunyuanVideoDiT(torch.nn.Module): def __init__(self, in_channels=16, hidden_size=3072, text_dim=4096, num_double_blocks=20, num_single_blocks=40, guidance_embed=True): super().__init__() self.img_in = PatchEmbed(in_channels=in_channels, embed_dim=hidden_size) self.txt_in = SingleTokenRefiner(in_channels=text_dim, hidden_size=hidden_size) self.time_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu") self.vector_in = torch.nn.Sequential( torch.nn.Linear(768, hidden_size), torch.nn.SiLU(), torch.nn.Linear(hidden_size, hidden_size) ) self.guidance_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu") if guidance_embed else None self.double_blocks = torch.nn.ModuleList([MMDoubleStreamBlock(hidden_size) for _ in range(num_double_blocks)]) self.single_blocks = torch.nn.ModuleList([MMSingleStreamBlock(hidden_size) for _ in range(num_single_blocks)]) self.final_layer = FinalLayer(hidden_size) # TODO: remove these parameters self.dtype = torch.bfloat16 self.patch_size = [1, 2, 2] self.hidden_size = 3072 self.heads_num = 24 self.rope_dim_list = [16, 56, 56] def unpatchify(self, x, T, H, W): x = rearrange(x, "B (T H W) (C pT pH pW) -> B C (T pT) (H pH) (W pW)", H=H, W=W, pT=1, pH=2, pW=2) return x def enable_block_wise_offload(self, warm_device="cuda", cold_device="cpu"): self.warm_device = warm_device self.cold_device = cold_device self.to(self.cold_device) def load_models_to_device(self, loadmodel_names=[], device="cpu"): for model_name in loadmodel_names: model = getattr(self, model_name) if model is not None: model.to(device) torch.cuda.empty_cache() def prepare_freqs(self, latents): return HunyuanVideoRope(latents) def forward( self, x: torch.Tensor, t: torch.Tensor, prompt_emb: torch.Tensor = None, text_mask: torch.Tensor = None, pooled_prompt_emb: torch.Tensor = None, freqs_cos: torch.Tensor = None, freqs_sin: torch.Tensor = None, guidance: torch.Tensor = None, **kwargs ): B, C, T, H, W = x.shape vec = self.time_in(t, dtype=torch.float32) + self.vector_in(pooled_prompt_emb) if self.guidance_in is not None: vec += self.guidance_in(guidance * 1000, dtype=torch.float32) img = self.img_in(x) txt = self.txt_in(prompt_emb, t, text_mask) for block in tqdm(self.double_blocks, desc="Double stream blocks"): img, txt = block(img, txt, vec, (freqs_cos, freqs_sin)) x = torch.concat([img, txt], dim=1) for block in tqdm(self.single_blocks, desc="Single stream blocks"): x = block(x, vec, (freqs_cos, freqs_sin)) img = x[:, :-256] img = self.final_layer(img, vec) img = self.unpatchify(img, T=T//1, H=H//2, W=W//2) return img def enable_auto_offload(self, dtype=torch.bfloat16, device="cuda"): def cast_to(weight, dtype=None, device=None, copy=False): if device is None or weight.device == device: if not copy: if dtype is None or weight.dtype == dtype: return weight return weight.to(dtype=dtype, copy=copy) r = torch.empty_like(weight, dtype=dtype, device=device) r.copy_(weight) return r def cast_weight(s, input=None, dtype=None, device=None): if input is not None: if dtype is None: dtype = input.dtype if device is None: device = input.device weight = cast_to(s.weight, dtype, device) return weight def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): if input is not None: if dtype is None: dtype = input.dtype if bias_dtype is None: bias_dtype = dtype if device is None: device = input.device weight = cast_to(s.weight, dtype, device) bias = cast_to(s.bias, bias_dtype, device) if s.bias is not None else None return weight, bias class quantized_layer: class Linear(torch.nn.Linear): def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs): super().__init__(*args, **kwargs) self.dtype = dtype self.device = device def block_forward_(self, x, i, j, dtype, device): weight_ = cast_to( self.weight[j * self.block_size: (j + 1) * self.block_size, i * self.block_size: (i + 1) * self.block_size], dtype=dtype, device=device ) if self.bias is None or i > 0: bias_ = None else: bias_ = cast_to(self.bias[j * self.block_size: (j + 1) * self.block_size], dtype=dtype, device=device) x_ = x[..., i * self.block_size: (i + 1) * self.block_size] y_ = torch.nn.functional.linear(x_, weight_, bias_) del x_, weight_, bias_ torch.cuda.empty_cache() return y_ def block_forward(self, x, **kwargs): # This feature can only reduce 2GB VRAM, so we disable it. y = torch.zeros(x.shape[:-1] + (self.out_features,), dtype=x.dtype, device=x.device) for i in range((self.in_features + self.block_size - 1) // self.block_size): for j in range((self.out_features + self.block_size - 1) // self.block_size): y[..., j * self.block_size: (j + 1) * self.block_size] += self.block_forward_(x, i, j, dtype=x.dtype, device=x.device) return y def forward(self, x, **kwargs): weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device) return torch.nn.functional.linear(x, weight, bias) class RMSNorm(torch.nn.Module): def __init__(self, module, dtype=torch.bfloat16, device="cuda"): super().__init__() self.module = module self.dtype = dtype self.device = device def forward(self, hidden_states, **kwargs): input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps) hidden_states = hidden_states.to(input_dtype) if self.module.weight is not None: weight = cast_weight(self.module, hidden_states, dtype=torch.bfloat16, device="cuda") hidden_states = hidden_states * weight return hidden_states class Conv3d(torch.nn.Conv3d): def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs): super().__init__(*args, **kwargs) self.dtype = dtype self.device = device def forward(self, x): weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device) return torch.nn.functional.conv3d(x, weight, bias, self.stride, self.padding, self.dilation, self.groups) class LayerNorm(torch.nn.LayerNorm): def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs): super().__init__(*args, **kwargs) self.dtype = dtype self.device = device def forward(self, x): if self.weight is not None and self.bias is not None: weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device) return torch.nn.functional.layer_norm(x, self.normalized_shape, weight, bias, self.eps) else: return torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) def replace_layer(model, dtype=torch.bfloat16, device="cuda"): for name, module in model.named_children(): if isinstance(module, torch.nn.Linear): with init_weights_on_device(): new_layer = quantized_layer.Linear( module.in_features, module.out_features, bias=module.bias is not None, dtype=dtype, device=device ) new_layer.load_state_dict(module.state_dict(), assign=True) setattr(model, name, new_layer) elif isinstance(module, torch.nn.Conv3d): with init_weights_on_device(): new_layer = quantized_layer.Conv3d( module.in_channels, module.out_channels, kernel_size=module.kernel_size, stride=module.stride, dtype=dtype, device=device ) new_layer.load_state_dict(module.state_dict(), assign=True) setattr(model, name, new_layer) elif isinstance(module, RMSNorm): new_layer = quantized_layer.RMSNorm( module, dtype=dtype, device=device ) setattr(model, name, new_layer) elif isinstance(module, torch.nn.LayerNorm): with init_weights_on_device(): new_layer = quantized_layer.LayerNorm( module.normalized_shape, elementwise_affine=module.elementwise_affine, eps=module.eps, dtype=dtype, device=device ) new_layer.load_state_dict(module.state_dict(), assign=True) setattr(model, name, new_layer) else: replace_layer(module, dtype=dtype, device=device) replace_layer(self, dtype=dtype, device=device) @staticmethod def state_dict_converter(): return HunyuanVideoDiTStateDictConverter() class HunyuanVideoDiTStateDictConverter: def __init__(self): pass def from_civitai(self, state_dict): origin_hash_key = hash_state_dict_keys(state_dict, with_shape=True) if "module" in state_dict: state_dict = state_dict["module"] direct_dict = { "img_in.proj": "img_in.proj", "time_in.mlp.0": "time_in.timestep_embedder.0", "time_in.mlp.2": "time_in.timestep_embedder.2", "vector_in.in_layer": "vector_in.0", "vector_in.out_layer": "vector_in.2", "guidance_in.mlp.0": "guidance_in.timestep_embedder.0", "guidance_in.mlp.2": "guidance_in.timestep_embedder.2", "txt_in.input_embedder": "txt_in.input_embedder", "txt_in.t_embedder.mlp.0": "txt_in.t_embedder.timestep_embedder.0", "txt_in.t_embedder.mlp.2": "txt_in.t_embedder.timestep_embedder.2", "txt_in.c_embedder.linear_1": "txt_in.c_embedder.0", "txt_in.c_embedder.linear_2": "txt_in.c_embedder.2", "final_layer.linear": "final_layer.linear", "final_layer.adaLN_modulation.1": "final_layer.adaLN_modulation.1", } txt_suffix_dict = { "norm1": "norm1", "self_attn_qkv": "self_attn_qkv", "self_attn_proj": "self_attn_proj", "norm2": "norm2", "mlp.fc1": "mlp.0", "mlp.fc2": "mlp.2", "adaLN_modulation.1": "adaLN_modulation.1", } double_suffix_dict = { "img_mod.linear": "component_a.mod.linear", "img_attn_qkv": "component_a.to_qkv", "img_attn_q_norm": "component_a.norm_q", "img_attn_k_norm": "component_a.norm_k", "img_attn_proj": "component_a.to_out", "img_mlp.fc1": "component_a.ff.0", "img_mlp.fc2": "component_a.ff.2", "txt_mod.linear": "component_b.mod.linear", "txt_attn_qkv": "component_b.to_qkv", "txt_attn_q_norm": "component_b.norm_q", "txt_attn_k_norm": "component_b.norm_k", "txt_attn_proj": "component_b.to_out", "txt_mlp.fc1": "component_b.ff.0", "txt_mlp.fc2": "component_b.ff.2", } single_suffix_dict = { "linear1": ["to_qkv", "ff.0"], "linear2": ["to_out", "ff.2"], "q_norm": "norm_q", "k_norm": "norm_k", "modulation.linear": "mod.linear", } # single_suffix_dict = { # "linear1": "linear1", # "linear2": "linear2", # "q_norm": "q_norm", # "k_norm": "k_norm", # "modulation.linear": "modulation.linear", # } state_dict_ = {} for name, param in state_dict.items(): names = name.split(".") direct_name = ".".join(names[:-1]) if direct_name in direct_dict: name_ = direct_dict[direct_name] + "." + names[-1] state_dict_[name_] = param elif names[0] == "double_blocks": prefix = ".".join(names[:2]) suffix = ".".join(names[2:-1]) name_ = prefix + "." + double_suffix_dict[suffix] + "." + names[-1] state_dict_[name_] = param elif names[0] == "single_blocks": prefix = ".".join(names[:2]) suffix = ".".join(names[2:-1]) if isinstance(single_suffix_dict[suffix], list): if suffix == "linear1": name_a, name_b = single_suffix_dict[suffix] param_a, param_b = torch.split(param, (3072*3, 3072*4), dim=0) state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b elif suffix == "linear2": if names[-1] == "weight": name_a, name_b = single_suffix_dict[suffix] param_a, param_b = torch.split(param, (3072*1, 3072*4), dim=-1) state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b else: name_a, name_b = single_suffix_dict[suffix] state_dict_[prefix + "." + name_a + "." + names[-1]] = param else: pass else: name_ = prefix + "." + single_suffix_dict[suffix] + "." + names[-1] state_dict_[name_] = param elif names[0] == "txt_in": prefix = ".".join(names[:4]).replace(".individual_token_refiner.", ".") suffix = ".".join(names[4:-1]) name_ = prefix + "." + txt_suffix_dict[suffix] + "." + names[-1] state_dict_[name_] = param else: pass return state_dict_ ================================================ FILE: diffsynth/models/hunyuan_video_text_encoder.py ================================================ from transformers import LlamaModel, LlamaConfig, DynamicCache, LlavaForConditionalGeneration from copy import deepcopy import torch class HunyuanVideoLLMEncoder(LlamaModel): def __init__(self, config: LlamaConfig): super().__init__(config) self.auto_offload = False def enable_auto_offload(self, **kwargs): self.auto_offload = True def forward(self, input_ids, attention_mask, hidden_state_skip_layer=2): embed_tokens = deepcopy(self.embed_tokens).to(input_ids.device) if self.auto_offload else self.embed_tokens inputs_embeds = embed_tokens(input_ids) past_key_values = DynamicCache() cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device) position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, None, False) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers rotary_emb = deepcopy(self.rotary_emb).to(input_ids.device) if self.auto_offload else self.rotary_emb position_embeddings = rotary_emb(hidden_states, position_ids) # decoder layers for layer_id, decoder_layer in enumerate(self.layers): if self.auto_offload: decoder_layer = deepcopy(decoder_layer).to(hidden_states.device) layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=False, use_cache=True, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = layer_outputs[0] if layer_id + hidden_state_skip_layer + 1 >= len(self.layers): break return hidden_states class HunyuanVideoMLLMEncoder(LlavaForConditionalGeneration): def __init__(self, config): super().__init__(config) self.auto_offload = False def enable_auto_offload(self, **kwargs): self.auto_offload = True # TODO: implement the low VRAM inference for MLLM. def forward(self, input_ids, pixel_values, attention_mask, hidden_state_skip_layer=2): outputs = super().forward(input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=True, pixel_values=pixel_values) hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)] return hidden_state ================================================ FILE: diffsynth/models/hunyuan_video_vae_decoder.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange import numpy as np from tqdm import tqdm from einops import repeat class CausalConv3d(nn.Module): def __init__(self, in_channel, out_channel, kernel_size, stride=1, dilation=1, pad_mode='replicate', **kwargs): super().__init__() self.pad_mode = pad_mode self.time_causal_padding = (kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size - 1, 0 ) # W, H, T self.conv = nn.Conv3d(in_channel, out_channel, kernel_size, stride=stride, dilation=dilation, **kwargs) def forward(self, x): x = F.pad(x, self.time_causal_padding, mode=self.pad_mode) return self.conv(x) class UpsampleCausal3D(nn.Module): def __init__(self, channels, use_conv=False, out_channels=None, kernel_size=None, bias=True, upsample_factor=(2, 2, 2)): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.upsample_factor = upsample_factor self.conv = None if use_conv: kernel_size = 3 if kernel_size is None else kernel_size self.conv = CausalConv3d(self.channels, self.out_channels, kernel_size=kernel_size, bias=bias) def forward(self, hidden_states): # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 dtype = hidden_states.dtype if dtype == torch.bfloat16: hidden_states = hidden_states.to(torch.float32) # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 if hidden_states.shape[0] >= 64: hidden_states = hidden_states.contiguous() # interpolate B, C, T, H, W = hidden_states.shape first_h, other_h = hidden_states.split((1, T - 1), dim=2) if T > 1: other_h = F.interpolate(other_h, scale_factor=self.upsample_factor, mode="nearest") first_h = F.interpolate(first_h.squeeze(2), scale_factor=self.upsample_factor[1:], mode="nearest").unsqueeze(2) hidden_states = torch.cat((first_h, other_h), dim=2) if T > 1 else first_h # If the input is bfloat16, we cast back to bfloat16 if dtype == torch.bfloat16: hidden_states = hidden_states.to(dtype) if self.conv: hidden_states = self.conv(hidden_states) return hidden_states class ResnetBlockCausal3D(nn.Module): def __init__(self, in_channels, out_channels=None, dropout=0.0, groups=32, eps=1e-6, conv_shortcut_bias=True): super().__init__() self.pre_norm = True self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.norm1 = nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1) self.norm2 = nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, stride=1) self.dropout = nn.Dropout(dropout) self.nonlinearity = nn.SiLU() self.conv_shortcut = None if in_channels != out_channels: self.conv_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, bias=conv_shortcut_bias) def forward(self, input_tensor): hidden_states = input_tensor # conv1 hidden_states = self.norm1(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.conv1(hidden_states) # conv2 hidden_states = self.norm2(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.conv2(hidden_states) # shortcut if self.conv_shortcut is not None: input_tensor = (self.conv_shortcut(input_tensor)) # shortcut and scale output_tensor = input_tensor + hidden_states return output_tensor def prepare_causal_attention_mask(n_frame, n_hw, dtype, device, batch_size=None): seq_len = n_frame * n_hw mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device) for i in range(seq_len): i_frame = i // n_hw mask[i, :(i_frame + 1) * n_hw] = 0 if batch_size is not None: mask = mask.unsqueeze(0).expand(batch_size, -1, -1) return mask class Attention(nn.Module): def __init__(self, in_channels, num_heads, head_dim, num_groups=32, dropout=0.0, eps=1e-6, bias=True, residual_connection=True): super().__init__() self.num_heads = num_heads self.head_dim = head_dim self.residual_connection = residual_connection dim_inner = head_dim * num_heads self.group_norm = nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, affine=True) self.to_q = nn.Linear(in_channels, dim_inner, bias=bias) self.to_k = nn.Linear(in_channels, dim_inner, bias=bias) self.to_v = nn.Linear(in_channels, dim_inner, bias=bias) self.to_out = nn.Sequential(nn.Linear(dim_inner, in_channels, bias=bias), nn.Dropout(dropout)) def forward(self, input_tensor, attn_mask=None): hidden_states = self.group_norm(input_tensor.transpose(1, 2)).transpose(1, 2) batch_size = hidden_states.shape[0] q = self.to_q(hidden_states) k = self.to_k(hidden_states) v = self.to_v(hidden_states) q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) if attn_mask is not None: attn_mask = attn_mask.view(batch_size, self.num_heads, -1, attn_mask.shape[-1]) hidden_states = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = self.to_out(hidden_states) if self.residual_connection: output_tensor = input_tensor + hidden_states return output_tensor class UNetMidBlockCausal3D(nn.Module): def __init__(self, in_channels, dropout=0.0, num_layers=1, eps=1e-6, num_groups=32, attention_head_dim=None): super().__init__() resnets = [ ResnetBlockCausal3D( in_channels=in_channels, out_channels=in_channels, dropout=dropout, groups=num_groups, eps=eps, ) ] attentions = [] attention_head_dim = attention_head_dim or in_channels for _ in range(num_layers): attentions.append( Attention( in_channels, num_heads=in_channels // attention_head_dim, head_dim=attention_head_dim, num_groups=num_groups, dropout=dropout, eps=eps, bias=True, residual_connection=True, )) resnets.append( ResnetBlockCausal3D( in_channels=in_channels, out_channels=in_channels, dropout=dropout, groups=num_groups, eps=eps, )) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward(self, hidden_states): hidden_states = self.resnets[0](hidden_states) for attn, resnet in zip(self.attentions, self.resnets[1:]): B, C, T, H, W = hidden_states.shape hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c") attn_mask = prepare_causal_attention_mask(T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B) hidden_states = attn(hidden_states, attn_mask=attn_mask) hidden_states = rearrange(hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W) hidden_states = resnet(hidden_states) return hidden_states class UpDecoderBlockCausal3D(nn.Module): def __init__( self, in_channels, out_channels, dropout=0.0, num_layers=1, eps=1e-6, num_groups=32, add_upsample=True, upsample_scale_factor=(2, 2, 2), ): super().__init__() resnets = [] for i in range(num_layers): cur_in_channel = in_channels if i == 0 else out_channels resnets.append( ResnetBlockCausal3D( in_channels=cur_in_channel, out_channels=out_channels, groups=num_groups, dropout=dropout, eps=eps, )) self.resnets = nn.ModuleList(resnets) self.upsamplers = None if add_upsample: self.upsamplers = nn.ModuleList([ UpsampleCausal3D( out_channels, use_conv=True, out_channels=out_channels, upsample_factor=upsample_scale_factor, ) ]) def forward(self, hidden_states): for resnet in self.resnets: hidden_states = resnet(hidden_states) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states class DecoderCausal3D(nn.Module): def __init__( self, in_channels=16, out_channels=3, eps=1e-6, dropout=0.0, block_out_channels=[128, 256, 512, 512], layers_per_block=2, num_groups=32, time_compression_ratio=4, spatial_compression_ratio=8, gradient_checkpointing=False, ): super().__init__() self.layers_per_block = layers_per_block self.conv_in = CausalConv3d(in_channels, block_out_channels[-1], kernel_size=3, stride=1) self.up_blocks = nn.ModuleList([]) # mid self.mid_block = UNetMidBlockCausal3D( in_channels=block_out_channels[-1], dropout=dropout, eps=eps, num_groups=num_groups, attention_head_dim=block_out_channels[-1], ) # up reversed_block_out_channels = list(reversed(block_out_channels)) output_channel = reversed_block_out_channels[0] for i in range(len(block_out_channels)): prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio)) num_time_upsample_layers = int(np.log2(time_compression_ratio)) add_spatial_upsample = bool(i < num_spatial_upsample_layers) add_time_upsample = bool(i >= len(block_out_channels) - 1 - num_time_upsample_layers and not is_final_block) upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1) upsample_scale_factor_T = (2,) if add_time_upsample else (1,) upsample_scale_factor = tuple(upsample_scale_factor_T + upsample_scale_factor_HW) up_block = UpDecoderBlockCausal3D( in_channels=prev_output_channel, out_channels=output_channel, dropout=dropout, num_layers=layers_per_block + 1, eps=eps, num_groups=num_groups, add_upsample=bool(add_spatial_upsample or add_time_upsample), upsample_scale_factor=upsample_scale_factor, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups, eps=eps) self.conv_act = nn.SiLU() self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3) self.gradient_checkpointing = gradient_checkpointing def forward(self, hidden_states): hidden_states = self.conv_in(hidden_states) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward # middle hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(self.mid_block), hidden_states, use_reentrant=False, ) # up for up_block in self.up_blocks: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(up_block), hidden_states, use_reentrant=False, ) else: # middle hidden_states = self.mid_block(hidden_states) # up for up_block in self.up_blocks: hidden_states = up_block(hidden_states) # post-process hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states class HunyuanVideoVAEDecoder(nn.Module): def __init__( self, in_channels=16, out_channels=3, eps=1e-6, dropout=0.0, block_out_channels=[128, 256, 512, 512], layers_per_block=2, num_groups=32, time_compression_ratio=4, spatial_compression_ratio=8, gradient_checkpointing=False, ): super().__init__() self.decoder = DecoderCausal3D( in_channels=in_channels, out_channels=out_channels, eps=eps, dropout=dropout, block_out_channels=block_out_channels, layers_per_block=layers_per_block, num_groups=num_groups, time_compression_ratio=time_compression_ratio, spatial_compression_ratio=spatial_compression_ratio, gradient_checkpointing=gradient_checkpointing, ) self.post_quant_conv = nn.Conv3d(in_channels, in_channels, kernel_size=1) self.scaling_factor = 0.476986 def forward(self, latents): latents = latents / self.scaling_factor latents = self.post_quant_conv(latents) dec = self.decoder(latents) return dec def build_1d_mask(self, length, left_bound, right_bound, border_width): x = torch.ones((length,)) if not left_bound: x[:border_width] = (torch.arange(border_width) + 1) / border_width if not right_bound: x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) return x def build_mask(self, data, is_bound, border_width): _, _, T, H, W = data.shape t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0]) h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1]) w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2]) t = repeat(t, "T -> T H W", T=T, H=H, W=W) h = repeat(h, "H -> T H W", T=T, H=H, W=W) w = repeat(w, "W -> T H W", T=T, H=H, W=W) mask = torch.stack([t, h, w]).min(dim=0).values mask = rearrange(mask, "T H W -> 1 1 T H W") return mask def tile_forward(self, hidden_states, tile_size, tile_stride): B, C, T, H, W = hidden_states.shape size_t, size_h, size_w = tile_size stride_t, stride_h, stride_w = tile_stride # Split tasks tasks = [] for t in range(0, T, stride_t): if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue for h in range(0, H, stride_h): if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue for w in range(0, W, stride_w): if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue t_, h_, w_ = t + size_t, h + size_h, w + size_w tasks.append((t, t_, h, h_, w, w_)) # Run torch_dtype = self.post_quant_conv.weight.dtype data_device = hidden_states.device computation_device = self.post_quant_conv.weight.device weight = torch.zeros((1, 1, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device) values = torch.zeros((B, 3, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device) for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"): hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device) hidden_states_batch = self.forward(hidden_states_batch).to(data_device) if t > 0: hidden_states_batch = hidden_states_batch[:, :, 1:] mask = self.build_mask( hidden_states_batch, is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W), border_width=((size_t - stride_t) * 4, (size_h - stride_h) * 8, (size_w - stride_w) * 8) ).to(dtype=torch_dtype, device=data_device) target_t = 0 if t==0 else t * 4 + 1 target_h = h * 8 target_w = w * 8 values[ :, :, target_t: target_t + hidden_states_batch.shape[2], target_h: target_h + hidden_states_batch.shape[3], target_w: target_w + hidden_states_batch.shape[4], ] += hidden_states_batch * mask weight[ :, :, target_t: target_t + hidden_states_batch.shape[2], target_h: target_h + hidden_states_batch.shape[3], target_w: target_w + hidden_states_batch.shape[4], ] += mask return values / weight def decode_video(self, latents, tile_size=(17, 32, 32), tile_stride=(12, 24, 24)): latents = latents.to(self.post_quant_conv.weight.dtype) return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride) @staticmethod def state_dict_converter(): return HunyuanVideoVAEDecoderStateDictConverter() class HunyuanVideoVAEDecoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = {} for name in state_dict: if name.startswith('decoder.') or name.startswith('post_quant_conv.'): state_dict_[name] = state_dict[name] return state_dict_ ================================================ FILE: diffsynth/models/hunyuan_video_vae_encoder.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat import numpy as np from tqdm import tqdm from .hunyuan_video_vae_decoder import CausalConv3d, ResnetBlockCausal3D, UNetMidBlockCausal3D class DownsampleCausal3D(nn.Module): def __init__(self, channels, out_channels, kernel_size=3, bias=True, stride=2): super().__init__() self.conv = CausalConv3d(channels, out_channels, kernel_size, stride=stride, bias=bias) def forward(self, hidden_states): hidden_states = self.conv(hidden_states) return hidden_states class DownEncoderBlockCausal3D(nn.Module): def __init__( self, in_channels, out_channels, dropout=0.0, num_layers=1, eps=1e-6, num_groups=32, add_downsample=True, downsample_stride=2, ): super().__init__() resnets = [] for i in range(num_layers): cur_in_channel = in_channels if i == 0 else out_channels resnets.append( ResnetBlockCausal3D( in_channels=cur_in_channel, out_channels=out_channels, groups=num_groups, dropout=dropout, eps=eps, )) self.resnets = nn.ModuleList(resnets) self.downsamplers = None if add_downsample: self.downsamplers = nn.ModuleList([DownsampleCausal3D( out_channels, out_channels, stride=downsample_stride, )]) def forward(self, hidden_states): for resnet in self.resnets: hidden_states = resnet(hidden_states) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) return hidden_states class EncoderCausal3D(nn.Module): def __init__( self, in_channels: int = 3, out_channels: int = 16, eps=1e-6, dropout=0.0, block_out_channels=[128, 256, 512, 512], layers_per_block=2, num_groups=32, time_compression_ratio: int = 4, spatial_compression_ratio: int = 8, gradient_checkpointing=False, ): super().__init__() self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1) self.down_blocks = nn.ModuleList([]) # down output_channel = block_out_channels[0] for i in range(len(block_out_channels)): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio)) num_time_downsample_layers = int(np.log2(time_compression_ratio)) add_spatial_downsample = bool(i < num_spatial_downsample_layers) add_time_downsample = bool(i >= (len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block) downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1) downsample_stride_T = (2,) if add_time_downsample else (1,) downsample_stride = tuple(downsample_stride_T + downsample_stride_HW) down_block = DownEncoderBlockCausal3D( in_channels=input_channel, out_channels=output_channel, dropout=dropout, num_layers=layers_per_block, eps=eps, num_groups=num_groups, add_downsample=bool(add_spatial_downsample or add_time_downsample), downsample_stride=downsample_stride, ) self.down_blocks.append(down_block) # mid self.mid_block = UNetMidBlockCausal3D( in_channels=block_out_channels[-1], dropout=dropout, eps=eps, num_groups=num_groups, attention_head_dim=block_out_channels[-1], ) # out self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=num_groups, eps=eps) self.conv_act = nn.SiLU() self.conv_out = CausalConv3d(block_out_channels[-1], 2 * out_channels, kernel_size=3) self.gradient_checkpointing = gradient_checkpointing def forward(self, hidden_states): hidden_states = self.conv_in(hidden_states) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward # down for down_block in self.down_blocks: torch.utils.checkpoint.checkpoint( create_custom_forward(down_block), hidden_states, use_reentrant=False, ) # middle hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(self.mid_block), hidden_states, use_reentrant=False, ) else: # down for down_block in self.down_blocks: hidden_states = down_block(hidden_states) # middle hidden_states = self.mid_block(hidden_states) # post-process hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states class HunyuanVideoVAEEncoder(nn.Module): def __init__( self, in_channels=3, out_channels=16, eps=1e-6, dropout=0.0, block_out_channels=[128, 256, 512, 512], layers_per_block=2, num_groups=32, time_compression_ratio=4, spatial_compression_ratio=8, gradient_checkpointing=False, ): super().__init__() self.encoder = EncoderCausal3D( in_channels=in_channels, out_channels=out_channels, eps=eps, dropout=dropout, block_out_channels=block_out_channels, layers_per_block=layers_per_block, num_groups=num_groups, time_compression_ratio=time_compression_ratio, spatial_compression_ratio=spatial_compression_ratio, gradient_checkpointing=gradient_checkpointing, ) self.quant_conv = nn.Conv3d(2 * out_channels, 2 * out_channels, kernel_size=1) self.scaling_factor = 0.476986 def forward(self, images): latents = self.encoder(images) latents = self.quant_conv(latents) latents = latents[:, :16] latents = latents * self.scaling_factor return latents def build_1d_mask(self, length, left_bound, right_bound, border_width): x = torch.ones((length,)) if not left_bound: x[:border_width] = (torch.arange(border_width) + 1) / border_width if not right_bound: x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) return x def build_mask(self, data, is_bound, border_width): _, _, T, H, W = data.shape t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0]) h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1]) w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2]) t = repeat(t, "T -> T H W", T=T, H=H, W=W) h = repeat(h, "H -> T H W", T=T, H=H, W=W) w = repeat(w, "W -> T H W", T=T, H=H, W=W) mask = torch.stack([t, h, w]).min(dim=0).values mask = rearrange(mask, "T H W -> 1 1 T H W") return mask def tile_forward(self, hidden_states, tile_size, tile_stride): B, C, T, H, W = hidden_states.shape size_t, size_h, size_w = tile_size stride_t, stride_h, stride_w = tile_stride # Split tasks tasks = [] for t in range(0, T, stride_t): if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue for h in range(0, H, stride_h): if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue for w in range(0, W, stride_w): if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue t_, h_, w_ = t + size_t, h + size_h, w + size_w tasks.append((t, t_, h, h_, w, w_)) # Run torch_dtype = self.quant_conv.weight.dtype data_device = hidden_states.device computation_device = self.quant_conv.weight.device weight = torch.zeros((1, 1, (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device) values = torch.zeros((B, 16, (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device) for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"): hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device) hidden_states_batch = self.forward(hidden_states_batch).to(data_device) if t > 0: hidden_states_batch = hidden_states_batch[:, :, 1:] mask = self.build_mask( hidden_states_batch, is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W), border_width=((size_t - stride_t) // 4, (size_h - stride_h) // 8, (size_w - stride_w) // 8) ).to(dtype=torch_dtype, device=data_device) target_t = 0 if t==0 else t // 4 + 1 target_h = h // 8 target_w = w // 8 values[ :, :, target_t: target_t + hidden_states_batch.shape[2], target_h: target_h + hidden_states_batch.shape[3], target_w: target_w + hidden_states_batch.shape[4], ] += hidden_states_batch * mask weight[ :, :, target_t: target_t + hidden_states_batch.shape[2], target_h: target_h + hidden_states_batch.shape[3], target_w: target_w + hidden_states_batch.shape[4], ] += mask return values / weight def encode_video(self, latents, tile_size=(65, 256, 256), tile_stride=(48, 192, 192)): latents = latents.to(self.quant_conv.weight.dtype) return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride) @staticmethod def state_dict_converter(): return HunyuanVideoVAEEncoderStateDictConverter() class HunyuanVideoVAEEncoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = {} for name in state_dict: if name.startswith('encoder.') or name.startswith('quant_conv.'): state_dict_[name] = state_dict[name] return state_dict_ ================================================ FILE: diffsynth/models/kolors_text_encoder.py ================================================ """ This model is copied from https://github.com/Kwai-Kolors/Kolors/tree/master/kolors/models. We didn't modify this model. The tensor operation is performed in the prompter. """ """ PyTorch ChatGLM model. """ import math import copy import warnings import re import sys import torch import torch.utils.checkpoint import torch.nn.functional as F from torch import nn from torch.nn import CrossEntropyLoss, LayerNorm from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss from torch.nn.utils import skip_init from typing import Optional, Tuple, Union, List, Callable, Dict, Any from copy import deepcopy from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging from transformers.generation.logits_process import LogitsProcessor from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput from transformers import PretrainedConfig from torch.nn.parameter import Parameter import bz2 import torch import base64 import ctypes from transformers.utils import logging from typing import List logger = logging.get_logger(__name__) try: from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up class Kernel: def __init__(self, code: bytes, function_names: List[str]): self.code = code self._function_names = function_names self._cmodule = LazyKernelCModule(self.code) for name in self._function_names: setattr(self, name, KernelFunction(self._cmodule, name)) quantization_code = "$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" kernels = Kernel( bz2.decompress(base64.b64decode(quantization_code)), [ "int4WeightCompression", "int4WeightExtractionFloat", "int4WeightExtractionHalf", "int8WeightExtractionFloat", "int8WeightExtractionHalf", ], ) except Exception as exception: kernels = None class W8A16Linear(torch.autograd.Function): @staticmethod def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width): ctx.inp_shape = inp.size() ctx.weight_bit_width = weight_bit_width out_features = quant_w.size(0) inp = inp.contiguous().view(-1, inp.size(-1)) weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width) ctx.weight_shape = weight.size() output = inp.mm(weight.t()) ctx.save_for_backward(inp, quant_w, scale_w) return output.view(*(ctx.inp_shape[:-1] + (out_features,))) @staticmethod def backward(ctx, grad_output: torch.Tensor): inp, quant_w, scale_w = ctx.saved_tensors weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width) grad_output = grad_output.contiguous().view(-1, weight.size(0)) grad_input = grad_output.mm(weight) grad_weight = grad_output.t().mm(inp) return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None def compress_int4_weight(weight: torch.Tensor): # (n, m) with torch.cuda.device(weight.device): n, m = weight.size(0), weight.size(1) assert m % 2 == 0 m = m // 2 out = torch.empty(n, m, dtype=torch.int8, device="cuda") stream = torch.cuda.current_stream() gridDim = (n, 1, 1) blockDim = (min(round_up(m, 32), 1024), 1, 1) kernels.int4WeightCompression( gridDim, blockDim, 0, stream, [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)], ) return out def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int): assert scale_list.dtype in [torch.half, torch.bfloat16] assert weight.dtype in [torch.int8] if source_bit_width == 8: return weight.to(scale_list.dtype) * scale_list[:, None] elif source_bit_width == 4: func = ( kernels.int4WeightExtractionHalf if scale_list.dtype == torch.half else kernels.int4WeightExtractionBFloat16 ) else: assert False, "Unsupported bit-width" with torch.cuda.device(weight.device): n, m = weight.size(0), weight.size(1) out = torch.empty(n, m * (8 // source_bit_width), dtype=scale_list.dtype, device="cuda") stream = torch.cuda.current_stream() gridDim = (n, 1, 1) blockDim = (min(round_up(m, 32), 1024), 1, 1) func( gridDim, blockDim, 0, stream, [ ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(scale_list.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m), ], ) return out class QuantizedLinear(torch.nn.Module): def __init__(self, weight_bit_width: int, weight, bias=None, device="cuda", dtype=None, empty_init=False): super().__init__() weight = weight.to(device) # ensure the weight is on the cuda device assert str(weight.device).startswith( 'cuda'), 'The weights that need to be quantified should be on the CUDA device' self.weight_bit_width = weight_bit_width shape = weight.shape if weight is None or empty_init: self.weight = torch.empty(shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=device) self.weight_scale = torch.empty(shape[0], dtype=dtype, device=device) else: self.weight_scale = weight.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1) self.weight = torch.round(weight / self.weight_scale[:, None]).to(torch.int8) if weight_bit_width == 4: self.weight = compress_int4_weight(self.weight) self.weight = Parameter(self.weight.to(device), requires_grad=False) self.weight_scale = Parameter(self.weight_scale.to(device), requires_grad=False) self.bias = Parameter(bias.to(device), requires_grad=False) if bias is not None else None def forward(self, input): output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width) if self.bias is not None: output = output + self.bias return output def quantize(model, weight_bit_width, empty_init=False, device=None): """Replace fp16 linear with quantized linear""" for layer in model.layers: layer.self_attention.query_key_value = QuantizedLinear( weight_bit_width=weight_bit_width, weight=layer.self_attention.query_key_value.weight, bias=layer.self_attention.query_key_value.bias, dtype=layer.self_attention.query_key_value.weight.dtype, device=layer.self_attention.query_key_value.weight.device if device is None else device, empty_init=empty_init ) layer.self_attention.dense = QuantizedLinear( weight_bit_width=weight_bit_width, weight=layer.self_attention.dense.weight, bias=layer.self_attention.dense.bias, dtype=layer.self_attention.dense.weight.dtype, device=layer.self_attention.dense.weight.device if device is None else device, empty_init=empty_init ) layer.mlp.dense_h_to_4h = QuantizedLinear( weight_bit_width=weight_bit_width, weight=layer.mlp.dense_h_to_4h.weight, bias=layer.mlp.dense_h_to_4h.bias, dtype=layer.mlp.dense_h_to_4h.weight.dtype, device=layer.mlp.dense_h_to_4h.weight.device if device is None else device, empty_init=empty_init ) layer.mlp.dense_4h_to_h = QuantizedLinear( weight_bit_width=weight_bit_width, weight=layer.mlp.dense_4h_to_h.weight, bias=layer.mlp.dense_4h_to_h.bias, dtype=layer.mlp.dense_4h_to_h.weight.dtype, device=layer.mlp.dense_4h_to_h.weight.device if device is None else device, empty_init=empty_init ) return model class ChatGLMConfig(PretrainedConfig): model_type = "chatglm" def __init__( self, num_layers=28, padded_vocab_size=65024, hidden_size=4096, ffn_hidden_size=13696, kv_channels=128, num_attention_heads=32, seq_length=2048, hidden_dropout=0.0, classifier_dropout=None, attention_dropout=0.0, layernorm_epsilon=1e-5, rmsnorm=True, apply_residual_connection_post_layernorm=False, post_layer_norm=True, add_bias_linear=False, add_qkv_bias=False, bias_dropout_fusion=True, multi_query_attention=False, multi_query_group_num=1, apply_query_key_layer_scaling=True, attention_softmax_in_fp32=True, fp32_residual_connection=False, quantization_bit=0, pre_seq_len=None, prefix_projection=False, **kwargs ): self.num_layers = num_layers self.vocab_size = padded_vocab_size self.padded_vocab_size = padded_vocab_size self.hidden_size = hidden_size self.ffn_hidden_size = ffn_hidden_size self.kv_channels = kv_channels self.num_attention_heads = num_attention_heads self.seq_length = seq_length self.hidden_dropout = hidden_dropout self.classifier_dropout = classifier_dropout self.attention_dropout = attention_dropout self.layernorm_epsilon = layernorm_epsilon self.rmsnorm = rmsnorm self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm self.post_layer_norm = post_layer_norm self.add_bias_linear = add_bias_linear self.add_qkv_bias = add_qkv_bias self.bias_dropout_fusion = bias_dropout_fusion self.multi_query_attention = multi_query_attention self.multi_query_group_num = multi_query_group_num self.apply_query_key_layer_scaling = apply_query_key_layer_scaling self.attention_softmax_in_fp32 = attention_softmax_in_fp32 self.fp32_residual_connection = fp32_residual_connection self.quantization_bit = quantization_bit self.pre_seq_len = pre_seq_len self.prefix_projection = prefix_projection super().__init__(**kwargs) # flags required to enable jit fusion kernels if sys.platform != 'darwin': torch._C._jit_set_profiling_mode(False) torch._C._jit_set_profiling_executor(False) torch._C._jit_override_can_fuse_on_cpu(True) torch._C._jit_override_can_fuse_on_gpu(True) logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "THUDM/ChatGLM" _CONFIG_FOR_DOC = "ChatGLM6BConfig" CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ "THUDM/chatglm3-6b-base", # See all ChatGLM models at https://huggingface.co/models?filter=chatglm ] def default_init(cls, *args, **kwargs): return cls(*args, **kwargs) class InvalidScoreLogitsProcessor(LogitsProcessor): def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: if torch.isnan(scores).any() or torch.isinf(scores).any(): scores.zero_() scores[..., 5] = 5e4 return scores class PrefixEncoder(torch.nn.Module): """ The torch.nn model to encode the prefix Input shape: (batch-size, prefix-length) Output shape: (batch-size, prefix-length, 2*layers*hidden) """ def __init__(self, config: ChatGLMConfig): super().__init__() self.prefix_projection = config.prefix_projection if self.prefix_projection: # Use a two-layer MLP to encode the prefix kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) self.trans = torch.nn.Sequential( torch.nn.Linear(kv_size, config.hidden_size), torch.nn.Tanh(), torch.nn.Linear(config.hidden_size, kv_size) ) else: self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.kv_channels * config.multi_query_group_num * 2) def forward(self, prefix: torch.Tensor): if self.prefix_projection: prefix_tokens = self.embedding(prefix) past_key_values = self.trans(prefix_tokens) else: past_key_values = self.embedding(prefix) return past_key_values def split_tensor_along_last_dim( tensor: torch.Tensor, num_partitions: int, contiguous_split_chunks: bool = False, ) -> List[torch.Tensor]: """Split a tensor along its last dimension. Arguments: tensor: input tensor. num_partitions: number of partitions to split the tensor contiguous_split_chunks: If True, make each chunk contiguous in memory. Returns: A list of Tensors """ # Get the size and dimension. last_dim = tensor.dim() - 1 last_dim_size = tensor.size()[last_dim] // num_partitions # Split. tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) # Note: torch.split does not create contiguous tensors by default. if contiguous_split_chunks: return tuple(chunk.contiguous() for chunk in tensor_list) return tensor_list class RotaryEmbedding(nn.Module): def __init__(self, dim, original_impl=False, device=None, dtype=None): super().__init__() inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) self.register_buffer("inv_freq", inv_freq) self.dim = dim self.original_impl = original_impl def forward_impl( self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 ): """Enhanced Transformer with Rotary Position Embedding. Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ transformers/rope/__init__.py. MIT License: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. """ # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem)) # Create position indexes `[0, 1, ..., seq_len - 1]` seq_idx = torch.arange(seq_len, dtype=torch.float, device=device) # Calculate the product of position index and $\theta_i$ idx_theta = torch.outer(seq_idx, theta).float() cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) # this is to mimic the behaviour of complex32, else we will get different results if dtype in (torch.float16, torch.bfloat16, torch.int8): cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() return cache def forward(self, max_seq_len, offset=0): return self.forward_impl( max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device ) @torch.jit.script def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: # x: [sq, b, np, hn] sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) rot_dim = rope_cache.shape[-2] * 2 x, x_pass = x[..., :rot_dim], x[..., rot_dim:] # truncate to support variable sizes rope_cache = rope_cache[:sq] xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) x_out2 = torch.stack( [ xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], ], -1, ) x_out2 = x_out2.flatten(3) return torch.cat((x_out2, x_pass), dim=-1) class RMSNorm(torch.nn.Module): def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): super().__init__() self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) self.eps = eps def forward(self, hidden_states: torch.Tensor): input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.eps) return (self.weight * hidden_states).to(input_dtype) class CoreAttention(torch.nn.Module): def __init__(self, config: ChatGLMConfig, layer_number): super(CoreAttention, self).__init__() self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 if self.apply_query_key_layer_scaling: self.attention_softmax_in_fp32 = True self.layer_number = max(1, layer_number) projection_size = config.kv_channels * config.num_attention_heads # Per attention head and per partition values. self.hidden_size_per_partition = projection_size self.hidden_size_per_attention_head = projection_size // config.num_attention_heads self.num_attention_heads_per_partition = config.num_attention_heads coeff = None self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) if self.apply_query_key_layer_scaling: coeff = self.layer_number self.norm_factor *= coeff self.coeff = coeff self.attention_dropout = torch.nn.Dropout(config.attention_dropout) def forward(self, query_layer, key_layer, value_layer, attention_mask): pytorch_major_version = int(torch.__version__.split('.')[0]) if pytorch_major_version >= 2: query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, is_causal=True) else: if attention_mask is not None: attention_mask = ~attention_mask context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, attention_mask) context_layer = context_layer.permute(2, 0, 1, 3) new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) context_layer = context_layer.reshape(*new_context_layer_shape) else: # Raw attention scores # [b, np, sq, sk] output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) # [sq, b, np, hn] -> [sq, b * np, hn] query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) # [sk, b, np, hn] -> [sk, b * np, hn] key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) # preallocting input tensor: [b * np, sq, sk] matmul_input_buffer = torch.empty( output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, device=query_layer.device ) # Raw attention scores. [b * np, sq, sk] matmul_result = torch.baddbmm( matmul_input_buffer, query_layer.transpose(0, 1), # [b * np, sq, hn] key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] beta=0.0, alpha=(1.0 / self.norm_factor), ) # change view to [b, np, sq, sk] attention_scores = matmul_result.view(*output_size) # =========================== # Attention probs and dropout # =========================== # attention scores and attention mask [b, np, sq, sk] if self.attention_softmax_in_fp32: attention_scores = attention_scores.float() if self.coeff is not None: attention_scores = attention_scores * self.coeff if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], device=attention_scores.device, dtype=torch.bool) attention_mask.tril_() attention_mask = ~attention_mask if attention_mask is not None: attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) attention_probs = F.softmax(attention_scores, dim=-1) attention_probs = attention_probs.type_as(value_layer) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.attention_dropout(attention_probs) # ========================= # Context layer. [sq, b, hp] # ========================= # value_layer -> context layer. # [sk, b, np, hn] --> [b, np, sq, hn] # context layer shape: [b, np, sq, hn] output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) # change view [sk, b * np, hn] value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) # change view [b * np, sq, sk] attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) # matmul: [b * np, sq, hn] context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) # change view [b, np, sq, hn] context_layer = context_layer.view(*output_size) # [b, np, sq, hn] --> [sq, b, np, hn] context_layer = context_layer.permute(2, 0, 1, 3).contiguous() # [sq, b, np, hn] --> [sq, b, hp] new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer class SelfAttention(torch.nn.Module): """Parallel self-attention layer abstract class. Self-attention layer takes input with size [s, b, h] and returns output of the same size. """ def __init__(self, config: ChatGLMConfig, layer_number, device=None): super(SelfAttention, self).__init__() self.layer_number = max(1, layer_number) self.projection_size = config.kv_channels * config.num_attention_heads # Per attention head and per partition values. self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads self.num_attention_heads_per_partition = config.num_attention_heads self.multi_query_attention = config.multi_query_attention self.qkv_hidden_size = 3 * self.projection_size if self.multi_query_attention: self.num_multi_query_groups_per_partition = config.multi_query_group_num self.qkv_hidden_size = ( self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num ) self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, bias=config.add_bias_linear or config.add_qkv_bias, device=device, **_config_to_kwargs(config) ) self.core_attention = CoreAttention(config, self.layer_number) # Output. self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, device=device, **_config_to_kwargs(config) ) def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): if self.multi_query_attention: num_attention_heads = self.num_multi_query_groups_per_partition else: num_attention_heads = self.num_attention_heads_per_partition return torch.empty( inference_max_sequence_len, batch_size, num_attention_heads, self.hidden_size_per_attention_head, dtype=dtype, device=device, ) def forward( self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True ): # hidden_states: [sq, b, h] # ================================================= # Pre-allocate memory for key-values for inference. # ================================================= # ===================== # Query, Key, and Value # ===================== # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] mixed_x_layer = self.query_key_value(hidden_states) if self.multi_query_attention: (query_layer, key_layer, value_layer) = mixed_x_layer.split( [ self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, ], dim=-1, ) query_layer = query_layer.view( query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) ) key_layer = key_layer.view( key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) ) value_layer = value_layer.view( value_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) ) else: new_tensor_shape = mixed_x_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, 3 * self.hidden_size_per_attention_head) mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) # apply relative positional encoding (rotary embedding) if rotary_pos_emb is not None: query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) # adjust key and value for inference if kv_cache is not None: cache_k, cache_v = kv_cache key_layer = torch.cat((cache_k, key_layer), dim=0) value_layer = torch.cat((cache_v, value_layer), dim=0) if use_cache: kv_cache = (key_layer, value_layer) else: kv_cache = None if self.multi_query_attention: key_layer = key_layer.unsqueeze(-2) key_layer = key_layer.expand( -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 ) key_layer = key_layer.contiguous().view( key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) ) value_layer = value_layer.unsqueeze(-2) value_layer = value_layer.expand( -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 ) value_layer = value_layer.contiguous().view( value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) ) # ================================== # core attention computation # ================================== context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) # ================= # Output. [sq, b, h] # ================= output = self.dense(context_layer) return output, kv_cache def _config_to_kwargs(args): common_kwargs = { "dtype": args.torch_dtype, } return common_kwargs class MLP(torch.nn.Module): """MLP. MLP will take the input with h hidden state, project it to 4*h hidden dimension, perform nonlinear transformation, and project the state back into h hidden dimension. """ def __init__(self, config: ChatGLMConfig, device=None): super(MLP, self).__init__() self.add_bias = config.add_bias_linear # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf self.dense_h_to_4h = nn.Linear( config.hidden_size, config.ffn_hidden_size * 2, bias=self.add_bias, device=device, **_config_to_kwargs(config) ) def swiglu(x): x = torch.chunk(x, 2, dim=-1) return F.silu(x[0]) * x[1] self.activation_func = swiglu # Project back to h. self.dense_4h_to_h = nn.Linear( config.ffn_hidden_size, config.hidden_size, bias=self.add_bias, device=device, **_config_to_kwargs(config) ) def forward(self, hidden_states): # [s, b, 4hp] intermediate_parallel = self.dense_h_to_4h(hidden_states) intermediate_parallel = self.activation_func(intermediate_parallel) # [s, b, h] output = self.dense_4h_to_h(intermediate_parallel) return output class GLMBlock(torch.nn.Module): """A single transformer layer. Transformer layer takes input with size [s, b, h] and returns an output of the same size. """ def __init__(self, config: ChatGLMConfig, layer_number, device=None): super(GLMBlock, self).__init__() self.layer_number = layer_number self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm self.fp32_residual_connection = config.fp32_residual_connection LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm # Layernorm on the input data. self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype) # Self attention. self.self_attention = SelfAttention(config, layer_number, device=device) self.hidden_dropout = config.hidden_dropout # Layernorm on the attention output self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype) # MLP self.mlp = MLP(config, device=device) def forward( self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, ): # hidden_states: [s, b, h] # Layer norm at the beginning of the transformer layer. layernorm_output = self.input_layernorm(hidden_states) # Self attention. attention_output, kv_cache = self.self_attention( layernorm_output, attention_mask, rotary_pos_emb, kv_cache=kv_cache, use_cache=use_cache ) # Residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = hidden_states layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) layernorm_input = residual + layernorm_input # Layer norm post the self attention. layernorm_output = self.post_attention_layernorm(layernorm_input) # MLP. mlp_output = self.mlp(layernorm_output) # Second residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = layernorm_input output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) output = residual + output return output, kv_cache class GLMTransformer(torch.nn.Module): """Transformer class.""" def __init__(self, config: ChatGLMConfig, device=None): super(GLMTransformer, self).__init__() self.fp32_residual_connection = config.fp32_residual_connection self.post_layer_norm = config.post_layer_norm # Number of layers. self.num_layers = config.num_layers # Transformer layers. def build_layer(layer_number): return GLMBlock(config, layer_number, device=device) self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) if self.post_layer_norm: LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm # Final layer norm before output. self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype) self.gradient_checkpointing = False def _get_layer(self, layer_number): return self.layers[layer_number] def forward( self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, use_cache: Optional[bool] = True, output_hidden_states: Optional[bool] = False, ): if not kv_caches: kv_caches = [None for _ in range(self.num_layers)] presents = () if use_cache else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False all_self_attentions = None all_hidden_states = () if output_hidden_states else None for index in range(self.num_layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer = self._get_layer(index) if self.gradient_checkpointing and self.training: layer_ret = torch.utils.checkpoint.checkpoint( layer, hidden_states, attention_mask, rotary_pos_emb, kv_caches[index], use_cache ) else: layer_ret = layer( hidden_states, attention_mask, rotary_pos_emb, kv_cache=kv_caches[index], use_cache=use_cache ) hidden_states, kv_cache = layer_ret if use_cache: presents = presents + (kv_cache,) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # Final layer norm. if self.post_layer_norm: hidden_states = self.final_layernorm(hidden_states) return hidden_states, presents, all_hidden_states, all_self_attentions class ChatGLMPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ is_parallelizable = False supports_gradient_checkpointing = True config_class = ChatGLMConfig base_model_prefix = "transformer" _no_split_modules = ["GLMBlock"] def _init_weights(self, module: nn.Module): """Initialize the weights.""" return def get_masks(self, input_ids, past_key_values, padding_mask=None): batch_size, seq_length = input_ids.shape full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) full_attention_mask.tril_() past_length = 0 if past_key_values: past_length = past_key_values[0][0].shape[0] if past_length: full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, device=input_ids.device), full_attention_mask), dim=-1) if padding_mask is not None: full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) if not past_length and padding_mask is not None: full_attention_mask -= padding_mask.unsqueeze(-1) - 1 full_attention_mask = (full_attention_mask < 0.5).bool() full_attention_mask.unsqueeze_(1) return full_attention_mask def get_position_ids(self, input_ids, device): batch_size, seq_length = input_ids.shape position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) return position_ids def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, GLMTransformer): module.gradient_checkpointing = value class Embedding(torch.nn.Module): """Language model embeddings.""" def __init__(self, config: ChatGLMConfig, device=None): super(Embedding, self).__init__() self.hidden_size = config.hidden_size # Word embeddings (parallel). self.word_embeddings = nn.Embedding( config.padded_vocab_size, self.hidden_size, dtype=config.torch_dtype, device=device ) self.fp32_residual_connection = config.fp32_residual_connection def forward(self, input_ids): # Embeddings. words_embeddings = self.word_embeddings(input_ids) embeddings = words_embeddings # Data format change to avoid explicit transposes : [b s h] --> [s b h]. embeddings = embeddings.transpose(0, 1).contiguous() # If the input flag for fp32 residual connection is set, convert for float. if self.fp32_residual_connection: embeddings = embeddings.float() return embeddings class ChatGLMModel(ChatGLMPreTrainedModel): def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): super().__init__(config) if empty_init: init_method = skip_init else: init_method = default_init init_kwargs = {} if device is not None: init_kwargs["device"] = device self.embedding = init_method(Embedding, config, **init_kwargs) self.num_layers = config.num_layers self.multi_query_group_num = config.multi_query_group_num self.kv_channels = config.kv_channels # Rotary positional embeddings self.seq_length = config.seq_length rotary_dim = ( config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels ) self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, dtype=config.torch_dtype) self.encoder = init_method(GLMTransformer, config, **init_kwargs) self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, dtype=config.torch_dtype, **init_kwargs) self.pre_seq_len = config.pre_seq_len self.prefix_projection = config.prefix_projection if self.pre_seq_len is not None: for param in self.parameters(): param.requires_grad = False self.prefix_tokens = torch.arange(self.pre_seq_len).long() self.prefix_encoder = PrefixEncoder(config) self.dropout = torch.nn.Dropout(0.1) def get_input_embeddings(self): return self.embedding.word_embeddings def get_prompt(self, batch_size, device, dtype=torch.half): prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) past_key_values = past_key_values.view( batch_size, self.pre_seq_len, self.num_layers * 2, self.multi_query_group_num, self.kv_channels ) # seq_len, b, nh, hidden_size past_key_values = self.dropout(past_key_values) past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) return past_key_values def forward( self, input_ids, position_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.BoolTensor] = None, full_attention_mask: Optional[torch.BoolTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, seq_length = input_ids.shape if inputs_embeds is None: inputs_embeds = self.embedding(input_ids) if self.pre_seq_len is not None: if past_key_values is None: past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, dtype=inputs_embeds.dtype) if attention_mask is not None: attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), attention_mask], dim=-1) if full_attention_mask is None: if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) # Rotary positional embeddings rotary_pos_emb = self.rotary_pos_emb(self.seq_length) if position_ids is not None: rotary_pos_emb = rotary_pos_emb[position_ids] else: rotary_pos_emb = rotary_pos_emb[None, :seq_length] rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() # Run encoder. hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states ) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, ) def quantize(self, weight_bit_width: int): # from .quantization import quantize quantize(self.encoder, weight_bit_width) return self class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): super().__init__(config) self.max_sequence_length = config.max_length self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) self.config = config self.quantized = False if self.config.quantization_bit: self.quantize(self.config.quantization_bit, empty_init=True) def _update_model_kwargs_for_generation( self, outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False, standardize_cache_format: bool = False, ) -> Dict[str, Any]: # update past_key_values model_kwargs["past_key_values"] = self._extract_past_from_model_output( outputs, standardize_cache_format=standardize_cache_format ) # update attention mask if "attention_mask" in model_kwargs: attention_mask = model_kwargs["attention_mask"] model_kwargs["attention_mask"] = torch.cat( [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 ) # update position ids if "position_ids" in model_kwargs: position_ids = model_kwargs["position_ids"] new_position_id = position_ids[..., -1:].clone() new_position_id += 1 model_kwargs["position_ids"] = torch.cat( [position_ids, new_position_id], dim=-1 ) model_kwargs["is_first_forward"] = False return model_kwargs def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, past_key_values: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, is_first_forward: bool = True, **kwargs ) -> dict: # only last token for input_ids if past is not None if position_ids is None: position_ids = self.get_position_ids(input_ids, device=input_ids.device) if not is_first_forward: if past_key_values is not None: position_ids = position_ids[..., -1:] input_ids = input_ids[:, -1:] return { "input_ids": input_ids, "past_key_values": past_key_values, "position_ids": position_ids, "attention_mask": attention_mask, "return_last_logit": True, "use_cache": use_cache } def forward( self, input_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, return_last_logit: Optional[bool] = False, ): use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] if return_last_logit: hidden_states = hidden_states[-1:] lm_logits = self.transformer.output_layer(hidden_states) lm_logits = lm_logits.transpose(0, 1).contiguous() loss = None if labels is not None: lm_logits = lm_logits.to(torch.float32) # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss(ignore_index=-100) loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) lm_logits = lm_logits.to(hidden_states.dtype) loss = loss.to(hidden_states.dtype) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @staticmethod def _reorder_cache( past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: """ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. Output shares the same memory storage as `past`. """ return tuple( ( layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), ) for layer_past in past ) def process_response(self, output, history): content = "" history = deepcopy(history) for response in output.split("<|assistant|>"): metadata, content = response.split("\n", maxsplit=1) if not metadata.strip(): content = content.strip() history.append({"role": "assistant", "metadata": metadata, "content": content}) content = content.replace("[[训练时间]]", "2023年") else: history.append({"role": "assistant", "metadata": metadata, "content": content}) if history[0]["role"] == "system" and "tools" in history[0]: content = "\n".join(content.split("\n")[1:-1]) def tool_call(**kwargs): return kwargs parameters = eval(content) content = {"name": metadata.strip(), "parameters": parameters} else: content = {"name": metadata.strip(), "content": content} return content, history @torch.inference_mode() def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, **kwargs): if history is None: history = [] if logits_processor is None: logits_processor = LogitsProcessorList() logits_processor.append(InvalidScoreLogitsProcessor()) gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, "temperature": temperature, "logits_processor": logits_processor, **kwargs} inputs = tokenizer.build_chat_input(query, history=history, role=role) inputs = inputs.to(self.device) eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), tokenizer.get_command("<|observation|>")] outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id) outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] response = tokenizer.decode(outputs) history.append({"role": role, "content": query}) response, history = self.process_response(response, history) return response, history @torch.inference_mode() def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", past_key_values=None,max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, return_past_key_values=False, **kwargs): if history is None: history = [] if logits_processor is None: logits_processor = LogitsProcessorList() logits_processor.append(InvalidScoreLogitsProcessor()) eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), tokenizer.get_command("<|observation|>")] gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, "temperature": temperature, "logits_processor": logits_processor, **kwargs} if past_key_values is None: inputs = tokenizer.build_chat_input(query, history=history, role=role) else: inputs = tokenizer.build_chat_input(query, role=role) inputs = inputs.to(self.device) if past_key_values is not None: past_length = past_key_values[0][0].shape[0] if self.transformer.pre_seq_len is not None: past_length -= self.transformer.pre_seq_len inputs.position_ids += past_length attention_mask = inputs.attention_mask attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) inputs['attention_mask'] = attention_mask history.append({"role": role, "content": query}) for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, eos_token_id=eos_token_id, return_past_key_values=return_past_key_values, **gen_kwargs): if return_past_key_values: outputs, past_key_values = outputs outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] response = tokenizer.decode(outputs) if response and response[-1] != "�": response, new_history = self.process_response(response, history) if return_past_key_values: yield response, new_history, past_key_values else: yield response, new_history @torch.inference_mode() def stream_generate( self, input_ids, generation_config: Optional[GenerationConfig] = None, logits_processor: Optional[LogitsProcessorList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None, prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, return_past_key_values=False, **kwargs, ): batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] if generation_config is None: generation_config = self.generation_config generation_config = copy.deepcopy(generation_config) model_kwargs = generation_config.update(**kwargs) model_kwargs["use_cache"] = generation_config.use_cache bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id if isinstance(eos_token_id, int): eos_token_id = [eos_token_id] eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None if has_default_max_length and generation_config.max_new_tokens is None: warnings.warn( f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" " recommend using `max_new_tokens` to control the maximum length of the generation.", UserWarning, ) elif generation_config.max_new_tokens is not None: generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length if not has_default_max_length: logger.warn( f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " "Please refer to the documentation for more information. " "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", UserWarning, ) if input_ids_seq_length >= generation_config.max_length: input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" logger.warning( f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" " increasing `max_new_tokens`." ) # 2. Set generation parameters if not already defined logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() logits_processor = self._get_logits_processor( generation_config=generation_config, input_ids_seq_length=input_ids_seq_length, encoder_input_ids=input_ids, prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, logits_processor=logits_processor, ) stopping_criteria = self._get_stopping_criteria( generation_config=generation_config, stopping_criteria=stopping_criteria ) logits_warper = self._get_logits_warper(generation_config) unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) scores = None while True: model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) # forward pass to get next token outputs = self( **model_inputs, return_dict=True, output_attentions=False, output_hidden_states=False, ) next_token_logits = outputs.logits[:, -1, :] # pre-process distribution next_token_scores = logits_processor(input_ids, next_token_logits) next_token_scores = logits_warper(input_ids, next_token_scores) # sample probs = nn.functional.softmax(next_token_scores, dim=-1) if generation_config.do_sample: next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) else: next_tokens = torch.argmax(probs, dim=-1) # update generated ids, model inputs, and length for next step input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) model_kwargs = self._update_model_kwargs_for_generation( outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder ) unfinished_sequences = unfinished_sequences.mul( next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0) ) if return_past_key_values: yield input_ids, outputs.past_key_values else: yield input_ids # stop when each sentence is finished, or if we exceed the maximum length if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): break def quantize(self, bits: int, empty_init=False, device=None, **kwargs): if bits == 0: return # from .quantization import quantize if self.quantized: logger.info("Already quantized.") return self self.quantized = True self.config.quantization_bit = bits self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, **kwargs) return self class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel): def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): super().__init__(config) self.num_labels = config.num_labels self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half) if config.classifier_dropout is not None: self.dropout = nn.Dropout(config.classifier_dropout) else: self.dropout = None self.config = config if self.config.quantization_bit: self.quantize(self.config.quantization_bit, empty_init=True) def forward( self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, full_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, inputs_embeds: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask, full_attention_mask=full_attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] pooled_hidden_states = hidden_states[-1] if self.dropout is not None: pooled_hidden_states = self.dropout(pooled_hidden_states) logits = self.classifier_head(pooled_hidden_states) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze().float(), labels.squeeze()) else: loss = loss_fct(logits.float(), labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits.float(), labels.view(-1, self.num_labels)) if not return_dict: output = (logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) ================================================ FILE: diffsynth/models/lora.py ================================================ import torch from .sd_unet import SDUNet from .sdxl_unet import SDXLUNet from .sd_text_encoder import SDTextEncoder from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 from .sd3_dit import SD3DiT from .flux_dit import FluxDiT from .hunyuan_dit import HunyuanDiT from .cog_dit import CogDiT from .hunyuan_video_dit import HunyuanVideoDiT from .wan_video_dit import WanModel class LoRAFromCivitai: def __init__(self): self.supported_model_classes = [] self.lora_prefix = [] self.renamed_lora_prefix = {} self.special_keys = {} def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alpha=1.0): for key in state_dict: if ".lora_up" in key: return self.convert_state_dict_up_down(state_dict, lora_prefix, alpha) return self.convert_state_dict_AB(state_dict, lora_prefix, alpha) def convert_state_dict_up_down(self, state_dict, lora_prefix="lora_unet_", alpha=1.0): renamed_lora_prefix = self.renamed_lora_prefix.get(lora_prefix, "") state_dict_ = {} for key in state_dict: if ".lora_up" not in key: continue if not key.startswith(lora_prefix): continue weight_up = state_dict[key].to(device="cuda", dtype=torch.float16) weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16) if len(weight_up.shape) == 4: weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32) weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32) lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) else: lora_weight = alpha * torch.mm(weight_up, weight_down) target_name = key.split(".")[0].replace(lora_prefix, renamed_lora_prefix).replace("_", ".") + ".weight" for special_key in self.special_keys: target_name = target_name.replace(special_key, self.special_keys[special_key]) state_dict_[target_name] = lora_weight.cpu() return state_dict_ def convert_state_dict_AB(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16): state_dict_ = {} for key in state_dict: if ".lora_B." not in key: continue if not key.startswith(lora_prefix): continue weight_up = state_dict[key].to(device=device, dtype=torch_dtype) weight_down = state_dict[key.replace(".lora_B.", ".lora_A.")].to(device=device, dtype=torch_dtype) if len(weight_up.shape) == 4: weight_up = weight_up.squeeze(3).squeeze(2) weight_down = weight_down.squeeze(3).squeeze(2) lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) else: lora_weight = alpha * torch.mm(weight_up, weight_down) keys = key.split(".") keys.pop(keys.index("lora_B")) target_name = ".".join(keys) target_name = target_name[len(lora_prefix):] state_dict_[target_name] = lora_weight.cpu() return state_dict_ def load(self, model, state_dict_lora, lora_prefix, alpha=1.0, model_resource=None): state_dict_model = model.state_dict() state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=alpha) if model_resource == "diffusers": state_dict_lora = model.__class__.state_dict_converter().from_diffusers(state_dict_lora) elif model_resource == "civitai": state_dict_lora = model.__class__.state_dict_converter().from_civitai(state_dict_lora) if isinstance(state_dict_lora, tuple): state_dict_lora = state_dict_lora[0] if len(state_dict_lora) > 0: print(f" {len(state_dict_lora)} tensors are updated.") for name in state_dict_lora: fp8=False if state_dict_model[name].dtype == torch.float8_e4m3fn: state_dict_model[name]= state_dict_model[name].to(state_dict_lora[name].dtype) fp8=True state_dict_model[name] += state_dict_lora[name].to( dtype=state_dict_model[name].dtype, device=state_dict_model[name].device) if fp8: state_dict_model[name] = state_dict_model[name].to(torch.float8_e4m3fn) model.load_state_dict(state_dict_model) def match(self, model, state_dict_lora): for lora_prefix, model_class in zip(self.lora_prefix, self.supported_model_classes): if not isinstance(model, model_class): continue state_dict_model = model.state_dict() for model_resource in ["diffusers", "civitai"]: try: state_dict_lora_ = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=1.0) converter_fn = model.__class__.state_dict_converter().from_diffusers if model_resource == "diffusers" \ else model.__class__.state_dict_converter().from_civitai state_dict_lora_ = converter_fn(state_dict_lora_) if isinstance(state_dict_lora_, tuple): state_dict_lora_ = state_dict_lora_[0] if len(state_dict_lora_) == 0: continue for name in state_dict_lora_: if name not in state_dict_model: break else: return lora_prefix, model_resource except: pass return None class SDLoRAFromCivitai(LoRAFromCivitai): def __init__(self): super().__init__() self.supported_model_classes = [SDUNet, SDTextEncoder] self.lora_prefix = ["lora_unet_", "lora_te_"] self.special_keys = { "down.blocks": "down_blocks", "up.blocks": "up_blocks", "mid.block": "mid_block", "proj.in": "proj_in", "proj.out": "proj_out", "transformer.blocks": "transformer_blocks", "to.q": "to_q", "to.k": "to_k", "to.v": "to_v", "to.out": "to_out", "text.model": "text_model", "self.attn.q.proj": "self_attn.q_proj", "self.attn.k.proj": "self_attn.k_proj", "self.attn.v.proj": "self_attn.v_proj", "self.attn.out.proj": "self_attn.out_proj", "input.blocks": "model.diffusion_model.input_blocks", "middle.block": "model.diffusion_model.middle_block", "output.blocks": "model.diffusion_model.output_blocks", } class SDXLLoRAFromCivitai(LoRAFromCivitai): def __init__(self): super().__init__() self.supported_model_classes = [SDXLUNet, SDXLTextEncoder, SDXLTextEncoder2] self.lora_prefix = ["lora_unet_", "lora_te1_", "lora_te2_"] self.renamed_lora_prefix = {"lora_te2_": "2"} self.special_keys = { "down.blocks": "down_blocks", "up.blocks": "up_blocks", "mid.block": "mid_block", "proj.in": "proj_in", "proj.out": "proj_out", "transformer.blocks": "transformer_blocks", "to.q": "to_q", "to.k": "to_k", "to.v": "to_v", "to.out": "to_out", "text.model": "conditioner.embedders.0.transformer.text_model", "self.attn.q.proj": "self_attn.q_proj", "self.attn.k.proj": "self_attn.k_proj", "self.attn.v.proj": "self_attn.v_proj", "self.attn.out.proj": "self_attn.out_proj", "input.blocks": "model.diffusion_model.input_blocks", "middle.block": "model.diffusion_model.middle_block", "output.blocks": "model.diffusion_model.output_blocks", "2conditioner.embedders.0.transformer.text_model.encoder.layers": "text_model.encoder.layers" } class FluxLoRAFromCivitai(LoRAFromCivitai): def __init__(self): super().__init__() self.supported_model_classes = [FluxDiT, FluxDiT] self.lora_prefix = ["lora_unet_", "transformer."] self.renamed_lora_prefix = {} self.special_keys = { "single.blocks": "single_blocks", "double.blocks": "double_blocks", "img.attn": "img_attn", "img.mlp": "img_mlp", "img.mod": "img_mod", "txt.attn": "txt_attn", "txt.mlp": "txt_mlp", "txt.mod": "txt_mod", } class GeneralLoRAFromPeft: def __init__(self): self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT, FluxDiT, CogDiT, WanModel] def get_name_dict(self, lora_state_dict): lora_name_dict = {} for key in lora_state_dict: if ".lora_B." not in key: continue keys = key.split(".") if len(keys) > keys.index("lora_B") + 2: keys.pop(keys.index("lora_B") + 1) keys.pop(keys.index("lora_B")) if keys[0] == "diffusion_model": keys.pop(0) target_name = ".".join(keys) lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A.")) return lora_name_dict def match(self, model: torch.nn.Module, state_dict_lora): lora_name_dict = self.get_name_dict(state_dict_lora) model_name_dict = {name: None for name, _ in model.named_parameters()} matched_num = sum([i in model_name_dict for i in lora_name_dict]) if matched_num == len(lora_name_dict): return "", "" else: return None def fetch_device_and_dtype(self, state_dict): device, dtype = None, None for name, param in state_dict.items(): device, dtype = param.device, param.dtype break computation_device = device computation_dtype = dtype if computation_device == torch.device("cpu"): if torch.cuda.is_available(): computation_device = torch.device("cuda") if computation_dtype == torch.float8_e4m3fn: computation_dtype = torch.float32 return device, dtype, computation_device, computation_dtype def load(self, model, state_dict_lora, lora_prefix="", alpha=1.0, model_resource=""): state_dict_model = model.state_dict() device, dtype, computation_device, computation_dtype = self.fetch_device_and_dtype(state_dict_model) lora_name_dict = self.get_name_dict(state_dict_lora) for name in lora_name_dict: weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=computation_device, dtype=computation_dtype) weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=computation_device, dtype=computation_dtype) if len(weight_up.shape) == 4: weight_up = weight_up.squeeze(3).squeeze(2) weight_down = weight_down.squeeze(3).squeeze(2) weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) else: weight_lora = alpha * torch.mm(weight_up, weight_down) weight_model = state_dict_model[name].to(device=computation_device, dtype=computation_dtype) weight_patched = weight_model + weight_lora state_dict_model[name] = weight_patched.to(device=device, dtype=dtype) print(f" {len(lora_name_dict)} tensors are updated.") model.load_state_dict(state_dict_model) class HunyuanVideoLoRAFromCivitai(LoRAFromCivitai): def __init__(self): super().__init__() self.supported_model_classes = [HunyuanVideoDiT, HunyuanVideoDiT] self.lora_prefix = ["diffusion_model.", "transformer."] self.special_keys = {} class FluxLoRAConverter: def __init__(self): pass @staticmethod def align_to_opensource_format(state_dict, alpha=1.0): prefix_rename_dict = { "single_blocks": "lora_unet_single_blocks", "blocks": "lora_unet_double_blocks", } middle_rename_dict = { "norm.linear": "modulation_lin", "to_qkv_mlp": "linear1", "proj_out": "linear2", "norm1_a.linear": "img_mod_lin", "norm1_b.linear": "txt_mod_lin", "attn.a_to_qkv": "img_attn_qkv", "attn.b_to_qkv": "txt_attn_qkv", "attn.a_to_out": "img_attn_proj", "attn.b_to_out": "txt_attn_proj", "ff_a.0": "img_mlp_0", "ff_a.2": "img_mlp_2", "ff_b.0": "txt_mlp_0", "ff_b.2": "txt_mlp_2", } suffix_rename_dict = { "lora_B.weight": "lora_up.weight", "lora_A.weight": "lora_down.weight", } state_dict_ = {} for name, param in state_dict.items(): names = name.split(".") if names[-2] != "lora_A" and names[-2] != "lora_B": names.pop(-2) prefix = names[0] middle = ".".join(names[2:-2]) suffix = ".".join(names[-2:]) block_id = names[1] if middle not in middle_rename_dict: continue rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix] state_dict_[rename] = param if rename.endswith("lora_up.weight"): state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((alpha,))[0] return state_dict_ @staticmethod def align_to_diffsynth_format(state_dict): rename_dict = { "lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight", "lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight", "lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight", "lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight", "lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight", "lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight", "lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight", "lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight", "lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight", "lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight", "lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight", } def guess_block_id(name): names = name.split("_") for i in names: if i.isdigit(): return i, name.replace(f"_{i}_", "_blockid_") return None, None state_dict_ = {} for name, param in state_dict.items(): block_id, source_name = guess_block_id(name) if source_name in rename_dict: target_name = rename_dict[source_name] target_name = target_name.replace(".blockid.", f".{block_id}.") state_dict_[target_name] = param else: state_dict_[name] = param return state_dict_ def get_lora_loaders(): return [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), FluxLoRAFromCivitai(), HunyuanVideoLoRAFromCivitai(), GeneralLoRAFromPeft()] ================================================ FILE: diffsynth/models/model_manager.py ================================================ import os, torch, json, importlib from typing import List from .downloader import download_models, download_customized_models, Preset_model_id, Preset_model_website from .sd_text_encoder import SDTextEncoder from .sd_unet import SDUNet from .sd_vae_encoder import SDVAEEncoder from .sd_vae_decoder import SDVAEDecoder from .lora import get_lora_loaders from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 from .sdxl_unet import SDXLUNet from .sdxl_vae_decoder import SDXLVAEDecoder from .sdxl_vae_encoder import SDXLVAEEncoder from .sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 from .sd3_dit import SD3DiT from .sd3_vae_decoder import SD3VAEDecoder from .sd3_vae_encoder import SD3VAEEncoder from .sd_controlnet import SDControlNet from .sdxl_controlnet import SDXLControlNetUnion from .sd_motion import SDMotionModel from .sdxl_motion import SDXLMotionModel from .svd_image_encoder import SVDImageEncoder from .svd_unet import SVDUNet from .svd_vae_decoder import SVDVAEDecoder from .svd_vae_encoder import SVDVAEEncoder from .sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder from .sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder from .hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder from .hunyuan_dit import HunyuanDiT from .hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder from .hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder from .flux_dit import FluxDiT from .flux_text_encoder import FluxTextEncoder2 from .flux_vae import FluxVAEEncoder, FluxVAEDecoder from .flux_ipadapter import FluxIpAdapter from .cog_vae import CogVAEEncoder, CogVAEDecoder from .cog_dit import CogDiT from ..extensions.RIFE import IFNet from ..extensions.ESRGAN import RRDBNet from ..configs.model_config import model_loader_configs, huggingface_model_loader_configs, patch_model_loader_configs from .utils import load_state_dict, init_weights_on_device, hash_state_dict_keys, split_state_dict_with_prefix def load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device): loaded_model_names, loaded_models = [], [] for model_name, model_class in zip(model_names, model_classes): print(f" model_name: {model_name} model_class: {model_class.__name__}") state_dict_converter = model_class.state_dict_converter() if model_resource == "civitai": state_dict_results = state_dict_converter.from_civitai(state_dict) elif model_resource == "diffusers": state_dict_results = state_dict_converter.from_diffusers(state_dict) if isinstance(state_dict_results, tuple): model_state_dict, extra_kwargs = state_dict_results print(f" This model is initialized with extra kwargs: {extra_kwargs}") else: model_state_dict, extra_kwargs = state_dict_results, {} torch_dtype = torch.float32 if extra_kwargs.get("upcast_to_float32", False) else torch_dtype with init_weights_on_device(): model = model_class(**extra_kwargs) if hasattr(model, "eval"): model = model.eval() model.load_state_dict(model_state_dict, assign=True) model = model.to(dtype=torch_dtype, device=device) loaded_model_names.append(model_name) loaded_models.append(model) return loaded_model_names, loaded_models def load_model_from_huggingface_folder(file_path, model_names, model_classes, torch_dtype, device): loaded_model_names, loaded_models = [], [] for model_name, model_class in zip(model_names, model_classes): if torch_dtype in [torch.float32, torch.float16, torch.bfloat16]: model = model_class.from_pretrained(file_path, torch_dtype=torch_dtype).eval() else: model = model_class.from_pretrained(file_path).eval().to(dtype=torch_dtype) if torch_dtype == torch.float16 and hasattr(model, "half"): model = model.half() try: model = model.to(device=device) except: pass loaded_model_names.append(model_name) loaded_models.append(model) return loaded_model_names, loaded_models def load_single_patch_model_from_single_file(state_dict, model_name, model_class, base_model, extra_kwargs, torch_dtype, device): print(f" model_name: {model_name} model_class: {model_class.__name__} extra_kwargs: {extra_kwargs}") base_state_dict = base_model.state_dict() base_model.to("cpu") del base_model model = model_class(**extra_kwargs) model.load_state_dict(base_state_dict, strict=False) model.load_state_dict(state_dict, strict=False) model.to(dtype=torch_dtype, device=device) return model def load_patch_model_from_single_file(state_dict, model_names, model_classes, extra_kwargs, model_manager, torch_dtype, device): loaded_model_names, loaded_models = [], [] for model_name, model_class in zip(model_names, model_classes): while True: for model_id in range(len(model_manager.model)): base_model_name = model_manager.model_name[model_id] if base_model_name == model_name: base_model_path = model_manager.model_path[model_id] base_model = model_manager.model[model_id] print(f" Adding patch model to {base_model_name} ({base_model_path})") patched_model = load_single_patch_model_from_single_file( state_dict, model_name, model_class, base_model, extra_kwargs, torch_dtype, device) loaded_model_names.append(base_model_name) loaded_models.append(patched_model) model_manager.model.pop(model_id) model_manager.model_path.pop(model_id) model_manager.model_name.pop(model_id) break else: break return loaded_model_names, loaded_models class ModelDetectorTemplate: def __init__(self): pass def match(self, file_path="", state_dict={}): return False def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs): return [], [] class ModelDetectorFromSingleFile: def __init__(self, model_loader_configs=[]): self.keys_hash_with_shape_dict = {} self.keys_hash_dict = {} for metadata in model_loader_configs: self.add_model_metadata(*metadata) def add_model_metadata(self, keys_hash, keys_hash_with_shape, model_names, model_classes, model_resource): self.keys_hash_with_shape_dict[keys_hash_with_shape] = (model_names, model_classes, model_resource) if keys_hash is not None: self.keys_hash_dict[keys_hash] = (model_names, model_classes, model_resource) def match(self, file_path="", state_dict={}): if isinstance(file_path, str) and os.path.isdir(file_path): return False if len(state_dict) == 0: state_dict = load_state_dict(file_path) keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True) if keys_hash_with_shape in self.keys_hash_with_shape_dict: return True keys_hash = hash_state_dict_keys(state_dict, with_shape=False) if keys_hash in self.keys_hash_dict: return True return False def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs): if len(state_dict) == 0: state_dict = load_state_dict(file_path) # Load models with strict matching keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True) if keys_hash_with_shape in self.keys_hash_with_shape_dict: model_names, model_classes, model_resource = self.keys_hash_with_shape_dict[keys_hash_with_shape] loaded_model_names, loaded_models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device) return loaded_model_names, loaded_models # Load models without strict matching # (the shape of parameters may be inconsistent, and the state_dict_converter will modify the model architecture) keys_hash = hash_state_dict_keys(state_dict, with_shape=False) if keys_hash in self.keys_hash_dict: model_names, model_classes, model_resource = self.keys_hash_dict[keys_hash] loaded_model_names, loaded_models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device) return loaded_model_names, loaded_models return loaded_model_names, loaded_models class ModelDetectorFromSplitedSingleFile(ModelDetectorFromSingleFile): def __init__(self, model_loader_configs=[]): super().__init__(model_loader_configs) def match(self, file_path="", state_dict={}): if isinstance(file_path, str) and os.path.isdir(file_path): return False if len(state_dict) == 0: state_dict = load_state_dict(file_path) splited_state_dict = split_state_dict_with_prefix(state_dict) for sub_state_dict in splited_state_dict: if super().match(file_path, sub_state_dict): return True return False def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs): # Split the state_dict and load from each component splited_state_dict = split_state_dict_with_prefix(state_dict) valid_state_dict = {} for sub_state_dict in splited_state_dict: if super().match(file_path, sub_state_dict): valid_state_dict.update(sub_state_dict) if super().match(file_path, valid_state_dict): loaded_model_names, loaded_models = super().load(file_path, valid_state_dict, device, torch_dtype) else: loaded_model_names, loaded_models = [], [] for sub_state_dict in splited_state_dict: if super().match(file_path, sub_state_dict): loaded_model_names_, loaded_models_ = super().load(file_path, valid_state_dict, device, torch_dtype) loaded_model_names += loaded_model_names_ loaded_models += loaded_models_ return loaded_model_names, loaded_models class ModelDetectorFromHuggingfaceFolder: def __init__(self, model_loader_configs=[]): self.architecture_dict = {} for metadata in model_loader_configs: self.add_model_metadata(*metadata) def add_model_metadata(self, architecture, huggingface_lib, model_name, redirected_architecture): self.architecture_dict[architecture] = (huggingface_lib, model_name, redirected_architecture) def match(self, file_path="", state_dict={}): if not isinstance(file_path, str) or os.path.isfile(file_path): return False file_list = os.listdir(file_path) if "config.json" not in file_list: return False with open(os.path.join(file_path, "config.json"), "r") as f: config = json.load(f) if "architectures" not in config and "_class_name" not in config: return False return True def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs): with open(os.path.join(file_path, "config.json"), "r") as f: config = json.load(f) loaded_model_names, loaded_models = [], [] architectures = config["architectures"] if "architectures" in config else [config["_class_name"]] for architecture in architectures: huggingface_lib, model_name, redirected_architecture = self.architecture_dict[architecture] if redirected_architecture is not None: architecture = redirected_architecture model_class = importlib.import_module(huggingface_lib).__getattribute__(architecture) loaded_model_names_, loaded_models_ = load_model_from_huggingface_folder(file_path, [model_name], [model_class], torch_dtype, device) loaded_model_names += loaded_model_names_ loaded_models += loaded_models_ return loaded_model_names, loaded_models class ModelDetectorFromPatchedSingleFile: def __init__(self, model_loader_configs=[]): self.keys_hash_with_shape_dict = {} for metadata in model_loader_configs: self.add_model_metadata(*metadata) def add_model_metadata(self, keys_hash_with_shape, model_name, model_class, extra_kwargs): self.keys_hash_with_shape_dict[keys_hash_with_shape] = (model_name, model_class, extra_kwargs) def match(self, file_path="", state_dict={}): if not isinstance(file_path, str) or os.path.isdir(file_path): return False if len(state_dict) == 0: state_dict = load_state_dict(file_path) keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True) if keys_hash_with_shape in self.keys_hash_with_shape_dict: return True return False def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, model_manager=None, **kwargs): if len(state_dict) == 0: state_dict = load_state_dict(file_path) # Load models with strict matching loaded_model_names, loaded_models = [], [] keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True) if keys_hash_with_shape in self.keys_hash_with_shape_dict: model_names, model_classes, extra_kwargs = self.keys_hash_with_shape_dict[keys_hash_with_shape] loaded_model_names_, loaded_models_ = load_patch_model_from_single_file( state_dict, model_names, model_classes, extra_kwargs, model_manager, torch_dtype, device) loaded_model_names += loaded_model_names_ loaded_models += loaded_models_ return loaded_model_names, loaded_models class ModelManager: def __init__( self, torch_dtype=torch.float16, device="cuda", model_id_list: List[Preset_model_id] = [], downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"], file_path_list: List[str] = [], ): self.torch_dtype = torch_dtype self.device = device self.model = [] self.model_path = [] self.model_name = [] downloaded_files = download_models(model_id_list, downloading_priority) if len(model_id_list) > 0 else [] self.model_detector = [ ModelDetectorFromSingleFile(model_loader_configs), ModelDetectorFromSplitedSingleFile(model_loader_configs), ModelDetectorFromHuggingfaceFolder(huggingface_model_loader_configs), ModelDetectorFromPatchedSingleFile(patch_model_loader_configs), ] self.load_models(downloaded_files + file_path_list) def load_model_from_single_file(self, file_path="", state_dict={}, model_names=[], model_classes=[], model_resource=None): print(f"Loading models from file: {file_path}") if len(state_dict) == 0: state_dict = load_state_dict(file_path) model_names, models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, self.torch_dtype, self.device) for model_name, model in zip(model_names, models): self.model.append(model) self.model_path.append(file_path) self.model_name.append(model_name) print(f" The following models are loaded: {model_names}.") def load_model_from_huggingface_folder(self, file_path="", model_names=[], model_classes=[]): print(f"Loading models from folder: {file_path}") model_names, models = load_model_from_huggingface_folder(file_path, model_names, model_classes, self.torch_dtype, self.device) for model_name, model in zip(model_names, models): self.model.append(model) self.model_path.append(file_path) self.model_name.append(model_name) print(f" The following models are loaded: {model_names}.") def load_patch_model_from_single_file(self, file_path="", state_dict={}, model_names=[], model_classes=[], extra_kwargs={}): print(f"Loading patch models from file: {file_path}") model_names, models = load_patch_model_from_single_file( state_dict, model_names, model_classes, extra_kwargs, self, self.torch_dtype, self.device) for model_name, model in zip(model_names, models): self.model.append(model) self.model_path.append(file_path) self.model_name.append(model_name) print(f" The following patched models are loaded: {model_names}.") def load_lora(self, file_path="", state_dict={}, lora_alpha=1.0): if isinstance(file_path, list): for file_path_ in file_path: self.load_lora(file_path_, state_dict=state_dict, lora_alpha=lora_alpha) else: print(f"Loading LoRA models from file: {file_path}") is_loaded = False if len(state_dict) == 0: state_dict = load_state_dict(file_path) for model_name, model, model_path in zip(self.model_name, self.model, self.model_path): for lora in get_lora_loaders(): match_results = lora.match(model, state_dict) if match_results is not None: print(f" Adding LoRA to {model_name} ({model_path}).") lora_prefix, model_resource = match_results lora.load(model, state_dict, lora_prefix, alpha=lora_alpha, model_resource=model_resource) is_loaded = True break if not is_loaded: print(f" Cannot load LoRA: {file_path}") def load_model(self, file_path, model_names=None, device=None, torch_dtype=None): print(f"Loading models from: {file_path}") if device is None: device = self.device if torch_dtype is None: torch_dtype = self.torch_dtype if isinstance(file_path, list): state_dict = {} for path in file_path: state_dict.update(load_state_dict(path)) elif os.path.isfile(file_path): state_dict = load_state_dict(file_path) else: state_dict = None for model_detector in self.model_detector: if model_detector.match(file_path, state_dict): model_names, models = model_detector.load( file_path, state_dict, device=device, torch_dtype=torch_dtype, allowed_model_names=model_names, model_manager=self ) for model_name, model in zip(model_names, models): self.model.append(model) self.model_path.append(file_path) self.model_name.append(model_name) print(f" The following models are loaded: {model_names}.") break else: print(f" We cannot detect the model type. No models are loaded.") def load_models(self, file_path_list, model_names=None, device=None, torch_dtype=None): for file_path in file_path_list: self.load_model(file_path, model_names, device=device, torch_dtype=torch_dtype) def fetch_model(self, model_name, file_path=None, require_model_path=False): fetched_models = [] fetched_model_paths = [] for model, model_path, model_name_ in zip(self.model, self.model_path, self.model_name): if file_path is not None and file_path != model_path: continue if model_name == model_name_: fetched_models.append(model) fetched_model_paths.append(model_path) if len(fetched_models) == 0: print(f"No {model_name} models available.") return None if len(fetched_models) == 1: print(f"Using {model_name} from {fetched_model_paths[0]}.") else: print(f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[0]}.") if require_model_path: return fetched_models[0], fetched_model_paths[0] else: return fetched_models[0] def to(self, device): for model in self.model: model.to(device) ================================================ FILE: diffsynth/models/omnigen.py ================================================ # The code is revised from DiT import os import torch import torch.nn as nn import numpy as np import math from safetensors.torch import load_file from typing import List, Optional, Tuple, Union import torch.utils.checkpoint from huggingface_hub import snapshot_download from transformers.modeling_outputs import BaseModelOutputWithPast from transformers import Phi3Config, Phi3Model from transformers.cache_utils import Cache, DynamicCache from transformers.utils import logging logger = logging.get_logger(__name__) class Phi3Transformer(Phi3Model): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`] We only modified the attention mask Args: config: Phi3Config """ def prefetch_layer(self, layer_idx: int, device: torch.device): "Starts prefetching the next layer cache" with torch.cuda.stream(self.prefetch_stream): # Prefetch next layer tensors to GPU for name, param in self.layers[layer_idx].named_parameters(): param.data = param.data.to(device, non_blocking=True) def evict_previous_layer(self, layer_idx: int): "Moves the previous layer cache to the CPU" prev_layer_idx = layer_idx - 1 for name, param in self.layers[prev_layer_idx].named_parameters(): param.data = param.data.to("cpu", non_blocking=True) def get_offlaod_layer(self, layer_idx: int, device: torch.device): # init stream if not hasattr(self, "prefetch_stream"): self.prefetch_stream = torch.cuda.Stream() # delete previous layer torch.cuda.current_stream().synchronize() self.evict_previous_layer(layer_idx) # make sure the current layer is ready torch.cuda.synchronize(self.prefetch_stream) # load next layer self.prefetch_layer((layer_idx + 1) % len(self.layers), device) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, offload_model: Optional[bool] = False, ) -> Union[Tuple, BaseModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = False if use_cache and not isinstance(past_key_values, Cache): return_legacy_cache = True if past_key_values is None: past_key_values = DynamicCache() else: past_key_values = DynamicCache.from_legacy_cache(past_key_values) logger.warning_once( "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" ) # if inputs_embeds is None: # inputs_embeds = self.embed_tokens(input_ids) # if cache_position is None: # past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 # cache_position = torch.arange( # past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device # ) # if position_ids is None: # position_ids = cache_position.unsqueeze(0) if attention_mask is not None and attention_mask.dim() == 3: dtype = inputs_embeds.dtype min_dtype = torch.finfo(dtype).min attention_mask = (1 - attention_mask) * min_dtype attention_mask = attention_mask.unsqueeze(1).to(inputs_embeds.dtype) else: raise Exception("attention_mask parameter was unavailable or invalid") # causal_mask = self._update_causal_mask( # attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions # ) hidden_states = inputs_embeds # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None layer_idx = -1 for decoder_layer in self.layers: layer_idx += 1 if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, ) else: if offload_model and not self.training: self.get_offlaod_layer(layer_idx, device=inputs_embeds.device) layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: print('************') all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if return_legacy_cache: next_cache = next_cache.to_legacy_cache() if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) def modulate(x, shift, scale): return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) class TimestepEmbedder(nn.Module): """ Embeds scalar timesteps into vector representations. """ def __init__(self, hidden_size, frequency_embedding_size=256): super().__init__() self.mlp = nn.Sequential( nn.Linear(frequency_embedding_size, hidden_size, bias=True), nn.SiLU(), nn.Linear(hidden_size, hidden_size, bias=True), ) self.frequency_embedding_size = frequency_embedding_size @staticmethod def timestep_embedding(t, dim, max_period=10000): """ Create sinusoidal timestep embeddings. :param t: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an (N, D) Tensor of positional embeddings. """ # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py half = dim // 2 freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half ).to(device=t.device) args = t[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) return embedding def forward(self, t, dtype=torch.float32): t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype) t_emb = self.mlp(t_freq) return t_emb class FinalLayer(nn.Module): """ The final layer of DiT. """ def __init__(self, hidden_size, patch_size, out_channels): super().__init__() self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) self.adaLN_modulation = nn.Sequential( nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True) ) def forward(self, x, c): shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) x = modulate(self.norm_final(x), shift, scale) x = self.linear(x) return x def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=1): """ grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) """ if isinstance(grid_size, int): grid_size = (grid_size, grid_size) grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) grid = grid.reshape([2, 1, grid_size[1], grid_size[0]]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if cls_token and extra_tokens > 0: pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) return pos_embed def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): assert embed_dim % 2 == 0 # use half of dimensions to encode grid_h emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) return emb def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim // 2, dtype=np.float64) omega /= embed_dim / 2. omega = 1. / 10000**omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) return emb class PatchEmbedMR(nn.Module): """ 2D Image to Patch Embedding """ def __init__( self, patch_size: int = 2, in_chans: int = 4, embed_dim: int = 768, bias: bool = True, ): super().__init__() self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) def forward(self, x): x = self.proj(x) x = x.flatten(2).transpose(1, 2) # NCHW -> NLC return x class OmniGenOriginalModel(nn.Module): """ Diffusion model with a Transformer backbone. """ def __init__( self, transformer_config: Phi3Config, patch_size=2, in_channels=4, pe_interpolation: float = 1.0, pos_embed_max_size: int = 192, ): super().__init__() self.in_channels = in_channels self.out_channels = in_channels self.patch_size = patch_size self.pos_embed_max_size = pos_embed_max_size hidden_size = transformer_config.hidden_size self.x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True) self.input_x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True) self.time_token = TimestepEmbedder(hidden_size) self.t_embedder = TimestepEmbedder(hidden_size) self.pe_interpolation = pe_interpolation pos_embed = get_2d_sincos_pos_embed(hidden_size, pos_embed_max_size, interpolation_scale=self.pe_interpolation, base_size=64) self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=True) self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) self.initialize_weights() self.llm = Phi3Transformer(config=transformer_config) self.llm.config.use_cache = False @classmethod def from_pretrained(cls, model_name): if not os.path.exists(model_name): cache_folder = os.getenv('HF_HUB_CACHE') model_name = snapshot_download(repo_id=model_name, cache_dir=cache_folder, ignore_patterns=['flax_model.msgpack', 'rust_model.ot', 'tf_model.h5']) config = Phi3Config.from_pretrained(model_name) model = cls(config) if os.path.exists(os.path.join(model_name, 'model.safetensors')): print("Loading safetensors") ckpt = load_file(os.path.join(model_name, 'model.safetensors')) else: ckpt = torch.load(os.path.join(model_name, 'model.pt'), map_location='cpu') model.load_state_dict(ckpt) return model def initialize_weights(self): assert not hasattr(self, "llama") # Initialize transformer layers: def _basic_init(module): if isinstance(module, nn.Linear): torch.nn.init.xavier_uniform_(module.weight) if module.bias is not None: nn.init.constant_(module.bias, 0) self.apply(_basic_init) # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): w = self.x_embedder.proj.weight.data nn.init.xavier_uniform_(w.view([w.shape[0], -1])) nn.init.constant_(self.x_embedder.proj.bias, 0) w = self.input_x_embedder.proj.weight.data nn.init.xavier_uniform_(w.view([w.shape[0], -1])) nn.init.constant_(self.x_embedder.proj.bias, 0) # Initialize timestep embedding MLP: nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) nn.init.normal_(self.time_token.mlp[0].weight, std=0.02) nn.init.normal_(self.time_token.mlp[2].weight, std=0.02) # Zero-out output layers: nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) nn.init.constant_(self.final_layer.linear.weight, 0) nn.init.constant_(self.final_layer.linear.bias, 0) def unpatchify(self, x, h, w): """ x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) """ c = self.out_channels x = x.reshape(shape=(x.shape[0], h//self.patch_size, w//self.patch_size, self.patch_size, self.patch_size, c)) x = torch.einsum('nhwpqc->nchpwq', x) imgs = x.reshape(shape=(x.shape[0], c, h, w)) return imgs def cropped_pos_embed(self, height, width): """Crops positional embeddings for SD3 compatibility.""" if self.pos_embed_max_size is None: raise ValueError("`pos_embed_max_size` must be set for cropping.") height = height // self.patch_size width = width // self.patch_size if height > self.pos_embed_max_size: raise ValueError( f"Height ({height}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}." ) if width > self.pos_embed_max_size: raise ValueError( f"Width ({width}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}." ) top = (self.pos_embed_max_size - height) // 2 left = (self.pos_embed_max_size - width) // 2 spatial_pos_embed = self.pos_embed.reshape(1, self.pos_embed_max_size, self.pos_embed_max_size, -1) spatial_pos_embed = spatial_pos_embed[:, top : top + height, left : left + width, :] # print(top, top + height, left, left + width, spatial_pos_embed.size()) spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1]) return spatial_pos_embed def patch_multiple_resolutions(self, latents, padding_latent=None, is_input_images:bool=False): if isinstance(latents, list): return_list = False if padding_latent is None: padding_latent = [None] * len(latents) return_list = True patched_latents, num_tokens, shapes = [], [], [] for latent, padding in zip(latents, padding_latent): height, width = latent.shape[-2:] if is_input_images: latent = self.input_x_embedder(latent) else: latent = self.x_embedder(latent) pos_embed = self.cropped_pos_embed(height, width) latent = latent + pos_embed if padding is not None: latent = torch.cat([latent, padding], dim=-2) patched_latents.append(latent) num_tokens.append(pos_embed.size(1)) shapes.append([height, width]) if not return_list: latents = torch.cat(patched_latents, dim=0) else: latents = patched_latents else: height, width = latents.shape[-2:] if is_input_images: latents = self.input_x_embedder(latents) else: latents = self.x_embedder(latents) pos_embed = self.cropped_pos_embed(height, width) latents = latents + pos_embed num_tokens = latents.size(1) shapes = [height, width] return latents, num_tokens, shapes def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False): """ """ input_is_list = isinstance(x, list) x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent) time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1) if input_img_latents is not None: input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True) if input_ids is not None: condition_embeds = self.llm.embed_tokens(input_ids).clone() input_img_inx = 0 for b_inx in input_image_sizes.keys(): for start_inx, end_inx in input_image_sizes[b_inx]: condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx] input_img_inx += 1 if input_img_latents is not None: assert input_img_inx == len(input_latents) input_emb = torch.cat([condition_embeds, time_token, x], dim=1) else: input_emb = torch.cat([time_token, x], dim=1) output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model) output, past_key_values = output.last_hidden_state, output.past_key_values if input_is_list: image_embedding = output[:, -max(num_tokens):] time_emb = self.t_embedder(timestep, dtype=x.dtype) x = self.final_layer(image_embedding, time_emb) latents = [] for i in range(x.size(0)): latent = x[i:i+1, :num_tokens[i]] latent = self.unpatchify(latent, shapes[i][0], shapes[i][1]) latents.append(latent) else: image_embedding = output[:, -num_tokens:] time_emb = self.t_embedder(timestep, dtype=x.dtype) x = self.final_layer(image_embedding, time_emb) latents = self.unpatchify(x, shapes[0], shapes[1]) if return_past_key_values: return latents, past_key_values return latents @torch.no_grad() def forward_with_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model): self.llm.config.use_cache = use_kv_cache model_out, past_key_values = self.forward(x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, past_key_values=past_key_values, return_past_key_values=True, offload_model=offload_model) if use_img_cfg: cond, uncond, img_cond = torch.split(model_out, len(model_out) // 3, dim=0) cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond) model_out = [cond, cond, cond] else: cond, uncond = torch.split(model_out, len(model_out) // 2, dim=0) cond = uncond + cfg_scale * (cond - uncond) model_out = [cond, cond] return torch.cat(model_out, dim=0), past_key_values @torch.no_grad() def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model): self.llm.config.use_cache = use_kv_cache if past_key_values is None: past_key_values = [None] * len(attention_mask) x = torch.split(x, len(x) // len(attention_mask), dim=0) timestep = timestep.to(x[0].dtype) timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0) model_out, pask_key_values = [], [] for i in range(len(input_ids)): temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model) model_out.append(temp_out) pask_key_values.append(temp_pask_key_values) if len(model_out) == 3: cond, uncond, img_cond = model_out cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond) model_out = [cond, cond, cond] elif len(model_out) == 2: cond, uncond = model_out cond = uncond + cfg_scale * (cond - uncond) model_out = [cond, cond] else: return model_out[0] return torch.cat(model_out, dim=0), pask_key_values class OmniGenTransformer(OmniGenOriginalModel): def __init__(self): config = { "_name_or_path": "Phi-3-vision-128k-instruct", "architectures": [ "Phi3ForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 1, "eos_token_id": 2, "hidden_act": "silu", "hidden_size": 3072, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "model_type": "phi3", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 32, "original_max_position_embeddings": 4096, "rms_norm_eps": 1e-05, "rope_scaling": { "long_factor": [ 1.0299999713897705, 1.0499999523162842, 1.0499999523162842, 1.0799999237060547, 1.2299998998641968, 1.2299998998641968, 1.2999999523162842, 1.4499999284744263, 1.5999999046325684, 1.6499998569488525, 1.8999998569488525, 2.859999895095825, 3.68999981880188, 5.419999599456787, 5.489999771118164, 5.489999771118164, 9.09000015258789, 11.579999923706055, 15.65999984741211, 15.769999504089355, 15.789999961853027, 18.360000610351562, 21.989999771118164, 23.079999923706055, 30.009998321533203, 32.35000228881836, 32.590003967285156, 35.56000518798828, 39.95000457763672, 53.840003967285156, 56.20000457763672, 57.95000457763672, 59.29000473022461, 59.77000427246094, 59.920005798339844, 61.190006256103516, 61.96000671386719, 62.50000762939453, 63.3700065612793, 63.48000717163086, 63.48000717163086, 63.66000747680664, 63.850006103515625, 64.08000946044922, 64.760009765625, 64.80001068115234, 64.81001281738281, 64.81001281738281 ], "short_factor": [ 1.05, 1.05, 1.05, 1.1, 1.1, 1.1, 1.2500000000000002, 1.2500000000000002, 1.4000000000000004, 1.4500000000000004, 1.5500000000000005, 1.8500000000000008, 1.9000000000000008, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.000000000000001, 2.1000000000000005, 2.1000000000000005, 2.2, 2.3499999999999996, 2.3499999999999996, 2.3499999999999996, 2.3499999999999996, 2.3999999999999995, 2.3999999999999995, 2.6499999999999986, 2.6999999999999984, 2.8999999999999977, 2.9499999999999975, 3.049999999999997, 3.049999999999997, 3.049999999999997 ], "type": "su" }, "rope_theta": 10000.0, "sliding_window": 131072, "tie_word_embeddings": False, "torch_dtype": "bfloat16", "transformers_version": "4.38.1", "use_cache": True, "vocab_size": 32064, "_attn_implementation": "sdpa" } config = Phi3Config(**config) super().__init__(config) def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False): input_is_list = isinstance(x, list) x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent) time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1) if input_img_latents is not None: input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True) if input_ids is not None: condition_embeds = self.llm.embed_tokens(input_ids).clone() input_img_inx = 0 for b_inx in input_image_sizes.keys(): for start_inx, end_inx in input_image_sizes[b_inx]: condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx] input_img_inx += 1 if input_img_latents is not None: assert input_img_inx == len(input_latents) input_emb = torch.cat([condition_embeds, time_token, x], dim=1) else: input_emb = torch.cat([time_token, x], dim=1) output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model) output, past_key_values = output.last_hidden_state, output.past_key_values if input_is_list: image_embedding = output[:, -max(num_tokens):] time_emb = self.t_embedder(timestep, dtype=x.dtype) x = self.final_layer(image_embedding, time_emb) latents = [] for i in range(x.size(0)): latent = x[i:i+1, :num_tokens[i]] latent = self.unpatchify(latent, shapes[i][0], shapes[i][1]) latents.append(latent) else: image_embedding = output[:, -num_tokens:] time_emb = self.t_embedder(timestep, dtype=x.dtype) x = self.final_layer(image_embedding, time_emb) latents = self.unpatchify(x, shapes[0], shapes[1]) if return_past_key_values: return latents, past_key_values return latents @torch.no_grad() def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model): self.llm.config.use_cache = use_kv_cache if past_key_values is None: past_key_values = [None] * len(attention_mask) x = torch.split(x, len(x) // len(attention_mask), dim=0) timestep = timestep.to(x[0].dtype) timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0) model_out, pask_key_values = [], [] for i in range(len(input_ids)): temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model) model_out.append(temp_out) pask_key_values.append(temp_pask_key_values) if len(model_out) == 3: cond, uncond, img_cond = model_out cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond) model_out = [cond, cond, cond] elif len(model_out) == 2: cond, uncond = model_out cond = uncond + cfg_scale * (cond - uncond) model_out = [cond, cond] else: return model_out[0] return torch.cat(model_out, dim=0), pask_key_values @staticmethod def state_dict_converter(): return OmniGenTransformerStateDictConverter() class OmniGenTransformerStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): return state_dict def from_civitai(self, state_dict): return state_dict ================================================ FILE: diffsynth/models/sd3_dit.py ================================================ import torch from einops import rearrange from .svd_unet import TemporalTimesteps from .tiler import TileWorker class RMSNorm(torch.nn.Module): def __init__(self, dim, eps, elementwise_affine=True): super().__init__() self.eps = eps if elementwise_affine: self.weight = torch.nn.Parameter(torch.ones((dim,))) else: self.weight = None def forward(self, hidden_states): input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.eps) hidden_states = hidden_states.to(input_dtype) if self.weight is not None: hidden_states = hidden_states * self.weight return hidden_states class PatchEmbed(torch.nn.Module): def __init__(self, patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192): super().__init__() self.pos_embed_max_size = pos_embed_max_size self.patch_size = patch_size self.proj = torch.nn.Conv2d(in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size) self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, embed_dim)) def cropped_pos_embed(self, height, width): height = height // self.patch_size width = width // self.patch_size top = (self.pos_embed_max_size - height) // 2 left = (self.pos_embed_max_size - width) // 2 spatial_pos_embed = self.pos_embed[:, top : top + height, left : left + width, :].flatten(1, 2) return spatial_pos_embed def forward(self, latent): height, width = latent.shape[-2:] latent = self.proj(latent) latent = latent.flatten(2).transpose(1, 2) pos_embed = self.cropped_pos_embed(height, width) return latent + pos_embed class TimestepEmbeddings(torch.nn.Module): def __init__(self, dim_in, dim_out, computation_device=None): super().__init__() self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device) self.timestep_embedder = torch.nn.Sequential( torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out) ) def forward(self, timestep, dtype): time_emb = self.time_proj(timestep).to(dtype) time_emb = self.timestep_embedder(time_emb) return time_emb class AdaLayerNorm(torch.nn.Module): def __init__(self, dim, single=False, dual=False): super().__init__() self.single = single self.dual = dual self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual]) self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) def forward(self, x, emb): emb = self.linear(torch.nn.functional.silu(emb)) if self.single: scale, shift = emb.unsqueeze(1).chunk(2, dim=2) x = self.norm(x) * (1 + scale) + shift return x elif self.dual: shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = emb.unsqueeze(1).chunk(9, dim=2) norm_x = self.norm(x) x = norm_x * (1 + scale_msa) + shift_msa norm_x2 = norm_x * (1 + scale_msa2) + shift_msa2 return x, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_x2, gate_msa2 else: shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2) x = self.norm(x) * (1 + scale_msa) + shift_msa return x, gate_msa, shift_mlp, scale_mlp, gate_mlp class JointAttention(torch.nn.Module): def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False, use_rms_norm=False): super().__init__() self.num_heads = num_heads self.head_dim = head_dim self.only_out_a = only_out_a self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3) self.a_to_out = torch.nn.Linear(dim_a, dim_a) if not only_out_a: self.b_to_out = torch.nn.Linear(dim_b, dim_b) if use_rms_norm: self.norm_q_a = RMSNorm(head_dim, eps=1e-6) self.norm_k_a = RMSNorm(head_dim, eps=1e-6) self.norm_q_b = RMSNorm(head_dim, eps=1e-6) self.norm_k_b = RMSNorm(head_dim, eps=1e-6) else: self.norm_q_a = None self.norm_k_a = None self.norm_q_b = None self.norm_k_b = None def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k): batch_size = hidden_states.shape[0] qkv = to_qkv(hidden_states) qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q, k, v = qkv.chunk(3, dim=1) if norm_q is not None: q = norm_q(q) if norm_k is not None: k = norm_k(k) return q, k, v def forward(self, hidden_states_a, hidden_states_b): batch_size = hidden_states_a.shape[0] qa, ka, va = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a) qb, kb, vb = self.process_qkv(hidden_states_b, self.b_to_qkv, self.norm_q_b, self.norm_k_b) q = torch.concat([qa, qb], dim=2) k = torch.concat([ka, kb], dim=2) v = torch.concat([va, vb], dim=2) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) hidden_states_a, hidden_states_b = hidden_states[:, :hidden_states_a.shape[1]], hidden_states[:, hidden_states_a.shape[1]:] hidden_states_a = self.a_to_out(hidden_states_a) if self.only_out_a: return hidden_states_a else: hidden_states_b = self.b_to_out(hidden_states_b) return hidden_states_a, hidden_states_b class SingleAttention(torch.nn.Module): def __init__(self, dim_a, num_heads, head_dim, use_rms_norm=False): super().__init__() self.num_heads = num_heads self.head_dim = head_dim self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) self.a_to_out = torch.nn.Linear(dim_a, dim_a) if use_rms_norm: self.norm_q_a = RMSNorm(head_dim, eps=1e-6) self.norm_k_a = RMSNorm(head_dim, eps=1e-6) else: self.norm_q_a = None self.norm_k_a = None def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k): batch_size = hidden_states.shape[0] qkv = to_qkv(hidden_states) qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q, k, v = qkv.chunk(3, dim=1) if norm_q is not None: q = norm_q(q) if norm_k is not None: k = norm_k(k) return q, k, v def forward(self, hidden_states_a): batch_size = hidden_states_a.shape[0] q, k, v = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) hidden_states = self.a_to_out(hidden_states) return hidden_states class DualTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, use_rms_norm=False): super().__init__() self.norm1_a = AdaLayerNorm(dim, dual=True) self.norm1_b = AdaLayerNorm(dim) self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) self.attn2 = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_a = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_b = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) def forward(self, hidden_states_a, hidden_states_b, temb): norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb) norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) # Attention attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b) # Part A hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2) norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) # Part B hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) return hidden_states_a, hidden_states_b class JointTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, use_rms_norm=False, dual=False): super().__init__() self.norm1_a = AdaLayerNorm(dim, dual=dual) self.norm1_b = AdaLayerNorm(dim) self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) if dual: self.attn2 = SingleAttention(dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_a = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_b = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) def forward(self, hidden_states_a, hidden_states_b, temb): if self.norm1_a.dual: norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb) else: norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) # Attention attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b) # Part A hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a if self.norm1_a.dual: hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2) norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) # Part B hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) return hidden_states_a, hidden_states_b class JointTransformerFinalBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, use_rms_norm=False): super().__init__() self.norm1_a = AdaLayerNorm(dim) self.norm1_b = AdaLayerNorm(dim, single=True) self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True, use_rms_norm=use_rms_norm) self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_a = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) def forward(self, hidden_states_a, hidden_states_b, temb): norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) norm_hidden_states_b = self.norm1_b(hidden_states_b, emb=temb) # Attention attn_output_a = self.attn(norm_hidden_states_a, norm_hidden_states_b) # Part A hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) return hidden_states_a, hidden_states_b class SD3DiT(torch.nn.Module): def __init__(self, embed_dim=1536, num_layers=24, use_rms_norm=False, num_dual_blocks=0, pos_embed_max_size=192): super().__init__() self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=embed_dim, pos_embed_max_size=pos_embed_max_size) self.time_embedder = TimestepEmbeddings(256, embed_dim) self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, embed_dim), torch.nn.SiLU(), torch.nn.Linear(embed_dim, embed_dim)) self.context_embedder = torch.nn.Linear(4096, embed_dim) self.blocks = torch.nn.ModuleList([JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm, dual=True) for _ in range(num_dual_blocks)] + [JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm) for _ in range(num_layers-1-num_dual_blocks)] + [JointTransformerFinalBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm)]) self.norm_out = AdaLayerNorm(embed_dim, single=True) self.proj_out = torch.nn.Linear(embed_dim, 64) def tiled_forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size=128, tile_stride=64): # Due to the global positional embedding, we cannot implement layer-wise tiled forward. hidden_states = TileWorker().tiled_forward( lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb), hidden_states, tile_size, tile_stride, tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) return hidden_states def forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tiled=False, tile_size=128, tile_stride=64, use_gradient_checkpointing=False): if tiled: return self.tiled_forward(hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size, tile_stride) conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb) prompt_emb = self.context_embedder(prompt_emb) height, width = hidden_states.shape[-2:] hidden_states = self.pos_embedder(hidden_states) def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward for block in self.blocks: if self.training and use_gradient_checkpointing: hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, prompt_emb, conditioning, use_reentrant=False, ) else: hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning) hidden_states = self.norm_out(hidden_states, conditioning) hidden_states = self.proj_out(hidden_states) hidden_states = rearrange(hidden_states, "B (H W) (P Q C) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2) return hidden_states @staticmethod def state_dict_converter(): return SD3DiTStateDictConverter() class SD3DiTStateDictConverter: def __init__(self): pass def infer_architecture(self, state_dict): embed_dim = state_dict["blocks.0.ff_a.0.weight"].shape[1] num_layers = 100 while num_layers > 0 and f"blocks.{num_layers-1}.ff_a.0.bias" not in state_dict: num_layers -= 1 use_rms_norm = "blocks.0.attn.norm_q_a.weight" in state_dict num_dual_blocks = 0 while f"blocks.{num_dual_blocks}.attn2.a_to_out.bias" in state_dict: num_dual_blocks += 1 pos_embed_max_size = state_dict["pos_embedder.pos_embed"].shape[1] return { "embed_dim": embed_dim, "num_layers": num_layers, "use_rms_norm": use_rms_norm, "num_dual_blocks": num_dual_blocks, "pos_embed_max_size": pos_embed_max_size } def from_diffusers(self, state_dict): rename_dict = { "context_embedder": "context_embedder", "pos_embed.pos_embed": "pos_embedder.pos_embed", "pos_embed.proj": "pos_embedder.proj", "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0", "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2", "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0", "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2", "norm_out.linear": "norm_out.linear", "proj_out": "proj_out", "norm1.linear": "norm1_a.linear", "norm1_context.linear": "norm1_b.linear", "attn.to_q": "attn.a_to_q", "attn.to_k": "attn.a_to_k", "attn.to_v": "attn.a_to_v", "attn.to_out.0": "attn.a_to_out", "attn.add_q_proj": "attn.b_to_q", "attn.add_k_proj": "attn.b_to_k", "attn.add_v_proj": "attn.b_to_v", "attn.to_add_out": "attn.b_to_out", "ff.net.0.proj": "ff_a.0", "ff.net.2": "ff_a.2", "ff_context.net.0.proj": "ff_b.0", "ff_context.net.2": "ff_b.2", "attn.norm_q": "attn.norm_q_a", "attn.norm_k": "attn.norm_k_a", "attn.norm_added_q": "attn.norm_q_b", "attn.norm_added_k": "attn.norm_k_b", } state_dict_ = {} for name, param in state_dict.items(): if name in rename_dict: if name == "pos_embed.pos_embed": param = param.reshape((1, 192, 192, param.shape[-1])) state_dict_[rename_dict[name]] = param elif name.endswith(".weight") or name.endswith(".bias"): suffix = ".weight" if name.endswith(".weight") else ".bias" prefix = name[:-len(suffix)] if prefix in rename_dict: state_dict_[rename_dict[prefix] + suffix] = param elif prefix.startswith("transformer_blocks."): names = prefix.split(".") names[0] = "blocks" middle = ".".join(names[2:]) if middle in rename_dict: name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]]) state_dict_[name_] = param merged_keys = [name for name in state_dict_ if ".a_to_q." in name or ".b_to_q." in name] for key in merged_keys: param = torch.concat([ state_dict_[key.replace("to_q", "to_q")], state_dict_[key.replace("to_q", "to_k")], state_dict_[key.replace("to_q", "to_v")], ], dim=0) name = key.replace("to_q", "to_qkv") state_dict_.pop(key.replace("to_q", "to_q")) state_dict_.pop(key.replace("to_q", "to_k")) state_dict_.pop(key.replace("to_q", "to_v")) state_dict_[name] = param return state_dict_, self.infer_architecture(state_dict_) def from_civitai(self, state_dict): rename_dict = { "model.diffusion_model.context_embedder.bias": "context_embedder.bias", "model.diffusion_model.context_embedder.weight": "context_embedder.weight", "model.diffusion_model.final_layer.linear.bias": "proj_out.bias", "model.diffusion_model.final_layer.linear.weight": "proj_out.weight", "model.diffusion_model.pos_embed": "pos_embedder.pos_embed", "model.diffusion_model.t_embedder.mlp.0.bias": "time_embedder.timestep_embedder.0.bias", "model.diffusion_model.t_embedder.mlp.0.weight": "time_embedder.timestep_embedder.0.weight", "model.diffusion_model.t_embedder.mlp.2.bias": "time_embedder.timestep_embedder.2.bias", "model.diffusion_model.t_embedder.mlp.2.weight": "time_embedder.timestep_embedder.2.weight", "model.diffusion_model.x_embedder.proj.bias": "pos_embedder.proj.bias", "model.diffusion_model.x_embedder.proj.weight": "pos_embedder.proj.weight", "model.diffusion_model.y_embedder.mlp.0.bias": "pooled_text_embedder.0.bias", "model.diffusion_model.y_embedder.mlp.0.weight": "pooled_text_embedder.0.weight", "model.diffusion_model.y_embedder.mlp.2.bias": "pooled_text_embedder.2.bias", "model.diffusion_model.y_embedder.mlp.2.weight": "pooled_text_embedder.2.weight", "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.weight": "blocks.23.norm1_b.linear.weight", "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.bias": "blocks.23.norm1_b.linear.bias", "model.diffusion_model.final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight", "model.diffusion_model.final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias", } for i in range(40): rename_dict.update({ f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_b.linear.bias", f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_b.linear.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.bias": f"blocks.{i}.attn.b_to_out.bias", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.weight": f"blocks.{i}.attn.b_to_out.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.bias": [f'blocks.{i}.attn.b_to_q.bias', f'blocks.{i}.attn.b_to_k.bias', f'blocks.{i}.attn.b_to_v.bias'], f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.weight": [f'blocks.{i}.attn.b_to_q.weight', f'blocks.{i}.attn.b_to_k.weight', f'blocks.{i}.attn.b_to_v.weight'], f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.bias": f"blocks.{i}.ff_b.0.bias", f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.weight": f"blocks.{i}.ff_b.0.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.bias": f"blocks.{i}.ff_b.2.bias", f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.weight": f"blocks.{i}.ff_b.2.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_a.linear.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_a.linear.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.bias": f"blocks.{i}.attn.a_to_out.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.weight": f"blocks.{i}.attn.a_to_out.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.bias": [f'blocks.{i}.attn.a_to_q.bias', f'blocks.{i}.attn.a_to_k.bias', f'blocks.{i}.attn.a_to_v.bias'], f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.weight": [f'blocks.{i}.attn.a_to_q.weight', f'blocks.{i}.attn.a_to_k.weight', f'blocks.{i}.attn.a_to_v.weight'], f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.bias": f"blocks.{i}.ff_a.0.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.weight": f"blocks.{i}.ff_a.0.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.bias": f"blocks.{i}.ff_a.2.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.weight": f"blocks.{i}.ff_a.2.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_a.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_a.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_b.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_b.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_q.weight": f"blocks.{i}.attn2.norm_q_a.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_k.weight": f"blocks.{i}.attn2.norm_k_a.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.weight": f"blocks.{i}.attn2.a_to_qkv.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.bias": f"blocks.{i}.attn2.a_to_qkv.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.weight": f"blocks.{i}.attn2.a_to_out.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.bias": f"blocks.{i}.attn2.a_to_out.bias", }) state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "model.diffusion_model.pos_embed": pos_embed_max_size = int(param.shape[1] ** 0.5 + 0.4) param = param.reshape((1, pos_embed_max_size, pos_embed_max_size, param.shape[-1])) if isinstance(rename_dict[name], str): state_dict_[rename_dict[name]] = param else: name_ = rename_dict[name][0].replace(".a_to_q.", ".a_to_qkv.").replace(".b_to_q.", ".b_to_qkv.") state_dict_[name_] = param extra_kwargs = self.infer_architecture(state_dict_) num_layers = extra_kwargs["num_layers"] for name in [ f"blocks.{num_layers-1}.norm1_b.linear.weight", f"blocks.{num_layers-1}.norm1_b.linear.bias", "norm_out.linear.weight", "norm_out.linear.bias", ]: param = state_dict_[name] dim = param.shape[0] // 2 param = torch.concat([param[dim:], param[:dim]], axis=0) state_dict_[name] = param return state_dict_, self.infer_architecture(state_dict_) ================================================ FILE: diffsynth/models/sd3_text_encoder.py ================================================ import torch from transformers import T5EncoderModel, T5Config from .sd_text_encoder import SDTextEncoder from .sdxl_text_encoder import SDXLTextEncoder2, SDXLTextEncoder2StateDictConverter class SD3TextEncoder1(SDTextEncoder): def __init__(self, vocab_size=49408): super().__init__(vocab_size=vocab_size) def forward(self, input_ids, clip_skip=2, extra_mask=None): embeds = self.token_embedding(input_ids) embeds = embeds + self.position_embeds.to(dtype=embeds.dtype, device=input_ids.device) attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) if extra_mask is not None: attn_mask[:, extra_mask[0]==0] = float("-inf") for encoder_id, encoder in enumerate(self.encoders): embeds = encoder(embeds, attn_mask=attn_mask) if encoder_id + clip_skip == len(self.encoders): hidden_states = embeds embeds = self.final_layer_norm(embeds) pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)] return pooled_embeds, hidden_states @staticmethod def state_dict_converter(): return SD3TextEncoder1StateDictConverter() class SD3TextEncoder2(SDXLTextEncoder2): def __init__(self): super().__init__() @staticmethod def state_dict_converter(): return SD3TextEncoder2StateDictConverter() class SD3TextEncoder3(T5EncoderModel): def __init__(self): config = T5Config( _name_or_path = ".", architectures = ["T5EncoderModel"], classifier_dropout = 0.0, d_ff = 10240, d_kv = 64, d_model = 4096, decoder_start_token_id = 0, dense_act_fn = "gelu_new", dropout_rate = 0.1, eos_token_id = 1, feed_forward_proj = "gated-gelu", initializer_factor = 1.0, is_encoder_decoder = True, is_gated_act = True, layer_norm_epsilon = 1e-06, model_type = "t5", num_decoder_layers = 24, num_heads = 64, num_layers = 24, output_past = True, pad_token_id = 0, relative_attention_max_distance = 128, relative_attention_num_buckets = 32, tie_word_embeddings = False, torch_dtype = torch.float16, transformers_version = "4.41.2", use_cache = True, vocab_size = 32128 ) super().__init__(config) self.eval() def forward(self, input_ids): outputs = super().forward(input_ids=input_ids) prompt_emb = outputs.last_hidden_state return prompt_emb @staticmethod def state_dict_converter(): return SD3TextEncoder3StateDictConverter() class SD3TextEncoder1StateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "text_model.embeddings.token_embedding.weight": "token_embedding.weight", "text_model.embeddings.position_embedding.weight": "position_embeds", "text_model.final_layer_norm.weight": "final_layer_norm.weight", "text_model.final_layer_norm.bias": "final_layer_norm.bias", } attn_rename_dict = { "self_attn.q_proj": "attn.to_q", "self_attn.k_proj": "attn.to_k", "self_attn.v_proj": "attn.to_v", "self_attn.out_proj": "attn.to_out", "layer_norm1": "layer_norm1", "layer_norm2": "layer_norm2", "mlp.fc1": "fc1", "mlp.fc2": "fc2", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param elif name.startswith("text_model.encoder.layers."): param = state_dict[name] names = name.split(".") layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) state_dict_[name_] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight": "position_embeds", "text_encoders.clip_l.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", 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"text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias", 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"encoders.9.fc1.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", "text_encoders.clip_l.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias", "text_encoders.clip_l.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param elif ("text_encoders.clip_l.transformer." + name) in rename_dict: param = state_dict[name] if name == "text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict["text_encoders.clip_l.transformer." + name]] = param return state_dict_ class SD3TextEncoder2StateDictConverter(SDXLTextEncoder2StateDictConverter): def __init__(self): pass def from_diffusers(self, state_dict): return super().from_diffusers(state_dict) def from_civitai(self, state_dict): rename_dict = { "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight": "position_embeds", "text_encoders.clip_g.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": "encoders.1.attn.to_q.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.v_proj.bias": "encoders.1.attn.to_v.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.v_proj.weight": "encoders.1.attn.to_v.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm1.bias": "encoders.10.layer_norm1.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm1.weight": "encoders.10.layer_norm1.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm2.bias": "encoders.10.layer_norm2.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm2.weight": "encoders.10.layer_norm2.weight", "text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc1.bias": "encoders.10.fc1.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc1.weight": 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"text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", "text_encoders.clip_g.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias", "text_encoders.clip_g.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight", "text_encoders.clip_g.transformer.text_projection.weight": "text_projection.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param elif ("text_encoders.clip_g.transformer." + name) in rename_dict: param = state_dict[name] if name == "text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict["text_encoders.clip_g.transformer." + name]] = param return state_dict_ class SD3TextEncoder3StateDictConverter(): def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = state_dict return state_dict_ def from_civitai(self, state_dict): prefix = "text_encoders.t5xxl.transformer." state_dict_ = {name[len(prefix):]: param for name, param in state_dict.items() if name.startswith(prefix)} if len(state_dict_) > 0: return self.from_diffusers(state_dict_) name_list = [ "encoder.block.0.layer.0.SelfAttention.k.weight", "encoder.block.0.layer.0.SelfAttention.o.weight", "encoder.block.0.layer.0.SelfAttention.q.weight", "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight", "encoder.block.0.layer.0.SelfAttention.v.weight", "encoder.block.0.layer.0.layer_norm.weight", "encoder.block.0.layer.1.DenseReluDense.wi_0.weight", "encoder.block.0.layer.1.DenseReluDense.wi_1.weight", "encoder.block.0.layer.1.DenseReluDense.wo.weight", "encoder.block.0.layer.1.layer_norm.weight", "encoder.block.1.layer.0.SelfAttention.k.weight", "encoder.block.1.layer.0.SelfAttention.o.weight", "encoder.block.1.layer.0.SelfAttention.q.weight", "encoder.block.1.layer.0.SelfAttention.v.weight", "encoder.block.1.layer.0.layer_norm.weight", "encoder.block.1.layer.1.DenseReluDense.wi_0.weight", "encoder.block.1.layer.1.DenseReluDense.wi_1.weight", "encoder.block.1.layer.1.DenseReluDense.wo.weight", "encoder.block.1.layer.1.layer_norm.weight", "encoder.block.10.layer.0.SelfAttention.k.weight", "encoder.block.10.layer.0.SelfAttention.o.weight", "encoder.block.10.layer.0.SelfAttention.q.weight", "encoder.block.10.layer.0.SelfAttention.v.weight", "encoder.block.10.layer.0.layer_norm.weight", "encoder.block.10.layer.1.DenseReluDense.wi_0.weight", "encoder.block.10.layer.1.DenseReluDense.wi_1.weight", "encoder.block.10.layer.1.DenseReluDense.wo.weight", "encoder.block.10.layer.1.layer_norm.weight", "encoder.block.11.layer.0.SelfAttention.k.weight", "encoder.block.11.layer.0.SelfAttention.o.weight", "encoder.block.11.layer.0.SelfAttention.q.weight", "encoder.block.11.layer.0.SelfAttention.v.weight", "encoder.block.11.layer.0.layer_norm.weight", "encoder.block.11.layer.1.DenseReluDense.wi_0.weight", "encoder.block.11.layer.1.DenseReluDense.wi_1.weight", "encoder.block.11.layer.1.DenseReluDense.wo.weight", "encoder.block.11.layer.1.layer_norm.weight", "encoder.block.12.layer.0.SelfAttention.k.weight", "encoder.block.12.layer.0.SelfAttention.o.weight", "encoder.block.12.layer.0.SelfAttention.q.weight", "encoder.block.12.layer.0.SelfAttention.v.weight", "encoder.block.12.layer.0.layer_norm.weight", "encoder.block.12.layer.1.DenseReluDense.wi_0.weight", "encoder.block.12.layer.1.DenseReluDense.wi_1.weight", "encoder.block.12.layer.1.DenseReluDense.wo.weight", "encoder.block.12.layer.1.layer_norm.weight", "encoder.block.13.layer.0.SelfAttention.k.weight", "encoder.block.13.layer.0.SelfAttention.o.weight", "encoder.block.13.layer.0.SelfAttention.q.weight", "encoder.block.13.layer.0.SelfAttention.v.weight", "encoder.block.13.layer.0.layer_norm.weight", "encoder.block.13.layer.1.DenseReluDense.wi_0.weight", "encoder.block.13.layer.1.DenseReluDense.wi_1.weight", "encoder.block.13.layer.1.DenseReluDense.wo.weight", "encoder.block.13.layer.1.layer_norm.weight", "encoder.block.14.layer.0.SelfAttention.k.weight", "encoder.block.14.layer.0.SelfAttention.o.weight", "encoder.block.14.layer.0.SelfAttention.q.weight", "encoder.block.14.layer.0.SelfAttention.v.weight", "encoder.block.14.layer.0.layer_norm.weight", "encoder.block.14.layer.1.DenseReluDense.wi_0.weight", "encoder.block.14.layer.1.DenseReluDense.wi_1.weight", "encoder.block.14.layer.1.DenseReluDense.wo.weight", "encoder.block.14.layer.1.layer_norm.weight", "encoder.block.15.layer.0.SelfAttention.k.weight", "encoder.block.15.layer.0.SelfAttention.o.weight", "encoder.block.15.layer.0.SelfAttention.q.weight", "encoder.block.15.layer.0.SelfAttention.v.weight", "encoder.block.15.layer.0.layer_norm.weight", "encoder.block.15.layer.1.DenseReluDense.wi_0.weight", "encoder.block.15.layer.1.DenseReluDense.wi_1.weight", "encoder.block.15.layer.1.DenseReluDense.wo.weight", "encoder.block.15.layer.1.layer_norm.weight", "encoder.block.16.layer.0.SelfAttention.k.weight", "encoder.block.16.layer.0.SelfAttention.o.weight", "encoder.block.16.layer.0.SelfAttention.q.weight", "encoder.block.16.layer.0.SelfAttention.v.weight", "encoder.block.16.layer.0.layer_norm.weight", "encoder.block.16.layer.1.DenseReluDense.wi_0.weight", "encoder.block.16.layer.1.DenseReluDense.wi_1.weight", "encoder.block.16.layer.1.DenseReluDense.wo.weight", "encoder.block.16.layer.1.layer_norm.weight", "encoder.block.17.layer.0.SelfAttention.k.weight", "encoder.block.17.layer.0.SelfAttention.o.weight", "encoder.block.17.layer.0.SelfAttention.q.weight", "encoder.block.17.layer.0.SelfAttention.v.weight", "encoder.block.17.layer.0.layer_norm.weight", "encoder.block.17.layer.1.DenseReluDense.wi_0.weight", "encoder.block.17.layer.1.DenseReluDense.wi_1.weight", "encoder.block.17.layer.1.DenseReluDense.wo.weight", "encoder.block.17.layer.1.layer_norm.weight", "encoder.block.18.layer.0.SelfAttention.k.weight", "encoder.block.18.layer.0.SelfAttention.o.weight", "encoder.block.18.layer.0.SelfAttention.q.weight", "encoder.block.18.layer.0.SelfAttention.v.weight", "encoder.block.18.layer.0.layer_norm.weight", "encoder.block.18.layer.1.DenseReluDense.wi_0.weight", "encoder.block.18.layer.1.DenseReluDense.wi_1.weight", "encoder.block.18.layer.1.DenseReluDense.wo.weight", "encoder.block.18.layer.1.layer_norm.weight", "encoder.block.19.layer.0.SelfAttention.k.weight", "encoder.block.19.layer.0.SelfAttention.o.weight", "encoder.block.19.layer.0.SelfAttention.q.weight", "encoder.block.19.layer.0.SelfAttention.v.weight", "encoder.block.19.layer.0.layer_norm.weight", "encoder.block.19.layer.1.DenseReluDense.wi_0.weight", "encoder.block.19.layer.1.DenseReluDense.wi_1.weight", "encoder.block.19.layer.1.DenseReluDense.wo.weight", "encoder.block.19.layer.1.layer_norm.weight", "encoder.block.2.layer.0.SelfAttention.k.weight", "encoder.block.2.layer.0.SelfAttention.o.weight", "encoder.block.2.layer.0.SelfAttention.q.weight", "encoder.block.2.layer.0.SelfAttention.v.weight", "encoder.block.2.layer.0.layer_norm.weight", "encoder.block.2.layer.1.DenseReluDense.wi_0.weight", "encoder.block.2.layer.1.DenseReluDense.wi_1.weight", "encoder.block.2.layer.1.DenseReluDense.wo.weight", "encoder.block.2.layer.1.layer_norm.weight", "encoder.block.20.layer.0.SelfAttention.k.weight", "encoder.block.20.layer.0.SelfAttention.o.weight", "encoder.block.20.layer.0.SelfAttention.q.weight", "encoder.block.20.layer.0.SelfAttention.v.weight", "encoder.block.20.layer.0.layer_norm.weight", "encoder.block.20.layer.1.DenseReluDense.wi_0.weight", "encoder.block.20.layer.1.DenseReluDense.wi_1.weight", "encoder.block.20.layer.1.DenseReluDense.wo.weight", "encoder.block.20.layer.1.layer_norm.weight", "encoder.block.21.layer.0.SelfAttention.k.weight", "encoder.block.21.layer.0.SelfAttention.o.weight", "encoder.block.21.layer.0.SelfAttention.q.weight", "encoder.block.21.layer.0.SelfAttention.v.weight", "encoder.block.21.layer.0.layer_norm.weight", "encoder.block.21.layer.1.DenseReluDense.wi_0.weight", "encoder.block.21.layer.1.DenseReluDense.wi_1.weight", "encoder.block.21.layer.1.DenseReluDense.wo.weight", "encoder.block.21.layer.1.layer_norm.weight", "encoder.block.22.layer.0.SelfAttention.k.weight", "encoder.block.22.layer.0.SelfAttention.o.weight", "encoder.block.22.layer.0.SelfAttention.q.weight", "encoder.block.22.layer.0.SelfAttention.v.weight", "encoder.block.22.layer.0.layer_norm.weight", "encoder.block.22.layer.1.DenseReluDense.wi_0.weight", "encoder.block.22.layer.1.DenseReluDense.wi_1.weight", "encoder.block.22.layer.1.DenseReluDense.wo.weight", "encoder.block.22.layer.1.layer_norm.weight", "encoder.block.23.layer.0.SelfAttention.k.weight", "encoder.block.23.layer.0.SelfAttention.o.weight", "encoder.block.23.layer.0.SelfAttention.q.weight", "encoder.block.23.layer.0.SelfAttention.v.weight", "encoder.block.23.layer.0.layer_norm.weight", "encoder.block.23.layer.1.DenseReluDense.wi_0.weight", "encoder.block.23.layer.1.DenseReluDense.wi_1.weight", "encoder.block.23.layer.1.DenseReluDense.wo.weight", "encoder.block.23.layer.1.layer_norm.weight", "encoder.block.3.layer.0.SelfAttention.k.weight", "encoder.block.3.layer.0.SelfAttention.o.weight", "encoder.block.3.layer.0.SelfAttention.q.weight", "encoder.block.3.layer.0.SelfAttention.v.weight", "encoder.block.3.layer.0.layer_norm.weight", "encoder.block.3.layer.1.DenseReluDense.wi_0.weight", "encoder.block.3.layer.1.DenseReluDense.wi_1.weight", "encoder.block.3.layer.1.DenseReluDense.wo.weight", "encoder.block.3.layer.1.layer_norm.weight", "encoder.block.4.layer.0.SelfAttention.k.weight", "encoder.block.4.layer.0.SelfAttention.o.weight", "encoder.block.4.layer.0.SelfAttention.q.weight", "encoder.block.4.layer.0.SelfAttention.v.weight", "encoder.block.4.layer.0.layer_norm.weight", "encoder.block.4.layer.1.DenseReluDense.wi_0.weight", "encoder.block.4.layer.1.DenseReluDense.wi_1.weight", "encoder.block.4.layer.1.DenseReluDense.wo.weight", "encoder.block.4.layer.1.layer_norm.weight", "encoder.block.5.layer.0.SelfAttention.k.weight", "encoder.block.5.layer.0.SelfAttention.o.weight", "encoder.block.5.layer.0.SelfAttention.q.weight", "encoder.block.5.layer.0.SelfAttention.v.weight", "encoder.block.5.layer.0.layer_norm.weight", "encoder.block.5.layer.1.DenseReluDense.wi_0.weight", "encoder.block.5.layer.1.DenseReluDense.wi_1.weight", "encoder.block.5.layer.1.DenseReluDense.wo.weight", "encoder.block.5.layer.1.layer_norm.weight", "encoder.block.6.layer.0.SelfAttention.k.weight", "encoder.block.6.layer.0.SelfAttention.o.weight", "encoder.block.6.layer.0.SelfAttention.q.weight", "encoder.block.6.layer.0.SelfAttention.v.weight", "encoder.block.6.layer.0.layer_norm.weight", "encoder.block.6.layer.1.DenseReluDense.wi_0.weight", "encoder.block.6.layer.1.DenseReluDense.wi_1.weight", "encoder.block.6.layer.1.DenseReluDense.wo.weight", "encoder.block.6.layer.1.layer_norm.weight", "encoder.block.7.layer.0.SelfAttention.k.weight", "encoder.block.7.layer.0.SelfAttention.o.weight", "encoder.block.7.layer.0.SelfAttention.q.weight", "encoder.block.7.layer.0.SelfAttention.v.weight", "encoder.block.7.layer.0.layer_norm.weight", "encoder.block.7.layer.1.DenseReluDense.wi_0.weight", "encoder.block.7.layer.1.DenseReluDense.wi_1.weight", "encoder.block.7.layer.1.DenseReluDense.wo.weight", "encoder.block.7.layer.1.layer_norm.weight", "encoder.block.8.layer.0.SelfAttention.k.weight", "encoder.block.8.layer.0.SelfAttention.o.weight", "encoder.block.8.layer.0.SelfAttention.q.weight", "encoder.block.8.layer.0.SelfAttention.v.weight", "encoder.block.8.layer.0.layer_norm.weight", "encoder.block.8.layer.1.DenseReluDense.wi_0.weight", "encoder.block.8.layer.1.DenseReluDense.wi_1.weight", "encoder.block.8.layer.1.DenseReluDense.wo.weight", "encoder.block.8.layer.1.layer_norm.weight", "encoder.block.9.layer.0.SelfAttention.k.weight", "encoder.block.9.layer.0.SelfAttention.o.weight", "encoder.block.9.layer.0.SelfAttention.q.weight", "encoder.block.9.layer.0.SelfAttention.v.weight", "encoder.block.9.layer.0.layer_norm.weight", "encoder.block.9.layer.1.DenseReluDense.wi_0.weight", "encoder.block.9.layer.1.DenseReluDense.wi_1.weight", "encoder.block.9.layer.1.DenseReluDense.wo.weight", "encoder.block.9.layer.1.layer_norm.weight", "encoder.embed_tokens.weight", "encoder.final_layer_norm.weight", "shared.weight", ] state_dict_ = {} for name, param in state_dict.items(): if name in name_list: state_dict_[name] = param return state_dict_ ================================================ FILE: diffsynth/models/sd3_vae_decoder.py ================================================ import torch from .sd_vae_decoder import VAEAttentionBlock, SDVAEDecoderStateDictConverter from .sd_unet import ResnetBlock, UpSampler from .tiler import TileWorker class SD3VAEDecoder(torch.nn.Module): def __init__(self): super().__init__() self.scaling_factor = 1.5305 # Different from SD 1.x self.shift_factor = 0.0609 # Different from SD 1.x self.conv_in = torch.nn.Conv2d(16, 512, kernel_size=3, padding=1) # Different from SD 1.x self.blocks = torch.nn.ModuleList([ # UNetMidBlock2D ResnetBlock(512, 512, eps=1e-6), VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), # UpDecoderBlock2D ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), UpSampler(512), # UpDecoderBlock2D ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), UpSampler(512), # UpDecoderBlock2D ResnetBlock(512, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), UpSampler(256), # UpDecoderBlock2D ResnetBlock(256, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-6) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) def tiled_forward(self, sample, tile_size=64, tile_stride=32): hidden_states = TileWorker().tiled_forward( lambda x: self.forward(x), sample, tile_size, tile_stride, tile_device=sample.device, tile_dtype=sample.dtype ) return hidden_states def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): # For VAE Decoder, we do not need to apply the tiler on each layer. if tiled: return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) # 1. pre-process hidden_states = sample / self.scaling_factor + self.shift_factor hidden_states = self.conv_in(hidden_states) time_emb = None text_emb = None res_stack = None # 2. blocks for i, block in enumerate(self.blocks): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) # 3. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states @staticmethod def state_dict_converter(): return SDVAEDecoderStateDictConverter() ================================================ FILE: diffsynth/models/sd3_vae_encoder.py ================================================ import torch from .sd_unet import ResnetBlock, DownSampler from .sd_vae_encoder import VAEAttentionBlock, SDVAEEncoderStateDictConverter from .tiler import TileWorker from einops import rearrange class SD3VAEEncoder(torch.nn.Module): def __init__(self): super().__init__() self.scaling_factor = 1.5305 # Different from SD 1.x self.shift_factor = 0.0609 # Different from SD 1.x self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1) self.blocks = torch.nn.ModuleList([ # DownEncoderBlock2D ResnetBlock(128, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), DownSampler(128, padding=0, extra_padding=True), # DownEncoderBlock2D ResnetBlock(128, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), DownSampler(256, padding=0, extra_padding=True), # DownEncoderBlock2D ResnetBlock(256, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), DownSampler(512, padding=0, extra_padding=True), # DownEncoderBlock2D ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), # UNetMidBlock2D ResnetBlock(512, 512, eps=1e-6), VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(512, 32, kernel_size=3, padding=1) def tiled_forward(self, sample, tile_size=64, tile_stride=32): hidden_states = TileWorker().tiled_forward( lambda x: self.forward(x), sample, tile_size, tile_stride, tile_device=sample.device, tile_dtype=sample.dtype ) return hidden_states def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): # For VAE Decoder, we do not need to apply the tiler on each layer. if tiled: return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) # 1. pre-process hidden_states = self.conv_in(sample) time_emb = None text_emb = None res_stack = None # 2. blocks for i, block in enumerate(self.blocks): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) # 3. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) hidden_states = hidden_states[:, :16] hidden_states = (hidden_states - self.shift_factor) * self.scaling_factor return hidden_states def encode_video(self, sample, batch_size=8): B = sample.shape[0] hidden_states = [] for i in range(0, sample.shape[2], batch_size): j = min(i + batch_size, sample.shape[2]) sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W") hidden_states_batch = self(sample_batch) hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B) hidden_states.append(hidden_states_batch) hidden_states = torch.concat(hidden_states, dim=2) return hidden_states @staticmethod def state_dict_converter(): return SDVAEEncoderStateDictConverter() ================================================ FILE: diffsynth/models/sd_controlnet.py ================================================ import torch from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler from .tiler import TileWorker class ControlNetConditioningLayer(torch.nn.Module): def __init__(self, channels = (3, 16, 32, 96, 256, 320)): super().__init__() self.blocks = torch.nn.ModuleList([]) self.blocks.append(torch.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1)) self.blocks.append(torch.nn.SiLU()) for i in range(1, len(channels) - 2): self.blocks.append(torch.nn.Conv2d(channels[i], channels[i], kernel_size=3, padding=1)) self.blocks.append(torch.nn.SiLU()) self.blocks.append(torch.nn.Conv2d(channels[i], channels[i+1], kernel_size=3, padding=1, stride=2)) self.blocks.append(torch.nn.SiLU()) self.blocks.append(torch.nn.Conv2d(channels[-2], channels[-1], kernel_size=3, padding=1)) def forward(self, conditioning): for block in self.blocks: conditioning = block(conditioning) return conditioning class SDControlNet(torch.nn.Module): def __init__(self, global_pool=False): super().__init__() self.time_proj = Timesteps(320) self.time_embedding = torch.nn.Sequential( torch.nn.Linear(320, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320)) self.blocks = torch.nn.ModuleList([ # CrossAttnDownBlock2D ResnetBlock(320, 320, 1280), AttentionBlock(8, 40, 320, 1, 768), PushBlock(), ResnetBlock(320, 320, 1280), AttentionBlock(8, 40, 320, 1, 768), PushBlock(), DownSampler(320), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(320, 640, 1280), AttentionBlock(8, 80, 640, 1, 768), PushBlock(), ResnetBlock(640, 640, 1280), AttentionBlock(8, 80, 640, 1, 768), PushBlock(), DownSampler(640), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(640, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768), PushBlock(), ResnetBlock(1280, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768), PushBlock(), DownSampler(1280), PushBlock(), # DownBlock2D ResnetBlock(1280, 1280, 1280), PushBlock(), ResnetBlock(1280, 1280, 1280), PushBlock(), # UNetMidBlock2DCrossAttn ResnetBlock(1280, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768), ResnetBlock(1280, 1280, 1280), PushBlock() ]) self.controlnet_blocks = torch.nn.ModuleList([ torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), ]) self.global_pool = global_pool def forward( self, sample, timestep, encoder_hidden_states, conditioning, tiled=False, tile_size=64, tile_stride=32, **kwargs ): # 1. time time_emb = self.time_proj(timestep).to(sample.dtype) time_emb = self.time_embedding(time_emb) time_emb = time_emb.repeat(sample.shape[0], 1) # 2. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = self.conv_in(sample) + self.controlnet_conv_in(conditioning) text_emb = encoder_hidden_states res_stack = [hidden_states] # 3. blocks for i, block in enumerate(self.blocks): if tiled and not isinstance(block, PushBlock): _, _, inter_height, _ = hidden_states.shape resize_scale = inter_height / height hidden_states = TileWorker().tiled_forward( lambda x: block(x, time_emb, text_emb, res_stack)[0], hidden_states, int(tile_size * resize_scale), int(tile_stride * resize_scale), tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) else: hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack) # 4. ControlNet blocks controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)] # pool if self.global_pool: controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack] return controlnet_res_stack @staticmethod def state_dict_converter(): return SDControlNetStateDictConverter() class SDControlNetStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): # architecture block_types = [ 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock' ] # controlnet_rename_dict controlnet_rename_dict = { "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight", "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias", "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight", "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias", "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight", "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias", "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight", "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias", "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight", "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias", "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight", "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias", "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight", "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias", "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight", "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias", } # Rename each parameter name_list = sorted([name for name in state_dict]) rename_dict = {} block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} for name in name_list: names = name.split(".") if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: pass elif name in controlnet_rename_dict: names = controlnet_rename_dict[name].split(".") elif names[0] == "controlnet_down_blocks": names[0] = "controlnet_blocks" elif names[0] == "controlnet_mid_block": names = ["controlnet_blocks", "12", names[-1]] elif names[0] in ["time_embedding", "add_embedding"]: if names[0] == "add_embedding": names[0] = "add_time_embedding" names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: if names[0] == "mid_block": names.insert(1, "0") block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] block_type_with_id = ".".join(names[:4]) if block_type_with_id != last_block_type_with_id[block_type]: block_id[block_type] += 1 last_block_type_with_id[block_type] = block_type_with_id while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: block_id[block_type] += 1 block_type_with_id = ".".join(names[:4]) names = ["blocks", str(block_id[block_type])] + names[4:] if "ff" in names: ff_index = names.index("ff") component = ".".join(names[ff_index:ff_index+3]) component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] names = names[:ff_index] + [component] + names[ff_index+3:] if "to_out" in names: names.pop(names.index("to_out") + 1) else: raise ValueError(f"Unknown parameters: {name}") rename_dict[name] = ".".join(names) # Convert state_dict state_dict_ = {} for name, param in state_dict.items(): if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() if rename_dict[name] in [ "controlnet_blocks.1.bias", "controlnet_blocks.2.bias", "controlnet_blocks.3.bias", "controlnet_blocks.5.bias", "controlnet_blocks.6.bias", "controlnet_blocks.8.bias", "controlnet_blocks.9.bias", "controlnet_blocks.10.bias", "controlnet_blocks.11.bias", "controlnet_blocks.12.bias" ]: continue state_dict_[rename_dict[name]] = param return state_dict_ def from_civitai(self, state_dict): if "mid_block.resnets.1.time_emb_proj.weight" in state_dict: # For controlnets in diffusers format return self.from_diffusers(state_dict) rename_dict = { "control_model.time_embed.0.weight": "time_embedding.0.weight", "control_model.time_embed.0.bias": "time_embedding.0.bias", "control_model.time_embed.2.weight": "time_embedding.2.weight", "control_model.time_embed.2.bias": "time_embedding.2.bias", "control_model.input_blocks.0.0.weight": "conv_in.weight", "control_model.input_blocks.0.0.bias": "conv_in.bias", "control_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", "control_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", "control_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", "control_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", "control_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", "control_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", "control_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", "control_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", "control_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", "control_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", "control_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight", "control_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias", "control_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight", "control_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias", "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight", "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias", "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight", "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias", "control_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight", "control_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias", "control_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.1.transformer_blocks.0.norm2.weight", "control_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.1.transformer_blocks.0.norm2.bias", "control_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.1.transformer_blocks.0.norm3.weight", "control_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.1.transformer_blocks.0.norm3.bias", "control_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight", "control_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias", "control_model.input_blocks.2.0.in_layers.0.weight": "blocks.3.norm1.weight", "control_model.input_blocks.2.0.in_layers.0.bias": "blocks.3.norm1.bias", "control_model.input_blocks.2.0.in_layers.2.weight": "blocks.3.conv1.weight", "control_model.input_blocks.2.0.in_layers.2.bias": "blocks.3.conv1.bias", "control_model.input_blocks.2.0.emb_layers.1.weight": "blocks.3.time_emb_proj.weight", "control_model.input_blocks.2.0.emb_layers.1.bias": "blocks.3.time_emb_proj.bias", "control_model.input_blocks.2.0.out_layers.0.weight": "blocks.3.norm2.weight", "control_model.input_blocks.2.0.out_layers.0.bias": "blocks.3.norm2.bias", "control_model.input_blocks.2.0.out_layers.3.weight": "blocks.3.conv2.weight", "control_model.input_blocks.2.0.out_layers.3.bias": "blocks.3.conv2.bias", "control_model.input_blocks.2.1.norm.weight": "blocks.4.norm.weight", "control_model.input_blocks.2.1.norm.bias": "blocks.4.norm.bias", "control_model.input_blocks.2.1.proj_in.weight": "blocks.4.proj_in.weight", "control_model.input_blocks.2.1.proj_in.bias": "blocks.4.proj_in.bias", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q.weight": "blocks.4.transformer_blocks.0.attn1.to_q.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k.weight": "blocks.4.transformer_blocks.0.attn1.to_k.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v.weight": "blocks.4.transformer_blocks.0.attn1.to_v.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.4.transformer_blocks.0.attn1.to_out.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.4.transformer_blocks.0.attn1.to_out.bias", "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.4.transformer_blocks.0.act_fn.proj.weight", "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.4.transformer_blocks.0.act_fn.proj.bias", "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.weight": "blocks.4.transformer_blocks.0.ff.weight", "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.bias": "blocks.4.transformer_blocks.0.ff.bias", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_q.weight": "blocks.4.transformer_blocks.0.attn2.to_q.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight": "blocks.4.transformer_blocks.0.attn2.to_k.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_v.weight": "blocks.4.transformer_blocks.0.attn2.to_v.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.4.transformer_blocks.0.attn2.to_out.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.4.transformer_blocks.0.attn2.to_out.bias", "control_model.input_blocks.2.1.transformer_blocks.0.norm1.weight": "blocks.4.transformer_blocks.0.norm1.weight", "control_model.input_blocks.2.1.transformer_blocks.0.norm1.bias": "blocks.4.transformer_blocks.0.norm1.bias", "control_model.input_blocks.2.1.transformer_blocks.0.norm2.weight": "blocks.4.transformer_blocks.0.norm2.weight", "control_model.input_blocks.2.1.transformer_blocks.0.norm2.bias": "blocks.4.transformer_blocks.0.norm2.bias", "control_model.input_blocks.2.1.transformer_blocks.0.norm3.weight": "blocks.4.transformer_blocks.0.norm3.weight", "control_model.input_blocks.2.1.transformer_blocks.0.norm3.bias": "blocks.4.transformer_blocks.0.norm3.bias", "control_model.input_blocks.2.1.proj_out.weight": "blocks.4.proj_out.weight", "control_model.input_blocks.2.1.proj_out.bias": "blocks.4.proj_out.bias", "control_model.input_blocks.3.0.op.weight": "blocks.6.conv.weight", "control_model.input_blocks.3.0.op.bias": "blocks.6.conv.bias", "control_model.input_blocks.4.0.in_layers.0.weight": "blocks.8.norm1.weight", "control_model.input_blocks.4.0.in_layers.0.bias": "blocks.8.norm1.bias", "control_model.input_blocks.4.0.in_layers.2.weight": "blocks.8.conv1.weight", "control_model.input_blocks.4.0.in_layers.2.bias": "blocks.8.conv1.bias", "control_model.input_blocks.4.0.emb_layers.1.weight": "blocks.8.time_emb_proj.weight", "control_model.input_blocks.4.0.emb_layers.1.bias": "blocks.8.time_emb_proj.bias", "control_model.input_blocks.4.0.out_layers.0.weight": "blocks.8.norm2.weight", "control_model.input_blocks.4.0.out_layers.0.bias": "blocks.8.norm2.bias", "control_model.input_blocks.4.0.out_layers.3.weight": "blocks.8.conv2.weight", "control_model.input_blocks.4.0.out_layers.3.bias": "blocks.8.conv2.bias", "control_model.input_blocks.4.0.skip_connection.weight": "blocks.8.conv_shortcut.weight", "control_model.input_blocks.4.0.skip_connection.bias": "blocks.8.conv_shortcut.bias", "control_model.input_blocks.4.1.norm.weight": "blocks.9.norm.weight", "control_model.input_blocks.4.1.norm.bias": "blocks.9.norm.bias", "control_model.input_blocks.4.1.proj_in.weight": "blocks.9.proj_in.weight", "control_model.input_blocks.4.1.proj_in.bias": "blocks.9.proj_in.bias", "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight": "blocks.9.transformer_blocks.0.attn1.to_q.weight", "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.9.transformer_blocks.0.attn1.to_k.weight", "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight": 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"control_model.middle_block.1.transformer_blocks.0.attn2.to_v.weight": "blocks.29.transformer_blocks.0.attn2.to_v.weight", "control_model.middle_block.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.29.transformer_blocks.0.attn2.to_out.weight", "control_model.middle_block.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.29.transformer_blocks.0.attn2.to_out.bias", "control_model.middle_block.1.transformer_blocks.0.norm1.weight": "blocks.29.transformer_blocks.0.norm1.weight", "control_model.middle_block.1.transformer_blocks.0.norm1.bias": "blocks.29.transformer_blocks.0.norm1.bias", "control_model.middle_block.1.transformer_blocks.0.norm2.weight": "blocks.29.transformer_blocks.0.norm2.weight", "control_model.middle_block.1.transformer_blocks.0.norm2.bias": "blocks.29.transformer_blocks.0.norm2.bias", "control_model.middle_block.1.transformer_blocks.0.norm3.weight": "blocks.29.transformer_blocks.0.norm3.weight", "control_model.middle_block.1.transformer_blocks.0.norm3.bias": "blocks.29.transformer_blocks.0.norm3.bias", "control_model.middle_block.1.proj_out.weight": "blocks.29.proj_out.weight", "control_model.middle_block.1.proj_out.bias": "blocks.29.proj_out.bias", "control_model.middle_block.2.in_layers.0.weight": "blocks.30.norm1.weight", "control_model.middle_block.2.in_layers.0.bias": "blocks.30.norm1.bias", "control_model.middle_block.2.in_layers.2.weight": "blocks.30.conv1.weight", "control_model.middle_block.2.in_layers.2.bias": "blocks.30.conv1.bias", "control_model.middle_block.2.emb_layers.1.weight": "blocks.30.time_emb_proj.weight", "control_model.middle_block.2.emb_layers.1.bias": "blocks.30.time_emb_proj.bias", "control_model.middle_block.2.out_layers.0.weight": "blocks.30.norm2.weight", "control_model.middle_block.2.out_layers.0.bias": "blocks.30.norm2.bias", "control_model.middle_block.2.out_layers.3.weight": "blocks.30.conv2.weight", "control_model.middle_block.2.out_layers.3.bias": "blocks.30.conv2.bias", "control_model.middle_block_out.0.weight": "controlnet_blocks.12.weight", "control_model.middle_block_out.0.bias": "controlnet_blocks.7.bias", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ ================================================ FILE: diffsynth/models/sd_ipadapter.py ================================================ from .svd_image_encoder import SVDImageEncoder from .sdxl_ipadapter import IpAdapterImageProjModel, IpAdapterModule, SDXLIpAdapterStateDictConverter from transformers import CLIPImageProcessor import torch class IpAdapterCLIPImageEmbedder(SVDImageEncoder): def __init__(self): super().__init__() self.image_processor = CLIPImageProcessor() def forward(self, image): pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype) return super().forward(pixel_values) class SDIpAdapter(torch.nn.Module): def __init__(self): super().__init__() shape_list = [(768, 320)] * 2 + [(768, 640)] * 2 + [(768, 1280)] * 5 + [(768, 640)] * 3 + [(768, 320)] * 3 + [(768, 1280)] * 1 self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list]) self.image_proj = IpAdapterImageProjModel(cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4) self.set_full_adapter() def set_full_adapter(self): block_ids = [1, 4, 9, 12, 17, 20, 40, 43, 46, 50, 53, 56, 60, 63, 66, 29] self.call_block_id = {(i, 0): j for j, i in enumerate(block_ids)} def set_less_adapter(self): # IP-Adapter for SD v1.5 doesn't support this feature. self.set_full_adapter() def forward(self, hidden_states, scale=1.0): hidden_states = self.image_proj(hidden_states) hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1]) ip_kv_dict = {} for (block_id, transformer_id) in self.call_block_id: ipadapter_id = self.call_block_id[(block_id, transformer_id)] ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states) if block_id not in ip_kv_dict: ip_kv_dict[block_id] = {} ip_kv_dict[block_id][transformer_id] = { "ip_k": ip_k, "ip_v": ip_v, "scale": scale } return ip_kv_dict @staticmethod def state_dict_converter(): return SDIpAdapterStateDictConverter() class SDIpAdapterStateDictConverter(SDXLIpAdapterStateDictConverter): def __init__(self): pass ================================================ FILE: diffsynth/models/sd_motion.py ================================================ from .sd_unet import SDUNet, Attention, GEGLU import torch from einops import rearrange, repeat class TemporalTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, attention_head_dim, max_position_embeddings=32): super().__init__() # 1. Self-Attn self.pe1 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim)) self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) # 2. Cross-Attn self.pe2 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim)) self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.attn2 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) # 3. Feed-forward self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.act_fn = GEGLU(dim, dim * 4) self.ff = torch.nn.Linear(dim * 4, dim) def forward(self, hidden_states, batch_size=1): # 1. Self-Attention norm_hidden_states = self.norm1(hidden_states) norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size) attn_output = self.attn1(norm_hidden_states + self.pe1[:, :norm_hidden_states.shape[1]]) attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size) hidden_states = attn_output + hidden_states # 2. Cross-Attention norm_hidden_states = self.norm2(hidden_states) norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size) attn_output = self.attn2(norm_hidden_states + self.pe2[:, :norm_hidden_states.shape[1]]) attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size) hidden_states = attn_output + hidden_states # 3. Feed-forward norm_hidden_states = self.norm3(hidden_states) ff_output = self.act_fn(norm_hidden_states) ff_output = self.ff(ff_output) hidden_states = ff_output + hidden_states return hidden_states class TemporalBlock(torch.nn.Module): def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): super().__init__() inner_dim = num_attention_heads * attention_head_dim self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) self.proj_in = torch.nn.Linear(in_channels, inner_dim) self.transformer_blocks = torch.nn.ModuleList([ TemporalTransformerBlock( inner_dim, num_attention_heads, attention_head_dim ) for d in range(num_layers) ]) self.proj_out = torch.nn.Linear(inner_dim, in_channels) def forward(self, hidden_states, time_emb, text_emb, res_stack, batch_size=1): batch, _, height, width = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) hidden_states = self.proj_in(hidden_states) for block in self.transformer_blocks: hidden_states = block( hidden_states, batch_size=batch_size ) hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() hidden_states = hidden_states + residual return hidden_states, time_emb, text_emb, res_stack class SDMotionModel(torch.nn.Module): def __init__(self): super().__init__() self.motion_modules = torch.nn.ModuleList([ TemporalBlock(8, 40, 320, eps=1e-6), TemporalBlock(8, 40, 320, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 40, 320, eps=1e-6), TemporalBlock(8, 40, 320, eps=1e-6), TemporalBlock(8, 40, 320, eps=1e-6), ]) self.call_block_id = { 1: 0, 4: 1, 9: 2, 12: 3, 17: 4, 20: 5, 24: 6, 26: 7, 29: 8, 32: 9, 34: 10, 36: 11, 40: 12, 43: 13, 46: 14, 50: 15, 53: 16, 56: 17, 60: 18, 63: 19, 66: 20 } def forward(self): pass @staticmethod def state_dict_converter(): return SDMotionModelStateDictConverter() class SDMotionModelStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "norm": "norm", "proj_in": "proj_in", "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q", "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k", "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v", "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out", "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1", "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q", "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k", "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v", "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out", "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2", "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1", "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2", "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj", "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff", "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3", "proj_out": "proj_out", } name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")]) name_list += sorted([i for i in state_dict if i.startswith("mid_block.")]) name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")]) state_dict_ = {} last_prefix, module_id = "", -1 for name in name_list: names = name.split(".") prefix_index = names.index("temporal_transformer") + 1 prefix = ".".join(names[:prefix_index]) if prefix != last_prefix: last_prefix = prefix module_id += 1 middle_name = ".".join(names[prefix_index:-1]) suffix = names[-1] if "pos_encoder" in names: rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]]) else: rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix]) state_dict_[rename] = state_dict[name] return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/sd_text_encoder.py ================================================ import torch from .attention import Attention class CLIPEncoderLayer(torch.nn.Module): def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True): super().__init__() self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True) self.layer_norm1 = torch.nn.LayerNorm(embed_dim) self.layer_norm2 = torch.nn.LayerNorm(embed_dim) self.fc1 = torch.nn.Linear(embed_dim, intermediate_size) self.fc2 = torch.nn.Linear(intermediate_size, embed_dim) self.use_quick_gelu = use_quick_gelu def quickGELU(self, x): return x * torch.sigmoid(1.702 * x) def forward(self, hidden_states, attn_mask=None): residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states = self.attn(hidden_states, attn_mask=attn_mask) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.fc1(hidden_states) if self.use_quick_gelu: hidden_states = self.quickGELU(hidden_states) else: hidden_states = torch.nn.functional.gelu(hidden_states) hidden_states = self.fc2(hidden_states) hidden_states = residual + hidden_states return hidden_states class SDTextEncoder(torch.nn.Module): def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072): super().__init__() # token_embedding self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) # position_embeds (This is a fixed tensor) self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) # encoders self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)]) # attn_mask self.attn_mask = self.attention_mask(max_position_embeddings) # final_layer_norm self.final_layer_norm = torch.nn.LayerNorm(embed_dim) def attention_mask(self, length): mask = torch.empty(length, length) mask.fill_(float("-inf")) mask.triu_(1) return mask def forward(self, input_ids, clip_skip=1): embeds = self.token_embedding(input_ids) + self.position_embeds attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) for encoder_id, encoder in enumerate(self.encoders): embeds = encoder(embeds, attn_mask=attn_mask) if encoder_id + clip_skip == len(self.encoders): break embeds = self.final_layer_norm(embeds) return embeds @staticmethod def state_dict_converter(): return SDTextEncoderStateDictConverter() class SDTextEncoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "text_model.embeddings.token_embedding.weight": "token_embedding.weight", "text_model.embeddings.position_embedding.weight": "position_embeds", "text_model.final_layer_norm.weight": "final_layer_norm.weight", "text_model.final_layer_norm.bias": "final_layer_norm.bias" } attn_rename_dict = { "self_attn.q_proj": "attn.to_q", "self_attn.k_proj": "attn.to_k", "self_attn.v_proj": "attn.to_v", "self_attn.out_proj": "attn.to_out", "layer_norm1": "layer_norm1", "layer_norm2": "layer_norm2", "mlp.fc1": "fc1", "mlp.fc2": "fc2", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param elif name.startswith("text_model.encoder.layers."): param = state_dict[name] names = name.split(".") layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) state_dict_[name_] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias", "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight", "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias", "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight", "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias", "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight", "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias", "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight", "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias", "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight", "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias", "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight", "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias", "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight", "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias", "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight", "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias", 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"cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", "cond_stage_model.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias", "cond_stage_model.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight", "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": "position_embeds" } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param return state_dict_ ================================================ FILE: diffsynth/models/sd_unet.py ================================================ import torch, math from .attention import Attention from .tiler import TileWorker class Timesteps(torch.nn.Module): def __init__(self, num_channels): super().__init__() self.num_channels = num_channels def forward(self, timesteps): half_dim = self.num_channels // 2 exponent = -math.log(10000) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) / half_dim timesteps = timesteps.unsqueeze(-1) emb = timesteps.float() * torch.exp(exponent) emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1) return emb class GEGLU(torch.nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = torch.nn.Linear(dim_in, dim_out * 2) def forward(self, hidden_states): hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1) return hidden_states * torch.nn.functional.gelu(gate) class BasicTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, attention_head_dim, cross_attention_dim): super().__init__() # 1. Self-Attn self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) # 2. Cross-Attn self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.attn2 = Attention(q_dim=dim, kv_dim=cross_attention_dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) # 3. Feed-forward self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.act_fn = GEGLU(dim, dim * 4) self.ff = torch.nn.Linear(dim * 4, dim) def forward(self, hidden_states, encoder_hidden_states, ipadapter_kwargs=None): # 1. Self-Attention norm_hidden_states = self.norm1(hidden_states) attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None) hidden_states = attn_output + hidden_states # 2. Cross-Attention norm_hidden_states = self.norm2(hidden_states) attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states, ipadapter_kwargs=ipadapter_kwargs) hidden_states = attn_output + hidden_states # 3. Feed-forward norm_hidden_states = self.norm3(hidden_states) ff_output = self.act_fn(norm_hidden_states) ff_output = self.ff(ff_output) hidden_states = ff_output + hidden_states return hidden_states class DownSampler(torch.nn.Module): def __init__(self, channels, padding=1, extra_padding=False): super().__init__() self.conv = torch.nn.Conv2d(channels, channels, 3, stride=2, padding=padding) self.extra_padding = extra_padding def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): if self.extra_padding: hidden_states = torch.nn.functional.pad(hidden_states, (0, 1, 0, 1), mode="constant", value=0) hidden_states = self.conv(hidden_states) return hidden_states, time_emb, text_emb, res_stack class UpSampler(torch.nn.Module): def __init__(self, channels): super().__init__() self.conv = torch.nn.Conv2d(channels, channels, 3, padding=1) def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): hidden_states = torch.nn.functional.interpolate(hidden_states, scale_factor=2.0, mode="nearest") hidden_states = self.conv(hidden_states) return hidden_states, time_emb, text_emb, res_stack class ResnetBlock(torch.nn.Module): def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5): super().__init__() self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels is not None: self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels) self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) self.nonlinearity = torch.nn.SiLU() self.conv_shortcut = None if in_channels != out_channels: self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True) def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): x = hidden_states x = self.norm1(x) x = self.nonlinearity(x) x = self.conv1(x) if time_emb is not None: emb = self.nonlinearity(time_emb) emb = self.time_emb_proj(emb)[:, :, None, None] x = x + emb x = self.norm2(x) x = self.nonlinearity(x) x = self.conv2(x) if self.conv_shortcut is not None: hidden_states = self.conv_shortcut(hidden_states) hidden_states = hidden_states + x return hidden_states, time_emb, text_emb, res_stack class AttentionBlock(torch.nn.Module): def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, cross_attention_dim=None, norm_num_groups=32, eps=1e-5, need_proj_out=True): super().__init__() inner_dim = num_attention_heads * attention_head_dim self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) self.proj_in = torch.nn.Linear(in_channels, inner_dim) self.transformer_blocks = torch.nn.ModuleList([ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, cross_attention_dim=cross_attention_dim ) for d in range(num_layers) ]) self.need_proj_out = need_proj_out if need_proj_out: self.proj_out = torch.nn.Linear(inner_dim, in_channels) def forward( self, hidden_states, time_emb, text_emb, res_stack, cross_frame_attention=False, tiled=False, tile_size=64, tile_stride=32, ipadapter_kwargs_list={}, **kwargs ): batch, _, height, width = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) hidden_states = self.proj_in(hidden_states) if cross_frame_attention: hidden_states = hidden_states.reshape(1, batch * height * width, inner_dim) encoder_hidden_states = text_emb.mean(dim=0, keepdim=True) else: encoder_hidden_states = text_emb if encoder_hidden_states.shape[0] != hidden_states.shape[0]: encoder_hidden_states = encoder_hidden_states.repeat(hidden_states.shape[0], 1, 1) if tiled: tile_size = min(tile_size, min(height, width)) hidden_states = hidden_states.permute(0, 2, 1).reshape(batch, inner_dim, height, width) def block_tile_forward(x): b, c, h, w = x.shape x = x.permute(0, 2, 3, 1).reshape(b, h*w, c) x = block(x, encoder_hidden_states) x = x.reshape(b, h, w, c).permute(0, 3, 1, 2) return x for block in self.transformer_blocks: hidden_states = TileWorker().tiled_forward( block_tile_forward, hidden_states, tile_size, tile_stride, tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) else: for block_id, block in enumerate(self.transformer_blocks): hidden_states = block( hidden_states, encoder_hidden_states=encoder_hidden_states, ipadapter_kwargs=ipadapter_kwargs_list.get(block_id, None) ) if cross_frame_attention: hidden_states = hidden_states.reshape(batch, height * width, inner_dim) if self.need_proj_out: hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() hidden_states = hidden_states + residual else: hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() return hidden_states, time_emb, text_emb, res_stack class PushBlock(torch.nn.Module): def __init__(self): super().__init__() def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): res_stack.append(hidden_states) return hidden_states, time_emb, text_emb, res_stack class PopBlock(torch.nn.Module): def __init__(self): super().__init__() def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): res_hidden_states = res_stack.pop() hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) return hidden_states, time_emb, text_emb, res_stack class SDUNet(torch.nn.Module): def __init__(self): super().__init__() self.time_proj = Timesteps(320) self.time_embedding = torch.nn.Sequential( torch.nn.Linear(320, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) self.blocks = torch.nn.ModuleList([ # CrossAttnDownBlock2D ResnetBlock(320, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), PushBlock(), ResnetBlock(320, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), PushBlock(), DownSampler(320), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(320, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), PushBlock(), ResnetBlock(640, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), PushBlock(), DownSampler(640), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(640, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), PushBlock(), ResnetBlock(1280, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), PushBlock(), DownSampler(1280), PushBlock(), # DownBlock2D ResnetBlock(1280, 1280, 1280), PushBlock(), ResnetBlock(1280, 1280, 1280), PushBlock(), # UNetMidBlock2DCrossAttn ResnetBlock(1280, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), ResnetBlock(1280, 1280, 1280), # UpBlock2D PopBlock(), ResnetBlock(2560, 1280, 1280), PopBlock(), ResnetBlock(2560, 1280, 1280), PopBlock(), ResnetBlock(2560, 1280, 1280), UpSampler(1280), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(2560, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(2560, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(1920, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), UpSampler(1280), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(1920, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(1280, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(960, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), UpSampler(640), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(960, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(640, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(640, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) def forward(self, sample, timestep, encoder_hidden_states, **kwargs): # 1. time time_emb = self.time_proj(timestep).to(sample.dtype) time_emb = self.time_embedding(time_emb) # 2. pre-process hidden_states = self.conv_in(sample) text_emb = encoder_hidden_states res_stack = [hidden_states] # 3. blocks for i, block in enumerate(self.blocks): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) # 4. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states @staticmethod def state_dict_converter(): return SDUNetStateDictConverter() class SDUNetStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): # architecture block_types = [ 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock' ] # Rename each parameter name_list = sorted([name for name in state_dict]) rename_dict = {} block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} for name in name_list: names = name.split(".") if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: pass elif names[0] in ["time_embedding", "add_embedding"]: if names[0] == "add_embedding": names[0] = "add_time_embedding" names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: if names[0] == "mid_block": names.insert(1, "0") block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] block_type_with_id = ".".join(names[:4]) if block_type_with_id != last_block_type_with_id[block_type]: block_id[block_type] += 1 last_block_type_with_id[block_type] = block_type_with_id while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: block_id[block_type] += 1 block_type_with_id = ".".join(names[:4]) names = ["blocks", str(block_id[block_type])] + names[4:] if "ff" in names: ff_index = names.index("ff") component = ".".join(names[ff_index:ff_index+3]) component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] names = names[:ff_index] + [component] + names[ff_index+3:] if "to_out" in names: names.pop(names.index("to_out") + 1) else: raise ValueError(f"Unknown parameters: {name}") rename_dict[name] = ".".join(names) # Convert state_dict state_dict_ = {} for name, param in state_dict.items(): if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", 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"model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias", "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ ================================================ FILE: diffsynth/models/sd_vae_decoder.py ================================================ import torch from .attention import Attention from .sd_unet import ResnetBlock, UpSampler from .tiler import TileWorker class VAEAttentionBlock(torch.nn.Module): def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): super().__init__() inner_dim = num_attention_heads * attention_head_dim self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) self.transformer_blocks = torch.nn.ModuleList([ Attention( inner_dim, num_attention_heads, attention_head_dim, bias_q=True, bias_kv=True, bias_out=True ) for d in range(num_layers) ]) def forward(self, hidden_states, time_emb, text_emb, res_stack): batch, _, height, width = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) for block in self.transformer_blocks: hidden_states = block(hidden_states) hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() hidden_states = hidden_states + residual return hidden_states, time_emb, text_emb, res_stack class SDVAEDecoder(torch.nn.Module): def __init__(self): super().__init__() self.scaling_factor = 0.18215 self.post_quant_conv = torch.nn.Conv2d(4, 4, kernel_size=1) self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1) self.blocks = torch.nn.ModuleList([ # UNetMidBlock2D ResnetBlock(512, 512, eps=1e-6), VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), # UpDecoderBlock2D ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), UpSampler(512), # UpDecoderBlock2D ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), UpSampler(512), # UpDecoderBlock2D ResnetBlock(512, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), UpSampler(256), # UpDecoderBlock2D ResnetBlock(256, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) def tiled_forward(self, sample, tile_size=64, tile_stride=32): hidden_states = TileWorker().tiled_forward( lambda x: self.forward(x), sample, tile_size, tile_stride, tile_device=sample.device, tile_dtype=sample.dtype ) return hidden_states def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): original_dtype = sample.dtype sample = sample.to(dtype=next(iter(self.parameters())).dtype) # For VAE Decoder, we do not need to apply the tiler on each layer. if tiled: return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) # 1. pre-process sample = sample / self.scaling_factor hidden_states = self.post_quant_conv(sample) hidden_states = self.conv_in(hidden_states) time_emb = None text_emb = None res_stack = None # 2. blocks for i, block in enumerate(self.blocks): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) # 3. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) hidden_states = hidden_states.to(original_dtype) return hidden_states @staticmethod def state_dict_converter(): return SDVAEDecoderStateDictConverter() class SDVAEDecoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): # architecture block_types = [ 'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', 'ResnetBlock', 'ResnetBlock', 'ResnetBlock' ] # Rename each parameter local_rename_dict = { "post_quant_conv": "post_quant_conv", "decoder.conv_in": "conv_in", "decoder.mid_block.attentions.0.group_norm": "blocks.1.norm", "decoder.mid_block.attentions.0.to_q": "blocks.1.transformer_blocks.0.to_q", "decoder.mid_block.attentions.0.to_k": "blocks.1.transformer_blocks.0.to_k", "decoder.mid_block.attentions.0.to_v": "blocks.1.transformer_blocks.0.to_v", "decoder.mid_block.attentions.0.to_out.0": "blocks.1.transformer_blocks.0.to_out", "decoder.mid_block.resnets.0.norm1": "blocks.0.norm1", "decoder.mid_block.resnets.0.conv1": "blocks.0.conv1", "decoder.mid_block.resnets.0.norm2": "blocks.0.norm2", "decoder.mid_block.resnets.0.conv2": "blocks.0.conv2", "decoder.mid_block.resnets.1.norm1": "blocks.2.norm1", "decoder.mid_block.resnets.1.conv1": "blocks.2.conv1", "decoder.mid_block.resnets.1.norm2": "blocks.2.norm2", "decoder.mid_block.resnets.1.conv2": "blocks.2.conv2", "decoder.conv_norm_out": "conv_norm_out", "decoder.conv_out": "conv_out", } name_list = sorted([name for name in state_dict]) rename_dict = {} block_id = {"ResnetBlock": 2, "DownSampler": 2, "UpSampler": 2} last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""} for name in name_list: names = name.split(".") name_prefix = ".".join(names[:-1]) if name_prefix in local_rename_dict: rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1] elif name.startswith("decoder.up_blocks"): block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]] block_type_with_id = ".".join(names[:5]) if block_type_with_id != last_block_type_with_id[block_type]: block_id[block_type] += 1 last_block_type_with_id[block_type] = block_type_with_id while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: block_id[block_type] += 1 block_type_with_id = ".".join(names[:5]) names = ["blocks", str(block_id[block_type])] + names[5:] rename_dict[name] = ".".join(names) # Convert state_dict state_dict_ = {} for name, param in state_dict.items(): if name in rename_dict: state_dict_[rename_dict[name]] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "first_stage_model.decoder.conv_in.bias": "conv_in.bias", "first_stage_model.decoder.conv_in.weight": "conv_in.weight", "first_stage_model.decoder.conv_out.bias": "conv_out.bias", "first_stage_model.decoder.conv_out.weight": "conv_out.weight", "first_stage_model.decoder.mid.attn_1.k.bias": 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state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if "transformer_blocks" in rename_dict[name]: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ ================================================ FILE: diffsynth/models/sd_vae_encoder.py ================================================ import torch from .sd_unet import ResnetBlock, DownSampler from .sd_vae_decoder import VAEAttentionBlock from .tiler import TileWorker from einops import rearrange class SDVAEEncoder(torch.nn.Module): def __init__(self): super().__init__() self.scaling_factor = 0.18215 self.quant_conv = torch.nn.Conv2d(8, 8, kernel_size=1) self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1) self.blocks = torch.nn.ModuleList([ # DownEncoderBlock2D ResnetBlock(128, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), DownSampler(128, padding=0, extra_padding=True), # DownEncoderBlock2D ResnetBlock(128, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), DownSampler(256, padding=0, extra_padding=True), # DownEncoderBlock2D ResnetBlock(256, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), DownSampler(512, padding=0, extra_padding=True), # DownEncoderBlock2D ResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), # UNetMidBlock2D ResnetBlock(512, 512, eps=1e-6), VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(512, 8, kernel_size=3, padding=1) def tiled_forward(self, sample, tile_size=64, tile_stride=32): hidden_states = TileWorker().tiled_forward( lambda x: self.forward(x), sample, tile_size, tile_stride, tile_device=sample.device, tile_dtype=sample.dtype ) return hidden_states def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): original_dtype = sample.dtype sample = sample.to(dtype=next(iter(self.parameters())).dtype) # For VAE Decoder, we do not need to apply the tiler on each layer. if tiled: return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) # 1. pre-process hidden_states = self.conv_in(sample) time_emb = None text_emb = None res_stack = None # 2. blocks for i, block in enumerate(self.blocks): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) # 3. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) hidden_states = self.quant_conv(hidden_states) hidden_states = hidden_states[:, :4] hidden_states *= self.scaling_factor hidden_states = hidden_states.to(original_dtype) return hidden_states def encode_video(self, sample, batch_size=8): B = sample.shape[0] hidden_states = [] for i in range(0, sample.shape[2], batch_size): j = min(i + batch_size, sample.shape[2]) sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W") hidden_states_batch = self(sample_batch) hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B) hidden_states.append(hidden_states_batch) hidden_states = torch.concat(hidden_states, dim=2) return hidden_states @staticmethod def state_dict_converter(): return SDVAEEncoderStateDictConverter() class SDVAEEncoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): # architecture block_types = [ 'ResnetBlock', 'ResnetBlock', 'DownSampler', 'ResnetBlock', 'ResnetBlock', 'DownSampler', 'ResnetBlock', 'ResnetBlock', 'DownSampler', 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock' ] # Rename each parameter local_rename_dict = { "quant_conv": "quant_conv", "encoder.conv_in": "conv_in", "encoder.mid_block.attentions.0.group_norm": "blocks.12.norm", "encoder.mid_block.attentions.0.to_q": "blocks.12.transformer_blocks.0.to_q", "encoder.mid_block.attentions.0.to_k": "blocks.12.transformer_blocks.0.to_k", "encoder.mid_block.attentions.0.to_v": "blocks.12.transformer_blocks.0.to_v", "encoder.mid_block.attentions.0.to_out.0": "blocks.12.transformer_blocks.0.to_out", "encoder.mid_block.resnets.0.norm1": "blocks.11.norm1", "encoder.mid_block.resnets.0.conv1": "blocks.11.conv1", "encoder.mid_block.resnets.0.norm2": "blocks.11.norm2", "encoder.mid_block.resnets.0.conv2": "blocks.11.conv2", "encoder.mid_block.resnets.1.norm1": "blocks.13.norm1", "encoder.mid_block.resnets.1.conv1": "blocks.13.conv1", "encoder.mid_block.resnets.1.norm2": "blocks.13.norm2", "encoder.mid_block.resnets.1.conv2": "blocks.13.conv2", "encoder.conv_norm_out": "conv_norm_out", "encoder.conv_out": "conv_out", } name_list = sorted([name for name in state_dict]) rename_dict = {} block_id = {"ResnetBlock": -1, "DownSampler": -1, "UpSampler": -1} last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""} for name in name_list: names = name.split(".") name_prefix = ".".join(names[:-1]) if name_prefix in local_rename_dict: rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1] elif name.startswith("encoder.down_blocks"): block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]] block_type_with_id = ".".join(names[:5]) if block_type_with_id != last_block_type_with_id[block_type]: block_id[block_type] += 1 last_block_type_with_id[block_type] = block_type_with_id while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: block_id[block_type] += 1 block_type_with_id = ".".join(names[:5]) names = ["blocks", str(block_id[block_type])] + names[5:] rename_dict[name] = ".".join(names) # Convert state_dict state_dict_ = {} for name, param in state_dict.items(): if name in rename_dict: state_dict_[rename_dict[name]] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "first_stage_model.encoder.conv_in.bias": "conv_in.bias", "first_stage_model.encoder.conv_in.weight": "conv_in.weight", "first_stage_model.encoder.conv_out.bias": "conv_out.bias", "first_stage_model.encoder.conv_out.weight": "conv_out.weight", "first_stage_model.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", "first_stage_model.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", "first_stage_model.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", "first_stage_model.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", "first_stage_model.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", "first_stage_model.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight", "first_stage_model.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias", "first_stage_model.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight", "first_stage_model.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias", "first_stage_model.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight", "first_stage_model.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias", "first_stage_model.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight", "first_stage_model.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias", "first_stage_model.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight", "first_stage_model.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias", "first_stage_model.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight", "first_stage_model.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias", "first_stage_model.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight", "first_stage_model.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias", "first_stage_model.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight", "first_stage_model.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias", "first_stage_model.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight", "first_stage_model.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias", "first_stage_model.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight", "first_stage_model.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias", "first_stage_model.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight", "first_stage_model.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias", "first_stage_model.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight", "first_stage_model.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias", "first_stage_model.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight", "first_stage_model.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias", "first_stage_model.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight", "first_stage_model.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias", "first_stage_model.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight", "first_stage_model.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias", "first_stage_model.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight", "first_stage_model.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias", "first_stage_model.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight", "first_stage_model.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias", "first_stage_model.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight", "first_stage_model.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias", "first_stage_model.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight", "first_stage_model.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias", "first_stage_model.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight", "first_stage_model.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias", "first_stage_model.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight", "first_stage_model.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias", "first_stage_model.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight", "first_stage_model.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias", "first_stage_model.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight", "first_stage_model.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias", "first_stage_model.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight", "first_stage_model.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias", "first_stage_model.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight", "first_stage_model.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias", "first_stage_model.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight", "first_stage_model.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias", "first_stage_model.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight", "first_stage_model.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias", "first_stage_model.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight", "first_stage_model.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias", "first_stage_model.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight", "first_stage_model.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias", "first_stage_model.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight", "first_stage_model.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias", "first_stage_model.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight", "first_stage_model.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias", "first_stage_model.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight", "first_stage_model.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias", "first_stage_model.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight", "first_stage_model.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias", "first_stage_model.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight", "first_stage_model.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias", "first_stage_model.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight", "first_stage_model.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias", "first_stage_model.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight", "first_stage_model.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias", "first_stage_model.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight", "first_stage_model.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias", "first_stage_model.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight", "first_stage_model.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias", "first_stage_model.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight", "first_stage_model.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias", "first_stage_model.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight", "first_stage_model.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", "first_stage_model.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", "first_stage_model.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", "first_stage_model.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", "first_stage_model.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", "first_stage_model.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", "first_stage_model.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", "first_stage_model.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", "first_stage_model.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", "first_stage_model.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", "first_stage_model.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", "first_stage_model.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", "first_stage_model.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", "first_stage_model.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", "first_stage_model.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", "first_stage_model.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", "first_stage_model.encoder.norm_out.bias": "conv_norm_out.bias", "first_stage_model.encoder.norm_out.weight": "conv_norm_out.weight", "first_stage_model.quant_conv.bias": "quant_conv.bias", "first_stage_model.quant_conv.weight": "quant_conv.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if "transformer_blocks" in rename_dict[name]: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ ================================================ FILE: diffsynth/models/sdxl_controlnet.py ================================================ import torch from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler from .sdxl_unet import SDXLUNet from .tiler import TileWorker from .sd_controlnet import ControlNetConditioningLayer from collections import OrderedDict class QuickGELU(torch.nn.Module): def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class ResidualAttentionBlock(torch.nn.Module): def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): super().__init__() self.attn = torch.nn.MultiheadAttention(d_model, n_head) self.ln_1 = torch.nn.LayerNorm(d_model) self.mlp = torch.nn.Sequential(OrderedDict([ ("c_fc", torch.nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), ("c_proj", torch.nn.Linear(d_model * 4, d_model)) ])) self.ln_2 = torch.nn.LayerNorm(d_model) self.attn_mask = attn_mask def attention(self, x: torch.Tensor): self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] def forward(self, x: torch.Tensor): x = x + self.attention(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class SDXLControlNetUnion(torch.nn.Module): def __init__(self, global_pool=False): super().__init__() self.time_proj = Timesteps(320) self.time_embedding = torch.nn.Sequential( torch.nn.Linear(320, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.add_time_proj = Timesteps(256) self.add_time_embedding = torch.nn.Sequential( torch.nn.Linear(2816, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.control_type_proj = Timesteps(256) self.control_type_embedding = torch.nn.Sequential( torch.nn.Linear(256 * 8, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320)) self.controlnet_transformer = ResidualAttentionBlock(320, 8) self.task_embedding = torch.nn.Parameter(torch.randn(8, 320)) self.spatial_ch_projs = torch.nn.Linear(320, 320) self.blocks = torch.nn.ModuleList([ # DownBlock2D ResnetBlock(320, 320, 1280), PushBlock(), ResnetBlock(320, 320, 1280), PushBlock(), DownSampler(320), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(320, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PushBlock(), ResnetBlock(640, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PushBlock(), DownSampler(640), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(640, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PushBlock(), ResnetBlock(1280, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PushBlock(), # UNetMidBlock2DCrossAttn ResnetBlock(1280, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), ResnetBlock(1280, 1280, 1280), PushBlock() ]) self.controlnet_blocks = torch.nn.ModuleList([ torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), ]) self.global_pool = global_pool # 0 -- openpose # 1 -- depth # 2 -- hed/pidi/scribble/ted # 3 -- canny/lineart/anime_lineart/mlsd # 4 -- normal # 5 -- segment # 6 -- tile # 7 -- repaint self.task_id = { "openpose": 0, "depth": 1, "softedge": 2, "canny": 3, "lineart": 3, "lineart_anime": 3, "tile": 6, "inpaint": 7 } def fuse_condition_to_input(self, hidden_states, task_id, conditioning): controlnet_cond = self.controlnet_conv_in(conditioning) feat_seq = torch.mean(controlnet_cond, dim=(2, 3)) feat_seq = feat_seq + self.task_embedding[task_id] x = torch.stack([feat_seq, torch.mean(hidden_states, dim=(2, 3))], dim=1) x = self.controlnet_transformer(x) alpha = self.spatial_ch_projs(x[:,0]).unsqueeze(-1).unsqueeze(-1) controlnet_cond_fuser = controlnet_cond + alpha hidden_states = hidden_states + controlnet_cond_fuser return hidden_states def forward( self, sample, timestep, encoder_hidden_states, conditioning, processor_id, add_time_id, add_text_embeds, tiled=False, tile_size=64, tile_stride=32, unet:SDXLUNet=None, **kwargs ): task_id = self.task_id[processor_id] # 1. time t_emb = self.time_proj(timestep).to(sample.dtype) t_emb = self.time_embedding(t_emb) time_embeds = self.add_time_proj(add_time_id) time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) add_embeds = add_embeds.to(sample.dtype) if unet is not None and unet.is_kolors: add_embeds = unet.add_time_embedding(add_embeds) else: add_embeds = self.add_time_embedding(add_embeds) control_type = torch.zeros((sample.shape[0], 8), dtype=sample.dtype, device=sample.device) control_type[:, task_id] = 1 control_embeds = self.control_type_proj(control_type.flatten()) control_embeds = control_embeds.reshape((sample.shape[0], -1)) control_embeds = control_embeds.to(sample.dtype) control_embeds = self.control_type_embedding(control_embeds) time_emb = t_emb + add_embeds + control_embeds # 2. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = self.conv_in(sample) hidden_states = self.fuse_condition_to_input(hidden_states, task_id, conditioning) text_emb = encoder_hidden_states if unet is not None and unet.is_kolors: text_emb = unet.text_intermediate_proj(text_emb) res_stack = [hidden_states] # 3. blocks for i, block in enumerate(self.blocks): if tiled and not isinstance(block, PushBlock): _, _, inter_height, _ = hidden_states.shape resize_scale = inter_height / height hidden_states = TileWorker().tiled_forward( lambda x: block(x, time_emb, text_emb, res_stack)[0], hidden_states, int(tile_size * resize_scale), int(tile_stride * resize_scale), tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) else: hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack) # 4. ControlNet blocks controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)] # pool if self.global_pool: controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack] return controlnet_res_stack @staticmethod def state_dict_converter(): return SDXLControlNetUnionStateDictConverter() class SDXLControlNetUnionStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): # architecture block_types = [ "ResnetBlock", "PushBlock", "ResnetBlock", "PushBlock", "DownSampler", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", "DownSampler", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "ResnetBlock", "PushBlock" ] # controlnet_rename_dict controlnet_rename_dict = { "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight", "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias", "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight", "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias", "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight", "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias", "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight", "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias", "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight", "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias", "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight", "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias", "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight", "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias", "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight", "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias", "control_add_embedding.linear_1.weight": "control_type_embedding.0.weight", "control_add_embedding.linear_1.bias": "control_type_embedding.0.bias", "control_add_embedding.linear_2.weight": "control_type_embedding.2.weight", "control_add_embedding.linear_2.bias": "control_type_embedding.2.bias", } # Rename each parameter name_list = sorted([name for name in state_dict]) rename_dict = {} block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} for name in name_list: names = name.split(".") if names[0] in ["conv_in", "conv_norm_out", "conv_out", "task_embedding", "spatial_ch_projs"]: pass elif name in controlnet_rename_dict: names = controlnet_rename_dict[name].split(".") elif names[0] == "controlnet_down_blocks": names[0] = "controlnet_blocks" elif names[0] == "controlnet_mid_block": names = ["controlnet_blocks", "9", names[-1]] elif names[0] in ["time_embedding", "add_embedding"]: if names[0] == "add_embedding": names[0] = "add_time_embedding" names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] elif names[0] == "control_add_embedding": names[0] = "control_type_embedding" elif names[0] == "transformer_layes": names[0] = "controlnet_transformer" names.pop(1) elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: if names[0] == "mid_block": names.insert(1, "0") block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] block_type_with_id = ".".join(names[:4]) if block_type_with_id != last_block_type_with_id[block_type]: block_id[block_type] += 1 last_block_type_with_id[block_type] = block_type_with_id while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: block_id[block_type] += 1 block_type_with_id = ".".join(names[:4]) names = ["blocks", str(block_id[block_type])] + names[4:] if "ff" in names: ff_index = names.index("ff") component = ".".join(names[ff_index:ff_index+3]) component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] names = names[:ff_index] + [component] + names[ff_index+3:] if "to_out" in names: names.pop(names.index("to_out") + 1) else: print(name, state_dict[name].shape) # raise ValueError(f"Unknown parameters: {name}") rename_dict[name] = ".".join(names) # Convert state_dict state_dict_ = {} for name, param in state_dict.items(): if name not in rename_dict: continue if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/sdxl_ipadapter.py ================================================ from .svd_image_encoder import SVDImageEncoder from transformers import CLIPImageProcessor import torch class IpAdapterXLCLIPImageEmbedder(SVDImageEncoder): def __init__(self): super().__init__(embed_dim=1664, encoder_intermediate_size=8192, projection_dim=1280, num_encoder_layers=48, num_heads=16, head_dim=104) self.image_processor = CLIPImageProcessor() def forward(self, image): pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype) return super().forward(pixel_values) class IpAdapterImageProjModel(torch.nn.Module): def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280, clip_extra_context_tokens=4): super().__init__() self.cross_attention_dim = cross_attention_dim self.clip_extra_context_tokens = clip_extra_context_tokens self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) self.norm = torch.nn.LayerNorm(cross_attention_dim) def forward(self, image_embeds): clip_extra_context_tokens = self.proj(image_embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) clip_extra_context_tokens = self.norm(clip_extra_context_tokens) return clip_extra_context_tokens class IpAdapterModule(torch.nn.Module): def __init__(self, input_dim, output_dim): super().__init__() self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False) self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False) def forward(self, hidden_states): ip_k = self.to_k_ip(hidden_states) ip_v = self.to_v_ip(hidden_states) return ip_k, ip_v class SDXLIpAdapter(torch.nn.Module): def __init__(self): super().__init__() shape_list = [(2048, 640)] * 4 + [(2048, 1280)] * 50 + [(2048, 640)] * 6 + [(2048, 1280)] * 10 self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list]) self.image_proj = IpAdapterImageProjModel() self.set_full_adapter() def set_full_adapter(self): map_list = sum([ [(7, i) for i in range(2)], [(10, i) for i in range(2)], [(15, i) for i in range(10)], [(18, i) for i in range(10)], [(25, i) for i in range(10)], [(28, i) for i in range(10)], [(31, i) for i in range(10)], [(35, i) for i in range(2)], [(38, i) for i in range(2)], [(41, i) for i in range(2)], [(21, i) for i in range(10)], ], []) self.call_block_id = {i: j for j, i in enumerate(map_list)} def set_less_adapter(self): map_list = sum([ [(7, i) for i in range(2)], [(10, i) for i in range(2)], [(15, i) for i in range(10)], [(18, i) for i in range(10)], [(25, i) for i in range(10)], [(28, i) for i in range(10)], [(31, i) for i in range(10)], [(35, i) for i in range(2)], [(38, i) for i in range(2)], [(41, i) for i in range(2)], [(21, i) for i in range(10)], ], []) self.call_block_id = {i: j for j, i in enumerate(map_list) if j>=34 and j<44} def forward(self, hidden_states, scale=1.0): hidden_states = self.image_proj(hidden_states) hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1]) ip_kv_dict = {} for (block_id, transformer_id) in self.call_block_id: ipadapter_id = self.call_block_id[(block_id, transformer_id)] ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states) if block_id not in ip_kv_dict: ip_kv_dict[block_id] = {} ip_kv_dict[block_id][transformer_id] = { "ip_k": ip_k, "ip_v": ip_v, "scale": scale } return ip_kv_dict @staticmethod def state_dict_converter(): return SDXLIpAdapterStateDictConverter() class SDXLIpAdapterStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): state_dict_ = {} for name in state_dict["ip_adapter"]: names = name.split(".") layer_id = str(int(names[0]) // 2) name_ = ".".join(["ipadapter_modules"] + [layer_id] + names[1:]) state_dict_[name_] = state_dict["ip_adapter"][name] for name in state_dict["image_proj"]: name_ = "image_proj." + name state_dict_[name_] = state_dict["image_proj"][name] return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/sdxl_motion.py ================================================ from .sd_motion import TemporalBlock import torch class SDXLMotionModel(torch.nn.Module): def __init__(self): super().__init__() self.motion_modules = torch.nn.ModuleList([ TemporalBlock(8, 320//8, 320, eps=1e-6), TemporalBlock(8, 320//8, 320, eps=1e-6), TemporalBlock(8, 640//8, 640, eps=1e-6), TemporalBlock(8, 640//8, 640, eps=1e-6), TemporalBlock(8, 1280//8, 1280, eps=1e-6), TemporalBlock(8, 1280//8, 1280, eps=1e-6), TemporalBlock(8, 1280//8, 1280, eps=1e-6), TemporalBlock(8, 1280//8, 1280, eps=1e-6), TemporalBlock(8, 1280//8, 1280, eps=1e-6), TemporalBlock(8, 640//8, 640, eps=1e-6), TemporalBlock(8, 640//8, 640, eps=1e-6), TemporalBlock(8, 640//8, 640, eps=1e-6), TemporalBlock(8, 320//8, 320, eps=1e-6), TemporalBlock(8, 320//8, 320, eps=1e-6), TemporalBlock(8, 320//8, 320, eps=1e-6), ]) self.call_block_id = { 0: 0, 2: 1, 7: 2, 10: 3, 15: 4, 18: 5, 25: 6, 28: 7, 31: 8, 35: 9, 38: 10, 41: 11, 44: 12, 46: 13, 48: 14, } def forward(self): pass @staticmethod def state_dict_converter(): return SDMotionModelStateDictConverter() class SDMotionModelStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "norm": "norm", "proj_in": "proj_in", "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q", "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k", "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v", "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out", "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1", "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q", "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k", "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v", "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out", "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2", "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1", "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2", "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj", "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff", "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3", "proj_out": "proj_out", } name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")]) name_list += sorted([i for i in state_dict if i.startswith("mid_block.")]) name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")]) state_dict_ = {} last_prefix, module_id = "", -1 for name in name_list: names = name.split(".") prefix_index = names.index("temporal_transformer") + 1 prefix = ".".join(names[:prefix_index]) if prefix != last_prefix: last_prefix = prefix module_id += 1 middle_name = ".".join(names[prefix_index:-1]) suffix = names[-1] if "pos_encoder" in names: rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]]) else: rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix]) state_dict_[rename] = state_dict[name] return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict) ================================================ FILE: diffsynth/models/sdxl_text_encoder.py ================================================ import torch from .sd_text_encoder import CLIPEncoderLayer class SDXLTextEncoder(torch.nn.Module): def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=11, encoder_intermediate_size=3072): super().__init__() # token_embedding self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) # position_embeds (This is a fixed tensor) self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) # encoders self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)]) # attn_mask self.attn_mask = self.attention_mask(max_position_embeddings) # The text encoder is different to that in Stable Diffusion 1.x. # It does not include final_layer_norm. def attention_mask(self, length): mask = torch.empty(length, length) mask.fill_(float("-inf")) mask.triu_(1) return mask def forward(self, input_ids, clip_skip=1): embeds = self.token_embedding(input_ids) + self.position_embeds attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) for encoder_id, encoder in enumerate(self.encoders): embeds = encoder(embeds, attn_mask=attn_mask) if encoder_id + clip_skip == len(self.encoders): break return embeds @staticmethod def state_dict_converter(): return SDXLTextEncoderStateDictConverter() class SDXLTextEncoder2(torch.nn.Module): def __init__(self, embed_dim=1280, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=32, encoder_intermediate_size=5120): super().__init__() # token_embedding self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) # position_embeds (This is a fixed tensor) self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) # encoders self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=20, head_dim=64, use_quick_gelu=False) for _ in range(num_encoder_layers)]) # attn_mask self.attn_mask = self.attention_mask(max_position_embeddings) # final_layer_norm self.final_layer_norm = torch.nn.LayerNorm(embed_dim) # text_projection self.text_projection = torch.nn.Linear(embed_dim, embed_dim, bias=False) def attention_mask(self, length): mask = torch.empty(length, length) mask.fill_(float("-inf")) mask.triu_(1) return mask def forward(self, input_ids, clip_skip=2): embeds = self.token_embedding(input_ids) + self.position_embeds attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) for encoder_id, encoder in enumerate(self.encoders): embeds = encoder(embeds, attn_mask=attn_mask) if encoder_id + clip_skip == len(self.encoders): hidden_states = embeds embeds = self.final_layer_norm(embeds) pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)] pooled_embeds = self.text_projection(pooled_embeds) return pooled_embeds, hidden_states @staticmethod def state_dict_converter(): return SDXLTextEncoder2StateDictConverter() class SDXLTextEncoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "text_model.embeddings.token_embedding.weight": "token_embedding.weight", "text_model.embeddings.position_embedding.weight": "position_embeds", "text_model.final_layer_norm.weight": "final_layer_norm.weight", "text_model.final_layer_norm.bias": "final_layer_norm.bias" } attn_rename_dict = { "self_attn.q_proj": "attn.to_q", "self_attn.k_proj": "attn.to_k", "self_attn.v_proj": "attn.to_v", "self_attn.out_proj": "attn.to_out", "layer_norm1": "layer_norm1", "layer_norm2": "layer_norm2", "mlp.fc1": "fc1", "mlp.fc2": "fc2", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param elif name.startswith("text_model.encoder.layers."): param = state_dict[name] names = name.split(".") layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) state_dict_[name_] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight": "position_embeds", "conditioner.embedders.0.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": "encoders.1.attn.to_q.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.v_proj.bias": "encoders.1.attn.to_v.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.v_proj.weight": "encoders.1.attn.to_v.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.layer_norm1.bias": "encoders.10.layer_norm1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.layer_norm1.weight": "encoders.10.layer_norm1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.layer_norm2.bias": "encoders.10.layer_norm2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.layer_norm2.weight": "encoders.10.layer_norm2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.mlp.fc1.bias": "encoders.10.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.mlp.fc1.weight": "encoders.10.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.mlp.fc2.bias": "encoders.10.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.mlp.fc2.weight": "encoders.10.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.k_proj.bias": "encoders.10.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.k_proj.weight": "encoders.10.attn.to_k.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.out_proj.bias": "encoders.10.attn.to_out.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.out_proj.weight": "encoders.10.attn.to_out.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.q_proj.bias": "encoders.10.attn.to_q.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.q_proj.weight": "encoders.10.attn.to_q.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.v_proj.bias": "encoders.10.attn.to_v.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.v_proj.weight": "encoders.10.attn.to_v.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.layer_norm1.bias": "encoders.2.layer_norm1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.layer_norm1.weight": "encoders.2.layer_norm1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.layer_norm2.bias": "encoders.2.layer_norm2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.layer_norm2.weight": "encoders.2.layer_norm2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.mlp.fc1.bias": "encoders.2.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.mlp.fc1.weight": "encoders.2.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.mlp.fc2.bias": "encoders.2.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.mlp.fc2.weight": "encoders.2.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.k_proj.bias": "encoders.2.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.k_proj.weight": "encoders.2.attn.to_k.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.out_proj.bias": "encoders.2.attn.to_out.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.out_proj.weight": "encoders.2.attn.to_out.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.q_proj.bias": "encoders.2.attn.to_q.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.q_proj.weight": "encoders.2.attn.to_q.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.v_proj.bias": "encoders.2.attn.to_v.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.v_proj.weight": "encoders.2.attn.to_v.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.layer_norm1.bias": "encoders.3.layer_norm1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.layer_norm1.weight": "encoders.3.layer_norm1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.layer_norm2.bias": "encoders.3.layer_norm2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.layer_norm2.weight": "encoders.3.layer_norm2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.mlp.fc1.bias": "encoders.3.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.mlp.fc1.weight": "encoders.3.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.mlp.fc2.bias": "encoders.3.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.mlp.fc2.weight": "encoders.3.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.k_proj.bias": "encoders.3.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.k_proj.weight": 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"conditioner.embedders.0.transformer.text_model.encoder.layers.4.layer_norm2.bias": "encoders.4.layer_norm2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.4.layer_norm2.weight": "encoders.4.layer_norm2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.4.mlp.fc1.bias": "encoders.4.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.4.mlp.fc1.weight": "encoders.4.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.4.mlp.fc2.bias": "encoders.4.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.4.mlp.fc2.weight": "encoders.4.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.k_proj.bias": "encoders.4.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.k_proj.weight": "encoders.4.attn.to_k.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.out_proj.bias": 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"conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc1.bias": "encoders.6.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc1.weight": "encoders.6.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc2.bias": "encoders.6.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc2.weight": "encoders.6.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.k_proj.bias": "encoders.6.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.k_proj.weight": "encoders.6.attn.to_k.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.out_proj.bias": "encoders.6.attn.to_out.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.out_proj.weight": "encoders.6.attn.to_out.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.q_proj.bias": 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"conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc1.weight": "encoders.7.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc2.bias": "encoders.7.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc2.weight": "encoders.7.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.k_proj.bias": "encoders.7.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.k_proj.weight": "encoders.7.attn.to_k.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.out_proj.bias": "encoders.7.attn.to_out.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.out_proj.weight": "encoders.7.attn.to_out.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.q_proj.bias": "encoders.7.attn.to_q.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc1.weight": "encoders.8.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc2.bias": "encoders.8.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc2.weight": "encoders.8.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.k_proj.weight": "encoders.8.attn.to_k.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.out_proj.bias": "encoders.8.attn.to_out.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.out_proj.weight": "encoders.8.attn.to_out.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.q_proj.bias": "encoders.8.attn.to_q.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.q_proj.weight": "encoders.8.attn.to_q.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.v_proj.bias": "encoders.8.attn.to_v.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.v_proj.weight": "encoders.8.attn.to_v.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param return state_dict_ class SDXLTextEncoder2StateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "text_model.embeddings.token_embedding.weight": "token_embedding.weight", "text_model.embeddings.position_embedding.weight": "position_embeds", "text_model.final_layer_norm.weight": "final_layer_norm.weight", "text_model.final_layer_norm.bias": "final_layer_norm.bias", "text_projection.weight": "text_projection.weight" } attn_rename_dict = { "self_attn.q_proj": "attn.to_q", "self_attn.k_proj": "attn.to_k", "self_attn.v_proj": "attn.to_v", "self_attn.out_proj": "attn.to_out", "layer_norm1": "layer_norm1", "layer_norm2": "layer_norm2", "mlp.fc1": "fc1", "mlp.fc2": "fc2", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param elif name.startswith("text_model.encoder.layers."): param = state_dict[name] names = name.split(".") layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) state_dict_[name_] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "conditioner.embedders.1.model.ln_final.bias": "final_layer_norm.bias", "conditioner.embedders.1.model.ln_final.weight": "final_layer_norm.weight", "conditioner.embedders.1.model.positional_embedding": "position_embeds", "conditioner.embedders.1.model.token_embedding.weight": "token_embedding.weight", "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'], "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'], "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias", "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight", "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias", "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight", "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias", "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight", "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias", "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight", "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias", "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight", "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'], "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'], "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias", "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight", "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias", "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight", "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias", "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight", "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias", "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight", "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias", "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight", "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'], "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_weight": ['encoders.10.attn.to_q.weight', 'encoders.10.attn.to_k.weight', 'encoders.10.attn.to_v.weight'], "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.bias": "encoders.10.attn.to_out.bias", "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.weight": "encoders.10.attn.to_out.weight", "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.bias": "encoders.10.layer_norm1.bias", "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.weight": "encoders.10.layer_norm1.weight", "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.bias": "encoders.10.layer_norm2.bias", "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.weight": "encoders.10.layer_norm2.weight", "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.bias": "encoders.10.fc1.bias", "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.weight": "encoders.10.fc1.weight", "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.bias": "encoders.10.fc2.bias", "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.weight": "encoders.10.fc2.weight", "conditioner.embedders.1.model.transformer.resblocks.11.attn.in_proj_bias": ['encoders.11.attn.to_q.bias', 'encoders.11.attn.to_k.bias', 'encoders.11.attn.to_v.bias'], "conditioner.embedders.1.model.transformer.resblocks.11.attn.in_proj_weight": ['encoders.11.attn.to_q.weight', 'encoders.11.attn.to_k.weight', 'encoders.11.attn.to_v.weight'], "conditioner.embedders.1.model.transformer.resblocks.11.attn.out_proj.bias": "encoders.11.attn.to_out.bias", "conditioner.embedders.1.model.transformer.resblocks.11.attn.out_proj.weight": "encoders.11.attn.to_out.weight", "conditioner.embedders.1.model.transformer.resblocks.11.ln_1.bias": "encoders.11.layer_norm1.bias", "conditioner.embedders.1.model.transformer.resblocks.11.ln_1.weight": "encoders.11.layer_norm1.weight", "conditioner.embedders.1.model.transformer.resblocks.11.ln_2.bias": "encoders.11.layer_norm2.bias", "conditioner.embedders.1.model.transformer.resblocks.11.ln_2.weight": "encoders.11.layer_norm2.weight", "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_fc.bias": "encoders.11.fc1.bias", "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_fc.weight": "encoders.11.fc1.weight", "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_proj.bias": "encoders.11.fc2.bias", "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_proj.weight": "encoders.11.fc2.weight", "conditioner.embedders.1.model.transformer.resblocks.12.attn.in_proj_bias": ['encoders.12.attn.to_q.bias', 'encoders.12.attn.to_k.bias', 'encoders.12.attn.to_v.bias'], "conditioner.embedders.1.model.transformer.resblocks.12.attn.in_proj_weight": ['encoders.12.attn.to_q.weight', 'encoders.12.attn.to_k.weight', 'encoders.12.attn.to_v.weight'], "conditioner.embedders.1.model.transformer.resblocks.12.attn.out_proj.bias": "encoders.12.attn.to_out.bias", "conditioner.embedders.1.model.transformer.resblocks.12.attn.out_proj.weight": "encoders.12.attn.to_out.weight", "conditioner.embedders.1.model.transformer.resblocks.12.ln_1.bias": "encoders.12.layer_norm1.bias", "conditioner.embedders.1.model.transformer.resblocks.12.ln_1.weight": "encoders.12.layer_norm1.weight", "conditioner.embedders.1.model.transformer.resblocks.12.ln_2.bias": "encoders.12.layer_norm2.bias", "conditioner.embedders.1.model.transformer.resblocks.12.ln_2.weight": "encoders.12.layer_norm2.weight", "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_fc.bias": "encoders.12.fc1.bias", 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"conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight", "conditioner.embedders.1.model.text_projection": "text_projection.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "conditioner.embedders.1.model.positional_embedding": param = param.reshape((1, param.shape[0], param.shape[1])) elif name == "conditioner.embedders.1.model.text_projection": param = param.T if isinstance(rename_dict[name], str): state_dict_[rename_dict[name]] = param else: length = param.shape[0] // 3 for i, rename in enumerate(rename_dict[name]): state_dict_[rename] = param[i*length: i*length+length] return state_dict_ ================================================ FILE: diffsynth/models/sdxl_unet.py ================================================ import torch from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler class SDXLUNet(torch.nn.Module): def __init__(self, is_kolors=False): super().__init__() self.time_proj = Timesteps(320) self.time_embedding = torch.nn.Sequential( torch.nn.Linear(320, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.add_time_proj = Timesteps(256) self.add_time_embedding = torch.nn.Sequential( torch.nn.Linear(5632 if is_kolors else 2816, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) self.text_intermediate_proj = torch.nn.Linear(4096, 2048) if is_kolors else None self.blocks = torch.nn.ModuleList([ # DownBlock2D ResnetBlock(320, 320, 1280), PushBlock(), ResnetBlock(320, 320, 1280), PushBlock(), DownSampler(320), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(320, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PushBlock(), ResnetBlock(640, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PushBlock(), DownSampler(640), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(640, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PushBlock(), ResnetBlock(1280, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PushBlock(), # UNetMidBlock2DCrossAttn ResnetBlock(1280, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), ResnetBlock(1280, 1280, 1280), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(2560, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PopBlock(), ResnetBlock(2560, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PopBlock(), ResnetBlock(1920, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), UpSampler(1280), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(1920, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PopBlock(), ResnetBlock(1280, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PopBlock(), ResnetBlock(960, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), UpSampler(640), # UpBlock2D PopBlock(), ResnetBlock(960, 320, 1280), PopBlock(), ResnetBlock(640, 320, 1280), PopBlock(), ResnetBlock(640, 320, 1280) ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) self.is_kolors = is_kolors def forward( self, sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds, tiled=False, tile_size=64, tile_stride=8, use_gradient_checkpointing=False, **kwargs ): # 1. time t_emb = self.time_proj(timestep).to(sample.dtype) t_emb = self.time_embedding(t_emb) time_embeds = self.add_time_proj(add_time_id) time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) add_embeds = add_embeds.to(sample.dtype) add_embeds = self.add_time_embedding(add_embeds) time_emb = t_emb + add_embeds # 2. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = self.conv_in(sample) text_emb = encoder_hidden_states if self.text_intermediate_proj is None else self.text_intermediate_proj(encoder_hidden_states) res_stack = [hidden_states] # 3. blocks def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward for i, block in enumerate(self.blocks): if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock)): hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, time_emb, text_emb, res_stack, use_reentrant=False, ) else: hidden_states, time_emb, text_emb, res_stack = block( hidden_states, time_emb, text_emb, res_stack, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride ) # 4. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states @staticmethod def state_dict_converter(): return SDXLUNetStateDictConverter() class SDXLUNetStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): # architecture block_types = [ 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock' ] # Rename each parameter name_list = sorted([name for name in state_dict]) rename_dict = {} block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} for name in name_list: names = name.split(".") if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: pass elif names[0] in ["encoder_hid_proj"]: names[0] = "text_intermediate_proj" elif names[0] in ["time_embedding", "add_embedding"]: if names[0] == "add_embedding": names[0] = "add_time_embedding" names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: if names[0] == "mid_block": names.insert(1, "0") block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] block_type_with_id = ".".join(names[:4]) if block_type_with_id != last_block_type_with_id[block_type]: block_id[block_type] += 1 last_block_type_with_id[block_type] = block_type_with_id while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: block_id[block_type] += 1 block_type_with_id = ".".join(names[:4]) names = ["blocks", str(block_id[block_type])] + names[4:] if "ff" in names: ff_index = names.index("ff") component = ".".join(names[ff_index:ff_index+3]) component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] names = names[:ff_index] + [component] + names[ff_index+3:] if "to_out" in names: names.pop(names.index("to_out") + 1) else: raise ValueError(f"Unknown parameters: {name}") rename_dict[name] = ".".join(names) # Convert state_dict state_dict_ = {} for name, param in state_dict.items(): if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param if "text_intermediate_proj.weight" in state_dict_: return state_dict_, {"is_kolors": True} else: return state_dict_ def from_civitai(self, state_dict): rename_dict = { "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", 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"model.diffusion_model.output_blocks.8.0.out_layers.0.weight": "blocks.48.norm2.weight", "model.diffusion_model.output_blocks.8.0.out_layers.3.bias": "blocks.48.conv2.bias", "model.diffusion_model.output_blocks.8.0.out_layers.3.weight": "blocks.48.conv2.weight", "model.diffusion_model.output_blocks.8.0.skip_connection.bias": "blocks.48.conv_shortcut.bias", "model.diffusion_model.output_blocks.8.0.skip_connection.weight": "blocks.48.conv_shortcut.weight", "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias", "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight", "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias", "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param if "text_intermediate_proj.weight" in state_dict_: return state_dict_, {"is_kolors": True} else: return state_dict_ ================================================ FILE: diffsynth/models/sdxl_vae_decoder.py ================================================ from .sd_vae_decoder import SDVAEDecoder, SDVAEDecoderStateDictConverter class SDXLVAEDecoder(SDVAEDecoder): def __init__(self, upcast_to_float32=True): super().__init__() self.scaling_factor = 0.13025 @staticmethod def state_dict_converter(): return SDXLVAEDecoderStateDictConverter() class SDXLVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter): def __init__(self): super().__init__() def from_diffusers(self, state_dict): state_dict = super().from_diffusers(state_dict) return state_dict, {"upcast_to_float32": True} def from_civitai(self, state_dict): state_dict = super().from_civitai(state_dict) return state_dict, {"upcast_to_float32": True} ================================================ FILE: diffsynth/models/sdxl_vae_encoder.py ================================================ from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder class SDXLVAEEncoder(SDVAEEncoder): def __init__(self, upcast_to_float32=True): super().__init__() self.scaling_factor = 0.13025 @staticmethod def state_dict_converter(): return SDXLVAEEncoderStateDictConverter() class SDXLVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter): def __init__(self): super().__init__() def from_diffusers(self, state_dict): state_dict = super().from_diffusers(state_dict) return state_dict, {"upcast_to_float32": True} def from_civitai(self, state_dict): state_dict = super().from_civitai(state_dict) return state_dict, {"upcast_to_float32": True} ================================================ FILE: diffsynth/models/stepvideo_dit.py ================================================ # Copyright 2025 StepFun Inc. All Rights Reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # ============================================================================== from typing import Dict, Optional, Tuple, Union, List import torch, math from torch import nn from einops import rearrange, repeat from tqdm import tqdm class RMSNorm(nn.Module): def __init__( self, dim: int, elementwise_affine=True, eps: float = 1e-6, device=None, dtype=None, ): """ Initialize the RMSNorm normalization layer. Args: dim (int): The dimension of the input tensor. eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. Attributes: eps (float): A small value added to the denominator for numerical stability. weight (nn.Parameter): Learnable scaling parameter. """ factory_kwargs = {"device": device, "dtype": dtype} super().__init__() self.eps = eps if elementwise_affine: self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs)) def _norm(self, x): """ Apply the RMSNorm normalization to the input tensor. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The normalized tensor. """ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): """ Forward pass through the RMSNorm layer. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The output tensor after applying RMSNorm. """ output = self._norm(x.float()).type_as(x) if hasattr(self, "weight"): output = output * self.weight return output ACTIVATION_FUNCTIONS = { "swish": nn.SiLU(), "silu": nn.SiLU(), "mish": nn.Mish(), "gelu": nn.GELU(), "relu": nn.ReLU(), } def get_activation(act_fn: str) -> nn.Module: """Helper function to get activation function from string. Args: act_fn (str): Name of activation function. Returns: nn.Module: Activation function. """ act_fn = act_fn.lower() if act_fn in ACTIVATION_FUNCTIONS: return ACTIVATION_FUNCTIONS[act_fn] else: raise ValueError(f"Unsupported activation function: {act_fn}") def get_timestep_embedding( timesteps: torch.Tensor, embedding_dim: int, flip_sin_to_cos: bool = False, downscale_freq_shift: float = 1, scale: float = 1, max_period: int = 10000, ): """ This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. """ assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" half_dim = embedding_dim // 2 exponent = -math.log(max_period) * torch.arange( start=0, end=half_dim, dtype=torch.float32, device=timesteps.device ) exponent = exponent / (half_dim - downscale_freq_shift) emb = torch.exp(exponent) emb = timesteps[:, None].float() * emb[None, :] # scale embeddings emb = scale * emb # concat sine and cosine embeddings emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) # flip sine and cosine embeddings if flip_sin_to_cos: emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) # zero pad if embedding_dim % 2 == 1: emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) return emb class Timesteps(nn.Module): def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float): super().__init__() self.num_channels = num_channels self.flip_sin_to_cos = flip_sin_to_cos self.downscale_freq_shift = downscale_freq_shift def forward(self, timesteps): t_emb = get_timestep_embedding( timesteps, self.num_channels, flip_sin_to_cos=self.flip_sin_to_cos, downscale_freq_shift=self.downscale_freq_shift, ) return t_emb class TimestepEmbedding(nn.Module): def __init__( self, in_channels: int, time_embed_dim: int, act_fn: str = "silu", out_dim: int = None, post_act_fn: Optional[str] = None, cond_proj_dim=None, sample_proj_bias=True ): super().__init__() linear_cls = nn.Linear self.linear_1 = linear_cls( in_channels, time_embed_dim, bias=sample_proj_bias, ) if cond_proj_dim is not None: self.cond_proj = linear_cls( cond_proj_dim, in_channels, bias=False, ) else: self.cond_proj = None self.act = get_activation(act_fn) if out_dim is not None: time_embed_dim_out = out_dim else: time_embed_dim_out = time_embed_dim self.linear_2 = linear_cls( time_embed_dim, time_embed_dim_out, bias=sample_proj_bias, ) if post_act_fn is None: self.post_act = None else: self.post_act = get_activation(post_act_fn) def forward(self, sample, condition=None): if condition is not None: sample = sample + self.cond_proj(condition) sample = self.linear_1(sample) if self.act is not None: sample = self.act(sample) sample = self.linear_2(sample) if self.post_act is not None: sample = self.post_act(sample) return sample class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module): def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False): super().__init__() self.outdim = size_emb_dim self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) self.use_additional_conditions = use_additional_conditions if self.use_additional_conditions: self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim) self.nframe_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) self.fps_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) def forward(self, timestep, resolution=None, nframe=None, fps=None): hidden_dtype = timestep.dtype timesteps_proj = self.time_proj(timestep) timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D) if self.use_additional_conditions: batch_size = timestep.shape[0] resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype) resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1) nframe_emb = self.additional_condition_proj(nframe.flatten()).to(hidden_dtype) nframe_emb = self.nframe_embedder(nframe_emb).reshape(batch_size, -1) conditioning = timesteps_emb + resolution_emb + nframe_emb if fps is not None: fps_emb = self.additional_condition_proj(fps.flatten()).to(hidden_dtype) fps_emb = self.fps_embedder(fps_emb).reshape(batch_size, -1) conditioning = conditioning + fps_emb else: conditioning = timesteps_emb return conditioning class AdaLayerNormSingle(nn.Module): r""" Norm layer adaptive layer norm single (adaLN-single). As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3). Parameters: embedding_dim (`int`): The size of each embedding vector. use_additional_conditions (`bool`): To use additional conditions for normalization or not. """ def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, time_step_rescale=1000): super().__init__() self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings( embedding_dim, size_emb_dim=embedding_dim // 2, use_additional_conditions=use_additional_conditions ) self.silu = nn.SiLU() self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) self.time_step_rescale = time_step_rescale ## timestep usually in [0, 1], we rescale it to [0,1000] for stability def forward( self, timestep: torch.Tensor, added_cond_kwargs: Dict[str, torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: embedded_timestep = self.emb(timestep*self.time_step_rescale, **added_cond_kwargs) out = self.linear(self.silu(embedded_timestep)) return out, embedded_timestep class PixArtAlphaTextProjection(nn.Module): """ Projects caption embeddings. Also handles dropout for classifier-free guidance. Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py """ def __init__(self, in_features, hidden_size): super().__init__() self.linear_1 = nn.Linear( in_features, hidden_size, bias=True, ) self.act_1 = nn.GELU(approximate="tanh") self.linear_2 = nn.Linear( hidden_size, hidden_size, bias=True, ) def forward(self, caption): hidden_states = self.linear_1(caption) hidden_states = self.act_1(hidden_states) hidden_states = self.linear_2(hidden_states) return hidden_states class Attention(nn.Module): def __init__(self): super().__init__() def attn_processor(self, attn_type): if attn_type == 'torch': return self.torch_attn_func elif attn_type == 'parallel': return self.parallel_attn_func else: raise Exception('Not supported attention type...') def torch_attn_func( self, q, k, v, attn_mask=None, causal=False, drop_rate=0.0, **kwargs ): if attn_mask is not None and attn_mask.dtype != torch.bool: attn_mask = attn_mask.to(q.dtype) if attn_mask is not None and attn_mask.ndim == 3: ## no head n_heads = q.shape[2] attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) q, k, v = map(lambda x: rearrange(x, 'b s h d -> b h s d'), (q, k, v)) if attn_mask is not None: attn_mask = attn_mask.to(q.device) x = torch.nn.functional.scaled_dot_product_attention( q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal ) x = rearrange(x, 'b h s d -> b s h d') return x class RoPE1D: def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0): self.base = freq self.F0 = F0 self.scaling_factor = scaling_factor self.cache = {} def get_cos_sin(self, D, seq_len, device, dtype): if (D, seq_len, device, dtype) not in self.cache: inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D)) t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype) freqs = torch.cat((freqs, freqs), dim=-1) cos = freqs.cos() # (Seq, Dim) sin = freqs.sin() self.cache[D, seq_len, device, dtype] = (cos, sin) return self.cache[D, seq_len, device, dtype] @staticmethod def rotate_half(x): x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:] return torch.cat((-x2, x1), dim=-1) def apply_rope1d(self, tokens, pos1d, cos, sin): assert pos1d.ndim == 2 cos = torch.nn.functional.embedding(pos1d, cos)[:, :, None, :] sin = torch.nn.functional.embedding(pos1d, sin)[:, :, None, :] return (tokens * cos) + (self.rotate_half(tokens) * sin) def __call__(self, tokens, positions): """ input: * tokens: batch_size x ntokens x nheads x dim * positions: batch_size x ntokens (t position of each token) output: * tokens after applying RoPE2D (batch_size x ntokens x nheads x dim) """ D = tokens.size(3) assert positions.ndim == 2 # Batch, Seq cos, sin = self.get_cos_sin(D, int(positions.max()) + 1, tokens.device, tokens.dtype) tokens = self.apply_rope1d(tokens, positions, cos, sin) return tokens class RoPE3D(RoPE1D): def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0): super(RoPE3D, self).__init__(freq, F0, scaling_factor) self.position_cache = {} def get_mesh_3d(self, rope_positions, bsz): f, h, w = rope_positions if f"{f}-{h}-{w}" not in self.position_cache: x = torch.arange(f, device='cpu') y = torch.arange(h, device='cpu') z = torch.arange(w, device='cpu') self.position_cache[f"{f}-{h}-{w}"] = torch.cartesian_prod(x, y, z).view(1, f*h*w, 3).expand(bsz, -1, 3) return self.position_cache[f"{f}-{h}-{w}"] def __call__(self, tokens, rope_positions, ch_split, parallel=False): """ input: * tokens: batch_size x ntokens x nheads x dim * rope_positions: list of (f, h, w) output: * tokens after applying RoPE2D (batch_size x ntokens x nheads x dim) """ assert sum(ch_split) == tokens.size(-1); mesh_grid = self.get_mesh_3d(rope_positions, bsz=tokens.shape[0]) out = [] for i, (D, x) in enumerate(zip(ch_split, torch.split(tokens, ch_split, dim=-1))): cos, sin = self.get_cos_sin(D, int(mesh_grid.max()) + 1, tokens.device, tokens.dtype) if parallel: pass else: mesh = mesh_grid[:, :, i].clone() x = self.apply_rope1d(x, mesh.to(tokens.device), cos, sin) out.append(x) tokens = torch.cat(out, dim=-1) return tokens class SelfAttention(Attention): def __init__(self, hidden_dim, head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type='torch'): super().__init__() self.head_dim = head_dim self.n_heads = hidden_dim // head_dim self.wqkv = nn.Linear(hidden_dim, hidden_dim*3, bias=bias) self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias) self.with_rope = with_rope self.with_qk_norm = with_qk_norm if self.with_qk_norm: self.q_norm = RMSNorm(head_dim, elementwise_affine=True) self.k_norm = RMSNorm(head_dim, elementwise_affine=True) if self.with_rope: self.rope_3d = RoPE3D(freq=1e4, F0=1.0, scaling_factor=1.0) self.rope_ch_split = [64, 32, 32] self.core_attention = self.attn_processor(attn_type=attn_type) self.parallel = attn_type=='parallel' def apply_rope3d(self, x, fhw_positions, rope_ch_split, parallel=True): x = self.rope_3d(x, fhw_positions, rope_ch_split, parallel) return x def forward( self, x, cu_seqlens=None, max_seqlen=None, rope_positions=None, attn_mask=None ): xqkv = self.wqkv(x) xqkv = xqkv.view(*x.shape[:-1], self.n_heads, 3*self.head_dim) xq, xk, xv = torch.split(xqkv, [self.head_dim]*3, dim=-1) ## seq_len, n, dim if self.with_qk_norm: xq = self.q_norm(xq) xk = self.k_norm(xk) if self.with_rope: xq = self.apply_rope3d(xq, rope_positions, self.rope_ch_split, parallel=self.parallel) xk = self.apply_rope3d(xk, rope_positions, self.rope_ch_split, parallel=self.parallel) output = self.core_attention( xq, xk, xv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, attn_mask=attn_mask ) output = rearrange(output, 'b s h d -> b s (h d)') output = self.wo(output) return output class CrossAttention(Attention): def __init__(self, hidden_dim, head_dim, bias=False, with_qk_norm=True, attn_type='torch'): super().__init__() self.head_dim = head_dim self.n_heads = hidden_dim // head_dim self.wq = nn.Linear(hidden_dim, hidden_dim, bias=bias) self.wkv = nn.Linear(hidden_dim, hidden_dim*2, bias=bias) self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias) self.with_qk_norm = with_qk_norm if self.with_qk_norm: self.q_norm = RMSNorm(head_dim, elementwise_affine=True) self.k_norm = RMSNorm(head_dim, elementwise_affine=True) self.core_attention = self.attn_processor(attn_type=attn_type) def forward( self, x: torch.Tensor, encoder_hidden_states: torch.Tensor, attn_mask=None ): xq = self.wq(x) xq = xq.view(*xq.shape[:-1], self.n_heads, self.head_dim) xkv = self.wkv(encoder_hidden_states) xkv = xkv.view(*xkv.shape[:-1], self.n_heads, 2*self.head_dim) xk, xv = torch.split(xkv, [self.head_dim]*2, dim=-1) ## seq_len, n, dim if self.with_qk_norm: xq = self.q_norm(xq) xk = self.k_norm(xk) output = self.core_attention( xq, xk, xv, attn_mask=attn_mask ) output = rearrange(output, 'b s h d -> b s (h d)') output = self.wo(output) return output class GELU(nn.Module): r""" GELU activation function with tanh approximation support with `approximate="tanh"`. Parameters: dim_in (`int`): The number of channels in the input. dim_out (`int`): The number of channels in the output. approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation. bias (`bool`, defaults to True): Whether to use a bias in the linear layer. """ def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True): super().__init__() self.proj = nn.Linear(dim_in, dim_out, bias=bias) self.approximate = approximate def gelu(self, gate: torch.Tensor) -> torch.Tensor: return torch.nn.functional.gelu(gate, approximate=self.approximate) def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states = self.gelu(hidden_states) return hidden_states class FeedForward(nn.Module): def __init__( self, dim: int, inner_dim: Optional[int] = None, dim_out: Optional[int] = None, mult: int = 4, bias: bool = False, ): super().__init__() inner_dim = dim*mult if inner_dim is None else inner_dim dim_out = dim if dim_out is None else dim_out self.net = nn.ModuleList([ GELU(dim, inner_dim, approximate="tanh", bias=bias), nn.Identity(), nn.Linear(inner_dim, dim_out, bias=bias) ]) def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor: for module in self.net: hidden_states = module(hidden_states) return hidden_states def modulate(x, scale, shift): x = x * (1 + scale) + shift return x def gate(x, gate): x = gate * x return x class StepVideoTransformerBlock(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, attention_head_dim: int, norm_eps: float = 1e-5, ff_inner_dim: Optional[int] = None, ff_bias: bool = False, attention_type: str = 'parallel' ): super().__init__() self.dim = dim self.norm1 = nn.LayerNorm(dim, eps=norm_eps) self.attn1 = SelfAttention(dim, attention_head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type=attention_type) self.norm2 = nn.LayerNorm(dim, eps=norm_eps) self.attn2 = CrossAttention(dim, attention_head_dim, bias=False, with_qk_norm=True, attn_type='torch') self.ff = FeedForward(dim=dim, inner_dim=ff_inner_dim, dim_out=dim, bias=ff_bias) self.scale_shift_table = nn.Parameter(torch.randn(6, dim) /dim**0.5) @torch.no_grad() def forward( self, q: torch.Tensor, kv: Optional[torch.Tensor] = None, timestep: Optional[torch.LongTensor] = None, attn_mask = None, rope_positions: list = None, ) -> torch.Tensor: shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( torch.clone(chunk) for chunk in (self.scale_shift_table[None].to(dtype=q.dtype, device=q.device) + timestep.reshape(-1, 6, self.dim)).chunk(6, dim=1) ) scale_shift_q = modulate(self.norm1(q), scale_msa, shift_msa) attn_q = self.attn1( scale_shift_q, rope_positions=rope_positions ) q = gate(attn_q, gate_msa) + q attn_q = self.attn2( q, kv, attn_mask ) q = attn_q + q scale_shift_q = modulate(self.norm2(q), scale_mlp, shift_mlp) ff_output = self.ff(scale_shift_q) q = gate(ff_output, gate_mlp) + q return q class PatchEmbed(nn.Module): """2D Image to Patch Embedding""" def __init__( self, patch_size=64, in_channels=3, embed_dim=768, layer_norm=False, flatten=True, bias=True, ): super().__init__() self.flatten = flatten self.layer_norm = layer_norm self.proj = nn.Conv2d( in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias ) def forward(self, latent): latent = self.proj(latent).to(latent.dtype) if self.flatten: latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC if self.layer_norm: latent = self.norm(latent) return latent class StepVideoModel(torch.nn.Module): def __init__( self, num_attention_heads: int = 48, attention_head_dim: int = 128, in_channels: int = 64, out_channels: Optional[int] = 64, num_layers: int = 48, dropout: float = 0.0, patch_size: int = 1, norm_type: str = "ada_norm_single", norm_elementwise_affine: bool = False, norm_eps: float = 1e-6, use_additional_conditions: Optional[bool] = False, caption_channels: Optional[Union[int, List, Tuple]] = [6144, 1024], attention_type: Optional[str] = "torch", ): super().__init__() # Set some common variables used across the board. self.inner_dim = num_attention_heads * attention_head_dim self.out_channels = in_channels if out_channels is None else out_channels self.use_additional_conditions = use_additional_conditions self.pos_embed = PatchEmbed( patch_size=patch_size, in_channels=in_channels, embed_dim=self.inner_dim, ) self.transformer_blocks = nn.ModuleList( [ StepVideoTransformerBlock( dim=self.inner_dim, attention_head_dim=attention_head_dim, attention_type=attention_type ) for _ in range(num_layers) ] ) # 3. Output blocks. self.norm_out = nn.LayerNorm(self.inner_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5) self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels) self.patch_size = patch_size self.adaln_single = AdaLayerNormSingle( self.inner_dim, use_additional_conditions=self.use_additional_conditions ) if isinstance(caption_channels, int): caption_channel = caption_channels else: caption_channel, clip_channel = caption_channels self.clip_projection = nn.Linear(clip_channel, self.inner_dim) self.caption_norm = nn.LayerNorm(caption_channel, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.caption_projection = PixArtAlphaTextProjection( in_features=caption_channel, hidden_size=self.inner_dim ) self.parallel = attention_type=='parallel' def patchfy(self, hidden_states): hidden_states = rearrange(hidden_states, 'b f c h w -> (b f) c h w') hidden_states = self.pos_embed(hidden_states) return hidden_states def prepare_attn_mask(self, encoder_attention_mask, encoder_hidden_states, q_seqlen): kv_seqlens = encoder_attention_mask.sum(dim=1).int() mask = torch.zeros([len(kv_seqlens), q_seqlen, max(kv_seqlens)], dtype=torch.bool, device=encoder_attention_mask.device) encoder_hidden_states = encoder_hidden_states[:,: max(kv_seqlens)] for i, kv_len in enumerate(kv_seqlens): mask[i, :, :kv_len] = 1 return encoder_hidden_states, mask def block_forward( self, hidden_states, encoder_hidden_states=None, timestep=None, rope_positions=None, attn_mask=None, parallel=True ): for block in tqdm(self.transformer_blocks, desc="Transformer blocks"): hidden_states = block( hidden_states, encoder_hidden_states, timestep=timestep, attn_mask=attn_mask, rope_positions=rope_positions ) return hidden_states @torch.inference_mode() def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_hidden_states_2: Optional[torch.Tensor] = None, timestep: Optional[torch.LongTensor] = None, added_cond_kwargs: Dict[str, torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, fps: torch.Tensor=None, return_dict: bool = False, ): assert hidden_states.ndim==5; "hidden_states's shape should be (bsz, f, ch, h ,w)" bsz, frame, _, height, width = hidden_states.shape height, width = height // self.patch_size, width // self.patch_size hidden_states = self.patchfy(hidden_states) len_frame = hidden_states.shape[1] if self.use_additional_conditions: added_cond_kwargs = { "resolution": torch.tensor([(height, width)]*bsz, device=hidden_states.device, dtype=hidden_states.dtype), "nframe": torch.tensor([frame]*bsz, device=hidden_states.device, dtype=hidden_states.dtype), "fps": fps } else: added_cond_kwargs = {} timestep, embedded_timestep = self.adaln_single( timestep, added_cond_kwargs=added_cond_kwargs ) encoder_hidden_states = self.caption_projection(self.caption_norm(encoder_hidden_states)) if encoder_hidden_states_2 is not None and hasattr(self, 'clip_projection'): clip_embedding = self.clip_projection(encoder_hidden_states_2) encoder_hidden_states = torch.cat([clip_embedding, encoder_hidden_states], dim=1) hidden_states = rearrange(hidden_states, '(b f) l d-> b (f l) d', b=bsz, f=frame, l=len_frame).contiguous() encoder_hidden_states, attn_mask = self.prepare_attn_mask(encoder_attention_mask, encoder_hidden_states, q_seqlen=frame*len_frame) hidden_states = self.block_forward( hidden_states, encoder_hidden_states, timestep=timestep, rope_positions=[frame, height, width], attn_mask=attn_mask, parallel=self.parallel ) hidden_states = rearrange(hidden_states, 'b (f l) d -> (b f) l d', b=bsz, f=frame, l=len_frame) embedded_timestep = repeat(embedded_timestep, 'b d -> (b f) d', f=frame).contiguous() shift, scale = (self.scale_shift_table[None].to(dtype=embedded_timestep.dtype, device=embedded_timestep.device) + embedded_timestep[:, None]).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) # Modulation hidden_states = hidden_states * (1 + scale) + shift hidden_states = self.proj_out(hidden_states) # unpatchify hidden_states = hidden_states.reshape( shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) ) hidden_states = rearrange(hidden_states, 'n h w p q c -> n c h p w q') output = hidden_states.reshape( shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) ) output = rearrange(output, '(b f) c h w -> b f c h w', f=frame) if return_dict: return {'x': output} return output @staticmethod def state_dict_converter(): return StepVideoDiTStateDictConverter() class StepVideoDiTStateDictConverter: def __init__(self): super().__init__() def from_diffusers(self, state_dict): return state_dict def from_civitai(self, state_dict): return state_dict ================================================ FILE: diffsynth/models/stepvideo_text_encoder.py ================================================ # Copyright 2025 StepFun Inc. All Rights Reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # ============================================================================== import os from typing import Optional import torch import torch.nn as nn import torch.nn.functional as F from .stepvideo_dit import RMSNorm from safetensors.torch import load_file from transformers.modeling_utils import PretrainedConfig, PreTrainedModel from einops import rearrange import json from typing import List from functools import wraps import warnings class EmptyInitOnDevice(torch.overrides.TorchFunctionMode): def __init__(self, device=None): self.device = device def __torch_function__(self, func, types, args=(), kwargs=None): kwargs = kwargs or {} if getattr(func, '__module__', None) == 'torch.nn.init': if 'tensor' in kwargs: return kwargs['tensor'] else: return args[0] if self.device is not None and func in torch.utils._device._device_constructors() and kwargs.get('device') is None: kwargs['device'] = self.device return func(*args, **kwargs) def with_empty_init(func): @wraps(func) def wrapper(*args, **kwargs): with EmptyInitOnDevice('cpu'): return func(*args, **kwargs) return wrapper class LLaMaEmbedding(nn.Module): """Language model embeddings. Arguments: hidden_size: hidden size vocab_size: vocabulary size max_sequence_length: maximum size of sequence. This is used for positional embedding embedding_dropout_prob: dropout probability for embeddings init_method: weight initialization method num_tokentypes: size of the token-type embeddings. 0 value will ignore this embedding """ def __init__(self, cfg, ): super().__init__() self.hidden_size = cfg.hidden_size self.params_dtype = cfg.params_dtype self.fp32_residual_connection = cfg.fp32_residual_connection self.embedding_weights_in_fp32 = cfg.embedding_weights_in_fp32 self.word_embeddings = torch.nn.Embedding( cfg.padded_vocab_size, self.hidden_size, ) self.embedding_dropout = torch.nn.Dropout(cfg.hidden_dropout) def forward(self, input_ids): # Embeddings. if self.embedding_weights_in_fp32: self.word_embeddings = self.word_embeddings.to(torch.float32) embeddings = self.word_embeddings(input_ids) if self.embedding_weights_in_fp32: embeddings = embeddings.to(self.params_dtype) self.word_embeddings = self.word_embeddings.to(self.params_dtype) # Data format change to avoid explicit transposes : [b s h] --> [s b h]. embeddings = embeddings.transpose(0, 1).contiguous() # If the input flag for fp32 residual connection is set, convert for float. if self.fp32_residual_connection: embeddings = embeddings.float() # Dropout. embeddings = self.embedding_dropout(embeddings) return embeddings class StepChatTokenizer: """Step Chat Tokenizer""" def __init__( self, model_file, name="StepChatTokenizer", bot_token="<|BOT|>", # Begin of Turn eot_token="<|EOT|>", # End of Turn call_start_token="<|CALL_START|>", # Call Start call_end_token="<|CALL_END|>", # Call End think_start_token="<|THINK_START|>", # Think Start think_end_token="<|THINK_END|>", # Think End mask_start_token="<|MASK_1e69f|>", # Mask start mask_end_token="<|UNMASK_1e69f|>", # Mask end ): import sentencepiece self._tokenizer = sentencepiece.SentencePieceProcessor(model_file=model_file) self._vocab = {} self._inv_vocab = {} self._special_tokens = {} self._inv_special_tokens = {} self._t5_tokens = [] for idx in range(self._tokenizer.get_piece_size()): text = self._tokenizer.id_to_piece(idx) self._inv_vocab[idx] = text self._vocab[text] = idx if self._tokenizer.is_control(idx) or self._tokenizer.is_unknown(idx): self._special_tokens[text] = idx self._inv_special_tokens[idx] = text self._unk_id = self._tokenizer.unk_id() self._bos_id = self._tokenizer.bos_id() self._eos_id = self._tokenizer.eos_id() for token in [ bot_token, eot_token, call_start_token, call_end_token, think_start_token, think_end_token ]: assert token in self._vocab, f"Token '{token}' not found in tokenizer" assert token in self._special_tokens, f"Token '{token}' is not a special token" for token in [mask_start_token, mask_end_token]: assert token in self._vocab, f"Token '{token}' not found in tokenizer" self._bot_id = self._tokenizer.piece_to_id(bot_token) self._eot_id = self._tokenizer.piece_to_id(eot_token) self._call_start_id = self._tokenizer.piece_to_id(call_start_token) self._call_end_id = self._tokenizer.piece_to_id(call_end_token) self._think_start_id = self._tokenizer.piece_to_id(think_start_token) self._think_end_id = self._tokenizer.piece_to_id(think_end_token) self._mask_start_id = self._tokenizer.piece_to_id(mask_start_token) self._mask_end_id = self._tokenizer.piece_to_id(mask_end_token) self._underline_id = self._tokenizer.piece_to_id("\u2581") @property def vocab(self): return self._vocab @property def inv_vocab(self): return self._inv_vocab @property def vocab_size(self): return self._tokenizer.vocab_size() def tokenize(self, text: str) -> List[int]: return self._tokenizer.encode_as_ids(text) def detokenize(self, token_ids: List[int]) -> str: return self._tokenizer.decode_ids(token_ids) class Tokens: def __init__(self, input_ids, cu_input_ids, attention_mask, cu_seqlens, max_seq_len) -> None: self.input_ids = input_ids self.attention_mask = attention_mask self.cu_input_ids = cu_input_ids self.cu_seqlens = cu_seqlens self.max_seq_len = max_seq_len def to(self, device): self.input_ids = self.input_ids.to(device) self.attention_mask = self.attention_mask.to(device) self.cu_input_ids = self.cu_input_ids.to(device) self.cu_seqlens = self.cu_seqlens.to(device) return self class Wrapped_StepChatTokenizer(StepChatTokenizer): def __call__(self, text, max_length=320, padding="max_length", truncation=True, return_tensors="pt"): # [bos, ..., eos, pad, pad, ..., pad] self.BOS = 1 self.EOS = 2 self.PAD = 2 out_tokens = [] attn_mask = [] if len(text) == 0: part_tokens = [self.BOS] + [self.EOS] valid_size = len(part_tokens) if len(part_tokens) < max_length: part_tokens += [self.PAD] * (max_length - valid_size) out_tokens.append(part_tokens) attn_mask.append([1]*valid_size+[0]*(max_length-valid_size)) else: for part in text: part_tokens = self.tokenize(part) part_tokens = part_tokens[:(max_length - 2)] # leave 2 space for bos and eos part_tokens = [self.BOS] + part_tokens + [self.EOS] valid_size = len(part_tokens) if len(part_tokens) < max_length: part_tokens += [self.PAD] * (max_length - valid_size) out_tokens.append(part_tokens) attn_mask.append([1]*valid_size+[0]*(max_length-valid_size)) out_tokens = torch.tensor(out_tokens, dtype=torch.long) attn_mask = torch.tensor(attn_mask, dtype=torch.long) # padding y based on tp size padded_len = 0 padded_flag = True if padded_len > 0 else False if padded_flag: pad_tokens = torch.tensor([[self.PAD] * max_length], device=out_tokens.device) pad_attn_mask = torch.tensor([[1]*padded_len+[0]*(max_length-padded_len)], device=attn_mask.device) out_tokens = torch.cat([out_tokens, pad_tokens], dim=0) attn_mask = torch.cat([attn_mask, pad_attn_mask], dim=0) # cu_seqlens cu_out_tokens = out_tokens.masked_select(attn_mask != 0).unsqueeze(0) seqlen = attn_mask.sum(dim=1).tolist() cu_seqlens = torch.cumsum(torch.tensor([0]+seqlen), 0).to(device=out_tokens.device,dtype=torch.int32) max_seq_len = max(seqlen) return Tokens(out_tokens, cu_out_tokens, attn_mask, cu_seqlens, max_seq_len) def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=True, return_attn_probs=False, tp_group_rank=0, tp_group_size=1): softmax_scale = q.size(-1) ** (-0.5) if softmax_scale is None else softmax_scale if hasattr(torch.ops.Optimus, "fwd"): results = torch.ops.Optimus.fwd(q, k, v, None, dropout_p, softmax_scale, causal, return_attn_probs, None, tp_group_rank, tp_group_size)[0] else: warnings.warn("Cannot load `torch.ops.Optimus.fwd`. Using `torch.nn.functional.scaled_dot_product_attention` instead.") results = torch.nn.functional.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True, scale=softmax_scale).transpose(1, 2) return results class FlashSelfAttention(torch.nn.Module): def __init__( self, attention_dropout=0.0, ): super().__init__() self.dropout_p = attention_dropout def forward(self, q, k, v, cu_seqlens=None, max_seq_len=None): if cu_seqlens is None: output = flash_attn_func(q, k, v, dropout_p=self.dropout_p) else: raise ValueError('cu_seqlens is not supported!') return output def safediv(n, d): q, r = divmod(n, d) assert r == 0 return q class MultiQueryAttention(nn.Module): def __init__(self, cfg, layer_id=None): super().__init__() self.head_dim = cfg.hidden_size // cfg.num_attention_heads self.max_seq_len = cfg.seq_length self.use_flash_attention = cfg.use_flash_attn assert self.use_flash_attention, 'FlashAttention is required!' self.n_groups = cfg.num_attention_groups self.tp_size = 1 self.n_local_heads = cfg.num_attention_heads self.n_local_groups = self.n_groups self.wqkv = nn.Linear( cfg.hidden_size, cfg.hidden_size + self.head_dim * 2 * self.n_groups, bias=False, ) self.wo = nn.Linear( cfg.hidden_size, cfg.hidden_size, bias=False, ) assert self.use_flash_attention, 'non-Flash attention not supported yet.' self.core_attention = FlashSelfAttention(attention_dropout=cfg.attention_dropout) self.layer_id = layer_id def forward( self, x: torch.Tensor, mask: Optional[torch.Tensor], cu_seqlens: Optional[torch.Tensor], max_seq_len: Optional[torch.Tensor], ): seqlen, bsz, dim = x.shape xqkv = self.wqkv(x) xq, xkv = torch.split( xqkv, (dim // self.tp_size, self.head_dim*2*self.n_groups // self.tp_size ), dim=-1, ) # gather on 1st dimension xq = xq.view(seqlen, bsz, self.n_local_heads, self.head_dim) xkv = xkv.view(seqlen, bsz, self.n_local_groups, 2 * self.head_dim) xk, xv = xkv.chunk(2, -1) # rotary embedding + flash attn xq = rearrange(xq, "s b h d -> b s h d") xk = rearrange(xk, "s b h d -> b s h d") xv = rearrange(xv, "s b h d -> b s h d") q_per_kv = self.n_local_heads // self.n_local_groups if q_per_kv > 1: b, s, h, d = xk.size() if h == 1: xk = xk.expand(b, s, q_per_kv, d) xv = xv.expand(b, s, q_per_kv, d) else: ''' To cover the cases where h > 1, we have the following implementation, which is equivalent to: xk = xk.repeat_interleave(q_per_kv, dim=-2) xv = xv.repeat_interleave(q_per_kv, dim=-2) but can avoid calling aten::item() that involves cpu. ''' idx = torch.arange(q_per_kv * h, device=xk.device).reshape(q_per_kv, -1).permute(1, 0).flatten() xk = torch.index_select(xk.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous() xv = torch.index_select(xv.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous() if self.use_flash_attention: output = self.core_attention(xq, xk, xv, cu_seqlens=cu_seqlens, max_seq_len=max_seq_len) # reduce-scatter only support first dimension now output = rearrange(output, "b s h d -> s b (h d)").contiguous() else: xq, xk, xv = [ rearrange(x, "b s ... -> s b ...").contiguous() for x in (xq, xk, xv) ] output = self.core_attention(xq, xk, xv, mask) output = self.wo(output) return output class FeedForward(nn.Module): def __init__( self, cfg, dim: int, hidden_dim: int, layer_id: int, multiple_of: int=256, ): super().__init__() hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) def swiglu(x): x = torch.chunk(x, 2, dim=-1) return F.silu(x[0]) * x[1] self.swiglu = swiglu self.w1 = nn.Linear( dim, 2 * hidden_dim, bias=False, ) self.w2 = nn.Linear( hidden_dim, dim, bias=False, ) def forward(self, x): x = self.swiglu(self.w1(x)) output = self.w2(x) return output class TransformerBlock(nn.Module): def __init__( self, cfg, layer_id: int ): super().__init__() self.n_heads = cfg.num_attention_heads self.dim = cfg.hidden_size self.head_dim = cfg.hidden_size // cfg.num_attention_heads self.attention = MultiQueryAttention( cfg, layer_id=layer_id, ) self.feed_forward = FeedForward( cfg, dim=cfg.hidden_size, hidden_dim=cfg.ffn_hidden_size, layer_id=layer_id, ) self.layer_id = layer_id self.attention_norm = RMSNorm( cfg.hidden_size, eps=cfg.layernorm_epsilon, ) self.ffn_norm = RMSNorm( cfg.hidden_size, eps=cfg.layernorm_epsilon, ) def forward( self, x: torch.Tensor, mask: Optional[torch.Tensor], cu_seqlens: Optional[torch.Tensor], max_seq_len: Optional[torch.Tensor], ): residual = self.attention.forward( self.attention_norm(x), mask, cu_seqlens, max_seq_len ) h = x + residual ffn_res = self.feed_forward.forward(self.ffn_norm(h)) out = h + ffn_res return out class Transformer(nn.Module): def __init__( self, config, max_seq_size=8192, ): super().__init__() self.num_layers = config.num_layers self.layers = self._build_layers(config) def _build_layers(self, config): layers = torch.nn.ModuleList() for layer_id in range(self.num_layers): layers.append( TransformerBlock( config, layer_id=layer_id + 1 , ) ) return layers def forward( self, hidden_states, attention_mask, cu_seqlens=None, max_seq_len=None, ): if max_seq_len is not None and not isinstance(max_seq_len, torch.Tensor): max_seq_len = torch.tensor(max_seq_len, dtype=torch.int32, device="cpu") for lid, layer in enumerate(self.layers): hidden_states = layer( hidden_states, attention_mask, cu_seqlens, max_seq_len, ) return hidden_states class Step1Model(PreTrainedModel): config_class=PretrainedConfig @with_empty_init def __init__( self, config, ): super().__init__(config) self.tok_embeddings = LLaMaEmbedding(config) self.transformer = Transformer(config) def forward( self, input_ids=None, attention_mask=None, ): hidden_states = self.tok_embeddings(input_ids) hidden_states = self.transformer( hidden_states, attention_mask, ) return hidden_states class STEP1TextEncoder(torch.nn.Module): def __init__(self, model_dir, max_length=320): super(STEP1TextEncoder, self).__init__() self.max_length = max_length self.text_tokenizer = Wrapped_StepChatTokenizer(os.path.join(model_dir, 'step1_chat_tokenizer.model')) text_encoder = Step1Model.from_pretrained(model_dir) self.text_encoder = text_encoder.eval().to(torch.bfloat16) @staticmethod def from_pretrained(path, torch_dtype=torch.bfloat16): model = STEP1TextEncoder(path).to(torch_dtype) return model @torch.no_grad def forward(self, prompts, with_mask=True, max_length=None, device="cuda"): self.device = device with torch.no_grad(), torch.amp.autocast(dtype=torch.bfloat16, device_type=device): if type(prompts) is str: prompts = [prompts] txt_tokens = self.text_tokenizer( prompts, max_length=max_length or self.max_length, padding="max_length", truncation=True, return_tensors="pt" ) y = self.text_encoder( txt_tokens.input_ids.to(self.device), attention_mask=txt_tokens.attention_mask.to(self.device) if with_mask else None ) y_mask = txt_tokens.attention_mask return y.transpose(0,1), y_mask ================================================ FILE: diffsynth/models/stepvideo_vae.py ================================================ # Copyright 2025 StepFun Inc. All Rights Reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # ============================================================================== import torch from einops import rearrange from torch import nn from torch.nn import functional as F from tqdm import tqdm from einops import repeat class BaseGroupNorm(nn.GroupNorm): def __init__(self, num_groups, num_channels): super().__init__(num_groups=num_groups, num_channels=num_channels) def forward(self, x, zero_pad=False, **kwargs): if zero_pad: return base_group_norm_with_zero_pad(x, self, **kwargs) else: return base_group_norm(x, self, **kwargs) def base_group_norm(x, norm_layer, act_silu=False, channel_last=False): if hasattr(base_group_norm, 'spatial') and base_group_norm.spatial: assert channel_last == True x_shape = x.shape x = x.flatten(0, 1) if channel_last: # Permute to NCHW format x = x.permute(0, 3, 1, 2) out = F.group_norm(x.contiguous(), norm_layer.num_groups, norm_layer.weight, norm_layer.bias, norm_layer.eps) if act_silu: out = F.silu(out) if channel_last: # Permute back to NHWC format out = out.permute(0, 2, 3, 1) out = out.view(x_shape) else: if channel_last: # Permute to NCHW format x = x.permute(0, 3, 1, 2) out = F.group_norm(x.contiguous(), norm_layer.num_groups, norm_layer.weight, norm_layer.bias, norm_layer.eps) if act_silu: out = F.silu(out) if channel_last: # Permute back to NHWC format out = out.permute(0, 2, 3, 1) return out def base_conv2d(x, conv_layer, channel_last=False, residual=None): if channel_last: x = x.permute(0, 3, 1, 2) # NHWC to NCHW out = F.conv2d(x, conv_layer.weight, conv_layer.bias, stride=conv_layer.stride, padding=conv_layer.padding) if residual is not None: if channel_last: residual = residual.permute(0, 3, 1, 2) # NHWC to NCHW out += residual if channel_last: out = out.permute(0, 2, 3, 1) # NCHW to NHWC return out def base_conv3d(x, conv_layer, channel_last=False, residual=None, only_return_output=False): if only_return_output: size = cal_outsize(x.shape, conv_layer.weight.shape, conv_layer.stride, conv_layer.padding) return torch.empty(size, device=x.device, dtype=x.dtype) if channel_last: x = x.permute(0, 4, 1, 2, 3) # NDHWC to NCDHW out = F.conv3d(x, conv_layer.weight, conv_layer.bias, stride=conv_layer.stride, padding=conv_layer.padding) if residual is not None: if channel_last: residual = residual.permute(0, 4, 1, 2, 3) # NDHWC to NCDHW out += residual if channel_last: out = out.permute(0, 2, 3, 4, 1) # NCDHW to NDHWC return out def cal_outsize(input_sizes, kernel_sizes, stride, padding): stride_d, stride_h, stride_w = stride padding_d, padding_h, padding_w = padding dilation_d, dilation_h, dilation_w = 1, 1, 1 in_d = input_sizes[1] in_h = input_sizes[2] in_w = input_sizes[3] in_channel = input_sizes[4] kernel_d = kernel_sizes[2] kernel_h = kernel_sizes[3] kernel_w = kernel_sizes[4] out_channels = kernel_sizes[0] out_d = calc_out_(in_d, padding_d, dilation_d, kernel_d, stride_d) out_h = calc_out_(in_h, padding_h, dilation_h, kernel_h, stride_h) out_w = calc_out_(in_w, padding_w, dilation_w, kernel_w, stride_w) size = [input_sizes[0], out_d, out_h, out_w, out_channels] return size def calc_out_(in_size, padding, dilation, kernel, stride): return (in_size + 2 * padding - dilation * (kernel - 1) - 1) // stride + 1 def base_conv3d_channel_last(x, conv_layer, residual=None): in_numel = x.numel() out_numel = int(x.numel() * conv_layer.out_channels / conv_layer.in_channels) if (in_numel >= 2**30) or (out_numel >= 2**30): assert conv_layer.stride[0] == 1, "time split asks time stride = 1" B,T,H,W,C = x.shape K = conv_layer.kernel_size[0] chunks = 4 chunk_size = T // chunks if residual is None: out_nhwc = base_conv3d(x, conv_layer, channel_last=True, residual=residual, only_return_output=True) else: out_nhwc = residual assert B == 1 outs = [] for i in range(chunks): if i == chunks-1: xi = x[:1,chunk_size*i:] out_nhwci = out_nhwc[:1,chunk_size*i:] else: xi = x[:1,chunk_size*i:chunk_size*(i+1)+K-1] out_nhwci = out_nhwc[:1,chunk_size*i:chunk_size*(i+1)] if residual is not None: if i == chunks-1: ri = residual[:1,chunk_size*i:] else: ri = residual[:1,chunk_size*i:chunk_size*(i+1)] else: ri = None out_nhwci.copy_(base_conv3d(xi, conv_layer, channel_last=True, residual=ri)) else: out_nhwc = base_conv3d(x, conv_layer, channel_last=True, residual=residual) return out_nhwc class Upsample2D(nn.Module): def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.use_conv_transpose = use_conv_transpose if use_conv: self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1) else: assert "Not Supported" self.conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1) def forward(self, x, output_size=None): assert x.shape[-1] == self.channels if self.use_conv_transpose: return self.conv(x) if output_size is None: x = F.interpolate( x.permute(0,3,1,2).to(memory_format=torch.channels_last), scale_factor=2.0, mode='nearest').permute(0,2,3,1).contiguous() else: x = F.interpolate( x.permute(0,3,1,2).to(memory_format=torch.channels_last), size=output_size, mode='nearest').permute(0,2,3,1).contiguous() # x = self.conv(x) x = base_conv2d(x, self.conv, channel_last=True) return x class Downsample2D(nn.Module): def __init__(self, channels, use_conv=False, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.padding = padding stride = 2 if use_conv: self.conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding) else: assert self.channels == self.out_channels self.conv = nn.AvgPool2d(kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[-1] == self.channels if self.use_conv and self.padding == 0: pad = (0, 0, 0, 1, 0, 1) x = F.pad(x, pad, mode="constant", value=0) assert x.shape[-1] == self.channels # x = self.conv(x) x = base_conv2d(x, self.conv, channel_last=True) return x class CausalConv(nn.Module): def __init__(self, chan_in, chan_out, kernel_size, **kwargs ): super().__init__() if isinstance(kernel_size, int): kernel_size = kernel_size if isinstance(kernel_size, tuple) else ((kernel_size,) * 3) time_kernel_size, height_kernel_size, width_kernel_size = kernel_size self.dilation = kwargs.pop('dilation', 1) self.stride = kwargs.pop('stride', 1) if isinstance(self.stride, int): self.stride = (self.stride, 1, 1) time_pad = self.dilation * (time_kernel_size - 1) + max((1 - self.stride[0]), 0) height_pad = height_kernel_size // 2 width_pad = width_kernel_size // 2 self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_pad, 0) self.time_uncausal_padding = (width_pad, width_pad, height_pad, height_pad, 0, 0) self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, **kwargs) self.is_first_run = True def forward(self, x, is_init=True, residual=None): x = nn.functional.pad(x, self.time_causal_padding if is_init else self.time_uncausal_padding) x = self.conv(x) if residual is not None: x.add_(residual) return x class ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, factor: int, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.factor = factor assert out_channels * factor**3 % in_channels == 0 self.repeats = out_channels * factor**3 // in_channels def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: x = x.repeat_interleave(self.repeats, dim=1) x = x.view(x.size(0), self.out_channels, self.factor, self.factor, self.factor, x.size(2), x.size(3), x.size(4)) x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() x = x.view(x.size(0), self.out_channels, x.size(2)*self.factor, x.size(4)*self.factor, x.size(6)*self.factor) x = x[:, :, self.factor - 1:, :, :] return x class ConvPixelShuffleUpSampleLayer3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, kernel_size: int, factor: int, ): super().__init__() self.factor = factor out_ratio = factor**3 self.conv = CausalConv( in_channels, out_channels * out_ratio, kernel_size=kernel_size ) def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: x = self.conv(x, is_init) x = self.pixel_shuffle_3d(x, self.factor) return x @staticmethod def pixel_shuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor: batch_size, channels, depth, height, width = x.size() new_channels = channels // (factor ** 3) new_depth = depth * factor new_height = height * factor new_width = width * factor x = x.view(batch_size, new_channels, factor, factor, factor, depth, height, width) x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() x = x.view(batch_size, new_channels, new_depth, new_height, new_width) x = x[:, :, factor - 1:, :, :] return x class ConvPixelUnshuffleDownSampleLayer3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, kernel_size: int, factor: int, ): super().__init__() self.factor = factor out_ratio = factor**3 assert out_channels % out_ratio == 0 self.conv = CausalConv( in_channels, out_channels // out_ratio, kernel_size=kernel_size ) def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: x = self.conv(x, is_init) x = self.pixel_unshuffle_3d(x, self.factor) return x @staticmethod def pixel_unshuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor: pad = (0, 0, 0, 0, factor-1, 0) # (left, right, top, bottom, front, back) x = F.pad(x, pad) B, C, D, H, W = x.shape x = x.view(B, C, D // factor, factor, H // factor, factor, W // factor, factor) x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() x = x.view(B, C * factor**3, D // factor, H // factor, W // factor) return x class PixelUnshuffleChannelAveragingDownSampleLayer3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, factor: int, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.factor = factor assert in_channels * factor**3 % out_channels == 0 self.group_size = in_channels * factor**3 // out_channels def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: pad = (0, 0, 0, 0, self.factor-1, 0) # (left, right, top, bottom, front, back) x = F.pad(x, pad) B, C, D, H, W = x.shape x = x.view(B, C, D // self.factor, self.factor, H // self.factor, self.factor, W // self.factor, self.factor) x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() x = x.view(B, C * self.factor**3, D // self.factor, H // self.factor, W // self.factor) x = x.view(B, self.out_channels, self.group_size, D // self.factor, H // self.factor, W // self.factor) x = x.mean(dim=2) return x def __init__( self, in_channels: int, out_channels: int, factor: int, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.factor = factor assert in_channels * factor**3 % out_channels == 0 self.group_size = in_channels * factor**3 // out_channels def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: pad = (0, 0, 0, 0, self.factor-1, 0) # (left, right, top, bottom, front, back) x = F.pad(x, pad) B, C, D, H, W = x.shape x = x.view(B, C, D // self.factor, self.factor, H // self.factor, self.factor, W // self.factor, self.factor) x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() x = x.view(B, C * self.factor**3, D // self.factor, H // self.factor, W // self.factor) x = x.view(B, self.out_channels, self.group_size, D // self.factor, H // self.factor, W // self.factor) x = x.mean(dim=2) return x def base_group_norm_with_zero_pad(x, norm_layer, act_silu=True, pad_size=2): out_shape = list(x.shape) out_shape[1] += pad_size out = torch.empty(out_shape, dtype=x.dtype, device=x.device) out[:, pad_size:] = base_group_norm(x, norm_layer, act_silu=act_silu, channel_last=True) out[:, :pad_size] = 0 return out class CausalConvChannelLast(CausalConv): def __init__(self, chan_in, chan_out, kernel_size, **kwargs ): super().__init__( chan_in, chan_out, kernel_size, **kwargs) self.time_causal_padding = (0, 0) + self.time_causal_padding self.time_uncausal_padding = (0, 0) + self.time_uncausal_padding def forward(self, x, is_init=True, residual=None): if self.is_first_run: self.is_first_run = False # self.conv.weight = nn.Parameter(self.conv.weight.permute(0,2,3,4,1).contiguous()) x = nn.functional.pad(x, self.time_causal_padding if is_init else self.time_uncausal_padding) x = base_conv3d_channel_last(x, self.conv, residual=residual) return x class CausalConvAfterNorm(CausalConv): def __init__(self, chan_in, chan_out, kernel_size, **kwargs ): super().__init__( chan_in, chan_out, kernel_size, **kwargs) if self.time_causal_padding == (1, 1, 1, 1, 2, 0): self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, padding=(0, 1, 1), **kwargs) else: self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, **kwargs) self.is_first_run = True def forward(self, x, is_init=True, residual=None): if self.is_first_run: self.is_first_run = False if self.time_causal_padding == (1, 1, 1, 1, 2, 0): pass else: x = nn.functional.pad(x, self.time_causal_padding).contiguous() x = base_conv3d_channel_last(x, self.conv, residual=residual) return x class AttnBlock(nn.Module): def __init__(self, in_channels ): super().__init__() self.norm = BaseGroupNorm(num_groups=32, num_channels=in_channels) self.q = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) self.k = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) self.v = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) self.proj_out = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) def attention(self, x, is_init=True): x = self.norm(x, act_silu=False, channel_last=True) q = self.q(x, is_init) k = self.k(x, is_init) v = self.v(x, is_init) b, t, h, w, c = q.shape q, k, v = map(lambda x: rearrange(x, "b t h w c -> b 1 (t h w) c"), (q, k, v)) x = nn.functional.scaled_dot_product_attention(q, k, v, is_causal=True) x = rearrange(x, "b 1 (t h w) c -> b t h w c", t=t, h=h, w=w) return x def forward(self, x): x = x.permute(0,2,3,4,1).contiguous() h = self.attention(x) x = self.proj_out(h, residual=x) x = x.permute(0,4,1,2,3) return x class Resnet3DBlock(nn.Module): def __init__(self, in_channels, out_channels=None, temb_channels=512, conv_shortcut=False, ): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.norm1 = BaseGroupNorm(num_groups=32, num_channels=in_channels) self.conv1 = CausalConvAfterNorm(in_channels, out_channels, kernel_size=3) if temb_channels > 0: self.temb_proj = nn.Linear(temb_channels, out_channels) self.norm2 = BaseGroupNorm(num_groups=32, num_channels=out_channels) self.conv2 = CausalConvAfterNorm(out_channels, out_channels, kernel_size=3) assert conv_shortcut is False self.use_conv_shortcut = conv_shortcut if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = CausalConvAfterNorm(in_channels, out_channels, kernel_size=3) else: self.nin_shortcut = CausalConvAfterNorm(in_channels, out_channels, kernel_size=1) def forward(self, x, temb=None, is_init=True): x = x.permute(0,2,3,4,1).contiguous() h = self.norm1(x, zero_pad=True, act_silu=True, pad_size=2) h = self.conv1(h) if temb is not None: h = h + self.temb_proj(nn.functional.silu(temb))[:, :, None, None] x = self.nin_shortcut(x) if self.in_channels != self.out_channels else x h = self.norm2(h, zero_pad=True, act_silu=True, pad_size=2) x = self.conv2(h, residual=x) x = x.permute(0,4,1,2,3) return x class Downsample3D(nn.Module): def __init__(self, in_channels, with_conv, stride ): super().__init__() self.with_conv = with_conv if with_conv: self.conv = CausalConv(in_channels, in_channels, kernel_size=3, stride=stride) def forward(self, x, is_init=True): if self.with_conv: x = self.conv(x, is_init) else: x = nn.functional.avg_pool3d(x, kernel_size=2, stride=2) return x class VideoEncoder(nn.Module): def __init__(self, ch=32, ch_mult=(4, 8, 16, 16), num_res_blocks=2, in_channels=3, z_channels=16, double_z=True, down_sampling_layer=[1, 2], resamp_with_conv=True, version=1, ): super().__init__() temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks # downsampling self.conv_in = CausalConv(in_channels, ch, kernel_size=3) self.down_sampling_layer = down_sampling_layer in_ch_mult = (1,) + tuple(ch_mult) self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch * in_ch_mult[i_level] block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append( Resnet3DBlock(in_channels=block_in, out_channels=block_out, temb_channels=temb_ch)) block_in = block_out down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions - 1: if i_level in self.down_sampling_layer: down.downsample = Downsample3D(block_in, resamp_with_conv, stride=(2, 2, 2)) else: down.downsample = Downsample2D(block_in, resamp_with_conv, padding=0) #DIFF self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) self.mid.attn_1 = AttnBlock(block_in) self.mid.block_2 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) # end self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in) self.version = version if version == 2: channels = 4 * z_channels * 2 ** 3 self.conv_patchify = ConvPixelUnshuffleDownSampleLayer3D(block_in, channels, kernel_size=3, factor=2) self.shortcut_pathify = PixelUnshuffleChannelAveragingDownSampleLayer3D(block_in, channels, 2) self.shortcut_out = PixelUnshuffleChannelAveragingDownSampleLayer3D(channels, 2 * z_channels if double_z else z_channels, 1) self.conv_out = CausalConvChannelLast(channels, 2 * z_channels if double_z else z_channels, kernel_size=3) else: self.conv_out = CausalConvAfterNorm(block_in, 2 * z_channels if double_z else z_channels, kernel_size=3) @torch.inference_mode() def forward(self, x, video_frame_num, is_init=True): # timestep embedding temb = None t = video_frame_num # downsampling h = self.conv_in(x, is_init) # make it real channel last, but behave like normal layout h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3) for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](h, temb, is_init) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) if i_level != self.num_resolutions - 1: if isinstance(self.down[i_level].downsample, Downsample2D): _, _, t, _, _ = h.shape h = rearrange(h, "b c t h w -> (b t) h w c", t=t) h = self.down[i_level].downsample(h) h = rearrange(h, "(b t) h w c -> b c t h w", t=t) else: h = self.down[i_level].downsample(h, is_init) h = self.mid.block_1(h, temb, is_init) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb, is_init) h = h.permute(0,2,3,4,1).contiguous() # b c l h w -> b l h w c if self.version == 2: h = base_group_norm(h, self.norm_out, act_silu=True, channel_last=True) h = h.permute(0,4,1,2,3).contiguous() shortcut = self.shortcut_pathify(h, is_init) h = self.conv_patchify(h, is_init) h = h.add_(shortcut) shortcut = self.shortcut_out(h, is_init).permute(0,2,3,4,1) h = self.conv_out(h.permute(0,2,3,4,1).contiguous(), is_init) h = h.add_(shortcut) else: h = base_group_norm_with_zero_pad(h, self.norm_out, act_silu=True, pad_size=2) h = self.conv_out(h, is_init) h = h.permute(0,4,1,2,3) # b l h w c -> b c l h w h = rearrange(h, "b c t h w -> b t c h w") return h class Res3DBlockUpsample(nn.Module): def __init__(self, input_filters, num_filters, down_sampling_stride, down_sampling=False ): super().__init__() self.input_filters = input_filters self.num_filters = num_filters self.act_ = nn.SiLU(inplace=True) self.conv1 = CausalConvChannelLast(num_filters, num_filters, kernel_size=[3, 3, 3]) self.norm1 = BaseGroupNorm(32, num_filters) self.conv2 = CausalConvChannelLast(num_filters, num_filters, kernel_size=[3, 3, 3]) self.norm2 = BaseGroupNorm(32, num_filters) self.down_sampling = down_sampling if down_sampling: self.down_sampling_stride = down_sampling_stride else: self.down_sampling_stride = [1, 1, 1] if num_filters != input_filters or down_sampling: self.conv3 = CausalConvChannelLast(input_filters, num_filters, kernel_size=[1, 1, 1], stride=self.down_sampling_stride) self.norm3 = BaseGroupNorm(32, num_filters) def forward(self, x, is_init=False): x = x.permute(0,2,3,4,1).contiguous() residual = x h = self.conv1(x, is_init) h = self.norm1(h, act_silu=True, channel_last=True) h = self.conv2(h, is_init) h = self.norm2(h, act_silu=False, channel_last=True) if self.down_sampling or self.num_filters != self.input_filters: x = self.conv3(x, is_init) x = self.norm3(x, act_silu=False, channel_last=True) h.add_(x) h = self.act_(h) if residual is not None: h.add_(residual) h = h.permute(0,4,1,2,3) return h class Upsample3D(nn.Module): def __init__(self, in_channels, scale_factor=2 ): super().__init__() self.scale_factor = scale_factor self.conv3d = Res3DBlockUpsample(input_filters=in_channels, num_filters=in_channels, down_sampling_stride=(1, 1, 1), down_sampling=False) def forward(self, x, is_init=True, is_split=True): b, c, t, h, w = x.shape # x = x.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3).to(memory_format=torch.channels_last_3d) if is_split: split_size = c // 8 x_slices = torch.split(x, split_size, dim=1) x = [nn.functional.interpolate(x, scale_factor=self.scale_factor) for x in x_slices] x = torch.cat(x, dim=1) else: x = nn.functional.interpolate(x, scale_factor=self.scale_factor) x = self.conv3d(x, is_init) return x class VideoDecoder(nn.Module): def __init__(self, ch=128, z_channels=16, out_channels=3, ch_mult=(1, 2, 4, 4), num_res_blocks=2, temporal_up_layers=[2, 3], temporal_downsample=4, resamp_with_conv=True, version=1, ): super().__init__() temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.temporal_downsample = temporal_downsample block_in = ch * ch_mult[self.num_resolutions - 1] self.version = version if version == 2: channels = 4 * z_channels * 2 ** 3 self.conv_in = CausalConv(z_channels, channels, kernel_size=3) self.shortcut_in = ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(z_channels, channels, 1) self.conv_unpatchify = ConvPixelShuffleUpSampleLayer3D(channels, block_in, kernel_size=3, factor=2) self.shortcut_unpathify = ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(channels, block_in, 2) else: self.conv_in = CausalConv(z_channels, block_in, kernel_size=3) # middle self.mid = nn.Module() self.mid.block_1 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) self.mid.attn_1 = AttnBlock(block_in) self.mid.block_2 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) # upsampling self.up_id = len(temporal_up_layers) self.video_frame_num = 1 self.cur_video_frame_num = self.video_frame_num // 2 ** self.up_id + 1 self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks + 1): block.append( Resnet3DBlock(in_channels=block_in, out_channels=block_out, temb_channels=temb_ch)) block_in = block_out up = nn.Module() up.block = block up.attn = attn if i_level != 0: if i_level in temporal_up_layers: up.upsample = Upsample3D(block_in) self.cur_video_frame_num = self.cur_video_frame_num * 2 else: up.upsample = Upsample2D(block_in, resamp_with_conv) self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in) self.conv_out = CausalConvAfterNorm(block_in, out_channels, kernel_size=3) @torch.inference_mode() def forward(self, z, is_init=True): z = rearrange(z, "b t c h w -> b c t h w") h = self.conv_in(z, is_init=is_init) if self.version == 2: shortcut = self.shortcut_in(z, is_init=is_init) h = h.add_(shortcut) shortcut = self.shortcut_unpathify(h, is_init=is_init) h = self.conv_unpatchify(h, is_init=is_init) h = h.add_(shortcut) temb = None h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3) h = self.mid.block_1(h, temb, is_init=is_init) h = self.mid.attn_1(h) h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3) h = self.mid.block_2(h, temb, is_init=is_init) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3) h = self.up[i_level].block[i_block](h, temb, is_init=is_init) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: if isinstance(self.up[i_level].upsample, Upsample2D) or (hasattr(self.up[i_level].upsample, "module") and isinstance(self.up[i_level].upsample.module, Upsample2D)): B = h.size(0) h = h.permute(0,2,3,4,1).flatten(0,1) h = self.up[i_level].upsample(h) h = h.unflatten(0, (B, -1)).permute(0,4,1,2,3) else: h = self.up[i_level].upsample(h, is_init=is_init) # end h = h.permute(0,2,3,4,1) # b c l h w -> b l h w c self.norm_out.to(dtype=h.dtype, device=h.device) # To be updated h = base_group_norm_with_zero_pad(h, self.norm_out, act_silu=True, pad_size=2) h = self.conv_out(h) h = h.permute(0,4,1,2,3) if is_init: h = h[:, :, (self.temporal_downsample - 1):] return h def rms_norm(input, normalized_shape, eps=1e-6): dtype = input.dtype input = input.to(torch.float32) variance = input.pow(2).flatten(-len(normalized_shape)).mean(-1)[(...,) + (None,) * len(normalized_shape)] input = input * torch.rsqrt(variance + eps) return input.to(dtype) class DiagonalGaussianDistribution(object): def __init__(self, parameters, deterministic=False, rms_norm_mean=False, only_return_mean=False): self.parameters = parameters self.mean, self.logvar = torch.chunk(parameters, 2, dim=-3) #N,[X],C,H,W self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) self.deterministic = deterministic if self.deterministic: self.var = self.std = torch.zeros_like( self.mean, device=self.parameters.device, dtype=self.parameters.dtype) if rms_norm_mean: self.mean = rms_norm(self.mean, self.mean.size()[1:]) self.only_return_mean = only_return_mean def sample(self, generator=None): # make sure sample is on the same device # as the parameters and has same dtype sample = torch.randn( self.mean.shape, generator=generator, device=self.parameters.device) sample = sample.to(dtype=self.parameters.dtype) x = self.mean + self.std * sample if self.only_return_mean: return self.mean else: return x class StepVideoVAE(nn.Module): def __init__(self, in_channels=3, out_channels=3, z_channels=64, num_res_blocks=2, model_path=None, weight_dict={}, world_size=1, version=2, ): super().__init__() self.frame_len = 17 self.latent_len = 3 if version == 2 else 5 base_group_norm.spatial = True if version == 2 else False self.encoder = VideoEncoder( in_channels=in_channels, z_channels=z_channels, num_res_blocks=num_res_blocks, version=version, ) self.decoder = VideoDecoder( z_channels=z_channels, out_channels=out_channels, num_res_blocks=num_res_blocks, version=version, ) if model_path is not None: weight_dict = self.init_from_ckpt(model_path) if len(weight_dict) != 0: self.load_from_dict(weight_dict) self.convert_channel_last() self.world_size = world_size def init_from_ckpt(self, model_path): from safetensors import safe_open p = {} with safe_open(model_path, framework="pt", device="cpu") as f: for k in f.keys(): tensor = f.get_tensor(k) if k.startswith("decoder.conv_out."): k = k.replace("decoder.conv_out.", "decoder.conv_out.conv.") p[k] = tensor return p def load_from_dict(self, p): self.load_state_dict(p) def convert_channel_last(self): #Conv2d NCHW->NHWC pass def naive_encode(self, x, is_init_image=True): b, l, c, h, w = x.size() x = rearrange(x, 'b l c h w -> b c l h w').contiguous() z = self.encoder(x, l, True) # 下采样[1, 4, 8, 16, 16] return z @torch.inference_mode() def encode(self, x): # b (nc cf) c h w -> (b nc) cf c h w -> encode -> (b nc) cf c h w -> b (nc cf) c h w chunks = list(x.split(self.frame_len, dim=1)) for i in range(len(chunks)): chunks[i] = self.naive_encode(chunks[i], True) z = torch.cat(chunks, dim=1) posterior = DiagonalGaussianDistribution(z) return posterior.sample() def decode_naive(self, z, is_init=True): z = z.to(next(self.decoder.parameters()).dtype) dec = self.decoder(z, is_init) return dec @torch.inference_mode() def decode_original(self, z): # b (nc cf) c h w -> (b nc) cf c h w -> decode -> (b nc) c cf h w -> b (nc cf) c h w chunks = list(z.split(self.latent_len, dim=1)) if self.world_size > 1: chunks_total_num = len(chunks) max_num_per_rank = (chunks_total_num + self.world_size - 1) // self.world_size rank = torch.distributed.get_rank() chunks_ = chunks[max_num_per_rank * rank : max_num_per_rank * (rank + 1)] if len(chunks_) < max_num_per_rank: chunks_.extend(chunks[:max_num_per_rank-len(chunks_)]) chunks = chunks_ for i in range(len(chunks)): chunks[i] = self.decode_naive(chunks[i], True).permute(0,2,1,3,4) x = torch.cat(chunks, dim=1) if self.world_size > 1: x_ = torch.empty([x.size(0), (self.world_size * max_num_per_rank) * self.frame_len, *x.shape[2:]], dtype=x.dtype, device=x.device) torch.distributed.all_gather_into_tensor(x_, x) x = x_[:, : chunks_total_num * self.frame_len] x = self.mix(x) return x def mix(self, x, smooth_scale = 0.6): remain_scale = smooth_scale mix_scale = 1. - remain_scale front = slice(self.frame_len - 1, x.size(1) - 1, self.frame_len) back = slice(self.frame_len, x.size(1), self.frame_len) x[:, front], x[:, back] = ( x[:, front] * remain_scale + x[:, back] * mix_scale, x[:, back] * remain_scale + x[:, front] * mix_scale ) return x def single_decode(self, hidden_states, device): chunks = list(hidden_states.split(self.latent_len, dim=1)) for i in range(len(chunks)): chunks[i] = self.decode_naive(chunks[i].to(device), True).permute(0,2,1,3,4).cpu() x = torch.cat(chunks, dim=1) return x def build_1d_mask(self, length, left_bound, right_bound, border_width): x = torch.ones((length,)) if not left_bound: x[:border_width] = (torch.arange(border_width) + 1) / border_width if not right_bound: x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) return x def build_mask(self, data, is_bound, border_width): _, _, _, H, W = data.shape h = self.build_1d_mask(H, is_bound[0], is_bound[1], border_width[0]) w = self.build_1d_mask(W, is_bound[2], is_bound[3], border_width[1]) h = repeat(h, "H -> H W", H=H, W=W) w = repeat(w, "W -> H W", H=H, W=W) mask = torch.stack([h, w]).min(dim=0).values mask = rearrange(mask, "H W -> 1 1 1 H W") return mask def tiled_decode(self, hidden_states, device, tile_size=(34, 34), tile_stride=(16, 16)): B, T, C, H, W = hidden_states.shape size_h, size_w = tile_size stride_h, stride_w = tile_stride # Split tasks tasks = [] for t in range(0, T, 3): for h in range(0, H, stride_h): if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue for w in range(0, W, stride_w): if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue t_, h_, w_ = t + 3, h + size_h, w + size_w tasks.append((t, t_, h, h_, w, w_)) # Run data_device = "cpu" computation_device = device weight = torch.zeros((1, 1, T//3*17, H * 16, W * 16), dtype=hidden_states.dtype, device=data_device) values = torch.zeros((B, 3, T//3*17, H * 16, W * 16), dtype=hidden_states.dtype, device=data_device) for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"): hidden_states_batch = hidden_states[:, t:t_, :, h:h_, w:w_].to(computation_device) hidden_states_batch = self.decode_naive(hidden_states_batch, True).to(data_device) mask = self.build_mask( hidden_states_batch, is_bound=(h==0, h_>=H, w==0, w_>=W), border_width=((size_h - stride_h) * 16, (size_w - stride_w) * 16) ).to(dtype=hidden_states.dtype, device=data_device) target_t = t // 3 * 17 target_h = h * 16 target_w = w * 16 values[ :, :, target_t: target_t + hidden_states_batch.shape[2], target_h: target_h + hidden_states_batch.shape[3], target_w: target_w + hidden_states_batch.shape[4], ] += hidden_states_batch * mask weight[ :, :, target_t: target_t + hidden_states_batch.shape[2], target_h: target_h + hidden_states_batch.shape[3], target_w: target_w + hidden_states_batch.shape[4], ] += mask return values / weight def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(16, 16), smooth_scale=0.6): hidden_states = hidden_states.to("cpu") if tiled: video = self.tiled_decode(hidden_states, device, tile_size, tile_stride) else: video = self.single_decode(hidden_states, device) video = self.mix(video, smooth_scale=smooth_scale) return video @staticmethod def state_dict_converter(): return StepVideoVAEStateDictConverter() class StepVideoVAEStateDictConverter: def __init__(self): super().__init__() def from_diffusers(self, state_dict): return self.from_civitai(state_dict) def from_civitai(self, state_dict): state_dict_ = {} for name, param in state_dict.items(): if name.startswith("decoder.conv_out."): name_ = name.replace("decoder.conv_out.", "decoder.conv_out.conv.") else: name_ = name state_dict_[name_] = param return state_dict_ ================================================ FILE: diffsynth/models/svd_image_encoder.py ================================================ import torch from .sd_text_encoder import CLIPEncoderLayer class CLIPVisionEmbeddings(torch.nn.Module): def __init__(self, embed_dim=1280, image_size=224, patch_size=14, num_channels=3): super().__init__() # class_embeds (This is a fixed tensor) self.class_embedding = torch.nn.Parameter(torch.randn(1, 1, embed_dim)) # position_embeds self.patch_embedding = torch.nn.Conv2d(in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=False) # position_embeds (This is a fixed tensor) self.position_embeds = torch.nn.Parameter(torch.zeros(1, (image_size // patch_size) ** 2 + 1, embed_dim)) def forward(self, pixel_values): batch_size = pixel_values.shape[0] patch_embeds = self.patch_embedding(pixel_values) patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.repeat(batch_size, 1, 1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + self.position_embeds return embeddings class SVDImageEncoder(torch.nn.Module): def __init__(self, embed_dim=1280, layer_norm_eps=1e-5, num_encoder_layers=32, encoder_intermediate_size=5120, projection_dim=1024, num_heads=16, head_dim=80): super().__init__() self.embeddings = CLIPVisionEmbeddings(embed_dim=embed_dim) self.pre_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps) self.encoders = torch.nn.ModuleList([ CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=num_heads, head_dim=head_dim, use_quick_gelu=False) for _ in range(num_encoder_layers)]) self.post_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps) self.visual_projection = torch.nn.Linear(embed_dim, projection_dim, bias=False) def forward(self, pixel_values): embeds = self.embeddings(pixel_values) embeds = self.pre_layernorm(embeds) for encoder_id, encoder in enumerate(self.encoders): embeds = encoder(embeds) embeds = self.post_layernorm(embeds[:, 0, :]) embeds = self.visual_projection(embeds) return embeds @staticmethod def state_dict_converter(): return SVDImageEncoderStateDictConverter() class SVDImageEncoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "vision_model.embeddings.patch_embedding.weight": "embeddings.patch_embedding.weight", "vision_model.embeddings.class_embedding": "embeddings.class_embedding", "vision_model.embeddings.position_embedding.weight": "embeddings.position_embeds", "vision_model.pre_layrnorm.weight": "pre_layernorm.weight", "vision_model.pre_layrnorm.bias": "pre_layernorm.bias", "vision_model.post_layernorm.weight": "post_layernorm.weight", "vision_model.post_layernorm.bias": "post_layernorm.bias", "visual_projection.weight": "visual_projection.weight" } attn_rename_dict = { "self_attn.q_proj": "attn.to_q", "self_attn.k_proj": "attn.to_k", "self_attn.v_proj": "attn.to_v", "self_attn.out_proj": "attn.to_out", "layer_norm1": "layer_norm1", "layer_norm2": "layer_norm2", "mlp.fc1": "fc1", "mlp.fc2": "fc2", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "vision_model.embeddings.class_embedding": param = state_dict[name].view(1, 1, -1) elif name == "vision_model.embeddings.position_embedding.weight": param = state_dict[name].unsqueeze(0) state_dict_[rename_dict[name]] = param elif name.startswith("vision_model.encoder.layers."): param = state_dict[name] names = name.split(".") layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) state_dict_[name_] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "conditioner.embedders.0.open_clip.model.visual.class_embedding": "embeddings.class_embedding", "conditioner.embedders.0.open_clip.model.visual.conv1.weight": "embeddings.patch_embedding.weight", "conditioner.embedders.0.open_clip.model.visual.ln_post.bias": "post_layernorm.bias", "conditioner.embedders.0.open_clip.model.visual.ln_post.weight": "post_layernorm.weight", "conditioner.embedders.0.open_clip.model.visual.ln_pre.bias": "pre_layernorm.bias", "conditioner.embedders.0.open_clip.model.visual.ln_pre.weight": "pre_layernorm.weight", "conditioner.embedders.0.open_clip.model.visual.positional_embedding": "embeddings.position_embeds", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'], "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'], "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'], "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'], "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'], 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"encoders.9.fc1.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_fc.weight": "encoders.9.fc1.weight", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.bias": "encoders.9.fc2.bias", "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight", "conditioner.embedders.0.open_clip.model.visual.proj": "visual_projection.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "conditioner.embedders.0.open_clip.model.visual.class_embedding": param = param.reshape((1, 1, param.shape[0])) elif name == "conditioner.embedders.0.open_clip.model.visual.positional_embedding": param = param.reshape((1, param.shape[0], param.shape[1])) elif name == "conditioner.embedders.0.open_clip.model.visual.proj": param = param.T if isinstance(rename_dict[name], str): state_dict_[rename_dict[name]] = param else: length = param.shape[0] // 3 for i, rename in enumerate(rename_dict[name]): state_dict_[rename] = param[i*length: i*length+length] return state_dict_ ================================================ FILE: diffsynth/models/svd_unet.py ================================================ import torch, math from einops import rearrange, repeat from .sd_unet import Timesteps, PushBlock, PopBlock, Attention, GEGLU, ResnetBlock, AttentionBlock, DownSampler, UpSampler class TemporalResnetBlock(torch.nn.Module): def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5): super().__init__() self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0)) if temb_channels is not None: self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels) self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0)) self.nonlinearity = torch.nn.SiLU() self.conv_shortcut = None if in_channels != out_channels: self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True) def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): x = rearrange(hidden_states, "f c h w -> 1 c f h w") x = self.norm1(x) x = self.nonlinearity(x) x = self.conv1(x) if time_emb is not None: emb = self.nonlinearity(time_emb) emb = self.time_emb_proj(emb) emb = repeat(emb, "b c -> b c f 1 1", f=hidden_states.shape[0]) x = x + emb x = self.norm2(x) x = self.nonlinearity(x) x = self.conv2(x) if self.conv_shortcut is not None: hidden_states = self.conv_shortcut(hidden_states) x = rearrange(x[0], "c f h w -> f c h w") hidden_states = hidden_states + x return hidden_states, time_emb, text_emb, res_stack def get_timestep_embedding( timesteps: torch.Tensor, embedding_dim: int, flip_sin_to_cos: bool = False, downscale_freq_shift: float = 1, scale: float = 1, max_period: int = 10000, computation_device = None, ): """ This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. """ assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" half_dim = embedding_dim // 2 exponent = -math.log(max_period) * torch.arange( start=0, end=half_dim, dtype=torch.float32, device=timesteps.device if computation_device is None else computation_device ) exponent = exponent / (half_dim - downscale_freq_shift) emb = torch.exp(exponent).to(timesteps.device) emb = timesteps[:, None].float() * emb[None, :] # scale embeddings emb = scale * emb # concat sine and cosine embeddings emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) # flip sine and cosine embeddings if flip_sin_to_cos: emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) # zero pad if embedding_dim % 2 == 1: emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) return emb class TemporalTimesteps(torch.nn.Module): def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, computation_device = None): super().__init__() self.num_channels = num_channels self.flip_sin_to_cos = flip_sin_to_cos self.downscale_freq_shift = downscale_freq_shift self.computation_device = computation_device def forward(self, timesteps): t_emb = get_timestep_embedding( timesteps, self.num_channels, flip_sin_to_cos=self.flip_sin_to_cos, downscale_freq_shift=self.downscale_freq_shift, computation_device=self.computation_device, ) return t_emb class TrainableTemporalTimesteps(torch.nn.Module): def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, num_frames: int): super().__init__() timesteps = PositionalID()(num_frames) embeddings = get_timestep_embedding(timesteps, num_channels, flip_sin_to_cos, downscale_freq_shift) self.embeddings = torch.nn.Parameter(embeddings) def forward(self, timesteps): t_emb = self.embeddings[timesteps] return t_emb class PositionalID(torch.nn.Module): def __init__(self, max_id=25, repeat_length=20): super().__init__() self.max_id = max_id self.repeat_length = repeat_length def frame_id_to_position_id(self, frame_id): if frame_id < self.max_id: position_id = frame_id else: position_id = (frame_id - self.max_id) % (self.repeat_length * 2) if position_id < self.repeat_length: position_id = self.max_id - 2 - position_id else: position_id = self.max_id - 2 * self.repeat_length + position_id return position_id def forward(self, num_frames, pivot_frame_id=0): position_ids = [self.frame_id_to_position_id(abs(i-pivot_frame_id)) for i in range(num_frames)] position_ids = torch.IntTensor(position_ids) return position_ids class TemporalAttentionBlock(torch.nn.Module): def __init__(self, num_attention_heads, attention_head_dim, in_channels, cross_attention_dim=None, add_positional_conv=None): super().__init__() self.positional_embedding_proj = torch.nn.Sequential( torch.nn.Linear(in_channels, in_channels * 4), torch.nn.SiLU(), torch.nn.Linear(in_channels * 4, in_channels) ) if add_positional_conv is not None: self.positional_embedding = TrainableTemporalTimesteps(in_channels, True, 0, add_positional_conv) self.positional_conv = torch.nn.Conv3d(in_channels, in_channels, kernel_size=3, padding=1, padding_mode="reflect") else: self.positional_embedding = TemporalTimesteps(in_channels, True, 0) self.positional_conv = None self.norm_in = torch.nn.LayerNorm(in_channels) self.act_fn_in = GEGLU(in_channels, in_channels * 4) self.ff_in = torch.nn.Linear(in_channels * 4, in_channels) self.norm1 = torch.nn.LayerNorm(in_channels) self.attn1 = Attention( q_dim=in_channels, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True ) self.norm2 = torch.nn.LayerNorm(in_channels) self.attn2 = Attention( q_dim=in_channels, kv_dim=cross_attention_dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True ) self.norm_out = torch.nn.LayerNorm(in_channels) self.act_fn_out = GEGLU(in_channels, in_channels * 4) self.ff_out = torch.nn.Linear(in_channels * 4, in_channels) def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): batch, inner_dim, height, width = hidden_states.shape pos_emb = torch.arange(batch) pos_emb = self.positional_embedding(pos_emb).to(dtype=hidden_states.dtype, device=hidden_states.device) pos_emb = self.positional_embedding_proj(pos_emb) hidden_states = rearrange(hidden_states, "T C H W -> 1 C T H W") + rearrange(pos_emb, "T C -> 1 C T 1 1") if self.positional_conv is not None: hidden_states = self.positional_conv(hidden_states) hidden_states = rearrange(hidden_states[0], "C T H W -> (H W) T C") residual = hidden_states hidden_states = self.norm_in(hidden_states) hidden_states = self.act_fn_in(hidden_states) hidden_states = self.ff_in(hidden_states) hidden_states = hidden_states + residual norm_hidden_states = self.norm1(hidden_states) attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None) hidden_states = attn_output + hidden_states norm_hidden_states = self.norm2(hidden_states) attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=text_emb.repeat(height * width, 1)) hidden_states = attn_output + hidden_states residual = hidden_states hidden_states = self.norm_out(hidden_states) hidden_states = self.act_fn_out(hidden_states) hidden_states = self.ff_out(hidden_states) hidden_states = hidden_states + residual hidden_states = hidden_states.reshape(height, width, batch, inner_dim).permute(2, 3, 0, 1) return hidden_states, time_emb, text_emb, res_stack class PopMixBlock(torch.nn.Module): def __init__(self, in_channels=None): super().__init__() self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5])) self.need_proj = in_channels is not None if self.need_proj: self.proj = torch.nn.Linear(in_channels, in_channels) def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): res_hidden_states = res_stack.pop() alpha = torch.sigmoid(self.mix_factor) hidden_states = alpha * res_hidden_states + (1 - alpha) * hidden_states if self.need_proj: hidden_states = hidden_states.permute(0, 2, 3, 1) hidden_states = self.proj(hidden_states) hidden_states = hidden_states.permute(0, 3, 1, 2) res_hidden_states = res_stack.pop() hidden_states = hidden_states + res_hidden_states return hidden_states, time_emb, text_emb, res_stack class SVDUNet(torch.nn.Module): def __init__(self, add_positional_conv=None): super().__init__() self.time_proj = Timesteps(320) self.time_embedding = torch.nn.Sequential( torch.nn.Linear(320, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.add_time_proj = Timesteps(256) self.add_time_embedding = torch.nn.Sequential( torch.nn.Linear(768, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.conv_in = torch.nn.Conv2d(8, 320, kernel_size=3, padding=1) self.blocks = torch.nn.ModuleList([ # CrossAttnDownBlockSpatioTemporal ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PushBlock(), ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PushBlock(), DownSampler(320), PushBlock(), # CrossAttnDownBlockSpatioTemporal ResnetBlock(320, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PushBlock(), ResnetBlock(640, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PushBlock(), DownSampler(640), PushBlock(), # CrossAttnDownBlockSpatioTemporal ResnetBlock(640, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(), ResnetBlock(1280, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(), DownSampler(1280), PushBlock(), # DownBlockSpatioTemporal ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), # UNetMidBlockSpatioTemporal ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), # UpBlockSpatioTemporal PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), UpSampler(1280), # CrossAttnUpBlockSpatioTemporal PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PopBlock(), ResnetBlock(1920, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), UpSampler(1280), # CrossAttnUpBlockSpatioTemporal PopBlock(), ResnetBlock(1920, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PopBlock(), ResnetBlock(1280, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PopBlock(), ResnetBlock(960, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), UpSampler(640), # CrossAttnUpBlockSpatioTemporal PopBlock(), ResnetBlock(960, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), ]) self.conv_norm_out = torch.nn.GroupNorm(32, 320, eps=1e-05, affine=True) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) def build_mask(self, data, is_bound): T, C, H, W = data.shape t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) border_width = (T + H + W) // 6 pad = torch.ones_like(t) * border_width mask = torch.stack([ pad if is_bound[0] else t + 1, pad if is_bound[1] else T - t, pad if is_bound[2] else h + 1, pad if is_bound[3] else H - h, pad if is_bound[4] else w + 1, pad if is_bound[5] else W - w ]).min(dim=0).values mask = mask.clip(1, border_width) mask = (mask / border_width).to(dtype=data.dtype, device=data.device) mask = rearrange(mask, "T H W -> T 1 H W") return mask def tiled_forward( self, sample, timestep, encoder_hidden_states, add_time_id, batch_time=25, batch_height=128, batch_width=128, stride_time=5, stride_height=64, stride_width=64, progress_bar=lambda x:x ): data_device = sample.device computation_device = self.conv_in.weight.device torch_dtype = sample.dtype T, C, H, W = sample.shape weight = torch.zeros((T, 1, H, W), dtype=torch_dtype, device=data_device) values = torch.zeros((T, 4, H, W), dtype=torch_dtype, device=data_device) # Split tasks tasks = [] for t in range(0, T, stride_time): for h in range(0, H, stride_height): for w in range(0, W, stride_width): if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\ or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\ or (w-stride_width >= 0 and w-stride_width+batch_width >= W): continue tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width)) # Run for tl, tr, hl, hr, wl, wr in progress_bar(tasks): sample_batch = sample[tl:tr, :, hl:hr, wl:wr].to(computation_device) sample_batch = self.forward(sample_batch, timestep, encoder_hidden_states, add_time_id).to(data_device) mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W)) values[tl:tr, :, hl:hr, wl:wr] += sample_batch * mask weight[tl:tr, :, hl:hr, wl:wr] += mask values /= weight return values def forward(self, sample, timestep, encoder_hidden_states, add_time_id, use_gradient_checkpointing=False, **kwargs): # 1. time timestep = torch.tensor((timestep,)).to(sample.device) t_emb = self.time_proj(timestep).to(sample.dtype) t_emb = self.time_embedding(t_emb) add_embeds = self.add_time_proj(add_time_id.flatten()).to(sample.dtype) add_embeds = add_embeds.reshape((-1, 768)) add_embeds = self.add_time_embedding(add_embeds) time_emb = t_emb + add_embeds # 2. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = self.conv_in(sample) text_emb = encoder_hidden_states res_stack = [hidden_states] # 3. blocks def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward for i, block in enumerate(self.blocks): if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock) or isinstance(block, PopMixBlock)): hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, time_emb, text_emb, res_stack, use_reentrant=False, ) else: hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) # 4. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states @staticmethod def state_dict_converter(): return SVDUNetStateDictConverter() class SVDUNetStateDictConverter: def __init__(self): pass def get_block_name(self, names): if names[0] in ["down_blocks", "mid_block", "up_blocks"]: if names[4] in ["norm", "proj_in"]: return ".".join(names[:4] + ["transformer_blocks"]) elif names[4] in ["time_pos_embed"]: return ".".join(names[:4] + ["temporal_transformer_blocks"]) elif names[4] in ["proj_out"]: return ".".join(names[:4] + ["time_mixer"]) else: return ".".join(names[:5]) return "" def from_diffusers(self, state_dict): rename_dict = { "time_embedding.linear_1": "time_embedding.0", "time_embedding.linear_2": "time_embedding.2", "add_embedding.linear_1": "add_time_embedding.0", "add_embedding.linear_2": "add_time_embedding.2", "conv_in": "conv_in", "conv_norm_out": "conv_norm_out", "conv_out": "conv_out", } blocks_rename_dict = [ "down_blocks.0.resnets.0.spatial_res_block", None, "down_blocks.0.resnets.0.temporal_res_block", "down_blocks.0.resnets.0.time_mixer", None, "down_blocks.0.attentions.0.transformer_blocks", None, "down_blocks.0.attentions.0.temporal_transformer_blocks", "down_blocks.0.attentions.0.time_mixer", None, "down_blocks.0.resnets.1.spatial_res_block", None, "down_blocks.0.resnets.1.temporal_res_block", "down_blocks.0.resnets.1.time_mixer", None, "down_blocks.0.attentions.1.transformer_blocks", None, "down_blocks.0.attentions.1.temporal_transformer_blocks", "down_blocks.0.attentions.1.time_mixer", None, "down_blocks.0.downsamplers.0.conv", None, "down_blocks.1.resnets.0.spatial_res_block", None, "down_blocks.1.resnets.0.temporal_res_block", "down_blocks.1.resnets.0.time_mixer", None, "down_blocks.1.attentions.0.transformer_blocks", None, "down_blocks.1.attentions.0.temporal_transformer_blocks", "down_blocks.1.attentions.0.time_mixer", None, "down_blocks.1.resnets.1.spatial_res_block", None, "down_blocks.1.resnets.1.temporal_res_block", "down_blocks.1.resnets.1.time_mixer", None, "down_blocks.1.attentions.1.transformer_blocks", None, "down_blocks.1.attentions.1.temporal_transformer_blocks", "down_blocks.1.attentions.1.time_mixer", None, "down_blocks.1.downsamplers.0.conv", None, "down_blocks.2.resnets.0.spatial_res_block", None, "down_blocks.2.resnets.0.temporal_res_block", "down_blocks.2.resnets.0.time_mixer", None, "down_blocks.2.attentions.0.transformer_blocks", None, "down_blocks.2.attentions.0.temporal_transformer_blocks", "down_blocks.2.attentions.0.time_mixer", None, "down_blocks.2.resnets.1.spatial_res_block", None, "down_blocks.2.resnets.1.temporal_res_block", "down_blocks.2.resnets.1.time_mixer", None, "down_blocks.2.attentions.1.transformer_blocks", None, "down_blocks.2.attentions.1.temporal_transformer_blocks", "down_blocks.2.attentions.1.time_mixer", None, "down_blocks.2.downsamplers.0.conv", None, "down_blocks.3.resnets.0.spatial_res_block", None, "down_blocks.3.resnets.0.temporal_res_block", "down_blocks.3.resnets.0.time_mixer", None, "down_blocks.3.resnets.1.spatial_res_block", None, "down_blocks.3.resnets.1.temporal_res_block", "down_blocks.3.resnets.1.time_mixer", None, "mid_block.mid_block.resnets.0.spatial_res_block", None, "mid_block.mid_block.resnets.0.temporal_res_block", "mid_block.mid_block.resnets.0.time_mixer", None, "mid_block.mid_block.attentions.0.transformer_blocks", None, "mid_block.mid_block.attentions.0.temporal_transformer_blocks", "mid_block.mid_block.attentions.0.time_mixer", "mid_block.mid_block.resnets.1.spatial_res_block", None, "mid_block.mid_block.resnets.1.temporal_res_block", "mid_block.mid_block.resnets.1.time_mixer", None, "up_blocks.0.resnets.0.spatial_res_block", None, "up_blocks.0.resnets.0.temporal_res_block", "up_blocks.0.resnets.0.time_mixer", None, "up_blocks.0.resnets.1.spatial_res_block", None, "up_blocks.0.resnets.1.temporal_res_block", "up_blocks.0.resnets.1.time_mixer", None, "up_blocks.0.resnets.2.spatial_res_block", None, "up_blocks.0.resnets.2.temporal_res_block", "up_blocks.0.resnets.2.time_mixer", "up_blocks.0.upsamplers.0.conv", None, "up_blocks.1.resnets.0.spatial_res_block", None, "up_blocks.1.resnets.0.temporal_res_block", "up_blocks.1.resnets.0.time_mixer", None, "up_blocks.1.attentions.0.transformer_blocks", None, "up_blocks.1.attentions.0.temporal_transformer_blocks", "up_blocks.1.attentions.0.time_mixer", None, "up_blocks.1.resnets.1.spatial_res_block", None, "up_blocks.1.resnets.1.temporal_res_block", "up_blocks.1.resnets.1.time_mixer", None, "up_blocks.1.attentions.1.transformer_blocks", None, "up_blocks.1.attentions.1.temporal_transformer_blocks", "up_blocks.1.attentions.1.time_mixer", None, "up_blocks.1.resnets.2.spatial_res_block", None, "up_blocks.1.resnets.2.temporal_res_block", "up_blocks.1.resnets.2.time_mixer", None, "up_blocks.1.attentions.2.transformer_blocks", None, "up_blocks.1.attentions.2.temporal_transformer_blocks", "up_blocks.1.attentions.2.time_mixer", "up_blocks.1.upsamplers.0.conv", None, "up_blocks.2.resnets.0.spatial_res_block", None, "up_blocks.2.resnets.0.temporal_res_block", "up_blocks.2.resnets.0.time_mixer", None, "up_blocks.2.attentions.0.transformer_blocks", None, "up_blocks.2.attentions.0.temporal_transformer_blocks", "up_blocks.2.attentions.0.time_mixer", None, "up_blocks.2.resnets.1.spatial_res_block", None, "up_blocks.2.resnets.1.temporal_res_block", "up_blocks.2.resnets.1.time_mixer", None, "up_blocks.2.attentions.1.transformer_blocks", None, "up_blocks.2.attentions.1.temporal_transformer_blocks", "up_blocks.2.attentions.1.time_mixer", None, "up_blocks.2.resnets.2.spatial_res_block", None, "up_blocks.2.resnets.2.temporal_res_block", "up_blocks.2.resnets.2.time_mixer", None, "up_blocks.2.attentions.2.transformer_blocks", None, "up_blocks.2.attentions.2.temporal_transformer_blocks", "up_blocks.2.attentions.2.time_mixer", "up_blocks.2.upsamplers.0.conv", None, "up_blocks.3.resnets.0.spatial_res_block", None, "up_blocks.3.resnets.0.temporal_res_block", "up_blocks.3.resnets.0.time_mixer", None, "up_blocks.3.attentions.0.transformer_blocks", None, "up_blocks.3.attentions.0.temporal_transformer_blocks", "up_blocks.3.attentions.0.time_mixer", None, "up_blocks.3.resnets.1.spatial_res_block", None, "up_blocks.3.resnets.1.temporal_res_block", "up_blocks.3.resnets.1.time_mixer", None, "up_blocks.3.attentions.1.transformer_blocks", None, "up_blocks.3.attentions.1.temporal_transformer_blocks", "up_blocks.3.attentions.1.time_mixer", None, "up_blocks.3.resnets.2.spatial_res_block", None, "up_blocks.3.resnets.2.temporal_res_block", "up_blocks.3.resnets.2.time_mixer", None, "up_blocks.3.attentions.2.transformer_blocks", None, "up_blocks.3.attentions.2.temporal_transformer_blocks", "up_blocks.3.attentions.2.time_mixer", ] blocks_rename_dict = {i:j for j,i in enumerate(blocks_rename_dict) if i is not None} state_dict_ = {} for name, param in sorted(state_dict.items()): names = name.split(".") if names[0] == "mid_block": names = ["mid_block"] + names if names[-1] in ["weight", "bias"]: name_prefix = ".".join(names[:-1]) if name_prefix in rename_dict: state_dict_[rename_dict[name_prefix] + "." + names[-1]] = param else: block_name = self.get_block_name(names) if "resnets" in block_name and block_name in blocks_rename_dict: rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[5:]) state_dict_[rename] = param elif ("downsamplers" in block_name or "upsamplers" in block_name) and block_name in blocks_rename_dict: rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[-2:]) state_dict_[rename] = param elif "attentions" in block_name and block_name in blocks_rename_dict: attention_id = names[5] if "transformer_blocks" in names: suffix_dict = { "attn1.to_out.0": "attn1.to_out", "attn2.to_out.0": "attn2.to_out", "ff.net.0.proj": "act_fn.proj", "ff.net.2": "ff", } suffix = ".".join(names[6:-1]) suffix = suffix_dict.get(suffix, suffix) rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "transformer_blocks", attention_id, suffix, names[-1]]) elif "temporal_transformer_blocks" in names: suffix_dict = { "attn1.to_out.0": "attn1.to_out", "attn2.to_out.0": "attn2.to_out", "ff_in.net.0.proj": "act_fn_in.proj", "ff_in.net.2": "ff_in", "ff.net.0.proj": "act_fn_out.proj", "ff.net.2": "ff_out", "norm3": "norm_out", } suffix = ".".join(names[6:-1]) suffix = suffix_dict.get(suffix, suffix) rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]]) elif "time_mixer" in block_name: rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "proj", names[-1]]) else: suffix_dict = { "linear_1": "positional_embedding_proj.0", "linear_2": "positional_embedding_proj.2", } suffix = names[-2] suffix = suffix_dict.get(suffix, suffix) rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]]) state_dict_[rename] = param else: print(name) else: block_name = self.get_block_name(names) if len(block_name)>0 and block_name in blocks_rename_dict: rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), names[-1]]) state_dict_[rename] = param return state_dict_ def from_civitai(self, state_dict, add_positional_conv=None): rename_dict = { "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", "model.diffusion_model.input_blocks.1.0.time_mixer.mix_factor": "blocks.3.mix_factor", "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.bias": "blocks.2.time_emb_proj.bias", "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.weight": "blocks.2.time_emb_proj.weight", "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.0.bias": 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"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.173.transformer_blocks.0.act_fn.proj.bias", "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.173.transformer_blocks.0.act_fn.proj.weight", "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.bias": "blocks.173.transformer_blocks.0.ff.bias", "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.weight": "blocks.173.transformer_blocks.0.ff.weight", "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.bias": "blocks.173.transformer_blocks.0.norm1.bias", "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.weight": "blocks.173.transformer_blocks.0.norm1.weight", "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.bias": "blocks.173.transformer_blocks.0.norm2.bias", "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.weight": "blocks.173.transformer_blocks.0.norm2.weight", "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.bias": "blocks.173.transformer_blocks.0.norm3.bias", "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight": "blocks.173.transformer_blocks.0.norm3.weight", "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias", "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight", "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias", "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param if add_positional_conv is not None: extra_names = [ "blocks.7.positional_conv", "blocks.17.positional_conv", "blocks.29.positional_conv", "blocks.39.positional_conv", "blocks.51.positional_conv", "blocks.61.positional_conv", "blocks.83.positional_conv", "blocks.113.positional_conv", "blocks.123.positional_conv", "blocks.133.positional_conv", "blocks.144.positional_conv", "blocks.154.positional_conv", "blocks.164.positional_conv", "blocks.175.positional_conv", "blocks.185.positional_conv", "blocks.195.positional_conv", ] extra_channels = [320, 320, 640, 640, 1280, 1280, 1280, 1280, 1280, 1280, 640, 640, 640, 320, 320, 320] for name, channels in zip(extra_names, extra_channels): weight = torch.zeros((channels, channels, 3, 3, 3)) weight[:,:,1,1,1] = torch.eye(channels, channels) bias = torch.zeros((channels,)) state_dict_[name + ".weight"] = weight state_dict_[name + ".bias"] = bias return state_dict_ ================================================ FILE: diffsynth/models/svd_vae_decoder.py ================================================ import torch from .attention import Attention from .sd_unet import ResnetBlock, UpSampler from .tiler import TileWorker from einops import rearrange, repeat class VAEAttentionBlock(torch.nn.Module): def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): super().__init__() inner_dim = num_attention_heads * attention_head_dim self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) self.transformer_blocks = torch.nn.ModuleList([ Attention( inner_dim, num_attention_heads, attention_head_dim, bias_q=True, bias_kv=True, bias_out=True ) for d in range(num_layers) ]) def forward(self, hidden_states, time_emb, text_emb, res_stack): batch, _, height, width = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) for block in self.transformer_blocks: hidden_states = block(hidden_states) hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() hidden_states = hidden_states + residual return hidden_states, time_emb, text_emb, res_stack class TemporalResnetBlock(torch.nn.Module): def __init__(self, in_channels, out_channels, groups=32, eps=1e-5): super().__init__() self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0)) self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0)) self.nonlinearity = torch.nn.SiLU() self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5])) def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): x_spatial = hidden_states x = rearrange(hidden_states, "T C H W -> 1 C T H W") x = self.norm1(x) x = self.nonlinearity(x) x = self.conv1(x) x = self.norm2(x) x = self.nonlinearity(x) x = self.conv2(x) x_temporal = hidden_states + x[0].permute(1, 0, 2, 3) alpha = torch.sigmoid(self.mix_factor) hidden_states = alpha * x_temporal + (1 - alpha) * x_spatial return hidden_states, time_emb, text_emb, res_stack class SVDVAEDecoder(torch.nn.Module): def __init__(self): super().__init__() self.scaling_factor = 0.18215 self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1) self.blocks = torch.nn.ModuleList([ # UNetMidBlock ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), # UpDecoderBlock ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), UpSampler(512), # UpDecoderBlock ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), UpSampler(512), # UpDecoderBlock ResnetBlock(512, 256, eps=1e-6), TemporalResnetBlock(256, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), TemporalResnetBlock(256, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), TemporalResnetBlock(256, 256, eps=1e-6), UpSampler(256), # UpDecoderBlock ResnetBlock(256, 128, eps=1e-6), TemporalResnetBlock(128, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), TemporalResnetBlock(128, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), TemporalResnetBlock(128, 128, eps=1e-6), ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) self.time_conv_out = torch.nn.Conv3d(3, 3, kernel_size=(3, 1, 1), padding=(1, 0, 0)) def forward(self, sample): # 1. pre-process hidden_states = rearrange(sample, "C T H W -> T C H W") hidden_states = hidden_states / self.scaling_factor hidden_states = self.conv_in(hidden_states) time_emb, text_emb, res_stack = None, None, None # 2. blocks for i, block in enumerate(self.blocks): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) # 3. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) hidden_states = rearrange(hidden_states, "T C H W -> C T H W") hidden_states = self.time_conv_out(hidden_states) return hidden_states def build_mask(self, data, is_bound): _, T, H, W = data.shape t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) border_width = (T + H + W) // 6 pad = torch.ones_like(t) * border_width mask = torch.stack([ pad if is_bound[0] else t + 1, pad if is_bound[1] else T - t, pad if is_bound[2] else h + 1, pad if is_bound[3] else H - h, pad if is_bound[4] else w + 1, pad if is_bound[5] else W - w ]).min(dim=0).values mask = mask.clip(1, border_width) mask = (mask / border_width).to(dtype=data.dtype, device=data.device) mask = rearrange(mask, "T H W -> 1 T H W") return mask def decode_video( self, sample, batch_time=8, batch_height=128, batch_width=128, stride_time=4, stride_height=32, stride_width=32, progress_bar=lambda x:x ): sample = sample.permute(1, 0, 2, 3) data_device = sample.device computation_device = self.conv_in.weight.device torch_dtype = sample.dtype _, T, H, W = sample.shape weight = torch.zeros((1, T, H*8, W*8), dtype=torch_dtype, device=data_device) values = torch.zeros((3, T, H*8, W*8), dtype=torch_dtype, device=data_device) # Split tasks tasks = [] for t in range(0, T, stride_time): for h in range(0, H, stride_height): for w in range(0, W, stride_width): if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\ or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\ or (w-stride_width >= 0 and w-stride_width+batch_width >= W): continue tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width)) # Run for tl, tr, hl, hr, wl, wr in progress_bar(tasks): sample_batch = sample[:, tl:tr, hl:hr, wl:wr].to(computation_device) sample_batch = self.forward(sample_batch).to(data_device) mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W)) values[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += sample_batch * mask weight[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += mask values /= weight return values @staticmethod def state_dict_converter(): return SVDVAEDecoderStateDictConverter() class SVDVAEDecoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): static_rename_dict = { "decoder.conv_in": "conv_in", "decoder.mid_block.attentions.0.group_norm": "blocks.2.norm", "decoder.mid_block.attentions.0.to_q": "blocks.2.transformer_blocks.0.to_q", "decoder.mid_block.attentions.0.to_k": "blocks.2.transformer_blocks.0.to_k", "decoder.mid_block.attentions.0.to_v": "blocks.2.transformer_blocks.0.to_v", "decoder.mid_block.attentions.0.to_out.0": "blocks.2.transformer_blocks.0.to_out", "decoder.up_blocks.0.upsamplers.0.conv": "blocks.11.conv", "decoder.up_blocks.1.upsamplers.0.conv": "blocks.18.conv", "decoder.up_blocks.2.upsamplers.0.conv": "blocks.25.conv", "decoder.conv_norm_out": "conv_norm_out", "decoder.conv_out": "conv_out", "decoder.time_conv_out": "time_conv_out" } prefix_rename_dict = { "decoder.mid_block.resnets.0.spatial_res_block": "blocks.0", "decoder.mid_block.resnets.0.temporal_res_block": "blocks.1", "decoder.mid_block.resnets.0.time_mixer": "blocks.1", "decoder.mid_block.resnets.1.spatial_res_block": "blocks.3", "decoder.mid_block.resnets.1.temporal_res_block": "blocks.4", "decoder.mid_block.resnets.1.time_mixer": "blocks.4", "decoder.up_blocks.0.resnets.0.spatial_res_block": "blocks.5", "decoder.up_blocks.0.resnets.0.temporal_res_block": "blocks.6", "decoder.up_blocks.0.resnets.0.time_mixer": "blocks.6", "decoder.up_blocks.0.resnets.1.spatial_res_block": "blocks.7", "decoder.up_blocks.0.resnets.1.temporal_res_block": "blocks.8", "decoder.up_blocks.0.resnets.1.time_mixer": "blocks.8", "decoder.up_blocks.0.resnets.2.spatial_res_block": "blocks.9", "decoder.up_blocks.0.resnets.2.temporal_res_block": "blocks.10", "decoder.up_blocks.0.resnets.2.time_mixer": "blocks.10", "decoder.up_blocks.1.resnets.0.spatial_res_block": "blocks.12", "decoder.up_blocks.1.resnets.0.temporal_res_block": "blocks.13", "decoder.up_blocks.1.resnets.0.time_mixer": "blocks.13", "decoder.up_blocks.1.resnets.1.spatial_res_block": "blocks.14", "decoder.up_blocks.1.resnets.1.temporal_res_block": "blocks.15", "decoder.up_blocks.1.resnets.1.time_mixer": "blocks.15", "decoder.up_blocks.1.resnets.2.spatial_res_block": "blocks.16", "decoder.up_blocks.1.resnets.2.temporal_res_block": "blocks.17", "decoder.up_blocks.1.resnets.2.time_mixer": "blocks.17", "decoder.up_blocks.2.resnets.0.spatial_res_block": "blocks.19", "decoder.up_blocks.2.resnets.0.temporal_res_block": "blocks.20", "decoder.up_blocks.2.resnets.0.time_mixer": "blocks.20", "decoder.up_blocks.2.resnets.1.spatial_res_block": "blocks.21", "decoder.up_blocks.2.resnets.1.temporal_res_block": "blocks.22", "decoder.up_blocks.2.resnets.1.time_mixer": "blocks.22", "decoder.up_blocks.2.resnets.2.spatial_res_block": "blocks.23", "decoder.up_blocks.2.resnets.2.temporal_res_block": "blocks.24", "decoder.up_blocks.2.resnets.2.time_mixer": "blocks.24", "decoder.up_blocks.3.resnets.0.spatial_res_block": "blocks.26", "decoder.up_blocks.3.resnets.0.temporal_res_block": "blocks.27", "decoder.up_blocks.3.resnets.0.time_mixer": "blocks.27", "decoder.up_blocks.3.resnets.1.spatial_res_block": "blocks.28", "decoder.up_blocks.3.resnets.1.temporal_res_block": "blocks.29", "decoder.up_blocks.3.resnets.1.time_mixer": "blocks.29", "decoder.up_blocks.3.resnets.2.spatial_res_block": "blocks.30", "decoder.up_blocks.3.resnets.2.temporal_res_block": "blocks.31", "decoder.up_blocks.3.resnets.2.time_mixer": "blocks.31", } suffix_rename_dict = { "norm1.weight": "norm1.weight", "conv1.weight": "conv1.weight", "norm2.weight": "norm2.weight", "conv2.weight": "conv2.weight", "conv_shortcut.weight": "conv_shortcut.weight", "norm1.bias": "norm1.bias", "conv1.bias": "conv1.bias", "norm2.bias": "norm2.bias", "conv2.bias": "conv2.bias", "conv_shortcut.bias": "conv_shortcut.bias", "mix_factor": "mix_factor", } state_dict_ = {} for name in static_rename_dict: state_dict_[static_rename_dict[name] + ".weight"] = state_dict[name + ".weight"] state_dict_[static_rename_dict[name] + ".bias"] = state_dict[name + ".bias"] for prefix_name in prefix_rename_dict: for suffix_name in suffix_rename_dict: name = prefix_name + "." + suffix_name name_ = prefix_rename_dict[prefix_name] + "." + suffix_rename_dict[suffix_name] if name in state_dict: state_dict_[name_] = state_dict[name] return state_dict_ def from_civitai(self, state_dict): rename_dict = { "first_stage_model.decoder.conv_in.bias": "conv_in.bias", "first_stage_model.decoder.conv_in.weight": "conv_in.weight", "first_stage_model.decoder.conv_out.bias": "conv_out.bias", "first_stage_model.decoder.conv_out.time_mix_conv.bias": "time_conv_out.bias", "first_stage_model.decoder.conv_out.time_mix_conv.weight": "time_conv_out.weight", "first_stage_model.decoder.conv_out.weight": "conv_out.weight", "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.2.transformer_blocks.0.to_k.bias", "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.2.transformer_blocks.0.to_k.weight", "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.2.norm.bias", "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.2.norm.weight", "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.2.transformer_blocks.0.to_out.bias", 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"first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.bias": "blocks.8.norm1.bias", "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.weight": "blocks.8.norm1.weight", "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.bias": "blocks.8.conv1.bias", "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.weight": "blocks.8.conv1.weight", "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.bias": "blocks.8.norm2.bias", "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.weight": "blocks.8.norm2.weight", "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.bias": "blocks.8.conv2.bias", "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.weight": "blocks.8.conv2.weight", "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.9.conv1.bias", "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.9.conv1.weight", "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.9.conv2.bias", "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.9.conv2.weight", "first_stage_model.decoder.up.3.block.2.mix_factor": "blocks.10.mix_factor", "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.9.norm1.bias", "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.9.norm1.weight", "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.9.norm2.bias", "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.9.norm2.weight", "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.bias": "blocks.10.norm1.bias", "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.weight": "blocks.10.norm1.weight", "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.bias": "blocks.10.conv1.bias", "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.weight": "blocks.10.conv1.weight", "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.bias": "blocks.10.norm2.bias", "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.weight": "blocks.10.norm2.weight", "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.bias": "blocks.10.conv2.bias", "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.weight": "blocks.10.conv2.weight", "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.11.conv.bias", "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.11.conv.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if "blocks.2.transformer_blocks.0" in rename_dict[name]: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ ================================================ FILE: diffsynth/models/svd_vae_encoder.py ================================================ from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder class SVDVAEEncoder(SDVAEEncoder): def __init__(self): super().__init__() self.scaling_factor = 0.13025 @staticmethod def state_dict_converter(): return SVDVAEEncoderStateDictConverter() class SVDVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter): def __init__(self): super().__init__() def from_diffusers(self, state_dict): return super().from_diffusers(state_dict) def from_civitai(self, state_dict): rename_dict = { "conditioner.embedders.3.encoder.encoder.conv_in.bias": "conv_in.bias", "conditioner.embedders.3.encoder.encoder.conv_in.weight": "conv_in.weight", "conditioner.embedders.3.encoder.encoder.conv_out.bias": "conv_out.bias", "conditioner.embedders.3.encoder.encoder.conv_out.weight": "conv_out.weight", "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight", "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias", "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight", "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias", "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight", "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias", "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight", "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias", "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight", "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias", "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight", "conditioner.embedders.3.encoder.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias", "conditioner.embedders.3.encoder.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight", "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias", "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight", "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias", "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight", "conditioner.embedders.3.encoder.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias", "conditioner.embedders.3.encoder.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight", "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias", "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight", "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias", "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight", "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias", "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight", "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias", "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight", "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias", "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight", "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias", "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight", "conditioner.embedders.3.encoder.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias", "conditioner.embedders.3.encoder.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight", "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias", "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight", "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias", "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight", "conditioner.embedders.3.encoder.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias", "conditioner.embedders.3.encoder.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight", "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias", "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight", "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias", "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight", "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias", "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight", "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias", "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight", "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias", "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight", "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias", "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight", "conditioner.embedders.3.encoder.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias", "conditioner.embedders.3.encoder.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight", "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias", "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight", "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias", "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight", "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias", "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight", "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias", "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight", "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias", "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight", "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias", "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight", "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias", "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight", "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias", "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight", "conditioner.embedders.3.encoder.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias", "conditioner.embedders.3.encoder.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight", "conditioner.embedders.3.encoder.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias", "conditioner.embedders.3.encoder.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight", "conditioner.embedders.3.encoder.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias", "conditioner.embedders.3.encoder.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight", "conditioner.embedders.3.encoder.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias", "conditioner.embedders.3.encoder.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight", "conditioner.embedders.3.encoder.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias", "conditioner.embedders.3.encoder.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight", "conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", "conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", "conditioner.embedders.3.encoder.encoder.norm_out.bias": "conv_norm_out.bias", "conditioner.embedders.3.encoder.encoder.norm_out.weight": "conv_norm_out.weight", "conditioner.embedders.3.encoder.quant_conv.bias": "quant_conv.bias", "conditioner.embedders.3.encoder.quant_conv.weight": "quant_conv.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if "transformer_blocks" in rename_dict[name]: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ ================================================ FILE: diffsynth/models/tiler.py ================================================ import torch from einops import rearrange, repeat class TileWorker: def __init__(self): pass def mask(self, height, width, border_width): # Create a mask with shape (height, width). # The centre area is filled with 1, and the border line is filled with values in range (0, 1]. x = torch.arange(height).repeat(width, 1).T y = torch.arange(width).repeat(height, 1) mask = torch.stack([x + 1, height - x, y + 1, width - y]).min(dim=0).values mask = (mask / border_width).clip(0, 1) return mask def tile(self, model_input, tile_size, tile_stride, tile_device, tile_dtype): # Convert a tensor (b, c, h, w) to (b, c, tile_size, tile_size, tile_num) batch_size, channel, _, _ = model_input.shape model_input = model_input.to(device=tile_device, dtype=tile_dtype) unfold_operator = torch.nn.Unfold( kernel_size=(tile_size, tile_size), stride=(tile_stride, tile_stride) ) model_input = unfold_operator(model_input) model_input = model_input.view((batch_size, channel, tile_size, tile_size, -1)) return model_input def tiled_inference(self, forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype): # Call y=forward_fn(x) for each tile tile_num = model_input.shape[-1] model_output_stack = [] for tile_id in range(0, tile_num, tile_batch_size): # process input tile_id_ = min(tile_id + tile_batch_size, tile_num) x = model_input[:, :, :, :, tile_id: tile_id_] x = x.to(device=inference_device, dtype=inference_dtype) x = rearrange(x, "b c h w n -> (n b) c h w") # process output y = forward_fn(x) y = rearrange(y, "(n b) c h w -> b c h w n", n=tile_id_-tile_id) y = y.to(device=tile_device, dtype=tile_dtype) model_output_stack.append(y) model_output = torch.concat(model_output_stack, dim=-1) return model_output def io_scale(self, model_output, tile_size): # Determine the size modification happened in forward_fn # We only consider the same scale on height and width. io_scale = model_output.shape[2] / tile_size return io_scale def untile(self, model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype): # The reversed function of tile mask = self.mask(tile_size, tile_size, border_width) mask = mask.to(device=tile_device, dtype=tile_dtype) mask = rearrange(mask, "h w -> 1 1 h w 1") model_output = model_output * mask fold_operator = torch.nn.Fold( output_size=(height, width), kernel_size=(tile_size, tile_size), stride=(tile_stride, tile_stride) ) mask = repeat(mask[0, 0, :, :, 0], "h w -> 1 (h w) n", n=model_output.shape[-1]) model_output = rearrange(model_output, "b c h w n -> b (c h w) n") model_output = fold_operator(model_output) / fold_operator(mask) return model_output def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_batch_size=1, tile_device="cpu", tile_dtype=torch.float32, border_width=None): # Prepare inference_device, inference_dtype = model_input.device, model_input.dtype height, width = model_input.shape[2], model_input.shape[3] border_width = int(tile_stride*0.5) if border_width is None else border_width # tile model_input = self.tile(model_input, tile_size, tile_stride, tile_device, tile_dtype) # inference model_output = self.tiled_inference(forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype) # resize io_scale = self.io_scale(model_output, tile_size) height, width = int(height*io_scale), int(width*io_scale) tile_size, tile_stride = int(tile_size*io_scale), int(tile_stride*io_scale) border_width = int(border_width*io_scale) # untile model_output = self.untile(model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype) # Done! model_output = model_output.to(device=inference_device, dtype=inference_dtype) return model_output class FastTileWorker: def __init__(self): pass def build_mask(self, data, is_bound): _, _, H, W = data.shape h = repeat(torch.arange(H), "H -> H W", H=H, W=W) w = repeat(torch.arange(W), "W -> H W", H=H, W=W) border_width = (H + W) // 4 pad = torch.ones_like(h) * border_width mask = torch.stack([ pad if is_bound[0] else h + 1, pad if is_bound[1] else H - h, pad if is_bound[2] else w + 1, pad if is_bound[3] else W - w ]).min(dim=0).values mask = mask.clip(1, border_width) mask = (mask / border_width).to(dtype=data.dtype, device=data.device) mask = rearrange(mask, "H W -> 1 H W") return mask def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_device="cpu", tile_dtype=torch.float32, border_width=None): # Prepare B, C, H, W = model_input.shape border_width = int(tile_stride*0.5) if border_width is None else border_width weight = torch.zeros((1, 1, H, W), dtype=tile_dtype, device=tile_device) values = torch.zeros((B, C, H, W), dtype=tile_dtype, device=tile_device) # Split tasks tasks = [] for h in range(0, H, tile_stride): for w in range(0, W, tile_stride): if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W): continue h_, w_ = h + tile_size, w + tile_size if h_ > H: h, h_ = H - tile_size, H if w_ > W: w, w_ = W - tile_size, W tasks.append((h, h_, w, w_)) # Run for hl, hr, wl, wr in tasks: # Forward hidden_states_batch = forward_fn(hl, hr, wl, wr).to(dtype=tile_dtype, device=tile_device) mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W)) values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask weight[:, :, hl:hr, wl:wr] += mask values /= weight return values class TileWorker2Dto3D: """ Process 3D tensors, but only enable TileWorker on 2D. """ def __init__(self): pass def build_mask(self, T, H, W, dtype, device, is_bound, border_width): t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) border_width = (H + W) // 4 if border_width is None else border_width pad = torch.ones_like(h) * border_width mask = torch.stack([ pad if is_bound[0] else t + 1, pad if is_bound[1] else T - t, pad if is_bound[2] else h + 1, pad if is_bound[3] else H - h, pad if is_bound[4] else w + 1, pad if is_bound[5] else W - w ]).min(dim=0).values mask = mask.clip(1, border_width) mask = (mask / border_width).to(dtype=dtype, device=device) mask = rearrange(mask, "T H W -> 1 1 T H W") return mask def tiled_forward( self, forward_fn, model_input, tile_size, tile_stride, tile_device="cpu", tile_dtype=torch.float32, computation_device="cuda", computation_dtype=torch.float32, border_width=None, scales=[1, 1, 1, 1], progress_bar=lambda x:x ): B, C, T, H, W = model_input.shape scale_C, scale_T, scale_H, scale_W = scales tile_size_H, tile_size_W = tile_size tile_stride_H, tile_stride_W = tile_stride value = torch.zeros((B, int(C*scale_C), int(T*scale_T), int(H*scale_H), int(W*scale_W)), dtype=tile_dtype, device=tile_device) weight = torch.zeros((1, 1, int(T*scale_T), int(H*scale_H), int(W*scale_W)), dtype=tile_dtype, device=tile_device) # Split tasks tasks = [] for h in range(0, H, tile_stride_H): for w in range(0, W, tile_stride_W): if (h-tile_stride_H >= 0 and h-tile_stride_H+tile_size_H >= H) or (w-tile_stride_W >= 0 and w-tile_stride_W+tile_size_W >= W): continue h_, w_ = h + tile_size_H, w + tile_size_W if h_ > H: h, h_ = max(H - tile_size_H, 0), H if w_ > W: w, w_ = max(W - tile_size_W, 0), W tasks.append((h, h_, w, w_)) # Run for hl, hr, wl, wr in progress_bar(tasks): mask = self.build_mask( int(T*scale_T), int((hr-hl)*scale_H), int((wr-wl)*scale_W), tile_dtype, tile_device, is_bound=(True, True, hl==0, hr>=H, wl==0, wr>=W), border_width=border_width ) grid_input = model_input[:, :, :, hl:hr, wl:wr].to(dtype=computation_dtype, device=computation_device) grid_output = forward_fn(grid_input).to(dtype=tile_dtype, device=tile_device) value[:, :, :, int(hl*scale_H):int(hr*scale_H), int(wl*scale_W):int(wr*scale_W)] += grid_output * mask weight[:, :, :, int(hl*scale_H):int(hr*scale_H), int(wl*scale_W):int(wr*scale_W)] += mask value = value / weight return value ================================================ FILE: diffsynth/models/utils.py ================================================ import torch, os from safetensors import safe_open from contextlib import contextmanager import hashlib @contextmanager def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False): old_register_parameter = torch.nn.Module.register_parameter if include_buffers: old_register_buffer = torch.nn.Module.register_buffer def register_empty_parameter(module, name, param): old_register_parameter(module, name, param) if param is not None: param_cls = type(module._parameters[name]) kwargs = module._parameters[name].__dict__ kwargs["requires_grad"] = param.requires_grad module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) def register_empty_buffer(module, name, buffer, persistent=True): old_register_buffer(module, name, buffer, persistent=persistent) if buffer is not None: module._buffers[name] = module._buffers[name].to(device) def patch_tensor_constructor(fn): def wrapper(*args, **kwargs): kwargs["device"] = device return fn(*args, **kwargs) return wrapper if include_buffers: tensor_constructors_to_patch = { torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ["empty", "zeros", "ones", "full"] } else: tensor_constructors_to_patch = {} try: torch.nn.Module.register_parameter = register_empty_parameter if include_buffers: torch.nn.Module.register_buffer = register_empty_buffer for torch_function_name in tensor_constructors_to_patch.keys(): setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name))) yield finally: torch.nn.Module.register_parameter = old_register_parameter if include_buffers: torch.nn.Module.register_buffer = old_register_buffer for torch_function_name, old_torch_function in tensor_constructors_to_patch.items(): setattr(torch, torch_function_name, old_torch_function) def load_state_dict_from_folder(file_path, torch_dtype=None): state_dict = {} for file_name in os.listdir(file_path): if "." in file_name and file_name.split(".")[-1] in [ "safetensors", "bin", "ckpt", "pth", "pt" ]: state_dict.update(load_state_dict(os.path.join(file_path, file_name), torch_dtype=torch_dtype)) return state_dict def load_state_dict(file_path, torch_dtype=None): if file_path.endswith(".safetensors"): return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype) else: return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype) def load_state_dict_from_safetensors(file_path, torch_dtype=None): state_dict = {} with safe_open(file_path, framework="pt", device="cpu") as f: for k in f.keys(): state_dict[k] = f.get_tensor(k) if torch_dtype is not None: state_dict[k] = state_dict[k].to(torch_dtype) return state_dict def load_state_dict_from_bin(file_path, torch_dtype=None): state_dict = torch.load(file_path, map_location="cpu", weights_only=True) if torch_dtype is not None: for i in state_dict: if isinstance(state_dict[i], torch.Tensor): state_dict[i] = state_dict[i].to(torch_dtype) return state_dict def search_for_embeddings(state_dict): embeddings = [] for k in state_dict: if isinstance(state_dict[k], torch.Tensor): embeddings.append(state_dict[k]) elif isinstance(state_dict[k], dict): embeddings += search_for_embeddings(state_dict[k]) return embeddings def search_parameter(param, state_dict): for name, param_ in state_dict.items(): if param.numel() == param_.numel(): if param.shape == param_.shape: if torch.dist(param, param_) < 1e-3: return name else: if torch.dist(param.flatten(), param_.flatten()) < 1e-3: return name return None def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False): matched_keys = set() with torch.no_grad(): for name in source_state_dict: rename = search_parameter(source_state_dict[name], target_state_dict) if rename is not None: print(f'"{name}": "{rename}",') matched_keys.add(rename) elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0: length = source_state_dict[name].shape[0] // 3 rename = [] for i in range(3): rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict)) if None not in rename: print(f'"{name}": {rename},') for rename_ in rename: matched_keys.add(rename_) for name in target_state_dict: if name not in matched_keys: print("Cannot find", name, target_state_dict[name].shape) def search_for_files(folder, extensions): files = [] if os.path.isdir(folder): for file in sorted(os.listdir(folder)): files += search_for_files(os.path.join(folder, file), extensions) elif os.path.isfile(folder): for extension in extensions: if folder.endswith(extension): files.append(folder) break return files def convert_state_dict_keys_to_single_str(state_dict, with_shape=True): keys = [] for key, value in state_dict.items(): if isinstance(key, str): if isinstance(value, torch.Tensor): if with_shape: shape = "_".join(map(str, list(value.shape))) keys.append(key + ":" + shape) keys.append(key) elif isinstance(value, dict): keys.append(key + "|" + convert_state_dict_keys_to_single_str(value, with_shape=with_shape)) keys.sort() keys_str = ",".join(keys) return keys_str def split_state_dict_with_prefix(state_dict): keys = sorted([key for key in state_dict if isinstance(key, str)]) prefix_dict = {} for key in keys: prefix = key if "." not in key else key.split(".")[0] if prefix not in prefix_dict: prefix_dict[prefix] = [] prefix_dict[prefix].append(key) state_dicts = [] for prefix, keys in prefix_dict.items(): sub_state_dict = {key: state_dict[key] for key in keys} state_dicts.append(sub_state_dict) return state_dicts def hash_state_dict_keys(state_dict, with_shape=True): keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape) keys_str = keys_str.encode(encoding="UTF-8") return hashlib.md5(keys_str).hexdigest() ================================================ FILE: diffsynth/models/wan_video_dit.py ================================================ import re import torch import torch.nn as nn import torch.nn.functional as F import math from typing import Tuple, Optional from einops import rearrange from .utils import hash_state_dict_keys try: import flash_attn_interface FLASH_ATTN_3_AVAILABLE = True except ModuleNotFoundError: FLASH_ATTN_3_AVAILABLE = False try: import flash_attn FLASH_ATTN_2_AVAILABLE = True except ModuleNotFoundError: FLASH_ATTN_2_AVAILABLE = False try: from sageattention import sageattn SAGE_ATTN_AVAILABLE = True except ModuleNotFoundError: SAGE_ATTN_AVAILABLE = False def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads: int, compatibility_mode=False): if compatibility_mode: q = rearrange(q, "b s (n d) -> b n s d", n=num_heads) k = rearrange(k, "b s (n d) -> b n s d", n=num_heads) v = rearrange(v, "b s (n d) -> b n s d", n=num_heads) x = F.scaled_dot_product_attention(q, k, v) x = rearrange(x, "b n s d -> b s (n d)", n=num_heads) elif FLASH_ATTN_3_AVAILABLE: q = rearrange(q, "b s (n d) -> b s n d", n=num_heads) k = rearrange(k, "b s (n d) -> b s n d", n=num_heads) v = rearrange(v, "b s (n d) -> b s n d", n=num_heads) x = flash_attn_interface.flash_attn_func(q, k, v) x = rearrange(x, "b s n d -> b s (n d)", n=num_heads) elif FLASH_ATTN_2_AVAILABLE: q = rearrange(q, "b s (n d) -> b s n d", n=num_heads) k = rearrange(k, "b s (n d) -> b s n d", n=num_heads) v = rearrange(v, "b s (n d) -> b s n d", n=num_heads) x = flash_attn.flash_attn_func(q, k, v) x = rearrange(x, "b s n d -> b s (n d)", n=num_heads) elif SAGE_ATTN_AVAILABLE: q = rearrange(q, "b s (n d) -> b n s d", n=num_heads) k = rearrange(k, "b s (n d) -> b n s d", n=num_heads) v = rearrange(v, "b s (n d) -> b n s d", n=num_heads) x = sageattn(q, k, v) x = rearrange(x, "b n s d -> b s (n d)", n=num_heads) else: q = rearrange(q, "b s (n d) -> b n s d", n=num_heads) k = rearrange(k, "b s (n d) -> b n s d", n=num_heads) v = rearrange(v, "b s (n d) -> b n s d", n=num_heads) x = F.scaled_dot_product_attention(q, k, v) x = rearrange(x, "b n s d -> b s (n d)", n=num_heads) return x def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor): return (x * (1 + scale) + shift) def sinusoidal_embedding_1d(dim, position): sinusoid = torch.outer(position.type(torch.float64), torch.pow( 10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2))) x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) return x.to(position.dtype) def precompute_freqs_cis_3d(dim: int, end: int = 1024, theta: float = 10000.0): # 3d rope precompute f_freqs_cis = precompute_freqs_cis(dim - 2 * (dim // 3), end, theta) h_freqs_cis = precompute_freqs_cis(dim // 3, end, theta) w_freqs_cis = precompute_freqs_cis(dim // 3, end, theta) return f_freqs_cis, h_freqs_cis, w_freqs_cis def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000.0): # 1d rope precompute freqs = 1.0 / (theta ** (torch.arange(0, dim, 2) [: (dim // 2)].double() / dim)) freqs = torch.outer(torch.arange(end, device=freqs.device), freqs) freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 return freqs_cis def rope_apply(x, freqs, num_heads): x = rearrange(x, "b s (n d) -> b s n d", n=num_heads) x_out = torch.view_as_complex(x.to(torch.float64).reshape( x.shape[0], x.shape[1], x.shape[2], -1, 2)) x_out = torch.view_as_real(x_out * freqs).flatten(2) return x_out.to(x.dtype) class RMSNorm(nn.Module): def __init__(self, dim, eps=1e-5): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def norm(self, x): return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) def forward(self, x): dtype = x.dtype return self.norm(x.float()).to(dtype) * self.weight class AttentionModule(nn.Module): def __init__(self, num_heads): super().__init__() self.num_heads = num_heads def forward(self, q, k, v): x = flash_attention(q=q, k=k, v=v, num_heads=self.num_heads) return x class SelfAttention(nn.Module): def __init__(self, dim: int, num_heads: int, eps: float = 1e-6): super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.q = nn.Linear(dim, dim) self.k = nn.Linear(dim, dim) self.v = nn.Linear(dim, dim) self.o = nn.Linear(dim, dim) self.norm_q = RMSNorm(dim, eps=eps) self.norm_k = RMSNorm(dim, eps=eps) self.attn = AttentionModule(self.num_heads) def forward(self, x, freqs): q = self.norm_q(self.q(x)) k = self.norm_k(self.k(x)) v = self.v(x) q = rope_apply(q, freqs, self.num_heads) k = rope_apply(k, freqs, self.num_heads) x = self.attn(q, k, v) return self.o(x) class CrossAttention(nn.Module): def __init__(self, dim: int, num_heads: int, eps: float = 1e-6, has_image_input: bool = False): super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.q = nn.Linear(dim, dim) self.k = nn.Linear(dim, dim) self.v = nn.Linear(dim, dim) self.o = nn.Linear(dim, dim) self.norm_q = RMSNorm(dim, eps=eps) self.norm_k = RMSNorm(dim, eps=eps) self.has_image_input = has_image_input if has_image_input: self.k_img = nn.Linear(dim, dim) self.v_img = nn.Linear(dim, dim) self.norm_k_img = RMSNorm(dim, eps=eps) self.attn = AttentionModule(self.num_heads) def forward(self, x: torch.Tensor, y: torch.Tensor): if self.has_image_input: img = y[:, :257] ctx = y[:, 257:] else: ctx = y q = self.norm_q(self.q(x)) k = self.norm_k(self.k(ctx)) v = self.v(ctx) x = self.attn(q, k, v) if self.has_image_input: k_img = self.norm_k_img(self.k_img(img)) v_img = self.v_img(img) y = flash_attention(q, k_img, v_img, num_heads=self.num_heads) x = x + y return self.o(x) class DiTBlock(nn.Module): def __init__(self, has_image_input: bool, dim: int, num_heads: int, ffn_dim: int, eps: float = 1e-6): super().__init__() self.dim = dim self.num_heads = num_heads self.ffn_dim = ffn_dim self.self_attn = SelfAttention(dim, num_heads, eps) self.cross_attn = CrossAttention( dim, num_heads, eps, has_image_input=has_image_input) self.norm1 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) self.norm2 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) self.norm3 = nn.LayerNorm(dim, eps=eps) self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU( approximate='tanh'), nn.Linear(ffn_dim, dim)) self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) def forward(self, x, context, cam_emb, t_mod, freqs, freqs_mvs): # wan2.1 forward # shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( # self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=1) # input_x = modulate(self.norm1(x), shift_msa, scale_msa) # x = x + gate_msa * self.self_attn(input_x, freqs) # x = x + self.cross_attn(self.norm3(x), context) # input_x = modulate(self.norm2(x), shift_mlp, scale_mlp) # x = x + gate_mlp * self.ffn(input_x) # msa: multi-head self-attention shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=1) input_x = modulate(self.norm1(x), shift_msa, scale_msa) x = x + gate_msa * self.self_attn(input_x, freqs) # mvs: multi-view synchronization module shift_mvs, scale_mvs, gate_mvs = ( self.modulation_mvs.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod[:, :3, :]).chunk(3, dim=1) input_x = modulate(self.norm_mvs(x), shift_mvs, scale_mvs) v, f, _ = cam_emb.shape h, w = 30, 52 # h, w hard code cam_emb = self.cam_encoder(cam_emb) cam_emb = cam_emb.unsqueeze(2).unsqueeze(3).repeat(1, 1, h, w, 1) cam_emb = rearrange(cam_emb, 'b f h w d -> b (f h w) d') input_x = input_x + cam_emb x = rearrange(x, '(b v) (f h w) d -> (b f) (v h w) d', v=v, f=f, h=h, w=w) input_x = rearrange(input_x, '(b v) (f h w) d -> (b f) (v h w) d', v=v, f=f, h=h, w=w) x = x + gate_mvs * self.projector(self.mvs_attn(input_x, freqs_mvs)) x = rearrange(x, '(b f) (v h w) d -> (b v) (f h w) d', v=v, f=f, h=h, w=w) # mlp: multi-layer perceptron x = x + self.cross_attn(self.norm3(x), context) input_x = modulate(self.norm2(x), shift_mlp, scale_mlp) x = x + gate_mlp * self.ffn(input_x) return x class MLP(torch.nn.Module): def __init__(self, in_dim, out_dim): super().__init__() self.proj = torch.nn.Sequential( nn.LayerNorm(in_dim), nn.Linear(in_dim, in_dim), nn.GELU(), nn.Linear(in_dim, out_dim), nn.LayerNorm(out_dim) ) def forward(self, x): return self.proj(x) class Head(nn.Module): def __init__(self, dim: int, out_dim: int, patch_size: Tuple[int, int, int], eps: float): super().__init__() self.dim = dim self.patch_size = patch_size self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=False) self.head = nn.Linear(dim, out_dim * math.prod(patch_size)) self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5) def forward(self, x, t_mod): shift, scale = (self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(2, dim=1) x = (self.head(self.norm(x) * (1 + scale) + shift)) return x class WanModel(torch.nn.Module): def __init__( self, dim: int, in_dim: int, ffn_dim: int, out_dim: int, text_dim: int, freq_dim: int, eps: float, patch_size: Tuple[int, int, int], num_heads: int, num_layers: int, has_image_input: bool, ): super().__init__() self.dim = dim self.freq_dim = freq_dim self.has_image_input = has_image_input self.patch_size = patch_size self.patch_embedding = nn.Conv3d( in_dim, dim, kernel_size=patch_size, stride=patch_size) self.text_embedding = nn.Sequential( nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), nn.Linear(dim, dim) ) self.time_embedding = nn.Sequential( nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim) ) self.time_projection = nn.Sequential( nn.SiLU(), nn.Linear(dim, dim * 6)) self.blocks = nn.ModuleList([ DiTBlock(has_image_input, dim, num_heads, ffn_dim, eps) for _ in range(num_layers) ]) self.head = Head(dim, out_dim, patch_size, eps) head_dim = dim // num_heads self.freqs = precompute_freqs_cis_3d(head_dim) if has_image_input: self.img_emb = MLP(1280, dim) # clip_feature_dim = 1280 def patchify(self, x: torch.Tensor): x = self.patch_embedding(x) grid_size = x.shape[2:] x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous() return x, grid_size # x, grid_size: (f, h, w) def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor): return rearrange( x, 'b (f h w) (x y z c) -> b c (f x) (h y) (w z)', f=grid_size[0], h=grid_size[1], w=grid_size[2], x=self.patch_size[0], y=self.patch_size[1], z=self.patch_size[2] ) def forward(self, x: torch.Tensor, timestep: torch.Tensor, cam_emb: torch.Tensor, context: torch.Tensor, clip_feature: Optional[torch.Tensor] = None, y: Optional[torch.Tensor] = None, use_gradient_checkpointing: bool = False, use_gradient_checkpointing_offload: bool = False, **kwargs, ): t = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, timestep)) t_mod = self.time_projection(t).unflatten(1, (6, self.dim)) context = self.text_embedding(context) if self.has_image_input: x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) clip_embdding = self.img_emb(clip_feature) context = torch.cat([clip_embdding, context], dim=1) x, (f, h, w) = self.patchify(x) freqs = torch.cat([ self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) v = 2 freqs_mvs = torch.cat([ self.freqs[0][:v].view(v, 1, 1, -1).expand(v, h, w, -1), self.freqs[1][:h].view(1, h, 1, -1).expand(v, h, w, -1), self.freqs[2][:w].view(1, 1, w, -1).expand(v, h, w, -1) ], dim=-1).reshape(v * h * w, 1, -1).to(x.device) def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward for block in self.blocks: if self.training and use_gradient_checkpointing: if use_gradient_checkpointing_offload: with torch.autograd.graph.save_on_cpu(): x = torch.utils.checkpoint.checkpoint( create_custom_forward(block), x, context, cam_emb, t_mod, freqs, freqs_mvs, use_reentrant=False, ) else: x = torch.utils.checkpoint.checkpoint( create_custom_forward(block), x, context, cam_emb, t_mod, freqs, freqs_mvs, use_reentrant=False, ) else: x = block(x, context, cam_emb, t_mod, freqs, freqs_mvs) x = self.head(x, t) x = self.unpatchify(x, (f, h, w)) return x @staticmethod def state_dict_converter(): return WanModelStateDictConverter() class WanModelStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "blocks.0.attn1.norm_k.weight": "blocks.0.self_attn.norm_k.weight", "blocks.0.attn1.norm_q.weight": "blocks.0.self_attn.norm_q.weight", "blocks.0.attn1.to_k.bias": "blocks.0.self_attn.k.bias", "blocks.0.attn1.to_k.weight": "blocks.0.self_attn.k.weight", "blocks.0.attn1.to_out.0.bias": "blocks.0.self_attn.o.bias", "blocks.0.attn1.to_out.0.weight": "blocks.0.self_attn.o.weight", "blocks.0.attn1.to_q.bias": "blocks.0.self_attn.q.bias", "blocks.0.attn1.to_q.weight": "blocks.0.self_attn.q.weight", "blocks.0.attn1.to_v.bias": "blocks.0.self_attn.v.bias", "blocks.0.attn1.to_v.weight": "blocks.0.self_attn.v.weight", "blocks.0.attn2.norm_k.weight": "blocks.0.cross_attn.norm_k.weight", "blocks.0.attn2.norm_q.weight": "blocks.0.cross_attn.norm_q.weight", "blocks.0.attn2.to_k.bias": "blocks.0.cross_attn.k.bias", "blocks.0.attn2.to_k.weight": "blocks.0.cross_attn.k.weight", "blocks.0.attn2.to_out.0.bias": "blocks.0.cross_attn.o.bias", "blocks.0.attn2.to_out.0.weight": "blocks.0.cross_attn.o.weight", "blocks.0.attn2.to_q.bias": "blocks.0.cross_attn.q.bias", "blocks.0.attn2.to_q.weight": "blocks.0.cross_attn.q.weight", "blocks.0.attn2.to_v.bias": "blocks.0.cross_attn.v.bias", "blocks.0.attn2.to_v.weight": "blocks.0.cross_attn.v.weight", "blocks.0.ffn.net.0.proj.bias": "blocks.0.ffn.0.bias", "blocks.0.ffn.net.0.proj.weight": "blocks.0.ffn.0.weight", "blocks.0.ffn.net.2.bias": "blocks.0.ffn.2.bias", "blocks.0.ffn.net.2.weight": "blocks.0.ffn.2.weight", "blocks.0.norm2.bias": "blocks.0.norm3.bias", "blocks.0.norm2.weight": "blocks.0.norm3.weight", "blocks.0.scale_shift_table": "blocks.0.modulation", "condition_embedder.text_embedder.linear_1.bias": "text_embedding.0.bias", "condition_embedder.text_embedder.linear_1.weight": "text_embedding.0.weight", "condition_embedder.text_embedder.linear_2.bias": "text_embedding.2.bias", "condition_embedder.text_embedder.linear_2.weight": "text_embedding.2.weight", "condition_embedder.time_embedder.linear_1.bias": "time_embedding.0.bias", "condition_embedder.time_embedder.linear_1.weight": "time_embedding.0.weight", "condition_embedder.time_embedder.linear_2.bias": "time_embedding.2.bias", "condition_embedder.time_embedder.linear_2.weight": "time_embedding.2.weight", "condition_embedder.time_proj.bias": "time_projection.1.bias", "condition_embedder.time_proj.weight": "time_projection.1.weight", "patch_embedding.bias": "patch_embedding.bias", "patch_embedding.weight": "patch_embedding.weight", "scale_shift_table": "head.modulation", "proj_out.bias": "head.head.bias", "proj_out.weight": "head.head.weight", } state_dict_ = {} for name, param in state_dict.items(): if name in rename_dict: state_dict_[rename_dict[name]] = param else: name_ = ".".join(name.split(".")[:1] + ["0"] + name.split(".")[2:]) if name_ in rename_dict: name_ = rename_dict[name_] name_ = ".".join(name_.split(".")[:1] + [name.split(".")[1]] + name_.split(".")[2:]) state_dict_[name_] = param if hash_state_dict_keys(state_dict) == "cb104773c6c2cb6df4f9529ad5c60d0b": config = { "model_type": "t2v", "patch_size": (1, 2, 2), "text_len": 512, "in_dim": 16, "dim": 5120, "ffn_dim": 13824, "freq_dim": 256, "text_dim": 4096, "out_dim": 16, "num_heads": 40, "num_layers": 40, "window_size": (-1, -1), "qk_norm": True, "cross_attn_norm": True, "eps": 1e-6, } else: config = {} return state_dict_, config def from_civitai(self, state_dict): if hash_state_dict_keys(state_dict) == "9269f8db9040a9d860eaca435be61814": config = { "has_image_input": False, "patch_size": [1, 2, 2], "in_dim": 16, "dim": 1536, "ffn_dim": 8960, "freq_dim": 256, "text_dim": 4096, "out_dim": 16, "num_heads": 12, "num_layers": 30, "eps": 1e-6 } elif hash_state_dict_keys(state_dict) == "aafcfd9672c3a2456dc46e1cb6e52c70": config = { "has_image_input": False, "patch_size": [1, 2, 2], "in_dim": 16, "dim": 5120, "ffn_dim": 13824, "freq_dim": 256, "text_dim": 4096, "out_dim": 16, "num_heads": 40, "num_layers": 40, "eps": 1e-6 } elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e": config = { "has_image_input": True, "patch_size": [1, 2, 2], "in_dim": 36, "dim": 5120, "ffn_dim": 13824, "freq_dim": 256, "text_dim": 4096, "out_dim": 16, "num_heads": 40, "num_layers": 40, "eps": 1e-6 } else: config = {} return state_dict, config ================================================ FILE: diffsynth/models/wan_video_image_encoder.py ================================================ """ Concise re-implementation of ``https://github.com/openai/CLIP'' and ``https://github.com/mlfoundations/open_clip''. """ import math import torch import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as T from .wan_video_dit import flash_attention class SelfAttention(nn.Module): def __init__(self, dim, num_heads, dropout=0.1, eps=1e-5): assert dim % num_heads == 0 super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.eps = eps # layers self.q = nn.Linear(dim, dim) self.k = nn.Linear(dim, dim) self.v = nn.Linear(dim, dim) self.o = nn.Linear(dim, dim) self.dropout = nn.Dropout(dropout) def forward(self, x, mask): """ x: [B, L, C]. """ b, s, c, n, d = *x.size(), self.num_heads, self.head_dim # compute query, key, value q = self.q(x).reshape(b, s, n, d).permute(0, 2, 1, 3) k = self.k(x).reshape(b, s, n, d).permute(0, 2, 1, 3) v = self.v(x).reshape(b, s, n, d).permute(0, 2, 1, 3) # compute attention p = self.dropout.p if self.training else 0.0 x = F.scaled_dot_product_attention(q, k, v, mask, p) x = x.permute(0, 2, 1, 3).reshape(b, s, c) # output x = self.o(x) x = self.dropout(x) return x class AttentionBlock(nn.Module): def __init__(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5): super().__init__() self.dim = dim self.num_heads = num_heads self.post_norm = post_norm self.eps = eps # layers self.attn = SelfAttention(dim, num_heads, dropout, eps) self.norm1 = nn.LayerNorm(dim, eps=eps) self.ffn = nn.Sequential( nn.Linear(dim, dim * 4), nn.GELU(), nn.Linear(dim * 4, dim), nn.Dropout(dropout)) self.norm2 = nn.LayerNorm(dim, eps=eps) def forward(self, x, mask): if self.post_norm: x = self.norm1(x + self.attn(x, mask)) x = self.norm2(x + self.ffn(x)) else: x = x + self.attn(self.norm1(x), mask) x = x + self.ffn(self.norm2(x)) return x class XLMRoberta(nn.Module): """ XLMRobertaModel with no pooler and no LM head. """ def __init__(self, vocab_size=250002, max_seq_len=514, type_size=1, pad_id=1, dim=1024, num_heads=16, num_layers=24, post_norm=True, dropout=0.1, eps=1e-5): super().__init__() self.vocab_size = vocab_size self.max_seq_len = max_seq_len self.type_size = type_size self.pad_id = pad_id self.dim = dim self.num_heads = num_heads self.num_layers = num_layers self.post_norm = post_norm self.eps = eps # embeddings self.token_embedding = nn.Embedding(vocab_size, dim, padding_idx=pad_id) self.type_embedding = nn.Embedding(type_size, dim) self.pos_embedding = nn.Embedding(max_seq_len, dim, padding_idx=pad_id) self.dropout = nn.Dropout(dropout) # blocks self.blocks = nn.ModuleList([ AttentionBlock(dim, num_heads, post_norm, dropout, eps) for _ in range(num_layers) ]) # norm layer self.norm = nn.LayerNorm(dim, eps=eps) def forward(self, ids): """ ids: [B, L] of torch.LongTensor. """ b, s = ids.shape mask = ids.ne(self.pad_id).long() # embeddings x = self.token_embedding(ids) + \ self.type_embedding(torch.zeros_like(ids)) + \ self.pos_embedding(self.pad_id + torch.cumsum(mask, dim=1) * mask) if self.post_norm: x = self.norm(x) x = self.dropout(x) # blocks mask = torch.where( mask.view(b, 1, 1, s).gt(0), 0.0, torch.finfo(x.dtype).min) for block in self.blocks: x = block(x, mask) # output if not self.post_norm: x = self.norm(x) return x def xlm_roberta_large(pretrained=False, return_tokenizer=False, device='cpu', **kwargs): """ XLMRobertaLarge adapted from Huggingface. """ # params cfg = dict( vocab_size=250002, max_seq_len=514, type_size=1, pad_id=1, dim=1024, num_heads=16, num_layers=24, post_norm=True, dropout=0.1, eps=1e-5) cfg.update(**kwargs) # init model if pretrained: from sora import DOWNLOAD_TO_CACHE # init a meta model with torch.device('meta'): model = XLMRoberta(**cfg) # load checkpoint model.load_state_dict( torch.load( DOWNLOAD_TO_CACHE('models/xlm_roberta/xlm_roberta_large.pth'), map_location=device), assign=True) else: # init a model on device with torch.device(device): model = XLMRoberta(**cfg) # init tokenizer if return_tokenizer: from sora.data import HuggingfaceTokenizer tokenizer = HuggingfaceTokenizer( name='xlm-roberta-large', seq_len=model.text_len, clean='whitespace') return model, tokenizer else: return model def pos_interpolate(pos, seq_len): if pos.size(1) == seq_len: return pos else: src_grid = int(math.sqrt(pos.size(1))) tar_grid = int(math.sqrt(seq_len)) n = pos.size(1) - src_grid * src_grid return torch.cat([ pos[:, :n], F.interpolate( pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute( 0, 3, 1, 2), size=(tar_grid, tar_grid), mode='bicubic', align_corners=False).flatten(2).transpose(1, 2) ], dim=1) class QuickGELU(nn.Module): def forward(self, x): return x * torch.sigmoid(1.702 * x) class LayerNorm(nn.LayerNorm): def forward(self, x): return super().forward(x).type_as(x) class SelfAttention(nn.Module): def __init__(self, dim, num_heads, causal=False, attn_dropout=0.0, proj_dropout=0.0): assert dim % num_heads == 0 super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.causal = causal self.attn_dropout = attn_dropout self.proj_dropout = proj_dropout # layers self.to_qkv = nn.Linear(dim, dim * 3) self.proj = nn.Linear(dim, dim) def forward(self, x): """ x: [B, L, C]. """ # compute query, key, value q, k, v = self.to_qkv(x).chunk(3, dim=-1) # compute attention x = flash_attention(q, k, v, num_heads=self.num_heads, compatibility_mode=True) # output x = self.proj(x) x = F.dropout(x, self.proj_dropout, self.training) return x class SwiGLU(nn.Module): def __init__(self, dim, mid_dim): super().__init__() self.dim = dim self.mid_dim = mid_dim # layers self.fc1 = nn.Linear(dim, mid_dim) self.fc2 = nn.Linear(dim, mid_dim) self.fc3 = nn.Linear(mid_dim, dim) def forward(self, x): x = F.silu(self.fc1(x)) * self.fc2(x) x = self.fc3(x) return x class AttentionBlock(nn.Module): def __init__(self, dim, mlp_ratio, num_heads, post_norm=False, causal=False, activation='quick_gelu', attn_dropout=0.0, proj_dropout=0.0, norm_eps=1e-5): assert activation in ['quick_gelu', 'gelu', 'swi_glu'] super().__init__() self.dim = dim self.mlp_ratio = mlp_ratio self.num_heads = num_heads self.post_norm = post_norm self.causal = causal self.norm_eps = norm_eps # layers self.norm1 = LayerNorm(dim, eps=norm_eps) self.attn = SelfAttention(dim, num_heads, causal, attn_dropout, proj_dropout) self.norm2 = LayerNorm(dim, eps=norm_eps) if activation == 'swi_glu': self.mlp = SwiGLU(dim, int(dim * mlp_ratio)) else: self.mlp = nn.Sequential( nn.Linear(dim, int(dim * mlp_ratio)), QuickGELU() if activation == 'quick_gelu' else nn.GELU(), nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout)) def forward(self, x): if self.post_norm: x = x + self.norm1(self.attn(x)) x = x + self.norm2(self.mlp(x)) else: x = x + self.attn(self.norm1(x)) x = x + self.mlp(self.norm2(x)) return x class AttentionPool(nn.Module): def __init__(self, dim, mlp_ratio, num_heads, activation='gelu', proj_dropout=0.0, norm_eps=1e-5): assert dim % num_heads == 0 super().__init__() self.dim = dim self.mlp_ratio = mlp_ratio self.num_heads = num_heads self.head_dim = dim // num_heads self.proj_dropout = proj_dropout self.norm_eps = norm_eps # layers gain = 1.0 / math.sqrt(dim) self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim)) self.to_q = nn.Linear(dim, dim) self.to_kv = nn.Linear(dim, dim * 2) self.proj = nn.Linear(dim, dim) self.norm = LayerNorm(dim, eps=norm_eps) self.mlp = nn.Sequential( nn.Linear(dim, int(dim * mlp_ratio)), QuickGELU() if activation == 'quick_gelu' else nn.GELU(), nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout)) def forward(self, x): """ x: [B, L, C]. """ b, s, c, n, d = *x.size(), self.num_heads, self.head_dim # compute query, key, value q = self.to_q(self.cls_embedding).view(1, 1, n*d).expand(b, -1, -1) k, v = self.to_kv(x).chunk(2, dim=-1) # compute attention x = flash_attention(q, k, v, num_heads=self.num_heads, compatibility_mode=True) x = x.reshape(b, 1, c) # output x = self.proj(x) x = F.dropout(x, self.proj_dropout, self.training) # mlp x = x + self.mlp(self.norm(x)) return x[:, 0] class VisionTransformer(nn.Module): def __init__(self, image_size=224, patch_size=16, dim=768, mlp_ratio=4, out_dim=512, num_heads=12, num_layers=12, pool_type='token', pre_norm=True, post_norm=False, activation='quick_gelu', attn_dropout=0.0, proj_dropout=0.0, embedding_dropout=0.0, norm_eps=1e-5): if image_size % patch_size != 0: print( '[WARNING] image_size is not divisible by patch_size', flush=True) assert pool_type in ('token', 'token_fc', 'attn_pool') out_dim = out_dim or dim super().__init__() self.image_size = image_size self.patch_size = patch_size self.num_patches = (image_size // patch_size)**2 self.dim = dim self.mlp_ratio = mlp_ratio self.out_dim = out_dim self.num_heads = num_heads self.num_layers = num_layers self.pool_type = pool_type self.post_norm = post_norm self.norm_eps = norm_eps # embeddings gain = 1.0 / math.sqrt(dim) self.patch_embedding = nn.Conv2d( 3, dim, kernel_size=patch_size, stride=patch_size, bias=not pre_norm) if pool_type in ('token', 'token_fc'): self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim)) self.pos_embedding = nn.Parameter(gain * torch.randn( 1, self.num_patches + (1 if pool_type in ('token', 'token_fc') else 0), dim)) self.dropout = nn.Dropout(embedding_dropout) # transformer self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None self.transformer = nn.Sequential(*[ AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False, activation, attn_dropout, proj_dropout, norm_eps) for _ in range(num_layers) ]) self.post_norm = LayerNorm(dim, eps=norm_eps) # head if pool_type == 'token': self.head = nn.Parameter(gain * torch.randn(dim, out_dim)) elif pool_type == 'token_fc': self.head = nn.Linear(dim, out_dim) elif pool_type == 'attn_pool': self.head = AttentionPool(dim, mlp_ratio, num_heads, activation, proj_dropout, norm_eps) def forward(self, x, interpolation=False, use_31_block=False): b = x.size(0) # embeddings x = self.patch_embedding(x).flatten(2).permute(0, 2, 1) if self.pool_type in ('token', 'token_fc'): x = torch.cat([self.cls_embedding.expand(b, -1, -1).to(dtype=x.dtype, device=x.device), x], dim=1) if interpolation: e = pos_interpolate(self.pos_embedding, x.size(1)) else: e = self.pos_embedding e = e.to(dtype=x.dtype, device=x.device) x = self.dropout(x + e) if self.pre_norm is not None: x = self.pre_norm(x) # transformer if use_31_block: x = self.transformer[:-1](x) return x else: x = self.transformer(x) return x class CLIP(nn.Module): def __init__(self, embed_dim=512, image_size=224, patch_size=16, vision_dim=768, vision_mlp_ratio=4, vision_heads=12, vision_layers=12, vision_pool='token', vision_pre_norm=True, vision_post_norm=False, vocab_size=49408, text_len=77, text_dim=512, text_mlp_ratio=4, text_heads=8, text_layers=12, text_causal=True, text_pool='argmax', text_head_bias=False, logit_bias=None, activation='quick_gelu', attn_dropout=0.0, proj_dropout=0.0, embedding_dropout=0.0, norm_eps=1e-5): super().__init__() self.embed_dim = embed_dim self.image_size = image_size self.patch_size = patch_size self.vision_dim = vision_dim self.vision_mlp_ratio = vision_mlp_ratio self.vision_heads = vision_heads self.vision_layers = vision_layers self.vision_pool = vision_pool self.vision_pre_norm = vision_pre_norm self.vision_post_norm = vision_post_norm self.vocab_size = vocab_size self.text_len = text_len self.text_dim = text_dim self.text_mlp_ratio = text_mlp_ratio self.text_heads = text_heads self.text_layers = text_layers self.text_causal = text_causal self.text_pool = text_pool self.text_head_bias = text_head_bias self.norm_eps = norm_eps # models self.visual = VisionTransformer( image_size=image_size, patch_size=patch_size, dim=vision_dim, mlp_ratio=vision_mlp_ratio, out_dim=embed_dim, num_heads=vision_heads, num_layers=vision_layers, pool_type=vision_pool, pre_norm=vision_pre_norm, post_norm=vision_post_norm, activation=activation, attn_dropout=attn_dropout, proj_dropout=proj_dropout, embedding_dropout=embedding_dropout, norm_eps=norm_eps) self.textual = TextTransformer( vocab_size=vocab_size, text_len=text_len, dim=text_dim, mlp_ratio=text_mlp_ratio, out_dim=embed_dim, num_heads=text_heads, num_layers=text_layers, causal=text_causal, pool_type=text_pool, head_bias=text_head_bias, activation=activation, attn_dropout=attn_dropout, proj_dropout=proj_dropout, embedding_dropout=embedding_dropout, norm_eps=norm_eps) self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([])) if logit_bias is not None: self.logit_bias = nn.Parameter(logit_bias * torch.ones([])) # initialize weights self.init_weights() def forward(self, imgs, txt_ids): """ imgs: [B, 3, H, W] of torch.float32. - mean: [0.48145466, 0.4578275, 0.40821073] - std: [0.26862954, 0.26130258, 0.27577711] txt_ids: [B, L] of torch.long. Encoded by data.CLIPTokenizer. """ xi = self.visual(imgs) xt = self.textual(txt_ids) return xi, xt def init_weights(self): # embeddings nn.init.normal_(self.textual.token_embedding.weight, std=0.02) nn.init.normal_(self.visual.patch_embedding.weight, std=0.1) # attentions for modality in ['visual', 'textual']: dim = self.vision_dim if modality == 'visual' else self.text_dim transformer = getattr(self, modality).transformer proj_gain = (1.0 / math.sqrt(dim)) * ( 1.0 / math.sqrt(2 * len(transformer))) attn_gain = 1.0 / math.sqrt(dim) mlp_gain = 1.0 / math.sqrt(2.0 * dim) for block in transformer: nn.init.normal_(block.attn.to_qkv.weight, std=attn_gain) nn.init.normal_(block.attn.proj.weight, std=proj_gain) nn.init.normal_(block.mlp[0].weight, std=mlp_gain) nn.init.normal_(block.mlp[2].weight, std=proj_gain) def param_groups(self): groups = [{ 'params': [ p for n, p in self.named_parameters() if 'norm' in n or n.endswith('bias') ], 'weight_decay': 0.0 }, { 'params': [ p for n, p in self.named_parameters() if not ('norm' in n or n.endswith('bias')) ] }] return groups class XLMRobertaWithHead(XLMRoberta): def __init__(self, **kwargs): self.out_dim = kwargs.pop('out_dim') super().__init__(**kwargs) # head mid_dim = (self.dim + self.out_dim) // 2 self.head = nn.Sequential( nn.Linear(self.dim, mid_dim, bias=False), nn.GELU(), nn.Linear(mid_dim, self.out_dim, bias=False)) def forward(self, ids): # xlm-roberta x = super().forward(ids) # average pooling mask = ids.ne(self.pad_id).unsqueeze(-1).to(x) x = (x * mask).sum(dim=1) / mask.sum(dim=1) # head x = self.head(x) return x class XLMRobertaCLIP(nn.Module): def __init__(self, embed_dim=1024, image_size=224, patch_size=14, vision_dim=1280, vision_mlp_ratio=4, vision_heads=16, vision_layers=32, vision_pool='token', vision_pre_norm=True, vision_post_norm=False, activation='gelu', vocab_size=250002, max_text_len=514, type_size=1, pad_id=1, text_dim=1024, text_heads=16, text_layers=24, text_post_norm=True, text_dropout=0.1, attn_dropout=0.0, proj_dropout=0.0, embedding_dropout=0.0, norm_eps=1e-5): super().__init__() self.embed_dim = embed_dim self.image_size = image_size self.patch_size = patch_size self.vision_dim = vision_dim self.vision_mlp_ratio = vision_mlp_ratio self.vision_heads = vision_heads self.vision_layers = vision_layers self.vision_pre_norm = vision_pre_norm self.vision_post_norm = vision_post_norm self.activation = activation self.vocab_size = vocab_size self.max_text_len = max_text_len self.type_size = type_size self.pad_id = pad_id self.text_dim = text_dim self.text_heads = text_heads self.text_layers = text_layers self.text_post_norm = text_post_norm self.norm_eps = norm_eps # models self.visual = VisionTransformer( image_size=image_size, patch_size=patch_size, dim=vision_dim, mlp_ratio=vision_mlp_ratio, out_dim=embed_dim, num_heads=vision_heads, num_layers=vision_layers, pool_type=vision_pool, pre_norm=vision_pre_norm, post_norm=vision_post_norm, activation=activation, attn_dropout=attn_dropout, proj_dropout=proj_dropout, embedding_dropout=embedding_dropout, norm_eps=norm_eps) self.textual = None self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([])) def forward(self, imgs, txt_ids): """ imgs: [B, 3, H, W] of torch.float32. - mean: [0.48145466, 0.4578275, 0.40821073] - std: [0.26862954, 0.26130258, 0.27577711] txt_ids: [B, L] of torch.long. Encoded by data.CLIPTokenizer. """ xi = self.visual(imgs) xt = self.textual(txt_ids) return xi, xt def param_groups(self): groups = [{ 'params': [ p for n, p in self.named_parameters() if 'norm' in n or n.endswith('bias') ], 'weight_decay': 0.0 }, { 'params': [ p for n, p in self.named_parameters() if not ('norm' in n or n.endswith('bias')) ] }] return groups def _clip(pretrained=False, pretrained_name=None, model_cls=CLIP, return_transforms=False, return_tokenizer=False, tokenizer_padding='eos', dtype=torch.float32, device='cpu', **kwargs): # init model if pretrained and pretrained_name: from sora import BUCKET, DOWNLOAD_TO_CACHE # init a meta model with torch.device('meta'): model = model_cls(**kwargs) # checkpoint path checkpoint = f'models/clip/{pretrained_name}' if dtype in (torch.float16, torch.bfloat16): suffix = '-' + { torch.float16: 'fp16', torch.bfloat16: 'bf16' }[dtype] if object_exists(BUCKET, f'{checkpoint}{suffix}.pth'): checkpoint = f'{checkpoint}{suffix}' checkpoint += '.pth' # load model.load_state_dict( torch.load(DOWNLOAD_TO_CACHE(checkpoint), map_location=device), assign=True, strict=False) else: # init a model on device with torch.device(device): model = model_cls(**kwargs) # set device output = (model,) # init transforms if return_transforms: # mean and std if 'siglip' in pretrained_name.lower(): mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] else: mean = [0.48145466, 0.4578275, 0.40821073] std = [0.26862954, 0.26130258, 0.27577711] # transforms transforms = T.Compose([ T.Resize((model.image_size, model.image_size), interpolation=T.InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=mean, std=std) ]) output += (transforms,) # init tokenizer if return_tokenizer: from sora import data if 'siglip' in pretrained_name.lower(): tokenizer = data.HuggingfaceTokenizer( name=f'timm/{pretrained_name}', seq_len=model.text_len, clean='canonicalize') elif 'xlm' in pretrained_name.lower(): tokenizer = data.HuggingfaceTokenizer( name='xlm-roberta-large', seq_len=model.max_text_len - 2, clean='whitespace') elif 'mba' in pretrained_name.lower(): tokenizer = data.HuggingfaceTokenizer( name='facebook/xlm-roberta-xl', seq_len=model.max_text_len - 2, clean='whitespace') else: tokenizer = data.CLIPTokenizer( seq_len=model.text_len, padding=tokenizer_padding) output += (tokenizer,) return output[0] if len(output) == 1 else output def clip_xlm_roberta_vit_h_14( pretrained=False, pretrained_name='open-clip-xlm-roberta-large-vit-huge-14', **kwargs): cfg = dict( embed_dim=1024, image_size=224, patch_size=14, vision_dim=1280, vision_mlp_ratio=4, vision_heads=16, vision_layers=32, vision_pool='token', activation='gelu', vocab_size=250002, max_text_len=514, type_size=1, pad_id=1, text_dim=1024, text_heads=16, text_layers=24, text_post_norm=True, text_dropout=0.1, attn_dropout=0.0, proj_dropout=0.0, embedding_dropout=0.0) cfg.update(**kwargs) return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg) class WanImageEncoder(torch.nn.Module): def __init__(self): super().__init__() # init model self.model, self.transforms = clip_xlm_roberta_vit_h_14( pretrained=False, return_transforms=True, return_tokenizer=False, dtype=torch.float32, device="cpu") def encode_image(self, videos): # preprocess size = (self.model.image_size,) * 2 videos = torch.cat([ F.interpolate( u, size=size, mode='bicubic', align_corners=False) for u in videos ]) videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5)) # forward dtype = next(iter(self.model.visual.parameters())).dtype videos = videos.to(dtype) out = self.model.visual(videos, use_31_block=True) return out @staticmethod def state_dict_converter(): return WanImageEncoderStateDictConverter() class WanImageEncoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): return state_dict def from_civitai(self, state_dict): state_dict_ = {} for name, param in state_dict.items(): if name.startswith("textual."): continue name = "model." + name state_dict_[name] = param return state_dict_ ================================================ FILE: diffsynth/models/wan_video_text_encoder.py ================================================ import math import torch import torch.nn as nn import torch.nn.functional as F def fp16_clamp(x): if x.dtype == torch.float16 and torch.isinf(x).any(): clamp = torch.finfo(x.dtype).max - 1000 x = torch.clamp(x, min=-clamp, max=clamp) return x class GELU(nn.Module): def forward(self, x): return 0.5 * x * (1.0 + torch.tanh( math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0)))) class T5LayerNorm(nn.Module): def __init__(self, dim, eps=1e-6): super(T5LayerNorm, self).__init__() self.dim = dim self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def forward(self, x): x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) + self.eps) if self.weight.dtype in [torch.float16, torch.bfloat16]: x = x.type_as(self.weight) return self.weight * x class T5Attention(nn.Module): def __init__(self, dim, dim_attn, num_heads, dropout=0.1): assert dim_attn % num_heads == 0 super(T5Attention, self).__init__() self.dim = dim self.dim_attn = dim_attn self.num_heads = num_heads self.head_dim = dim_attn // num_heads # layers self.q = nn.Linear(dim, dim_attn, bias=False) self.k = nn.Linear(dim, dim_attn, bias=False) self.v = nn.Linear(dim, dim_attn, bias=False) self.o = nn.Linear(dim_attn, dim, bias=False) self.dropout = nn.Dropout(dropout) def forward(self, x, context=None, mask=None, pos_bias=None): """ x: [B, L1, C]. context: [B, L2, C] or None. mask: [B, L2] or [B, L1, L2] or None. """ # check inputs context = x if context is None else context b, n, c = x.size(0), self.num_heads, self.head_dim # compute query, key, value q = self.q(x).view(b, -1, n, c) k = self.k(context).view(b, -1, n, c) v = self.v(context).view(b, -1, n, c) # attention bias attn_bias = x.new_zeros(b, n, q.size(1), k.size(1)) if pos_bias is not None: attn_bias += pos_bias if mask is not None: assert mask.ndim in [2, 3] mask = mask.view(b, 1, 1, -1) if mask.ndim == 2 else mask.unsqueeze(1) attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min) # compute attention (T5 does not use scaling) attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias attn = F.softmax(attn.float(), dim=-1).type_as(attn) x = torch.einsum('bnij,bjnc->binc', attn, v) # output x = x.reshape(b, -1, n * c) x = self.o(x) x = self.dropout(x) return x class T5FeedForward(nn.Module): def __init__(self, dim, dim_ffn, dropout=0.1): super(T5FeedForward, self).__init__() self.dim = dim self.dim_ffn = dim_ffn # layers self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU()) self.fc1 = nn.Linear(dim, dim_ffn, bias=False) self.fc2 = nn.Linear(dim_ffn, dim, bias=False) self.dropout = nn.Dropout(dropout) def forward(self, x): x = self.fc1(x) * self.gate(x) x = self.dropout(x) x = self.fc2(x) x = self.dropout(x) return x class T5SelfAttention(nn.Module): def __init__(self, dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos=True, dropout=0.1): super(T5SelfAttention, self).__init__() self.dim = dim self.dim_attn = dim_attn self.dim_ffn = dim_ffn self.num_heads = num_heads self.num_buckets = num_buckets self.shared_pos = shared_pos # layers self.norm1 = T5LayerNorm(dim) self.attn = T5Attention(dim, dim_attn, num_heads, dropout) self.norm2 = T5LayerNorm(dim) self.ffn = T5FeedForward(dim, dim_ffn, dropout) self.pos_embedding = None if shared_pos else T5RelativeEmbedding( num_buckets, num_heads, bidirectional=True) def forward(self, x, mask=None, pos_bias=None): e = pos_bias if self.shared_pos else self.pos_embedding( x.size(1), x.size(1)) x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e)) x = fp16_clamp(x + self.ffn(self.norm2(x))) return x class T5RelativeEmbedding(nn.Module): def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128): super(T5RelativeEmbedding, self).__init__() self.num_buckets = num_buckets self.num_heads = num_heads self.bidirectional = bidirectional self.max_dist = max_dist # layers self.embedding = nn.Embedding(num_buckets, num_heads) def forward(self, lq, lk): device = self.embedding.weight.device # rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \ # torch.arange(lq).unsqueeze(1).to(device) rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \ torch.arange(lq, device=device).unsqueeze(1) rel_pos = self._relative_position_bucket(rel_pos) rel_pos_embeds = self.embedding(rel_pos) rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze( 0) # [1, N, Lq, Lk] return rel_pos_embeds.contiguous() def _relative_position_bucket(self, rel_pos): # preprocess if self.bidirectional: num_buckets = self.num_buckets // 2 rel_buckets = (rel_pos > 0).long() * num_buckets rel_pos = torch.abs(rel_pos) else: num_buckets = self.num_buckets rel_buckets = 0 rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos)) # embeddings for small and large positions max_exact = num_buckets // 2 rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) / math.log(self.max_dist / max_exact) * (num_buckets - max_exact)).long() rel_pos_large = torch.min( rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1)) rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large) return rel_buckets def init_weights(m): if isinstance(m, T5LayerNorm): nn.init.ones_(m.weight) elif isinstance(m, T5FeedForward): nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5) nn.init.normal_(m.fc1.weight, std=m.dim**-0.5) nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5) elif isinstance(m, T5Attention): nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn)**-0.5) nn.init.normal_(m.k.weight, std=m.dim**-0.5) nn.init.normal_(m.v.weight, std=m.dim**-0.5) nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn)**-0.5) elif isinstance(m, T5RelativeEmbedding): nn.init.normal_( m.embedding.weight, std=(2 * m.num_buckets * m.num_heads)**-0.5) class WanTextEncoder(torch.nn.Module): def __init__(self, vocab=256384, dim=4096, dim_attn=4096, dim_ffn=10240, num_heads=64, num_layers=24, num_buckets=32, shared_pos=False, dropout=0.1): super(WanTextEncoder, self).__init__() self.dim = dim self.dim_attn = dim_attn self.dim_ffn = dim_ffn self.num_heads = num_heads self.num_layers = num_layers self.num_buckets = num_buckets self.shared_pos = shared_pos # layers self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \ else nn.Embedding(vocab, dim) self.pos_embedding = T5RelativeEmbedding( num_buckets, num_heads, bidirectional=True) if shared_pos else None self.dropout = nn.Dropout(dropout) self.blocks = nn.ModuleList([ T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos, dropout) for _ in range(num_layers) ]) self.norm = T5LayerNorm(dim) # initialize weights self.apply(init_weights) def forward(self, ids, mask=None): x = self.token_embedding(ids) x = self.dropout(x) e = self.pos_embedding(x.size(1), x.size(1)) if self.shared_pos else None for block in self.blocks: x = block(x, mask, pos_bias=e) x = self.norm(x) x = self.dropout(x) return x @staticmethod def state_dict_converter(): return WanTextEncoderStateDictConverter() class WanTextEncoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): return state_dict def from_civitai(self, state_dict): return state_dict ================================================ FILE: diffsynth/models/wan_video_vae.py ================================================ from einops import rearrange, repeat import torch import torch.nn as nn import torch.nn.functional as F from tqdm import tqdm CACHE_T = 2 def check_is_instance(model, module_class): if isinstance(model, module_class): return True if hasattr(model, "module") and isinstance(model.module, module_class): return True return False def block_causal_mask(x, block_size): # params b, n, s, _, device = *x.size(), x.device assert s % block_size == 0 num_blocks = s // block_size # build mask mask = torch.zeros(b, n, s, s, dtype=torch.bool, device=device) for i in range(num_blocks): mask[:, :, i * block_size:(i + 1) * block_size, :(i + 1) * block_size] = 1 return mask class CausalConv3d(nn.Conv3d): """ Causal 3d convolusion. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._padding = (self.padding[2], self.padding[2], self.padding[1], self.padding[1], 2 * self.padding[0], 0) self.padding = (0, 0, 0) def forward(self, x, cache_x=None): padding = list(self._padding) if cache_x is not None and self._padding[4] > 0: cache_x = cache_x.to(x.device) x = torch.cat([cache_x, x], dim=2) padding[4] -= cache_x.shape[2] x = F.pad(x, padding) return super().forward(x) class RMS_norm(nn.Module): def __init__(self, dim, channel_first=True, images=True, bias=False): super().__init__() broadcastable_dims = (1, 1, 1) if not images else (1, 1) shape = (dim, *broadcastable_dims) if channel_first else (dim,) self.channel_first = channel_first self.scale = dim**0.5 self.gamma = nn.Parameter(torch.ones(shape)) self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0. def forward(self, x): return F.normalize( x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias class Upsample(nn.Upsample): def forward(self, x): """ Fix bfloat16 support for nearest neighbor interpolation. """ return super().forward(x.float()).type_as(x) class Resample(nn.Module): def __init__(self, dim, mode): assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', 'downsample3d') super().__init__() self.dim = dim self.mode = mode # layers if mode == 'upsample2d': self.resample = nn.Sequential( Upsample(scale_factor=(2., 2.), mode='nearest-exact'), nn.Conv2d(dim, dim // 2, 3, padding=1)) elif mode == 'upsample3d': self.resample = nn.Sequential( Upsample(scale_factor=(2., 2.), mode='nearest-exact'), nn.Conv2d(dim, dim // 2, 3, padding=1)) self.time_conv = CausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) elif mode == 'downsample2d': self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2))) elif mode == 'downsample3d': self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2))) self.time_conv = CausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) else: self.resample = nn.Identity() def forward(self, x, feat_cache=None, feat_idx=[0]): b, c, t, h, w = x.size() if self.mode == 'upsample3d': if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = 'Rep' feat_idx[0] += 1 else: cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[ idx] is not None and feat_cache[idx] != 'Rep': # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) if cache_x.shape[2] < 2 and feat_cache[ idx] is not None and feat_cache[idx] == 'Rep': cache_x = torch.cat([ torch.zeros_like(cache_x).to(cache_x.device), cache_x ], dim=2) if feat_cache[idx] == 'Rep': x = self.time_conv(x) else: x = self.time_conv(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 x = x.reshape(b, 2, c, t, h, w) x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3) x = x.reshape(b, c, t * 2, h, w) t = x.shape[2] x = rearrange(x, 'b c t h w -> (b t) c h w') x = self.resample(x) x = rearrange(x, '(b t) c h w -> b c t h w', t=t) if self.mode == 'downsample3d': if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = x.clone() feat_idx[0] += 1 else: cache_x = x[:, :, -1:, :, :].clone() x = self.time_conv( torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) feat_cache[idx] = cache_x feat_idx[0] += 1 return x def init_weight(self, conv): conv_weight = conv.weight nn.init.zeros_(conv_weight) c1, c2, t, h, w = conv_weight.size() one_matrix = torch.eye(c1, c2) init_matrix = one_matrix nn.init.zeros_(conv_weight) conv_weight.data[:, :, 1, 0, 0] = init_matrix conv.weight.data.copy_(conv_weight) nn.init.zeros_(conv.bias.data) def init_weight2(self, conv): conv_weight = conv.weight.data nn.init.zeros_(conv_weight) c1, c2, t, h, w = conv_weight.size() init_matrix = torch.eye(c1 // 2, c2) conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix conv.weight.data.copy_(conv_weight) nn.init.zeros_(conv.bias.data) class ResidualBlock(nn.Module): def __init__(self, in_dim, out_dim, dropout=0.0): super().__init__() self.in_dim = in_dim self.out_dim = out_dim # layers self.residual = nn.Sequential( RMS_norm(in_dim, images=False), nn.SiLU(), CausalConv3d(in_dim, out_dim, 3, padding=1), RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), CausalConv3d(out_dim, out_dim, 3, padding=1)) self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ if in_dim != out_dim else nn.Identity() def forward(self, x, feat_cache=None, feat_idx=[0]): h = self.shortcut(x) for layer in self.residual: if check_is_instance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x + h class AttentionBlock(nn.Module): """ Causal self-attention with a single head. """ def __init__(self, dim): super().__init__() self.dim = dim # layers self.norm = RMS_norm(dim) self.to_qkv = nn.Conv2d(dim, dim * 3, 1) self.proj = nn.Conv2d(dim, dim, 1) # zero out the last layer params nn.init.zeros_(self.proj.weight) def forward(self, x): identity = x b, c, t, h, w = x.size() x = rearrange(x, 'b c t h w -> (b t) c h w') x = self.norm(x) # compute query, key, value q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, -1).permute( 0, 1, 3, 2).contiguous().chunk(3, dim=-1) # apply attention x = F.scaled_dot_product_attention( q, k, v, #attn_mask=block_causal_mask(q, block_size=h * w) ) x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w) # output x = self.proj(x) x = rearrange(x, '(b t) c h w-> b c t h w', t=t) return x + identity class Encoder3d(nn.Module): def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample # dimensions dims = [dim * u for u in [1] + dim_mult] scale = 1.0 # init block self.conv1 = CausalConv3d(3, dims[0], 3, padding=1) # downsample blocks downsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): # residual (+attention) blocks for _ in range(num_res_blocks): downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) if scale in attn_scales: downsamples.append(AttentionBlock(out_dim)) in_dim = out_dim # downsample block if i != len(dim_mult) - 1: mode = 'downsample3d' if temperal_downsample[ i] else 'downsample2d' downsamples.append(Resample(out_dim, mode=mode)) scale /= 2.0 self.downsamples = nn.Sequential(*downsamples) # middle blocks self.middle = nn.Sequential(ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), ResidualBlock(out_dim, out_dim, dropout)) # output blocks self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, z_dim, 3, padding=1)) def forward(self, x, feat_cache=None, feat_idx=[0]): if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) ## downsamples for layer in self.downsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## middle for layer in self.middle: if check_is_instance(layer, ResidualBlock) and feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## head for layer in self.head: if check_is_instance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x class Decoder3d(nn.Module): def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_upsample=[False, True, True], dropout=0.0): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_upsample = temperal_upsample # dimensions dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] scale = 1.0 / 2**(len(dim_mult) - 2) # init block self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) # middle blocks self.middle = nn.Sequential(ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), ResidualBlock(dims[0], dims[0], dropout)) # upsample blocks upsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): # residual (+attention) blocks if i == 1 or i == 2 or i == 3: in_dim = in_dim // 2 for _ in range(num_res_blocks + 1): upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) if scale in attn_scales: upsamples.append(AttentionBlock(out_dim)) in_dim = out_dim # upsample block if i != len(dim_mult) - 1: mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' upsamples.append(Resample(out_dim, mode=mode)) scale *= 2.0 self.upsamples = nn.Sequential(*upsamples) # output blocks self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, 3, 3, padding=1)) def forward(self, x, feat_cache=None, feat_idx=[0]): ## conv1 if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) ## middle for layer in self.middle: if check_is_instance(layer, ResidualBlock) and feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## upsamples for layer in self.upsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## head for layer in self.head: if check_is_instance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat([ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x ], dim=2) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x def count_conv3d(model): count = 0 for m in model.modules(): if check_is_instance(m, CausalConv3d): count += 1 return count class VideoVAE_(nn.Module): def __init__(self, dim=96, z_dim=16, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[False, True, True], dropout=0.0): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample self.temperal_upsample = temperal_downsample[::-1] # modules self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout) self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) self.conv2 = CausalConv3d(z_dim, z_dim, 1) self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout) def forward(self, x): mu, log_var = self.encode(x) z = self.reparameterize(mu, log_var) x_recon = self.decode(z) return x_recon, mu, log_var def encode(self, x, scale): self.clear_cache() ## cache t = x.shape[2] iter_ = 1 + (t - 1) // 4 for i in range(iter_): self._enc_conv_idx = [0] if i == 0: out = self.encoder(x[:, :, :1, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx) else: out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx) out = torch.cat([out, out_], 2) mu, log_var = self.conv1(out).chunk(2, dim=1) if isinstance(scale[0], torch.Tensor): scale = [s.to(dtype=mu.dtype, device=mu.device) for s in scale] mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view( 1, self.z_dim, 1, 1, 1) else: scale = scale.to(dtype=mu.dtype, device=mu.device) mu = (mu - scale[0]) * scale[1] return mu def decode(self, z, scale): self.clear_cache() # z: [b,c,t,h,w] if isinstance(scale[0], torch.Tensor): scale = [s.to(dtype=z.dtype, device=z.device) for s in scale] z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view( 1, self.z_dim, 1, 1, 1) else: scale = scale.to(dtype=z.dtype, device=z.device) z = z / scale[1] + scale[0] iter_ = z.shape[2] x = self.conv2(z) for i in range(iter_): self._conv_idx = [0] if i == 0: out = self.decoder(x[:, :, i:i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx) else: out_ = self.decoder(x[:, :, i:i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx) out = torch.cat([out, out_], 2) # may add tensor offload return out def reparameterize(self, mu, log_var): std = torch.exp(0.5 * log_var) eps = torch.randn_like(std) return eps * std + mu def sample(self, imgs, deterministic=False): mu, log_var = self.encode(imgs) if deterministic: return mu std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) return mu + std * torch.randn_like(std) def clear_cache(self): self._conv_num = count_conv3d(self.decoder) self._conv_idx = [0] self._feat_map = [None] * self._conv_num # cache encode self._enc_conv_num = count_conv3d(self.encoder) self._enc_conv_idx = [0] self._enc_feat_map = [None] * self._enc_conv_num class WanVideoVAE(nn.Module): def __init__(self, z_dim=16): super().__init__() mean = [ -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 ] std = [ 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 ] self.mean = torch.tensor(mean) self.std = torch.tensor(std) self.scale = [self.mean, 1.0 / self.std] # init model self.model = VideoVAE_(z_dim=z_dim).eval().requires_grad_(False) self.upsampling_factor = 8 def build_1d_mask(self, length, left_bound, right_bound, border_width): x = torch.ones((length,)) if not left_bound: x[:border_width] = (torch.arange(border_width) + 1) / border_width if not right_bound: x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) return x def build_mask(self, data, is_bound, border_width): _, _, _, H, W = data.shape h = self.build_1d_mask(H, is_bound[0], is_bound[1], border_width[0]) w = self.build_1d_mask(W, is_bound[2], is_bound[3], border_width[1]) h = repeat(h, "H -> H W", H=H, W=W) w = repeat(w, "W -> H W", H=H, W=W) mask = torch.stack([h, w]).min(dim=0).values mask = rearrange(mask, "H W -> 1 1 1 H W") return mask def tiled_decode(self, hidden_states, device, tile_size, tile_stride): _, _, T, H, W = hidden_states.shape size_h, size_w = tile_size stride_h, stride_w = tile_stride # Split tasks tasks = [] for h in range(0, H, stride_h): if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue for w in range(0, W, stride_w): if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue h_, w_ = h + size_h, w + size_w tasks.append((h, h_, w, w_)) data_device = "cpu" computation_device = device out_T = T * 4 - 3 weight = torch.zeros((1, 1, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device) values = torch.zeros((1, 3, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device) for h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"): hidden_states_batch = hidden_states[:, :, :, h:h_, w:w_].to(computation_device) hidden_states_batch = self.model.decode(hidden_states_batch, self.scale).to(data_device) mask = self.build_mask( hidden_states_batch, is_bound=(h==0, h_>=H, w==0, w_>=W), border_width=((size_h - stride_h) * self.upsampling_factor, (size_w - stride_w) * self.upsampling_factor) ).to(dtype=hidden_states.dtype, device=data_device) target_h = h * self.upsampling_factor target_w = w * self.upsampling_factor values[ :, :, :, target_h:target_h + hidden_states_batch.shape[3], target_w:target_w + hidden_states_batch.shape[4], ] += hidden_states_batch * mask weight[ :, :, :, target_h: target_h + hidden_states_batch.shape[3], target_w: target_w + hidden_states_batch.shape[4], ] += mask values = values / weight values = values.clamp_(-1, 1) return values def tiled_encode(self, video, device, tile_size, tile_stride): _, _, T, H, W = video.shape size_h, size_w = tile_size stride_h, stride_w = tile_stride # Split tasks tasks = [] for h in range(0, H, stride_h): if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue for w in range(0, W, stride_w): if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue h_, w_ = h + size_h, w + size_w tasks.append((h, h_, w, w_)) data_device = "cpu" computation_device = device out_T = (T + 3) // 4 weight = torch.zeros((1, 1, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device) values = torch.zeros((1, 16, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device) for h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"): hidden_states_batch = video[:, :, :, h:h_, w:w_].to(computation_device) hidden_states_batch = self.model.encode(hidden_states_batch, self.scale).to(data_device) mask = self.build_mask( hidden_states_batch, is_bound=(h==0, h_>=H, w==0, w_>=W), border_width=((size_h - stride_h) // self.upsampling_factor, (size_w - stride_w) // self.upsampling_factor) ).to(dtype=video.dtype, device=data_device) target_h = h // self.upsampling_factor target_w = w // self.upsampling_factor values[ :, :, :, target_h:target_h + hidden_states_batch.shape[3], target_w:target_w + hidden_states_batch.shape[4], ] += hidden_states_batch * mask weight[ :, :, :, target_h: target_h + hidden_states_batch.shape[3], target_w: target_w + hidden_states_batch.shape[4], ] += mask values = values / weight return values def single_encode(self, video, device): video = video.to(device) x = self.model.encode(video, self.scale) return x def single_decode(self, hidden_state, device): hidden_state = hidden_state.to(device) video = self.model.decode(hidden_state, self.scale) return video.clamp_(-1, 1) def encode(self, videos, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)): videos = [video.to("cpu") for video in videos] hidden_states = [] for video in videos: video = video.unsqueeze(0) if tiled: tile_size = (tile_size[0] * 8, tile_size[1] * 8) tile_stride = (tile_stride[0] * 8, tile_stride[1] * 8) hidden_state = self.tiled_encode(video, device, tile_size, tile_stride) else: hidden_state = self.single_encode(video, device) hidden_state = hidden_state.squeeze(0) hidden_states.append(hidden_state) hidden_states = torch.stack(hidden_states) return hidden_states def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)): hidden_states = [hidden_state.to("cpu") for hidden_state in hidden_states] videos = [] for hidden_state in hidden_states: hidden_state = hidden_state.unsqueeze(0) if tiled: video = self.tiled_decode(hidden_state, device, tile_size, tile_stride) else: video = self.single_decode(hidden_state, device) video = video.squeeze(0) videos.append(video) videos = torch.stack(videos) return videos @staticmethod def state_dict_converter(): return WanVideoVAEStateDictConverter() class WanVideoVAEStateDictConverter: def __init__(self): pass def from_civitai(self, state_dict): state_dict_ = {} if 'model_state' in state_dict: state_dict = state_dict['model_state'] for name in state_dict: state_dict_['model.' + name] = state_dict[name] return state_dict_ ================================================ FILE: diffsynth/pipelines/__init__.py ================================================ from .sd_image import SDImagePipeline from .sd_video import SDVideoPipeline from .sdxl_image import SDXLImagePipeline from .sdxl_video import SDXLVideoPipeline from .sd3_image import SD3ImagePipeline from .hunyuan_image import HunyuanDiTImagePipeline from .svd_video import SVDVideoPipeline from .flux_image import FluxImagePipeline from .cog_video import CogVideoPipeline from .omnigen_image import OmnigenImagePipeline from .pipeline_runner import SDVideoPipelineRunner from .hunyuan_video import HunyuanVideoPipeline from .step_video import StepVideoPipeline from .wan_video import WanVideoPipeline from .wan_video_syncammaster import WanVideoSynCamMasterPipeline KolorsImagePipeline = SDXLImagePipeline ================================================ FILE: diffsynth/pipelines/base.py ================================================ import torch import numpy as np from PIL import Image from torchvision.transforms import GaussianBlur class BasePipeline(torch.nn.Module): def __init__(self, device="cuda", torch_dtype=torch.float16, height_division_factor=64, width_division_factor=64): super().__init__() self.device = device self.torch_dtype = torch_dtype self.height_division_factor = height_division_factor self.width_division_factor = width_division_factor self.cpu_offload = False self.model_names = [] def check_resize_height_width(self, height, width): if height % self.height_division_factor != 0: height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor print(f"The height cannot be evenly divided by {self.height_division_factor}. We round it up to {height}.") if width % self.width_division_factor != 0: width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor print(f"The width cannot be evenly divided by {self.width_division_factor}. We round it up to {width}.") return height, width def preprocess_image(self, image): image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) return image def preprocess_images(self, images): return [self.preprocess_image(image) for image in images] def vae_output_to_image(self, vae_output): image = vae_output[0].cpu().float().permute(1, 2, 0).numpy() image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) return image def vae_output_to_video(self, vae_output): video = vae_output.cpu().permute(1, 2, 0).numpy() video = [Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) for image in video] return video def merge_latents(self, value, latents, masks, scales, blur_kernel_size=33, blur_sigma=10.0): if len(latents) > 0: blur = GaussianBlur(kernel_size=blur_kernel_size, sigma=blur_sigma) height, width = value.shape[-2:] weight = torch.ones_like(value) for latent, mask, scale in zip(latents, masks, scales): mask = self.preprocess_image(mask.resize((width, height))).mean(dim=1, keepdim=True) > 0 mask = mask.repeat(1, latent.shape[1], 1, 1).to(dtype=latent.dtype, device=latent.device) mask = blur(mask) value += latent * mask * scale weight += mask * scale value /= weight return value def control_noise_via_local_prompts(self, prompt_emb_global, prompt_emb_locals, masks, mask_scales, inference_callback, special_kwargs=None, special_local_kwargs_list=None): if special_kwargs is None: noise_pred_global = inference_callback(prompt_emb_global) else: noise_pred_global = inference_callback(prompt_emb_global, special_kwargs) if special_local_kwargs_list is None: noise_pred_locals = [inference_callback(prompt_emb_local) for prompt_emb_local in prompt_emb_locals] else: noise_pred_locals = [inference_callback(prompt_emb_local, special_kwargs) for prompt_emb_local, special_kwargs in zip(prompt_emb_locals, special_local_kwargs_list)] noise_pred = self.merge_latents(noise_pred_global, noise_pred_locals, masks, mask_scales) return noise_pred def extend_prompt(self, prompt, local_prompts, masks, mask_scales): local_prompts = local_prompts or [] masks = masks or [] mask_scales = mask_scales or [] extended_prompt_dict = self.prompter.extend_prompt(prompt) prompt = extended_prompt_dict.get("prompt", prompt) local_prompts += extended_prompt_dict.get("prompts", []) masks += extended_prompt_dict.get("masks", []) mask_scales += [100.0] * len(extended_prompt_dict.get("masks", [])) return prompt, local_prompts, masks, mask_scales def enable_cpu_offload(self): self.cpu_offload = True def load_models_to_device(self, loadmodel_names=[]): # only load models to device if cpu_offload is enabled if not self.cpu_offload: return # offload the unneeded models to cpu for model_name in self.model_names: if model_name not in loadmodel_names: model = getattr(self, model_name) if model is not None: if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: for module in model.modules(): if hasattr(module, "offload"): module.offload() else: model.cpu() # load the needed models to device for model_name in loadmodel_names: model = getattr(self, model_name) if model is not None: if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: for module in model.modules(): if hasattr(module, "onload"): module.onload() else: model.to(self.device) # fresh the cuda cache torch.cuda.empty_cache() def generate_noise(self, shape, seed=None, device="cpu", dtype=torch.float16): generator = None if seed is None else torch.Generator(device).manual_seed(seed) noise = torch.randn(shape, generator=generator, device=device, dtype=dtype) return noise ================================================ FILE: diffsynth/pipelines/cog_video.py ================================================ from ..models import ModelManager, FluxTextEncoder2, CogDiT, CogVAEEncoder, CogVAEDecoder from ..prompters import CogPrompter from ..schedulers import EnhancedDDIMScheduler from .base import BasePipeline import torch from tqdm import tqdm from PIL import Image import numpy as np from einops import rearrange class CogVideoPipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) self.scheduler = EnhancedDDIMScheduler(rescale_zero_terminal_snr=True, prediction_type="v_prediction") self.prompter = CogPrompter() # models self.text_encoder: FluxTextEncoder2 = None self.dit: CogDiT = None self.vae_encoder: CogVAEEncoder = None self.vae_decoder: CogVAEDecoder = None def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): self.text_encoder = model_manager.fetch_model("flux_text_encoder_2") self.dit = model_manager.fetch_model("cog_dit") self.vae_encoder = model_manager.fetch_model("cog_vae_encoder") self.vae_decoder = model_manager.fetch_model("cog_vae_decoder") self.prompter.fetch_models(self.text_encoder) self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) @staticmethod def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[]): pipe = CogVideoPipeline( device=model_manager.device, torch_dtype=model_manager.torch_dtype ) pipe.fetch_models(model_manager, prompt_refiner_classes) return pipe def tensor2video(self, frames): frames = rearrange(frames, "C T H W -> T H W C") frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) frames = [Image.fromarray(frame) for frame in frames] return frames def encode_prompt(self, prompt, positive=True): prompt_emb = self.prompter.encode_prompt(prompt, device=self.device, positive=positive) return {"prompt_emb": prompt_emb} def prepare_extra_input(self, latents): return {"image_rotary_emb": self.dit.prepare_rotary_positional_embeddings(latents.shape[3], latents.shape[4], latents.shape[2], device=self.device)} @torch.no_grad() def __call__( self, prompt, negative_prompt="", input_video=None, cfg_scale=7.0, denoising_strength=1.0, num_frames=49, height=480, width=720, num_inference_steps=20, tiled=False, tile_size=(60, 90), tile_stride=(30, 45), seed=None, progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Tiler parameters tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength) # Prepare latent tensors noise = self.generate_noise((1, 16, num_frames // 4 + 1, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype) if denoising_strength == 1.0: latents = noise.clone() else: input_video = self.preprocess_images(input_video) input_video = torch.stack(input_video, dim=2) latents = self.vae_encoder.encode_video(input_video, **tiler_kwargs, progress_bar=progress_bar_cmd).to(dtype=self.torch_dtype) latents = self.scheduler.add_noise(latents, noise, self.scheduler.timesteps[0]) if not tiled: latents = latents.to(self.device) # Encode prompt prompt_emb_posi = self.encode_prompt(prompt, positive=True) if cfg_scale != 1.0: prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) # Extra input extra_input = self.prepare_extra_input(latents) # Denoise for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(self.device) # Classifier-free guidance noise_pred_posi = self.dit( latents, timestep=timestep, **prompt_emb_posi, **tiler_kwargs, **extra_input ) if cfg_scale != 1.0: noise_pred_nega = self.dit( latents, timestep=timestep, **prompt_emb_nega, **tiler_kwargs, **extra_input ) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) else: noise_pred = noise_pred_posi # DDIM latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) # Update progress bar if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image video = self.vae_decoder.decode_video(latents.to("cpu"), **tiler_kwargs, progress_bar=progress_bar_cmd) video = self.tensor2video(video[0]) return video ================================================ FILE: diffsynth/pipelines/dancer.py ================================================ import torch from ..models import SDUNet, SDMotionModel, SDXLUNet, SDXLMotionModel from ..models.sd_unet import PushBlock, PopBlock from ..controlnets import MultiControlNetManager def lets_dance( unet: SDUNet, motion_modules: SDMotionModel = None, controlnet: MultiControlNetManager = None, sample = None, timestep = None, encoder_hidden_states = None, ipadapter_kwargs_list = {}, controlnet_frames = None, unet_batch_size = 1, controlnet_batch_size = 1, cross_frame_attention = False, tiled=False, tile_size=64, tile_stride=32, device = "cuda", vram_limit_level = 0, ): # 0. Text embedding alignment (only for video processing) if encoder_hidden_states.shape[0] != sample.shape[0]: encoder_hidden_states = encoder_hidden_states.repeat(sample.shape[0], 1, 1, 1) # 1. ControlNet # This part will be repeated on overlapping frames if animatediff_batch_size > animatediff_stride. # I leave it here because I intend to do something interesting on the ControlNets. controlnet_insert_block_id = 30 if controlnet is not None and controlnet_frames is not None: res_stacks = [] # process controlnet frames with batch for batch_id in range(0, sample.shape[0], controlnet_batch_size): batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0]) res_stack = controlnet( sample[batch_id: batch_id_], timestep, encoder_hidden_states[batch_id: batch_id_], controlnet_frames[:, batch_id: batch_id_], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride ) if vram_limit_level >= 1: res_stack = [res.cpu() for res in res_stack] res_stacks.append(res_stack) # concat the residual additional_res_stack = [] for i in range(len(res_stacks[0])): res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0) additional_res_stack.append(res) else: additional_res_stack = None # 2. time time_emb = unet.time_proj(timestep).to(sample.dtype) time_emb = unet.time_embedding(time_emb) # 3. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = unet.conv_in(sample) text_emb = encoder_hidden_states res_stack = [hidden_states.cpu() if vram_limit_level>=1 else hidden_states] # 4. blocks for block_id, block in enumerate(unet.blocks): # 4.1 UNet if isinstance(block, PushBlock): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) if vram_limit_level>=1: res_stack[-1] = res_stack[-1].cpu() elif isinstance(block, PopBlock): if vram_limit_level>=1: res_stack[-1] = res_stack[-1].to(device) hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) else: hidden_states_input = hidden_states hidden_states_output = [] for batch_id in range(0, sample.shape[0], unet_batch_size): batch_id_ = min(batch_id + unet_batch_size, sample.shape[0]) hidden_states, _, _, _ = block( hidden_states_input[batch_id: batch_id_], time_emb, text_emb[batch_id: batch_id_], res_stack, cross_frame_attention=cross_frame_attention, ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride ) hidden_states_output.append(hidden_states) hidden_states = torch.concat(hidden_states_output, dim=0) # 4.2 AnimateDiff if motion_modules is not None: if block_id in motion_modules.call_block_id: motion_module_id = motion_modules.call_block_id[block_id] hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( hidden_states, time_emb, text_emb, res_stack, batch_size=1 ) # 4.3 ControlNet if block_id == controlnet_insert_block_id and additional_res_stack is not None: hidden_states += additional_res_stack.pop().to(device) if vram_limit_level>=1: res_stack = [(res.to(device) + additional_res.to(device)).cpu() for res, additional_res in zip(res_stack, additional_res_stack)] else: res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)] # 5. output hidden_states = unet.conv_norm_out(hidden_states) hidden_states = unet.conv_act(hidden_states) hidden_states = unet.conv_out(hidden_states) return hidden_states def lets_dance_xl( unet: SDXLUNet, motion_modules: SDXLMotionModel = None, controlnet: MultiControlNetManager = None, sample = None, add_time_id = None, add_text_embeds = None, timestep = None, encoder_hidden_states = None, ipadapter_kwargs_list = {}, controlnet_frames = None, unet_batch_size = 1, controlnet_batch_size = 1, cross_frame_attention = False, tiled=False, tile_size=64, tile_stride=32, device = "cuda", vram_limit_level = 0, ): # 0. Text embedding alignment (only for video processing) if encoder_hidden_states.shape[0] != sample.shape[0]: encoder_hidden_states = encoder_hidden_states.repeat(sample.shape[0], 1, 1, 1) if add_text_embeds.shape[0] != sample.shape[0]: add_text_embeds = add_text_embeds.repeat(sample.shape[0], 1) # 1. ControlNet controlnet_insert_block_id = 22 if controlnet is not None and controlnet_frames is not None: res_stacks = [] # process controlnet frames with batch for batch_id in range(0, sample.shape[0], controlnet_batch_size): batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0]) res_stack = controlnet( sample[batch_id: batch_id_], timestep, encoder_hidden_states[batch_id: batch_id_], controlnet_frames[:, batch_id: batch_id_], add_time_id=add_time_id, add_text_embeds=add_text_embeds, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, unet=unet, # for Kolors, some modules in ControlNets will be replaced. ) if vram_limit_level >= 1: res_stack = [res.cpu() for res in res_stack] res_stacks.append(res_stack) # concat the residual additional_res_stack = [] for i in range(len(res_stacks[0])): res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0) additional_res_stack.append(res) else: additional_res_stack = None # 2. time t_emb = unet.time_proj(timestep).to(sample.dtype) t_emb = unet.time_embedding(t_emb) time_embeds = unet.add_time_proj(add_time_id) time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) add_embeds = add_embeds.to(sample.dtype) add_embeds = unet.add_time_embedding(add_embeds) time_emb = t_emb + add_embeds # 3. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = unet.conv_in(sample) text_emb = encoder_hidden_states if unet.text_intermediate_proj is None else unet.text_intermediate_proj(encoder_hidden_states) res_stack = [hidden_states] # 4. blocks for block_id, block in enumerate(unet.blocks): # 4.1 UNet if isinstance(block, PushBlock): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) if vram_limit_level>=1: res_stack[-1] = res_stack[-1].cpu() elif isinstance(block, PopBlock): if vram_limit_level>=1: res_stack[-1] = res_stack[-1].to(device) hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) else: hidden_states_input = hidden_states hidden_states_output = [] for batch_id in range(0, sample.shape[0], unet_batch_size): batch_id_ = min(batch_id + unet_batch_size, sample.shape[0]) hidden_states, _, _, _ = block( hidden_states_input[batch_id: batch_id_], time_emb[batch_id: batch_id_], text_emb[batch_id: batch_id_], res_stack, cross_frame_attention=cross_frame_attention, ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, ) hidden_states_output.append(hidden_states) hidden_states = torch.concat(hidden_states_output, dim=0) # 4.2 AnimateDiff if motion_modules is not None: if block_id in motion_modules.call_block_id: motion_module_id = motion_modules.call_block_id[block_id] hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( hidden_states, time_emb, text_emb, res_stack, batch_size=1 ) # 4.3 ControlNet if block_id == controlnet_insert_block_id and additional_res_stack is not None: hidden_states += additional_res_stack.pop().to(device) res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)] # 5. output hidden_states = unet.conv_norm_out(hidden_states) hidden_states = unet.conv_act(hidden_states) hidden_states = unet.conv_out(hidden_states) return hidden_states ================================================ FILE: diffsynth/pipelines/flux_image.py ================================================ from ..models import ModelManager, FluxDiT, SD3TextEncoder1, FluxTextEncoder2, FluxVAEDecoder, FluxVAEEncoder, FluxIpAdapter from ..controlnets import FluxMultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator from ..prompters import FluxPrompter from ..schedulers import FlowMatchScheduler from .base import BasePipeline from typing import List import torch from tqdm import tqdm import numpy as np from PIL import Image from ..models.tiler import FastTileWorker from transformers import SiglipVisionModel from copy import deepcopy from transformers.models.t5.modeling_t5 import T5LayerNorm, T5DenseActDense, T5DenseGatedActDense from ..models.flux_dit import RMSNorm from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear class FluxImagePipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) self.scheduler = FlowMatchScheduler() self.prompter = FluxPrompter() # models self.text_encoder_1: SD3TextEncoder1 = None self.text_encoder_2: FluxTextEncoder2 = None self.dit: FluxDiT = None self.vae_decoder: FluxVAEDecoder = None self.vae_encoder: FluxVAEEncoder = None self.controlnet: FluxMultiControlNetManager = None self.ipadapter: FluxIpAdapter = None self.ipadapter_image_encoder: SiglipVisionModel = None self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter', 'ipadapter_image_encoder'] def enable_vram_management(self, num_persistent_param_in_dit=None): dtype = next(iter(self.text_encoder_1.parameters())).dtype enable_vram_management( self.text_encoder_1, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Embedding: AutoWrappedModule, torch.nn.LayerNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.text_encoder_2.parameters())).dtype enable_vram_management( self.text_encoder_2, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Embedding: AutoWrappedModule, T5LayerNorm: AutoWrappedModule, T5DenseActDense: AutoWrappedModule, T5DenseGatedActDense: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.dit.parameters())).dtype enable_vram_management( self.dit, module_map = { RMSNorm: AutoWrappedModule, torch.nn.Linear: AutoWrappedLinear, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cuda", computation_dtype=self.torch_dtype, computation_device=self.device, ), max_num_param=num_persistent_param_in_dit, overflow_module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.vae_decoder.parameters())).dtype enable_vram_management( self.vae_decoder, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv2d: AutoWrappedModule, torch.nn.GroupNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.vae_encoder.parameters())).dtype enable_vram_management( self.vae_encoder, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv2d: AutoWrappedModule, torch.nn.GroupNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) self.enable_cpu_offload() def denoising_model(self): return self.dit def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], prompt_extender_classes=[]): self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") self.text_encoder_2 = model_manager.fetch_model("flux_text_encoder_2") self.dit = model_manager.fetch_model("flux_dit") self.vae_decoder = model_manager.fetch_model("flux_vae_decoder") self.vae_encoder = model_manager.fetch_model("flux_vae_encoder") self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2) self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) self.prompter.load_prompt_extenders(model_manager, prompt_extender_classes) # ControlNets controlnet_units = [] for config in controlnet_config_units: controlnet_unit = ControlNetUnit( Annotator(config.processor_id, device=self.device, skip_processor=config.skip_processor), model_manager.fetch_model("flux_controlnet", config.model_path), config.scale ) controlnet_units.append(controlnet_unit) self.controlnet = FluxMultiControlNetManager(controlnet_units) # IP-Adapters self.ipadapter = model_manager.fetch_model("flux_ipadapter") self.ipadapter_image_encoder = model_manager.fetch_model("siglip_vision_model") @staticmethod def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], prompt_extender_classes=[], device=None, torch_dtype=None): pipe = FluxImagePipeline( device=model_manager.device if device is None else device, torch_dtype=model_manager.torch_dtype if torch_dtype is None else torch_dtype, ) pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes, prompt_extender_classes) return pipe def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return latents def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) image = self.vae_output_to_image(image) return image def encode_prompt(self, prompt, positive=True, t5_sequence_length=512): prompt_emb, pooled_prompt_emb, text_ids = self.prompter.encode_prompt( prompt, device=self.device, positive=positive, t5_sequence_length=t5_sequence_length ) return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_ids": text_ids} def prepare_extra_input(self, latents=None, guidance=1.0): latent_image_ids = self.dit.prepare_image_ids(latents) guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype) return {"image_ids": latent_image_ids, "guidance": guidance} def apply_controlnet_mask_on_latents(self, latents, mask): mask = (self.preprocess_image(mask) + 1) / 2 mask = mask.mean(dim=1, keepdim=True) mask = mask.to(dtype=self.torch_dtype, device=self.device) mask = 1 - torch.nn.functional.interpolate(mask, size=latents.shape[-2:]) latents = torch.concat([latents, mask], dim=1) return latents def apply_controlnet_mask_on_image(self, image, mask): mask = mask.resize(image.size) mask = self.preprocess_image(mask).mean(dim=[0, 1]) image = np.array(image) image[mask > 0] = 0 image = Image.fromarray(image) return image def prepare_controlnet_input(self, controlnet_image, controlnet_inpaint_mask, tiler_kwargs): if isinstance(controlnet_image, Image.Image): controlnet_image = [controlnet_image] * len(self.controlnet.processors) controlnet_frames = [] for i in range(len(self.controlnet.processors)): # image annotator image = self.controlnet.process_image(controlnet_image[i], processor_id=i)[0] if controlnet_inpaint_mask is not None and self.controlnet.processors[i].processor_id == "inpaint": image = self.apply_controlnet_mask_on_image(image, controlnet_inpaint_mask) # image to tensor image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) # vae encoder image = self.encode_image(image, **tiler_kwargs) if controlnet_inpaint_mask is not None and self.controlnet.processors[i].processor_id == "inpaint": image = self.apply_controlnet_mask_on_latents(image, controlnet_inpaint_mask) # store it controlnet_frames.append(image) return controlnet_frames def prepare_ipadapter_inputs(self, images, height=384, width=384): images = [image.convert("RGB").resize((width, height), resample=3) for image in images] images = [self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) for image in images] return torch.cat(images, dim=0) def inpaint_fusion(self, latents, inpaint_latents, pred_noise, fg_mask, bg_mask, progress_id, background_weight=0.): # inpaint noise inpaint_noise = (latents - inpaint_latents) / self.scheduler.sigmas[progress_id] # merge noise weight = torch.ones_like(inpaint_noise) inpaint_noise[fg_mask] = pred_noise[fg_mask] inpaint_noise[bg_mask] += pred_noise[bg_mask] * background_weight weight[bg_mask] += background_weight inpaint_noise /= weight return inpaint_noise def preprocess_masks(self, masks, height, width, dim): out_masks = [] for mask in masks: mask = self.preprocess_image(mask.resize((width, height), resample=Image.NEAREST)).mean(dim=1, keepdim=True) > 0 mask = mask.repeat(1, dim, 1, 1).to(device=self.device, dtype=self.torch_dtype) out_masks.append(mask) return out_masks def prepare_entity_inputs(self, entity_prompts, entity_masks, width, height, t5_sequence_length=512, enable_eligen_inpaint=False): fg_mask, bg_mask = None, None if enable_eligen_inpaint: masks_ = deepcopy(entity_masks) fg_masks = torch.cat([self.preprocess_image(mask.resize((width//8, height//8))).mean(dim=1, keepdim=True) for mask in masks_]) fg_masks = (fg_masks > 0).float() fg_mask = fg_masks.sum(dim=0, keepdim=True).repeat(1, 16, 1, 1) > 0 bg_mask = ~fg_mask entity_masks = self.preprocess_masks(entity_masks, height//8, width//8, 1) entity_masks = torch.cat(entity_masks, dim=0).unsqueeze(0) # b, n_mask, c, h, w entity_prompts = self.encode_prompt(entity_prompts, t5_sequence_length=t5_sequence_length)['prompt_emb'].unsqueeze(0) return entity_prompts, entity_masks, fg_mask, bg_mask def prepare_latents(self, input_image, height, width, seed, tiled, tile_size, tile_stride): if input_image is not None: self.load_models_to_device(['vae_encoder']) image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) input_latents = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) noise = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) latents = self.scheduler.add_noise(input_latents, noise, timestep=self.scheduler.timesteps[0]) else: latents = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) input_latents = None return latents, input_latents def prepare_ipadapter(self, ipadapter_images, ipadapter_scale): if ipadapter_images is not None: self.load_models_to_device(['ipadapter_image_encoder']) ipadapter_images = self.prepare_ipadapter_inputs(ipadapter_images) ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images).pooler_output self.load_models_to_device(['ipadapter']) ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} else: ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} return ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega def prepare_controlnet(self, controlnet_image, masks, controlnet_inpaint_mask, tiler_kwargs, enable_controlnet_on_negative): if controlnet_image is not None: self.load_models_to_device(['vae_encoder']) controlnet_kwargs_posi = {"controlnet_frames": self.prepare_controlnet_input(controlnet_image, controlnet_inpaint_mask, tiler_kwargs)} if len(masks) > 0 and controlnet_inpaint_mask is not None: print("The controlnet_inpaint_mask will be overridden by masks.") local_controlnet_kwargs = [{"controlnet_frames": self.prepare_controlnet_input(controlnet_image, mask, tiler_kwargs)} for mask in masks] else: local_controlnet_kwargs = None else: controlnet_kwargs_posi, local_controlnet_kwargs = {"controlnet_frames": None}, [{}] * len(masks) controlnet_kwargs_nega = controlnet_kwargs_posi if enable_controlnet_on_negative else {} return controlnet_kwargs_posi, controlnet_kwargs_nega, local_controlnet_kwargs def prepare_eligen(self, prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint, enable_eligen_on_negative, cfg_scale): if eligen_entity_masks is not None: entity_prompt_emb_posi, entity_masks_posi, fg_mask, bg_mask = self.prepare_entity_inputs(eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint) if enable_eligen_on_negative and cfg_scale != 1.0: entity_prompt_emb_nega = prompt_emb_nega['prompt_emb'].unsqueeze(1).repeat(1, entity_masks_posi.shape[1], 1, 1) entity_masks_nega = entity_masks_posi else: entity_prompt_emb_nega, entity_masks_nega = None, None else: entity_prompt_emb_posi, entity_masks_posi, entity_prompt_emb_nega, entity_masks_nega = None, None, None, None fg_mask, bg_mask = None, None eligen_kwargs_posi = {"entity_prompt_emb": entity_prompt_emb_posi, "entity_masks": entity_masks_posi} eligen_kwargs_nega = {"entity_prompt_emb": entity_prompt_emb_nega, "entity_masks": entity_masks_nega} return eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask def prepare_prompts(self, prompt, local_prompts, masks, mask_scales, t5_sequence_length, negative_prompt, cfg_scale): # Extend prompt self.load_models_to_device(['text_encoder_1', 'text_encoder_2']) prompt, local_prompts, masks, mask_scales = self.extend_prompt(prompt, local_prompts, masks, mask_scales) # Encode prompts prompt_emb_posi = self.encode_prompt(prompt, t5_sequence_length=t5_sequence_length) prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False, t5_sequence_length=t5_sequence_length) if cfg_scale != 1.0 else None prompt_emb_locals = [self.encode_prompt(prompt_local, t5_sequence_length=t5_sequence_length) for prompt_local in local_prompts] return prompt_emb_posi, prompt_emb_nega, prompt_emb_locals @torch.no_grad() def __call__( self, # Prompt prompt, negative_prompt="", cfg_scale=1.0, embedded_guidance=3.5, t5_sequence_length=512, # Image input_image=None, denoising_strength=1.0, height=1024, width=1024, seed=None, # Steps num_inference_steps=30, # local prompts local_prompts=(), masks=(), mask_scales=(), # ControlNet controlnet_image=None, controlnet_inpaint_mask=None, enable_controlnet_on_negative=False, # IP-Adapter ipadapter_images=None, ipadapter_scale=1.0, # EliGen eligen_entity_prompts=None, eligen_entity_masks=None, enable_eligen_on_negative=False, enable_eligen_inpaint=False, # TeaCache tea_cache_l1_thresh=None, # Tile tiled=False, tile_size=128, tile_stride=64, # Progress bar progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Tiler parameters tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors latents, input_latents = self.prepare_latents(input_image, height, width, seed, tiled, tile_size, tile_stride) # Prompt prompt_emb_posi, prompt_emb_nega, prompt_emb_locals = self.prepare_prompts(prompt, local_prompts, masks, mask_scales, t5_sequence_length, negative_prompt, cfg_scale) # Extra input extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance) # Entity control eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask = self.prepare_eligen(prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint, enable_eligen_on_negative, cfg_scale) # IP-Adapter ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = self.prepare_ipadapter(ipadapter_images, ipadapter_scale) # ControlNets controlnet_kwargs_posi, controlnet_kwargs_nega, local_controlnet_kwargs = self.prepare_controlnet(controlnet_image, masks, controlnet_inpaint_mask, tiler_kwargs, enable_controlnet_on_negative) # TeaCache tea_cache_kwargs = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh) if tea_cache_l1_thresh is not None else None} # Denoise self.load_models_to_device(['dit', 'controlnet']) for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(self.device) # Positive side inference_callback = lambda prompt_emb_posi, controlnet_kwargs: lets_dance_flux( dit=self.dit, controlnet=self.controlnet, hidden_states=latents, timestep=timestep, **prompt_emb_posi, **tiler_kwargs, **extra_input, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **eligen_kwargs_posi, **tea_cache_kwargs, ) noise_pred_posi = self.control_noise_via_local_prompts( prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback, special_kwargs=controlnet_kwargs_posi, special_local_kwargs_list=local_controlnet_kwargs ) # Inpaint if enable_eligen_inpaint: noise_pred_posi = self.inpaint_fusion(latents, input_latents, noise_pred_posi, fg_mask, bg_mask, progress_id) # Classifier-free guidance if cfg_scale != 1.0: # Negative side noise_pred_nega = lets_dance_flux( dit=self.dit, controlnet=self.controlnet, hidden_states=latents, timestep=timestep, **prompt_emb_nega, **tiler_kwargs, **extra_input, **controlnet_kwargs_nega, **ipadapter_kwargs_list_nega, **eligen_kwargs_nega, ) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) else: noise_pred = noise_pred_posi # Iterate latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) # UI if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image self.load_models_to_device(['vae_decoder']) image = self.decode_image(latents, **tiler_kwargs) # Offload all models self.load_models_to_device([]) return image class TeaCache: def __init__(self, num_inference_steps, rel_l1_thresh): self.num_inference_steps = num_inference_steps self.step = 0 self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = None self.rel_l1_thresh = rel_l1_thresh self.previous_residual = None self.previous_hidden_states = None def check(self, dit: FluxDiT, hidden_states, conditioning): inp = hidden_states.clone() temb_ = conditioning.clone() modulated_inp, _, _, _, _ = dit.blocks[0].norm1_a(inp, emb=temb_) if self.step == 0 or self.step == self.num_inference_steps - 1: should_calc = True self.accumulated_rel_l1_distance = 0 else: coefficients = [4.98651651e+02, -2.83781631e+02, 5.58554382e+01, -3.82021401e+00, 2.64230861e-01] rescale_func = np.poly1d(coefficients) self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) if self.accumulated_rel_l1_distance < self.rel_l1_thresh: should_calc = False else: should_calc = True self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = modulated_inp self.step += 1 if self.step == self.num_inference_steps: self.step = 0 if should_calc: self.previous_hidden_states = hidden_states.clone() return not should_calc def store(self, hidden_states): self.previous_residual = hidden_states - self.previous_hidden_states self.previous_hidden_states = None def update(self, hidden_states): hidden_states = hidden_states + self.previous_residual return hidden_states def lets_dance_flux( dit: FluxDiT, controlnet: FluxMultiControlNetManager = None, hidden_states=None, timestep=None, prompt_emb=None, pooled_prompt_emb=None, guidance=None, text_ids=None, image_ids=None, controlnet_frames=None, tiled=False, tile_size=128, tile_stride=64, entity_prompt_emb=None, entity_masks=None, ipadapter_kwargs_list={}, tea_cache: TeaCache = None, **kwargs ): if tiled: def flux_forward_fn(hl, hr, wl, wr): tiled_controlnet_frames = [f[:, :, hl: hr, wl: wr] for f in controlnet_frames] if controlnet_frames is not None else None return lets_dance_flux( dit=dit, controlnet=controlnet, hidden_states=hidden_states[:, :, hl: hr, wl: wr], timestep=timestep, prompt_emb=prompt_emb, pooled_prompt_emb=pooled_prompt_emb, guidance=guidance, text_ids=text_ids, image_ids=None, controlnet_frames=tiled_controlnet_frames, tiled=False, **kwargs ) return FastTileWorker().tiled_forward( flux_forward_fn, hidden_states, tile_size=tile_size, tile_stride=tile_stride, tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) # ControlNet if controlnet is not None and controlnet_frames is not None: controlnet_extra_kwargs = { "hidden_states": hidden_states, "timestep": timestep, "prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "guidance": guidance, "text_ids": text_ids, "image_ids": image_ids, "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride, } controlnet_res_stack, controlnet_single_res_stack = controlnet( controlnet_frames, **controlnet_extra_kwargs ) if image_ids is None: image_ids = dit.prepare_image_ids(hidden_states) conditioning = dit.time_embedder(timestep, hidden_states.dtype) + dit.pooled_text_embedder(pooled_prompt_emb) if dit.guidance_embedder is not None: guidance = guidance * 1000 conditioning = conditioning + dit.guidance_embedder(guidance, hidden_states.dtype) height, width = hidden_states.shape[-2:] hidden_states = dit.patchify(hidden_states) hidden_states = dit.x_embedder(hidden_states) if entity_prompt_emb is not None and entity_masks is not None: prompt_emb, image_rotary_emb, attention_mask = dit.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids) else: prompt_emb = dit.context_embedder(prompt_emb) image_rotary_emb = dit.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) attention_mask = None # TeaCache if tea_cache is not None: tea_cache_update = tea_cache.check(dit, hidden_states, conditioning) else: tea_cache_update = False if tea_cache_update: hidden_states = tea_cache.update(hidden_states) else: # Joint Blocks for block_id, block in enumerate(dit.blocks): hidden_states, prompt_emb = block( hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None) ) # ControlNet if controlnet is not None and controlnet_frames is not None: hidden_states = hidden_states + controlnet_res_stack[block_id] # Single Blocks hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) num_joint_blocks = len(dit.blocks) for block_id, block in enumerate(dit.single_blocks): hidden_states, prompt_emb = block( hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None) ) # ControlNet if controlnet is not None and controlnet_frames is not None: hidden_states[:, prompt_emb.shape[1]:] = hidden_states[:, prompt_emb.shape[1]:] + controlnet_single_res_stack[block_id] hidden_states = hidden_states[:, prompt_emb.shape[1]:] if tea_cache is not None: tea_cache.store(hidden_states) hidden_states = dit.final_norm_out(hidden_states, conditioning) hidden_states = dit.final_proj_out(hidden_states) hidden_states = dit.unpatchify(hidden_states, height, width) return hidden_states ================================================ FILE: diffsynth/pipelines/hunyuan_image.py ================================================ from ..models.hunyuan_dit import HunyuanDiT from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder from ..models.sdxl_vae_encoder import SDXLVAEEncoder from ..models.sdxl_vae_decoder import SDXLVAEDecoder from ..models import ModelManager from ..prompters import HunyuanDiTPrompter from ..schedulers import EnhancedDDIMScheduler from .base import BasePipeline import torch from tqdm import tqdm import numpy as np class ImageSizeManager: def __init__(self): pass def _to_tuple(self, x): if isinstance(x, int): return x, x else: return x def get_fill_resize_and_crop(self, src, tgt): th, tw = self._to_tuple(tgt) h, w = self._to_tuple(src) tr = th / tw # base 分辨率 r = h / w # 目标分辨率 # resize if r > tr: resize_height = th resize_width = int(round(th / h * w)) else: resize_width = tw resize_height = int(round(tw / w * h)) # 根据base分辨率，将目标分辨率resize下来 crop_top = int(round((th - resize_height) / 2.0)) crop_left = int(round((tw - resize_width) / 2.0)) return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) def get_meshgrid(self, start, *args): if len(args) == 0: # start is grid_size num = self._to_tuple(start) start = (0, 0) stop = num elif len(args) == 1: # start is start, args[0] is stop, step is 1 start = self._to_tuple(start) stop = self._to_tuple(args[0]) num = (stop[0] - start[0], stop[1] - start[1]) elif len(args) == 2: # start is start, args[0] is stop, args[1] is num start = self._to_tuple(start) # 左上角 eg: 12,0 stop = self._to_tuple(args[0]) # 右下角 eg: 20,32 num = self._to_tuple(args[1]) # 目标大小 eg: 32,124 else: raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) # 12-20 中间差值32份 0-32 中间差值124份 grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) # [2, W, H] return grid def get_2d_rotary_pos_embed(self, embed_dim, start, *args, use_real=True): grid = self.get_meshgrid(start, *args) # [2, H, w] grid = grid.reshape([2, 1, *grid.shape[1:]]) # 返回一个采样矩阵 分辨率与目标分辨率一致 pos_embed = self.get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) return pos_embed def get_2d_rotary_pos_embed_from_grid(self, embed_dim, grid, use_real=False): assert embed_dim % 4 == 0 # use half of dimensions to encode grid_h emb_h = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) emb_w = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) if use_real: cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) return cos, sin else: emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) return emb def get_1d_rotary_pos_embed(self, dim: int, pos, theta: float = 10000.0, use_real=False): if isinstance(pos, int): pos = np.arange(pos) freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] if use_real: freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] return freqs_cos, freqs_sin else: freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] return freqs_cis def calc_rope(self, height, width): patch_size = 2 head_size = 88 th = height // 8 // patch_size tw = width // 8 // patch_size base_size = 512 // 8 // patch_size start, stop = self.get_fill_resize_and_crop((th, tw), base_size) sub_args = [start, stop, (th, tw)] rope = self.get_2d_rotary_pos_embed(head_size, *sub_args) return rope class HunyuanDiTImagePipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) self.scheduler = EnhancedDDIMScheduler(prediction_type="v_prediction", beta_start=0.00085, beta_end=0.03) self.prompter = HunyuanDiTPrompter() self.image_size_manager = ImageSizeManager() # models self.text_encoder: HunyuanDiTCLIPTextEncoder = None self.text_encoder_t5: HunyuanDiTT5TextEncoder = None self.dit: HunyuanDiT = None self.vae_decoder: SDXLVAEDecoder = None self.vae_encoder: SDXLVAEEncoder = None self.model_names = ['text_encoder', 'text_encoder_t5', 'dit', 'vae_decoder', 'vae_encoder'] def denoising_model(self): return self.dit def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): # Main models self.text_encoder = model_manager.fetch_model("hunyuan_dit_clip_text_encoder") self.text_encoder_t5 = model_manager.fetch_model("hunyuan_dit_t5_text_encoder") self.dit = model_manager.fetch_model("hunyuan_dit") self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") self.prompter.fetch_models(self.text_encoder, self.text_encoder_t5) self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) @staticmethod def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None): pipe = HunyuanDiTImagePipeline( device=model_manager.device if device is None else device, torch_dtype=model_manager.torch_dtype, ) pipe.fetch_models(model_manager, prompt_refiner_classes) return pipe def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return latents def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) image = self.vae_output_to_image(image) return image def encode_prompt(self, prompt, clip_skip=1, clip_skip_2=1, positive=True): text_emb, text_emb_mask, text_emb_t5, text_emb_mask_t5 = self.prompter.encode_prompt( prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=positive, device=self.device ) return { "text_emb": text_emb, "text_emb_mask": text_emb_mask, "text_emb_t5": text_emb_t5, "text_emb_mask_t5": text_emb_mask_t5 } def prepare_extra_input(self, latents=None, tiled=False, tile_size=64, tile_stride=32): batch_size, height, width = latents.shape[0], latents.shape[2] * 8, latents.shape[3] * 8 if tiled: height, width = tile_size * 16, tile_size * 16 image_meta_size = torch.as_tensor([width, height, width, height, 0, 0]).to(device=self.device) freqs_cis_img = self.image_size_manager.calc_rope(height, width) image_meta_size = torch.stack([image_meta_size] * batch_size) return { "size_emb": image_meta_size, "freq_cis_img": (freqs_cis_img[0].to(dtype=self.torch_dtype, device=self.device), freqs_cis_img[1].to(dtype=self.torch_dtype, device=self.device)), "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride } @torch.no_grad() def __call__( self, prompt, local_prompts=[], masks=[], mask_scales=[], negative_prompt="", cfg_scale=7.5, clip_skip=1, clip_skip_2=1, input_image=None, reference_strengths=[0.4], denoising_strength=1.0, height=1024, width=1024, num_inference_steps=20, tiled=False, tile_size=64, tile_stride=32, seed=None, progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) if input_image is not None: self.load_models_to_device(['vae_encoder']) image = self.preprocess_image(input_image).to(device=self.device, dtype=torch.float32) latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) else: latents = noise.clone() # Encode prompts self.load_models_to_device(['text_encoder', 'text_encoder_t5']) prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) if cfg_scale != 1.0: prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) for prompt_local in local_prompts] # Prepare positional id extra_input = self.prepare_extra_input(latents, tiled, tile_size) # Denoise self.load_models_to_device(['dit']) for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = torch.tensor([timestep]).to(dtype=self.torch_dtype, device=self.device) # Positive side inference_callback = lambda prompt_emb_posi: self.dit(latents, timestep=timestep, **prompt_emb_posi, **extra_input) noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) if cfg_scale != 1.0: # Negative side noise_pred_nega = self.dit( latents, timestep=timestep, **prompt_emb_nega, **extra_input, ) # Classifier-free guidance noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) else: noise_pred = noise_pred_posi latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image self.load_models_to_device(['vae_decoder']) image = self.decode_image(latents.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) # Offload all models self.load_models_to_device([]) return image ================================================ FILE: diffsynth/pipelines/hunyuan_video.py ================================================ from ..models import ModelManager, SD3TextEncoder1, HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder from ..models.hunyuan_video_dit import HunyuanVideoDiT from ..models.hunyuan_video_text_encoder import HunyuanVideoLLMEncoder from ..schedulers.flow_match import FlowMatchScheduler from .base import BasePipeline from ..prompters import HunyuanVideoPrompter import torch import torchvision.transforms as transforms from einops import rearrange import numpy as np from PIL import Image from tqdm import tqdm class HunyuanVideoPipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = FlowMatchScheduler(shift=7.0, sigma_min=0.0, extra_one_step=True) self.prompter = HunyuanVideoPrompter() self.text_encoder_1: SD3TextEncoder1 = None self.text_encoder_2: HunyuanVideoLLMEncoder = None self.dit: HunyuanVideoDiT = None self.vae_decoder: HunyuanVideoVAEDecoder = None self.vae_encoder: HunyuanVideoVAEEncoder = None self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder'] self.vram_management = False def enable_vram_management(self): self.vram_management = True self.enable_cpu_offload() self.text_encoder_2.enable_auto_offload(dtype=self.torch_dtype, device=self.device) self.dit.enable_auto_offload(dtype=self.torch_dtype, device=self.device) def fetch_models(self, model_manager: ModelManager): self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") self.text_encoder_2 = model_manager.fetch_model("hunyuan_video_text_encoder_2") self.dit = model_manager.fetch_model("hunyuan_video_dit") self.vae_decoder = model_manager.fetch_model("hunyuan_video_vae_decoder") self.vae_encoder = model_manager.fetch_model("hunyuan_video_vae_encoder") self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2) @staticmethod def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, enable_vram_management=True): if device is None: device = model_manager.device if torch_dtype is None: torch_dtype = model_manager.torch_dtype pipe = HunyuanVideoPipeline(device=device, torch_dtype=torch_dtype) pipe.fetch_models(model_manager) if enable_vram_management: pipe.enable_vram_management() return pipe def generate_crop_size_list(self, base_size=256, patch_size=32, max_ratio=4.0): num_patches = round((base_size / patch_size)**2) assert max_ratio >= 1.0 crop_size_list = [] wp, hp = num_patches, 1 while wp > 0: if max(wp, hp) / min(wp, hp) <= max_ratio: crop_size_list.append((wp * patch_size, hp * patch_size)) if (hp + 1) * wp <= num_patches: hp += 1 else: wp -= 1 return crop_size_list def get_closest_ratio(self, height: float, width: float, ratios: list, buckets: list): aspect_ratio = float(height) / float(width) closest_ratio_id = np.abs(ratios - aspect_ratio).argmin() closest_ratio = min(ratios, key=lambda ratio: abs(float(ratio) - aspect_ratio)) return buckets[closest_ratio_id], float(closest_ratio) def prepare_vae_images_inputs(self, semantic_images, i2v_resolution="720p"): if i2v_resolution == "720p": bucket_hw_base_size = 960 elif i2v_resolution == "540p": bucket_hw_base_size = 720 elif i2v_resolution == "360p": bucket_hw_base_size = 480 else: raise ValueError(f"i2v_resolution: {i2v_resolution} must be in [360p, 540p, 720p]") origin_size = semantic_images[0].size crop_size_list = self.generate_crop_size_list(bucket_hw_base_size, 32) aspect_ratios = np.array([round(float(h) / float(w), 5) for h, w in crop_size_list]) closest_size, closest_ratio = self.get_closest_ratio(origin_size[1], origin_size[0], aspect_ratios, crop_size_list) ref_image_transform = transforms.Compose([ transforms.Resize(closest_size), transforms.CenterCrop(closest_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]) ]) semantic_image_pixel_values = [ref_image_transform(semantic_image) for semantic_image in semantic_images] semantic_image_pixel_values = torch.cat(semantic_image_pixel_values).unsqueeze(0).unsqueeze(2).to(self.device) target_height, target_width = closest_size return semantic_image_pixel_values, target_height, target_width def encode_prompt(self, prompt, positive=True, clip_sequence_length=77, llm_sequence_length=256, input_images=None): prompt_emb, pooled_prompt_emb, text_mask = self.prompter.encode_prompt( prompt, device=self.device, positive=positive, clip_sequence_length=clip_sequence_length, llm_sequence_length=llm_sequence_length, images=input_images ) return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_mask": text_mask} def prepare_extra_input(self, latents=None, guidance=1.0): freqs_cos, freqs_sin = self.dit.prepare_freqs(latents) guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype) return {"freqs_cos": freqs_cos, "freqs_sin": freqs_sin, "guidance": guidance} def tensor2video(self, frames): frames = rearrange(frames, "C T H W -> T H W C") frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) frames = [Image.fromarray(frame) for frame in frames] return frames def encode_video(self, frames, tile_size=(17, 30, 30), tile_stride=(12, 20, 20)): tile_size = ((tile_size[0] - 1) * 4 + 1, tile_size[1] * 8, tile_size[2] * 8) tile_stride = (tile_stride[0] * 4, tile_stride[1] * 8, tile_stride[2] * 8) latents = self.vae_encoder.encode_video(frames, tile_size=tile_size, tile_stride=tile_stride) return latents @torch.no_grad() def __call__( self, prompt, negative_prompt="", input_video=None, input_images=None, i2v_resolution="720p", i2v_stability=True, denoising_strength=1.0, seed=None, rand_device=None, height=720, width=1280, num_frames=129, embedded_guidance=6.0, cfg_scale=1.0, num_inference_steps=30, tea_cache_l1_thresh=None, tile_size=(17, 30, 30), tile_stride=(12, 20, 20), step_processor=None, progress_bar_cmd=lambda x: x, progress_bar_st=None, ): # Tiler parameters tiler_kwargs = {"tile_size": tile_size, "tile_stride": tile_stride} # Scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # encoder input images if input_images is not None: self.load_models_to_device(['vae_encoder']) image_pixel_values, height, width = self.prepare_vae_images_inputs(input_images, i2v_resolution=i2v_resolution) with torch.autocast(device_type=self.device, dtype=torch.float16, enabled=True): image_latents = self.vae_encoder(image_pixel_values) # Initialize noise rand_device = self.device if rand_device is None else rand_device noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=self.torch_dtype).to(self.device) if input_video is not None: self.load_models_to_device(['vae_encoder']) input_video = self.preprocess_images(input_video) input_video = torch.stack(input_video, dim=2) latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) elif input_images is not None and i2v_stability: noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=image_latents.dtype).to(self.device) t = torch.tensor([0.999]).to(device=self.device) latents = noise * t + image_latents.repeat(1, 1, (num_frames - 1) // 4 + 1, 1, 1) * (1 - t) latents = latents.to(dtype=image_latents.dtype) else: latents = noise # Encode prompts # current mllm does not support vram_management self.load_models_to_device(["text_encoder_1"] if self.vram_management and input_images is None else ["text_encoder_1", "text_encoder_2"]) prompt_emb_posi = self.encode_prompt(prompt, positive=True, input_images=input_images) if cfg_scale != 1.0: prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) # Extra input extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance) # TeaCache tea_cache_kwargs = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh) if tea_cache_l1_thresh is not None else None} # Denoise self.load_models_to_device([] if self.vram_management else ["dit"]) for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(self.device) print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}") forward_func = lets_dance_hunyuan_video if input_images is not None: latents = torch.concat([image_latents, latents[:, :, 1:, :, :]], dim=2) forward_func = lets_dance_hunyuan_video_i2v # Inference with torch.autocast(device_type=self.device, dtype=self.torch_dtype): noise_pred_posi = forward_func(self.dit, latents, timestep, **prompt_emb_posi, **extra_input, **tea_cache_kwargs) if cfg_scale != 1.0: noise_pred_nega = forward_func(self.dit, latents, timestep, **prompt_emb_nega, **extra_input) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) else: noise_pred = noise_pred_posi # (Experimental feature, may be removed in the future) if step_processor is not None: self.load_models_to_device(['vae_decoder']) rendered_frames = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents, to_final=True) rendered_frames = self.vae_decoder.decode_video(rendered_frames, **tiler_kwargs) rendered_frames = self.tensor2video(rendered_frames[0]) rendered_frames = step_processor(rendered_frames, original_frames=input_video) self.load_models_to_device(['vae_encoder']) rendered_frames = self.preprocess_images(rendered_frames) rendered_frames = torch.stack(rendered_frames, dim=2) target_latents = self.encode_video(rendered_frames).to(dtype=self.torch_dtype, device=self.device) noise_pred = self.scheduler.return_to_timestep(self.scheduler.timesteps[progress_id], latents, target_latents) self.load_models_to_device([] if self.vram_management else ["dit"]) # Scheduler if input_images is not None: latents = self.scheduler.step(noise_pred[:, :, 1:, :, :], self.scheduler.timesteps[progress_id], latents[:, :, 1:, :, :]) latents = torch.concat([image_latents, latents], dim=2) else: latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) # Decode self.load_models_to_device(['vae_decoder']) frames = self.vae_decoder.decode_video(latents, **tiler_kwargs) self.load_models_to_device([]) frames = self.tensor2video(frames[0]) return frames class TeaCache: def __init__(self, num_inference_steps, rel_l1_thresh): self.num_inference_steps = num_inference_steps self.step = 0 self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = None self.rel_l1_thresh = rel_l1_thresh self.previous_residual = None self.previous_hidden_states = None def check(self, dit: HunyuanVideoDiT, img, vec): img_ = img.clone() vec_ = vec.clone() img_mod1_shift, img_mod1_scale, _, _, _, _ = dit.double_blocks[0].component_a.mod(vec_).chunk(6, dim=-1) normed_inp = dit.double_blocks[0].component_a.norm1(img_) modulated_inp = normed_inp * (1 + img_mod1_scale.unsqueeze(1)) + img_mod1_shift.unsqueeze(1) if self.step == 0 or self.step == self.num_inference_steps - 1: should_calc = True self.accumulated_rel_l1_distance = 0 else: coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02] rescale_func = np.poly1d(coefficients) self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) if self.accumulated_rel_l1_distance < self.rel_l1_thresh: should_calc = False else: should_calc = True self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = modulated_inp self.step += 1 if self.step == self.num_inference_steps: self.step = 0 if should_calc: self.previous_hidden_states = img.clone() return not should_calc def store(self, hidden_states): self.previous_residual = hidden_states - self.previous_hidden_states self.previous_hidden_states = None def update(self, hidden_states): hidden_states = hidden_states + self.previous_residual return hidden_states def lets_dance_hunyuan_video( dit: HunyuanVideoDiT, x: torch.Tensor, t: torch.Tensor, prompt_emb: torch.Tensor = None, text_mask: torch.Tensor = None, pooled_prompt_emb: torch.Tensor = None, freqs_cos: torch.Tensor = None, freqs_sin: torch.Tensor = None, guidance: torch.Tensor = None, tea_cache: TeaCache = None, **kwargs ): B, C, T, H, W = x.shape vec = dit.time_in(t, dtype=torch.float32) + dit.vector_in(pooled_prompt_emb) + dit.guidance_in(guidance * 1000, dtype=torch.float32) img = dit.img_in(x) txt = dit.txt_in(prompt_emb, t, text_mask) # TeaCache if tea_cache is not None: tea_cache_update = tea_cache.check(dit, img, vec) else: tea_cache_update = False if tea_cache_update: print("TeaCache skip forward.") img = tea_cache.update(img) else: split_token = int(text_mask.sum(dim=1)) txt_len = int(txt.shape[1]) for block in tqdm(dit.double_blocks, desc="Double stream blocks"): img, txt = block(img, txt, vec, (freqs_cos, freqs_sin), split_token=split_token) x = torch.concat([img, txt], dim=1) for block in tqdm(dit.single_blocks, desc="Single stream blocks"): x = block(x, vec, (freqs_cos, freqs_sin), txt_len=txt_len, split_token=split_token) img = x[:, :-txt_len] if tea_cache is not None: tea_cache.store(img) img = dit.final_layer(img, vec) img = dit.unpatchify(img, T=T//1, H=H//2, W=W//2) return img def lets_dance_hunyuan_video_i2v( dit: HunyuanVideoDiT, x: torch.Tensor, t: torch.Tensor, prompt_emb: torch.Tensor = None, text_mask: torch.Tensor = None, pooled_prompt_emb: torch.Tensor = None, freqs_cos: torch.Tensor = None, freqs_sin: torch.Tensor = None, guidance: torch.Tensor = None, tea_cache: TeaCache = None, **kwargs ): B, C, T, H, W = x.shape # Uncomment below to keep same as official implementation # guidance = guidance.to(dtype=torch.float32).to(torch.bfloat16) vec = dit.time_in(t, dtype=torch.bfloat16) vec_2 = dit.vector_in(pooled_prompt_emb) vec = vec + vec_2 vec = vec + dit.guidance_in(guidance * 1000., dtype=torch.bfloat16) token_replace_vec = dit.time_in(torch.zeros_like(t), dtype=torch.bfloat16) tr_token = (H // 2) * (W // 2) token_replace_vec = token_replace_vec + vec_2 img = dit.img_in(x) txt = dit.txt_in(prompt_emb, t, text_mask) # TeaCache if tea_cache is not None: tea_cache_update = tea_cache.check(dit, img, vec) else: tea_cache_update = False if tea_cache_update: print("TeaCache skip forward.") img = tea_cache.update(img) else: split_token = int(text_mask.sum(dim=1)) txt_len = int(txt.shape[1]) for block in tqdm(dit.double_blocks, desc="Double stream blocks"): img, txt = block(img, txt, vec, (freqs_cos, freqs_sin), token_replace_vec, tr_token, split_token) x = torch.concat([img, txt], dim=1) for block in tqdm(dit.single_blocks, desc="Single stream blocks"): x = block(x, vec, (freqs_cos, freqs_sin), txt_len, token_replace_vec, tr_token, split_token) img = x[:, :-txt_len] if tea_cache is not None: tea_cache.store(img) img = dit.final_layer(img, vec) img = dit.unpatchify(img, T=T//1, H=H//2, W=W//2) return img ================================================ FILE: diffsynth/pipelines/omnigen_image.py ================================================ from ..models.omnigen import OmniGenTransformer from ..models.sdxl_vae_encoder import SDXLVAEEncoder from ..models.sdxl_vae_decoder import SDXLVAEDecoder from ..models.model_manager import ModelManager from ..prompters.omnigen_prompter import OmniGenPrompter from ..schedulers import FlowMatchScheduler from .base import BasePipeline from typing import Optional, Dict, Any, Tuple, List from transformers.cache_utils import DynamicCache import torch, os from tqdm import tqdm class OmniGenCache(DynamicCache): def __init__(self, num_tokens_for_img: int, offload_kv_cache: bool=False) -> None: if not torch.cuda.is_available(): print("No available GPU, offload_kv_cache will be set to False, which will result in large memory usage and time cost when input multiple images!!!") offload_kv_cache = False raise RuntimeError("OffloadedCache can only be used with a GPU") super().__init__() self.original_device = [] self.prefetch_stream = torch.cuda.Stream() self.num_tokens_for_img = num_tokens_for_img self.offload_kv_cache = offload_kv_cache def prefetch_layer(self, layer_idx: int): "Starts prefetching the next layer cache" if layer_idx < len(self): with torch.cuda.stream(self.prefetch_stream): # Prefetch next layer tensors to GPU device = self.original_device[layer_idx] self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device, non_blocking=True) self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device, non_blocking=True) def evict_previous_layer(self, layer_idx: int): "Moves the previous layer cache to the CPU" if len(self) > 2: # We do it on the default stream so it occurs after all earlier computations on these tensors are done if layer_idx == 0: prev_layer_idx = -1 else: prev_layer_idx = (layer_idx - 1) % len(self) self.key_cache[prev_layer_idx] = self.key_cache[prev_layer_idx].to("cpu", non_blocking=True) self.value_cache[prev_layer_idx] = self.value_cache[prev_layer_idx].to("cpu", non_blocking=True) def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]: "Gets the cache for this layer to the device. Prefetches the next and evicts the previous layer." if layer_idx < len(self): if self.offload_kv_cache: # Evict the previous layer if necessary torch.cuda.current_stream().synchronize() self.evict_previous_layer(layer_idx) # Load current layer cache to its original device if not already there original_device = self.original_device[layer_idx] # self.prefetch_stream.synchronize(original_device) torch.cuda.synchronize(self.prefetch_stream) key_tensor = self.key_cache[layer_idx] value_tensor = self.value_cache[layer_idx] # Prefetch the next layer self.prefetch_layer((layer_idx + 1) % len(self)) else: key_tensor = self.key_cache[layer_idx] value_tensor = self.value_cache[layer_idx] return (key_tensor, value_tensor) else: raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}") def update( self, key_states: torch.Tensor, value_states: torch.Tensor, layer_idx: int, cache_kwargs: Optional[Dict[str, Any]] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`. Parameters: key_states (`torch.Tensor`): The new key states to cache. value_states (`torch.Tensor`): The new value states to cache. layer_idx (`int`): The index of the layer to cache the states for. cache_kwargs (`Dict[str, Any]`, `optional`): Additional arguments for the cache subclass. No additional arguments are used in `OffloadedCache`. Return: A tuple containing the updated key and value states. """ # Update the cache if len(self.key_cache) < layer_idx: raise ValueError("OffloadedCache does not support model usage where layers are skipped. Use DynamicCache.") elif len(self.key_cache) == layer_idx: # only cache the states for condition tokens key_states = key_states[..., :-(self.num_tokens_for_img+1), :] value_states = value_states[..., :-(self.num_tokens_for_img+1), :] # Update the number of seen tokens if layer_idx == 0: self._seen_tokens += key_states.shape[-2] self.key_cache.append(key_states) self.value_cache.append(value_states) self.original_device.append(key_states.device) if self.offload_kv_cache: self.evict_previous_layer(layer_idx) return self.key_cache[layer_idx], self.value_cache[layer_idx] else: # only cache the states for condition tokens key_tensor, value_tensor = self[layer_idx] k = torch.cat([key_tensor, key_states], dim=-2) v = torch.cat([value_tensor, value_states], dim=-2) return k, v class OmnigenImagePipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = FlowMatchScheduler(num_train_timesteps=1, shift=1, inverse_timesteps=True, sigma_min=0, sigma_max=1) # models self.vae_decoder: SDXLVAEDecoder = None self.vae_encoder: SDXLVAEEncoder = None self.transformer: OmniGenTransformer = None self.prompter: OmniGenPrompter = None self.model_names = ['transformer', 'vae_decoder', 'vae_encoder'] def denoising_model(self): return self.transformer def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): # Main models self.transformer, model_path = model_manager.fetch_model("omnigen_transformer", require_model_path=True) self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") self.prompter = OmniGenPrompter.from_pretrained(os.path.dirname(model_path)) @staticmethod def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None): pipe = OmnigenImagePipeline( device=model_manager.device if device is None else device, torch_dtype=model_manager.torch_dtype, ) pipe.fetch_models(model_manager, prompt_refiner_classes=[]) return pipe def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return latents def encode_images(self, images, tiled=False, tile_size=64, tile_stride=32): latents = [self.encode_image(image.to(device=self.device), tiled, tile_size, tile_stride).to(self.torch_dtype) for image in images] return latents def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) image = self.vae_output_to_image(image) return image def encode_prompt(self, prompt, clip_skip=1, positive=True): prompt_emb = self.prompter.encode_prompt(prompt, clip_skip=clip_skip, device=self.device, positive=positive) return {"encoder_hidden_states": prompt_emb} def prepare_extra_input(self, latents=None): return {} def crop_position_ids_for_cache(self, position_ids, num_tokens_for_img): if isinstance(position_ids, list): for i in range(len(position_ids)): position_ids[i] = position_ids[i][:, -(num_tokens_for_img+1):] else: position_ids = position_ids[:, -(num_tokens_for_img+1):] return position_ids def crop_attention_mask_for_cache(self, attention_mask, num_tokens_for_img): if isinstance(attention_mask, list): return [x[..., -(num_tokens_for_img+1):, :] for x in attention_mask] return attention_mask[..., -(num_tokens_for_img+1):, :] @torch.no_grad() def __call__( self, prompt, reference_images=[], cfg_scale=2.0, image_cfg_scale=2.0, use_kv_cache=True, offload_kv_cache=True, input_image=None, denoising_strength=1.0, height=1024, width=1024, num_inference_steps=20, tiled=False, tile_size=64, tile_stride=32, seed=None, progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Tiler parameters tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors if input_image is not None: self.load_models_to_device(['vae_encoder']) image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) latents = self.encode_image(image, **tiler_kwargs) noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) else: latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) latents = latents.repeat(3, 1, 1, 1) # Encode prompts input_data = self.prompter(prompt, reference_images, height=height, width=width, use_img_cfg=True, separate_cfg_input=True, use_input_image_size_as_output=False) # Encode images reference_latents = [self.encode_images(images, **tiler_kwargs) for images in input_data['input_pixel_values']] # Pack all parameters model_kwargs = dict(input_ids=[input_ids.to(self.device) for input_ids in input_data['input_ids']], input_img_latents=reference_latents, input_image_sizes=input_data['input_image_sizes'], attention_mask=[attention_mask.to(self.device) for attention_mask in input_data["attention_mask"]], position_ids=[position_ids.to(self.device) for position_ids in input_data["position_ids"]], cfg_scale=cfg_scale, img_cfg_scale=image_cfg_scale, use_img_cfg=True, use_kv_cache=use_kv_cache, offload_model=False, ) # Denoise self.load_models_to_device(['transformer']) cache = [OmniGenCache(latents.size(-1)*latents.size(-2) // 4, offload_kv_cache) for _ in range(len(model_kwargs['input_ids']))] if use_kv_cache else None for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).repeat(latents.shape[0]).to(self.device) # Forward noise_pred, cache = self.transformer.forward_with_separate_cfg(latents, timestep, past_key_values=cache, **model_kwargs) # Scheduler latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) # Update KV cache if progress_id == 0 and use_kv_cache: num_tokens_for_img = latents.size(-1)*latents.size(-2) // 4 if isinstance(cache, list): model_kwargs['input_ids'] = [None] * len(cache) else: model_kwargs['input_ids'] = None model_kwargs['position_ids'] = self.crop_position_ids_for_cache(model_kwargs['position_ids'], num_tokens_for_img) model_kwargs['attention_mask'] = self.crop_attention_mask_for_cache(model_kwargs['attention_mask'], num_tokens_for_img) # UI if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image del cache self.load_models_to_device(['vae_decoder']) image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) # offload all models self.load_models_to_device([]) return image ================================================ FILE: diffsynth/pipelines/pipeline_runner.py ================================================ import os, torch, json from .sd_video import ModelManager, SDVideoPipeline, ControlNetConfigUnit from ..processors.sequencial_processor import SequencialProcessor from ..data import VideoData, save_frames, save_video class SDVideoPipelineRunner: def __init__(self, in_streamlit=False): self.in_streamlit = in_streamlit def load_pipeline(self, model_list, textual_inversion_folder, device, lora_alphas, controlnet_units): # Load models model_manager = ModelManager(torch_dtype=torch.float16, device=device) model_manager.load_models(model_list) pipe = SDVideoPipeline.from_model_manager( model_manager, [ ControlNetConfigUnit( processor_id=unit["processor_id"], model_path=unit["model_path"], scale=unit["scale"] ) for unit in controlnet_units ] ) textual_inversion_paths = [] for file_name in os.listdir(textual_inversion_folder): if file_name.endswith(".pt") or file_name.endswith(".bin") or file_name.endswith(".pth") or file_name.endswith(".safetensors"): textual_inversion_paths.append(os.path.join(textual_inversion_folder, file_name)) pipe.prompter.load_textual_inversions(textual_inversion_paths) return model_manager, pipe def load_smoother(self, model_manager, smoother_configs): smoother = SequencialProcessor.from_model_manager(model_manager, smoother_configs) return smoother def synthesize_video(self, model_manager, pipe, seed, smoother, **pipeline_inputs): torch.manual_seed(seed) if self.in_streamlit: import streamlit as st progress_bar_st = st.progress(0.0) output_video = pipe(**pipeline_inputs, smoother=smoother, progress_bar_st=progress_bar_st) progress_bar_st.progress(1.0) else: output_video = pipe(**pipeline_inputs, smoother=smoother) model_manager.to("cpu") return output_video def load_video(self, video_file, image_folder, height, width, start_frame_id, end_frame_id): video = VideoData(video_file=video_file, image_folder=image_folder, height=height, width=width) if start_frame_id is None: start_frame_id = 0 if end_frame_id is None: end_frame_id = len(video) frames = [video[i] for i in range(start_frame_id, end_frame_id)] return frames def add_data_to_pipeline_inputs(self, data, pipeline_inputs): pipeline_inputs["input_frames"] = self.load_video(**data["input_frames"]) pipeline_inputs["num_frames"] = len(pipeline_inputs["input_frames"]) pipeline_inputs["width"], pipeline_inputs["height"] = pipeline_inputs["input_frames"][0].size if len(data["controlnet_frames"]) > 0: pipeline_inputs["controlnet_frames"] = [self.load_video(**unit) for unit in data["controlnet_frames"]] return pipeline_inputs def save_output(self, video, output_folder, fps, config): os.makedirs(output_folder, exist_ok=True) save_frames(video, os.path.join(output_folder, "frames")) save_video(video, os.path.join(output_folder, "video.mp4"), fps=fps) config["pipeline"]["pipeline_inputs"]["input_frames"] = [] config["pipeline"]["pipeline_inputs"]["controlnet_frames"] = [] with open(os.path.join(output_folder, "config.json"), 'w') as file: json.dump(config, file, indent=4) def run(self, config): if self.in_streamlit: import streamlit as st if self.in_streamlit: st.markdown("Loading videos ...") config["pipeline"]["pipeline_inputs"] = self.add_data_to_pipeline_inputs(config["data"], config["pipeline"]["pipeline_inputs"]) if self.in_streamlit: st.markdown("Loading videos ... done!") if self.in_streamlit: st.markdown("Loading models ...") model_manager, pipe = self.load_pipeline(**config["models"]) if self.in_streamlit: st.markdown("Loading models ... done!") if "smoother_configs" in config: if self.in_streamlit: st.markdown("Loading smoother ...") smoother = self.load_smoother(model_manager, config["smoother_configs"]) if self.in_streamlit: st.markdown("Loading smoother ... done!") else: smoother = None if self.in_streamlit: st.markdown("Synthesizing videos ...") output_video = self.synthesize_video(model_manager, pipe, config["pipeline"]["seed"], smoother, **config["pipeline"]["pipeline_inputs"]) if self.in_streamlit: st.markdown("Synthesizing videos ... done!") if self.in_streamlit: st.markdown("Saving videos ...") self.save_output(output_video, config["data"]["output_folder"], config["data"]["fps"], config) if self.in_streamlit: st.markdown("Saving videos ... done!") if self.in_streamlit: st.markdown("Finished!") video_file = open(os.path.join(os.path.join(config["data"]["output_folder"], "video.mp4")), 'rb') if self.in_streamlit: st.video(video_file.read()) ================================================ FILE: diffsynth/pipelines/sd3_image.py ================================================ from ..models import ModelManager, SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEDecoder, SD3VAEEncoder from ..prompters import SD3Prompter from ..schedulers import FlowMatchScheduler from .base import BasePipeline import torch from tqdm import tqdm class SD3ImagePipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) self.scheduler = FlowMatchScheduler() self.prompter = SD3Prompter() # models self.text_encoder_1: SD3TextEncoder1 = None self.text_encoder_2: SD3TextEncoder2 = None self.text_encoder_3: SD3TextEncoder3 = None self.dit: SD3DiT = None self.vae_decoder: SD3VAEDecoder = None self.vae_encoder: SD3VAEEncoder = None self.model_names = ['text_encoder_1', 'text_encoder_2', 'text_encoder_3', 'dit', 'vae_decoder', 'vae_encoder'] def denoising_model(self): return self.dit def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") self.text_encoder_2 = model_manager.fetch_model("sd3_text_encoder_2") self.text_encoder_3 = model_manager.fetch_model("sd3_text_encoder_3") self.dit = model_manager.fetch_model("sd3_dit") self.vae_decoder = model_manager.fetch_model("sd3_vae_decoder") self.vae_encoder = model_manager.fetch_model("sd3_vae_encoder") self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2, self.text_encoder_3) self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) @staticmethod def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None): pipe = SD3ImagePipeline( device=model_manager.device if device is None else device, torch_dtype=model_manager.torch_dtype, ) pipe.fetch_models(model_manager, prompt_refiner_classes) return pipe def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return latents def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) image = self.vae_output_to_image(image) return image def encode_prompt(self, prompt, positive=True, t5_sequence_length=77): prompt_emb, pooled_prompt_emb = self.prompter.encode_prompt( prompt, device=self.device, positive=positive, t5_sequence_length=t5_sequence_length ) return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb} def prepare_extra_input(self, latents=None): return {} @torch.no_grad() def __call__( self, prompt, local_prompts=[], masks=[], mask_scales=[], negative_prompt="", cfg_scale=7.5, input_image=None, denoising_strength=1.0, height=1024, width=1024, num_inference_steps=20, t5_sequence_length=77, tiled=False, tile_size=128, tile_stride=64, seed=None, progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Tiler parameters tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors if input_image is not None: self.load_models_to_device(['vae_encoder']) image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) latents = self.encode_image(image, **tiler_kwargs) noise = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) else: latents = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) # Encode prompts self.load_models_to_device(['text_encoder_1', 'text_encoder_2', 'text_encoder_3']) prompt_emb_posi = self.encode_prompt(prompt, positive=True, t5_sequence_length=t5_sequence_length) prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False, t5_sequence_length=t5_sequence_length) prompt_emb_locals = [self.encode_prompt(prompt_local, t5_sequence_length=t5_sequence_length) for prompt_local in local_prompts] # Denoise self.load_models_to_device(['dit']) for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(self.device) # Classifier-free guidance inference_callback = lambda prompt_emb_posi: self.dit( latents, timestep=timestep, **prompt_emb_posi, **tiler_kwargs, ) noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) noise_pred_nega = self.dit( latents, timestep=timestep, **prompt_emb_nega, **tiler_kwargs, ) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) # DDIM latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) # UI if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image self.load_models_to_device(['vae_decoder']) image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) # offload all models self.load_models_to_device([]) return image ================================================ FILE: diffsynth/pipelines/sd_image.py ================================================ from ..models import SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder from ..models.model_manager import ModelManager from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator from ..prompters import SDPrompter from ..schedulers import EnhancedDDIMScheduler from .base import BasePipeline from .dancer import lets_dance from typing import List import torch from tqdm import tqdm class SDImagePipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = EnhancedDDIMScheduler() self.prompter = SDPrompter() # models self.text_encoder: SDTextEncoder = None self.unet: SDUNet = None self.vae_decoder: SDVAEDecoder = None self.vae_encoder: SDVAEEncoder = None self.controlnet: MultiControlNetManager = None self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None self.ipadapter: SDIpAdapter = None self.model_names = ['text_encoder', 'unet', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter_image_encoder', 'ipadapter'] def denoising_model(self): return self.unet def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): # Main models self.text_encoder = model_manager.fetch_model("sd_text_encoder") self.unet = model_manager.fetch_model("sd_unet") self.vae_decoder = model_manager.fetch_model("sd_vae_decoder") self.vae_encoder = model_manager.fetch_model("sd_vae_encoder") self.prompter.fetch_models(self.text_encoder) self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) # ControlNets controlnet_units = [] for config in controlnet_config_units: controlnet_unit = ControlNetUnit( Annotator(config.processor_id, device=self.device), model_manager.fetch_model("sd_controlnet", config.model_path), config.scale ) controlnet_units.append(controlnet_unit) self.controlnet = MultiControlNetManager(controlnet_units) # IP-Adapters self.ipadapter = model_manager.fetch_model("sd_ipadapter") self.ipadapter_image_encoder = model_manager.fetch_model("sd_ipadapter_clip_image_encoder") @staticmethod def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], device=None): pipe = SDImagePipeline( device=model_manager.device if device is None else device, torch_dtype=model_manager.torch_dtype, ) pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes=[]) return pipe def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return latents def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) image = self.vae_output_to_image(image) return image def encode_prompt(self, prompt, clip_skip=1, positive=True): prompt_emb = self.prompter.encode_prompt(prompt, clip_skip=clip_skip, device=self.device, positive=positive) return {"encoder_hidden_states": prompt_emb} def prepare_extra_input(self, latents=None): return {} @torch.no_grad() def __call__( self, prompt, local_prompts=[], masks=[], mask_scales=[], negative_prompt="", cfg_scale=7.5, clip_skip=1, input_image=None, ipadapter_images=None, ipadapter_scale=1.0, controlnet_image=None, denoising_strength=1.0, height=512, width=512, num_inference_steps=20, tiled=False, tile_size=64, tile_stride=32, seed=None, progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Tiler parameters tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors if input_image is not None: self.load_models_to_device(['vae_encoder']) image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) latents = self.encode_image(image, **tiler_kwargs) noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) else: latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) # Encode prompts self.load_models_to_device(['text_encoder']) prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, positive=True) for prompt_local in local_prompts] # IP-Adapter if ipadapter_images is not None: self.load_models_to_device(['ipadapter_image_encoder']) ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) self.load_models_to_device(['ipadapter']) ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} else: ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} # Prepare ControlNets if controlnet_image is not None: self.load_models_to_device(['controlnet']) controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype) controlnet_image = controlnet_image.unsqueeze(1) controlnet_kwargs = {"controlnet_frames": controlnet_image} else: controlnet_kwargs = {"controlnet_frames": None} # Denoise self.load_models_to_device(['controlnet', 'unet']) for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(self.device) # Classifier-free guidance inference_callback = lambda prompt_emb_posi: lets_dance( self.unet, motion_modules=None, controlnet=self.controlnet, sample=latents, timestep=timestep, **prompt_emb_posi, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_posi, device=self.device, ) noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) noise_pred_nega = lets_dance( self.unet, motion_modules=None, controlnet=self.controlnet, sample=latents, timestep=timestep, **prompt_emb_nega, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_nega, device=self.device, ) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) # DDIM latents = self.scheduler.step(noise_pred, timestep, latents) # UI if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image self.load_models_to_device(['vae_decoder']) image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) # offload all models self.load_models_to_device([]) return image ================================================ FILE: diffsynth/pipelines/sd_video.py ================================================ from ..models import SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder, SDMotionModel from ..models.model_manager import ModelManager from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator from ..prompters import SDPrompter from ..schedulers import EnhancedDDIMScheduler from .sd_image import SDImagePipeline from .dancer import lets_dance from typing import List import torch from tqdm import tqdm def lets_dance_with_long_video( unet: SDUNet, motion_modules: SDMotionModel = None, controlnet: MultiControlNetManager = None, sample = None, timestep = None, encoder_hidden_states = None, ipadapter_kwargs_list = {}, controlnet_frames = None, unet_batch_size = 1, controlnet_batch_size = 1, cross_frame_attention = False, tiled=False, tile_size=64, tile_stride=32, device="cuda", animatediff_batch_size=16, animatediff_stride=8, ): num_frames = sample.shape[0] hidden_states_output = [(torch.zeros(sample[0].shape, dtype=sample[0].dtype), 0) for i in range(num_frames)] for batch_id in range(0, num_frames, animatediff_stride): batch_id_ = min(batch_id + animatediff_batch_size, num_frames) # process this batch hidden_states_batch = lets_dance( unet, motion_modules, controlnet, sample[batch_id: batch_id_].to(device), timestep, encoder_hidden_states, ipadapter_kwargs_list=ipadapter_kwargs_list, controlnet_frames=controlnet_frames[:, batch_id: batch_id_].to(device) if controlnet_frames is not None else None, unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size, cross_frame_attention=cross_frame_attention, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, device=device ).cpu() # update hidden_states for i, hidden_states_updated in zip(range(batch_id, batch_id_), hidden_states_batch): bias = max(1 - abs(i - (batch_id + batch_id_ - 1) / 2) / ((batch_id_ - batch_id - 1 + 1e-2) / 2), 1e-2) hidden_states, num = hidden_states_output[i] hidden_states = hidden_states * (num / (num + bias)) + hidden_states_updated * (bias / (num + bias)) hidden_states_output[i] = (hidden_states, num + bias) if batch_id_ == num_frames: break # output hidden_states = torch.stack([h for h, _ in hidden_states_output]) return hidden_states class SDVideoPipeline(SDImagePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16, use_original_animatediff=True): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_original_animatediff else "scaled_linear") self.prompter = SDPrompter() # models self.text_encoder: SDTextEncoder = None self.unet: SDUNet = None self.vae_decoder: SDVAEDecoder = None self.vae_encoder: SDVAEEncoder = None self.controlnet: MultiControlNetManager = None self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None self.ipadapter: SDIpAdapter = None self.motion_modules: SDMotionModel = None def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): # Main models self.text_encoder = model_manager.fetch_model("sd_text_encoder") self.unet = model_manager.fetch_model("sd_unet") self.vae_decoder = model_manager.fetch_model("sd_vae_decoder") self.vae_encoder = model_manager.fetch_model("sd_vae_encoder") self.prompter.fetch_models(self.text_encoder) self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) # ControlNets controlnet_units = [] for config in controlnet_config_units: controlnet_unit = ControlNetUnit( Annotator(config.processor_id, device=self.device), model_manager.fetch_model("sd_controlnet", config.model_path), config.scale ) controlnet_units.append(controlnet_unit) self.controlnet = MultiControlNetManager(controlnet_units) # IP-Adapters self.ipadapter = model_manager.fetch_model("sd_ipadapter") self.ipadapter_image_encoder = model_manager.fetch_model("sd_ipadapter_clip_image_encoder") # Motion Modules self.motion_modules = model_manager.fetch_model("sd_motion_modules") if self.motion_modules is None: self.scheduler = EnhancedDDIMScheduler(beta_schedule="scaled_linear") @staticmethod def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): pipe = SDVideoPipeline( device=model_manager.device, torch_dtype=model_manager.torch_dtype, ) pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) return pipe def decode_video(self, latents, tiled=False, tile_size=64, tile_stride=32): images = [ self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) for frame_id in range(latents.shape[0]) ] return images def encode_video(self, processed_images, tiled=False, tile_size=64, tile_stride=32): latents = [] for image in processed_images: image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) latent = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) latents.append(latent.cpu()) latents = torch.concat(latents, dim=0) return latents @torch.no_grad() def __call__( self, prompt, negative_prompt="", cfg_scale=7.5, clip_skip=1, num_frames=None, input_frames=None, ipadapter_images=None, ipadapter_scale=1.0, controlnet_frames=None, denoising_strength=1.0, height=512, width=512, num_inference_steps=20, animatediff_batch_size = 16, animatediff_stride = 8, unet_batch_size = 1, controlnet_batch_size = 1, cross_frame_attention = False, smoother=None, smoother_progress_ids=[], tiled=False, tile_size=64, tile_stride=32, seed=None, progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Tiler parameters, batch size ... tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} other_kwargs = { "animatediff_batch_size": animatediff_batch_size, "animatediff_stride": animatediff_stride, "unet_batch_size": unet_batch_size, "controlnet_batch_size": controlnet_batch_size, "cross_frame_attention": cross_frame_attention, } # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors if self.motion_modules is None: noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1) else: noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype) if input_frames is None or denoising_strength == 1.0: latents = noise else: latents = self.encode_video(input_frames, **tiler_kwargs) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) # Encode prompts prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) # IP-Adapter if ipadapter_images is not None: ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} else: ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} # Prepare ControlNets if controlnet_frames is not None: if isinstance(controlnet_frames[0], list): controlnet_frames_ = [] for processor_id in range(len(controlnet_frames)): controlnet_frames_.append( torch.stack([ self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype) for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id]) ], dim=1) ) controlnet_frames = torch.concat(controlnet_frames_, dim=0) else: controlnet_frames = torch.stack([ self.controlnet.process_image(controlnet_frame).to(self.torch_dtype) for controlnet_frame in progress_bar_cmd(controlnet_frames) ], dim=1) controlnet_kwargs = {"controlnet_frames": controlnet_frames} else: controlnet_kwargs = {"controlnet_frames": None} # Denoise for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(self.device) # Classifier-free guidance noise_pred_posi = lets_dance_with_long_video( self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, sample=latents, timestep=timestep, **prompt_emb_posi, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **other_kwargs, **tiler_kwargs, device=self.device, ) noise_pred_nega = lets_dance_with_long_video( self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, sample=latents, timestep=timestep, **prompt_emb_nega, **controlnet_kwargs, **ipadapter_kwargs_list_nega, **other_kwargs, **tiler_kwargs, device=self.device, ) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) # DDIM and smoother if smoother is not None and progress_id in smoother_progress_ids: rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True) rendered_frames = self.decode_video(rendered_frames) rendered_frames = smoother(rendered_frames, original_frames=input_frames) target_latents = self.encode_video(rendered_frames) noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents) latents = self.scheduler.step(noise_pred, timestep, latents) # UI if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image output_frames = self.decode_video(latents, **tiler_kwargs) # Post-process if smoother is not None and (num_inference_steps in smoother_progress_ids or -1 in smoother_progress_ids): output_frames = smoother(output_frames, original_frames=input_frames) return output_frames ================================================ FILE: diffsynth/pipelines/sdxl_image.py ================================================ from ..models import SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder from ..models.kolors_text_encoder import ChatGLMModel from ..models.model_manager import ModelManager from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator from ..prompters import SDXLPrompter, KolorsPrompter from ..schedulers import EnhancedDDIMScheduler from .base import BasePipeline from .dancer import lets_dance_xl from typing import List import torch from tqdm import tqdm from einops import repeat class SDXLImagePipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = EnhancedDDIMScheduler() self.prompter = SDXLPrompter() # models self.text_encoder: SDXLTextEncoder = None self.text_encoder_2: SDXLTextEncoder2 = None self.text_encoder_kolors: ChatGLMModel = None self.unet: SDXLUNet = None self.vae_decoder: SDXLVAEDecoder = None self.vae_encoder: SDXLVAEEncoder = None self.controlnet: MultiControlNetManager = None self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None self.ipadapter: SDXLIpAdapter = None self.model_names = ['text_encoder', 'text_encoder_2', 'text_encoder_kolors', 'unet', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter_image_encoder', 'ipadapter'] def denoising_model(self): return self.unet def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): # Main models self.text_encoder = model_manager.fetch_model("sdxl_text_encoder") self.text_encoder_2 = model_manager.fetch_model("sdxl_text_encoder_2") self.text_encoder_kolors = model_manager.fetch_model("kolors_text_encoder") self.unet = model_manager.fetch_model("sdxl_unet") self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") # ControlNets controlnet_units = [] for config in controlnet_config_units: controlnet_unit = ControlNetUnit( Annotator(config.processor_id, device=self.device), model_manager.fetch_model("sdxl_controlnet", config.model_path), config.scale ) controlnet_units.append(controlnet_unit) self.controlnet = MultiControlNetManager(controlnet_units) # IP-Adapters self.ipadapter = model_manager.fetch_model("sdxl_ipadapter") self.ipadapter_image_encoder = model_manager.fetch_model("sdxl_ipadapter_clip_image_encoder") # Kolors if self.text_encoder_kolors is not None: print("Switch to Kolors. The prompter and scheduler will be replaced.") self.prompter = KolorsPrompter() self.prompter.fetch_models(self.text_encoder_kolors) self.scheduler = EnhancedDDIMScheduler(beta_end=0.014, num_train_timesteps=1100) else: self.prompter.fetch_models(self.text_encoder, self.text_encoder_2) self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) @staticmethod def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], device=None): pipe = SDXLImagePipeline( device=model_manager.device if device is None else device, torch_dtype=model_manager.torch_dtype, ) pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) return pipe def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return latents def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) image = self.vae_output_to_image(image) return image def encode_prompt(self, prompt, clip_skip=1, clip_skip_2=2, positive=True): add_prompt_emb, prompt_emb = self.prompter.encode_prompt( prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, device=self.device, positive=positive, ) return {"encoder_hidden_states": prompt_emb, "add_text_embeds": add_prompt_emb} def prepare_extra_input(self, latents=None): height, width = latents.shape[2] * 8, latents.shape[3] * 8 add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device).repeat(latents.shape[0]) return {"add_time_id": add_time_id} @torch.no_grad() def __call__( self, prompt, local_prompts=[], masks=[], mask_scales=[], negative_prompt="", cfg_scale=7.5, clip_skip=1, clip_skip_2=2, input_image=None, ipadapter_images=None, ipadapter_scale=1.0, ipadapter_use_instant_style=False, controlnet_image=None, denoising_strength=1.0, height=1024, width=1024, num_inference_steps=20, tiled=False, tile_size=64, tile_stride=32, seed=None, progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Tiler parameters tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors if input_image is not None: self.load_models_to_device(['vae_encoder']) image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) latents = self.encode_image(image, **tiler_kwargs) noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) else: latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) # Encode prompts self.load_models_to_device(['text_encoder', 'text_encoder_2', 'text_encoder_kolors']) prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=False) prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) for prompt_local in local_prompts] # IP-Adapter if ipadapter_images is not None: if ipadapter_use_instant_style: self.ipadapter.set_less_adapter() else: self.ipadapter.set_full_adapter() self.load_models_to_device(['ipadapter_image_encoder']) ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) self.load_models_to_device(['ipadapter']) ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} else: ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} # Prepare ControlNets if controlnet_image is not None: self.load_models_to_device(['controlnet']) controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype) controlnet_image = controlnet_image.unsqueeze(1) controlnet_kwargs = {"controlnet_frames": controlnet_image} else: controlnet_kwargs = {"controlnet_frames": None} # Prepare extra input extra_input = self.prepare_extra_input(latents) # Denoise self.load_models_to_device(['controlnet', 'unet']) for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(self.device) # Classifier-free guidance inference_callback = lambda prompt_emb_posi: lets_dance_xl( self.unet, motion_modules=None, controlnet=self.controlnet, sample=latents, timestep=timestep, **extra_input, **prompt_emb_posi, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_posi, device=self.device, ) noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) if cfg_scale != 1.0: noise_pred_nega = lets_dance_xl( self.unet, motion_modules=None, controlnet=self.controlnet, sample=latents, timestep=timestep, **extra_input, **prompt_emb_nega, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_nega, device=self.device, ) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) else: noise_pred = noise_pred_posi # DDIM latents = self.scheduler.step(noise_pred, timestep, latents) # UI if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image self.load_models_to_device(['vae_decoder']) image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) # offload all models self.load_models_to_device([]) return image ================================================ FILE: diffsynth/pipelines/sdxl_video.py ================================================ from ..models import SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder, SDXLMotionModel from ..models.kolors_text_encoder import ChatGLMModel from ..models.model_manager import ModelManager from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator from ..prompters import SDXLPrompter, KolorsPrompter from ..schedulers import EnhancedDDIMScheduler from .sdxl_image import SDXLImagePipeline from .dancer import lets_dance_xl from typing import List import torch from tqdm import tqdm class SDXLVideoPipeline(SDXLImagePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16, use_original_animatediff=True): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_original_animatediff else "scaled_linear") self.prompter = SDXLPrompter() # models self.text_encoder: SDXLTextEncoder = None self.text_encoder_2: SDXLTextEncoder2 = None self.text_encoder_kolors: ChatGLMModel = None self.unet: SDXLUNet = None self.vae_decoder: SDXLVAEDecoder = None self.vae_encoder: SDXLVAEEncoder = None # self.controlnet: MultiControlNetManager = None (TODO) self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None self.ipadapter: SDXLIpAdapter = None self.motion_modules: SDXLMotionModel = None def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): # Main models self.text_encoder = model_manager.fetch_model("sdxl_text_encoder") self.text_encoder_2 = model_manager.fetch_model("sdxl_text_encoder_2") self.text_encoder_kolors = model_manager.fetch_model("kolors_text_encoder") self.unet = model_manager.fetch_model("sdxl_unet") self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") self.prompter.fetch_models(self.text_encoder) self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) # ControlNets (TODO) # IP-Adapters self.ipadapter = model_manager.fetch_model("sdxl_ipadapter") self.ipadapter_image_encoder = model_manager.fetch_model("sdxl_ipadapter_clip_image_encoder") # Motion Modules self.motion_modules = model_manager.fetch_model("sdxl_motion_modules") if self.motion_modules is None: self.scheduler = EnhancedDDIMScheduler(beta_schedule="scaled_linear") # Kolors if self.text_encoder_kolors is not None: print("Switch to Kolors. The prompter will be replaced.") self.prompter = KolorsPrompter() self.prompter.fetch_models(self.text_encoder_kolors) # The schedulers of AniamteDiff and Kolors are incompatible. We align it with AniamteDiff. if self.motion_modules is None: self.scheduler = EnhancedDDIMScheduler(beta_end=0.014, num_train_timesteps=1100) else: self.prompter.fetch_models(self.text_encoder, self.text_encoder_2) @staticmethod def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): pipe = SDXLVideoPipeline( device=model_manager.device, torch_dtype=model_manager.torch_dtype, ) pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) return pipe def decode_video(self, latents, tiled=False, tile_size=64, tile_stride=32): images = [ self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) for frame_id in range(latents.shape[0]) ] return images def encode_video(self, processed_images, tiled=False, tile_size=64, tile_stride=32): latents = [] for image in processed_images: image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) latent = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) latents.append(latent.cpu()) latents = torch.concat(latents, dim=0) return latents @torch.no_grad() def __call__( self, prompt, negative_prompt="", cfg_scale=7.5, clip_skip=1, num_frames=None, input_frames=None, ipadapter_images=None, ipadapter_scale=1.0, ipadapter_use_instant_style=False, controlnet_frames=None, denoising_strength=1.0, height=512, width=512, num_inference_steps=20, animatediff_batch_size = 16, animatediff_stride = 8, unet_batch_size = 1, controlnet_batch_size = 1, cross_frame_attention = False, smoother=None, smoother_progress_ids=[], tiled=False, tile_size=64, tile_stride=32, seed=None, progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Tiler parameters, batch size ... tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors if self.motion_modules is None: noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1) else: noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype) if input_frames is None or denoising_strength == 1.0: latents = noise else: latents = self.encode_video(input_frames, **tiler_kwargs) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) latents = latents.to(self.device) # will be deleted for supporting long videos # Encode prompts prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) # IP-Adapter if ipadapter_images is not None: if ipadapter_use_instant_style: self.ipadapter.set_less_adapter() else: self.ipadapter.set_full_adapter() ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} else: ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} # Prepare ControlNets if controlnet_frames is not None: if isinstance(controlnet_frames[0], list): controlnet_frames_ = [] for processor_id in range(len(controlnet_frames)): controlnet_frames_.append( torch.stack([ self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype) for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id]) ], dim=1) ) controlnet_frames = torch.concat(controlnet_frames_, dim=0) else: controlnet_frames = torch.stack([ self.controlnet.process_image(controlnet_frame).to(self.torch_dtype) for controlnet_frame in progress_bar_cmd(controlnet_frames) ], dim=1) controlnet_kwargs = {"controlnet_frames": controlnet_frames} else: controlnet_kwargs = {"controlnet_frames": None} # Prepare extra input extra_input = self.prepare_extra_input(latents) # Denoise for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(self.device) # Classifier-free guidance noise_pred_posi = lets_dance_xl( self.unet, motion_modules=self.motion_modules, controlnet=None, sample=latents, timestep=timestep, **prompt_emb_posi, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **extra_input, **tiler_kwargs, device=self.device, ) noise_pred_nega = lets_dance_xl( self.unet, motion_modules=self.motion_modules, controlnet=None, sample=latents, timestep=timestep, **prompt_emb_nega, **controlnet_kwargs, **ipadapter_kwargs_list_nega, **extra_input, **tiler_kwargs, device=self.device, ) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) # DDIM and smoother if smoother is not None and progress_id in smoother_progress_ids: rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True) rendered_frames = self.decode_video(rendered_frames) rendered_frames = smoother(rendered_frames, original_frames=input_frames) target_latents = self.encode_video(rendered_frames) noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents) latents = self.scheduler.step(noise_pred, timestep, latents) # UI if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image output_frames = self.decode_video(latents, **tiler_kwargs) # Post-process if smoother is not None and (num_inference_steps in smoother_progress_ids or -1 in smoother_progress_ids): output_frames = smoother(output_frames, original_frames=input_frames) return output_frames ================================================ FILE: diffsynth/pipelines/step_video.py ================================================ from ..models import ModelManager from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder from ..models.stepvideo_text_encoder import STEP1TextEncoder from ..models.stepvideo_dit import StepVideoModel from ..models.stepvideo_vae import StepVideoVAE from ..schedulers.flow_match import FlowMatchScheduler from .base import BasePipeline from ..prompters import StepVideoPrompter import torch from einops import rearrange import numpy as np from PIL import Image from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear from transformers.models.bert.modeling_bert import BertEmbeddings from ..models.stepvideo_dit import RMSNorm from ..models.stepvideo_vae import CausalConv, CausalConvAfterNorm, Upsample2D, BaseGroupNorm class StepVideoPipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = FlowMatchScheduler(sigma_min=0.0, extra_one_step=True, shift=13.0, reverse_sigmas=True, num_train_timesteps=1) self.prompter = StepVideoPrompter() self.text_encoder_1: HunyuanDiTCLIPTextEncoder = None self.text_encoder_2: STEP1TextEncoder = None self.dit: StepVideoModel = None self.vae: StepVideoVAE = None self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae'] def enable_vram_management(self, num_persistent_param_in_dit=None): dtype = next(iter(self.text_encoder_1.parameters())).dtype enable_vram_management( self.text_encoder_1, module_map = { torch.nn.Linear: AutoWrappedLinear, BertEmbeddings: AutoWrappedModule, torch.nn.LayerNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=torch.float32, computation_device=self.device, ), ) dtype = next(iter(self.text_encoder_2.parameters())).dtype enable_vram_management( self.text_encoder_2, module_map = { torch.nn.Linear: AutoWrappedLinear, RMSNorm: AutoWrappedModule, torch.nn.Embedding: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.dit.parameters())).dtype enable_vram_management( self.dit, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv2d: AutoWrappedModule, torch.nn.LayerNorm: AutoWrappedModule, RMSNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device=self.device, computation_dtype=self.torch_dtype, computation_device=self.device, ), max_num_param=num_persistent_param_in_dit, overflow_module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.vae.parameters())).dtype enable_vram_management( self.vae, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv3d: AutoWrappedModule, CausalConv: AutoWrappedModule, CausalConvAfterNorm: AutoWrappedModule, Upsample2D: AutoWrappedModule, BaseGroupNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) self.enable_cpu_offload() def fetch_models(self, model_manager: ModelManager): self.text_encoder_1 = model_manager.fetch_model("hunyuan_dit_clip_text_encoder") self.text_encoder_2 = model_manager.fetch_model("stepvideo_text_encoder_2") self.dit = model_manager.fetch_model("stepvideo_dit") self.vae = model_manager.fetch_model("stepvideo_vae") self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2) @staticmethod def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None): if device is None: device = model_manager.device if torch_dtype is None: torch_dtype = model_manager.torch_dtype pipe = StepVideoPipeline(device=device, torch_dtype=torch_dtype) pipe.fetch_models(model_manager) return pipe def encode_prompt(self, prompt, positive=True): clip_embeds, llm_embeds, llm_mask = self.prompter.encode_prompt(prompt, device=self.device, positive=positive) clip_embeds = clip_embeds.to(dtype=self.torch_dtype, device=self.device) llm_embeds = llm_embeds.to(dtype=self.torch_dtype, device=self.device) llm_mask = llm_mask.to(dtype=self.torch_dtype, device=self.device) return {"encoder_hidden_states_2": clip_embeds, "encoder_hidden_states": llm_embeds, "encoder_attention_mask": llm_mask} def tensor2video(self, frames): frames = rearrange(frames, "C T H W -> T H W C") frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) frames = [Image.fromarray(frame) for frame in frames] return frames @torch.no_grad() def __call__( self, prompt, negative_prompt="", input_video=None, denoising_strength=1.0, seed=None, rand_device="cpu", height=544, width=992, num_frames=204, cfg_scale=9.0, num_inference_steps=30, tiled=True, tile_size=(34, 34), tile_stride=(16, 16), smooth_scale=0.6, progress_bar_cmd=lambda x: x, progress_bar_st=None, ): # Tiler parameters tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Initialize noise latents = self.generate_noise((1, max(num_frames//17*3, 1), 64, height//16, width//16), seed=seed, device=rand_device, dtype=self.torch_dtype).to(self.device) # Encode prompts self.load_models_to_device(["text_encoder_1", "text_encoder_2"]) prompt_emb_posi = self.encode_prompt(prompt, positive=True) if cfg_scale != 1.0: prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) # Denoise self.load_models_to_device(["dit"]) for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}") # Inference noise_pred_posi = self.dit(latents, timestep=timestep, **prompt_emb_posi) if cfg_scale != 1.0: noise_pred_nega = self.dit(latents, timestep=timestep, **prompt_emb_nega) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) else: noise_pred = noise_pred_posi # Scheduler latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) # Decode self.load_models_to_device(['vae']) frames = self.vae.decode(latents, device=self.device, smooth_scale=smooth_scale, **tiler_kwargs) self.load_models_to_device([]) frames = self.tensor2video(frames[0]) return frames ================================================ FILE: diffsynth/pipelines/svd_video.py ================================================ from ..models import ModelManager, SVDImageEncoder, SVDUNet, SVDVAEEncoder, SVDVAEDecoder from ..schedulers import ContinuousODEScheduler from .base import BasePipeline import torch from tqdm import tqdm from PIL import Image import numpy as np from einops import rearrange, repeat class SVDVideoPipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = ContinuousODEScheduler() # models self.image_encoder: SVDImageEncoder = None self.unet: SVDUNet = None self.vae_encoder: SVDVAEEncoder = None self.vae_decoder: SVDVAEDecoder = None def fetch_models(self, model_manager: ModelManager): self.image_encoder = model_manager.fetch_model("svd_image_encoder") self.unet = model_manager.fetch_model("svd_unet") self.vae_encoder = model_manager.fetch_model("svd_vae_encoder") self.vae_decoder = model_manager.fetch_model("svd_vae_decoder") @staticmethod def from_model_manager(model_manager: ModelManager, **kwargs): pipe = SVDVideoPipeline( device=model_manager.device, torch_dtype=model_manager.torch_dtype ) pipe.fetch_models(model_manager) return pipe def encode_image_with_clip(self, image): image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) image = SVDCLIPImageProcessor().resize_with_antialiasing(image, (224, 224)) image = (image + 1.0) / 2.0 mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype) std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype) image = (image - mean) / std image_emb = self.image_encoder(image) return image_emb def encode_image_with_vae(self, image, noise_aug_strength, seed=None): image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) noise = self.generate_noise(image.shape, seed=seed, device=self.device, dtype=self.torch_dtype) image = image + noise_aug_strength * noise image_emb = self.vae_encoder(image) / self.vae_encoder.scaling_factor return image_emb def encode_video_with_vae(self, video): video = torch.concat([self.preprocess_image(frame) for frame in video], dim=0) video = rearrange(video, "T C H W -> 1 C T H W") video = video.to(device=self.device, dtype=self.torch_dtype) latents = self.vae_encoder.encode_video(video) latents = rearrange(latents[0], "C T H W -> T C H W") return latents def tensor2video(self, frames): frames = rearrange(frames, "C T H W -> T H W C") frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) frames = [Image.fromarray(frame) for frame in frames] return frames def calculate_noise_pred( self, latents, timestep, add_time_id, cfg_scales, image_emb_vae_posi, image_emb_clip_posi, image_emb_vae_nega, image_emb_clip_nega ): # Positive side noise_pred_posi = self.unet( torch.cat([latents, image_emb_vae_posi], dim=1), timestep, image_emb_clip_posi, add_time_id ) # Negative side noise_pred_nega = self.unet( torch.cat([latents, image_emb_vae_nega], dim=1), timestep, image_emb_clip_nega, add_time_id ) # Classifier-free guidance noise_pred = noise_pred_nega + cfg_scales * (noise_pred_posi - noise_pred_nega) return noise_pred def post_process_latents(self, latents, post_normalize=True, contrast_enhance_scale=1.0): if post_normalize: mean, std = latents.mean(), latents.std() latents = (latents - latents.mean(dim=[1, 2, 3], keepdim=True)) / latents.std(dim=[1, 2, 3], keepdim=True) * std + mean latents = latents * contrast_enhance_scale return latents @torch.no_grad() def __call__( self, input_image=None, input_video=None, mask_frames=[], mask_frame_ids=[], min_cfg_scale=1.0, max_cfg_scale=3.0, denoising_strength=1.0, num_frames=25, height=576, width=1024, fps=7, motion_bucket_id=127, noise_aug_strength=0.02, num_inference_steps=20, post_normalize=True, contrast_enhance_scale=1.2, seed=None, progress_bar_cmd=tqdm, progress_bar_st=None, ): height, width = self.check_resize_height_width(height, width) # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength) # Prepare latent tensors noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) if denoising_strength == 1.0: latents = noise.clone() else: latents = self.encode_video_with_vae(input_video) latents = self.scheduler.add_noise(latents, noise, self.scheduler.timesteps[0]) # Prepare mask frames if len(mask_frames) > 0: mask_latents = self.encode_video_with_vae(mask_frames) # Encode image image_emb_clip_posi = self.encode_image_with_clip(input_image) image_emb_clip_nega = torch.zeros_like(image_emb_clip_posi) image_emb_vae_posi = repeat(self.encode_image_with_vae(input_image, noise_aug_strength, seed=seed), "B C H W -> (B T) C H W", T=num_frames) image_emb_vae_nega = torch.zeros_like(image_emb_vae_posi) # Prepare classifier-free guidance cfg_scales = torch.linspace(min_cfg_scale, max_cfg_scale, num_frames) cfg_scales = cfg_scales.reshape(num_frames, 1, 1, 1).to(device=self.device, dtype=self.torch_dtype) # Prepare positional id add_time_id = torch.tensor([[fps-1, motion_bucket_id, noise_aug_strength]], device=self.device) # Denoise for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): # Mask frames for frame_id, mask_frame_id in enumerate(mask_frame_ids): latents[mask_frame_id] = self.scheduler.add_noise(mask_latents[frame_id], noise[mask_frame_id], timestep) # Fetch model output noise_pred = self.calculate_noise_pred( latents, timestep, add_time_id, cfg_scales, image_emb_vae_posi, image_emb_clip_posi, image_emb_vae_nega, image_emb_clip_nega ) # Forward Euler latents = self.scheduler.step(noise_pred, timestep, latents) # Update progress bar if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image latents = self.post_process_latents(latents, post_normalize=post_normalize, contrast_enhance_scale=contrast_enhance_scale) video = self.vae_decoder.decode_video(latents, progress_bar=progress_bar_cmd) video = self.tensor2video(video) return video class SVDCLIPImageProcessor: def __init__(self): pass def resize_with_antialiasing(self, input, size, interpolation="bicubic", align_corners=True): h, w = input.shape[-2:] factors = (h / size[0], w / size[1]) # First, we have to determine sigma # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171 sigmas = ( max((factors[0] - 1.0) / 2.0, 0.001), max((factors[1] - 1.0) / 2.0, 0.001), ) # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206 # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3)) # Make sure it is odd if (ks[0] % 2) == 0: ks = ks[0] + 1, ks[1] if (ks[1] % 2) == 0: ks = ks[0], ks[1] + 1 input = self._gaussian_blur2d(input, ks, sigmas) output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners) return output def _compute_padding(self, kernel_size): """Compute padding tuple.""" # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom) # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad if len(kernel_size) < 2: raise AssertionError(kernel_size) computed = [k - 1 for k in kernel_size] # for even kernels we need to do asymmetric padding :( out_padding = 2 * len(kernel_size) * [0] for i in range(len(kernel_size)): computed_tmp = computed[-(i + 1)] pad_front = computed_tmp // 2 pad_rear = computed_tmp - pad_front out_padding[2 * i + 0] = pad_front out_padding[2 * i + 1] = pad_rear return out_padding def _filter2d(self, input, kernel): # prepare kernel b, c, h, w = input.shape tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype) tmp_kernel = tmp_kernel.expand(-1, c, -1, -1) height, width = tmp_kernel.shape[-2:] padding_shape: list[int] = self._compute_padding([height, width]) input = torch.nn.functional.pad(input, padding_shape, mode="reflect") # kernel and input tensor reshape to align element-wise or batch-wise params tmp_kernel = tmp_kernel.reshape(-1, 1, height, width) input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1)) # convolve the tensor with the kernel. output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) out = output.view(b, c, h, w) return out def _gaussian(self, window_size: int, sigma): if isinstance(sigma, float): sigma = torch.tensor([[sigma]]) batch_size = sigma.shape[0] x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) if window_size % 2 == 0: x = x + 0.5 gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) return gauss / gauss.sum(-1, keepdim=True) def _gaussian_blur2d(self, input, kernel_size, sigma): if isinstance(sigma, tuple): sigma = torch.tensor([sigma], dtype=input.dtype) else: sigma = sigma.to(dtype=input.dtype) ky, kx = int(kernel_size[0]), int(kernel_size[1]) bs = sigma.shape[0] kernel_x = self._gaussian(kx, sigma[:, 1].view(bs, 1)) kernel_y = self._gaussian(ky, sigma[:, 0].view(bs, 1)) out_x = self._filter2d(input, kernel_x[..., None, :]) out = self._filter2d(out_x, kernel_y[..., None]) return out ================================================ FILE: diffsynth/pipelines/wan_video.py ================================================ from ..models import ModelManager from ..models.wan_video_dit import WanModel from ..models.wan_video_text_encoder import WanTextEncoder from ..models.wan_video_vae import WanVideoVAE from ..models.wan_video_image_encoder import WanImageEncoder from ..schedulers.flow_match import FlowMatchScheduler from .base import BasePipeline from ..prompters import WanPrompter import torch, os from einops import rearrange import numpy as np from PIL import Image from tqdm import tqdm from typing import Optional from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear from ..models.wan_video_text_encoder import T5RelativeEmbedding, T5LayerNorm from ..models.wan_video_dit import RMSNorm, sinusoidal_embedding_1d from ..models.wan_video_vae import RMS_norm, CausalConv3d, Upsample class WanVideoPipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16, tokenizer_path=None): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = FlowMatchScheduler(shift=5, sigma_min=0.0, extra_one_step=True) self.prompter = WanPrompter(tokenizer_path=tokenizer_path) self.text_encoder: WanTextEncoder = None self.image_encoder: WanImageEncoder = None self.dit: WanModel = None self.vae: WanVideoVAE = None self.model_names = ['text_encoder', 'dit', 'vae'] self.height_division_factor = 16 self.width_division_factor = 16 def enable_vram_management(self, num_persistent_param_in_dit=None): dtype = next(iter(self.text_encoder.parameters())).dtype enable_vram_management( self.text_encoder, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Embedding: AutoWrappedModule, T5RelativeEmbedding: AutoWrappedModule, T5LayerNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.dit.parameters())).dtype enable_vram_management( self.dit, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv3d: AutoWrappedModule, torch.nn.LayerNorm: AutoWrappedModule, RMSNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device=self.device, computation_dtype=self.torch_dtype, computation_device=self.device, ), max_num_param=num_persistent_param_in_dit, overflow_module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.vae.parameters())).dtype enable_vram_management( self.vae, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv2d: AutoWrappedModule, RMS_norm: AutoWrappedModule, CausalConv3d: AutoWrappedModule, Upsample: AutoWrappedModule, torch.nn.SiLU: AutoWrappedModule, torch.nn.Dropout: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device=self.device, computation_dtype=self.torch_dtype, computation_device=self.device, ), ) if self.image_encoder is not None: dtype = next(iter(self.image_encoder.parameters())).dtype enable_vram_management( self.image_encoder, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv2d: AutoWrappedModule, torch.nn.LayerNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=dtype, computation_device=self.device, ), ) self.enable_cpu_offload() def fetch_models(self, model_manager: ModelManager): text_encoder_model_and_path = model_manager.fetch_model("wan_video_text_encoder", require_model_path=True) if text_encoder_model_and_path is not None: self.text_encoder, tokenizer_path = text_encoder_model_and_path self.prompter.fetch_models(self.text_encoder) self.prompter.fetch_tokenizer(os.path.join(os.path.dirname(tokenizer_path), "google/umt5-xxl")) self.dit = model_manager.fetch_model("wan_video_dit") self.vae = model_manager.fetch_model("wan_video_vae") self.image_encoder = model_manager.fetch_model("wan_video_image_encoder") @staticmethod def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None): if device is None: device = model_manager.device if torch_dtype is None: torch_dtype = model_manager.torch_dtype pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype) pipe.fetch_models(model_manager) return pipe def denoising_model(self): return self.dit def encode_prompt(self, prompt, positive=True): prompt_emb = self.prompter.encode_prompt(prompt, positive=positive) return {"context": prompt_emb} def encode_image(self, image, num_frames, height, width): image = self.preprocess_image(image.resize((width, height))).to(self.device) clip_context = self.image_encoder.encode_image([image]) msk = torch.ones(1, num_frames, height//8, width//8, device=self.device) msk[:, 1:] = 0 msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8) msk = msk.transpose(1, 2)[0] vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1) y = self.vae.encode([vae_input.to(dtype=self.torch_dtype, device=self.device)], device=self.device)[0] y = torch.concat([msk, y]) y = y.unsqueeze(0) clip_context = clip_context.to(dtype=self.torch_dtype, device=self.device) y = y.to(dtype=self.torch_dtype, device=self.device) return {"clip_feature": clip_context, "y": y} def tensor2video(self, frames): frames = rearrange(frames, "C T H W -> T H W C") frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) frames = [Image.fromarray(frame) for frame in frames] return frames def prepare_extra_input(self, latents=None): return {} def encode_video(self, input_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): latents = self.vae.encode(input_video, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return latents def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return frames @torch.no_grad() def __call__( self, prompt, negative_prompt="", input_image=None, input_video=None, denoising_strength=1.0, seed=None, rand_device="cpu", height=480, width=832, num_frames=81, cfg_scale=5.0, num_inference_steps=50, sigma_shift=5.0, tiled=True, tile_size=(30, 52), tile_stride=(15, 26), tea_cache_l1_thresh=None, tea_cache_model_id="", progress_bar_cmd=tqdm, progress_bar_st=None, ): # Parameter check height, width = self.check_resize_height_width(height, width) if num_frames % 4 != 1: num_frames = (num_frames + 2) // 4 * 4 + 1 print(f"Only `num_frames % 4 != 1` is acceptable. We round it up to {num_frames}.") # Tiler parameters tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift) # Initialize noise noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=torch.float32) noise = noise.to(dtype=self.torch_dtype, device=self.device) if input_video is not None: self.load_models_to_device(['vae']) input_video = self.preprocess_images(input_video) input_video = torch.stack(input_video, dim=2).to(dtype=self.torch_dtype, device=self.device) latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) else: latents = noise # Encode prompts self.load_models_to_device(["text_encoder"]) prompt_emb_posi = self.encode_prompt(prompt, positive=True) if cfg_scale != 1.0: prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) # Encode image if input_image is not None and self.image_encoder is not None: self.load_models_to_device(["image_encoder", "vae"]) image_emb = self.encode_image(input_image, num_frames, height, width) else: image_emb = {} # Extra input extra_input = self.prepare_extra_input(latents) # TeaCache tea_cache_posi = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} tea_cache_nega = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} # Denoise self.load_models_to_device(["dit"]) for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) # Inference noise_pred_posi = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_posi, **image_emb, **extra_input, **tea_cache_posi) if cfg_scale != 1.0: noise_pred_nega = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_nega, **image_emb, **extra_input, **tea_cache_nega) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) else: noise_pred = noise_pred_posi # Scheduler latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) # Decode self.load_models_to_device(['vae']) frames = self.decode_video(latents, **tiler_kwargs) self.load_models_to_device([]) frames = self.tensor2video(frames[0]) return frames class TeaCache: def __init__(self, num_inference_steps, rel_l1_thresh, model_id): self.num_inference_steps = num_inference_steps self.step = 0 self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = None self.rel_l1_thresh = rel_l1_thresh self.previous_residual = None self.previous_hidden_states = None self.coefficients_dict = { "Wan2.1-T2V-1.3B": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02], "Wan2.1-T2V-14B": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01], "Wan2.1-I2V-14B-480P": [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01], "Wan2.1-I2V-14B-720P": [ 8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02], } if model_id not in self.coefficients_dict: supported_model_ids = ", ".join([i for i in self.coefficients_dict]) raise ValueError(f"{model_id} is not a supported TeaCache model id. Please choose a valid model id in ({supported_model_ids}).") self.coefficients = self.coefficients_dict[model_id] def check(self, dit: WanModel, x, t_mod): modulated_inp = t_mod.clone() if self.step == 0 or self.step == self.num_inference_steps - 1: should_calc = True self.accumulated_rel_l1_distance = 0 else: coefficients = self.coefficients rescale_func = np.poly1d(coefficients) self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) if self.accumulated_rel_l1_distance < self.rel_l1_thresh: should_calc = False else: should_calc = True self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = modulated_inp self.step += 1 if self.step == self.num_inference_steps: self.step = 0 if should_calc: self.previous_hidden_states = x.clone() return not should_calc def store(self, hidden_states): self.previous_residual = hidden_states - self.previous_hidden_states self.previous_hidden_states = None def update(self, hidden_states): hidden_states = hidden_states + self.previous_residual return hidden_states def model_fn_wan_video( dit: WanModel, x: torch.Tensor, timestep: torch.Tensor, context: torch.Tensor, clip_feature: Optional[torch.Tensor] = None, y: Optional[torch.Tensor] = None, tea_cache: TeaCache = None, **kwargs, ): t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep)) t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim)) context = dit.text_embedding(context) if dit.has_image_input: x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) clip_embdding = dit.img_emb(clip_feature) context = torch.cat([clip_embdding, context], dim=1) x, (f, h, w) = dit.patchify(x) freqs = torch.cat([ dit.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), dit.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), dit.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) # TeaCache if tea_cache is not None: tea_cache_update = tea_cache.check(dit, x, t_mod) else: tea_cache_update = False if tea_cache_update: x = tea_cache.update(x) else: # blocks for block in dit.blocks: x = block(x, context, t_mod, freqs) if tea_cache is not None: tea_cache.store(x) x = dit.head(x, t) x = dit.unpatchify(x, (f, h, w)) return x ================================================ FILE: diffsynth/pipelines/wan_video_syncammaster.py ================================================ from ..models import ModelManager from ..models.wan_video_dit import WanModel from ..models.wan_video_text_encoder import WanTextEncoder from ..models.wan_video_vae import WanVideoVAE from ..models.wan_video_image_encoder import WanImageEncoder from ..schedulers.flow_match import FlowMatchScheduler from .base import BasePipeline from ..prompters import WanPrompter import torch, os from einops import rearrange import numpy as np from PIL import Image from tqdm import tqdm from typing import Optional from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear from ..models.wan_video_text_encoder import T5RelativeEmbedding, T5LayerNorm from ..models.wan_video_dit import RMSNorm, sinusoidal_embedding_1d from ..models.wan_video_vae import RMS_norm, CausalConv3d, Upsample class WanVideoSynCamMasterPipeline(BasePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16, tokenizer_path=None): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = FlowMatchScheduler(shift=5, sigma_min=0.0, extra_one_step=True) self.prompter = WanPrompter(tokenizer_path=tokenizer_path) self.text_encoder: WanTextEncoder = None self.image_encoder: WanImageEncoder = None self.dit: WanModel = None self.vae: WanVideoVAE = None self.model_names = ['text_encoder', 'dit', 'vae'] self.height_division_factor = 16 self.width_division_factor = 16 def enable_vram_management(self, num_persistent_param_in_dit=None): dtype = next(iter(self.text_encoder.parameters())).dtype enable_vram_management( self.text_encoder, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Embedding: AutoWrappedModule, T5RelativeEmbedding: AutoWrappedModule, T5LayerNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.dit.parameters())).dtype enable_vram_management( self.dit, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv3d: AutoWrappedModule, torch.nn.LayerNorm: AutoWrappedModule, RMSNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device=self.device, computation_dtype=self.torch_dtype, computation_device=self.device, ), max_num_param=num_persistent_param_in_dit, overflow_module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=self.torch_dtype, computation_device=self.device, ), ) dtype = next(iter(self.vae.parameters())).dtype enable_vram_management( self.vae, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv2d: AutoWrappedModule, RMS_norm: AutoWrappedModule, CausalConv3d: AutoWrappedModule, Upsample: AutoWrappedModule, torch.nn.SiLU: AutoWrappedModule, torch.nn.Dropout: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device=self.device, computation_dtype=self.torch_dtype, computation_device=self.device, ), ) if self.image_encoder is not None: dtype = next(iter(self.image_encoder.parameters())).dtype enable_vram_management( self.image_encoder, module_map = { torch.nn.Linear: AutoWrappedLinear, torch.nn.Conv2d: AutoWrappedModule, torch.nn.LayerNorm: AutoWrappedModule, }, module_config = dict( offload_dtype=dtype, offload_device="cpu", onload_dtype=dtype, onload_device="cpu", computation_dtype=dtype, computation_device=self.device, ), ) self.enable_cpu_offload() def fetch_models(self, model_manager: ModelManager): text_encoder_model_and_path = model_manager.fetch_model("wan_video_text_encoder", require_model_path=True) if text_encoder_model_and_path is not None: self.text_encoder, tokenizer_path = text_encoder_model_and_path self.prompter.fetch_models(self.text_encoder) self.prompter.fetch_tokenizer(os.path.join(os.path.dirname(tokenizer_path), "google/umt5-xxl")) self.dit = model_manager.fetch_model("wan_video_dit") self.vae = model_manager.fetch_model("wan_video_vae") self.image_encoder = model_manager.fetch_model("wan_video_image_encoder") @staticmethod def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None): if device is None: device = model_manager.device if torch_dtype is None: torch_dtype = model_manager.torch_dtype pipe = WanVideoSynCamMasterPipeline(device=device, torch_dtype=torch_dtype) pipe.fetch_models(model_manager) return pipe def denoising_model(self): return self.dit def encode_prompt(self, prompt, positive=True): prompt_emb = self.prompter.encode_prompt(prompt, positive=positive) return {"context": prompt_emb} def encode_image(self, image, num_frames, height, width): image = self.preprocess_image(image.resize((width, height))).to(self.device) clip_context = self.image_encoder.encode_image([image]) msk = torch.ones(1, num_frames, height//8, width//8, device=self.device) msk[:, 1:] = 0 msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8) msk = msk.transpose(1, 2)[0] vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1) y = self.vae.encode([vae_input.to(dtype=self.torch_dtype, device=self.device)], device=self.device)[0] y = torch.concat([msk, y]) y = y.unsqueeze(0) clip_context = clip_context.to(dtype=self.torch_dtype, device=self.device) y = y.to(dtype=self.torch_dtype, device=self.device) return {"clip_feature": clip_context, "y": y} def tensor2video(self, frames): frames = rearrange(frames, "C T H W -> T H W C") frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) frames = [Image.fromarray(frame) for frame in frames] return frames def prepare_extra_input(self, latents=None): return {} def encode_video(self, input_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): latents = self.vae.encode(input_video, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return latents def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return frames @torch.no_grad() def __call__( self, prompt, negative_prompt="", camera=None, input_image=None, input_video=None, denoising_strength=1.0, seed=None, rand_device="cpu", height=480, width=832, num_frames=81, cfg_scale=5.0, num_inference_steps=50, sigma_shift=5.0, tiled=True, tile_size=(30, 52), tile_stride=(15, 26), tea_cache_l1_thresh=None, tea_cache_model_id="", progress_bar_cmd=tqdm, progress_bar_st=None, ): # Parameter check height, width = self.check_resize_height_width(height, width) if num_frames % 4 != 1: num_frames = (num_frames + 2) // 4 * 4 + 1 print(f"Only `num_frames % 4 != 1` is acceptable. We round it up to {num_frames}.") # Tiler parameters tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} # Scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift) # Initialize noise # (b v) c f h w [b=1 v=2] noise = self.generate_noise((2, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=torch.float32) noise = noise.to(dtype=self.torch_dtype, device=self.device) if input_video is not None: self.load_models_to_device(['vae']) input_video = self.preprocess_images(input_video) input_video = torch.stack(input_video, dim=2).to(dtype=self.torch_dtype, device=self.device) latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) else: latents = noise # Process multi-view cameras (syncammaster) cam_emb = camera.to(dtype=self.torch_dtype, device=self.device) # Encode prompts self.load_models_to_device(["text_encoder"]) prompt_emb_posi = self.encode_prompt(prompt, positive=True) prompt_emb_posi['context'] = prompt_emb_posi['context'].repeat(2, 1, 1) if cfg_scale != 1.0: prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) prompt_emb_nega['context'] = prompt_emb_nega['context'].repeat(2, 1, 1) # Encode image if input_image is not None and self.image_encoder is not None: self.load_models_to_device(["image_encoder", "vae"]) image_emb = self.encode_image(input_image, num_frames, height, width) else: image_emb = {} # Extra input extra_input = self.prepare_extra_input(latents) # TeaCache tea_cache_posi = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} tea_cache_nega = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} # Denoise self.load_models_to_device(["dit"]) for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) # Inference noise_pred_posi = model_fn_wan_video(self.dit, latents, timestep=timestep, cam_emb=cam_emb, **prompt_emb_posi, **image_emb, **extra_input, **tea_cache_posi) if cfg_scale != 1.0: noise_pred_nega = model_fn_wan_video(self.dit, latents, timestep=timestep, cam_emb=cam_emb, **prompt_emb_nega, **image_emb, **extra_input, **tea_cache_nega) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) else: noise_pred = noise_pred_posi # Scheduler latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) # Decode self.load_models_to_device(['vae']) frames1 = self.decode_video(latents[0:1], **tiler_kwargs) self.load_models_to_device([]) frames1 = self.tensor2video(frames1[0]) frames2 = self.decode_video(latents[1:2], **tiler_kwargs) self.load_models_to_device([]) frames2 = self.tensor2video(frames2[0]) return frames1, frames2 class TeaCache: def __init__(self, num_inference_steps, rel_l1_thresh, model_id): self.num_inference_steps = num_inference_steps self.step = 0 self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = None self.rel_l1_thresh = rel_l1_thresh self.previous_residual = None self.previous_hidden_states = None self.coefficients_dict = { "Wan2.1-T2V-1.3B": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02], "Wan2.1-T2V-14B": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01], "Wan2.1-I2V-14B-480P": [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01], "Wan2.1-I2V-14B-720P": [ 8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02], } if model_id not in self.coefficients_dict: supported_model_ids = ", ".join([i for i in self.coefficients_dict]) raise ValueError(f"{model_id} is not a supported TeaCache model id. Please choose a valid model id in ({supported_model_ids}).") self.coefficients = self.coefficients_dict[model_id] def check(self, dit: WanModel, x, t_mod): modulated_inp = t_mod.clone() if self.step == 0 or self.step == self.num_inference_steps - 1: should_calc = True self.accumulated_rel_l1_distance = 0 else: coefficients = self.coefficients rescale_func = np.poly1d(coefficients) self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) if self.accumulated_rel_l1_distance < self.rel_l1_thresh: should_calc = False else: should_calc = True self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = modulated_inp self.step += 1 if self.step == self.num_inference_steps: self.step = 0 if should_calc: self.previous_hidden_states = x.clone() return not should_calc def store(self, hidden_states): self.previous_residual = hidden_states - self.previous_hidden_states self.previous_hidden_states = None def update(self, hidden_states): hidden_states = hidden_states + self.previous_residual return hidden_states def model_fn_wan_video( dit: WanModel, x: torch.Tensor, timestep: torch.Tensor, cam_emb: torch.Tensor, context: torch.Tensor, clip_feature: Optional[torch.Tensor] = None, y: Optional[torch.Tensor] = None, tea_cache: TeaCache = None, **kwargs, ): t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep)) t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim)) context = dit.text_embedding(context) if dit.has_image_input: x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) clip_embdding = dit.img_emb(clip_feature) context = torch.cat([clip_embdding, context], dim=1) x, (f, h, w) = dit.patchify(x) freqs = torch.cat([ dit.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), dit.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), dit.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) v = 2 freqs_mvs = torch.cat([ dit.freqs[0][:v].view(v, 1, 1, -1).expand(v, h, w, -1), dit.freqs[1][:h].view(1, h, 1, -1).expand(v, h, w, -1), dit.freqs[2][:w].view(1, 1, w, -1).expand(v, h, w, -1) ], dim=-1).reshape(v * h * w, 1, -1).to(x.device) # TeaCache tea_cache=None if tea_cache is not None: tea_cache_update = tea_cache.check(dit, x, t_mod) else: tea_cache_update = False if tea_cache_update: x = tea_cache.update(x) else: # blocks for block in dit.blocks: x = block(x, context, cam_emb, t_mod, freqs, freqs_mvs) if tea_cache is not None: tea_cache.store(x) x = dit.head(x, t) x = dit.unpatchify(x, (f, h, w)) return x ================================================ FILE: diffsynth/processors/FastBlend.py ================================================ from PIL import Image import cupy as cp import numpy as np from tqdm import tqdm from ..extensions.FastBlend.patch_match import PyramidPatchMatcher from ..extensions.FastBlend.runners.fast import TableManager from .base import VideoProcessor class FastBlendSmoother(VideoProcessor): def __init__( self, inference_mode="fast", batch_size=8, window_size=60, minimum_patch_size=5, threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, initialize="identity", tracking_window_size=0 ): self.inference_mode = inference_mode self.batch_size = batch_size self.window_size = window_size self.ebsynth_config = { "minimum_patch_size": minimum_patch_size, "threads_per_block": threads_per_block, "num_iter": num_iter, "gpu_id": gpu_id, "guide_weight": guide_weight, "initialize": initialize, "tracking_window_size": tracking_window_size } @staticmethod def from_model_manager(model_manager, **kwargs): # TODO: fetch GPU ID from model_manager return FastBlendSmoother(**kwargs) def inference_fast(self, frames_guide, frames_style): table_manager = TableManager() patch_match_engine = PyramidPatchMatcher( image_height=frames_style[0].shape[0], image_width=frames_style[0].shape[1], channel=3, **self.ebsynth_config ) # left part table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, self.batch_size, desc="Fast Mode Step 1/4") table_l = table_manager.remapping_table_to_blending_table(table_l) table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, self.window_size, self.batch_size, desc="Fast Mode Step 2/4") # right part table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, self.batch_size, desc="Fast Mode Step 3/4") table_r = table_manager.remapping_table_to_blending_table(table_r) table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, self.window_size, self.batch_size, desc="Fast Mode Step 4/4")[::-1] # merge frames = [] for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r): weight_m = -1 weight = weight_l + weight_m + weight_r frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight) frames.append(frame) frames = [frame.clip(0, 255).astype("uint8") for frame in frames] frames = [Image.fromarray(frame) for frame in frames] return frames def inference_balanced(self, frames_guide, frames_style): patch_match_engine = PyramidPatchMatcher( image_height=frames_style[0].shape[0], image_width=frames_style[0].shape[1], channel=3, **self.ebsynth_config ) output_frames = [] # tasks n = len(frames_style) tasks = [] for target in range(n): for source in range(target - self.window_size, target + self.window_size + 1): if source >= 0 and source < n and source != target: tasks.append((source, target)) # run frames = [(None, 1) for i in range(n)] for batch_id in tqdm(range(0, len(tasks), self.batch_size), desc="Balanced Mode"): tasks_batch = tasks[batch_id: min(batch_id+self.batch_size, len(tasks))] source_guide = np.stack([frames_guide[source] for source, target in tasks_batch]) target_guide = np.stack([frames_guide[target] for source, target in tasks_batch]) source_style = np.stack([frames_style[source] for source, target in tasks_batch]) _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) for (source, target), result in zip(tasks_batch, target_style): frame, weight = frames[target] if frame is None: frame = frames_style[target] frames[target] = ( frame * (weight / (weight + 1)) + result / (weight + 1), weight + 1 ) if weight + 1 == min(n, target + self.window_size + 1) - max(0, target - self.window_size): frame = frame.clip(0, 255).astype("uint8") output_frames.append(Image.fromarray(frame)) frames[target] = (None, 1) return output_frames def inference_accurate(self, frames_guide, frames_style): patch_match_engine = PyramidPatchMatcher( image_height=frames_style[0].shape[0], image_width=frames_style[0].shape[1], channel=3, use_mean_target_style=True, **self.ebsynth_config ) output_frames = [] # run n = len(frames_style) for target in tqdm(range(n), desc="Accurate Mode"): l, r = max(target - self.window_size, 0), min(target + self.window_size + 1, n) remapped_frames = [] for i in range(l, r, self.batch_size): j = min(i + self.batch_size, r) source_guide = np.stack([frames_guide[source] for source in range(i, j)]) target_guide = np.stack([frames_guide[target]] * (j - i)) source_style = np.stack([frames_style[source] for source in range(i, j)]) _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) remapped_frames.append(target_style) frame = np.concatenate(remapped_frames, axis=0).mean(axis=0) frame = frame.clip(0, 255).astype("uint8") output_frames.append(Image.fromarray(frame)) return output_frames def release_vram(self): mempool = cp.get_default_memory_pool() pinned_mempool = cp.get_default_pinned_memory_pool() mempool.free_all_blocks() pinned_mempool.free_all_blocks() def __call__(self, rendered_frames, original_frames=None, **kwargs): rendered_frames = [np.array(frame) for frame in rendered_frames] original_frames = [np.array(frame) for frame in original_frames] if self.inference_mode == "fast": output_frames = self.inference_fast(original_frames, rendered_frames) elif self.inference_mode == "balanced": output_frames = self.inference_balanced(original_frames, rendered_frames) elif self.inference_mode == "accurate": output_frames = self.inference_accurate(original_frames, rendered_frames) else: raise ValueError("inference_mode must be fast, balanced or accurate") self.release_vram() return output_frames ================================================ FILE: diffsynth/processors/PILEditor.py ================================================ from PIL import ImageEnhance from .base import VideoProcessor class ContrastEditor(VideoProcessor): def __init__(self, rate=1.5): self.rate = rate @staticmethod def from_model_manager(model_manager, **kwargs): return ContrastEditor(**kwargs) def __call__(self, rendered_frames, **kwargs): rendered_frames = [ImageEnhance.Contrast(i).enhance(self.rate) for i in rendered_frames] return rendered_frames class SharpnessEditor(VideoProcessor): def __init__(self, rate=1.5): self.rate = rate @staticmethod def from_model_manager(model_manager, **kwargs): return SharpnessEditor(**kwargs) def __call__(self, rendered_frames, **kwargs): rendered_frames = [ImageEnhance.Sharpness(i).enhance(self.rate) for i in rendered_frames] return rendered_frames ================================================ FILE: diffsynth/processors/RIFE.py ================================================ import torch import numpy as np from PIL import Image from .base import VideoProcessor class RIFESmoother(VideoProcessor): def __init__(self, model, device="cuda", scale=1.0, batch_size=4, interpolate=True): self.model = model self.device = device # IFNet only does not support float16 self.torch_dtype = torch.float32 # Other parameters self.scale = scale self.batch_size = batch_size self.interpolate = interpolate @staticmethod def from_model_manager(model_manager, **kwargs): return RIFESmoother(model_manager.RIFE, device=model_manager.device, **kwargs) def process_image(self, image): width, height = image.size if width % 32 != 0 or height % 32 != 0: width = (width + 31) // 32 height = (height + 31) // 32 image = image.resize((width, height)) image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1) return image def process_images(self, images): images = [self.process_image(image) for image in images] images = torch.stack(images) return images def decode_images(self, images): images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) images = [Image.fromarray(image) for image in images] return images def process_tensors(self, input_tensor, scale=1.0, batch_size=4): output_tensor = [] for batch_id in range(0, input_tensor.shape[0], batch_size): batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) batch_input_tensor = input_tensor[batch_id: batch_id_] batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) output_tensor.append(merged[2].cpu()) output_tensor = torch.concat(output_tensor, dim=0) return output_tensor @torch.no_grad() def __call__(self, rendered_frames, **kwargs): # Preprocess processed_images = self.process_images(rendered_frames) # Input input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1) # Interpolate output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size) if self.interpolate: # Blend input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1) output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size) processed_images[1:-1] = output_tensor else: processed_images[1:-1] = (processed_images[1:-1] + output_tensor) / 2 # To images output_images = self.decode_images(processed_images) if output_images[0].size != rendered_frames[0].size: output_images = [image.resize(rendered_frames[0].size) for image in output_images] return output_images ================================================ FILE: diffsynth/processors/__init__.py ================================================ ================================================ FILE: diffsynth/processors/base.py ================================================ class VideoProcessor: def __init__(self): pass def __call__(self): raise NotImplementedError ================================================ FILE: diffsynth/processors/sequencial_processor.py ================================================ from .base import VideoProcessor class AutoVideoProcessor(VideoProcessor): def __init__(self): pass @staticmethod def from_model_manager(model_manager, processor_type, **kwargs): if processor_type == "FastBlend": from .FastBlend import FastBlendSmoother return FastBlendSmoother.from_model_manager(model_manager, **kwargs) elif processor_type == "Contrast": from .PILEditor import ContrastEditor return ContrastEditor.from_model_manager(model_manager, **kwargs) elif processor_type == "Sharpness": from .PILEditor import SharpnessEditor return SharpnessEditor.from_model_manager(model_manager, **kwargs) elif processor_type == "RIFE": from .RIFE import RIFESmoother return RIFESmoother.from_model_manager(model_manager, **kwargs) else: raise ValueError(f"invalid processor_type: {processor_type}") class SequencialProcessor(VideoProcessor): def __init__(self, processors=[]): self.processors = processors @staticmethod def from_model_manager(model_manager, configs): processors = [ AutoVideoProcessor.from_model_manager(model_manager, config["processor_type"], **config["config"]) for config in configs ] return SequencialProcessor(processors) def __call__(self, rendered_frames, **kwargs): for processor in self.processors: rendered_frames = processor(rendered_frames, **kwargs) return rendered_frames ================================================ FILE: diffsynth/prompters/__init__.py ================================================ from .prompt_refiners import Translator, BeautifulPrompt, QwenPrompt from .sd_prompter import SDPrompter from .sdxl_prompter import SDXLPrompter from .sd3_prompter import SD3Prompter from .hunyuan_dit_prompter import HunyuanDiTPrompter from .kolors_prompter import KolorsPrompter from .flux_prompter import FluxPrompter from .omost import OmostPromter from .cog_prompter import CogPrompter from .hunyuan_video_prompter import HunyuanVideoPrompter from .stepvideo_prompter import StepVideoPrompter from .wan_prompter import WanPrompter ================================================ FILE: diffsynth/prompters/base_prompter.py ================================================ from ..models.model_manager import ModelManager import torch def tokenize_long_prompt(tokenizer, prompt, max_length=None): # Get model_max_length from self.tokenizer length = tokenizer.model_max_length if max_length is None else max_length # To avoid the warning. set self.tokenizer.model_max_length to +oo. tokenizer.model_max_length = 99999999 # Tokenize it! input_ids = tokenizer(prompt, return_tensors="pt").input_ids # Determine the real length. max_length = (input_ids.shape[1] + length - 1) // length * length # Restore tokenizer.model_max_length tokenizer.model_max_length = length # Tokenize it again with fixed length. input_ids = tokenizer( prompt, return_tensors="pt", padding="max_length", max_length=max_length, truncation=True ).input_ids # Reshape input_ids to fit the text encoder. num_sentence = input_ids.shape[1] // length input_ids = input_ids.reshape((num_sentence, length)) return input_ids class BasePrompter: def __init__(self): self.refiners = [] self.extenders = [] def load_prompt_refiners(self, model_manager: ModelManager, refiner_classes=[]): for refiner_class in refiner_classes: refiner = refiner_class.from_model_manager(model_manager) self.refiners.append(refiner) def load_prompt_extenders(self,model_manager:ModelManager,extender_classes=[]): for extender_class in extender_classes: extender = extender_class.from_model_manager(model_manager) self.extenders.append(extender) @torch.no_grad() def process_prompt(self, prompt, positive=True): if isinstance(prompt, list): prompt = [self.process_prompt(prompt_, positive=positive) for prompt_ in prompt] else: for refiner in self.refiners: prompt = refiner(prompt, positive=positive) return prompt @torch.no_grad() def extend_prompt(self, prompt:str, positive=True): extended_prompt = dict(prompt=prompt) for extender in self.extenders: extended_prompt = extender(extended_prompt) return extended_prompt ================================================ FILE: diffsynth/prompters/cog_prompter.py ================================================ from .base_prompter import BasePrompter from ..models.flux_text_encoder import FluxTextEncoder2 from transformers import T5TokenizerFast import os class CogPrompter(BasePrompter): def __init__( self, tokenizer_path=None ): if tokenizer_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_path = os.path.join(base_path, "tokenizer_configs/cog/tokenizer") super().__init__() self.tokenizer = T5TokenizerFast.from_pretrained(tokenizer_path) self.text_encoder: FluxTextEncoder2 = None def fetch_models(self, text_encoder: FluxTextEncoder2 = None): self.text_encoder = text_encoder def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): input_ids = tokenizer( prompt, return_tensors="pt", padding="max_length", max_length=max_length, truncation=True, ).input_ids.to(device) prompt_emb = text_encoder(input_ids) prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) return prompt_emb def encode_prompt( self, prompt, positive=True, device="cuda" ): prompt = self.process_prompt(prompt, positive=positive) prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder, self.tokenizer, 226, device) return prompt_emb ================================================ FILE: diffsynth/prompters/flux_prompter.py ================================================ from .base_prompter import BasePrompter from ..models.flux_text_encoder import FluxTextEncoder2 from ..models.sd3_text_encoder import SD3TextEncoder1 from transformers import CLIPTokenizer, T5TokenizerFast import os, torch class FluxPrompter(BasePrompter): def __init__( self, tokenizer_1_path=None, tokenizer_2_path=None ): if tokenizer_1_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_1") if tokenizer_2_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_2") super().__init__() self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) self.tokenizer_2 = T5TokenizerFast.from_pretrained(tokenizer_2_path) self.text_encoder_1: SD3TextEncoder1 = None self.text_encoder_2: FluxTextEncoder2 = None def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: FluxTextEncoder2 = None): self.text_encoder_1 = text_encoder_1 self.text_encoder_2 = text_encoder_2 def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device): input_ids = tokenizer( prompt, return_tensors="pt", padding="max_length", max_length=max_length, truncation=True ).input_ids.to(device) pooled_prompt_emb, _ = text_encoder(input_ids) return pooled_prompt_emb def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): input_ids = tokenizer( prompt, return_tensors="pt", padding="max_length", max_length=max_length, truncation=True, ).input_ids.to(device) prompt_emb = text_encoder(input_ids) return prompt_emb def encode_prompt( self, prompt, positive=True, device="cuda", t5_sequence_length=512, ): prompt = self.process_prompt(prompt, positive=positive) # CLIP pooled_prompt_emb = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) # T5 prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder_2, self.tokenizer_2, t5_sequence_length, device) # text_ids text_ids = torch.zeros(prompt_emb.shape[0], prompt_emb.shape[1], 3).to(device=device, dtype=prompt_emb.dtype) return prompt_emb, pooled_prompt_emb, text_ids ================================================ FILE: diffsynth/prompters/hunyuan_dit_prompter.py ================================================ from .base_prompter import BasePrompter from ..models.model_manager import ModelManager from ..models import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder from transformers import BertTokenizer, AutoTokenizer import warnings, os class HunyuanDiTPrompter(BasePrompter): def __init__( self, tokenizer_path=None, tokenizer_t5_path=None ): if tokenizer_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer") if tokenizer_t5_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_t5_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer_t5") super().__init__() self.tokenizer = BertTokenizer.from_pretrained(tokenizer_path) with warnings.catch_warnings(): warnings.simplefilter("ignore") self.tokenizer_t5 = AutoTokenizer.from_pretrained(tokenizer_t5_path) self.text_encoder: HunyuanDiTCLIPTextEncoder = None self.text_encoder_t5: HunyuanDiTT5TextEncoder = None def fetch_models(self, text_encoder: HunyuanDiTCLIPTextEncoder = None, text_encoder_t5: HunyuanDiTT5TextEncoder = None): self.text_encoder = text_encoder self.text_encoder_t5 = text_encoder_t5 def encode_prompt_using_signle_model(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): text_inputs = tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids attention_mask = text_inputs.attention_mask.to(device) prompt_embeds = text_encoder( text_input_ids.to(device), attention_mask=attention_mask, clip_skip=clip_skip ) return prompt_embeds, attention_mask def encode_prompt( self, prompt, clip_skip=1, clip_skip_2=1, positive=True, device="cuda" ): prompt = self.process_prompt(prompt, positive=positive) # CLIP prompt_emb, attention_mask = self.encode_prompt_using_signle_model(prompt, self.text_encoder, self.tokenizer, self.tokenizer.model_max_length, clip_skip, device) # T5 prompt_emb_t5, attention_mask_t5 = self.encode_prompt_using_signle_model(prompt, self.text_encoder_t5, self.tokenizer_t5, self.tokenizer_t5.model_max_length, clip_skip_2, device) return prompt_emb, attention_mask, prompt_emb_t5, attention_mask_t5 ================================================ FILE: diffsynth/prompters/hunyuan_video_prompter.py ================================================ from .base_prompter import BasePrompter from ..models.sd3_text_encoder import SD3TextEncoder1 from ..models.hunyuan_video_text_encoder import HunyuanVideoLLMEncoder, HunyuanVideoMLLMEncoder from transformers import CLIPTokenizer, LlamaTokenizerFast, CLIPImageProcessor import os, torch from typing import Union PROMPT_TEMPLATE_ENCODE = ( "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, " "quantity, text, spatial relationships of the objects and background:<|eot_id|>" "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>") PROMPT_TEMPLATE_ENCODE_VIDEO = ( "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: " "1. The main content and theme of the video." "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." "4. background environment, light, style and atmosphere." "5. camera angles, movements, and transitions used in the video:<|eot_id|>" "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>") PROMPT_TEMPLATE_ENCODE_I2V = ( "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the image by detailing the color, shape, size, texture, " "quantity, text, spatial relationships of the objects and background:<|eot_id|>" "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" "<|start_header_id|>assistant<|end_header_id|>\n\n" ) PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = ( "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " "1. The main content and theme of the video." "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." "4. background environment, light, style and atmosphere." "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n" "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" "<|start_header_id|>assistant<|end_header_id|>\n\n" ) PROMPT_TEMPLATE = { "dit-llm-encode": { "template": PROMPT_TEMPLATE_ENCODE, "crop_start": 36, }, "dit-llm-encode-video": { "template": PROMPT_TEMPLATE_ENCODE_VIDEO, "crop_start": 95, }, "dit-llm-encode-i2v": { "template": PROMPT_TEMPLATE_ENCODE_I2V, "crop_start": 36, "image_emb_start": 5, "image_emb_end": 581, "image_emb_len": 576, "double_return_token_id": 271 }, "dit-llm-encode-video-i2v": { "template": PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, "crop_start": 103, "image_emb_start": 5, "image_emb_end": 581, "image_emb_len": 576, "double_return_token_id": 271 }, } NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion" class HunyuanVideoPrompter(BasePrompter): def __init__( self, tokenizer_1_path=None, tokenizer_2_path=None, ): if tokenizer_1_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_1_path = os.path.join( base_path, "tokenizer_configs/hunyuan_video/tokenizer_1") if tokenizer_2_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_2_path = os.path.join( base_path, "tokenizer_configs/hunyuan_video/tokenizer_2") super().__init__() self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) self.tokenizer_2 = LlamaTokenizerFast.from_pretrained(tokenizer_2_path, padding_side='right') self.text_encoder_1: SD3TextEncoder1 = None self.text_encoder_2: HunyuanVideoLLMEncoder = None self.prompt_template = PROMPT_TEMPLATE['dit-llm-encode'] self.prompt_template_video = PROMPT_TEMPLATE['dit-llm-encode-video'] def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: Union[HunyuanVideoLLMEncoder, HunyuanVideoMLLMEncoder] = None): self.text_encoder_1 = text_encoder_1 self.text_encoder_2 = text_encoder_2 if isinstance(text_encoder_2, HunyuanVideoMLLMEncoder): # processor # TODO: may need to replace processor with local implementation base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/hunyuan_video/tokenizer_2") self.processor = CLIPImageProcessor.from_pretrained(tokenizer_2_path) # template self.prompt_template = PROMPT_TEMPLATE['dit-llm-encode-i2v'] self.prompt_template_video = PROMPT_TEMPLATE['dit-llm-encode-video-i2v'] def apply_text_to_template(self, text, template): assert isinstance(template, str) if isinstance(text, list): return [self.apply_text_to_template(text_) for text_ in text] elif isinstance(text, str): # Will send string to tokenizer. Used for llm return template.format(text) else: raise TypeError(f"Unsupported prompt type: {type(text)}") def encode_prompt_using_clip(self, prompt, max_length, device): tokenized_result = self.tokenizer_1( prompt, return_tensors="pt", padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True ) input_ids = tokenized_result.input_ids.to(device) attention_mask = tokenized_result.attention_mask.to(device) return self.text_encoder_1(input_ids=input_ids, extra_mask=attention_mask)[0] def encode_prompt_using_llm(self, prompt, max_length, device, crop_start, hidden_state_skip_layer=2, use_attention_mask=True): max_length += crop_start inputs = self.tokenizer_2(prompt, return_tensors="pt", padding="max_length", max_length=max_length, truncation=True) input_ids = inputs.input_ids.to(device) attention_mask = inputs.attention_mask.to(device) last_hidden_state = self.text_encoder_2(input_ids, attention_mask, hidden_state_skip_layer) # crop out if crop_start > 0: last_hidden_state = last_hidden_state[:, crop_start:] attention_mask = (attention_mask[:, crop_start:] if use_attention_mask else None) return last_hidden_state, attention_mask def encode_prompt_using_mllm(self, prompt, images, max_length, device, crop_start, hidden_state_skip_layer=2, use_attention_mask=True, image_embed_interleave=4): image_outputs = self.processor(images, return_tensors="pt")["pixel_values"].to(device) max_length += crop_start inputs = self.tokenizer_2(prompt, return_tensors="pt", padding="max_length", max_length=max_length, truncation=True) input_ids = inputs.input_ids.to(device) attention_mask = inputs.attention_mask.to(device) last_hidden_state = self.text_encoder_2(input_ids=input_ids, attention_mask=attention_mask, hidden_state_skip_layer=hidden_state_skip_layer, pixel_values=image_outputs) text_crop_start = (crop_start - 1 + self.prompt_template_video.get("image_emb_len", 576)) image_crop_start = self.prompt_template_video.get("image_emb_start", 5) image_crop_end = self.prompt_template_video.get("image_emb_end", 581) batch_indices, last_double_return_token_indices = torch.where( input_ids == self.prompt_template_video.get("double_return_token_id", 271)) if last_double_return_token_indices.shape[0] == 3: # in case the prompt is too long last_double_return_token_indices = torch.cat(( last_double_return_token_indices, torch.tensor([input_ids.shape[-1]]), )) batch_indices = torch.cat((batch_indices, torch.tensor([0]))) last_double_return_token_indices = (last_double_return_token_indices.reshape(input_ids.shape[0], -1)[:, -1]) batch_indices = batch_indices.reshape(input_ids.shape[0], -1)[:, -1] assistant_crop_start = (last_double_return_token_indices - 1 + self.prompt_template_video.get("image_emb_len", 576) - 4) assistant_crop_end = (last_double_return_token_indices - 1 + self.prompt_template_video.get("image_emb_len", 576)) attention_mask_assistant_crop_start = (last_double_return_token_indices - 4) attention_mask_assistant_crop_end = last_double_return_token_indices text_last_hidden_state = [] text_attention_mask = [] image_last_hidden_state = [] image_attention_mask = [] for i in range(input_ids.shape[0]): text_last_hidden_state.append( torch.cat([ last_hidden_state[i, text_crop_start:assistant_crop_start[i].item()], last_hidden_state[i, assistant_crop_end[i].item():], ])) text_attention_mask.append( torch.cat([ attention_mask[ i, crop_start:attention_mask_assistant_crop_start[i].item(), ], attention_mask[i, attention_mask_assistant_crop_end[i].item():], ]) if use_attention_mask else None) image_last_hidden_state.append(last_hidden_state[i, image_crop_start:image_crop_end]) image_attention_mask.append( torch.ones(image_last_hidden_state[-1].shape[0]).to(last_hidden_state.device). to(attention_mask.dtype) if use_attention_mask else None) text_last_hidden_state = torch.stack(text_last_hidden_state) text_attention_mask = torch.stack(text_attention_mask) image_last_hidden_state = torch.stack(image_last_hidden_state) image_attention_mask = torch.stack(image_attention_mask) image_last_hidden_state = image_last_hidden_state[:, ::image_embed_interleave, :] image_attention_mask = image_attention_mask[:, ::image_embed_interleave] assert (text_last_hidden_state.shape[0] == text_attention_mask.shape[0] and image_last_hidden_state.shape[0] == image_attention_mask.shape[0]) last_hidden_state = torch.cat([image_last_hidden_state, text_last_hidden_state], dim=1) attention_mask = torch.cat([image_attention_mask, text_attention_mask], dim=1) return last_hidden_state, attention_mask def encode_prompt(self, prompt, images=None, positive=True, device="cuda", clip_sequence_length=77, llm_sequence_length=256, data_type='video', use_template=True, hidden_state_skip_layer=2, use_attention_mask=True, image_embed_interleave=4): prompt = self.process_prompt(prompt, positive=positive) # apply template if use_template: template = self.prompt_template_video if data_type == 'video' else self.prompt_template prompt_formated = self.apply_text_to_template(prompt, template['template']) else: prompt_formated = prompt # Text encoder if data_type == 'video': crop_start = self.prompt_template_video.get("crop_start", 0) else: crop_start = self.prompt_template.get("crop_start", 0) # CLIP pooled_prompt_emb = self.encode_prompt_using_clip(prompt, clip_sequence_length, device) # LLM if images is None: prompt_emb, attention_mask = self.encode_prompt_using_llm(prompt_formated, llm_sequence_length, device, crop_start, hidden_state_skip_layer, use_attention_mask) else: prompt_emb, attention_mask = self.encode_prompt_using_mllm(prompt_formated, images, llm_sequence_length, device, crop_start, hidden_state_skip_layer, use_attention_mask, image_embed_interleave) return prompt_emb, pooled_prompt_emb, attention_mask ================================================ FILE: diffsynth/prompters/kolors_prompter.py ================================================ from .base_prompter import BasePrompter from ..models.model_manager import ModelManager import json, os, re from typing import List, Optional, Union, Dict from sentencepiece import SentencePieceProcessor from transformers import PreTrainedTokenizer from transformers.utils import PaddingStrategy from transformers.tokenization_utils_base import EncodedInput, BatchEncoding from ..models.kolors_text_encoder import ChatGLMModel class SPTokenizer: def __init__(self, model_path: str): # reload tokenizer assert os.path.isfile(model_path), model_path self.sp_model = SentencePieceProcessor(model_file=model_path) # BOS / EOS token IDs self.n_words: int = self.sp_model.vocab_size() self.bos_id: int = self.sp_model.bos_id() self.eos_id: int = self.sp_model.eos_id() self.pad_id: int = self.sp_model.unk_id() assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"] special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens self.special_tokens = {} self.index_special_tokens = {} for token in special_tokens: self.special_tokens[token] = self.n_words self.index_special_tokens[self.n_words] = token self.n_words += 1 self.role_special_token_expression = "|".join([re.escape(token) for token in role_special_tokens]) def tokenize(self, s: str, encode_special_tokens=False): if encode_special_tokens: last_index = 0 t = [] for match in re.finditer(self.role_special_token_expression, s): if last_index < match.start(): t.extend(self.sp_model.EncodeAsPieces(s[last_index:match.start()])) t.append(s[match.start():match.end()]) last_index = match.end() if last_index < len(s): t.extend(self.sp_model.EncodeAsPieces(s[last_index:])) return t else: return self.sp_model.EncodeAsPieces(s) def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]: assert type(s) is str t = self.sp_model.encode(s) if bos: t = [self.bos_id] + t if eos: t = t + [self.eos_id] return t def decode(self, t: List[int]) -> str: text, buffer = "", [] for token in t: if token in self.index_special_tokens: if buffer: text += self.sp_model.decode(buffer) buffer = [] text += self.index_special_tokens[token] else: buffer.append(token) if buffer: text += self.sp_model.decode(buffer) return text def decode_tokens(self, tokens: List[str]) -> str: text = self.sp_model.DecodePieces(tokens) return text def convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ if token in self.special_tokens: return self.special_tokens[token] return self.sp_model.PieceToId(token) def convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.index_special_tokens: return self.index_special_tokens[index] if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0: return "" return self.sp_model.IdToPiece(index) class ChatGLMTokenizer(PreTrainedTokenizer): vocab_files_names = {"vocab_file": "tokenizer.model"} model_input_names = ["input_ids", "attention_mask", "position_ids"] def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, encode_special_tokens=False, **kwargs): self.name = "GLMTokenizer" self.vocab_file = vocab_file self.tokenizer = SPTokenizer(vocab_file) self.special_tokens = { "": self.tokenizer.bos_id, "": self.tokenizer.eos_id, "": self.tokenizer.pad_id } self.encode_special_tokens = encode_special_tokens super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, encode_special_tokens=encode_special_tokens, **kwargs) def get_command(self, token): if token in self.special_tokens: return self.special_tokens[token] assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}" return self.tokenizer.special_tokens[token] @property def unk_token(self) -> str: return "" @property def pad_token(self) -> str: return "" @property def pad_token_id(self): return self.get_command("") @property def eos_token(self) -> str: return "" @property def eos_token_id(self): return self.get_command("") @property def vocab_size(self): return self.tokenizer.n_words def get_vocab(self): """ Returns vocab as a dict """ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, text, **kwargs): return self.tokenizer.tokenize(text, encode_special_tokens=self.encode_special_tokens) def _convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ return self.tokenizer.convert_token_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.tokenizer.convert_id_to_token(index) def convert_tokens_to_string(self, tokens: List[str]) -> str: return self.tokenizer.decode_tokens(tokens) def save_vocabulary(self, save_directory, filename_prefix=None): """ Save the vocabulary and special tokens file to a directory. Args: save_directory (`str`): The directory in which to save the vocabulary. filename_prefix (`str`, *optional*): An optional prefix to add to the named of the saved files. Returns: `Tuple(str)`: Paths to the files saved. """ if os.path.isdir(save_directory): vocab_file = os.path.join( save_directory, self.vocab_files_names["vocab_file"] ) else: vocab_file = save_directory with open(self.vocab_file, 'rb') as fin: proto_str = fin.read() with open(vocab_file, "wb") as writer: writer.write(proto_str) return (vocab_file,) def get_prefix_tokens(self): prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")] return prefix_tokens def build_single_message(self, role, metadata, message): assert role in ["system", "user", "assistant", "observation"], role role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n") message_tokens = self.tokenizer.encode(message) tokens = role_tokens + message_tokens return tokens def build_chat_input(self, query, history=None, role="user"): if history is None: history = [] input_ids = [] for item in history: content = item["content"] if item["role"] == "system" and "tools" in item: content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False) input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content)) input_ids.extend(self.build_single_message(role, "", query)) input_ids.extend([self.get_command("<|assistant|>")]) return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERT sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ prefix_tokens = self.get_prefix_tokens() token_ids_0 = prefix_tokens + token_ids_0 if token_ids_1 is not None: token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("")] return token_ids_0 def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, padding_side: Optional[str] = None, ) -> dict: """ Pad encoded inputs (on left/right and up to predefined length or max length in the batch) Args: encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability `>= 7.5` (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ # Load from model defaults assert self.padding_side == "left" required_input = encoded_inputs[self.model_input_names[0]] seq_length = len(required_input) if padding_strategy == PaddingStrategy.LONGEST: max_length = len(required_input) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length # Initialize attention mask if not present. if "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * seq_length if "position_ids" not in encoded_inputs: encoded_inputs["position_ids"] = list(range(seq_length)) if needs_to_be_padded: difference = max_length - len(required_input) if "attention_mask" in encoded_inputs: encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] if "position_ids" in encoded_inputs: encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input return encoded_inputs class KolorsPrompter(BasePrompter): def __init__( self, tokenizer_path=None ): if tokenizer_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_path = os.path.join(base_path, "tokenizer_configs/kolors/tokenizer") super().__init__() self.tokenizer = ChatGLMTokenizer.from_pretrained(tokenizer_path) self.text_encoder: ChatGLMModel = None def fetch_models(self, text_encoder: ChatGLMModel = None): self.text_encoder = text_encoder def encode_prompt_using_ChatGLM(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): text_inputs = tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ).to(device) output = text_encoder( input_ids=text_inputs['input_ids'] , attention_mask=text_inputs['attention_mask'], position_ids=text_inputs['position_ids'], output_hidden_states=True ) prompt_emb = output.hidden_states[-clip_skip].permute(1, 0, 2).clone() pooled_prompt_emb = output.hidden_states[-1][-1, :, :].clone() return prompt_emb, pooled_prompt_emb def encode_prompt( self, prompt, clip_skip=1, clip_skip_2=2, positive=True, device="cuda" ): prompt = self.process_prompt(prompt, positive=positive) prompt_emb, pooled_prompt_emb = self.encode_prompt_using_ChatGLM(prompt, self.text_encoder, self.tokenizer, 256, clip_skip_2, device) return pooled_prompt_emb, prompt_emb ================================================ FILE: diffsynth/prompters/omnigen_prompter.py ================================================ import os import re from typing import Dict, List import torch from PIL import Image from torchvision import transforms from transformers import AutoTokenizer from huggingface_hub import snapshot_download import numpy as np def crop_arr(pil_image, max_image_size): while min(*pil_image.size) >= 2 * max_image_size: pil_image = pil_image.resize( tuple(x // 2 for x in pil_image.size), resample=Image.BOX ) if max(*pil_image.size) > max_image_size: scale = max_image_size / max(*pil_image.size) pil_image = pil_image.resize( tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC ) if min(*pil_image.size) < 16: scale = 16 / min(*pil_image.size) pil_image = pil_image.resize( tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC ) arr = np.array(pil_image) crop_y1 = (arr.shape[0] % 16) // 2 crop_y2 = arr.shape[0] % 16 - crop_y1 crop_x1 = (arr.shape[1] % 16) // 2 crop_x2 = arr.shape[1] % 16 - crop_x1 arr = arr[crop_y1:arr.shape[0]-crop_y2, crop_x1:arr.shape[1]-crop_x2] return Image.fromarray(arr) class OmniGenPrompter: def __init__(self, text_tokenizer, max_image_size: int=1024): self.text_tokenizer = text_tokenizer self.max_image_size = max_image_size self.image_transform = transforms.Compose([ transforms.Lambda(lambda pil_image: crop_arr(pil_image, max_image_size)), transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True) ]) self.collator = OmniGenCollator() self.separate_collator = OmniGenSeparateCollator() @classmethod def from_pretrained(cls, model_name): if not os.path.exists(model_name): cache_folder = os.getenv('HF_HUB_CACHE') model_name = snapshot_download(repo_id=model_name, cache_dir=cache_folder, allow_patterns="*.json") text_tokenizer = AutoTokenizer.from_pretrained(model_name) return cls(text_tokenizer) def process_image(self, image): return self.image_transform(image) def process_multi_modal_prompt(self, text, input_images): text = self.add_prefix_instruction(text) if input_images is None or len(input_images) == 0: model_inputs = self.text_tokenizer(text) return {"input_ids": model_inputs.input_ids, "pixel_values": None, "image_sizes": None} pattern = r"<\|image_\d+\|>" prompt_chunks = [self.text_tokenizer(chunk).input_ids for chunk in re.split(pattern, text)] for i in range(1, len(prompt_chunks)): if prompt_chunks[i][0] == 1: prompt_chunks[i] = prompt_chunks[i][1:] image_tags = re.findall(pattern, text) image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags] unique_image_ids = sorted(list(set(image_ids))) assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}" # total images must be the same as the number of image tags assert len(unique_image_ids) == len(input_images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(input_images)} images" input_images = [input_images[x-1] for x in image_ids] all_input_ids = [] img_inx = [] idx = 0 for i in range(len(prompt_chunks)): all_input_ids.extend(prompt_chunks[i]) if i != len(prompt_chunks) -1: start_inx = len(all_input_ids) size = input_images[i].size(-2) * input_images[i].size(-1) // 16 // 16 img_inx.append([start_inx, start_inx+size]) all_input_ids.extend([0]*size) return {"input_ids": all_input_ids, "pixel_values": input_images, "image_sizes": img_inx} def add_prefix_instruction(self, prompt): user_prompt = '<|user|>\n' generation_prompt = 'Generate an image according to the following instructions\n' assistant_prompt = '<|assistant|>\n<|diffusion|>' prompt_suffix = "<|end|>\n" prompt = f"{user_prompt}{generation_prompt}{prompt}{prompt_suffix}{assistant_prompt}" return prompt def __call__(self, instructions: List[str], input_images: List[List[str]] = None, height: int = 1024, width: int = 1024, negative_prompt: str = "low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers.", use_img_cfg: bool = True, separate_cfg_input: bool = False, use_input_image_size_as_output: bool=False, ) -> Dict: if input_images is None: use_img_cfg = False if isinstance(instructions, str): instructions = [instructions] input_images = [input_images] input_data = [] for i in range(len(instructions)): cur_instruction = instructions[i] cur_input_images = None if input_images is None else input_images[i] if cur_input_images is not None and len(cur_input_images) > 0: cur_input_images = [self.process_image(x) for x in cur_input_images] else: cur_input_images = None assert "<|image_1|>" not in cur_instruction mllm_input = self.process_multi_modal_prompt(cur_instruction, cur_input_images) neg_mllm_input, img_cfg_mllm_input = None, None neg_mllm_input = self.process_multi_modal_prompt(negative_prompt, None) if use_img_cfg: if cur_input_images is not None and len(cur_input_images) >= 1: img_cfg_prompt = [f"<|image_{i+1}|>" for i in range(len(cur_input_images))] img_cfg_mllm_input = self.process_multi_modal_prompt(" ".join(img_cfg_prompt), cur_input_images) else: img_cfg_mllm_input = neg_mllm_input if use_input_image_size_as_output: input_data.append((mllm_input, neg_mllm_input, img_cfg_mllm_input, [mllm_input['pixel_values'][0].size(-2), mllm_input['pixel_values'][0].size(-1)])) else: input_data.append((mllm_input, neg_mllm_input, img_cfg_mllm_input, [height, width])) if separate_cfg_input: return self.separate_collator(input_data) return self.collator(input_data) class OmniGenCollator: def __init__(self, pad_token_id=2, hidden_size=3072): self.pad_token_id = pad_token_id self.hidden_size = hidden_size def create_position(self, attention_mask, num_tokens_for_output_images): position_ids = [] text_length = attention_mask.size(-1) img_length = max(num_tokens_for_output_images) for mask in attention_mask: temp_l = torch.sum(mask) temp_position = [0]*(text_length-temp_l) + [i for i in range(temp_l+img_length+1)] # we add a time embedding into the sequence, so add one more token position_ids.append(temp_position) return torch.LongTensor(position_ids) def create_mask(self, attention_mask, num_tokens_for_output_images): extended_mask = [] padding_images = [] text_length = attention_mask.size(-1) img_length = max(num_tokens_for_output_images) seq_len = text_length + img_length + 1 # we add a time embedding into the sequence, so add one more token inx = 0 for mask in attention_mask: temp_l = torch.sum(mask) pad_l = text_length - temp_l temp_mask = torch.tril(torch.ones(size=(temp_l+1, temp_l+1))) image_mask = torch.zeros(size=(temp_l+1, img_length)) temp_mask = torch.cat([temp_mask, image_mask], dim=-1) image_mask = torch.ones(size=(img_length, temp_l+img_length+1)) temp_mask = torch.cat([temp_mask, image_mask], dim=0) if pad_l > 0: pad_mask = torch.zeros(size=(temp_l+1+img_length, pad_l)) temp_mask = torch.cat([pad_mask, temp_mask], dim=-1) pad_mask = torch.ones(size=(pad_l, seq_len)) temp_mask = torch.cat([pad_mask, temp_mask], dim=0) true_img_length = num_tokens_for_output_images[inx] pad_img_length = img_length - true_img_length if pad_img_length > 0: temp_mask[:, -pad_img_length:] = 0 temp_padding_imgs = torch.zeros(size=(1, pad_img_length, self.hidden_size)) else: temp_padding_imgs = None extended_mask.append(temp_mask.unsqueeze(0)) padding_images.append(temp_padding_imgs) inx += 1 return torch.cat(extended_mask, dim=0), padding_images def adjust_attention_for_input_images(self, attention_mask, image_sizes): for b_inx in image_sizes.keys(): for start_inx, end_inx in image_sizes[b_inx]: attention_mask[b_inx][start_inx:end_inx, start_inx:end_inx] = 1 return attention_mask def pad_input_ids(self, input_ids, image_sizes): max_l = max([len(x) for x in input_ids]) padded_ids = [] attention_mask = [] new_image_sizes = [] for i in range(len(input_ids)): temp_ids = input_ids[i] temp_l = len(temp_ids) pad_l = max_l - temp_l if pad_l == 0: attention_mask.append([1]*max_l) padded_ids.append(temp_ids) else: attention_mask.append([0]*pad_l+[1]*temp_l) padded_ids.append([self.pad_token_id]*pad_l+temp_ids) if i in image_sizes: new_inx = [] for old_inx in image_sizes[i]: new_inx.append([x+pad_l for x in old_inx]) image_sizes[i] = new_inx return torch.LongTensor(padded_ids), torch.LongTensor(attention_mask), image_sizes def process_mllm_input(self, mllm_inputs, target_img_size): num_tokens_for_output_images = [] for img_size in target_img_size: num_tokens_for_output_images.append(img_size[0]*img_size[1]//16//16) pixel_values, image_sizes = [], {} b_inx = 0 for x in mllm_inputs: if x['pixel_values'] is not None: pixel_values.extend(x['pixel_values']) for size in x['image_sizes']: if b_inx not in image_sizes: image_sizes[b_inx] = [size] else: image_sizes[b_inx].append(size) b_inx += 1 pixel_values = [x.unsqueeze(0) for x in pixel_values] input_ids = [x['input_ids'] for x in mllm_inputs] padded_input_ids, attention_mask, image_sizes = self.pad_input_ids(input_ids, image_sizes) position_ids = self.create_position(attention_mask, num_tokens_for_output_images) attention_mask, padding_images = self.create_mask(attention_mask, num_tokens_for_output_images) attention_mask = self.adjust_attention_for_input_images(attention_mask, image_sizes) return padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes def __call__(self, features): mllm_inputs = [f[0] for f in features] cfg_mllm_inputs = [f[1] for f in features] img_cfg_mllm_input = [f[2] for f in features] target_img_size = [f[3] for f in features] if img_cfg_mllm_input[0] is not None: mllm_inputs = mllm_inputs + cfg_mllm_inputs + img_cfg_mllm_input target_img_size = target_img_size + target_img_size + target_img_size else: mllm_inputs = mllm_inputs + cfg_mllm_inputs target_img_size = target_img_size + target_img_size all_padded_input_ids, all_position_ids, all_attention_mask, all_padding_images, all_pixel_values, all_image_sizes = self.process_mllm_input(mllm_inputs, target_img_size) data = {"input_ids": all_padded_input_ids, "attention_mask": all_attention_mask, "position_ids": all_position_ids, "input_pixel_values": all_pixel_values, "input_image_sizes": all_image_sizes, "padding_images": all_padding_images, } return data class OmniGenSeparateCollator(OmniGenCollator): def __call__(self, features): mllm_inputs = [f[0] for f in features] cfg_mllm_inputs = [f[1] for f in features] img_cfg_mllm_input = [f[2] for f in features] target_img_size = [f[3] for f in features] all_padded_input_ids, all_attention_mask, all_position_ids, all_pixel_values, all_image_sizes, all_padding_images = [], [], [], [], [], [] padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(mllm_inputs, target_img_size) all_padded_input_ids.append(padded_input_ids) all_attention_mask.append(attention_mask) all_position_ids.append(position_ids) all_pixel_values.append(pixel_values) all_image_sizes.append(image_sizes) all_padding_images.append(padding_images) if cfg_mllm_inputs[0] is not None: padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(cfg_mllm_inputs, target_img_size) all_padded_input_ids.append(padded_input_ids) all_attention_mask.append(attention_mask) all_position_ids.append(position_ids) all_pixel_values.append(pixel_values) all_image_sizes.append(image_sizes) all_padding_images.append(padding_images) if img_cfg_mllm_input[0] is not None: padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(img_cfg_mllm_input, target_img_size) all_padded_input_ids.append(padded_input_ids) all_attention_mask.append(attention_mask) all_position_ids.append(position_ids) all_pixel_values.append(pixel_values) all_image_sizes.append(image_sizes) all_padding_images.append(padding_images) data = {"input_ids": all_padded_input_ids, "attention_mask": all_attention_mask, "position_ids": all_position_ids, "input_pixel_values": all_pixel_values, "input_image_sizes": all_image_sizes, "padding_images": all_padding_images, } return data ================================================ FILE: diffsynth/prompters/omost.py ================================================ from transformers import AutoTokenizer, TextIteratorStreamer import difflib import torch import numpy as np import re from ..models.model_manager import ModelManager from PIL import Image valid_colors = { # r, g, b 'aliceblue': (240, 248, 255), 'antiquewhite': (250, 235, 215), 'aqua': (0, 255, 255), 'aquamarine': (127, 255, 212), 'azure': (240, 255, 255), 'beige': (245, 245, 220), 'bisque': (255, 228, 196), 'black': (0, 0, 0), 'blanchedalmond': (255, 235, 205), 'blue': (0, 0, 255), 'blueviolet': (138, 43, 226), 'brown': (165, 42, 42), 'burlywood': (222, 184, 135), 'cadetblue': (95, 158, 160), 'chartreuse': (127, 255, 0), 'chocolate': (210, 105, 30), 'coral': (255, 127, 80), 'cornflowerblue': (100, 149, 237), 'cornsilk': (255, 248, 220), 'crimson': (220, 20, 60), 'cyan': (0, 255, 255), 'darkblue': (0, 0, 139), 'darkcyan': (0, 139, 139), 'darkgoldenrod': (184, 134, 11), 'darkgray': (169, 169, 169), 'darkgrey': (169, 169, 169), 'darkgreen': (0, 100, 0), 'darkkhaki': (189, 183, 107), 'darkmagenta': (139, 0, 139), 'darkolivegreen': (85, 107, 47), 'darkorange': (255, 140, 0), 'darkorchid': (153, 50, 204), 'darkred': (139, 0, 0), 'darksalmon': (233, 150, 122), 'darkseagreen': (143, 188, 143), 'darkslateblue': (72, 61, 139), 'darkslategray': (47, 79, 79), 'darkslategrey': (47, 79, 79), 'darkturquoise': (0, 206, 209), 'darkviolet': (148, 0, 211), 'deeppink': (255, 20, 147), 'deepskyblue': (0, 191, 255), 'dimgray': (105, 105, 105), 'dimgrey': (105, 105, 105), 'dodgerblue': (30, 144, 255), 'firebrick': (178, 34, 34), 'floralwhite': (255, 250, 240), 'forestgreen': (34, 139, 34), 'fuchsia': (255, 0, 255), 'gainsboro': (220, 220, 220), 'ghostwhite': (248, 248, 255), 'gold': (255, 215, 0), 'goldenrod': (218, 165, 32), 'gray': (128, 128, 128), 'grey': (128, 128, 128), 'green': (0, 128, 0), 'greenyellow': (173, 255, 47), 'honeydew': (240, 255, 240), 'hotpink': (255, 105, 180), 'indianred': (205, 92, 92), 'indigo': (75, 0, 130), 'ivory': (255, 255, 240), 'khaki': (240, 230, 140), 'lavender': (230, 230, 250), 'lavenderblush': (255, 240, 245), 'lawngreen': (124, 252, 0), 'lemonchiffon': (255, 250, 205), 'lightblue': (173, 216, 230), 'lightcoral': (240, 128, 128), 'lightcyan': (224, 255, 255), 'lightgoldenrodyellow': (250, 250, 210), 'lightgray': (211, 211, 211), 'lightgrey': (211, 211, 211), 'lightgreen': (144, 238, 144), 'lightpink': (255, 182, 193), 'lightsalmon': (255, 160, 122), 'lightseagreen': (32, 178, 170), 'lightskyblue': (135, 206, 250), 'lightslategray': (119, 136, 153), 'lightslategrey': (119, 136, 153), 'lightsteelblue': (176, 196, 222), 'lightyellow': (255, 255, 224), 'lime': (0, 255, 0), 'limegreen': (50, 205, 50), 'linen': (250, 240, 230), 'magenta': (255, 0, 255), 'maroon': (128, 0, 0), 'mediumaquamarine': (102, 205, 170), 'mediumblue': (0, 0, 205), 'mediumorchid': (186, 85, 211), 'mediumpurple': (147, 112, 219), 'mediumseagreen': (60, 179, 113), 'mediumslateblue': (123, 104, 238), 'mediumspringgreen': (0, 250, 154), 'mediumturquoise': (72, 209, 204), 'mediumvioletred': (199, 21, 133), 'midnightblue': (25, 25, 112), 'mintcream': (245, 255, 250), 'mistyrose': (255, 228, 225), 'moccasin': (255, 228, 181), 'navajowhite': (255, 222, 173), 'navy': (0, 0, 128), 'navyblue': (0, 0, 128), 'oldlace': (253, 245, 230), 'olive': (128, 128, 0), 'olivedrab': (107, 142, 35), 'orange': (255, 165, 0), 'orangered': (255, 69, 0), 'orchid': (218, 112, 214), 'palegoldenrod': (238, 232, 170), 'palegreen': (152, 251, 152), 'paleturquoise': (175, 238, 238), 'palevioletred': (219, 112, 147), 'papayawhip': (255, 239, 213), 'peachpuff': (255, 218, 185), 'peru': (205, 133, 63), 'pink': (255, 192, 203), 'plum': (221, 160, 221), 'powderblue': (176, 224, 230), 'purple': (128, 0, 128), 'rebeccapurple': (102, 51, 153), 'red': (255, 0, 0), 'rosybrown': (188, 143, 143), 'royalblue': (65, 105, 225), 'saddlebrown': (139, 69, 19), 'salmon': (250, 128, 114), 'sandybrown': (244, 164, 96), 'seagreen': (46, 139, 87), 'seashell': (255, 245, 238), 'sienna': (160, 82, 45), 'silver': (192, 192, 192), 'skyblue': (135, 206, 235), 'slateblue': (106, 90, 205), 'slategray': (112, 128, 144), 'slategrey': (112, 128, 144), 'snow': (255, 250, 250), 'springgreen': (0, 255, 127), 'steelblue': (70, 130, 180), 'tan': (210, 180, 140), 'teal': (0, 128, 128), 'thistle': (216, 191, 216), 'tomato': (255, 99, 71), 'turquoise': (64, 224, 208), 'violet': (238, 130, 238), 'wheat': (245, 222, 179), 'white': (255, 255, 255), 'whitesmoke': (245, 245, 245), 'yellow': (255, 255, 0), 'yellowgreen': (154, 205, 50) } valid_locations = { # x, y in 90*90 'in the center': (45, 45), 'on the left': (15, 45), 'on the right': (75, 45), 'on the top': (45, 15), 'on the bottom': (45, 75), 'on the top-left': (15, 15), 'on the top-right': (75, 15), 'on the bottom-left': (15, 75), 'on the bottom-right': (75, 75) } valid_offsets = { # x, y in 90*90 'no offset': (0, 0), 'slightly to the left': (-10, 0), 'slightly to the right': (10, 0), 'slightly to the upper': (0, -10), 'slightly to the lower': (0, 10), 'slightly to the upper-left': (-10, -10), 'slightly to the upper-right': (10, -10), 'slightly to the lower-left': (-10, 10), 'slightly to the lower-right': (10, 10)} valid_areas = { # w, h in 90*90 "a small square area": (50, 50), "a small vertical area": (40, 60), "a small horizontal area": (60, 40), "a medium-sized square area": (60, 60), "a medium-sized vertical area": (50, 80), "a medium-sized horizontal area": (80, 50), "a large square area": (70, 70), "a large vertical area": (60, 90), "a large horizontal area": (90, 60) } def safe_str(x): return x.strip(',. ') + '.' def closest_name(input_str, options): input_str = input_str.lower() closest_match = difflib.get_close_matches(input_str, list(options.keys()), n=1, cutoff=0.5) assert isinstance(closest_match, list) and len(closest_match) > 0, f'The value [{input_str}] is not valid!' result = closest_match[0] if result != input_str: print(f'Automatically corrected [{input_str}] -> [{result}].') return result class Canvas: @staticmethod def from_bot_response(response: str): matched = re.search(r'```python\n(.*?)\n```', response, re.DOTALL) assert matched, 'Response does not contain codes!' code_content = matched.group(1) assert 'canvas = Canvas()' in code_content, 'Code block must include valid canvas var!' local_vars = {'Canvas': Canvas} exec(code_content, {}, local_vars) canvas = local_vars.get('canvas', None) assert isinstance(canvas, Canvas), 'Code block must produce valid canvas var!' return canvas def __init__(self): self.components = [] self.color = None self.record_tags = True self.prefixes = [] self.suffixes = [] return def set_global_description(self, description: str, detailed_descriptions: list, tags: str, HTML_web_color_name: str): assert isinstance(description, str), 'Global description is not valid!' assert isinstance(detailed_descriptions, list) and all(isinstance(item, str) for item in detailed_descriptions), \ 'Global detailed_descriptions is not valid!' assert isinstance(tags, str), 'Global tags is not valid!' HTML_web_color_name = closest_name(HTML_web_color_name, valid_colors) self.color = np.array([[valid_colors[HTML_web_color_name]]], dtype=np.uint8) self.prefixes = [description] self.suffixes = detailed_descriptions if self.record_tags: self.suffixes = self.suffixes + [tags] self.prefixes = [safe_str(x) for x in self.prefixes] self.suffixes = [safe_str(x) for x in self.suffixes] return def add_local_description(self, location: str, offset: str, area: str, distance_to_viewer: float, description: str, detailed_descriptions: list, tags: str, atmosphere: str, style: str, quality_meta: str, HTML_web_color_name: str): assert isinstance(description, str), 'Local description is wrong!' assert isinstance(distance_to_viewer, (int, float)) and distance_to_viewer > 0, \ f'The distance_to_viewer for [{description}] is not positive float number!' assert isinstance(detailed_descriptions, list) and all(isinstance(item, str) for item in detailed_descriptions), \ f'The detailed_descriptions for [{description}] is not valid!' assert isinstance(tags, str), f'The tags for [{description}] is not valid!' assert isinstance(atmosphere, str), f'The atmosphere for [{description}] is not valid!' assert isinstance(style, str), f'The style for [{description}] is not valid!' assert isinstance(quality_meta, str), f'The quality_meta for [{description}] is not valid!' location = closest_name(location, valid_locations) offset = closest_name(offset, valid_offsets) area = closest_name(area, valid_areas) HTML_web_color_name = closest_name(HTML_web_color_name, valid_colors) xb, yb = valid_locations[location] xo, yo = valid_offsets[offset] w, h = valid_areas[area] rect = (yb + yo - h // 2, yb + yo + h // 2, xb + xo - w // 2, xb + xo + w // 2) rect = [max(0, min(90, i)) for i in rect] color = np.array([[valid_colors[HTML_web_color_name]]], dtype=np.uint8) prefixes = self.prefixes + [description] suffixes = detailed_descriptions if self.record_tags: suffixes = suffixes + [tags, atmosphere, style, quality_meta] prefixes = [safe_str(x) for x in prefixes] suffixes = [safe_str(x) for x in suffixes] self.components.append(dict( rect=rect, distance_to_viewer=distance_to_viewer, color=color, prefixes=prefixes, suffixes=suffixes, location=location, )) return def process(self): # sort components self.components = sorted(self.components, key=lambda x: x['distance_to_viewer'], reverse=True) # compute initial latent # print(self.color) initial_latent = np.zeros(shape=(90, 90, 3), dtype=np.float32) + self.color for component in self.components: a, b, c, d = component['rect'] initial_latent[a:b, c:d] = 0.7 * component['color'] + 0.3 * initial_latent[a:b, c:d] initial_latent = initial_latent.clip(0, 255).astype(np.uint8) # compute conditions bag_of_conditions = [ dict(mask=np.ones(shape=(90, 90), dtype=np.float32), prefixes=self.prefixes, suffixes=self.suffixes,location= "full") ] for i, component in enumerate(self.components): a, b, c, d = component['rect'] m = np.zeros(shape=(90, 90), dtype=np.float32) m[a:b, c:d] = 1.0 bag_of_conditions.append(dict( mask = m, prefixes = component['prefixes'], suffixes = component['suffixes'], location = component['location'], )) return dict( initial_latent = initial_latent, bag_of_conditions = bag_of_conditions, ) class OmostPromter(torch.nn.Module): def __init__(self,model = None,tokenizer = None, template = "",device="cpu"): super().__init__() self.model=model self.tokenizer = tokenizer self.device = device if template == "": template = r'''You are a helpful AI assistant to compose images using the below python class `Canvas`: ```python class Canvas: def set_global_description(self, description: str, detailed_descriptions: list[str], tags: str, HTML_web_color_name: str): pass def add_local_description(self, location: str, offset: str, area: str, distance_to_viewer: float, description: str, detailed_descriptions: list[str], tags: str, atmosphere: str, style: str, quality_meta: str, HTML_web_color_name: str): assert location in ["in the center", "on the left", "on the right", "on the top", "on the bottom", "on the top-left", "on the top-right", "on the bottom-left", "on the bottom-right"] assert offset in ["no offset", "slightly to the left", "slightly to the right", "slightly to the upper", "slightly to the lower", "slightly to the upper-left", "slightly to the upper-right", "slightly to the lower-left", "slightly to the lower-right"] assert area in ["a small square area", "a small vertical area", "a small horizontal area", "a medium-sized square area", "a medium-sized vertical area", "a medium-sized horizontal area", "a large square area", "a large vertical area", "a large horizontal area"] assert distance_to_viewer > 0 pass ```''' self.template = template @staticmethod def from_model_manager(model_manager: ModelManager): model, model_path = model_manager.fetch_model("omost_prompt", require_model_path=True) tokenizer = AutoTokenizer.from_pretrained(model_path) omost = OmostPromter( model= model, tokenizer = tokenizer, device = model_manager.device ) return omost def __call__(self,prompt_dict:dict): raw_prompt=prompt_dict["prompt"] conversation = [{"role": "system", "content": self.template}] conversation.append({"role": "user", "content": raw_prompt}) input_ids = self.tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True).to(self.device) streamer = TextIteratorStreamer(self.tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) attention_mask = torch.ones(input_ids.shape, dtype=torch.bfloat16, device=self.device) generate_kwargs = dict( input_ids = input_ids, streamer = streamer, # stopping_criteria=stopping_criteria, # max_new_tokens=max_new_tokens, do_sample = True, attention_mask = attention_mask, pad_token_id = self.tokenizer.eos_token_id, # temperature=temperature, # top_p=top_p, ) self.model.generate(**generate_kwargs) outputs = [] for text in streamer: outputs.append(text) llm_outputs = "".join(outputs) canvas = Canvas.from_bot_response(llm_outputs) canvas_output = canvas.process() prompts = [" ".join(_["prefixes"]+_["suffixes"][:2]) for _ in canvas_output["bag_of_conditions"]] canvas_output["prompt"] = prompts[0] canvas_output["prompts"] = prompts[1:] raw_masks = [_["mask"] for _ in canvas_output["bag_of_conditions"]] masks=[] for mask in raw_masks: mask[mask>0.5]=255 mask = np.stack([mask] * 3, axis=-1).astype("uint8") masks.append(Image.fromarray(mask)) canvas_output["masks"] = masks prompt_dict.update(canvas_output) print(f"Your prompt is extended by Omost:\n") cnt = 0 for component,pmt in zip(canvas_output["bag_of_conditions"],prompts): loc = component["location"] cnt += 1 print(f"Component {cnt} - Location : {loc}\nPrompt:{pmt}\n") return prompt_dict ================================================ FILE: diffsynth/prompters/prompt_refiners.py ================================================ from transformers import AutoTokenizer from ..models.model_manager import ModelManager import torch from .omost import OmostPromter class BeautifulPrompt(torch.nn.Module): def __init__(self, tokenizer_path=None, model=None, template=""): super().__init__() self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) self.model = model self.template = template @staticmethod def from_model_manager(model_manager: ModelManager): model, model_path = model_manager.fetch_model("beautiful_prompt", require_model_path=True) template = 'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:' if model_path.endswith("v2"): template = """Converts a simple image description into a prompt. \ Prompts are formatted as multiple related tags separated by commas, plus you can use () to increase the weight, [] to decrease the weight, \ or use a number to specify the weight. You should add appropriate words to make the images described in the prompt more aesthetically pleasing, \ but make sure there is a correlation between the input and output.\n\ ### Input: {raw_prompt}\n### Output:""" beautiful_prompt = BeautifulPrompt( tokenizer_path=model_path, model=model, template=template ) return beautiful_prompt def __call__(self, raw_prompt, positive=True, **kwargs): if positive: model_input = self.template.format(raw_prompt=raw_prompt) input_ids = self.tokenizer.encode(model_input, return_tensors='pt').to(self.model.device) outputs = self.model.generate( input_ids, max_new_tokens=384, do_sample=True, temperature=0.9, top_k=50, top_p=0.95, repetition_penalty=1.1, num_return_sequences=1 ) prompt = raw_prompt + ", " + self.tokenizer.batch_decode( outputs[:, input_ids.size(1):], skip_special_tokens=True )[0].strip() print(f"Your prompt is refined by BeautifulPrompt: {prompt}") return prompt else: return raw_prompt class QwenPrompt(torch.nn.Module): # This class leverages the open-source Qwen model to translate Chinese prompts into English, # with an integrated optimization mechanism for enhanced translation quality. def __init__(self, tokenizer_path=None, model=None, system_prompt=""): super().__init__() self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) self.model = model self.system_prompt = system_prompt @staticmethod def from_model_manager(model_nameger: ModelManager): model, model_path = model_nameger.fetch_model("qwen_prompt", require_model_path=True) system_prompt = """You are an English image describer. Here are some example image styles:\n\n1. Extreme close-up: Clear focus on a single object with a blurred background, highlighted under natural sunlight.\n2. Vintage: A photograph of a historical scene, using techniques such as Daguerreotype or cyanotype.\n3. Anime: A stylized cartoon image, emphasizing hyper-realistic portraits and luminous brushwork.\n4. Candid: A natural, unposed shot capturing spontaneous moments, often with cinematic qualities.\n5. Landscape: A photorealistic image of natural scenery, such as a sunrise over the sea.\n6. Design: Colorful and detailed illustrations, often in the style of 2D game art or botanical illustrations.\n7. Urban: An ultrarealistic scene in a modern setting, possibly a cityscape viewed from indoors.\n\nYour task is to translate a given Chinese image description into a concise and precise English description. Ensure that the imagery is vivid and descriptive, and include stylistic elements to enrich the description.\nPlease note the following points:\n\n1. Capture the essence and mood of the Chinese description without including direct phrases or words from the examples provided.\n2. You should add appropriate words to make the images described in the prompt more aesthetically pleasing. If the Chinese description does not specify a style, you need to add some stylistic descriptions based on the essence of the Chinese text.\n3. The generated English description should not exceed 200 words.\n\n""" qwen_prompt = QwenPrompt( tokenizer_path=model_path, model=model, system_prompt=system_prompt ) return qwen_prompt def __call__(self, raw_prompt, positive=True, **kwargs): if positive: messages = [{ 'role': 'system', 'content': self.system_prompt }, { 'role': 'user', 'content': raw_prompt }] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device) generated_ids = self.model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] prompt = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(f"Your prompt is refined by Qwen: {prompt}") return prompt else: return raw_prompt class Translator(torch.nn.Module): def __init__(self, tokenizer_path=None, model=None): super().__init__() self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) self.model = model @staticmethod def from_model_manager(model_manager: ModelManager): model, model_path = model_manager.fetch_model("translator", require_model_path=True) translator = Translator(tokenizer_path=model_path, model=model) return translator def __call__(self, prompt, **kwargs): input_ids = self.tokenizer.encode(prompt, return_tensors='pt').to(self.model.device) output_ids = self.model.generate(input_ids) prompt = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] print(f"Your prompt is translated: {prompt}") return prompt ================================================ FILE: diffsynth/prompters/sd3_prompter.py ================================================ from .base_prompter import BasePrompter from ..models.model_manager import ModelManager from ..models import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 from transformers import CLIPTokenizer, T5TokenizerFast import os, torch class SD3Prompter(BasePrompter): def __init__( self, tokenizer_1_path=None, tokenizer_2_path=None, tokenizer_3_path=None ): if tokenizer_1_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_1") if tokenizer_2_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_2") if tokenizer_3_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_3_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_3") super().__init__() self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path) self.text_encoder_1: SD3TextEncoder1 = None self.text_encoder_2: SD3TextEncoder2 = None self.text_encoder_3: SD3TextEncoder3 = None def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: SD3TextEncoder2 = None, text_encoder_3: SD3TextEncoder3 = None): self.text_encoder_1 = text_encoder_1 self.text_encoder_2 = text_encoder_2 self.text_encoder_3 = text_encoder_3 def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device): input_ids = tokenizer( prompt, return_tensors="pt", padding="max_length", max_length=max_length, truncation=True ).input_ids.to(device) pooled_prompt_emb, prompt_emb = text_encoder(input_ids) return pooled_prompt_emb, prompt_emb def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): input_ids = tokenizer( prompt, return_tensors="pt", padding="max_length", max_length=max_length, truncation=True, add_special_tokens=True, ).input_ids.to(device) prompt_emb = text_encoder(input_ids) prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) return prompt_emb def encode_prompt( self, prompt, positive=True, device="cuda", t5_sequence_length=77, ): prompt = self.process_prompt(prompt, positive=positive) # CLIP pooled_prompt_emb_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(prompt, self.text_encoder_2, self.tokenizer_2, 77, device) # T5 if self.text_encoder_3 is None: prompt_emb_3 = torch.zeros((prompt_emb_1.shape[0], t5_sequence_length, 4096), dtype=prompt_emb_1.dtype, device=device) else: prompt_emb_3 = self.encode_prompt_using_t5(prompt, self.text_encoder_3, self.tokenizer_3, t5_sequence_length, device) prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16 # Merge prompt_emb = torch.cat([ torch.nn.functional.pad(torch.cat([prompt_emb_1, prompt_emb_2], dim=-1), (0, 4096 - 768 - 1280)), prompt_emb_3 ], dim=-2) pooled_prompt_emb = torch.cat([pooled_prompt_emb_1, pooled_prompt_emb_2], dim=-1) return prompt_emb, pooled_prompt_emb ================================================ FILE: diffsynth/prompters/sd_prompter.py ================================================ from .base_prompter import BasePrompter, tokenize_long_prompt from ..models.utils import load_state_dict, search_for_embeddings from ..models import SDTextEncoder from transformers import CLIPTokenizer import torch, os class SDPrompter(BasePrompter): def __init__(self, tokenizer_path=None): if tokenizer_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer") super().__init__() self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) self.text_encoder: SDTextEncoder = None self.textual_inversion_dict = {} self.keyword_dict = {} def fetch_models(self, text_encoder: SDTextEncoder = None): self.text_encoder = text_encoder def add_textual_inversions_to_model(self, textual_inversion_dict, text_encoder): dtype = next(iter(text_encoder.parameters())).dtype state_dict = text_encoder.token_embedding.state_dict() token_embeddings = [state_dict["weight"]] for keyword in textual_inversion_dict: _, embeddings = textual_inversion_dict[keyword] token_embeddings.append(embeddings.to(dtype=dtype, device=token_embeddings[0].device)) token_embeddings = torch.concat(token_embeddings, dim=0) state_dict["weight"] = token_embeddings text_encoder.token_embedding = torch.nn.Embedding(token_embeddings.shape[0], token_embeddings.shape[1]) text_encoder.token_embedding = text_encoder.token_embedding.to(dtype=dtype, device=token_embeddings[0].device) text_encoder.token_embedding.load_state_dict(state_dict) def add_textual_inversions_to_tokenizer(self, textual_inversion_dict, tokenizer): additional_tokens = [] for keyword in textual_inversion_dict: tokens, _ = textual_inversion_dict[keyword] additional_tokens += tokens self.keyword_dict[keyword] = " " + " ".join(tokens) + " " tokenizer.add_tokens(additional_tokens) def load_textual_inversions(self, model_paths): for model_path in model_paths: keyword = os.path.splitext(os.path.split(model_path)[-1])[0] state_dict = load_state_dict(model_path) # Search for embeddings for embeddings in search_for_embeddings(state_dict): if len(embeddings.shape) == 2 and embeddings.shape[1] == 768: tokens = [f"{keyword}_{i}" for i in range(embeddings.shape[0])] self.textual_inversion_dict[keyword] = (tokens, embeddings) self.add_textual_inversions_to_model(self.textual_inversion_dict, self.text_encoder) self.add_textual_inversions_to_tokenizer(self.textual_inversion_dict, self.tokenizer) def encode_prompt(self, prompt, clip_skip=1, device="cuda", positive=True): prompt = self.process_prompt(prompt, positive=positive) for keyword in self.keyword_dict: if keyword in prompt: print(f"Textual inversion {keyword} is enabled.") prompt = prompt.replace(keyword, self.keyword_dict[keyword]) input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device) prompt_emb = self.text_encoder(input_ids, clip_skip=clip_skip) prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) return prompt_emb ================================================ FILE: diffsynth/prompters/sdxl_prompter.py ================================================ from .base_prompter import BasePrompter, tokenize_long_prompt from ..models.model_manager import ModelManager from ..models import SDXLTextEncoder, SDXLTextEncoder2 from transformers import CLIPTokenizer import torch, os class SDXLPrompter(BasePrompter): def __init__( self, tokenizer_path=None, tokenizer_2_path=None ): if tokenizer_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer") if tokenizer_2_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_xl/tokenizer_2") super().__init__() self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) self.text_encoder: SDXLTextEncoder = None self.text_encoder_2: SDXLTextEncoder2 = None def fetch_models(self, text_encoder: SDXLTextEncoder = None, text_encoder_2: SDXLTextEncoder2 = None): self.text_encoder = text_encoder self.text_encoder_2 = text_encoder_2 def encode_prompt( self, prompt, clip_skip=1, clip_skip_2=2, positive=True, device="cuda" ): prompt = self.process_prompt(prompt, positive=positive) # 1 input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device) prompt_emb_1 = self.text_encoder(input_ids, clip_skip=clip_skip) # 2 input_ids_2 = tokenize_long_prompt(self.tokenizer_2, prompt).to(device) add_text_embeds, prompt_emb_2 = self.text_encoder_2(input_ids_2, clip_skip=clip_skip_2) # Merge if prompt_emb_1.shape[0] != prompt_emb_2.shape[0]: max_batch_size = min(prompt_emb_1.shape[0], prompt_emb_2.shape[0]) prompt_emb_1 = prompt_emb_1[: max_batch_size] prompt_emb_2 = prompt_emb_2[: max_batch_size] prompt_emb = torch.concatenate([prompt_emb_1, prompt_emb_2], dim=-1) # For very long prompt, we only use the first 77 tokens to compute `add_text_embeds`. add_text_embeds = add_text_embeds[0:1] prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) return add_text_embeds, prompt_emb ================================================ FILE: diffsynth/prompters/stepvideo_prompter.py ================================================ from .base_prompter import BasePrompter from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder from ..models.stepvideo_text_encoder import STEP1TextEncoder from transformers import BertTokenizer import os, torch class StepVideoPrompter(BasePrompter): def __init__( self, tokenizer_1_path=None, ): if tokenizer_1_path is None: base_path = os.path.dirname(os.path.dirname(__file__)) tokenizer_1_path = os.path.join( base_path, "tokenizer_configs/hunyuan_dit/tokenizer") super().__init__() self.tokenizer_1 = BertTokenizer.from_pretrained(tokenizer_1_path) def fetch_models(self, text_encoder_1: HunyuanDiTCLIPTextEncoder = None, text_encoder_2: STEP1TextEncoder = None): self.text_encoder_1 = text_encoder_1 self.text_encoder_2 = text_encoder_2 def encode_prompt_using_clip(self, prompt, max_length, device): text_inputs = self.tokenizer_1( prompt, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, return_tensors="pt", ) prompt_embeds = self.text_encoder_1( text_inputs.input_ids.to(device), attention_mask=text_inputs.attention_mask.to(device), ) return prompt_embeds def encode_prompt_using_llm(self, prompt, max_length, device): y, y_mask = self.text_encoder_2(prompt, max_length=max_length, device=device) return y, y_mask def encode_prompt(self, prompt, positive=True, device="cuda"): prompt = self.process_prompt(prompt, positive=positive) clip_embeds = self.encode_prompt_using_clip(prompt, max_length=77, device=device) llm_embeds, llm_mask = self.encode_prompt_using_llm(prompt, max_length=320, device=device) llm_mask = torch.nn.functional.pad(llm_mask, (clip_embeds.shape[1], 0), value=1) return clip_embeds, llm_embeds, llm_mask ================================================ FILE: diffsynth/prompters/wan_prompter.py ================================================ from .base_prompter import BasePrompter from ..models.wan_video_text_encoder import WanTextEncoder from transformers import AutoTokenizer import os, torch import ftfy import html import string import regex as re def basic_clean(text): text = ftfy.fix_text(text) text = html.unescape(html.unescape(text)) return text.strip() def whitespace_clean(text): text = re.sub(r'\s+', ' ', text) text = text.strip() return text def canonicalize(text, keep_punctuation_exact_string=None): text = text.replace('_', ' ') if keep_punctuation_exact_string: text = keep_punctuation_exact_string.join( part.translate(str.maketrans('', '', string.punctuation)) for part in text.split(keep_punctuation_exact_string)) else: text = text.translate(str.maketrans('', '', string.punctuation)) text = text.lower() text = re.sub(r'\s+', ' ', text) return text.strip() class HuggingfaceTokenizer: def __init__(self, name, seq_len=None, clean=None, **kwargs): assert clean in (None, 'whitespace', 'lower', 'canonicalize') self.name = name self.seq_len = seq_len self.clean = clean # init tokenizer self.tokenizer = AutoTokenizer.from_pretrained(name, **kwargs) self.vocab_size = self.tokenizer.vocab_size def __call__(self, sequence, **kwargs): return_mask = kwargs.pop('return_mask', False) # arguments _kwargs = {'return_tensors': 'pt'} if self.seq_len is not None: _kwargs.update({ 'padding': 'max_length', 'truncation': True, 'max_length': self.seq_len }) _kwargs.update(**kwargs) # tokenization if isinstance(sequence, str): sequence = [sequence] if self.clean: sequence = [self._clean(u) for u in sequence] ids = self.tokenizer(sequence, **_kwargs) # output if return_mask: return ids.input_ids, ids.attention_mask else: return ids.input_ids def _clean(self, text): if self.clean == 'whitespace': text = whitespace_clean(basic_clean(text)) elif self.clean == 'lower': text = whitespace_clean(basic_clean(text)).lower() elif self.clean == 'canonicalize': text = canonicalize(basic_clean(text)) return text class WanPrompter(BasePrompter): def __init__(self, tokenizer_path=None, text_len=512): super().__init__() self.text_len = text_len self.text_encoder = None self.fetch_tokenizer(tokenizer_path) def fetch_tokenizer(self, tokenizer_path=None): if tokenizer_path is not None: self.tokenizer = HuggingfaceTokenizer(name=tokenizer_path, seq_len=self.text_len, clean='whitespace') def fetch_models(self, text_encoder: WanTextEncoder = None): self.text_encoder = text_encoder def encode_prompt(self, prompt, positive=True, device="cuda"): prompt = self.process_prompt(prompt, positive=positive) ids, mask = self.tokenizer(prompt, return_mask=True, add_special_tokens=True) ids = ids.to(device) mask = mask.to(device) seq_lens = mask.gt(0).sum(dim=1).long() prompt_emb = self.text_encoder(ids, mask) for i, v in enumerate(seq_lens): prompt_emb[:, v:] = 0 return prompt_emb ================================================ FILE: diffsynth/schedulers/__init__.py ================================================ from .ddim import EnhancedDDIMScheduler from .continuous_ode import ContinuousODEScheduler from .flow_match import FlowMatchScheduler ================================================ FILE: diffsynth/schedulers/continuous_ode.py ================================================ import torch class ContinuousODEScheduler(): def __init__(self, num_inference_steps=100, sigma_max=700.0, sigma_min=0.002, rho=7.0): self.sigma_max = sigma_max self.sigma_min = sigma_min self.rho = rho self.set_timesteps(num_inference_steps) def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, **kwargs): ramp = torch.linspace(1-denoising_strength, 1, num_inference_steps) min_inv_rho = torch.pow(torch.tensor((self.sigma_min,)), (1 / self.rho)) max_inv_rho = torch.pow(torch.tensor((self.sigma_max,)), (1 / self.rho)) self.sigmas = torch.pow(max_inv_rho + ramp * (min_inv_rho - max_inv_rho), self.rho) self.timesteps = torch.log(self.sigmas) * 0.25 def step(self, model_output, timestep, sample, to_final=False): timestep_id = torch.argmin((self.timesteps - timestep).abs()) sigma = self.sigmas[timestep_id] sample *= (sigma*sigma + 1).sqrt() estimated_sample = -sigma / (sigma*sigma + 1).sqrt() * model_output + 1 / (sigma*sigma + 1) * sample if to_final or timestep_id + 1 >= len(self.timesteps): prev_sample = estimated_sample else: sigma_ = self.sigmas[timestep_id + 1] derivative = 1 / sigma * (sample - estimated_sample) prev_sample = sample + derivative * (sigma_ - sigma) prev_sample /= (sigma_*sigma_ + 1).sqrt() return prev_sample def return_to_timestep(self, timestep, sample, sample_stablized): # This scheduler doesn't support this function. pass def add_noise(self, original_samples, noise, timestep): timestep_id = torch.argmin((self.timesteps - timestep).abs()) sigma = self.sigmas[timestep_id] sample = (original_samples + noise * sigma) / (sigma*sigma + 1).sqrt() return sample def training_target(self, sample, noise, timestep): timestep_id = torch.argmin((self.timesteps - timestep).abs()) sigma = self.sigmas[timestep_id] target = (-(sigma*sigma + 1).sqrt() / sigma + 1 / (sigma*sigma + 1).sqrt() / sigma) * sample + 1 / (sigma*sigma + 1).sqrt() * noise return target def training_weight(self, timestep): timestep_id = torch.argmin((self.timesteps - timestep).abs()) sigma = self.sigmas[timestep_id] weight = (1 + sigma*sigma).sqrt() / sigma return weight ================================================ FILE: diffsynth/schedulers/ddim.py ================================================ import torch, math class EnhancedDDIMScheduler(): def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="epsilon", rescale_zero_terminal_snr=False): self.num_train_timesteps = num_train_timesteps if beta_schedule == "scaled_linear": betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32)) elif beta_schedule == "linear": betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) else: raise NotImplementedError(f"{beta_schedule} is not implemented") self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0) if rescale_zero_terminal_snr: self.alphas_cumprod = self.rescale_zero_terminal_snr(self.alphas_cumprod) self.alphas_cumprod = self.alphas_cumprod.tolist() self.set_timesteps(10) self.prediction_type = prediction_type def rescale_zero_terminal_snr(self, alphas_cumprod): alphas_bar_sqrt = alphas_cumprod.sqrt() # Store old values. alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() # Shift so the last timestep is zero. alphas_bar_sqrt -= alphas_bar_sqrt_T # Scale so the first timestep is back to the old value. alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) # Convert alphas_bar_sqrt to betas alphas_bar = alphas_bar_sqrt.square() # Revert sqrt return alphas_bar def set_timesteps(self, num_inference_steps, denoising_strength=1.0, **kwargs): # The timesteps are aligned to 999...0, which is different from other implementations, # but I think this implementation is more reasonable in theory. max_timestep = max(round(self.num_train_timesteps * denoising_strength) - 1, 0) num_inference_steps = min(num_inference_steps, max_timestep + 1) if num_inference_steps == 1: self.timesteps = torch.Tensor([max_timestep]) else: step_length = max_timestep / (num_inference_steps - 1) self.timesteps = torch.Tensor([round(max_timestep - i*step_length) for i in range(num_inference_steps)]) def denoise(self, model_output, sample, alpha_prod_t, alpha_prod_t_prev): if self.prediction_type == "epsilon": weight_e = math.sqrt(1 - alpha_prod_t_prev) - math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t) / alpha_prod_t) weight_x = math.sqrt(alpha_prod_t_prev / alpha_prod_t) prev_sample = sample * weight_x + model_output * weight_e elif self.prediction_type == "v_prediction": weight_e = -math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t)) + math.sqrt(alpha_prod_t * (1 - alpha_prod_t_prev)) weight_x = math.sqrt(alpha_prod_t * alpha_prod_t_prev) + math.sqrt((1 - alpha_prod_t) * (1 - alpha_prod_t_prev)) prev_sample = sample * weight_x + model_output * weight_e else: raise NotImplementedError(f"{self.prediction_type} is not implemented") return prev_sample def step(self, model_output, timestep, sample, to_final=False): alpha_prod_t = self.alphas_cumprod[int(timestep.flatten().tolist()[0])] if isinstance(timestep, torch.Tensor): timestep = timestep.cpu() timestep_id = torch.argmin((self.timesteps - timestep).abs()) if to_final or timestep_id + 1 >= len(self.timesteps): alpha_prod_t_prev = 1.0 else: timestep_prev = int(self.timesteps[timestep_id + 1]) alpha_prod_t_prev = self.alphas_cumprod[timestep_prev] return self.denoise(model_output, sample, alpha_prod_t, alpha_prod_t_prev) def return_to_timestep(self, timestep, sample, sample_stablized): alpha_prod_t = self.alphas_cumprod[int(timestep.flatten().tolist()[0])] noise_pred = (sample - math.sqrt(alpha_prod_t) * sample_stablized) / math.sqrt(1 - alpha_prod_t) return noise_pred def add_noise(self, original_samples, noise, timestep): sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples def training_target(self, sample, noise, timestep): if self.prediction_type == "epsilon": return noise else: sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) target = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample return target def training_weight(self, timestep): return 1.0 ================================================ FILE: diffsynth/schedulers/flow_match.py ================================================ import torch class FlowMatchScheduler(): def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003/1.002, inverse_timesteps=False, extra_one_step=False, reverse_sigmas=False): self.num_train_timesteps = num_train_timesteps self.shift = shift self.sigma_max = sigma_max self.sigma_min = sigma_min self.inverse_timesteps = inverse_timesteps self.extra_one_step = extra_one_step self.reverse_sigmas = reverse_sigmas self.set_timesteps(num_inference_steps) def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, shift=None): if shift is not None: self.shift = shift sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength if self.extra_one_step: self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps + 1)[:-1] else: self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps) if self.inverse_timesteps: self.sigmas = torch.flip(self.sigmas, dims=[0]) self.sigmas = self.shift * self.sigmas / (1 + (self.shift - 1) * self.sigmas) if self.reverse_sigmas: self.sigmas = 1 - self.sigmas self.timesteps = self.sigmas * self.num_train_timesteps if training: x = self.timesteps y = torch.exp(-2 * ((x - num_inference_steps / 2) / num_inference_steps) ** 2) y_shifted = y - y.min() bsmntw_weighing = y_shifted * (num_inference_steps / y_shifted.sum()) self.linear_timesteps_weights = bsmntw_weighing def step(self, model_output, timestep, sample, to_final=False, **kwargs): if isinstance(timestep, torch.Tensor): timestep = timestep.cpu() timestep_id = torch.argmin((self.timesteps - timestep).abs()) sigma = self.sigmas[timestep_id] if to_final or timestep_id + 1 >= len(self.timesteps): sigma_ = 1 if (self.inverse_timesteps or self.reverse_sigmas) else 0 else: sigma_ = self.sigmas[timestep_id + 1] prev_sample = sample + model_output * (sigma_ - sigma) return prev_sample def return_to_timestep(self, timestep, sample, sample_stablized): if isinstance(timestep, torch.Tensor): timestep = timestep.cpu() timestep_id = torch.argmin((self.timesteps - timestep).abs()) sigma = self.sigmas[timestep_id] model_output = (sample - sample_stablized) / sigma return model_output def add_noise(self, original_samples, noise, timestep): if isinstance(timestep, torch.Tensor): timestep = timestep.cpu() timestep_id = torch.argmin((self.timesteps - timestep).abs()) sigma = self.sigmas[timestep_id] sample = (1 - sigma) * original_samples + sigma * noise return sample def training_target(self, sample, noise, timestep): target = noise - sample return target def training_weight(self, timestep): timestep_id = torch.argmin((self.timesteps - timestep.to(self.timesteps.device)).abs()) weights = self.linear_timesteps_weights[timestep_id] return weights ================================================ FILE: diffsynth/tokenizer_configs/__init__.py ================================================ ================================================ FILE: diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json ================================================ { "": 32099, "": 32089, "": 32088, "": 32087, "": 32086, "": 32085, "": 32084, "": 32083, "": 32082, "": 32081, "": 32080, "": 32098, "": 32079, "": 32078, "": 32077, "": 32076, "": 32075, "": 32074, "": 32073, "": 32072, "": 32071, "": 32070, "": 32097, "": 32069, "": 32068, "": 32067, "": 32066, "": 32065, "": 32064, "": 32063, "": 32062, "": 32061, "": 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diffsynth/tokenizer_configs/flux/tokenizer_1/merges.txt ================================================ #version: 0.2 i n t h a n r e a r e r th e in g o u o n s t o r e n o n a l a t e r i t i n t o r o i s l e i c a t an d e d o f c h o r e s i l e l s t a c o m a m l o a n a y s h r i l i t i f or n e ð Ł r a h a d e o l v e s i u r a l s e ' s u n d i b e l a w h o o d ay e n m a n o l e t o ou r i r g h w it i t y o a s s p th is t s at i yo u wit h a d i s a b l y w e th e t e a s a g v i p p s u h o m y . . b u c om s e er s m e m e al l c on m o k e g e ou t en t c o f e v er a r f ro a u p o c e gh t ar e s s fro m c h t r ou n on e b y d o t h w or er e k e p ro f or d s b o t a w e g o h e t er in g d e b e ati on m or a y e x il l p e k s s c l u f u q u v er ðŁ ĺ j u m u at e an d v e k ing m ar o p h i .. . p re a d r u th at j o o f c e ne w a m a p g re s s d u no w y e t ing y our it y n i c i p ar g u f i a f p er t er u p s o g i on s g r g e b r p l ' t m i in e we e b i u s sh o ha ve to day a v m an en t ac k ur e ou r â Ģ c u l d lo o i m ic e s om f in re d re n oo d w as ti on p i i r th er t y p h ar d e c ! ! m on mor e w ill t ra c an c ol p u t e w n m b s o it i ju st n ing h ere t u p a p r bu t wh at al ly f ir m in c a an t s a t ed e v m ent f a ge t am e ab out g ra no t ha pp ay s m an h is ti me li ke g h ha s th an lo ve ar t st e d ing h e c re w s w at d er it e s er ac e ag e en d st r a w st or r e c ar el l al l p s f ri p ho p or d o a k w i f re wh o sh i b oo s on el l wh en il l ho w gre at w in e l b l s si al i som e ðŁ Ĵ t on d er le s p la ï ¸ e d s ch h u on g d on k i s h an n c or . . oun d a z in e ar y fu l st u ou ld st i g o se e ab le ar s l l m is b er c k w a en ts n o si g f e fir st e t sp e ac k i f ou s ' m st er a pp an g an ce an s g ood b re e ver the y t ic com e of f b ack as e ing s ol d i ght f o h er happ y p ic it s v ing u s m at h om d y e m s k y ing the ir le d r y u l h ar c k t on on al 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##騁 ##騎 ##騏 ##験 ##騙 ##騨 ##騰 ##騷 ##驀 ##驅 ##驊 ##驍 ##驒 ##驕 ##驗 ##驚 ##驛 ##驟 ##驢 ##驥 ##马 ##驭 ##驮 ##驯 ##驰 ##驱 ##驳 ##驴 ##驶 ##驷 ##驸 ##驹 ##驻 ##驼 ##驾 ##驿 ##骁 ##骂 ##骄 ##骅 ##骆 ##骇 ##骈 ##骊 ##骋 ##验 ##骏 ##骐 ##骑 ##骗 ##骚 ##骛 ##骜 ##骞 ##骠 ##骡 ##骤 ##骥 ##骧 ##骨 ##骯 ##骰 ##骶 ##骷 ##骸 ##骼 ##髂 ##髅 ##髋 ##髏 ##髒 ##髓 ##體 ##髖 ##高 ##髦 ##髪 ##髮 ##髯 ##髻 ##鬃 ##鬆 ##鬍 ##鬓 ##鬚 ##鬟 ##鬢 ##鬣 ##鬥 ##鬧 ##鬱 ##鬼 ##魁 ##魂 ##魄 ##魅 ##魇 ##魍 ##魏 ##魔 ##魘 ##魚 ##魯 ##魷 ##鮑 ##鮨 ##鮪 ##鮭 ##鮮 ##鯉 ##鯊 ##鯖 ##鯛 ##鯨 ##鯰 ##鯽 ##鰍 ##鰓 ##鰭 ##鰲 ##鰻 ##鰾 ##鱈 ##鱉 ##鱔 ##鱗 ##鱷 ##鱸 ##鱼 ##鱿 ##鲁 ##鲈 ##鲍 ##鲑 ##鲛 ##鲜 ##鲟 ##鲢 ##鲤 ##鲨 ##鲫 ##鲱 ##鲲 ##鲶 ##鲷 ##鲸 ##鳃 ##鳄 ##鳅 ##鳌 ##鳍 ##鳕 ##鳖 ##鳗 ##鳝 ##鳞 ##鳥 ##鳩 ##鳳 ##鳴 ##鳶 ##鴉 ##鴕 ##鴛 ##鴦 ##鴨 ##鴻 ##鴿 ##鵑 ##鵜 ##鵝 ##鵡 ##鵬 ##鵰 ##鵲 ##鶘 ##鶩 ##鶯 ##鶴 ##鷗 ##鷲 ##鷹 ##鷺 ##鸚 ##鸞 ##鸟 ##鸠 ##鸡 ##鸢 ##鸣 ##鸥 ##鸦 ##鸨 ##鸪 ##鸭 ##鸯 ##鸳 ##鸵 ##鸽 ##鸾 ##鸿 ##鹂 ##鹃 ##鹄 ##鹅 ##鹈 ##鹉 ##鹊 ##鹌 ##鹏 ##鹑 ##鹕 ##鹘 ##鹜 ##鹞 ##鹤 ##鹦 ##鹧 ##鹫 ##鹭 ##鹰 ##鹳 ##鹵 ##鹹 ##鹼 ##鹽 ##鹿 ##麂 ##麋 ##麒 ##麓 ##麗 ##麝 ##麟 ##麥 ##麦 ##麩 ##麴 ##麵 ##麸 ##麺 ##麻 ##麼 ##麽 ##麾 ##黃 ##黄 ##黍 ##黎 ##黏 ##黑 ##黒 ##黔 ##默 ##黛 ##黜 ##黝 ##點 ##黠 ##黨 ##黯 ##黴 ##鼋 ##鼎 ##鼐 ##鼓 ##鼠 ##鼬 ##鼹 ##鼻 ##鼾 ##齁 ##齊 ##齋 ##齐 ##齒 ##齡 ##齢 ##齣 ##齦 ##齿 ##龄 ##龅 ##龈 ##龊 ##龋 ##龌 ##龍 ##龐 ##龔 ##龕 ##龙 ##龚 ##龛 ##龜 ##龟 ##︰ ##︱ ##︶ ##︿ ##﹁ ##﹂ ##﹍ ##﹏ ##﹐ ##﹑ ##﹒ ##﹔ ##﹕ ##﹖ ##﹗ ##﹙ ##﹚ ##﹝ ##﹞ ##﹡ ##﹣ ##！ ##＂ ##＃ ##＄ ##％ ##＆ ##＇ ##（ ##） ##＊ ##， ##－ ##． ##／ ##： ##； ##＜ ##？ ##＠ ##［ ##＼ ##］ ##＾ ##＿ ##｀ ##ｆ ##ｈ ##ｊ ##ｕ ##ｗ ##ｚ ##｛ ##｝ ##｡ ##｢ ##｣ ##､ ##･ ##ｯ ##ｰ ##ｲ ##ｸ ##ｼ ##ｽ ##ﾄ ##ﾉ ##ﾌ ##ﾗ ##ﾙ ##ﾝ ##ﾞ ##ﾟ ##￣ ##￥ ##👍 ##🔥 ##😂 ##😎 [unused0] [unused100] [unused101] [unused102] [unused103] [unused104] [unused105] [unused106] [unused107] [unused108] [unused109] [unused110] [unused111] [unused112] [unused113] [unused114] [unused115] [unused116] [unused117] [unused118] [unused119] [unused120] [unused121] [unused122] [unused123] [unused124] [unused125] [unused126] [unused127] [unused128] [unused129] [unused130] [unused131] [unused132] [unused133] [unused134] [unused135] [unused136] [unused137] [unused138] [unused139] [unused140] [unused141] [unused142] [unused143] [unused144] [unused145] [unused146] [unused147] [unused148] [unused149] [unused150] [unused151] [unused152] [unused153] [unused154] [unused155] [unused156] [unused157] [unused158] [unused159] [unused160] [unused161] [unused162] [unused163] [unused164] [unused165] [unused166] [unused167] [unused168] [unused169] [unused170] [unused171] [unused172] [unused173] [unused174] [unused175] [unused176] [unused177] [unused178] [unused179] [unused180] [unused181] [unused182] [unused183] [unused184] [unused185] [unused186] [unused187] [unused188] [unused189] [unused190] [unused191] [unused192] [unused193] [unused194] [unused195] [unused196] [unused197] [unused198] [unused199] [unused200] [unused201] [unused202] [unused203] [unused204] [unused205] [unused206] [unused207] [unused208] [unused209] [unused210] [unused211] [unused212] [unused213] [unused214] [unused215] [unused216] [unused217] [unused218] [unused219] [unused220] [unused221] [unused222] [unused223] [unused224] [unused225] [unused226] [unused227] [unused228] [unused229] [unused230] [unused231] [unused232] [unused233] [unused234] [unused235] [unused236] [unused237] [unused238] [unused239] [unused240] [unused241] [unused242] [unused243] [unused244] [unused245] [unused246] [unused247] [unused248] [unused249] [unused250] [unused251] [unused252] [unused253] [unused254] [unused255] [unused256] [unused257] [unused258] [unused259] [unused260] [unused261] [unused262] [unused263] [unused264] [unused265] [unused266] [unused267] [unused268] [unused269] [unused270] [unused271] [unused272] [unused273] [unused274] [unused275] [unused276] [unused277] [unused278] [unused279] [unused280] [unused281] [unused282] [unused283] [unused284] [unused285] [unused286] [unused287] [unused288] [unused289] [unused290] [unused291] [unused292] [unused293] [unused294] [unused295] [unused296] [unused297] [unused298] [unused299] [unused300] [unused301] [unused302] [unused303] [unused304] [unused305] [unused306] [unused307] [unused308] [unused309] [unused310] [unused311] [unused312] [unused313] [unused314] [unused315] [unused316] [unused317] [unused318] [unused319] [unused320] [unused321] [unused322] [unused323] [unused324] [unused325] [unused326] [unused327] [unused328] [unused329] [unused330] [unused331] [unused332] [unused333] [unused334] [unused335] [unused336] [unused337] [unused338] [unused339] [unused340] [unused341] [unused342] [unused343] [unused344] [unused345] [unused346] [unused347] [unused348] [unused349] [unused350] [unused351] [unused352] [unused353] [unused354] [unused355] [unused356] [unused357] [unused358] [unused359] [unused360] [unused361] [unused362] [unused363] [unused364] [unused365] [unused366] [unused367] [unused368] [unused369] [unused370] [unused371] [unused372] [unused373] [unused374] [unused375] [unused376] [unused377] [unused378] [unused379] [unused380] [unused381] [unused382] [unused383] [unused384] [unused385] [unused386] [unused387] [unused388] [unused389] [unused390] [unused391] [unused392] [unused393] [unused394] [unused395] [unused396] [unused397] [unused398] [unused399] [unused400] [unused401] [unused402] [unused403] [unused404] [unused405] [unused406] [unused407] [unused408] [unused409] [unused410] [unused411] [unused412] [unused413] [unused414] [unused415] [unused416] [unused417] [unused418] [unused419] [unused420] [unused421] [unused422] [unused423] [unused424] [unused425] [unused426] [unused427] [unused428] [unused429] [unused430] [unused431] [unused432] [unused433] [unused434] [unused435] [unused436] [unused437] [unused438] [unused439] [unused440] [unused441] [unused442] [unused443] [unused444] [unused445] [unused446] [unused447] [unused448] [unused449] [unused450] [unused451] [unused452] [unused453] [unused454] [unused455] [unused456] [unused457] [unused458] [unused459] [unused460] [unused461] [unused462] [unused463] [unused464] [unused465] [unused466] [unused467] [unused468] [unused469] [unused470] [unused471] [unused472] [unused473] [unused474] [unused475] [unused476] [unused477] [unused478] [unused479] [unused480] [unused481] [unused482] [unused483] [unused484] [unused485] [unused486] [unused487] [unused488] [unused489] [unused490] [unused491] [unused492] [unused493] [unused494] [unused495] [unused496] [unused497] [unused498] [unused499] [unused500] [unused501] [unused502] [unused503] [unused504] [unused505] [unused506] [unused507] [unused508] [unused509] [unused510] [unused511] [unused512] [unused513] [unused514] [unused515] [unused516] [unused517] [unused518] [unused519] [unused520] [unused521] [unused522] [unused523] [unused524] [unused525] [unused526] [unused527] [unused528] [unused529] [unused530] [unused531] [unused532] [unused533] [unused534] [unused535] [unused536] [unused537] [unused538] [unused539] [unused540] [unused541] [unused542] [unused543] [unused544] [unused545] [unused546] [unused547] [unused548] [unused549] [unused550] [unused551] [unused552] [unused553] [unused554] [unused555] [unused556] [unused557] [unused558] [unused559] [unused560] [unused561] [unused562] [unused563] [unused564] [unused565] [unused566] [unused567] [unused568] [unused569] [unused570] [unused571] [unused572] [unused573] [unused574] [unused575] [unused576] [unused577] [unused578] [unused579] [unused580] [unused581] [unused582] [unused583] [unused584] [unused585] [unused586] [unused587] [unused588] [unused589] [unused590] [unused591] [unused592] [unused593] [unused594] [unused595] [unused596] [unused597] [unused598] [unused599] [unused600] [unused601] [unused602] [unused603] [unused604] [unused605] [unused606] [unused607] [unused608] [unused609] [unused610] [unused611] [unused612] [unused613] [unused614] [unused615] [unused616] [unused617] [unused618] [unused619] [unused620] [unused621] [unused622] [unused623] [unused624] [unused625] [unused626] [unused627] [unused628] [unused629] [unused630] [unused631] [unused632] [unused633] [unused634] [unused635] [unused636] [unused637] [unused638] [unused639] [unused640] [unused641] [unused642] [unused643] [unused644] [unused645] [unused646] [unused647] [unused648] [unused649] [unused650] [unused651] [unused652] [unused653] [unused654] [unused655] [unused656] [unused657] [unused658] [unused659] [unused660] [unused661] [unused662] [unused663] [unused664] [unused665] [unused666] [unused667] [unused668] [unused669] [unused670] [unused671] [unused672] [unused673] [unused674] [unused675] [unused676] [unused677] [unused678] [unused679] [unused680] [unused681] [unused682] [unused683] [unused684] [unused685] [unused686] [unused687] [unused688] [unused689] [unused690] [unused691] [unused692] [unused693] [unused694] [unused695] [unused696] [unused697] [unused698] [unused699] [unused700] [unused701] [unused702] [unused703] [unused704] [unused705] [unused706] [unused707] [unused708] [unused709] [unused710] [unused711] [unused712] [unused713] [unused714] [unused715] [unused716] [unused717] [unused718] [unused719] [unused720] 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court short round seen always become sure almost director council career things using couldn better students married nothing worked others record anything continued give military established returned does written thing feet far already championship western department role various production television produced working region present period looking least total england wife per brother soon political taken created further able reached joined upon done important either appeared position ground lead election arms police instead words moment someone announced less wrote past followed founded finally india taking records considered northern toward european outside described track playing heard professional australia miles yet trying blood southern maybe everything mouth race recorded above daughter points middle move tried elected closed ten minister chief person similar brought rest formed floor doing killed training needed turn finished railway rather sent example ran term coming currently forces despite areas fact dead originally germany probably developed pulled stood signed songs child eventually met average teams minutes current kind decided usually eastern seemed episode bed added indian route available throughout addition appointed eight construction mean remained schools sometimes events possible australian forward debut seat performance committee features character herself lot russian range hours sold quickly directed guitar performed players smile myself placed province towards wouldn leading whole designed census europe attack japanese getting alone lower wide hospital believe changed sister gone hadn ship studies academy shot below involved kept largest especially beginning movement section female professor lord longer walked actually civil families thus aircraft completed includes captain fight vocals featured fourth officer hear means medical groups lips competition entire lived leaving federal tournament passed independent kingdom spent fine doesn reported fall raised itself replaced leader theatre whose parents spanish canadian degree writing awarded higher coast provided senior organization stopped onto countries parts conference interest saying allowed earlier matter winning try happened moving los breath nearly mid certain italian african standing fell artist shows deal mine industry everyone republic provide student primary owned older heavy 1st makes attention anyone africa stated length ended fingers command staff foreign opening governor okay medal kill introduced chest hell feeling success meet reason meeting novel trade buildings guy goal native husband previously entered producer operations takes covered forced roman complete successful texas cold traditional films clear approximately nine prince question tracks ireland regional personal operation economic holding twenty additional hour regular historic places whom shook km² secretary prior scored units ask property ready immediately month listed contract themselves lines navy writer meant runs practice championships singer commission required starting generally giving attended couple stand catholic caught executive thinking chair quite shoulder hope decision plays defeated municipality whether offered slowly pain direction mission mostly noted individual managed lives plant helped except studied computer figure relationship issue significant loss smiled gun highest male bring goals mexico problem distance commercial completely location annual famous neck caused italy understand greek highway wrong comes appearance issues musical companies castle income assembly bass initially parliament artists experience particular walk foot engineering talking dropped boys stars remember carried train stadium angeles evidence becoming assistant soviet upper youth reach actor numerous nodded arrived minute believed complex victory associated temple chance perhaps bishop launched particularly retired subject prize contains yeah theory empire suddenly waiting trust recording terms champion religious zealand names 2nd ancient corner represented legal justice cause watched brothers material changes simply response answer historical stories straight feature increased administration virginia activities cultural overall winner programs basketball legs guard cast doctor flight results remains cost effect winter larger islands problems chairman grew commander isn failed selected hurt fort regiment majority plans shown pretty irish characters directly scene likely operated allow matches looks houses fellow marriage rules florida expected nearby congress peace recent wait subsequently variety serving agreed poor attempt wood democratic rural mile appears township soldiers ##ized pennsylvania closer fighting claimed score physical filled genus specific sitting mom therefore supported status fear cases meaning wales minor spain vice parish separate horse fifth remaining branch presented stared uses forms baseball exactly choice discovered composed truth russia dad ring referred numbers greater metres slightly direct increase responsible crew rule trees troops broke goes individuals hundred weight creek sleep defense provides ordered jewish safe judge whatever corps realized growing cities gaze lies spread letter showed situation mayor transport watching workers extended expression normal chart multiple border mrs walls piano heat cannot earned products drama era authority seasons join grade difficult territory mainly stations squadron stepped iron 19th serve appear speak broken charge knowledge kilometres removed ships campus pushed britain leaves recently boston latter acquired poland quality officers presence planned nations mass broadcast influence wild emperor electric headed ability promoted yellow ministry throat smaller politician latin spoke cars males lack acting seeing consists estate pressure newspaper olympics conditions beat elements walking vote needs carolina featuring levels francisco purpose females dutch duke ahead gas safety serious turning highly lieutenant firm amount mixed proposed perfect agreement affairs 3rd seconds contemporary paid prison label administrative intended constructed academic teacher races formerly nation issued shut drums housing seems graduated mentioned picked recognized shortly protection picture notable elections 1980s loved percent racing elizabeth volume hockey beside settled competed replied drew actress marine scotland steel glanced farm risk tonight positive singles effects gray screen residents sides none secondary literature polish destroyed flying founder households lay reserve industrial younger approach appearances ones finish powerful fully growth honor jersey projects revealed infantry pair equipment visit evening grant effort treatment buried republican primarily bottom owner 1970s israel gives remain spot produce champions accepted ways ##ally losing split capacity basis trial questions 20th guess officially memorial naval initial ##ization whispered median engineer sydney columbia strength tears senate asian draw warm supposed transferred leaned candidate escape mountains potential activity seem traffic murder slow orchestra haven agency taught website comedy unable storm planning albums rugby environment scientific grabbed protect boat typically damage principal divided dedicated ohio pick fought driver empty shoulders sort thank berlin prominent account freedom necessary efforts headquarters follows alongside suggested operating steps technical begin easily teeth speaking settlement scale renamed enemy semi joint compared scottish leadership analysis offers georgia pieces captured animal deputy organized combined method challenge 1960s huge wants battalion sons rise crime types facilities telling platform sit 1990s tells assigned pull commonly alive letters concept conducted wearing happen bought becomes holy gets defeat languages purchased occurred titled declared applied sciences concert sounds jazz brain painting fleet tax michigan animals leaders episodes birth clubs palace critical refused fair leg laughed returning surrounding participated formation lifted pointed connected rome medicine laid powers tall shared focused knowing yards entrance falls calling sources chosen beneath resources yard nominated silence defined gained thirty bodies adopted christmas widely register apart iran premier serves unknown parties generation continues fields brigade quiet teaching clothes impact weapons partner flat theater relations plants suffered begins seats armed models worth laws communities classes background knows thanks quarter reaching humans carry killing format setting architecture disease railroad possibly arthur thoughts doors density crowd illinois stomach tone unique reports anyway liberal vehicle thick dry drug faced largely facility theme holds creation strange colonel revolution politics turns silent rail relief independence combat shape determined sales learned 4th finger providing heritage fiction situated designated allowing hosted sight interview estimated reduced toronto footballer keeping guys damn claim motion sixth stayed rear receive handed twelve dress audience granted brazil spirit ##ated noticed olympic representative tight trouble reviews drink vampire missing roles ranked newly household finals critics phase massachusetts pilot unlike philadelphia bright guns crown organizations roof respectively clearly tongue marked circle bronze expanded sexual supply yourself inspired labour reference draft connection reasons driving jesus cells entry neither trail claims atlantic orders labor nose afraid identified intelligence calls cancer attacked passing positions imperial grey swedish avoid extra uncle covers allows surprise materials fame hunter citizens figures environmental confirmed shit titles performing difference acts attacks existing votes opportunity nor entirely trains opposite pakistan develop resulted representatives actions reality pressed barely conversation faculty northwest ends documentary nuclear stock sets eat alternative resulting creating surprised cemetery drop finding cricket streets tradition ride ear explained composer injury apartment municipal educational occupied netherlands clean billion constitution learn maximum classical lose opposition ontario hills rolled ending drawn permanent lewis sites chamber scoring height lyrics staring officials snow oldest qualified interior apparently succeeded thousand dinner lights existence heavily greatest conservative send bowl catch duty speech authorities princess performances versions shall graduate pictures effective remembered poetry desk crossed starring starts passenger sharp acres ass weather falling rank fund supporting adult heads southeast lane condition transfer prevent regions earl federation relatively answered besides obtained portion reaction liked peak counter religion chain rare convention aid lie vehicles perform squad wonder lying crazy sword attempted centuries weren philosophy interested sweden wolf frequently abandoned literary alliance task entitled threw promotion tiny soccer visited achieved defence internal persian methods arrested otherwise programming villages elementary districts rooms criminal conflict worry trained attempts waited signal truck subsequent programme communist faith sector carrying laugh controlled korean showing origin fuel evil brief identity darkness pool missed publication wings invited briefly standards kissed ideas climate causing walter worse albert winners desire aged northeast dangerous gate doubt wooden poet rising funding communications communication violence copies prepared investigation skills pulling containing ultimately offices singing understanding tomorrow christ ward pope stands 5th flow studios aired commissioned contained exist americans wrestling approved kid employed respect suit asking increasing frame angry selling 1950s thin finds temperature statement ali explain inhabitants towns extensive narrow flowers promise somewhere closely bureau cape weekly presidential legislative launch founding artillery strike un institutions roll writers landing chose anymore attorney billboard receiving agricultural breaking sought dave admitted lands mexican ##bury specifically hole moscow roads accident proved struck guards stuff slid expansion melbourne opposed sub southwest architect failure plane tank listen regarding wet introduction metropolitan fighter inch grown gene anger fixed khan domestic worldwide chapel mill functions examples developing turkey hits pocket antonio papers grow unless circuit 18th concerned attached journalist selection journey converted provincial painted hearing aren bands negative aside wondered knight lap noise billy shooting bedroom priest resistance motor homes sounded giant scenes equal comic patients hidden solid actual bringing afternoon touched funds consisted marie canal treaty turkish recognition residence cathedral broad knees incident shaped fired norwegian handle cheek contest represent representing birds advantage emergency wrapped drawing notice broadcasting somehow bachelor seventh collected registered establishment assumed chemical personnel retirement portuguese wore tied device threat progress advance ##ised banks hired manchester nfl teachers structures forever tennis helping saturday applications junction incorporated neighborhood dressed ceremony influenced hers stairs decades inner kansas hung hoped gain scheduled downtown engaged austria clock norway certainly pale victor employees plate putting surrounded ##ists finishing blues tropical minnesota consider philippines accept retrieved concern anderson properties institution gordon successfully vietnam backing outstanding muslim crossing folk producing usual demand occurs observed lawyer educated pleasure budget items quietly colorado philip typical ##worth derived survived asks mental jake jews distinguished sri extremely athletic loud thousands worried transportation horses weapon arena importance users objects contributed douglas aware senator johnny sisters engines flag investment samuel shock capable clark row wheel refers familiar biggest wins hate maintained drove hamilton expressed injured underground churches wars tunnel passes stupid agriculture softly cabinet regarded joining indiana dates spend behavior woods protein gently chase morgan mention burning wake combination occur mirror leads indeed impossible paintings covering soldier locations attendance sell historian wisconsin invasion argued painter diego changing egypt experienced inches missouri grounds spoken switzerland reform rolling forget massive resigned burned tennessee locked values improved wounded universe sick dating facing purchase ##pur moments merged anniversary coal brick understood causes dynasty queensland establish stores crisis promote hoping cards referee extension raise arizona improve colonial formal charged palm hide rescue faces feelings candidates juan 6th courses weekend luke cash fallen delivered affected installed carefully tries hollywood costs lincoln responsibility shore proper normally maryland assistance constant offering friendly waters persons realize contain trophy partnership factor musicians bound oregon indicated houston medium consisting somewhat cycle beer moore frederick gotten worst weak approached arranged chin loan bond fifteen pattern disappeared translated ##zed lip arab capture interests insurance shifted cave prix warning sections courts coat plot smell golf favorite maintain knife voted degrees finance quebec opinion translation manner ruled operate productions choose musician confused tired separated stream techniques committed attend ranking kings throw passengers measure horror mining sand danger salt calm decade dam require runner rush associate greece rivers consecutive matthew ##ski sighed sq documents closing tie accused islamic distributed directors organisation 7th breathing mad lit arrival concrete taste composition shaking faster amateur adjacent stating twin flew publications obviously ridge storage carl pages concluded desert driven universities ages terminal sequence borough constituency cousin economics dreams margaret notably reduce montreal 17th ears saved vocal riding roughly threatened meters meanwhile landed compete repeated grass czech regularly charges sudden appeal solution describes classification glad parking belt physics rachel hungarian participate expedition damaged gift childhood fifty mathematics jumped letting defensive mph testing hundreds shoot owners matters smoke israeli kentucky dancing mounted grandfather designs profit argentina truly lawrence cole begun detroit willing branches smiling decide miami enjoyed recordings ##dale poverty ethnic arabic accompanied fishing determine residential acid returns starred strategy forty businesses equivalent commonwealth distinct ill seriously ##ped harris replace rio imagine formula ensure additionally scheme conservation occasionally purposes feels favor 1930s contrast hanging hunt movies instruments victims danish christopher busy demon sugar earliest colony studying duties belgium slipped carter visible stages iraq commune forming continuing talked counties legend bathroom option tail clay daughters afterwards severe jaw visitors devices aviation entering subjects temporary swimming forth smooth bush operates rocks movements signs eddie voices honorary memories dallas measures racial promised harvard 16th parliamentary indicate benefit flesh dublin louisiana patient sleeping membership coastal medieval wanting element scholars rice limit survive makeup rating definitely collaboration obvious baron birthday linked soil diocese ncaa offensive shouldn waist plain ross organ resolution manufacturing adding relative kennedy whilst moth gardens crash heading partners credited carlos moves cable marshall depending bottle represents rejected responded existed denmark ##ating treated graham routes talent commissioner drugs secure tests reign restored photography contributions oklahoma designer disc grin seattle robin paused atlanta unusual praised las laughing satellite hungary visiting interesting factors deck poems norman ##water stuck speaker rifle premiered comics actors reputation eliminated 8th ceiling prisoners leather austin mississippi rapidly admiral parallel charlotte guilty tools gender divisions fruit laboratory nelson marry rapid aunt tribe requirements aspects suicide amongst adams bone ukraine kick sees edinburgh clothing column rough gods hunting broadway gathered concerns spending ty 12th snapped requires solar bones cavalry iowa drinking waste franklin charity thompson stewart tip landscape enjoy singh poem listening eighth fred differences adapted bomb ukrainian surgery corporate masters anywhere waves odd portugal orleans dick debate kent eating puerto cleared expect cinema guitarist blocks electrical agree involving depth dying panel struggle peninsula adults novels emerged vienna debuted shoes tamil songwriter meets prove beating instance heaven scared sending marks artistic passage superior significantly retained ##izing technique cheeks warren maintenance destroy extreme allied appearing fill advice alabama qualifying policies cleveland hat battery authors 10th soundtrack acted dated lb glance equipped coalition funny outer ambassador roy possibility couples campbell loose ethan supplies gonna monster shake agents frequency springs dogs practices gang plastic easier suggests gulf blade exposed colors industries markets nervous electoral charts legislation ownership ##idae appointment shield assault socialist abbey monument license throne employment replacement charter suffering accounts oak connecticut strongly wright colour 13th context welsh networks voiced gabriel forehead manage schedule totally remix forests occupation print nicholas brazilian strategic vampires engineers roots seek correct instrumental und alfred backed stanley robinson traveled wayne austrian achieve exit rates strip whereas sing deeply adventure bobby jamie careful components cap useful personality knee pushing hosts protest ottoman symphony boundary processes considering considerable tons cooper trading conduct illegal revolutionary definition harder jacob circumstances destruction popularity grip classified liverpool baltimore flows seeking honour approval mechanical till happening statue critic increasingly immediate describe commerce stare indonesia meat rounds boats baker orthodox depression formally worn naked muttered sentence 11th document criticism wished vessel spiritual bent virgin minimum murray lunch danny printed compilation keyboards blow belonged raising cutting pittsburgh 9th shadows hated indigenous jon 15th barry scholar oliver stick susan meetings attracted spell romantic ye demanded customers logan revival keys modified commanded jeans upset phil detective hiding resident ##bly experiences diamond defeating coverage lucas external parks franchise helen bible successor percussion celebrated lift clan romania ##ied mills nobody achievement shrugged fault rhythm initiative breakfast carbon lasted violent wound killer gradually filmed °c processing remove criticized guests sang chemistry legislature ##bridge uniform escaped integrated proposal purple denied liquid influential morris nights stones intense experimental twisted pace nazi mitchell ny blind reporter newspapers 14th centers burn basin forgotten surviving filed collections monastery losses manual couch description appropriate merely missions sebastian restoration replacing triple elder julia warriors benjamin julian convinced stronger amazing declined versus merchant happens output finland bare barbara absence ignored dawn injuries producers luis ##ities kw admit expensive electricity exception symbol ladies shower sheriff characteristics ##je aimed button ratio effectively summit angle jury bears foster vessels pants executed evans dozen advertising kicked patrol competitions lifetime principles athletics birmingham sponsored rob nomination acoustic creature longest credits harbor dust josh territories milk infrastructure completion thailand indians leon archbishop assist pitch blake arrangement girlfriend serbian operational hence sad scent fur sessions refer rarely exists 1892 scientists dirty penalty burst portrait seed pole limits rival stable grave constitutional alcohol arrest flower mystery devil architectural relationships greatly habitat ##istic larry progressive remote cotton preserved reaches cited vast scholarship decisions teach editions knocked eve searching partly participation animated fate excellent alternate saints youngest climbed suggest discussion staying choir lakes jacket revenue nevertheless peaked instrument wondering annually managing neil 1891 signing terry apply clinical brooklyn aim catherine fuck farmers figured ninth pride hugh ordinary involvement comfortable shouted encouraged representation sharing panic exact cargo competing fat cried 1920s occasions cabin borders utah marcus ##isation badly muscles victorian transition warner bet permission slave terrible similarly shares seth uefa possession medals benefits colleges lowered perfectly transit ##kar publisher ##ened harrison deaths elevation asleep machines sigh ash hardly argument occasion parent decline contribution concentration opportunities hispanic guardian extent emotions hips mason volumes bloody controversy diameter steady mistake phoenix identify violin departure richmond spin funeral enemies 1864 literally connor random sergeant grab confusion 1865 transmission informed leaning sacred suspended thinks gates portland luck agencies yours hull expert muscle layer practical sculpture jerusalem latest lloyd statistics deeper recommended warrior arkansas mess supports greg eagle recovered rated concerts rushed stops eggs premiere keith delhi turner pit affair belief paint ##zing victim withdrew bonus styles fled glasgow technologies funded adaptation portrayed cooperation supporters judges bernard hallway ralph graduating controversial distant continental spider bite recognize intention mixing egyptian bow tourism suppose claiming dominated participants nurse partially tape psychology essential touring duo voting civilian emotional channels apparent hebrew 1887 tommy carrier intersection beast hudson bench discuss costa ##ered detailed behalf drivers unfortunately obtain rocky ##dae siege friendship 1861 hang governments collins respond wildlife preferred operator laura pregnant videos dennis suspected boots instantly weird automatic businessman alleged placing throwing mood 1862 perry venue jet remainder passion biological boyfriend 1863 dirt buffalo ron segment abuse genre thrown stroke colored stress exercise displayed struggled abroad dramatic wonderful thereafter madrid component widespread ##sed tale citizen todd vancouver overseas forcing crying descent discussed substantial ranks regime provinces drum zane tribes proof researchers volunteer manor silk milan donated allies venture principle delivery enterprise bars traditionally witch reminded copper pete inter colin grinned elsewhere competitive frequent scream tension texts submarine finnish defending defend pat detail affiliated stuart themes periods tool belgian ruling crimes answers folded licensed demolished hans lucy 1881 lion traded photographs writes craig trials generated beth noble debt percentage yorkshire erected viewed grades confidence ceased islam telephone retail chile m² roberts sixteen commented hampshire innocent dual pounds checked regulations afghanistan sung rico liberty assets bigger options angels relegated tribute wells attending leaf romanian monthly patterns gmina madison hurricane rev ##ians bristol elite valuable disaster democracy awareness germans freyja loop absolutely paying populations maine sole prayer spencer releases doorway bull lover midnight conclusion thirteen mediterranean nhl proud sample ##hill drummer guinea murphy climb instant attributed horn ain railways autumn ferry opponent traveling secured corridor stretched tales sheet trinity cattle helps indicates manhattan murdered fitted gentle grandmother mines shocked vegas produces caribbean belong continuous desperate drunk historically trio waved raf dealing nathan murmured interrupted residing scientist pioneer harold aaron delta attempting minority believes chorus tend lots eyed indoor load shots updated jail concerning connecting wealth slaves arrive rangers sufficient rebuilt ##wick cardinal flood muhammad whenever relation runners moral repair viewers arriving revenge punk assisted bath fairly breathe lists innings illustrated whisper nearest voters clinton ties ultimate screamed beijing lions andre fictional gathering comfort radar suitable dismissed hms ban pine wrist atmosphere voivodeship bid timber ##ned giants cameron recovery uss identical categories switched serbia laughter noah ensemble therapy peoples touching ##off locally pearl platforms everywhere ballet tables lanka herbert outdoor toured derek 1883 spaces contested swept 1878 exclusive slight connections winds prisoner collective bangladesh tube publicly wealthy isolated insisted fortune ticket spotted reportedly animation enforcement tanks decides wider lowest owen nod hitting gregory furthermore magazines fighters solutions pointing requested peru reed chancellor knights mask worker eldest flames reduction volunteers reporting wire advisory endemic origins settlers pursue knock consumer 1876 eu compound creatures mansion sentenced ivan deployed guitars frowned involves mechanism kilometers perspective shops terminus duncan alien fist bridges ##pers heroes derby swallowed patent sara illness characterized adventures slide hawaii jurisdiction organised adelaide walks biology rogers swing tightly boundaries prepare implementation stolen certified colombia edwards garage recalled rage harm nigeria breast furniture pupils settle cuba balls alaska 21st linear thrust celebration latino genetic terror ##ening lightning fee witness lodge establishing skull earning hood rebellion sporting warned missile devoted activist porch worship fourteen package decorated ##shire housed chess sailed doctors oscar joan treat garcia harbour jeremy traditions dominant jacques ##gon relocated 1879 amendment sized companion simultaneously volleyball spun acre increases stopping loves belongs affect drafted tossed scout battles 1875 filming shoved munich tenure vertical romance argue craft ranging opens honest tyler yesterday muslims reveal snake immigrants radical screaming speakers firing saving belonging ease lighting prefecture blame farmer hungry grows rubbed beam sur subsidiary armenian dropping conventional qualify spots sweat festivals immigration physician discover exposure sandy explanation isaac implemented ##fish hart initiated stakes presents heights householder pleased tourist regardless slip closest surely sultan brings riley preparation aboard slammed baptist experiment ongoing interstate organic playoffs 1877 hindu tours tier plenty arrangements talks trapped excited sank athens 1872 denver welfare suburb athletes trick diverse belly exclusively yelled conversion 1874 internationally computers conductor abilities sensitive dispute measured globe rocket prices amsterdam flights tigers municipalities emotion references explains airlines manufactured archaeological 1873 interpretation devon ##ites settlements kissing absolute improvement impressed barcelona sullivan jefferson towers jesse julie grandson gauge regard rings interviews trace raymond thumb departments burns serial bulgarian scores demonstrated 1866 kyle alberta underneath romanized relieved acquisition phrase cliff reveals cuts merger custom nee gilbert graduation assessment difficulty demands swung democrat commons 1940s grove completing focuses sum substitute bearing stretch reception reflected essentially destination pairs ##ched survival resource ##bach promoting doubles messages tear ##fully parade florence harvey incumbent partial pedro frozen procedure olivia controls shelter personally temperatures brisbane tested sits marble comprehensive oxygen leonard ##kov inaugural iranian referring quarters attitude mainstream lined mars dakota norfolk unsuccessful explosion helicopter congressional ##sing inspector bitch seal departed divine coaching examination punishment manufacturer sink columns unincorporated signals nevada squeezed dylan dining martial manuel eighteen elevator brushed plates ministers congregation slept specialized taxes restricted negotiations likes statistical arnold inspiration execution bold intermediate significance margin ruler wheels gothic intellectual dependent listened eligible buses widow syria earn cincinnati collapsed recipient secrets accessible philippine maritime goddess clerk surrender breaks playoff ideal beetle aspect soap regulation strings expand anglo shorter crosses retreat tough coins wallace directions pressing shipping locomotives comparison topics nephew distinction honors travelled sierra ibn fortress recognised carved 1869 clients intent coaches describing bread ##ington beaten northwestern merit collapse challenges historians objective submitted virus attacking drake assume diseases stem leeds farming glasses visits nowhere fellowship relevant carries restaurants experiments constantly bases targets shah tenth opponents verse territorial writings corruption instruction inherited reverse emphasis employee arch keeps rabbi watson payment uh nancy ##tre venice fastest sexy banned adrian properly ruth touchdown dollar boards metre circles edges favour travels liberation scattered firmly holland permitted diesel kenya den originated demons resumed dragged rider servant blinked extend torn ##sey input meal everybody cylinder kinds camps bullet logic croatian evolved healthy fool wise preserve pradesh respective artificial gross corresponding convicted cage caroline dialogue ##dor narrative stranger mario christianity failing trent commanding buddhist 1848 maurice focusing yale bike altitude mouse revised ##sley veteran pulls theology crashed campaigns legion ##ability drag excellence customer cancelled intensity excuse liga participating contributing printing ##burn variable curious legacy renaissance symptoms binding vocalist dancer grammar gospel democrats enters diplomatic hitler clouds mathematical quit defended oriented ##heim fundamental hardware impressive equally convince confederate guilt chuck sliding magnetic narrowed petersburg bulgaria otto phd skill hopes pitcher reservoir hearts automatically expecting mysterious bennett extensively imagined seeds monitor fix ##ative journalism struggling signature ranch encounter photographer observation protests influences calendar cruz croatia locomotive hughes naturally shakespeare basement hook uncredited faded theories approaches dare phillips filling fury obama efficient arc deliver breeding inducted leagues efficiency axis montana eagles ##ked supplied instructions karen picking indicating trap anchor practically christians tomb vary occasional electronics lords readers newcastle faint innovation collect situations engagement claude mixture ##feld peer tissue lean °f floors architects reducing rope 1859 ottawa ##har samples banking declaration proteins resignation francois saudi advocate exhibited armor twins divorce ##ras abraham reviewed temporarily matrix physically pulse curled difficulties bengal usage ##ban riders certificate holes warsaw distinctive mutual 1857 customs circular eugene removal loaded mere vulnerable depicted generations dame heir enormous lightly climbing pitched lessons pilots nepal preparing brad louise renowned liam ##ably shaw brilliant bills ##nik fucking mainland pleasant seized veterans jerked fail brush radiation stored warmth southeastern nate sin raced berkeley joke athlete designation trunk roland qualification heels artwork receives judicial reserves ##bed woke installation abu floating fake lesser excitement interface concentrated addressed characteristic amanda saxophone monk releasing egg dies interaction defender outbreak glory loving sequel consciousness awake ski enrolled handling rookie brow somebody biography warfare amounts contracts presentation fabric dissolved challenged meter psychological elevated rally accurate ##tha hospitals undergraduate specialist venezuela exhibit shed nursing protestant fluid structural footage jared consistent prey ##ska succession reflect exile lebanon wiped suspect shanghai resting integration preservation marvel variant pirates sheep rounded capita sailing colonies manuscript deemed variations clarke functional emerging boxing relaxed curse azerbaijan heavyweight nickname editorial rang grid tightened earthquake flashed miguel rushing ##ches improvements boxes brooks consumption molecular felix societies repeatedly variation aids civic graphics professionals realm autonomous receiver delayed workshop militia chairs canyon harsh extending lovely happiness ##jan stake eyebrows embassy wellington hannah corners bishops swear cloth contents namely commenced 1854 stanford nashville courage graphic commitment garrison hamlet clearing rebels attraction literacy cooking ruins temples jenny humanity celebrate hasn freight sixty rebel bastard newton deer ##ges ##ching smiles delaware singers approaching assists flame boulevard barrel planted pursuit consequences shallow invitation rode depot ernest kane rod concepts preston topic chambers striking blast arrives descendants montgomery ranges worlds chaos praise fewer 1855 sanctuary mud programmes maintaining harper bore handsome closure tournaments nebraska linda facade puts satisfied argentine dale cork dome panama ##yl 1858 tasks experts ##ates feeding equation engage bryan um quartet disbanded sheffield blocked gasped delay kisses connects ##non sts poured creator publishers guided ellis extinct hug gaining ##ord complicated poll clenched investigate thereby quantum spine cdp humor kills administered semifinals encountered ignore commentary ##maker bother roosevelt plains halfway flowing cultures crack imprisoned neighboring airline gather wolves marathon transformed cruise organisations punch exhibitions numbered alarm ratings daddy silently ##stein queens colours impression guidance tactical ##rat marshal della arrow rested feared tender owns bitter advisor escort ##ides spare farms grants dragons encourage colleagues cameras sucked pile spirits prague statements suspension landmark fence torture recreation bags permanently survivors pond spy predecessor bombing coup protecting transformation glow ##lands dug priests andrea feat barn jumping ##ologist casualties stern auckland pipe serie revealing trevor mercy spectrum consist governing collaborated possessed epic comprises blew shane lopez honored magical sacrifice judgment perceived hammer baronet tune das missionary sheets neutral oral threatening attractive shade aims seminary estates 1856 michel wounds refugees manufacturers mercury syndrome porter ##iya ##din hamburg identification upstairs purse widened pause cared breathed affiliate santiago prevented celtic fisher recruited byzantine reconstruction farther diet sake spite sensation blank separation ##hon vladimir armies anime accommodate orbit cult sofia ##ify founders sustained disorder honours northeastern mia crops violet threats blanket fires canton followers southwestern prototype voyage assignment altered moderate protocol pistol questioned brass lifting 1852 math authored doug dimensional dynamic 1851 pronounced grateful quest uncomfortable boom presidency stevens relating politicians barrier quinn diana mosque tribal palmer portions sometime chester treasure bend millions reforms registration consequently monitoring ate preliminary brandon invented eaten exterior intervention ports documented displays lecture sally favourite vermont invisible isle breed journalists relay speaks backward explore midfielder actively stefan procedures cannon blond kenneth centered servants chains libraries malcolm essex henri slavery ##hal facts fairy coached cassie cats washed cop announcement 2000s vinyl activated marco frontier growled curriculum ##das loyal accomplished leslie ritual kenny vii napoleon hollow hybrid jungle stationed friedrich counted ##ulated platinum theatrical seated col rubber glen diversity healing extends provisions administrator columbus tributary assured ##uous prestigious examined lectures grammy ronald associations bailey allan essays flute believing consultant proceedings travelling 1853 kerala yugoslavia buddy methodist burial centres batman discontinued dock stockholm lungs severely citing manga steal mumbai iraqi robot celebrity bride broadcasts abolished pot joel overhead franz packed reconnaissance johann acknowledged introduce handled doctorate developments drinks alley palestine ##aki proceeded recover bradley grain patch afford infection nationalist legendary interchange virtually gen gravity exploration amber vital wishes powell doctrine elbow screenplay ##bird contribute indonesian creates enzyme kylie discipline drops manila hunger layers suffer fever bits monica keyboard manages ##hood searched appeals ##bad testament grande reid ##war beliefs congo requiring casey 1849 regret streak rape depends syrian sprint pound tourists upcoming pub tense ##els practiced nationwide guild motorcycle liz ##zar chiefs desired elena precious absorbed relatives booth pianist ##mal citizenship exhausted wilhelm ##ceae ##hed noting quarterback urge hectares ##gue holly blonde davies parked sustainable stepping twentieth airfield nest chip ##nell shaft paulo requirement paradise tobacco trans renewed vietnamese suggesting catching holmes enjoying trips colt holder butterfly nerve reformed cherry bowling trailer carriage goodbye appreciate toy joshua interactive enabled involve ##kan collar determination bunch recall shorts superintendent episcopal frustration giovanni nineteenth laser privately array circulation ##ovic armstrong deals painful permit discrimination aires retiring cottage horizon ellen jamaica ripped fernando chapters patron lecturer behaviour genes georgian export solomon rivals seventeen rodriguez princeton independently sox 1847 arguing entity casting hank criteria oakland geographic milwaukee reflection expanding conquest dubbed halt brave brunswick arched curtis divorced predominantly somerset streams ugly zoo horrible curved buenos fierce dictionary vector theological unions handful stability punjab segments altar ignoring gesture monsters pastor thighs unexpected operators abruptly coin compiled associates improving migration compact collegiate quarterfinals roster restore assembled hurry oval ##cies 1846 flags martha victories sharply ##rated argues deadly drawings symbols performer griffin restrictions editing andrews journals arabia compositions dee pierce removing hindi casino runway civilians minds ##zation refuge rent retain potentially conferences suburban conducting descended massacre ammunition terrain fork souls counts chelsea durham drives cab perth realizing palestinian finn simpson ##dal betty moreover particles cardinals tent evaluation extraordinary inscription wednesday chloe maintains panels ashley trucks ##nation cluster sunlight strikes zhang dialect tucked collecting ##mas ##sville quoted evan franco aria buying cleaning closet provision apollo clinic rat necessarily ##ising venues flipped cent spreading trustees checking authorized disappointed ##ado notion duration trumpet hesitated topped brussels rolls theoretical hint define aggressive repeat wash peaceful optical width allegedly mcdonald strict ##illa investors jam witnesses sounding miranda michelle hugo harmony valid lynn glared nina headquartered diving boarding gibson albanian marsh routine dealt enhanced intelligent substance targeted enlisted discovers spinning observations pissed smoking capitol varied costume seemingly indies compensation surgeon thursday arsenal westminster suburbs rid anglican ##ridge knots foods alumni lighter fraser whoever portal scandal gavin advised instructor flooding terrorist teenage interim senses duck teen thesis abby eager overcome newport glenn rises shame prompted priority forgot bomber nicolas protective cartoon katherine breeze lonely trusted henderson richardson relax palms remarkable legends cricketer essay ordained edmund rifles trigger ##uri ##away sail alert 1830 audiences penn sussex siblings pursued indianapolis resist rosa consequence succeed avoided 1845 ##ulation inland ##tie ##nna counsel profession chronicle hurried ##una eyebrow eventual bleeding innovative cure committees accounting scope hardy heather tenor gut herald codes tore scales wagon luxury tin prefer fountain triangle bonds darling convoy dried traced beings troy accidentally slam findings smelled joey lawyers outcome steep bosnia configuration shifting toll brook performers lobby philosophical construct shrine aggregate cox phenomenon savage insane solely reynolds nationally holdings consideration enable edgar fights relegation chances atomic hub conjunction awkward reactions currency finale kumar underwent steering elaborate gifts comprising melissa veins reasonable sunshine solve trails inhabited elimination ethics huh ana molly consent apartments layout marines hunters bulk ##oma hometown ##wall ##mont cracked reads neighbouring withdrawn admission wingspan damned anthology lancashire brands batting forgive cuban awful ##lyn dimensions imagination dante tracking desperately goalkeeper ##yne groaned workshops confident burton gerald milton circus uncertain slope copenhagen sophia fog philosopher portraits accent cycling varying gripped larvae garrett specified scotia mature luther kurt rap ##kes aerial ferdinand heated transported ##shan safely nonetheless ##orn ##gal motors demanding ##sburg startled ##brook ally generate caps ghana stained mentions beds afterward ##bling utility ##iro richards 1837 conspiracy conscious shining footsteps observer cyprus urged loyalty developer probability olive upgraded gym miracle insects graves 1844 ourselves hydrogen katie tickets poets planes prevention witnessed dense jin randy tang warehouse monroe archived elderly investigations alec granite mineral conflicts controlling aboriginal mechanics stan stark rhode skirt est bombs respected ##horn imposed limestone deny nominee memphis grabbing disabled amusement frankfurt corn referendum varies slowed disk firms unconscious incredible clue sue ##zhou twist ##cio joins idaho chad developers computing destroyer mortal tucker kingston choices carson whitney geneva pretend dimension staged plateau maya ##une freestyle rovers ##ids tristan classroom prospect ##hus honestly diploma lied thermal auxiliary feast unlikely iata morocco pounding treasury lithuania considerably 1841 dish 1812 geological matching stumbled destroying marched brien advances nicole settling measuring directing ##mie tuesday bassist capabilities stunned fraud torpedo ##phone anton wisdom surveillance ruined ##ulate lawsuit healthcare theorem halls trend aka horizontal dozens acquire lasting swim hawk gorgeous fees vicinity decrease adoption tactics ##ography pakistani ##ole draws ##hall willie burke heath algorithm integral powder elliott brigadier jackie tate varieties darker ##cho lately cigarette specimens adds ##ensis ##inger exploded finalist murders wilderness arguments nicknamed acceptance onwards manufacture robertson jets tampa enterprises loudly composers nominations 1838 malta inquiry automobile hosting viii rays tilted grief museums strategies furious euro equality cohen poison surrey wireless governed ridiculous moses ##esh vanished barnes attract morrison istanbul ##iness absent rotation petition janet ##logical satisfaction custody deliberately observatory comedian surfaces pinyin novelist strictly canterbury oslo monks embrace jealous photograph continent dorothy marina excess holden allegations explaining stack avoiding lance storyline majesty poorly spike bradford raven travis classics proven voltage pillow fists butt 1842 interpreted 1839 gage telegraph lens promising expelled casual collector zones silly nintendo ##kh downstairs chef suspicious afl flies vacant uganda pregnancy condemned lutheran estimates cheap decree saxon proximity stripped idiot deposits contrary presenter magnus glacier offense edwin ##ori upright ##long bolt ##ois toss geographical ##izes environments delicate marking abstract xavier nails windsor plantation occurring equity saskatchewan fears drifted sequences vegetation revolt ##stic 1843 sooner fusion opposing nato skating 1836 secretly ruin lease flora anxiety ##ological ##mia bout taxi emmy frost rainbow compounds foundations rainfall assassination nightmare dominican achievements deserve orlando intact armenia ##nte calgary valentine marion proclaimed theodore bells courtyard thigh gonzalez console troop minimal everyday supporter terrorism buck openly presbyterian activists carpet ##iers rubbing uprising cute conceived legally ##cht millennium cello velocity rescued cardiff 1835 rex concentrate senators beard rendered glowing battalions scouts competitors sculptor catalogue arctic ion raja bicycle glancing lawn ##woman gentleman lighthouse publish predicted calculated variants ##gne strain winston deceased touchdowns brady caleb sinking echoed crush hon blessed protagonist hayes endangered magnitude editors ##tine estimate responsibilities ##mel backup laying consumed sealed zurich lovers frustrated ##eau ahmed kicking treasurer 1832 biblical refuse terrified pump agrees genuine imprisonment refuses plymouth lou ##nen tara trembling antarctic ton learns ##tas crap crucial faction atop ##borough wrap lancaster odds hopkins erik lyon ##eon bros snap locality empress crowned cal acclaimed chuckled clara sends mild towel wishing assuming interviewed ##bal interactions eden cups helena indie beck ##fire batteries filipino wizard parted traces ##born rows idol albany delegates ##ees ##sar discussions notre instructed belgrade highways suggestion lauren possess orientation alexandria abdul beats salary reunion ludwig alright wagner intimate pockets slovenia hugged brighton merchants cruel stole trek slopes repairs enrollment politically underlying promotional counting boeing isabella naming keen bacteria listing separately belfast ussr lithuanian anybody ribs sphere martinez cock embarrassed proposals fragments nationals ##wski premises fin alpine matched freely bounded jace sleeve pier populated evident ##like frances flooded ##dle frightened pour trainer framed visitor challenging pig wickets ##fold infected ##pes arose reward ecuador oblast vale shuttle ##usa bach rankings forbidden cornwall accordance salem consumers bruno fantastic toes machinery resolved julius remembering propaganda iceland bombardment tide contacts wives ##rah concerto macdonald albania implement daisy tapped sudan helmet mistress crop sunk finest ##craft hostile boxer fr paths adjusted habit ballot supervision soprano bullets wicked sunset regiments disappear lamp performs ##gia rabbit digging incidents entries ##cion dishes introducing ##ati ##fied freshman slot jill tackles baroque backs ##iest lone sponsor destiny altogether convert ##aro consensus shapes demonstration basically feminist auction artifacts ##bing strongest halifax allmusic mighty smallest precise alexandra viola ##los ##ille manuscripts ##illo dancers ari managers monuments blades barracks springfield maiden consolidated electron berry airing wheat nobel inclusion blair payments geography bee eleanor react ##hurst afc manitoba lineup fitness recreational investments airborne disappointment ##dis edmonton viewing renovation infant bankruptcy roses aftermath pavilion carpenter withdrawal ladder discussing popped reliable agreements rochester ##abad curves bombers rao reverend decreased choosing stiff consulting naples crawford tracy ribbon cops crushed deciding unified teenager accepting flagship poles sanchez inspection revived skilled induced exchanged flee locals tragedy swallow hanna demonstrate ##ela salvador flown contestants civilization ##ines wanna rhodes fletcher hector knocking considers nash mechanisms sensed mentally walt unclear ##eus renovated madame crews governmental undertaken monkey ##ben ##ato fatal armored copa caves governance grasp perception certification froze damp tugged wyoming ##rg ##ero newman nerves curiosity graph ##ami withdraw tunnels dull meredith moss exhibits neighbors communicate accuracy explored raiders republicans secular kat superman penny criticised freed conviction ham likewise delegation gotta doll promises technological myth nationality resolve convent sharon dig sip coordinator entrepreneur fold ##dine capability councillor synonym blown swan cursed 1815 jonas haired sofa canvas keeper rivalry ##hart rapper speedway swords postal maxwell estonia potter recurring errors ##oni cognitive 1834 claws nadu roberto bce wrestler ellie infinite ink ##tia presumably finite staircase noel patricia nacional chill eternal tu preventing prussia fossil limbs ##logist ernst frog perez rene prussian ##ios molecules regulatory answering opinions sworn lengths supposedly hypothesis upward habitats seating ancestors drank yield synthesis researcher modest ##var mothers peered voluntary homeland acclaim ##igan static valve luxembourg alto carroll receptor norton ambulance ##tian johnston catholics depicting jointly elephant gloria mentor badge ahmad distinguish remarked councils precisely allison advancing detection crowded cooperative ankle mercedes dagger surrendered pollution commit subway jeffrey lesson sculptures provider ##fication membrane timothy rectangular fiscal heating teammate basket particle anonymous deployment missiles courthouse proportion shoe sec complaints forbes blacks abandon remind sizes overwhelming autobiography natalie ##awa risks contestant countryside babies scorer invaded enclosed proceed hurling disorders ##cu reflecting continuously cruiser graduates freeway investigated ore deserved maid blocking phillip jorge shakes dove mann variables lacked burden accompanying que consistently organizing provisional complained endless tubes juice georges krishna mick thriller laps arcade sage snail shannon laurence seoul vacation presenting hire churchill surprisingly prohibited savannah technically ##oli ##lessly testimony suited speeds toys romans flowering measurement talented kay settings charleston expectations shattered achieving triumph ceremonies portsmouth lanes mandatory loser stretching cologne realizes seventy cornell careers webb ##ulating americas budapest ava suspicion yo conrad sterling jessie rector ##az 1831 transform organize loans christine volcanic warrant slender summers subfamily newer danced dynamics rhine proceeds heinrich gastropod commands sings facilitate easter positioned responses expense fruits yanked imported 25th velvet vic primitive tribune baldwin neighbourhood donna rip hay ##uro 1814 espn welcomed ##aria qualifier glare highland timing ##cted shells eased geometry louder exciting slovakia ##iz savings prairie marching rafael tonnes ##lled curtain preceding shy heal greene worthy ##pot detachment bury sherman ##eck reinforced seeks bottles contracted duchess outfit walsh mickey geoffrey archer squeeze dawson eliminate invention ##enberg neal ##eth stance dealer coral maple retire simplified 1833 hid watts backwards jules ##oke genesis frames rebounds burma woodland moist santos whispers drained subspecies streaming ulster burnt correspondence maternal gerard denis stealing genius duchy ##oria inaugurated momentum suits placement sovereign clause thames ##hara confederation reservation sketch yankees lets rotten charm hal verses commercially dot salon citation adopt winnipeg mist allocated cairo jenkins interference objectives ##wind 1820 portfolio armoured sectors initiatives integrity exercises robe tap gazed ##tones distracted rulers favorable jerome tended cart factories ##eri diplomat valued gravel charitable calvin exploring shepherd terrace pupil ##ural reflects ##rch governors shelf depths ##nberg trailed crest tackle ##nian hatred ##kai clare makers ethiopia longtime detected embedded lacking slapped rely thomson anticipation morton successive agnes screenwriter straightened philippe playwright haunted licence iris intentions sutton logical correctly ##weight branded licked tipped silva ricky narrator requests ##ents greeted supernatural cow ##wald lung refusing employer strait gaelic liner ##piece zoe sabha ##mba driveway harvest prints bates reluctantly threshold algebra ira wherever coupled assumption picks designers raids gentlemen roller blowing leipzig locks screw dressing strand ##lings scar dwarf depicts ##nu nods differ boris ##eur yuan flip ##gie mob invested questioning applying shout ##sel gameplay blamed illustrations bothered weakness rehabilitation ##zes envelope rumors miners leicester subtle kerry ferguson premiership bengali prof catches remnants dana ##rily shouting presidents baltic ought ghosts dances sailors shirley fancy dominic ##bie madonna ##rick bark buttons gymnasium ashes liver toby oath providence doyle evangelical nixon cement carnegie embarked hatch surroundings guarantee needing pirate essence filter crane hammond projected immune percy twelfth regent doctoral damon mikhail ##ichi critically elect realised abortion acute screening mythology steadily frown nottingham kirk wa minneapolis ##rra module algeria nautical encounters surprising statues availability shirts pie alma brows munster mack soup crater tornado sanskrit cedar explosive bordered dixon planets stamp exam happily ##bble carriers kidnapped accommodation emigrated ##met knockout correspondent violation profits peaks lang specimen agenda ancestry pottery spelling equations obtaining ki linking 1825 debris asylum buddhism ##ants gazette dental eligibility fathers averaged zimbabwe francesco coloured hissed translator lynch mandate humanities mackenzie uniforms ##iana asset fitting samantha genera rim beloved shark riot entities expressions indo carmen slipping owing abbot neighbor sidney rats recommendations encouraging squadrons anticipated commanders conquered donations diagnosed divide ##iva guessed decoration vernon auditorium revelation conversations ##kers ##power herzegovina dash alike protested lateral herman accredited ##gent freeman mel fiji crow crimson ##rine livestock ##pped humanitarian bored oz whip ##lene ##ali legitimate alter grinning spelled anxious oriental wesley ##nin ##hole carnival controller detect ##ssa bowed educator kosovo macedonia ##sin occupy mastering stephanie janeiro para unaware nurses noon hopefully ranger combine sociology polar rica ##eer neill ##sman holocaust doubled lust 1828 decent cooling unveiled 1829 nsw homer chapman meyer dive mae reagan expertise ##gled darwin brooke sided prosecution investigating comprised petroleum genres reluctant differently trilogy johns vegetables corpse highlighted lounge pension unsuccessfully elegant aided ivory beatles amelia cain dubai immigrant babe underwater combining mumbled atlas horns accessed ballad physicians homeless gestured rpm freak louisville corporations patriots prizes rational warn modes decorative overnight din troubled phantom monarch sheer ##dorf generals guidelines organs addresses enhance curling parishes cord ##kie caesar deutsche bavaria coleman cyclone ##eria bacon petty ##yama ##old hampton diagnosis 1824 throws complexity rita disputed pablo marketed trafficking ##ulus examine plague formats vault faithful ##bourne webster highlights ##ient phones vacuum sandwich modeling ##gated bolivia clergy qualities isabel ##nas ##ars wears screams reunited annoyed bra ##ancy ##rate differential transmitter tattoo container poker ##och excessive resides cowboys ##tum augustus trash providers statute retreated balcony reversed void storey preceded masses leap laughs neighborhoods wards schemes falcon santo battlefield ronnie lesbian venus ##dian beg sandstone daylight punched gwen analog stroked wwe acceptable measurements toxic ##kel adequate surgical economist parameters varsity ##sberg quantity ##chy ##rton countess generating precision diamonds expressway ##ı 1821 uruguay talents galleries expenses scanned colleague outlets ryder lucien ##ila paramount syracuse dim fangs gown sweep ##sie missionaries websites sentences adviser val trademark spells ##plane patience starter slim ##borg toe incredibly shoots elliot nobility ##wyn cowboy endorsed gardner tendency persuaded organisms emissions kazakhstan amused boring chips themed ##hand constantinople chasing systematic guatemala borrowed erin carey ##hard highlands struggles 1810 ##ifying ##ced exceptions develops enlarged kindergarten castro ##rina leigh zombie juvenile ##most consul sailor hyde clarence intensive pinned nasty useless jung clayton stuffed exceptional ix apostolic transactions exempt swinging cove religions shields dairy bypass pursuing joyce bombay chassis southampton chat interact redesignated ##pen nascar pray salmon rigid regained malaysian grim publicity constituted capturing toilet delegate purely tray drift loosely striker weakened trinidad mitch itv defines transmitted scarlet nodding fitzgerald narrowly tooth standings virtue ##wara ##cting chateau gloves lid hurting conservatory ##pel sinclair reopened sympathy nigerian strode advocated optional chronic discharge suck compatible laurel stella fails wage dodge informal sorts levi buddha villagers chronicles heavier summoned gateway eleventh jewelry translations accordingly seas ##ency fiber pyramid cubic dragging ##ista caring ##ops contacted lunar lisbon patted 1826 sacramento theft madagascar subtropical disputes holidays piper willow mare cane newfoundland benny companions dong raj observe roar charming plaque tibetan fossils enacted manning bubble tanzania ##eda ##hir funk swamp deputies cloak ufc scenario par scratch metals anthem guru engaging specially ##boat dialects nineteen cecil duet disability unofficial ##lies defunct moonlight drainage surname puzzle switching conservatives mammals knox broadcaster sidewalk cope ##ried benson princes peterson ##sal bedford sharks eli wreck alberto gasp archaeology lgbt teaches securities madness compromise waving coordination davidson visions leased possibilities eighty fernandez enthusiasm assassin sponsorship reviewer kingdoms estonian laboratories ##fy ##nal applies verb celebrations ##zzo rowing lightweight sadness submit balanced dude explicitly metric magnificent mound brett mohammad mistakes irregular sanders betrayed shipped surge ##enburg reporters termed georg pity verbal bulls abbreviated enabling appealed sicily sting heel sweetheart bart spacecraft brutal monarchy aberdeen cameo diane survivor clyde ##aries complaint ##makers clarinet delicious chilean karnataka coordinates 1818 panties ##rst pretending dramatically kiev tends distances catalog launching instances telecommunications portable lindsay vatican ##eim angles aliens marker stint screens bolton ##rne judy wool benedict plasma europa imaging filmmaker swiftly contributor opted stamps apologize financing butter gideon sophisticated alignment avery chemicals yearly speculation prominence professionally immortal institutional inception wrists identifying tribunal derives gains papal preference linguistic vince operative brewery ##ont unemployment boyd ##ured ##outs albeit prophet 1813 ##rad quarterly asteroid cleaned radius temper ##llen telugu jerk viscount ##ote glimpse ##aya yacht hawaiian baden laptop readily ##gu monetary offshore scots watches ##yang ##arian upgrade needle lea encyclopedia flank fingertips delight teachings confirm roth beaches midway winters ##iah teasing daytime beverly gambling ##backs regulated clement hermann tricks knot ##shing ##uring ##vre detached ecological owed specialty byron inventor bats stays screened unesco midland trim affection ##ander jess thoroughly feedback chennai strained heartbeat wrapping overtime pleaded ##sworth leisure oclc ##tate ##ele feathers angelo thirds nuts surveys clever gill commentator ##dos darren rides gibraltar dissolution dedication shin meals saddle elvis reds chaired taller appreciation functioning niece favored advocacy robbie criminals suffolk yugoslav passport constable congressman hastings ##rov consecrated sparks ecclesiastical confined ##ovich muller floyd nora 1822 paved 1827 cumberland ned saga spiral appreciated collaborative treating similarities feminine finishes ##ib jade import ##hot champagne mice securing celebrities helsinki attributes ##gos cousins phases ache lucia gandhi submission vicar spear shine tasmania biting detention constitute tighter seasonal ##gus terrestrial matthews effectiveness parody philharmonic ##onic 1816 strangers encoded consortium guaranteed regards shifts tortured collision supervisor inform broader insight theaters armour emeritus blink incorporates mapping handball flexible ##nta substantially generous thief carr loses 1793 prose ucla romeo generic metallic realization damages commissioners zach default helicopters lengthy stems partnered spectators rogue indication penalties teresa 1801 sen ##tric dalton ##wich irving photographic ##vey deaf peters excluded unsure ##vable patterson crawled ##zio resided whipped latvia slower ecole pipes employers maharashtra comparable textile pageant ##gel alphabet binary irrigation chartered choked antoine offs waking supplement quantities demolition regain locate urdu folks scary andreas whites ##ava classrooms mw aesthetic publishes valleys guides cubs johannes bryant conventions affecting ##itt drain awesome isolation prosecutor ambitious apology captive downs atmospheric lorenzo aisle beef foul ##onia kidding composite disturbed illusion natives ##ffer rockets riverside wartime painters adolf melted uncertainty simulation hawks progressed meantime builder spray breach unhappy regina russians determining tram 1806 ##quin aging 1823 garion rented mister diaz terminated clip 1817 depend nervously disco owe defenders shiva notorious disbelief shiny worcester ##gation ##yr trailing undertook islander belarus limitations watershed fuller overlooking utilized raphael 1819 synthetic breakdown klein ##nate moaned memoir lamb practicing ##erly cellular arrows exotic witches charted rey hut hierarchy subdivision freshwater giuseppe aloud reyes qatar marty sideways utterly sexually jude prayers mccarthy softball blend damien ##gging ##metric wholly erupted lebanese negro revenues tasted comparative teamed transaction labeled maori sovereignty parkway trauma gran malay advancement descendant buzz salvation inventory symbolic ##making antarctica mps ##bro mohammed myanmar holt submarines tones ##lman locker patriarch bangkok emerson remarks predators kin afghan confession norwich rental emerge advantages ##zel rca ##hold shortened storms aidan ##matic autonomy compliance ##quet dudley ##osis 1803 motto documentation summary professors spectacular christina archdiocese flashing innocence remake ##dell psychic reef scare employ sticks meg gus leans accompany bergen tomas doom wages pools ##bes breasts scholarly alison outline brittany breakthrough willis realistic ##cut ##boro competitor ##stan pike picnic designing commercials washing villain skiing costumes auburn halted executives logistics cycles vowel applicable barrett exclaimed eurovision eternity ramon ##umi modifications sweeping disgust torch aviv ensuring rude dusty sonic donovan outskirts cu pathway ##band ##gun disciplines acids cadet paired sketches ##sive marriages folding peers slovak implies admired ##beck 1880s leopold instinct attained weston megan horace ##ination dorsal ingredients evolutionary complications deity lethal brushing levy deserted institutes posthumously delivering telescope coronation motivated rapids luc flicked pays volcano tanner weighed ##nica crowds frankie gifted addressing granddaughter winding ##rna constantine gomez ##front landscapes rudolf anthropology slate werewolf astronomy circa rouge dreaming sack knelt drowned naomi prolific tracked freezing herb agony randall twisting wendy deposit touches vein wheeler ##bbled batted retaining tire presently compare specification daemon nigel ##grave merry recommendation czechoslovakia sandra roma ##sts lambert inheritance sheikh winchester cries examining ##yle comeback cuisine nave ##iv retrieve tomatoes barker polished defining irene lantern personalities begging tract swore 1809 ##gic omaha brotherhood haiti ##ots exeter ##ete ##zia steele dumb pearson surveyed elisabeth trends fritz bugs fraction calmly viking ##birds tug inserted unusually ##ield confronted distress crashing brent turks resign ##olo cambodia gabe sauce ##kal evelyn extant clusters quarry teenagers luna ##lers ##ister affiliation drill ##ashi panthers scenic libya anita strengthen inscriptions ##cated lace sued judith riots ##uted mint ##eta preparations midst dub challenger ##vich mock displaced wicket breaths enables schmidt analyst ##lum highlight automotive axe josef newark sufficiently resembles 50th ##pal flushed mum traits ##ante commodore incomplete warming titular ceremonial ethical celebrating eighteenth cao lima medalist mobility strips snakes miniature zagreb barton escapes umbrella automated doubted differs cooled georgetown dresden cooked fade wyatt jacobs carlton abundant stereo madras inning spur malayalam begged osaka groan escaping charging dose ##aj bud papa communists advocates edged tri resemble peaking necklace fried montenegro saxony goose glances stuttgart curator recruit grocery sympathetic ##tting ##fort lotus randolph ancestor ##rand succeeding jupiter 1798 macedonian ##heads hiking 1808 handing fischer ##itive garbage ##pies prone singular papua inclined attractions italia pouring motioned grandma garnered jacksonville corp ego ringing aluminum ##hausen ordering ##foot drawer traders synagogue ##kawa resistant wandering fragile fiona teased hardcore soaked jubilee decisive exposition mercer poster valencia hale kuwait 1811 ##ises ##wr ##eed tavern gamma johan ##uer airways amino gil vocational domains torres generator folklore outcomes ##keeper canberra shooter fl beams confrontation ##gram aligned forestry pipeline jax motorway conception decay coffin ##cott stalin 1805 escorted minded ##nam sitcom purchasing twilight veronica additions passive tensions straw frequencies 1804 refugee cultivation ##iate christie clary bulletin crept disposal ##rich ##zong processor crescent ##rol emphasized whale nazis aurora dwelling hauled sponsors toledo ideology theatres tessa cerambycidae saves turtle cone suspects kara rusty yelling greeks mozart shades cocked participant shire spit freeze necessity ##cos inmates nielsen councillors loaned uncommon omar peasants botanical offspring daniels formations jokes 1794 pioneers sigma licensing ##sus wheelchair polite 1807 liquor pratt trustee ##uta forewings balloon kilometre camping explicit casually shawn foolish teammates nm hassan carrie judged satisfy vanessa knives selective flowed ##lice stressed eliza mathematician cease cultivated ##roy commissions browns ##ania destroyers sheridan meadow ##rius minerals ##cial downstream clash gram memoirs ventures baha seymour archie midlands edith fare flynn invite canceled tiles stabbed boulder incorporate amended camden facial mollusk unreleased descriptions grabs raises ramp shiver ##rose coined pioneering tunes qing warwick tops melanie giles ##rous wandered ##inal annexed 30th unnamed ##ished organizational airplane normandy stoke whistle blessing violations chased holders shotgun ##ctic reactor ##vik tires tearing shores fortified mascot constituencies columnist productive tibet ##rta lineage hooked tapes judging cody ##gger hansen kashmir triggered ##eva solved cliffs ##tree resisted anatomy protesters transparent implied ##iga injection mattress excluding ##mbo defenses helpless devotion ##elli growl liberals weber phenomena atoms plug ##iff mortality apprentice howe convincing swimmer barber leone promptly sodium def nowadays arise ##oning gloucester corrected dignity norm erie ##ders elders evacuated compression ##yar hartford backpack reasoning accepts 24th wipe millimetres marcel ##oda dodgers albion 1790 overwhelmed aerospace oaks 1795 showcase acknowledge recovering nolan ashe hurts geology fashioned disappearance farewell swollen shrug marquis wimbledon rue 1792 commemorate reduces experiencing inevitable calcutta ##court murderer sticking fisheries imagery bloom ##inus gustav hesitation memorable viral beans accidents tunisia antenna spilled consort treatments aye perimeter ##gard donation hostage migrated banker addiction apex lil trout ##ously conscience ##nova rams sands genome passionate troubles ##lets amid ##ibility ##ret higgins exceed vikings ##vie payne ##zan muscular defendant sucking ##wal ibrahim fuselage claudia vfl europeans snails interval ##garh preparatory statewide tasked lacrosse viktor ##lation angola ##hra flint implications employs teens patrons stall weekends barriers scrambled nucleus tehran jenna parsons lifelong robots displacement ##bles precipitation knuckles clutched 1802 marrying ecology marx accusations declare scars kolkata mat meadows bermuda skeleton finalists vintage crawl coordinate affects subjected orchestral mistaken mirrors dipped relied arches candle ##nick incorporating wildly fond basilica owl fringe rituals whispering stirred feud tertiary slick goat honorable whereby ricardo stripes parachute adjoining submerged synthesizer ##gren intend positively ninety phi beaver partition fellows alexis prohibition carlisle bizarre fraternity doubts icy aquatic sneak sonny combines airports crude supervised spatial merge alfonso ##bic corrupt scan undergo ##ams disabilities colombian comparing dolphins perkins reprinted unanimous bounced hairs underworld midwest semester bucket paperback miniseries coventry demise ##leigh demonstrations sensor rotating yan ##hler arrange soils ##idge hyderabad labs brakes grandchildren ##nde negotiated rover ferrari continuation directorate augusta stevenson counterpart gore ##rda nursery rican ave collectively broadly pastoral repertoire asserted discovering nordic styled fiba cunningham harley middlesex survives tumor tempo zack aiming lok urgent ##nto devils contractor turin ##wl bliss repaired simmons moan astronomical negotiate lyric 1890s lara bred clad angus pbs engineered posed hernandez possessions elbows psychiatric strokes confluence electorate lifts campuses lava alps ##ution ##date physicist woody ##ographic ##itis juliet reformation sparhawk complement suppressed jewel ##½ floated ##kas continuity sadly ##ische inability melting scanning paula flour judaism safer vague solving curb ##stown financially gable bees expired miserable cassidy dominion 1789 cupped robbery facto amos warden resume tallest marvin pounded declaring gasoline ##aux darkened sophomore ##mere erection gossip televised risen dial ##eu pillars passages profound arabian ashton silicon nail ##lated ##hardt fleming firearms ducked circuits blows waterloo titans fireplace cheshire financed activation algorithms constituent catcher cherokee partnerships sexuality platoon tragic vivian guarded whiskey meditation poetic ##nga porto listeners dominance kendra mona chandler factions 22nd salisbury attitudes derivative ##ido ##haus intake paced javier illustrator barrels bias cockpit burnett dreamed ensuing receptors someday hawkins mattered ##lal slavic 1799 jesuit cameroon wasted wax lowering victorious freaking outright hancock librarian sensing bald calcium myers tablet announcing barack shipyard pharmaceutical greenwich flush medley patches wolfgang speeches acquiring exams nikolai hayden kannada reilly waitress abdomen devastated capped pseudonym pharmacy fulfill paraguay 1796 clicked ##trom archipelago syndicated ##hman lumber orgasm rejection clifford lorraine advent mafia rodney brock ##used ##elia cassette chamberlain despair mongolia sensors developmental upstream ##alis spanning trombone basque seeded interred renewable rhys leapt revision molecule ##ages chord vicious nord shivered 23rd arlington debts corpus sunrise bays blackburn centimetres ##uded shuddered strangely gripping cartoons isabelle orbital ##ppa seals proving refusal strengthened bust assisting baghdad batsman portrayal mara pushes spears og ##cock reside nathaniel brennan 1776 confirmation caucus ##worthy markings yemen nobles ku lazy viewer catalan encompasses sawyer ##fall sparked substances patents braves arranger evacuation sergio persuade dover tolerance penguin cum jockey insufficient townships occupying declining plural processed projection puppet flanders introduces liability ##yon gymnastics antwerp hobart candles jeep wes observers chaplain bundle glorious ##hine hazel flung sol excavations dumped stares bangalore triangular icelandic intervals expressing turbine ##vers songwriting crafts ##igo jasmine ditch rite entertaining comply sorrow wrestlers basel emirates marian rivera helpful ##some caution downward networking ##atory ##tered darted genocide emergence replies specializing spokesman convenient unlocked fading augustine concentrations resemblance elijah investigator andhra ##uda promotes ##rrell fleeing simone announcer lydia weaver residency modification ##fest stretches alternatively nat lowe lacks ##ented pam tile concealed inferior abdullah residences tissues vengeance ##ided moisture peculiar groove bologna jennings ninja oversaw zombies pumping batch livingston emerald installations 1797 peel nitrogen rama ##fying schooling strands responding werner lime casa accurately targeting ##rod underway ##uru hemisphere lester ##yard occupies griffith angrily reorganized ##owing courtney deposited estadio ##ifies dunn exiled ##ying checks ##combe successes unexpectedly blu assessed ##flower observing sacked spiders kn nodes prosperity audrey divisional broncos tangled adjust feeds erosion paolo surf directory snatched humid admiralty screwed reddish ##nese modules trench lamps bind leah bucks competes ##nz transcription isles violently clutching pga cyclist inflation flats ragged unnecessary ##hian stubborn coordinated harriet baba disqualified insect wolfe ##fies reinforcements rocked duel winked embraced bricks ##raj hiatus defeats pending brightly jealousy ##xton ##uki lena colorful ##dley stein kidney ##shu underwear wanderers ##haw ##icus guardians m³ roared habits ##wise permits uranium punished disguise bundesliga elise dundee erotic partisan collectors float individually rendering behavioral bucharest ser hare valerie corporal nutrition proportional immense ##kis pavement ##zie ##eld sutherland crouched 1775 suzuki trades endurance operas crosby prayed priory rory socially gujarat walton cube pasha privilege lennon floods thorne waterfall nipple scouting approve ##lov minorities voter dwight extensions assure ballroom slap dripping privileges rejoined confessed demonstrating patriotic yell investor ##uth pagan slumped squares confront bert embarrassment aston urging sweater starr yuri brains williamson commuter mortar structured selfish exports ##jon cds ##him unfinished ##rre mortgage destinations ##nagar canoe solitary buchanan delays magistrate fk ##pling motivation ##lier ##vier recruiting assess ##mouth malik antique 1791 pius rahman reich tub zhou smashed airs galway xii conditioning honduras discharged dexter ##pf lionel debates lemon volunteered dioxide procession devi sic tremendous advertisements colts transferring verdict hanover decommissioned utter relate pac racism beacon limp similarity terra occurrence ant becky capt updates armament richie pal ##graph halloween mayo ##ssen ##bone cara serena fcc dolls obligations ##dling violated lafayette jakarta exploitation infamous iconic ##lah ##park moody reginald dread spill crystals olivier modeled bluff equilibrium separating notices ordnance extinction onset cosmic attachment sammy expose privy anchored ##bil abbott admits bending baritone emmanuel policeman vaughan winged climax dresses denny polytechnic mohamed burmese authentic nikki genetics grandparents homestead gaza postponed metacritic una ##sby unstable dissertation ##cian curls obscure uncovered bronx praying disappearing ##hoe prehistoric coke turret mutations nonprofit pits monaco ##usion prominently dispatched podium ##mir uci ##uation fortifications birthplace kendall ##lby ##oll preacher rack goodman persistent ##ott countless jaime recorder lexington persecution jumps renewal wagons crushing ##holder decorations ##lake abundance wrath laundry £1 garde jeanne beetles peasant splitting caste sergei ##rer ##ema scripts ##ively rub satellites ##vor inscribed verlag scrapped gale packages chick potato slogan kathleen arabs ##culture counterparts reminiscent choral ##tead rand retains bushes dane accomplish courtesy closes ##oth slaughter hague krakow lawson tailed elias ginger ##ttes canopy betrayal rebuilding turf ##hof frowning allegiance brigades kicks rebuild polls alias nationalism rowan audition bowie fortunately recognizes harp dillon horrified ##oro renault ropes presumed rewarded infrared wiping accelerated illustration presses practitioners badminton ##iard detained ##tera recognizing relates misery ##sies ##tly reproduction piercing potatoes thornton esther manners hbo ##aan ours bullshit ernie perennial sensitivity illuminated rupert ##iss rfc nassau ##dock staggered socialism ##haven appointments nonsense prestige sharma haul solidarity ##rata igor pedestrian ##uit baxter tenants wires medication unlimited guiding impacts diabetes ##rama sasha pas clive extraction continually constraints ##bilities sonata hunted sixteenth chu planting quote mayer pretended spat ceramic ##cci curtains pigs pitching ##dad latvian sore dayton ##sted patrols slice playground ##nted shone stool apparatus inadequate mates treason ##ija desires ##liga ##croft somalia laurent mir grape obliged chevrolet thirteenth stunning enthusiastic ##ede accounted concludes currents basil ##kovic drought ##rica mai ##aire shove posting ##shed pilgrimage humorous packing fry pencil wines smells marilyn aching newest clung bon neighbours sanctioned ##pie mug ##stock drowning hydraulic ##vil hiring reminder lilly investigators ##ncies sour ##eous compulsory packet ##rion ##graphic ##elle cannes ##inate depressed ##rit heroic importantly theresa ##tled conway saturn marginal rae ##xia corresponds royce pact jasper explosives packaging aluminium ##ttered denotes rhythmic spans assignments hereditary outlined originating sundays lad reissued greeting beatrice ##dic pillar marcos plots handbook alcoholic judiciary avant slides extract masculine blur ##eum homage trembled owens hymn trey signaling socks accumulated reacted attic theo lining angie distraction primera talbot creativity billed ##hey deacon eduardo identifies proposition dizzy gunner hogan ##yam ##pping ##hol ja ##chan jensen reconstructed ##berger clearance darius ##nier abe harlem plea dei circled emotionally notation fascist neville exceeded upwards viable ducks workforce racer limiting shri ##lson possesses kerr moths devastating laden disturbing locking gal fearing accreditation flavor aide 1870s mountainous ##baum melt ##ures texture servers soda herd ##nium erect puzzled hum peggy examinations gould testified geoff ren devised sacks ##law denial posters grunted cesar tutor gerry offerings byrne falcons combinations incoming pardon rocking 26th avengers flared mankind seller uttar loch nadia stroking exposing fertile ancestral instituted ##has noises prophecy taxation eminent vivid pol ##bol dart indirect multimedia notebook upside displaying adrenaline referenced geometric ##iving progression ##ddy blunt announce ##far implementing ##lav aggression liaison cooler cares headache plantations gorge dots impulse thickness ashamed averaging kathy obligation precursor fowler symmetry thee hears ##rai undergoing butcher bowler ##lip cigarettes subscription goodness ##ically browne ##hos kyoto donor ##erty damaging friction drifting expeditions hardened prostitution fauna blankets claw tossing snarled butterflies recruits investigative coated healed communal hai xiii academics boone psychologist restless lahore stephens brendan foreigners printer ached explode 27th deed scratched dared ##pole cardiac 1780 okinawa proto commando compelled oddly electrons replica thanksgiving ##rist sheila deliberate stafford tidal representations hercules ou ##path ##iated kidnapping lenses ##tling deficit samoa mouths consuming computational maze granting smirk razor fixture ideals inviting aiden nominal issuing julio pitt ramsey docks ##oss exhaust ##owed bavarian draped anterior mating ethiopian explores noticing ##nton discarded convenience hoffman endowment beasts cartridge mormon paternal probe sleeves interfere lump deadline jenks bulldogs scrap alternating justified reproductive nam seize descending secretariat kirby grouped smash panther sedan tapping lola cheer germanic unfortunate ##eter unrelated ##fan subordinate ##sdale suzanne advertisement ##ility horsepower ##lda cautiously discourse luigi ##mans ##fields noun prevalent mao schneider everett surround governorate kira ##avia westward ##take misty rails sustainability unused ##rating packs toast unwilling regulate thy suffrage nile awe assam definitions travelers affordable ##rb conferred sells undefeated beneficial torso basal repeating remixes bahrain cables fang ##itated excavated numbering statutory deluxe ##lian forested ramirez derbyshire zeus slamming transfers astronomer banana lottery berg histories bamboo ##uchi resurrection posterior bowls vaguely ##thi thou preserving tensed offence ##inas meyrick callum ridden watt langdon tying lowland snorted daring truman ##hale ##girl aura overly filing weighing goa infections philanthropist saunders eponymous ##owski latitude perspectives reviewing mets commandant radial ##kha flashlight reliability koch vowels amazed ada elaine supper ##encies predator debated soviets cola ##boards ##nah compartment crooked arbitrary fourteenth havana majors steelers clips profitable ambush exited packers ##tile nude cracks fungi limb trousers josie shelby tens frederic ##ος definite smoothly constellation insult baton discs lingering ##nco conclusions lent staging becker grandpa shaky ##tron einstein obstacles adverse economically ##moto mccartney thor dismissal motions readings nostrils treatise ##pace squeezing evidently prolonged 1783 venezuelan je marguerite beirut takeover shareholders ##vent denise digit airplay norse ##bbling imaginary pills hubert blaze vacated eliminating vine mansfield retrospective barrow borne clutch bail forensic weaving ##nett ##witz desktop citadel promotions worrying dorset subdivided ##iating manned expeditionary pickup synod chuckle barney ##rz ##ffin functionality karachi litigation meanings lick anders ##ffed execute curl oppose ankles typhoon ##ache linguistics compassion pressures grazing perfection ##iting immunity monopoly muddy backgrounds namibia francesca monitors attracting stunt tuition ##ии vegetable ##mates ##quent mgm jen complexes forts cellar bites seventeenth royals flemish failures mast charities ##cular peruvian capitals macmillan ipswich outward frigate postgraduate folds employing ##ouse concurrently fiery ##tai contingent nightmares monumental nicaragua ##kowski lizard mal fielding gig reject harding ##ipe coastline ##cin beethoven humphrey innovations ##tam norris doris solicitor obey niagara shelves bourbon nightclub specifications hilton ##ndo centennial dispersed worm neglected briggs kuala uneasy ##nstein ##bound ##aking ##burgh awaiting pronunciation ##bbed ##quest eh optimal zhu raped greens presided brenda worries venetian marxist turnout ##lius refined braced sins grasped sunderland nickel speculated lowell cyrillic communism fundraising resembling colonists mutant freddie usc ##mos gratitude ##run mural ##lous chemist reminds 28th steals tess pietro ##ingen promoter ri microphone honoured rai sant ##qui feather ##nson burlington kurdish terrorists deborah sickness ##wed hazard irritated desperation veil clarity ##rik jewels xv ##gged ##ows ##cup berkshire unfair mysteries orchid winced exhaustion renovations stranded obe infinity ##nies adapt redevelopment thanked registry olga domingo noir tudor ole commenting behaviors ##ais crisp pauline probable stirling wigan paralympics panting surpassed ##rew luca barred famed ##sters cassandra waiter carolyn exported ##orted andres destructive deeds jonah castles vacancy ##glass 1788 orchard yep famine belarusian sprang ##forth skinny ##mis administrators rotterdam zambia zhao boiler discoveries ##ride ##physics lucius disappointing outreach spoon ##frame qualifications unanimously enjoys regency ##iidae stade realism veterinary rodgers dump alain chestnut castile censorship rumble gibbs communion reggae inactivated logs loads ##houses homosexual ##iano ale informs ##cas phrases plaster linebacker ambrose kaiser fascinated limerick recruitment forge mastered ##nding leinster rooted threaten ##strom borneo ##hes suggestions scholarships propeller documentaries patronage coats constructing invest neurons comet entirety shouts identities annoying unchanged wary ##antly ##ogy neat oversight ##kos phillies replay constance ##kka incarnation humble skies minus ##acy smithsonian guerrilla jar cadets ##plate surplus audit ##aru cracking joanna louisa pacing ##lights intentionally ##iri diner nwa imprint australians tong unprecedented bunker naive specialists ark nichols railing leaked pedal ##uka shrub longing roofs captains neural tuned ##ntal ##jet emission medina frantic codex definitive sid abolition intensified stocks enrique sustain genoa oxide ##written clues cha ##gers tributaries fragment venom ##ente ##sca muffled vain sire laos ##ingly ##hana hastily snapping surfaced sentiment motive ##oft contests approximate mesa luckily dinosaur exchanges propelled accord bourne relieve tow masks offended ##ues cynthia ##mmer rains bartender zinc reviewers lois ##sai legged arrogant rafe comprise handicap blockade inlet lagoon copied drilling shelley petals ##inian mandarin obsolete ##inated onward arguably productivity praising seldom busch discusses raleigh shortage ranged stanton encouragement firstly conceded overs temporal ##uke cbe ##bos woo certainty pumps ##pton stalked ##uli lizzie periodic thieves weaker gases shoving chooses wc ##chemical prompting weights ##kill robust flanked sticky tuberculosis ##eb ##eal christchurch resembled wallet reese inappropriate pictured distract fixing fiddle giggled burger heirs hairy mechanic torque obsessed chiefly cheng logging extracted meaningful numb ##vsky gloucestershire reminding unite ##lit breeds diminished clown glove 1860s archibald focal freelance sliced depiction ##yk organism switches sights stray crawling ##ril lever leningrad interpretations loops anytime reel alicia delighted ##ech inhaled xiv suitcase bernie vega licenses northampton exclusion induction monasteries racecourse homosexuality ##sfield ##rky dimitri michele alternatives ions commentators genuinely objected pork hospitality fencing stephan warships peripheral wit drunken wrinkled quentin spends departing chung numerical spokesperson johannesburg caliber killers ##udge assumes neatly demographic abigail bloc mounting ##lain bentley slightest xu recipients ##jk merlin ##writer seniors prisons blinking hindwings flickered kappa ##hel 80s strengthening appealing brewing gypsy mali lashes hulk unpleasant harassment bio treaties predict instrumentation pulp troupe boiling mantle ##ffe ##vn dividing handles verbs ##onal coconut senegal thorough gum momentarily ##sto cocaine panicked destined ##turing teatro denying weary captained mans ##hawks wakefield bollywood thankfully cyril amendments ##bahn consultation stud reflections kindness 1787 internally ##ovo tex mosaic distribute paddy seeming ##hic piers ##mura popularly winger kang sentinel mccoy ##anza covenant ##bag verge fireworks suppress thrilled dominate ##jar swansea reconciliation stiffened cue dorian ##uf damascus amor ida foremost ##aga porsche unseen dir ##had ##azi stony lexi melodies ##nko angular integer podcast ants inherent jaws justify persona ##olved josephine ##nr ##ressed customary flashes gala cyrus glaring backyard ariel physiology greenland stir avon atletico finch methodology ked mas catholicism townsend branding quincy fits containers 1777 ashore aragon forearm poisoning adopting conquer grinding amnesty keller finances evaluate forged lankan instincts ##uto guam bosnian photographed workplace desirable protector allocation intently encourages willy ##sten bodyguard electro brighter bihar ##chev lasts opener amphibious sal verde arte ##cope captivity vocabulary yields ##tted agreeing desmond pioneered ##chus strap campaigned railroads ##ович emblem ##dre stormed ##ulous marijuana northumberland ##nath bowen landmarks beaumont ##qua danube ##bler attorneys th flyers critique villains cass mutation acc ##0s colombo mckay motif sampling concluding syndicate ##rell neon stables warnings clint mourning wilkinson ##tated merrill leopard evenings exhaled emil sonia ezra discrete stove farrell fifteenth prescribed superhero ##rier worms helm wren ##duction expo ##rator hq unfamiliar antony prevents acceleration fiercely mari painfully calculations cheaper ign clifton irvine davenport mozambique pierced ##evich wonders ##wig ##cate ##iling crusade ware enzymes reasonably mls ##coe mater ambition bunny eliot kernel ##fin asphalt headmaster torah aden lush pins waived ##yas joao substrate enforce ##grad ##ules alvarez selections epidemic tempted bremen translates ensured waterfront 29th forrest manny malone kramer reigning simpler absorption engraved ##ffy evaluated 1778 haze comforting crossover ##abe thorn ##rift ##imo suppression fatigue cutter wurttemberg ##orf enforced hovering proprietary samurai syllable ascent lacey tick lars tractor merchandise rep bouncing defendants ##yre huntington ##oko standardized ##hor ##hima assassinated predecessors rainy liar assurance lyrical ##uga secondly flattened parameter undercover ##mity bordeaux punish ridges markers exodus inactive hesitate debbie nyc pledge savoy nagar offset organist ##tium hesse marin converting ##iver diagram propulsion validity reverted supportive ministries clans responds proclamation ##inae ein pleading patriot birch islanders strauss hates ##dh brandenburg concession 1900s killings textbook antiquity cinematography wharf embarrassing setup creed farmland inequality centred signatures fallon ##ingham ##uts ceylon gazing directive laurie ##tern globally ##uated ##dent allah excavation threads ##cross frantically icc utilize determines respiratory thoughtful receptions ##dicate merging chandra seine builders builds diagnostic dev visibility goddamn analyses dhaka proves chancel concurrent curiously canadians pumped restoring 1850s turtles jaguar sinister spinal declan vows 1784 glowed capitalism swirling universidad ##lder ##oat soloist ##genic ##oor coincidence beginnings nissan dip resorts caucasus combustion infectious ##eno pigeon serpent ##itating conclude masked salad jew ##gr surreal toni ##wc harmonica ##gins ##etic ##coat fishermen intending bravery ##wave klaus titan wembley taiwanese ransom 40th incorrect hussein eyelids cooke dramas utilities ##etta ##print 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favoured appoint transgender elephants poked greenwood defences fulfilled militant somali 1758 chalk potent ##ucci migrants wink assistants nos restriction activism niger ##ario colon shaun ##sat daphne ##erated swam congregations reprise considerations magnet playable xvi overthrow tobias knob chavez coding ##mers propped katrina orient newcomer ##suke temperate ##pool farmhouse interrogation committing ##vert forthcoming strawberry joaquin macau ponds shocking siberia ##cellular chant contributors ##nant ##ologists sped absorb hail 1782 spared ##hore barbados karate opus originates saul ##xie evergreen leaped ##rock correlation exaggerated weekday unification bump tracing brig afb pathways utilizing disturbance kneeling ##stad ##guchi 100th pune ##thy decreasing manipulation miriam academia ecosystem occupational rbi ##lem rift rotary stacked incorporation awakening generators guerrero racist ##omy cyber derivatives culminated allie annals panzer sainte pops zu austro ##vate algerian politely nicholson mornings educate tastes thrill dartmouth ##gating ##jee regan differing concentrating choreography divinity pledged alexandre routing gregor madeline ##idal apocalypse ##hora gunfire culminating elves fined liang lam programmed tar guessing transparency gabrielle ##gna cancellation flexibility ##lining accession shea stronghold nets specializes ##rgan abused hasan sgt exceeding admiration supermarket photographers specialised tilt resonance hmm perfume sami threatens garland botany guarding boiled greet puppy russo supplier wilmington vibrant vijay ##bius paralympic grumbled paige faa licking margins hurricanes ##gong fest grenade ripping ##uz counseling weigh ##sian needles wiltshire edison costly ##not fulton tramway redesigned staffordshire gasping watkins sleepy candidacy monkeys timeline throbbing ##bid ##sos berth uzbekistan vanderbilt bothering overturned ballots gem ##iger sunglasses subscribers hooker compelling ang exceptionally saloon stab ##rdi carla terrifying ##vision coil ##oids satisfying vendors 31st mackay deities overlooked ambient bahamas felipe olympia whirled botanist advertised tugging disciples morales unionist rites foley morse motives creepy ##₀ soo ##sz bargain highness frightening turnpike tory reorganization depict biographer unopposed manifesto ##gles institut emile accidental kapoor ##dam kilkenny cortex lively romanesque jain shan cannons ##ske petrol echoing amalgamated disappears cautious proposes sanctions trenton flotilla aus contempt tor canary cote theirs ##hun conceptual deleted fascinating paso blazing elf honourable hutchinson ##eiro ##outh ##zin surveyor amidst wooded reissue intro ##ono cobb shelters newsletter hanson brace encoding confiscated dem caravan marino scroll melodic cows imam ##adi ##aneous northward searches biodiversity cora roaring ##bers connell theologian halo compose pathetic unmarried dynamo az calculation toulouse deserves humour nr forgiveness tam undergone martyr pamela myths whore counselor hicks heavens battleship electromagnetic stellar establishments presley hopped ##chin temptation 90s wills ##yuan nhs ##nya seminars ##yev adaptations gong asher lex indicator sikh tobago cites goin ##yte satirical ##gies characterised correspond bubbles lure participates ##vid eruption skate therapeutic 1785 canals wholesale defaulted sac petit ##zzled virgil leak ravens portraying ##yx ghetto creators dams portray vicente ##rington fae namesake bounty ##arium joachim ##ota ##iser aforementioned axle snout depended dismantled reuben ##ibly gallagher ##lau earnest ##ieu ##iary inflicted objections ##llar asa gritted ##athy jericho ##sea ##was flick underside ceramics undead substituted eastward undoubtedly wheeled chimney ##iche guinness siding traitor baptiste disguised inauguration tipperary choreographer perched warmed stationary ##ntes bacterial ##aurus flores phosphate attacker invaders alvin intersects indirectly immigrated businessmen cornelius valves narrated pill sober nationale monastic applicants scenery ##jack motifs constitutes ##osh jurisdictions tuning irritation woven ##uddin fertility gao ##erie antagonist impatient glacial hides boarded denominations interception ##jas nicola algebraic marquess bahn parole buyers bait turbines paperwork bestowed natasha renee oceans purchases vaccine ##tock fixtures playhouse integrate jai oswald intellectuals booked nests mortimer ##isi obsession sept ##gler ##sum scrutiny simultaneous squinted ##shin collects oven shankar penned remarkably slips luggage spectral 1786 collaborations louie consolidation ##ailed ##ivating hoover blackpool harness ignition vest tails belmont mongol skinner ##nae visually mage derry ##tism ##unce stevie transitional ##rdy redskins drying prep prospective annoyance oversee ##loaded fills ##books announces scowled respects prasad mystic tucson ##vale revue springer bankrupt 1772 aristotle habsburg ##geny dal natal nut pod chewing darts moroccan walkover rosario lenin punjabi ##ße grossed scattering wired invasive hui polynomial corridors wakes gina portrays ##cratic arid retreating erich irwin sniper ##dha linen lindsey maneuver butch shutting socio bounce commemorative postseason jeremiah pines mystical beads abbas furnace bidding consulted assaulted empirical rubble enclosure sob weakly cancel polly yielded ##emann curly prediction battered 70s vhs jacqueline render sails barked detailing grayson riga sloane raging ##yah herbs bravo ##athlon alloy giggle imminent suffers assumptions waltz ##itate accomplishments ##ited bathing remixed deception ##emia deepest ##eis balkan frogs ##rong slab ##pate philosophers peterborough grains imports dickinson rwanda ##atics 1774 dirk tablets ##rove clone ##rice caretaker hostilities mclean ##gre regimental treasures norms impose tsar tango diplomacy variously complain recognise arrests 1779 celestial pulitzer ##dus libretto ##moor adele splash expectation lds confronts ##izer spontaneous harmful wedge entrepreneurs buyer bilingual translate rugged conner circulated uae eaton ##gra ##zzle lingered lockheed vishnu reelection alonso ##oom joints yankee headline cooperate heinz laureate invading ##sford echoes scandinavian ##dham hugging vitamin salute micah hind trader ##sper radioactive ##ndra militants poisoned ratified remark campeonato deprived wander prop ##dong ##tani ##eye chiang darcy ##oping mandolin spice statesman babylon walled forgetting afro ##cap giorgio buffer ##polis planetary ##gis overlap terminals kinda centenary ##bir arising manipulate elm ke 1770 ##tad chrysler mapped moose pomeranian quad macarthur assemblies shoreline recalls stratford ##rted noticeable ##evic imp ##rita ##sque accustomed supplying tents disgusted sipped filters khz reno selecting luftwaffe mcmahon tyne masterpiece carriages collided dunes exercised flare remembers muzzle heck ##rson burgess lunged middleton boycott bilateral ##sity hazardous lumpur multiplayer spotlight jackets goldman liege porcelain rag waterford attracts hopeful battling ottomans kensington baked hymns cheyenne lattice levine borrow polymer clashes michaels monitored commitments denounced ##von cavity ##oney hobby akin ##holders futures intricate cornish patty ##oned illegally dolphin ##lag barlow yellowish maddie apologized luton plagued ##puram ##rds sway fanny łodz ##rino psi suspicions hanged ##eding initiate charlton ##por nak competent analytical annex wardrobe reservations sect fairfax hedge piled buckingham uneven bauer simplicity snyder interpret accountability donors moderately byrd continents ##cite disciple jamaican nominees ##uss mongolian diver attackers eagerly ideological pillows miracles apartheid revolver sulfur clinics moran ##enko ile katy rhetoric ##icated chronology recycling ##hrer elongated mughal pascal profiles vibration databases domination ##fare matthias digest rehearsal polling weiss initiation reeves clinging flourished impress ##hoff buckley symposium rhythms weed emphasize transforming ##taking ##yman accountant analyze flicker foil priesthood voluntarily decreases ##hya slater sv charting mcgill ##lde moreno besieged zur robes ##phic admitting deported turmoil peyton earthquakes ##ares nationalists beau clair brethren interrupt welch curated galerie requesting ##ested impending steward viper ##vina complaining beautifully brandy foam nl 1660 alessandro punches laced explanations ##lim attribute clit reggie discomfort ##cards smoothed whales ##cene adler countered duffy disciplinary widening recipe reliance conducts goats gradient preaching ##shaw matilda quasi striped meridian cannabis cordoba certificates ##agh ##tering graffiti hangs pilgrims repeats ##ych revive urine etat ##hawk fueled belts fuzzy susceptible mauritius salle sincere beers hooks ##cki arbitration entrusted advise sniffed seminar junk donnell processors principality strapped celia mendoza everton fortunes prejudice starving reassigned steamer ##lund tuck evenly foreman ##ffen dans envisioned slit baseman liberia rosemary ##weed electrified periodically potassium stride contexts sperm slade mariners influx bianca subcommittee ##rane spilling icao estuary ##nock delivers ##ulata isa mira bohemian dessert ##sbury welcoming proudly slowing ##chs musee ascension russ ##vian waits ##psy africans exploit ##morphic eccentric crab peck entrances formidable marketplace groom bolted metabolism patton robbins courier payload endure ##ifier andes refrigerator ornate ##uca ruthless illegitimate masonry strasbourg bikes apples quintet willingly niche bakery corpses energetic ##cliffe ##sser ##ards centimeters centro fuscous cretaceous rancho ##yde andrei telecom tottenham oasis ordination vulnerability presiding corey penguins sims ##pis malawi piss correction ##cked ##ffle ##ryn countdown detectives psychiatrist psychedelic dinosaurs blouse choi vowed randomly ##pol 49ers scrub blanche bruins dusseldorf ##using unwanted ##ums dominique elevations headlights om laguna ##oga 1750 famously ignorance shrewsbury breuning che confederacy greco overhaul ##screen paz skirts disagreement cruelty jagged phoebe shifter hovered viruses ##wes ##lined landlord squirrel dashed ornamental gag wally grange literal spurs undisclosed proceeding billie orphan spanned humidity indy weighted presentations explosions lucian ##tary vaughn hindus ##anga ##hell psycho daytona protects efficiently rematch sly tandem ##oya rebranded impaired hee metropolis peach godfrey diaspora ethnicity prosperous gleaming dar grossing playback ##rden stripe pistols ##tain births labelled ##cating rudy alba ##onne aquarium hostility ##tase shudder sumatra hardest lakers consonant creeping demos homicide capsule zeke liberties expulsion pueblo ##comb trait transporting ##ddin ##neck ##yna depart gregg mold ledge hangar oldham playboy termination analysts gmbh romero ##itic insist cradle filthy brightness slash shootout deposed bordering ##truct microwave tumbled sheltered cathy 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statutes wilde gail kung sabine comfortably motorcycles ##rgo pneumonia fetch ##sonic axel faintly parallels ##oop mclaren spouse compton interdisciplinary miner ##eni clamped ##chal ##llah separates versa ##mler scarborough labrador ##lity ##osing rutgers hurdles como burt divers wichita cade coincided bruised mla vineyard ##ili ##brush notch mentioning jase hearted kits doe ##acle pomerania ##ady ronan seizure pavel problematic ##zaki domenico ##ulin catering penelope dependence parental emilio ministerial atkinson ##bolic clarkson chargers colby grill peeked arises summon ##aged fools ##grapher faculties qaeda ##vial garner refurbished ##hwa geelong disasters nudged bs shareholder lori algae reinstated rot ##ades ##nous invites stainless inclusive ##itude diocesan til ##icz denomination ##xa benton floral registers ##erman ##kell absurd brunei guangzhou hitter retaliation ##uled ##eve blanc nh consistency contamination ##eres dire palermo broadcasters diaries inspire vols brewer 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armistice ##estinal elton aj encompassing camel commemorated malaria woodward calf cigar penetrate ##oso willard ##rno ##uche illustrate amusing convergence noteworthy ##lma ##rva journeys realise manfred ##sable ##vocation hearings fiance ##posed educators provoked adjusting ##cturing modular stockton paterson vlad rejects electors selena maureen ##tres ##rce swirled ##num proportions nanny pawn naturalist parma apostles awoke ethel wen ##bey monsoon overview ##inating mccain rendition risky adorned ##ih equestrian germain nj conspicuous confirming ##yoshi shivering ##imeter milestone rumours flinched bounds smacked token ##bei lectured automobiles ##shore impacted ##iable nouns nero ##leaf ismail prostitute trams bridget sud stimulus impressions reins revolves ##gned giro honeymoon ##swell criterion ##sms ##uil libyan prefers ##osition preview sucks accusation bursts metaphor diffusion tolerate faye betting cinematographer liturgical specials bitterly humboldt ##ckle flux rattled ##itzer archaeologists odor authorised marshes discretion ##ов alarmed archaic inverse ##leton explorers ##pine drummond tsunami woodlands ##minate ##tland booklet insanity owning insert crafted calculus receivers stung ##eca ##nched prevailing travellers eyeing lila graphs ##borne julien ##won morale adaptive therapist erica cw libertarian bowman pitches vita ##ional crook ##entation caledonia mutiny ##sible 1840s automation flock ##pia ironic pathology ##imus remarried joker withstand energies ##att shropshire hostages madeleine tentatively conflicting mateo recipes euros mercenaries nico ##ndon albuquerque augmented mythical bel freud ##child cough ##lica freddy lillian genetically nuremberg calder bonn outdoors paste suns urgency vin restraint tyson ##cera ##selle barrage bethlehem kahn ##par mounts nippon barony happier ryu makeshift sheldon blushed castillo barking listener taped bethel fluent headlines pornography rum disclosure sighing mace doubling gunther manly ##plex interventions physiological forwards emerges ##tooth ##gny compliment rib recession visibly barge faults connector exquisite prefect ##rlin patio ##cured elevators italics pena wasp satin botswana graceful respectable ##jima ##rter ##oic franciscan generates ##dl alfredo disgusting ##olate ##iously sherwood warns cod promo cheryl sino ##escu twitch ##zhi brownish thom ortiz ##dron densely ##beat carmel reinforce ##bana anastasia downhill vertex contaminated remembrance harmonic homework fiancee gears olds angelica ramsay quiz colliery sevens ##cape autism ##hil walkway ##boats ruben abnormal ounce khmer ##bbe zachary bedside morphology punching ##olar sparrow convinces hewitt queer remastered rods mabel solemn notified lyricist symmetric ##xide encore passports wildcats ##uni baja ##pac mildly ##ease bleed commodity mounds glossy orchestras ##omo damian prelude ambitions ##vet awhile remotely ##aud asserts imply ##iques distinctly modelling remedy ##dded windshield dani xiao ##endra 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unto ##yuki localities ##cko ##ln darlington slain academie lobbying sediment puzzles ##grass defiance dickens manifest tongues alumnus arbor coincide appalachian mustafa examiner cabaret traumatic yves bracelet draining heroin magnum baths odessa consonants mitsubishi ##gua kellan vaudeville joked straps probation ##ław ceded interfaces ##pas ##zawa blinding viet rothschild museo huddersfield tactic ##storm brackets dazed incorrectly ##vu reg glazed fearful manifold benefited irony stumbling ##rte willingness balkans mei wraps ##aba injected ##lea gu syed harmless ##hammer bray takeoff poppy timor cardboard astronaut purdue weeping southbound cursing stalls diagonal ##neer lamar bryce comte weekdays harrington ##uba negatively ##see lays grouping ##cken ##henko affirmed halle modernist ##lai hodges smelling aristocratic baptized dismiss justification oilers coupling qin snack healer ##qing gardener layla battled formulated stephenson gravitational ##gill 1768 granny coordinating 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marries squared tentative ##chman boer ##isch bolts swap fisherman assyrian impatiently guthrie martins murdoch tanya nicely dolly lacy med syn decks fashionable millionaire surfing heaved tammy consulate attendees routinely fuse saxophonist backseat malaya ##lord scowl tau ##ishly sighted steaming ##rks ##holes ##hong ching ##wife bless conserved jurassic stacey zion chunk rigorous blaine peabody slayer dismay brewers nz ##jer det ##glia glover postwar penetration sylvester imitation vertically airlift heiress knoxville viva ##uin macon ##rim ##fighter ##gonal janice ##orescence ##wari marius belongings leicestershire blanco inverted preseason sanity sobbing ##due ##elt ##dled collingwood regeneration flickering shortest ##mount ##osi feminism ##lat sherlock cabinets fumbled northbound precedent snaps ##mme researching ##akes guillaume insights manipulated vapor neighbour gangster frey stalking scarcely callie barnett tendencies doomed assessing slung panchayat ambiguous bartlett 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memorandum armenians ##ddle popularized rhodesia 60s lame ##illon sans bikini header orbits ##finger ##ulator sharif spines biotechnology strolled naughty yates ##wire fremantle milo ##mour abducted removes ##atin humming ##chrome ##ester hume pivotal ##rates armand grams believers elector rte apron bis scraped ##yria endorsement initials ##llation dotted hints buzzing emigration nearer indicators ##ulu coarse neutron protectorate ##uze directional exploits pains loire 1830s proponents guggenheim rabbits ritchie hectare inputs hutton ##raz verify ##ako boilers longitude ##lev skeletal yer emilia citrus compromised ##gau prescription paragraph eduard cadillac attire categorized kenyan weddings charley ##bourg entertain monmouth ##lles nutrients davey mesh incentive practised ecosystems kemp subdued overheard ##rya bodily maxim ##nius apprenticeship ursula ##fight lodged rug silesian unconstitutional patel inspected coyote unbeaten ##hak 34th disruption convict parcel ##nham collier implicated mallory ##iac susannah winkler ##rber shia phelps sediments graphical robotic ##sner adulthood mart smoked ##isto kathryn clarified ##aran divides convictions oppression pausing burying ##mt federico mathias eileen ##tana kite hunched ##acies ##atz disadvantage liza kinetic greedy paradox yokohama dowager trunks ventured ##gement gupta vilnius olaf ##thest crimean hopper ##ej progressively arturo mouthed arrondissement ##fusion rubin simulcast oceania ##orum ##stra ##rred busiest intensely navigator cary ##vine ##hini ##bies fife rowe rowland posing insurgents shafts lawsuits activate conor inward culturally garlic ##eering eclectic ##hui ##kee ##nl furrowed vargas meteorological rendezvous ##aus culinary commencement ##dition quota ##notes mommy salaries overlapping mule ##iology ##mology sums wentworth ##isk ##zione mainline subgroup ##illy hack plaintiff verdi bulb differentiation engagements multinational supplemented bertrand caller regis ##naire ##sler ##arts ##imated 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akbar migrant syllables indefinitely ##ogical destroys replaces applause ##phine pest ##fide articulated bertie ##cars ##ptic courtroom crowley aesthetics cummings tehsil hormones titanic dangerously ##ibe stadion jaenelle auguste ciudad ##chu mysore partisans lucan philipp ##aly debating henley interiors ##rano ##tious homecoming beyonce usher henrietta prepares weeds ely plucked ##pire ##dable luxurious ##aq artifact password pasture juno maddy minsk ##dder ##ologies ##rone assessments martian royalist 1765 examines ##mani nino parry scooped relativity ##eli ##uting ##cao congregational noisy traverse ##agawa strikeouts nickelodeon obituary transylvania binds depictions polk trolley ##yed ##lard breeders ##under dryly hokkaido 1762 strengths stacks bonaparte neared prostitutes stamped anaheim gutierrez sinai ##zzling bram fresno madhya proton ##lena ##llum ##phon reelected wanda ##anus ##lb ample distinguishing ##yler grasping sermons tomato bland stimulation avenues ##eux spreads 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colton ##founded outsider espionage kelsey edible ##ulf dora establishes ##sham ##tries contracting ##tania cinematic costello nesting ##uron connolly duff ##nology mma ##mata fergus sexes optics spectator woodstock banning ##hee ##fle differentiate outfielder refinery gerhard horde lair drastically ##udi landfall ##cheng motorsport odi ##achi predominant quay skins ##ental edna harshly complementary murdering ##aves wreckage ono outstretched lennox munitions galen reconcile scalp bicycles gillespie questionable rosenberg guillermo jarvis kabul opium yd ##twined abuses decca outpost ##cino sensible neutrality ponce anchorage atkins turrets inadvertently disagree libre vodka reassuring weighs ##yal glide jumper ceilings repertory outs stain ##bial envy ##ucible smashing heightened policing hyun mixes lai prima ##ples celeste ##bina lucrative intervened kc manually ##rned stature staffed bun bastards nairobi priced ##auer thatcher ##kia tripped comune ##ogan ##pled brasil incentives 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revelations jolly gibbons paw ##dro ##quel freeing shack fries palatine ##hiko accompaniment cruising recycled ##aver erwin sorting synthesizers dyke realities strides enslaved wetland ##ghan competence gunpowder grassy maroon reactors objection ##oms carlson gearbox macintosh radios shelton ##sho clergyman prakash mongols trophies oricon stimuli twenty20 cantonese cortes mirrored ##saurus bhp cristina melancholy ##lating enjoyable nuevo ##wny downfall schumacher ##ind banging lausanne rumbled paramilitary reflex ax amplitude migratory ##gall ##ups midi barnard lastly sherry ##nall keystone ##kra carleton slippery coloring foe socket otter ##rgos mats ##tose consultants bafta bison topping primal abandonment transplant atoll hideous mort pained reproduced tae howling ##turn unlawful billionaire hotter poised lansing ##chang dinamo retro messing domesday ##mina blitz timed ##athing ##kley ascending gesturing ##izations signaled tis chinatown mermaid savanna jameson ##aint catalina ##pet ##hers cochrane cy chatting ##kus alerted computation mused noelle majestic mohawk campo octagonal ##sant ##hend aspiring ##mart comprehend iona paralyzed shimmering swindon rhone ##eley reputed configurations pitchfork agitation francais gillian lipstick ##ilo outsiders pontifical resisting bitterness sewer rockies ##edd ##ucher misleading 1756 exiting galloway ##nging risked ##heart commemoration schultz ##rka integrating ##rsa poses shrieked ##weiler guineas gladys jerking owls goldsmith nightly penetrating ##unced lia ignited betsy ##aring ##thorpe follower vigorously ##rave coded kiran knit zoology tbilisi ##bered repository govt deciduous dino growling ##bba enhancement unleashed chanting pussy biochemistry ##eric kettle repression toxicity nrhp ##arth ##kko ##bush ernesto commended outspoken mca parchment kristen ##aton bisexual raked glamour navajo conditioned showcased ##hma spacious youthful ##esa usl appliances junta brest layne conglomerate enchanted chao loosened picasso circulating inspect montevideo ##centric ##kti piazza spurred ##aith bari freedoms poultry stamford lieu indigo sarcastic bahia stump attach dvds frankenstein lille approx scriptures pollen ##script nmi overseen ##ivism tides proponent newmarket inherit milling ##erland centralized ##rou distributors credentials drawers abbreviation ##lco downing uncomfortably ripe ##oes erase franchises populace ##bery ##khar decomposition pleas ##tet daryl sabah ##wide fearless genie lesions annette ##ogist oboe appendix nair dripped petitioned maclean mosquito parrot hampered 1648 operatic reservoirs ##tham irrelevant jolt summarized ##fp medallion ##taff clawed harlow narrower goddard marcia bodied fremont suarez altering tempest mussolini porn ##isms sweetly oversees walkers solitude grimly shrines ich supervisors hostess dietrich legitimacy brushes expressive ##yp dissipated ##rse localized systemic ##nikov gettysburg ##uaries dialogues muttering housekeeper sicilian discouraged ##frey beamed kaladin halftime kidnap ##amo ##llet 1754 synonymous depleted instituto insulin reprised ##opsis clashed ##ctric interrupting radcliffe insisting medici 1715 ejected playfully turbulent starvation ##rini shipment rebellious petersen verification merits ##rified cakes ##charged 1757 milford shortages spying fidelity ##aker emitted storylines harvested seismic ##iform cheung kilda theoretically barbie lynx ##rgy ##tius goblin mata poisonous ##nburg reactive residues obedience ##евич conjecture ##rac hating sixties kicker moaning motown ##bha emancipation neoclassical ##hering consoles ebert professorship ##tures sustaining assaults obeyed affluent incurred tornadoes ##eber ##zow emphasizing highlanders cheated helmets ##ctus internship terence bony executions legislators berries peninsular tinged ##aco 1689 amplifier corvette ribbons lavish pennant ##lander worthless ##chfield ##forms mariano pyrenees expenditures ##icides chesterfield mandir tailor 39th sergey nestled willed aristocracy 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rollins atheist ominous ##ault herr chariot martina strung ##fell ##farlane horrific sahib gazes saetan erased ptolemy ##olic flushing lauderdale analytic ##ices navarro beak gorilla herrera broom guadalupe raiding sykes bsc deliveries 1720 invasions carmichael tajikistan thematic ecumenical sentiments onstage ##rians ##brand ##sume catastrophic flanks molten ##arns waller aimee terminating ##icing alternately ##oche nehru printers outraged ##eving empires template banners repetitive za ##oise vegetarian ##tell guiana opt cavendish lucknow synthesized ##hani ##mada finalized ##ctable fictitious mayoral unreliable ##enham embracing peppers rbis ##chio ##neo inhibition slashed togo orderly embroidered salty barron benito totaled ##dak pubs simulated caden devin tolkien momma welding sesame ##ept gottingen hardness shaman temeraire adequately pediatric assertion radicals composure cadence seafood beaufort lazarus mani warily cunning kurdistan cantata ##kir ares ##clusive nape townland 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integers wallis yamaha ##isha hushed oblivion aviator evangelist friars ##eller monograph ode ##nary airplanes labourers charms ##nee 1661 hagen tnt rudder fiesta transcript dorothea ska inhibitor maccabi retorted raining encompassed clauses menacing 1642 lineman ##gist vamps ##dick gloom ##rera dealings easing seekers ##nut ##pment helens unmanned ##anu ##isson basics ##amy ##ckman adjustments 1688 brutality horne ##zell ##mable aggregator ##thal rhino ##drick ##vira counters ##rting mn montenegrin packard ##unciation ##♭ ##kki reclaim scholastic thugs pulsed ##icia syriac quan saddam banda kobe blaming buddies dissent ##lusion ##usia corbett jaya delle erratic lexie ##hesis amiga hermes ##pressing ##leen chapels gospels jamal ##uating compute revolving warp ##sso ##thes armory ##eras ##gol antrim loki ##kow ##asian ##good ##zano braid handwriting subdistrict funky pantheon ##iculate concurrency estimation improper juliana ##his newcomers johnstone staten communicated ##oco ##alle 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mcintyre repay tarzan darting harrisburg margarita repulsed ##lding belinda hamburger novo compliant runways bingham registrar skyscraper cuthbert improvisation livelihood ##corp ##elial admiring ##dened sporadic believer casablanca popcorn asha shovel ##bek ##dice coiled tangible ##dez casper elsie resin tenderness rectory ##ivision avail sonar ##mori boutique ##dier guerre bathed upbringing vaulted sandals blessings ##naut ##utnant 1680 foxes pia corrosion hesitantly confederates crystalline footprints shapiro tirana valentin drones 45th microscope shipments texted inquisition wry guernsey unauthorized resigning ripple schubert stu reassure felony ##ardo brittle koreans ##havan ##ives dun implicit tyres ##aldi ##lth magnolia ##ehan ##puri ##poulos aggressively fei gr familiarity ##poo indicative ##trust fundamentally jimmie overrun anchors moans ##opus britannia armagh purposely seizing ##vao bewildered mundane avoidance cosmopolitan geometridae quartermaster caf chatter engulfed gleam purge ##icate juliette jurisprudence guerra revisions ##bn casimir brew ##jm 1749 clapton cloudy conde hermitage simulations torches vincenzo matteo ##rill hidalgo booming westbound accomplishment tentacles unaffected ##sius annabelle flopped sloping ##litz dreamer interceptor vu ##loh consecration copying messaging breaker climates hospitalized 1752 torino afternoons winfield witnessing ##teacher breakers choirs sawmill coldly ##ege sipping haste uninhabited conical bibliography pamphlets severn edict ##oca deux illnesses grips rehearsals sis thinkers tame ##keepers 1690 acacia reformer ##osed ##rys shuffling ##iring ##shima eastbound ionic rhea flees littered ##oum rocker vomiting groaning champ overwhelmingly civilizations paces sloop adoptive ##tish skaters ##vres aiding nikola shriek ##ignon pharmaceuticals tuna calvert gustavo stocked yearbook ##urai ##mana computed subsp riff hanoi kelvin hamid moors pastures summons jihad nectar ##ctors bayou untitled pleasing vastly republics intellect ##ulio ##tou crumbling stylistic ##ی consolation frequented h₂o walden widows ##iens ##ignment chunks improves grit recited ##dev snarl sociological ##arte ##gul inquired ##held bruise clube consultancy homogeneous hornets multiplication pasta prick savior ##grin ##kou ##phile yoon ##gara grimes vanishing cheering reacting bn distillery ##quisite ##vity coe dockyard massif ##jord escorts voss ##valent byte chopped hawke illusions workings floats ##koto ##vac kv annapolis madden ##onus alvaro noctuidae ##cum ##scopic avenge steamboat forte illustrates erika ##trip dew nationalities bran manifested thirsty diversified muscled reborn ##standing arson ##lessness ##dran ##logram ##boys ##kushima ##vious willoughby ##phobia alsace dashboard yuki ##chai granville myspace publicized tricked ##gang adjective ##ater relic reorganisation enthusiastically indications saxe ##lassified consolidate iec padua helplessly ramps renaming regulars pedestrians accents convicts 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swearing ##nob ##tically fleeting prostate amulet educating ##mined ##tler 75th jens respondents cavaliers papacy raju ##iente ##ulum ##tip funnel disneyland ##lley sociologist ##iam faulkner louvre menon ##dson ##ower afterlife mannheim peptide referees comedians meaningless ##anger ##laise fabrics hurley renal sleeps ##bour ##icle breakout kristin roadside animator clover disdain unsafe redesign ##urity firth barnsley portage reset narrows commandos expansive speechless tubular essendon eyelashes smashwords ##yad ##bang ##claim craved sprinted chet somme astor wrocław orton bane ##erving ##uing mischief ##amps ##sund scaling terre ##xious impairment offenses undermine moi soy contiguous arcadia inuit seam ##tops macbeth rebelled ##icative ##iot elaborated frs uniformed ##dberg powerless priscilla stimulated qc arboretum frustrating trieste bullock ##nified enriched glistening intern ##adia locus nouvelle ollie ike lash starboard tapestry headlined hove rigged ##vite pollock ##yme thrive clustered cas roi gleamed olympiad ##lino pressured regimes ##hosis ##lick ripley ##ophone kickoff gallon rockwell ##arable crusader glue revolutions scrambling 1714 grover ##jure englishman aztec contemplating coven preach triumphant tufts ##esian rotational ##phus falkland ##brates strewn clarissa rejoin environmentally glint banded drenched moat albanians johor rr maestro malley nouveau shaded taxonomy adhere bunk airfields ##ritan 1741 encompass remington tran ##erative amelie mazda friar morals passions ##zai breadth vis ##hae argus burnham caressing insider rudd ##imov ##rso italianate murderous textual wainwright armada bam weave timer ##taken ##nh fra ##crest ardent salazar taps tunis ##ntino allegro gland philanthropic ##chester implication ##optera esq judas noticeably wynn ##dara inched indexed crises villiers bandit royalties patterned cupboard interspersed accessory isla kendrick entourage stitches ##esthesia headwaters ##ior interlude distraught draught 1727 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rounding sired ##emon ##nist remade uncover ##mack complied lei newsweek ##jured ##parts ##enting ##pg finer guerrillas athenian deng disused stepmother accuse gingerly seduction confronting ##going gora nostalgia sabres virginity wrenched ##minated syndication wielding eyre ##gnon ##igny behaved taxpayer sweeps ##growth childless gallant ##ywood amplified geraldine scrape ##ffi babylonian fresco ##rdan ##kney ##position 1718 restricting tack fukuoka osborn selector partnering ##dlow kia tak whitley gables ##mania mri softness immersion ##bots ##evsky 1713 chilling insignificant pcs ##uis elites lina purported supplemental teaming ##americana ##dding ##inton proficient rouen ##nage ##rret niccolo selects ##bread fluffy 1621 gruff knotted mukherjee polgara thrash nicholls secluded smoothing thru corsica loaf whitaker inquiries ##rrier ##kam indochina marlins myles peking ##tea extracts pastry superhuman connacht vogel ##ditional ##het ##udged ##lash gloss quarries refit teaser ##alic 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chimed incline zoning archduke hellenistic ##oses ##sions candi thong ##ople magnate rustic ##rsk projective slant ##offs danes hollis vocalists ##ammed congenital contend gesellschaft ##ocating ##pressive douglass quieter ##kshi howled salim spontaneously townsville buena southport ##bold kato 1638 faerie stiffly ##vus ##rled flawless realising taboo ##7th straightening jena ##hid cartwright berber bertram soloists noses coping fission hardin inca ##cen 1717 mobilized vhf ##raf biscuits curate ##anial gaunt neighbourhoods 1540 ##abas blanca bypassed sockets behold coincidentally ##bane nara shave splinter terrific ##arion ##erian commonplace juris redwood waistband boxed caitlin fingerprints jennie naturalized ##ired balfour craters jody bungalow hugely quilt glitter pigeons undertaker bulging constrained ##sil ##akh assimilation reworked ##person persuasion ##pants felicia ##cliff ##ulent 1732 explodes ##dun ##inium ##zic lyman vulture hog overlook begs northwards ow spoil ##urer 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##rud completes nipples flavour hirsch ##laus calderon sneakers moravian ##ksha 1622 ##imeters bodo ##isance ##pre ##ronia anatomical excerpt ##lke dh kunst ##tablished ##scoe biomass panted unharmed gael housemates montpellier coa rodents tonic hickory singleton ##taro 1719 aldo breaststroke dempsey och rocco ##cuit merton dissemination midsummer serials ##idi haji polynomials enoch prematurely shutter taunton £3 ##grating ##inates archangel harassed ##asco archway dazzling ##ecin 1736 sumo wat ##kovich 1086 honneur ##ently ##nostic ##ttal ##idon 1605 1716 rents ##gnan hires ##ikh ##dant howie ##rons handler retracted shocks 1632 arun duluth kepler trumpeter ##lary peeking seasoned trooper ##mara laszlo ##iciencies ##rti heterosexual ##inatory indira jogging ##inga ##lism beit dissatisfaction malice ##ately nedra peeling ##rgeon 47th stadiums vertigo ##ains iced restroom ##plify ##tub illustrating pear ##chner ##sibility inorganic rappers receipts watery ##kura lucinda ##oulos 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annum boomed seminole sugarcane ##dna departmental dismissing innsbruck arteries ashok batavia daze kun overtook ##rga ##tlan beheaded gaddafi holm electronically faulty galilee fractures kobayashi ##lized gunmen magma aramaic mala eastenders inference messengers bf ##qu bathrooms ##vere 1658 flashbacks ideally misunderstood ##jali ##weather mendez ##grounds uncanny ##iii 1709 friendships ##nbc sacrament accommodated reiterated logistical pebbles thumped ##escence administering decrees drafts ##flight ##cased ##tula futuristic picket intimidation winthrop ##fahan interfered afar francoise morally uta cochin croft dwarfs ##bruck ##dents ##nami biker ##hner ##meral ##isen ##ometric ##pres ##ан brightened meek parcels securely gunners ##jhl ##zko agile hysteria ##lten ##rcus bukit champs chevy cuckoo leith sadler theologians welded ##section 1663 plurality xander ##rooms ##formed shredded temps intimately pau tormented ##lok ##stellar 1618 charred essen ##mmel alarms spraying ascot blooms twinkle ##abia ##apes internment obsidian ##chaft snoop ##dav ##ooping malibu ##tension quiver ##itia hays mcintosh travers walsall ##ffie 1623 beverley schwarz plunging structurally rosenthal vikram ##tsk ghz ##onda ##tiv chalmers groningen pew reckon unicef ##rvis 55th ##gni 1651 sulawesi avila cai metaphysical screwing turbulence ##mberg augusto samba 56th baffled momentary toxin ##urian ##wani aachen condoms dali steppe ##oed ##year adolescence dauphin electrically inaccessible microscopy nikita ##ega atv ##enter ##oles ##oteric accountants punishments wrongly bribes adventurous clinch flinders southland ##hem ##kata gough ##ciency lads soared ##ה undergoes deformation outlawed rubbish ##arus ##mussen ##nidae ##rzburg arcs ##ingdon ##tituted 1695 wheelbase wheeling bombardier campground zebra ##lices ##oj ##bain lullaby ##ecure donetsk wylie grenada ##arding ##ης squinting eireann opposes ##andra maximal runes ##broken ##cuting ##iface ##ror ##rosis additive britney adultery 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indices ##flict frowns resolving weightlifting tugs cleric contentious 1653 mania rms ##miya ##reate ##ruck ##tucket bien eels marek ##ayton ##cence discreet unofficially ##ife leaks ##bber 1705 dung compressor hillsborough pandit shillings distal ##skin ##tat nosed ##nir mangrove undeveloped ##idia textures ##inho ##rise irritating nay amazingly bancroft apologetic compassionate kata symphonies ##lovic airspace ##lch gifford precautions fulfillment sevilla vulgar martinique ##urities looting piccolo tidy ##dermott quadrant armchair incomes mathematicians stampede nilsson ##inking ##scan foo quarterfinal ##ostal shang shouldered squirrels ##owe vinegar ##bner ##rchy ##systems delaying ##trics ars dwyer rhapsody sponsoring ##gration bipolar cinder starters ##olio ##urst signage ##nty aground figurative mons acquaintances duets erroneously soyuz elliptic recreated ##cultural ##quette ##ssed ##tma ##zcz moderator scares ##itaire ##stones ##udence juniper sighting ##just ##nsen britten calabria ry bop cramer forsyth stillness airmen gathers unfit ##umber ##upt taunting seeker streamlined ##bution holster schumann tread vox ##gano ##onzo strive dil reforming covent newbury predicting ##orro decorate tre ##puted andover asahi dept dunkirk gills ##tori buren huskies ##stis ##stov abstracts bets loosen ##opa 1682 yearning ##glio ##sir berman effortlessly enamel napoli persist ##peration ##uez attache elisa invitations ##kic accelerating reindeer boardwalk clutches nelly polka ##kei adamant huey lough unbroken adventurer embroidery inspecting stanza ##ducted naia taluka ##pone ##roids chases deprivation florian ##ppet earthly ##lib ##ssee colossal foreigner vet freaks patrice rosewood triassic upstate ##pkins dominates ata chants ks vo ##bley ##raya ##rmed agra infiltrate ##ailing ##ilation ##tzer ##uppe ##werk binoculars enthusiast fujian squeak ##avs abolitionist almeida boredom hampstead marsden rations ##ands inflated bonuses rosalie patna ##rco detachments penitentiary 54th flourishing woolf ##dion ##etched papyrus ##lster ##nsor ##toy bobbed dismounted endelle inhuman motorola wince wreath ##ticus hideout inspections sanjay disgrace infused pudding stalks ##urbed arsenic leases ##hyl ##rrard collarbone ##waite ##wil dowry ##bant ##edance genealogical nitrate salamanca scandals thyroid necessitated ##` ##¡ ##¢ ##¦ ##¨ ##ª ##¬ ##´ ##¶ ##¾ ##¿ ##ð ##þ ##ħ ##œ ##ƒ ##ɐ ##ɑ ##ɒ ##ɕ ##ɣ ##ɨ ##ɪ ##ɫ ##ɬ ##ɯ ##ɲ ##ɴ ##ɹ ##ɾ ##ʀ ##ʁ ##ʂ ##ʃ ##ʉ ##ʊ ##ʋ ##ʌ ##ʎ ##ʐ ##ʑ ##ʒ ##ʔ ##ʲ ##ʳ ##ʷ ##ʸ ##ʻ ##ʼ ##ʾ ##ʿ ##ˡ ##ˣ ##ˤ ##ζ ##ξ ##щ ##ъ ##э ##ю ##ђ ##є ##ј ##љ ##њ ##ћ ##ӏ ##ա ##բ ##գ ##դ ##ե ##թ ##ի ##լ ##կ ##հ ##մ ##յ ##ն ##ո ##պ ##ս ##վ ##տ ##ր ##ւ ##ք ##־ ##א ##ב ##ג ##ד ##ו ##ז ##ח ##ט ##י ##ך ##כ ##ל ##ם ##מ ##ן ##נ ##ס ##ע ##ף ##פ ##ץ ##צ ##ק ##ר ##ש ##ת ##، ##ء ##ث ##ج ##ح ##خ ##ذ ##ز ##ش ##ص ##ض ##ط ##ظ ##غ ##ـ ##ف ##ق ##ك ##ى ##ٹ ##پ ##چ ##ک ##گ ##ں ##ھ ##ہ ##ے ##अ ##आ ##उ ##ए ##क ##ख ##ग ##च ##ज ##ट ##ड ##ण ##त ##थ ##द ##ध ##न ##प ##ब ##भ ##म ##य ##र ##ल ##व ##श ##ष ##स ##ह ##ा ##ि ##ी ##ो ##। ##॥ ##ং ##অ ##আ ##ই ##উ ##এ ##ও ##ক ##খ ##গ ##চ ##ছ ##জ ##ট ##ড ##ণ ##ত ##থ ##দ ##ধ ##ন ##প ##ব ##ভ ##ম ##য ##র ##ল ##শ ##ষ ##স ##হ ##া ##ি ##ী ##ে ##க ##ச ##ட ##த ##ந ##ன ##ப ##ம ##ய ##ர ##ல ##ள ##வ ##ா ##ி ##ு ##ே ##ை ##ನ ##ರ ##ಾ ##ක ##ය ##ර ##ල ##ව ##ා ##ต ##ท ##พ ##ล ##ว ##ส ##། ##ག ##ང ##ད ##ན ##པ ##བ ##མ ##འ ##ར ##ལ ##ས ##မ ##ა ##ბ ##გ ##დ ##ე ##ვ ##თ ##ი ##კ ##ლ ##მ ##ნ ##ო ##რ ##ს ##ტ ##უ ##ᄊ ##ᴬ ##ᴮ ##ᴰ ##ᴵ ##ᴺ ##ᵀ ##ᵇ ##ᵈ ##ᵖ ##ᵗ ##ᵣ ##ᵤ ##ᵥ ##ᶜ ##ᶠ ##‐ ##‑ ##‒ ##– ##— ##― ##‘ ##’ ##‚ ##“ ##” ##‡ ##… ##⁰ ##⁴ ##⁵ ##⁶ ##⁷ ##⁸ ##⁹ ##⁻ ##₅ ##₆ ##₇ ##₈ ##₉ ##₊ ##₍ ##₎ ##ₐ ##ₑ ##ₒ ##ₓ ##ₕ ##ₖ ##ₗ ##ₘ ##ₚ ##ₛ ##ₜ ##₤ ##₩ ##₱ ##₹ ##ℓ ##ℝ ##⅓ ##⅔ ##↦ ##⇄ ##⇌ ##∂ ##∅ ##∆ ##∇ ##∈ ##∗ ##∘ ##∧ ##∨ ##∪ ##⊂ ##⊆ ##⊕ ##⊗ ##☉ ##♯ ##⟨ ##⟩ ##ⱼ ##⺩ ##⺼ ##⽥ ##亻 ##宀 ##彳 ##忄 ##扌 ##氵 ##疒 ##糹 ##訁 ##辶 ##阝 ##龸 ##fi ##fl ================================================ FILE: diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/vocab_org.txt 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##黑 ##黒 ##黔 ##默 ##黛 ##黜 ##黝 ##點 ##黠 ##黨 ##黯 ##黴 ##鼋 ##鼎 ##鼐 ##鼓 ##鼠 ##鼬 ##鼹 ##鼻 ##鼾 ##齁 ##齊 ##齋 ##齐 ##齒 ##齡 ##齢 ##齣 ##齦 ##齿 ##龄 ##龅 ##龈 ##龊 ##龋 ##龌 ##龍 ##龐 ##龔 ##龕 ##龙 ##龚 ##龛 ##龜 ##龟 ##︰ ##︱ ##︶ ##︿ ##﹁ ##﹂ ##﹍ ##﹏ ##﹐ ##﹑ ##﹒ ##﹔ ##﹕ ##﹖ ##﹗ ##﹙ ##﹚ ##﹝ ##﹞ ##﹡ ##﹣ ##！ ##＂ ##＃ ##＄ ##％ ##＆ ##＇ ##（ ##） ##＊ ##， ##－ ##． ##／ ##： ##； ##＜ ##？ ##＠ ##［ ##＼ ##］ ##＾ ##＿ ##｀ ##ｆ ##ｈ ##ｊ ##ｕ ##ｗ ##ｚ ##｛ ##｝ ##｡ ##｢ ##｣ ##､ ##･ ##ｯ ##ｰ ##ｲ ##ｸ ##ｼ ##ｽ ##ﾄ ##ﾉ ##ﾌ ##ﾗ ##ﾙ ##ﾝ ##ﾞ ##ﾟ ##￣ ##￥ ##👍 ##🔥 ##😂 ##😎 ================================================ FILE: diffsynth/tokenizer_configs/hunyuan_dit/tokenizer_t5/config.json ================================================ { "_name_or_path": "/home/patrick/t5/mt5-xl", "architectures": [ "MT5ForConditionalGeneration" ], "d_ff": 5120, "d_kv": 64, "d_model": 2048, "decoder_start_token_id": 0, "dropout_rate": 0.1, "eos_token_id": 1, "feed_forward_proj": "gated-gelu", "initializer_factor": 1.0, "is_encoder_decoder": true, "layer_norm_epsilon": 1e-06, 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FILE: diffsynth/tokenizer_configs/hunyuan_video/tokenizer_1/merges.txt ================================================ #version: 0.2 i n t h a n r e a r e r th e in g o u o n s t o r e n o n a l a t e r i t i n t o r o i s l e i c a t an d e d o f c h o r e s i l e l s t a c o m a m l o a n a y s h r i l i t i f or n e ð Ł r a h a d e o l v e s i u r a l s e ' s u n d i b e l a w h o o d ay e n m a n o l e t o ou r i r g h w it i t y o a s s p th is t s at i yo u wit h a d i s a b l y w e th e t e a s a g v i p p s u h o m y . . b u c om s e er s m e m e al l c on m o k e g e ou t en t c o f e v er a r f ro a u p o c e gh t ar e s s fro m c h t r ou n on e b y d o t h w or er e k e p ro f or d s b o t a w e g o h e t er in g d e b e ati on m or a y e x il l p e k s s c l u f u q u v er ðŁ ĺ j u m u at e an d v e k ing m ar o p h i .. . p re a d r u th at j o o f c e ne w a m a p g re s s d u no w y e t ing y our it y n i c i p ar g u f i a f p er t er u p s o g i on s g r g e b r p l ' t m i in e we e b i u s sh o ha ve to day a v m an en t ac k ur e ou r â Ģ c u l d lo o i m ic e s om f in re d re n oo d w as ti on p i i r th er t y p h ar d e c ! ! m on mor e w ill t ra c an c ol p u t e w n m b s o it i ju st n ing h ere t u p a p r bu t wh at al ly f ir m in c a an t s a t ed e v m ent f a ge t am e ab out g ra no t ha pp ay s m an h is ti me li ke g h ha s th an lo ve ar t st e d ing h e c re w s w at d er it e s er ac e ag e en d st r a w st or r e c ar el l al l p s f ri p ho p or d o a k w i f re wh o sh i b oo s on el l wh en il l ho w gre at w in e l b l s si al i som e ðŁ Ĵ t on d er le s p la ï ¸ e d s ch h u on g d on k i s h an n c or . . oun d a z in e ar y fu l st u ou ld st i g o se e ab le ar s l l m is b er c k w a en ts n o si g f e fir st e t sp e ac k i f ou s ' m st er a pp an g an ce an s g ood b re e ver the y t ic com e of f b ack as e ing s ol d i ght f o h er happ y p ic it s v ing u s m at h om d y e m s k y ing the ir le d r y u l h ar 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false, "single_word": false, "special": true }, "128249": { "content": "<|reserved_special_token_244|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128250": { "content": "<|reserved_special_token_245|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128251": { "content": "<|reserved_special_token_246|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128252": { "content": "<|reserved_special_token_247|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128253": { "content": "<|reserved_special_token_248|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128254": { "content": "<|reserved_special_token_249|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128255": { "content": "<|reserved_special_token_250|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128256": { "content": "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128257": { "content": "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128258": { "content": "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true } }, "bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", "clean_up_tokenization_spaces": true, "eos_token": "<|end_of_text|>", "legacy": true, "model_input_names": [ "input_ids", "attention_mask" ], "model_max_length": 1000000000000000019884624838656, "pad_token": "", "padding_side": "right", "processor_class": "LlavaProcessor", "tokenizer_class": "LlamaTokenizer", "unk_token": "", "use_default_system_prompt": false } ================================================ FILE: diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer_config.json ================================================ { "name_or_path": "THUDM/chatglm3-6b-base", "remove_space": false, "do_lower_case": false, "tokenizer_class": "ChatGLMTokenizer", "auto_map": { "AutoTokenizer": [ "tokenization_chatglm.ChatGLMTokenizer", null ] } } ================================================ FILE: diffsynth/tokenizer_configs/stable_diffusion/tokenizer/merges.txt ================================================ #version: 0.2 i n t h a n r e a r e r th e in g o u o n s t o r e n o n a l a t e r i t i n t o r o i s l e i c a t an d e d o f c h o r e s i l e l s t a c o m a m l o a n a y s h r i l i t i f or n e ð Ł r a h a d e o l v e s i u r a l s e ' s u n d i b e l a w h o o d ay e n m a n o l e t o ou r i r g h w it i t y o a s s p th is t s at i yo u wit h a d i s a b l y w e th e t e a s a g v i p p s u h o m y . . b u c om s e er s m e m e al l c on m o k e g e ou t en t c o f e v er a r f ro a u p o c e gh t ar e s s fro m c h t r ou n on e b y d o t h w or er e k e p ro f or d s b o t a w e g o h e t er in g d e b e ati on m or a y e x il l p e k s s c l u f u q u v er ðŁ ĺ j u m u at e an d v e k ing m ar o p h i .. . p re a d r u th at j o o f c e ne w a m a p g re s s d u no w y e t ing y our it y n i c i p ar g u f i a f p er t er u p s o g i on s g r g e b r p l ' t m i in e we e b i u s sh o ha ve to day a v m an en t ac k ur e ou r â Ģ c u l d lo o i m ic e s om f in re d re n oo d w as ti on p i i r th er t y p h ar d e c ! ! m on mor e w ill t ra c an c ol p u t e w n m b s o it i ju st n ing h ere t u p a p r bu t wh at al ly f ir m in c a an t s a t ed e v m ent f a ge t am e ab out g ra no t ha pp ay s m an h is ti me li ke g h ha s th an lo ve ar t st e d ing h e c re w s w at d er it e s er ac e ag e en d st r a w st or r e c ar el l al l p s f ri p ho p or d o a k w i f re wh o sh i b oo s on el l wh en il l ho w gre at w in e l b l s si al i som e ðŁ Ĵ t on d er le s p la ï ¸ e d s ch h u on g d on k i s h an n c or . . oun d a z in e ar y fu l st u ou ld st i g o se e ab le ar s l l m is b er c k w a en ts n o si g f e fir st e t sp e ac k i f ou s ' m st er a pp an g an ce an s g ood b re e ver the y t ic com e of f b ack as e ing s ol d i ght f o h er happ y p ic it s v ing u s m at h om d y e m s k y ing the ir le d r y u l h ar c k t on on al h el r ic b ir vi e w ay t ri d a p le b ro st o oo l ni ght tr u b a re ad re s ye ar f r t or al s c oun c la t ure v el at ed le c en d th ing v o ic i be st c an wor k la st af ter en ce p ri p e e s i l âĢ ¦ d re y s o ver i es ðŁ ij com m t w in k s un c l li fe t t a ch l and s y t re t al p ol s m du c s al f t ' re ch e w ar t ur ati ons ac h m s il e p m ou gh at e st ar wee k ! !! c lu th ere n er t om s el ï¸ ı wor ld v es c am go t in ter of f u m ton ight o ther h ou loo k j e i d si on be au at t el i or t re c f f st er su pp g en be en il y te am m m i c pe op it t at s on ly mb er en g b ri m p k now b ur b ar in s lo w sh e ro w â Ŀ t ro peop le vi a lo w ag a be t x t f ac ch ar e ar w al s en f am b le n ati is h n or g ame li ve s co le y d on ic k b all ver y the se p an i a at ing c r a re g ir ma ke st re sho w . " f l u p d r than ks il li w om st s i g s ur ever y c ur vie w le t in to mo st n a in di g ar ha d s ou v ed an t iti on ma de f ol un i it ed ðŁ ı ic al th r read y ch ec d ra k es boo k e p si c mor ning ne ws c au c t w ell an c pho to th an or s bir th g g ou t ne xt som e en ing stor y ch ri do wn hom e f fe fre e d a b or f il ci al than k si de le ar qu e l ine t en at es ye ars m y pho to beau ti ri ght n u for m shi p b an th er d ays g am as on g y ðŁ İ birth day se t ic k e t st ill com ing ta ke ðŁ ĩ b b s ol s on d en e p mu sic the m de n wh y f oo c ra am az w n h ol t ting w r u e ma g c ro l an c lo b ra a k s ing c al re ad ' ve jo h b ab d ri b lo bi g er ic in t t or tr y l a le g hou se m ic v al beauti ful l itt chec k ne w ver s s w ar i pla y h er âĢ ĵ w in m a con gr sch ool f un . @ he al ic h d el wh ere l on ke t tw o mu ch wat ch v en d ed a st k ed b as go ing m p e ver w ays ro o de sig l y s ed to p l in ch an to o it ing d ent gh ts t y sp o ne ed b lu in st be ing âĿ ¤ w el l s hi m m ay st ing n a el y litt le g a n at tom or m c h on w ant a ir pi c am eric p er le ss wee k ve l a h c ap ch am g er ti m tomor row ne ss st ate h al ser v z e o s p at v is ex c s in f f c ity c en an y b el su mm t in w ould loo king k o ce le fam ily m er po w hel p bu s c o c le sel f en s ic s th o an i ch o le ad b s t wee th ink for e ch il vi de di d al e ch i v il en ds w ing p as ' ll v ol s a g s man y j ec be fore gra ph n y ur ing w il d d bu il f av st ed tr an l ing ou d d ge fi el nati onal st a c er w ere in a se ason c ou n ed amaz ing ti ons cele br n s a th he ad s day d ar lo c v in an other g oo s at n y jo in pre s s es s ing an a in ing .. .. c our ï¸ ı ac t cau se li ght am s t a b al f c hi gh off ici t t chri st d ic d ay ra l h or : ) vi si n am o b ma s gh t re ally t un fin d thr ough por t u t ti ve st y n e or e ðŁĺ Ĥ supp ort ne ver ev en ðŁ Ķ h a y a l d u k r an j am wi th me di d es ne y ch ing al e h y k in ! ! d y pl ace al so b le wh ich bl ack b li s ay par k pl ay ir e vide o week end a il ke y p t w ard fri day d in ine ss g ro b en al ways t ball ag o m il c y pro duc di sc un der ple ase sp or fu ll e y ðŁ Ļ is e iti es c at k no u se fo re k er ar t hi gh op en s an e f our s sh ed st ri d ro aga in i m ðŁ ĵ en jo fu n ge tting p en g er c li an y ever y e u wom en â ľ e st c ould r y " @ th ou sh a comm un b er d ents di s wh ile aw ay di o h am g la d ate k a mis s un ch w on in f roo m g a re al ex per di rec sh ould sp r g ol l ong bet ter or i e y i ence il s z z h an f ound v s â Ļ po st ti c par t m en ren ce ce ss v ic s il sho p ðŁĺ Ĥ f ood v al sti c y ou s ays e lec st ar o c l and i d c tion fiel d s of st art wat er fri ends on es ðŁ Į f la f ar wh ite par ty in st gr ou t v every one m ent j a ch a pr in an ts d uring l at l ar we st th en k a y oun in sp in te we en visi t aga inst re le he ad c es to wn loo ks th re re gi ren t pro jec gir l se ar w o m om c ar h un pu bli d i p le c all c ri u m for d per fe fri end h ard ssi on te st pla ying ar ound be cause ke ts me et sat ur ar ti wor k j un v en r un me mber por t su per t wit s am el s t ly ad v ati ve at h s ure av ail la r s qu ar ds ev ent m en l l o ver lo gy it al tim es m al b ack c oo ma king st ru â ģ it u sh ar g an c as s n summ er pic ture f an h in christ mas c y pr oud cham pi desig n pp ing ho pe c a avail able ma y we d photo graph spe cial sal e sto p er y a we al ity hi story am a pre si b ru wor king d one d r k en fe at w ood ate st sun day mo vi vel y s le f ace sp ec stu dents b y ha m sp on bus iness d at i e i p so ci g lo h and re cor r s me e ke ep p ur heal th sh e com ple go d da vi col lec li st r a clu b t ers in clu th ings pl an â ĺ joh n sh ing at ul so on blu e g or satur day w on congr atul se e âĿ¤ ï¸ı tho se ðŁĺ į fin al d ou it h o wn ro ad t our a st indi a ti l n d f er fav or su l lear n fir e ju st grou p a h r ac bo dy u r c are à ¸ p lo o h po s gi ve te ch su b c ent er ing y m il ity f ic lon don v ir gu ys b a ðŁ ¤ bab y sc re ðŁĺ į tru mp un der chan ge i an col le ss es l er ss ed n ice ann oun pow er s ar a king min i s li s wee k ar fu l c ru ac tion a ther ) . st and de vel a a g an le ft lo l re l tran s m ents in t e f man ag di g gen er do wn p au ti v k u th ur k en st on f ans tal k twee t t oo sty le pro te se con fr on awe some g l p al ne t s or la u g on sin ce t ty ser ies me mor b eli fil m di d di es o t congratul ations p ra e ve w oo offici al su c in cre b on par t pp ed cla ss si ve bo y cu l perfe ct t ou d am wel come foo tball h i p ap wa it ad a congr ats youn g exc ited re ce j an v a re d st ra medi a ' d do es le t mu l ill s gre en m el to ge fu ture ye ster vers ity for m ta in i de ch es ki ds qu i ha ha de ta bi g favor ite gir ls con tin do m sear ch u al a ir d ers mon th c er yester day commun ity ad e do g vil le ic es d eli sy ste ru n is m he art c up en ti fe w presi dent e ds un til fe sti o k f lo sa id ol e me d tra vel  £ ph one toge ther fa st lo t gam es sh ir bet ween y es th ers do ing m ac at or b and fol low projec t devel op di ffe con fe spe ci ca st y s bo ard r d i al sh oo r am ha ving sh are fol low on e n ame m r pu t disc u or y c ame ou s s ite twit ter t b t it fin ally z ed su per com pan us ing all s li st r is sho t g al t ar de l joh n âĢ Ķ some thing ra m inte re wh e b it ðŁ į stre et oun d a i tic kets movi e re al k y ta king o pp c c l am m oun in ve bl ack us ed on line y or loc al gu e c ks o w ge st bo ys illi on con t re ci in ed eu ro no w se en p h te ach de f sou th su ch aw ard mu st is su ca re fe el p lu l atest spor ts we b te x e ment s k fi c w an te ch o t bo x n er fre e t al a sh c ase ho t won der mee ting er a ch all ðŁ IJ jo b il i c ool j our th s m o f el di e mic ha e le te am serv ice st and ma kes p ing ear ly com es e k ho li v ers ag ue s au thre e mon day fa shi some one th ro se a b ad supp or tur n ur y m ing photograph y n ic mar k pre tty ss ing wat ching me mb ar ri coun ty be ach fr an cen ter pol ice b at publi c t an pre ss s af s y ge ts ro y n ers y our bu y st ers sho w as ed chil dre af ric in es sp ace sc ri h all pa in ar ing hom e m ur heal th ch ed s and rece i gu y e a americ an re si childre n - - i ri ing ton coun try ro ss le n ann a boo ks b c e ce d om lo vely k h pe t g y g ri st age off ice ro ck m on b ay t able su n m ed th in l or f low ( @ uni versity stor e fron t goo d z a vo te nor th he y an im or der mi d with out a de re member mar ket ? ? mu s tra ining e duc bu t co ver st an sc en b la bre ak l ou s ame g old a in o s bo th l it ver n a i al bu p a enjo y be g ell ing thur sday inf o s an americ a ha ir te l mar ch con cer colle ge confe rence ap p h our ch ang â ļ s our ol s we ather w ar p hi festi val secon d cu te pr ac en er str y le a pol it s av se n o w m i ne ar ou ght z e co ffe w illi d an se y davi d e se f an de ci the at no v ati on tr ac sc i re view c el e m u n ju ly or ig ti on d ru form er st ay af ter in v too k dat a b al tu es d an ev ening ðŁĺĤ ðŁĺĤ d ol u res pro vi t s e st sig n j ac u k s ong ye t bo w in du j ap h oo po int any one z y i st h ur it al buil ding wom an ch ur j er per for co ach le ague ce ss ne t i mag nati on br it qu e aw ards ag es wor ks c ed man ce l ate ig n mon ey tru e i i t ell pl ac p ac as y wor ld be hin im port read ing gra m gi ving me t h it for ward st om pres ent jun e so cial no on mar t hal f s we go vern k er deta ils li sh _ _ ac y si a ber t f all ! !!! ) , th i d iti sp ort k ing f it st af c at mu se cen tr y er con tro b loo wal k ac tu did n li m lear ning re search wed ne au th h ours k y f ar h en .. .. it ch ri l str ong sk y que sti jam es r on d g f ur c in do es app ro mar ke tu res ful ly ch at behin d te m fin i mis sion b att fe el he av every thing b ar w ish pre mi i ma exper ience e ach re port swee t tic s spr ing re spon syste m vic tor l in sa w al ready gh ter f le ã ĥ br ing albu m - - ell s st an to m inter national w ent an ni mat ch pp er st one sm all ra in fashi on are a v an ag ram k o thou ght wor th v an m er coffe e it es g n arti st c on ar ch c ir se cre gr ound is o h and co m bri dge h s x i l ink pu l sp l r ace f li ri ver g as di sco d al play er f it photo s it y o k j or tr a ap ril ad s a di sol u beau ty do or me ss up date ali a sch o en ed mom ent sco t sc ience i or ti es ac ross ous ly sh es does n p age wat er m illion cla ssi l ic ca st form ation micha el ell o s mo in ts vi sion op ening ld n au str tues day win ner po ssi r ound shir t di t b o u es il led al ong tri p star ting im pro k an per son no t re co ne eds c le li e re st r ing win ter si mp mo m be er fac e tor s us a collec tion ge or se ssion tr ying la s la ke j en orig in stu dent se cur v in pic s ex pe com p gon na e qu b ad le y a u memb ers bre ak w all gi c din ner bu l insp ir r i min d ic a win ning tal king t ren s is t en 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gram ho tel ic e ma d secur ity en ge d c en ough st a e ty de ad g un he ar m ir hu man gre ss oun ds pi ece bre aking gar den fi ght vie ws f ish star ted run ning gre en ser i s m as k d or de ath e conom er i ir d s er l unch âģ ¦ bo x nat u ba se b an f al glo bal wil d wo w out side mo ve le ad an al muse um on g ha w pow er than k b ac char ac cam pa dig ital r o op er de v w ol p ati f a m ale pap er ill ing c s â ĥ educ ation ta ken e ffe m ou s ad " . bas ed staf f inclu ding li ving a c ch ina mo b stor m lu ck ph il o o y n tra vel k el ti al pr ice boo k import ant bi o p ool ny c f ab lo ad ? ! chall enge cr y ser ve we ar bu s ta in nu mber ro r k at i z th ough ho sp m m fa ir ut es ho t po p fi ed cam p develop ment li br c ali em s âģ¦ @ b ol is ed stand ing mo del it a g le bro wn ima ge ve red for ce o il par tic sh u da ily la w se c cla ss cam p holi day cl in k ers pres ent gam e incre di er ship inter view b ill du e and y ab o in nov ke y ac ade p il mo der st ars br and f er wee ks con si pr e sa fe wr it di um la unch marke ting ann ual as si cour t la dy c ted and a in side chil d opp or sm ith centr e gu e âģ © f ren st y for t ent ly is n ke ep to ber on y bo y al d col la de mo le vel com pet ad o b our fanta stic m ate s u sou th oppor tun vers ary lat er bu d face book la un ster n p it ! " ma j gr am tb t fi re happ y a ks wh ole actu ally ill er ell a lo ts al ex an ge lan ds ðŁĺ Ń en ter r ou episo de p ed in ten sh ire wh o pl an h o ca ke we st mag az fre sh c c n ar ch ris wr iting w er n om l o mi dd dre am o l ti onal de b > > be come s i gr and all ing hi stor ri de i red saf e que en ci l in tro vi l d ani .. . ar tic st at sh ort or ing sel fi mis si do c b it g all b om i re se lec d ition ðŁĶ ¥ fri end be at gh ting ðŁĺ Ĭ pe ace ex hi ant a ab ility il lu j on qu ality tri bu m es play ers fa ir cu t c ab suc cess b i su s pro mo sch e an ge ic o comm it cat ch ill a kin d feel ing qu o s ay anni versary spo t mo ther an e p end your self op s app le min utes p o gr and ri es ha ha care er ed ition de c ric k am i concer t iti ve ge ous d ly t te adv ent i g li ghts ak er sk y âĥ £ r ay fini shed w ay s d ac coun ðŁĴ ķ ck y ch el lit er pain ting lo s st un techno logy n as ma r b il afric a ki e ey es gol f plu s ni a it ec serv ices wed ding kno wn te le .. ... star ts pa ren w ants ati onal mon ths win do fav our er t magaz ine ex clu re ve b c origin al e ss n al an ti st ro t ice stu dy à ¤ v ac nation al fi ve ra in ve ment u te ver se em er ar my possi ble gue ss val ley ther n cro w m r col or on to pic k cle ar dar k t ac wan ted it ting can cer govern ment di e ri se z ing col d f oun stu dio str ation bro ther a head sh el mic ro ic ally d au sig ned vi ol a x as se i o w re spl ay ch ick augu st pl at ti ps sp i hu man e asy lo gi mi ke gro w ag re w w sh ad mo tiv wi de tur ns om g v ar de fin su g j im ðŁĶ ¥ t d campa ign nam ed re tweet co p t v le av k is dou ble s mar issu e vil la in formation li es sto ck n t di stric sh or mi x er o se p me x see ing li ve re min co de g ur s c wil d l un h ood spo t fa ther fore ver up d tra f f ly ne ed gra du tra in ma ke s ab be y si ze lead er tal ks e u lo g fo x gor geous le ss le ts sur pri my self no te li ves f ru lo ved se ver de m j i so c h old do gs n i â ŀ lea ve air port ben ef ex pl shi ps comple te ach i gre at vin tage j ack ro c woo d pri v off er ey e ver sion te a co ach off ic w ell g en s at h h you th o x ? 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sh ðŁij Į mi ami be th v ity se cu che f cri me graph y ma x arti sts re volu gu ard spee ch u c upd ates fac es st ant chang ed repor ts low er pe ar n c k il loo ked spe aker s f re spect ok ay oce an s itting architec ture tra il se at i ra le g japan ese d am u lar sw im polit ics finan cial ol d mou th at temp de stin fi shing atten tion me m chang es deci ded reli gi g in c av z z ad am ma c wr ite beg in sc ul al ter is s ath on imag es m oo jo ined ðŁĺ ī âŀ ¡ï¸ı pas sed mu sli h ir lar gest cam er com ic gh ted rug by bur gh gg ing te sting pre par lau gh al ed impro ve beli ev adv ice sha res he art tur ning s b t el caf e n es dani el pat ter t z se tt par k c and st ick happ ens bri an ne west e pic ad or ki es war ning anim als custo m ar c di an gol d cor e t f c ity pan ts re ality con fi in ju fo x gu il k new âĺ º cor rec itu de d den . # re duc pas s f on y a ow ner re turns n c e ast ap ol in sur th o si m juni or be e ang el att le elec tric hor ror cra sh e ye pat h sou thern emplo ye ge o t an ha z r ally ðŁı » proper ty was n enjo yed gre y g as bre w nor thern hol ding g p ta ke ch art ly n dr ama z o pa id throw back cu p discu ssion down town w ill le w b is t ary bre ad up on r ate teach ers it ation anc ed cy cle choo se d c ir an co w da ve ra ise prin cess fa ith - > indu stri sp ain guit ar fac ts m n sp en cour te go tt projec ts au di o sc pe ter s and intere st happ iness ven ue sol di surpri se poten tial per io custom er i i g ni manu fac e co bro ken sing er vel s wal es hu s in j f our tal ent d ying mat the fil m jo ining s ell j ar lma o sur ger bb c sour ces au stin ni k char les f am prin ci ange l cas h lo t o red pla ys pl ate don e memor y br ings n ba solu tions teach ing gr ace cir cu hel ps foun der mar y expl ore de cor par ts ch o inte gr ha u is es pu tting in er r it v y mic hel blu es every day for ms bi o ye ar p in t ter spr ing ) ) po t al ing perform ing sh an plan et mus ical head s it alian stru gg âĢį âĻ w 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a c o m a m l o a n a y s h r i l i t i f or n e ð Ł r a h a d e o l v e s i u r a l s e ' s u n d i b e l a w h o o d ay e n m a n o l e t o ou r i r g h w it i t y o a s s p th is t s at i yo u wit h a d i s a b l y w e th e t e a s a g v i p p s u h o m y . . b u c om s e er s m e m e al l c on m o k e g e ou t en t c o f e v er a r f ro a u p o c e gh t ar e s s fro m c h t r ou n on e b y d o t h w or er e k e p ro f or d s b o t a w e g o h e t er in g d e b e ati on m or a y e x il l p e k s s c l u f u q u v er ðŁ ĺ j u m u at e an d v e k ing m ar o p h i .. . p re a d r u th at j o o f c e ne w a m a p g re s s d u no w y e t ing y our it y n i c i p ar g u f i a f p er t er u p s o g i on s g r g e b r p l ' t m i in e we e b i u s sh o ha ve to day a v m an en t ac k ur e ou r â Ģ c u l d lo o i m ic e s om f in re d re n oo d w as ti on p i i r th er t y p h ar d e c ! ! m on mor e w ill t ra c an c ol p u t e w n m b s o it i ju st n ing h ere t u p a p r bu t wh at al ly f ir m in c a an t s a t ed e v m ent f a ge t am e ab out g ra no t ha pp ay s m an h is ti me li ke g h ha s th an lo ve ar t st e d ing h e c re w s w at d er it e s er ac e ag e en d st r a w st or r e c ar el l al l p s f ri p ho p or d o a k w i f re wh o sh i b oo s on el l wh en il l ho w gre at w in e l b l s si al i som e ðŁ Ĵ t on d er le s p la ï ¸ e d s ch h u on g d on k i s h an n c or . . oun d a z in e ar y fu l st u ou ld st i g o se e ab le ar s l l m is b er c k w a en ts n o si g f e fir st e t sp e ac k i f ou s ' m st er a pp an g an ce an s g ood b re e ver the y t ic com e of f b ack as e ing s ol d i ght f o h er happ y p ic it s v ing u s m at h om d y e m s k y ing the ir le d r y u l h ar c k t on on al h el r ic b ir vi e w ay t ri d a p le b ro st o oo l ni ght tr u b a re ad re s ye ar f r t or al s c oun c la t ure v el at ed le c en d th ing v o ic i be st c an wor k la st af ter en ce p ri p e e s i l âĢ ¦ d re y s o ver i es ðŁ ij com m t w in k s un c l li fe t t a ch l and s y t re t al p ol s m du c s al f t ' re ch e w ar t ur ati ons ac h m s il e p m ou gh at e st ar wee k ! !! c lu th ere n er t om s el ï¸ ı wor ld v es c am go t in ter of f u m ton ight o ther h ou loo k j e i d si on be au at t el i or t re c f f st er su pp g en be en il y te am m m i c pe op it t at s on ly mb er en g b ri m p k now b ur b ar in s lo w sh e ro w â Ŀ t ro peop le vi a lo w ag a be t x t f ac ch ar e ar w al s en f am b le n ati is h n or g ame li ve s co le y d on ic k b all ver y the se p an i a at ing c r a re g ir ma ke st re sho w . " f l u p d r than ks il li w om st s i g s ur ever y c ur vie w le t in to mo st n a in di g ar ha d s ou v ed an t iti on ma de f ol un i it ed ðŁ ı ic al th r read y ch ec d ra k es boo k e p si c mor ning ne ws c au c t w ell an c pho to th an or s bir th g g ou t ne xt som e en ing stor y ch ri do wn hom e f fe fre e d a b or f il ci al than k si de le ar qu e l ine t en at es ye ars m y pho to beau ti ri ght n u for m shi p b an th er d ays g am as on g y ðŁ İ birth day se t ic k e t st ill com ing ta ke ðŁ ĩ b b s ol s on d en e p mu sic the m de n wh y f oo c ra am az w n h ol t ting w r u e ma g c ro l an c lo b ra a k s ing c al re ad ' ve jo h b ab d ri b lo bi g er ic in t t or tr y l a le g hou se m ic v al beauti ful l itt chec k ne w ver s s w ar i pla y h er âĢ ĵ w in m a con gr sch ool f un . @ he al ic h d el wh ere l on ke t tw o mu ch wat ch v en d ed a st k ed b as go ing m p e ver w ays ro o de sig l y s ed to p l in ch an to o it ing d ent gh ts t y sp o ne ed b 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================================================ #version: 0.2 i n t h a n r e a r e r th e in g o u o n s t o r e n o n a l a t e r i t i n t o r o i s l e i c a t an d e d o f c h o r e s i l e l s t a c o m a m l o a n a y s h r i l i t i f or n e ð Ł r a h a d e o l v e s i u r a l s e ' s u n d i b e l a w h o o d ay e n m a n o l e t o ou r i r g h w it i t y o a s s p th is t s at i yo u wit h a d i s a b l y w e th e t e a s a g v i p p s u h o m y . . b u c om s e er s m e m e al l c on m o k e g e ou t en t c o f e v er a r f ro a u p o c e gh t ar e s s fro m c h t r ou n on e b y d o t h w or er e k e p ro f or d s b o t a w e g o h e t er in g d e b e ati on m or a y e x il l p e k s s c l u f u q u v er ðŁ ĺ j u m u at e an d v e k ing m ar o p h i .. . p re a d r u th at j o o f c e ne w a m a p g re s s d u no w y e t ing y our it y n i c i p ar g u f i a f p er t er u p s o g i on s g r g e b r p l ' t m i in e we e b i u s sh o ha ve to day a v m an en t ac k ur e ou r â Ģ c u l d lo o i m ic e s om f in re d re n oo d w as ti on p i i r th er t y p h ar d e c ! ! m on mor e w ill t ra c an c ol p u t e w n m b s o it i ju st n ing h ere t u p a p r bu t wh at al ly f ir m in c a an t s a t ed e v m ent f a ge t am e ab out g ra no t ha pp ay s m an h is ti me li ke g h ha s th an lo ve ar t st e d ing h e c re w s w at d er it e s er ac e ag e en d st r a w st or r e c ar el l al l p s f ri p ho p or d o a k w i f re wh o sh i b oo s on el l wh en il l ho w gre at w in e l b l s si al i som e ðŁ Ĵ t on d er le s p la ï ¸ e d s ch h u on g d on k i s h an n c or . . oun d a z in e ar y fu l st u ou ld st i g o se e ab le ar s l l m is b er c k w a en ts n o si g f e fir st e t sp e ac k i f ou s ' m st er a pp an g an ce an s g ood b re e ver the y t ic com e of f b ack as e ing s ol d i ght f o h er happ y p ic it s v ing u s m at h om d y e m s k y ing the ir le d r y u l h ar c k t on on al h el r ic b ir vi e w ay t ri d a p le b ro st o oo l ni ght tr u b a re ad re s ye ar f r t or al s c oun c la t ure v el at ed le c en d th ing v o ic i be st c an wor k la st af ter en ce p ri p e e s i l âĢ ¦ d re y s o ver i es ðŁ ij com m t w in k s un c l li fe t t a ch l and s y t re t al p ol s m du c s al f t ' re ch e w ar t ur ati ons ac h m s il e p m ou gh at e st ar wee k ! !! c lu th ere n er t om s el ï¸ ı wor ld v es c am go t in ter of f u m ton ight o ther h ou loo k j e i d si on be au at t el i or t re c f f st er su pp g en be en il y te am m m i c pe op it t at s on ly mb er en g b ri m p k now b ur b ar in s lo w sh e ro w â Ŀ t ro peop le vi a lo w ag a be t x t f ac ch ar e ar w al s en f am b le n ati is h n or g ame li ve s co le y d on ic k b all ver y the se p an i a at ing c r a re g ir ma ke st re sho w . " f l u p d r than ks il li w om st s i g s ur ever y c ur vie w le t in to mo st n a in di g ar ha d s ou v ed an t iti on ma de f ol un i it ed ðŁ ı ic al th r read y ch ec d ra k es boo k e p si c mor ning ne ws c au c t w ell an c pho to th an or s bir th g g ou t ne xt som e en ing stor y ch ri do wn hom e f fe fre e d a b or f il ci al than k si de le ar qu e l ine t en at es ye ars m y pho to beau ti ri ght n u for m shi p b an th er d ays g am as on g y ðŁ İ birth day se t ic k e t st ill com ing ta ke ðŁ ĩ b b s ol s on d en e p mu sic the m de n wh y f oo c ra am az w n h ol t ting w r u e ma g c ro l an c lo b ra a k s ing c al re ad ' ve jo h b ab d ri b lo bi g er ic in t t or tr y l a le g hou se m ic v al beauti ful l itt chec k ne w ver s s w ar i pla y h er âĢ ĵ w in m a con gr sch ool f un . @ he al ic h d el wh ere l on ke t tw o mu ch wat ch v en d ed a st k ed b as go ing m p e ver w ays ro o de sig l y s ed to p l in ch an to o it ing d ent gh ts t y sp o ne ed b lu in st be ing âĿ ¤ w el l s hi m m ay st ing n a el y litt le g a n at tom or m c h on w ant a ir pi c am eric p er le ss wee k ve l a h c ap ch am g er ti m tomor row ne ss st ate h al ser v z e o s p at v is ex c s in f f c ity c en an y b el su mm t in w ould loo king k o ce le fam ily m er po w hel p bu s c o c le sel f en s ic s th o an i ch o le ad b s t wee th ink for e ch il vi de di d al e ch i v il en ds w ing p as ' ll v ol s a g s man y j ec be fore gra ph n y ur ing w il d d bu il f av st ed tr an l ing ou d d ge fi el nati onal st a c er w ere in a se ason c ou n ed amaz ing ti ons cele br n s a th he ad s day d ar lo c v in an other g oo s at n y jo in pre s s es s ing an a in ing .. .. c our ï¸ ı ac t cau se li ght am s t a b al f c hi gh off ici t t chri st d ic d ay ra l h or : ) vi si n am o b ma s gh t re ally t un fin d thr ough por t u t ti ve st y n e or e ðŁĺ Ĥ supp ort ne ver ev en ðŁ Ķ h a y a l d u k r an j am wi th me di d es ne y ch ing al e h y k in ! ! d y pl ace al so b le wh ich bl ack b li s ay par k pl ay ir e vide o week end a il ke y p t w ard fri day d in ine ss g ro b en al ways t ball ag o m il c y pro duc di sc un der ple ase sp or fu ll e y ðŁ Ļ is e iti es c at k no u se fo re k er ar t hi gh op en s an e f our s sh ed st ri d ro aga in i m ðŁ ĵ en jo fu n ge tting p en g er c li an y ever y e u wom en â ľ e st c ould r y " @ th ou sh a comm un b er d ents di s wh ile aw ay di o h am g la d ate k a mis s un ch w on in f roo m g a re al ex per di rec sh ould sp r g ol l ong bet ter or i e y i ence il s z z h an f ound v s â Ļ po st ti c par t m en ren ce ce ss v ic s il sho p ðŁĺ Ĥ f ood v al sti c y ou s ays e lec st ar o c l and i d c tion fiel d s of st art wat er fri ends on es ðŁ Į f la f ar wh ite par ty in st gr ou t v every one m ent j a ch a pr in an ts d uring l at l ar we st th en k a y oun in sp in te we en visi t aga inst re le he ad c es to wn loo ks th re re gi ren t pro jec gir l se ar w o m om c ar h un pu 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te ch su b c ent er ing y m il ity f ic lon don v ir gu ys b a ðŁ ¤ bab y sc re ðŁĺ į tru mp un der chan ge i an col le ss es l er ss ed n ice ann oun pow er s ar a king min i s li s wee k ar fu l c ru ac tion a ther ) . st and de vel a a g an le ft lo l re l tran s m ents in t e f man ag di g gen er do wn p au ti v k u th ur k en st on f ans tal k twee t t oo sty le pro te se con fr on awe some g l p al ne t s or la u g on sin ce t ty ser ies me mor b eli fil m di d di es o t congratul ations p ra e ve w oo offici al su c in cre b on par t pp ed cla ss si ve bo y cu l perfe ct t ou d am wel come foo tball h i p ap wa it ad a congr ats youn g exc ited re ce j an v a re d st ra medi a ' d do es le t mu l ill s gre en m el to ge fu ture ye ster vers ity for m ta in i de ch es ki ds qu i ha ha de ta bi g favor ite gir ls con tin do m sear ch u al a ir d ers mon th c er yester day commun ity ad e do g vil le ic es d eli sy ste ru n is m he art c up en ti fe w presi dent e ds un til fe sti 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e k e p ro f or d s b o t a w e g o h e t er in g d e b e ati on m or a y e x il l p e k s s c l u f u q u v er ðŁ ĺ j u m u at e an d v e k ing m ar o p h i .. . p re a d r u th at j o o f c e ne w a m a p g re s s d u no w y e t ing y our it y n i c i p ar g u f i a f p er t er u p s o g i on s g r g e b r p l ' t m i in e we e b i u s sh o ha ve to day a v m an en t ac k ur e ou r â Ģ c u l d lo o i m ic e s om f in re d re n oo d w as ti on p i i r th er t y p h ar d e c ! ! m on mor e w ill t ra c an c ol p u t e w n m b s o it i ju st n ing h ere t u p a p r bu t wh at al ly f ir m in c a an t s a t ed e v m ent f a ge t am e ab out g ra no t ha pp ay s m an h is ti me li ke g h ha s th an lo ve ar t st e d ing h e c re w s w at d er it e s er ac e ag e en d st r a w st or r e c ar el l al l p s f ri p ho p or d o a k w i f re wh o sh i b oo s on el l wh en il l ho w gre at w in e l b l s si al i som e ðŁ Ĵ t on d er le s p la ï ¸ e d s ch h u on g d on k i s h an n c or . . oun d a z in e ar y fu l st u ou ld st i g o se e ab le ar s l l m is b er c k w a en ts n o si g f e fir st e t sp e ac k i f ou s ' m st er a pp an g an ce an s g ood b re e ver the y t ic com e of f b ack as e ing s ol d i ght f o h er happ y p ic it s v ing u s m at h om d y e m s k y ing the ir le d r y u l h ar c k t on on al h el r ic b ir vi e w ay t ri d a p le b ro st o oo l ni ght tr u b a re ad re s ye ar f r t or al s c oun c la t ure v el at ed le c en d th ing v o ic i be st c an wor k la st af ter en ce p ri p e e s i l âĢ ¦ d re y s o ver i es ðŁ ij com m t w in k s un c l li fe t t a ch l and s y t re t al p ol s m du c s al f t ' re ch e w ar t ur ati ons ac h m s il e p m ou gh at e st ar wee k ! !! c lu th ere n er t om s el ï¸ ı wor ld v es c am go t in ter of f u m ton ight o ther h ou loo k j e i d si on be au at t el i or t re c f f st er su pp g en be en il y te am m m i c pe op it t at s on ly mb er en g b ri m p k now b ur b ar in s lo w sh e ro w â Ŀ t ro peop le vi a lo w ag a be t x t f ac ch ar e ar w al s en f am b le n ati is h n or g ame li ve s co le y d on ic k b all ver y the se p an i a at ing c r a re g ir ma ke st re sho w . " f l u p d r than ks il li w om st s i g s ur ever y c ur vie w le t in to mo st n a in di g ar ha d s ou v ed an t iti on ma de f ol un i it ed ðŁ ı ic al th r read y ch ec d ra k es boo k e p si c mor ning ne ws c au c t w ell an c pho to th an or s bir th g g ou t ne xt som e en ing stor y ch ri do wn hom e f fe fre e d a b or f il ci al than k si de le ar qu e l ine t en at es ye ars m y pho to beau ti ri ght n u for m shi p b an th er d ays g am as on g y ðŁ İ birth day se t ic k e t st ill com ing ta ke ðŁ ĩ b b s ol s on d en e p mu sic the m de n wh y f oo c ra am az w n h ol t ting w r u e ma g c ro l an c lo b ra a k s ing c al re ad ' ve jo h b ab d ri b lo bi g er ic in t t or tr y l a le g hou se m ic v al beauti ful l itt chec k ne w ver s s w ar i pla y h er âĢ ĵ w in m a con gr sch ool f un . @ he al ic h d el wh ere l on ke t tw o mu ch wat ch v en d ed a st k ed b as go ing m p e ver w ays ro o de sig l y s ed to p l in ch an to o it ing d ent gh ts t y sp o ne ed b lu in st be ing âĿ ¤ w el l s hi m m ay st ing n a el y litt le g a n at tom or m c h on w ant a ir pi c am eric p er le ss wee k ve l a h c ap ch am g er ti m tomor row ne ss st ate h al ser v z e o s p at v is ex c s in f f c ity c en an y b el su mm t in w ould loo king k o ce le fam ily m er po w hel p bu s c o c le sel f en s ic s th o an i ch o le ad b s t wee th ink for e ch il vi de di d al e ch i v il en ds w ing p as ' ll v ol s a g s man y j ec be fore gra ph n y ur ing w il d d bu il f av st ed tr an l ing ou d d ge fi el nati onal st a c er w ere in a se ason c ou n ed amaz ing ti ons cele br n s a th he ad s day d ar lo c v in an other g oo s at n y jo in pre s s es s ing an a in ing .. .. c our ï¸ ı ac t cau se li ght am s t a b al f c hi gh off ici t t chri st d ic d ay ra l h or : ) vi si n am o b ma s gh t re ally t un fin d thr ough por t u t ti ve st y n e or e ðŁĺ Ĥ supp ort ne ver ev en ðŁ Ķ h a y a l d u k r an j am wi th me di d es ne y ch ing al e h y k in ! ! d y pl ace al so b le wh ich bl ack b li s ay par k pl ay ir e vide o week end a il ke y p t w ard fri day d in ine ss g ro b en al ways t ball ag o m il c y pro duc di sc un der ple ase sp or fu ll e y ðŁ Ļ is e iti es c at k no u se fo re k er ar t hi gh op en s an e f our s sh ed st ri d ro aga in i m ðŁ ĵ en jo fu n ge tting p en g er c li an y ever y e u wom en â ľ e st c ould r y " @ th ou sh a comm un b er d ents di s wh ile aw ay di o h am g la d ate k a mis s un ch w on in f roo m g a re al ex per di rec sh ould sp r g ol l ong bet ter or i e y i ence il s z z h an f ound v s â Ļ po st ti c par t m en ren ce ce ss v ic s il sho p ðŁĺ Ĥ f ood v al sti c y ou s ays e lec st ar o c l and i d c tion fiel d s of st art wat er fri ends on es ðŁ Į f la f ar wh ite par ty in st gr ou t v every one m ent j a ch a pr in an ts d uring l at l ar we st th en k a y oun in sp in te we en visi t aga inst re le he ad c es to wn loo ks th re re gi ren t pro jec gir l se ar w o m om c ar h un pu bli d i p le c all c ri u m for d per fe fri end h ard ssi on te st pla ying ar ound be cause ke ts me et sat ur ar ti wor k j un v en r un me mber por t su per t wit s am el s t ly ad v ati ve at h s ure av ail la r s qu ar ds ev ent m en l l o ver lo gy it al tim es m al b ack c oo ma king st ru â ģ it u sh ar g an c as s n summ er pic ture f an h in christ mas c y pr oud cham pi desig n pp ing ho pe c a avail able ma y we d photo graph spe cial sal e sto p er y a we al ity hi story am a pre si b ru wor king d one d r k en fe at w ood ate st sun day mo vi vel y s le f ace sp ec stu dents b y ha m sp on bus iness d at i e i p so ci g lo h and re cor r s me e ke ep p ur heal th sh e com ple go d da vi col lec li st r a clu b t ers in clu th ings pl an â ĺ joh n sh ing at ul so on blu e g or satur day w on congr atul se e âĿ¤ ï¸ı tho se ðŁĺ į fin al d ou it h o wn ro ad t our a st indi a ti l n d f er fav or su l lear n fir e ju st grou p a h r ac bo dy u r c are à ¸ p lo o h po s gi ve te ch su b c ent er ing y m il ity f ic lon don v ir gu ys b a ðŁ ¤ bab y sc re ðŁĺ į tru mp un der chan ge i an col le ss es l er ss ed n ice ann oun pow er s ar a king min i s li s wee k ar fu l c ru ac tion a ther ) . st and de vel a a g an le ft lo l re l tran s m ents in t e f man ag di g gen er do wn p au ti v k u th ur k en st on f ans tal k twee t t oo sty le pro te se con fr on awe some g l p al ne t s or la u g on sin ce t ty ser ies me mor b eli fil m di d di es o t congratul ations p ra e ve w oo offici al su c in cre b on par t pp ed cla ss si ve bo y cu l perfe ct t ou d am wel come foo tball h i p ap wa it ad a congr ats youn g exc ited re ce j an v a re d st ra medi a ' d do es le t mu l ill s gre en m el to ge fu ture ye ster vers ity for m ta in i de ch es ki ds qu i ha ha de ta bi g favor ite gir ls con tin do m sear ch u al a ir d ers mon th c er yester day commun ity ad e do g vil le ic es d eli sy ste ru n is m he art c up en ti fe w presi dent e ds un til fe sti o k f lo sa id ol e me d tra vel  £ ph one toge ther fa st lo t gam es sh ir bet ween y es th ers do ing m ac at or b and fol low projec t devel op di ffe con fe spe ci ca st y s bo ard r d i al sh oo r am ha ving sh are fol low on e n ame m r pu t disc u or y c ame ou s s ite twit ter t b t it fin ally z ed su per com pan us ing all s li st r is sho t g al t ar de l joh n âĢ Ķ some thing ra m inte re wh e b it ðŁ į stre et oun d a i tic kets movi e re al k y ta king o pp c c l am m oun in ve bl ack us ed on line y or loc al gu e c ks o w ge st bo ys illi on con t re ci in ed eu ro no w se en p h te ach de f sou th su ch aw ard mu st is su ca re fe el p lu l atest spor ts we b te x e ment s k fi c w an te ch o t bo x n er fre e t al a sh c ase ho t won der mee ting er a ch all ðŁ IJ jo b il i c ool j our th s m o f el di e mic ha e le te am serv ice st and ma kes p ing ear ly com es e k ho li v ers ag ue s au thre e mon day fa shi some one th ro se a b ad supp or tur n ur y m ing photograph y n ic mar k pre tty ss ing wat ching me mb ar ri coun ty be ach fr an cen ter pol ice b at publi c t an pre ss s af s y ge ts ro y n ers y our bu y st ers sho w as ed chil dre af ric in es sp ace sc ri h all pa in ar ing hom e m ur heal th ch ed s and rece i gu y e a americ an re si childre n - - i ri ing ton coun try ro ss le n ann a boo ks b c e ce d om lo vely k h pe t g y g ri st age off ice ro ck m on b ay t able su n m ed th in l or f low ( @ uni versity stor e fron t goo d z a vo te nor th he y an im or der mi d with out a de re member mar ket ? ? mu s tra ining e duc bu t co ver st an sc en b la bre ak l ou s ame g old a in o s bo th l it ver n a i al bu p a enjo y be g ell ing thur sday inf o s an americ a ha ir te l mar ch con cer colle ge confe rence ap p h our ch ang â ļ s our ol s we ather w ar p hi festi val secon d cu te pr ac en er str y le a pol it s av se n o w m i ne ar ou ght z e co ffe w illi d an se y davi d e se f an de ci the at no v ati on tr ac sc i re view c el e m u n ju ly or ig ti on d ru form er st ay af ter in v too k dat a b al tu es d an ev ening ðŁĺĤ ðŁĺĤ d ol u res pro vi t s e st sig n j ac u k s ong ye t bo w in du j ap h oo po int any one z y i st h ur it al buil ding wom an ch ur j er per for co ach le ague ce ss ne t i mag nati on br it qu e aw ards ag es wor ks c ed man ce l ate ig n mon ey tru e i i t ell pl ac p ac as y wor ld be hin im port read ing gra m gi ving me t h it for ward st om pres ent jun e so cial no on mar t hal f s we go vern k er deta ils li sh _ _ ac y si a ber t f all ! !!! ) , th i d iti sp ort k ing f it st af c at mu se cen tr y er con tro b loo wal k ac tu did n li m lear ning re search wed ne au th h ours k y f ar h en .. .. it ch ri l str ong sk y que sti jam es r on d g f ur c in do es app ro mar ke tu res ful ly ch at behin d te m fin i mis sion b att fe el he av every thing b ar w ish pre mi i ma exper ience e ach re port swee t tic s spr ing re spon syste m vic tor l in sa w al ready gh ter f le ã ĥ br ing albu m - - ell s st an to m inter national w ent an ni mat ch pp er st one sm all ra in fashi on are a v an ag ram k o thou ght wor th v an m er coffe e it es g n arti st c on ar ch c ir se cre gr ound is o h and co m bri dge h s x i l ink pu l sp l r ace f li ri ver g as di sco d al play er f it photo s it y o k j or tr a ap ril ad s a di sol u beau ty do or me ss up date ali a sch o en ed mom ent sco t sc ience i or ti es ac ross ous ly sh es does n p age wat er m illion cla ssi l ic ca st form ation micha el ell o s mo in ts vi sion op ening ld n au str tues day win ner po ssi r ound shir t di t b o u es il led al ong tri p star ting im pro k an per son no t re co ne eds c le li e re st r ing win ter si mp mo m be er fac e tor s us a collec tion ge or se ssion tr ying la s la ke j en orig in stu dent se cur v in pic s ex pe com p gon na e qu b ad le y a u memb ers bre ak w all gi c din ner bu l insp ir r i min d ic a win ning tal king t ren s is t en wonder ful s now he ar th om no thing gu i st in blo g fe st b un le e war ds ch ance dre ss re n pau l p es tech no ru ssi c ard e ast mar i w ine t i la w str ic k i ap e au gu pro fe as h cour se ma il ren tly d un m un lo ve is land dri ve s l end ed ma in lo st nat ure âĿ¤ ï¸ı ch ic re por p in pr o st ation ce p ta kes compan y go es on d ma ch ra dio d ad ro ck j a p ay champi on e e in de tt a ati c t ab beli eve ener gy z i t at wor d on ce re sul y l and re an o inst agram clo se t am cu stom w a con om sho ws li fe k in ro b t age n ation al most list en sa ve re li ac e mar y tre e for get j ack wa iting direc tor h ill bor n te mp f l st e on a sing le wedne sday un ited in o @ _ ne l celebr ate en ding de al j i can ada hu ge tr ack âĢ ¢ f y fan ta an g yor k rele ase p un ep iso wor ds t our p ack i gh classi c perfor mance ke t after noon recor d win s pro ble âĿ ¤ f our b ed ban k d ance s la cal led mi ght a p pa st ðŁ ļ diffe rent it e gi ft ssi ve chur ch c us pro gram ho tel ic e ma d secur ity en ge d c en ough st a e ty de ad g un he ar m ir hu man gre ss oun ds pi ece bre aking gar den fi ght vie ws f ish star ted run ning gre en ser i s m as k d or de ath e conom er i ir d s er l unch âģ ¦ bo x nat u ba se b an f al glo bal wil d wo w out side mo ve le ad an al muse um on g ha w pow er than k b ac char ac cam pa dig ital r o op er de v w ol p ati f a m ale pap er ill ing c s â ĥ educ ation ta ken e ffe m ou s ad " . bas ed staf f inclu ding li ving a c ch ina mo b stor m lu ck ph il o o y n tra vel k el ti al pr ice boo k import ant bi o p ool ny c f ab lo ad ? ! chall enge cr y ser ve we ar bu s ta in nu mber ro r k at i z th ough ho sp m m fa ir ut es ho t po p fi ed cam p develop ment li br c ali em s âģ¦ @ b ol is ed stand ing mo del it a g le bro wn ima ge ve red for ce o il par tic sh u da ily la w se c cla ss cam p holi day cl in k ers pres ent gam e incre di er ship inter view b ill du e and y ab o in nov ke y ac ade p il mo der st ars br and f er wee ks con si pr e sa fe wr it di um la unch marke ting ann ual as si cour t la dy c ted and a in side chil d opp or sm ith centr e gu e âģ © f ren st y for t ent ly is n ke ep to ber on y bo y al d col la de mo le vel com pet ad o b our fanta stic m ate s u sou th oppor tun vers ary lat er bu d face book la un ster n p it ! " ma j gr am tb t fi re happ y a ks wh ole actu ally ill er ell a lo ts al ex an ge lan ds ðŁĺ Ń en ter r ou episo de p ed in ten sh ire wh o pl an h o ca ke we st mag az fre sh c c n ar ch ris wr iting w er n om l o mi dd dre am o l ti onal de b > > be come s i gr and all ing hi stor ri de i red saf e que en ci l in tro vi l d ani .. . ar tic st at sh ort or ing sel fi mis si do c b it g all b om i re se lec d ition ðŁĶ ¥ fri end be at gh ting ðŁĺ Ĭ pe ace ex hi ant a ab ility il lu j on qu ality tri bu m es play ers fa ir cu t c ab suc cess b i su s pro mo sch e an ge ic o comm it cat ch ill a kin d feel ing qu o s ay anni versary spo t mo ther an e p end your self op s app le min utes p o gr and ri es ha ha care er ed ition de c ric k am i concer t iti ve ge ous d ly t te adv ent i g li ghts ak er sk y âĥ £ r ay fini shed w ay s d ac coun ðŁĴ ķ ck y ch el lit er pain ting lo s st un techno logy n as ma r b il afric a ki e ey es gol f plu s ni a it ec serv ices wed ding kno wn te le .. ... star ts pa ren w ants ati onal mon ths win do fav our er t magaz ine ex clu re ve b c origin al e ss n al an ti st ro t ice stu dy à ¤ v ac nation al fi ve ra in ve ment u te ver se em er ar my possi ble gue ss val ley ther n cro w m r col or on to pic k cle ar dar k t ac wan ted it ting can cer govern ment di e ri se z ing col d f oun stu dio str ation bro ther a head sh el mic ro ic ally d au sig ned vi ol a x as se i o w re spl ay ch ick augu st pl at ti ps sp i hu man e asy lo gi mi ke gro w ag re w w sh ad mo tiv wi de tur ns om g v ar de fin su g j im ðŁĶ ¥ t d campa ign nam ed re tweet co p t v le av k is dou ble s mar issu e vil la in formation li es sto ck n t di stric sh or mi x er o se p me x see ing li ve re min co de g ur s c wil d l un h ood spo t fa ther fore ver up d tra f f ly ne ed gra du tra in ma ke s ab be y si ze lead er tal ks e u lo g fo x gor geous le ss le ts sur pri my self no te li ves f ru lo ved se ver de m j i so c h old do gs n i â ŀ lea ve air port ben ef ex pl shi ps comple te ach i gre at vin tage j ack ro c woo d pri v off er ey e ver sion te a co ach off ic w ell g en s at h h you th o x ? " m t mi x g g d le natu ral buil d break fast thin king theat re mo on ber g go als geor ge en e exc ell il ing tun e y ed g ate m it net work jo e h ello f b tu be we aring ath le stru c har d gla ss g ers thro w g es b t indu stry manag ement ali st go al stre am y el a vi ici ous o thers s ki chri sti bir d e sc m in tr o l t j an im p ri ghts sh a or gan cent ral ar a ro ll favour ite che ster el se p ay car s m ine ste p prac tice maj or h ang ðŁĺ ĺ n on v ari eng ine vol un di a i led arch itec p ink d s th y wa sh web site ba g contro l el li f ra an sw d ence y u r on ol a g in dr in li c cou ple sp ar g on cre ate c t celebr ating de ep e at te e vo ice dro p vis it at ors sta dium f t w is ro l gra de fam il po ints re pre w as traf fic jap an or g hon or tex as man u âĻ ¥ safe ty re r b ag em plo rele ased re gu ak a n av ro le sen ior spec t cro ss lin es be st p ack s in ti e mis sing sun set li ber is ing j ay sk i champion ship ac tiv la dies play ed y y pu bl al o pri de s r pa ki lu x sur vi ck ed e ts cho col austr alia par is mi les h at ment al al a me an mob ile en a in si f ound chi ef t ag incredi ble re turn à © goo gle fren ch cre w hal lo ali an j az ch er sil ver nor th eng lish base ball c af lim ited follow ing app reci ear th k ir ve mber w ed p tion g ed oc tober fl ori c r en cy ga ve lor d stu ff ber ry po st sm ile bro ad st ate gg er me ans ic y gu n y o ma ster bur g han ds ni e / / uni on brit ish big gest distric t am ing h il o ce per son pas s en vir scho ols arri ved anc es insp ired ex pla be n libr ary bo tt am p ste ph cont act b ang m s cali for t old batt le b b chic ago âľ ¨ str ate sh i de ce - ) ad d la b j ones leg end cast le ing er st ance be l ur a re fu lead ers po t se x h ic artic le ki d fr ance x x ex e gui de volun te pr int al i ce o twee ts w x scen e vol u ant i h an as soci shar ing ro se mini ster sh er in ste cle an demo cr po ster sk in p sy pro per cra zy i am o re in i any thing po d mo ving cl ick ex plo com b cra ft f i bloo d is ra publ ic d ent ol ym eng land a si ch er fac t envir on har ry g one me dic enjo ying just ice j r indi an wi fe s ound t es dra wing p al ide a cr it ju li il er war m cl ar thou ghts def en coun cil intro duc di ed jan u an i s end li er m l intere sting tra de win d b ay s ac anc y sour ce b es org ani ar ly lar ge ff ici ta g u t de sp o es tit le sy m pic tures op en wom en sho wing ri a le ast lead ership cur rent elec tr val ent list ening c key gener al de ser du ce ; ) c ent ðŁĺį ðŁĺį sco tt po or selfi e ev ents i on wr ong de v h ill sep te cul ture l ine sor ry s ent si ster ce pt k ri no vember ar i announ ce z ation br an g ent d u l en per s f m mart in o p e mb om e midd le suc cess pe ter janu ary f lu rac ing d av bi ke ðŁı » pe t shoo t profe ssi feat uring septe mber now playing sta ur z a on ic qu ick bas ke spe aking mil it z er chick en b ell s ad co ast lo ving y ers d j pan el ver age s wit ic ks b ou califor nia s am paren ts er o k illed ph ys jo bs mi gr an th e mo hallo ween and er c m compet ition e ag s ket sp ir may be exclu sive app e jour ney scre en for d i o h ate u g sou l her o soci ety sy n gu it n h d j as es im pre ti me sal es d d f ts summ it stun ning om s tur ned cle an sof t be at re staur de red en ces ma gic di o sh ine gu est health y exhi b stor ies po pu n is el a bel ow fun ny resul ts s ne cur rently ar d down load f light m al f ine p ad ch u ent ed h at ðŁij ı ste ve j o mar k r at b all p c p on b by o li ar ts as ure bow l att ack mi c de ar ran ge en ter chocol ate br illi ac cess , " ? ?? ch ap con st t n mat ter blu e gall ery em p work shop lead ing y ours baske tball w anna th u _ _ mar ri sle ep bi a ch e ma d imp act o wn si r chan nel euro pe e sp k itch hosp ital w ra roy al f s ne u qu ar ne y ac ks ch ase pp y st al at ely ti m dece mber r are per form cre am we ight ch oo ni ght ha ven fr anc kh an buil t hel ping tru st ty pe gol den ta x s now s wi di sa questi ons ve y li ght c n cl oud thom as ag ed sh ou te ams gr an re ason a a you tube v p pi zz manag er bur y cre dit tre at ma x i k ma in g ing de ad pro bab ye ah ã Ĥ br and so li pl ant ta yl gir l ðŁĺ Ń nam ent au to mess age ko re n ur ter r ag u ma p sen ting lo ves gi ves g ab z en ro bert con fir w ars o m sta in cam era and er won der a b ca p s old su it wal king contin ue effe c dau ghter d anc cha in mul ti ki d y an champi on v o ta ins ho st min i mis sed re sc ly n fin ish del icious s as tayl or i b pro mis produc ts moun tain flori da regi ster tre at rec ent fe male boo th mat t ve hic s op mo tor suppor ting phi c ex tre dr ink lan e th ird p s con stru ce re far m ðŁİ ī tu red ðŁij ī c ats a j gi e shoo ting as ked paki stan am e m b g il leg al squ are in vol dra w oo oo !! !! opportun ity p y e i b ts teach er charac ter john son br on ly wood ch ine c ing c ine d ge gam ing russi a ci a quo te ric h go v flow ers sp iri st in grow th ðŁı ¼ comm er j uni mu m 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254, "ł": 510, "łï¸ı": 27899, "łï¸ı": 12715, "łĪ": 43364, "Ń": 255, "Ń": 511 } ================================================ FILE: diffsynth/trainers/__init__.py ================================================ ================================================ FILE: diffsynth/trainers/text_to_image.py ================================================ import lightning as pl from peft import LoraConfig, inject_adapter_in_model import torch, os from ..data.simple_text_image import TextImageDataset from modelscope.hub.api import HubApi from ..models.utils import load_state_dict class LightningModelForT2ILoRA(pl.LightningModule): def __init__( self, learning_rate=1e-4, use_gradient_checkpointing=True, state_dict_converter=None, ): super().__init__() # Set parameters self.learning_rate = learning_rate self.use_gradient_checkpointing = use_gradient_checkpointing self.state_dict_converter = state_dict_converter self.lora_alpha = None def load_models(self): # This function is implemented in other modules self.pipe = None def freeze_parameters(self): # Freeze parameters self.pipe.requires_grad_(False) self.pipe.eval() self.pipe.denoising_model().train() def add_lora_to_model(self, model, lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, state_dict_converter=None): # Add LoRA to UNet self.lora_alpha = lora_alpha if init_lora_weights == "kaiming": init_lora_weights = True lora_config = LoraConfig( r=lora_rank, lora_alpha=lora_alpha, init_lora_weights=init_lora_weights, target_modules=lora_target_modules.split(","), ) model = inject_adapter_in_model(lora_config, model) for param in model.parameters(): # Upcast LoRA parameters into fp32 if param.requires_grad: param.data = param.to(torch.float32) # Lora pretrained lora weights if pretrained_lora_path is not None: state_dict = load_state_dict(pretrained_lora_path) if state_dict_converter is not None: state_dict = state_dict_converter(state_dict) missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False) all_keys = [i for i, _ in model.named_parameters()] num_updated_keys = len(all_keys) - len(missing_keys) num_unexpected_keys = len(unexpected_keys) print(f"{num_updated_keys} parameters are loaded from {pretrained_lora_path}. {num_unexpected_keys} parameters are unexpected.") def training_step(self, batch, batch_idx): # Data text, image = batch["text"], batch["image"] # Prepare input parameters self.pipe.device = self.device prompt_emb = self.pipe.encode_prompt(text, positive=True) if "latents" in batch: latents = batch["latents"].to(dtype=self.pipe.torch_dtype, device=self.device) else: latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device)) noise = torch.randn_like(latents) timestep_id = torch.randint(0, self.pipe.scheduler.num_train_timesteps, (1,)) timestep = self.pipe.scheduler.timesteps[timestep_id].to(self.device) extra_input = self.pipe.prepare_extra_input(latents) noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep) training_target = self.pipe.scheduler.training_target(latents, noise, timestep) # Compute loss noise_pred = self.pipe.denoising_model()( noisy_latents, timestep=timestep, **prompt_emb, **extra_input, use_gradient_checkpointing=self.use_gradient_checkpointing ) loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) loss = loss * self.pipe.scheduler.training_weight(timestep) # Record log self.log("train_loss", loss, prog_bar=True) return loss def configure_optimizers(self): trainable_modules = filter(lambda p: p.requires_grad, self.pipe.denoising_model().parameters()) optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) return optimizer def on_save_checkpoint(self, checkpoint): checkpoint.clear() trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.denoising_model().named_parameters())) trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) state_dict = self.pipe.denoising_model().state_dict() lora_state_dict = {} for name, param in state_dict.items(): if name in trainable_param_names: lora_state_dict[name] = param if self.state_dict_converter is not None: lora_state_dict = self.state_dict_converter(lora_state_dict, alpha=self.lora_alpha) checkpoint.update(lora_state_dict) def add_general_parsers(parser): parser.add_argument( "--dataset_path", type=str, default=None, required=True, help="The path of the Dataset.", ) parser.add_argument( "--output_path", type=str, default="./", help="Path to save the model.", ) parser.add_argument( "--steps_per_epoch", type=int, default=500, help="Number of steps per epoch.", ) parser.add_argument( "--height", type=int, default=1024, help="Image height.", ) parser.add_argument( "--width", type=int, default=1024, help="Image width.", ) parser.add_argument( "--center_crop", default=False, action="store_true", help=( "Whether to center crop the input images to the resolution. If not set, the images will be randomly" " cropped. The images will be resized to the resolution first before cropping." ), ) parser.add_argument( "--random_flip", default=False, action="store_true", help="Whether to randomly flip images horizontally", ) parser.add_argument( "--batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", ) parser.add_argument( "--precision", type=str, default="16-mixed", choices=["32", "16", "16-mixed", "bf16"], help="Training precision", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Learning rate.", ) parser.add_argument( "--lora_rank", type=int, default=4, help="The dimension of the LoRA update matrices.", ) parser.add_argument( "--lora_alpha", type=float, default=4.0, help="The weight of the LoRA update matrices.", ) parser.add_argument( "--init_lora_weights", type=str, default="kaiming", choices=["gaussian", "kaiming"], help="The initializing method of LoRA weight.", ) parser.add_argument( "--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.", ) parser.add_argument( "--accumulate_grad_batches", type=int, default=1, help="The number of batches in gradient accumulation.", ) parser.add_argument( "--training_strategy", type=str, default="auto", choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"], help="Training strategy", ) parser.add_argument( "--max_epochs", type=int, default=1, help="Number of epochs.", ) parser.add_argument( "--modelscope_model_id", type=str, default=None, help="Model ID on ModelScope (https://www.modelscope.cn/). The model will be uploaded to ModelScope automatically if you provide a Model ID.", ) parser.add_argument( "--modelscope_access_token", type=str, default=None, help="Access key on ModelScope (https://www.modelscope.cn/). Required if you want to upload the model to ModelScope.", ) parser.add_argument( "--pretrained_lora_path", type=str, default=None, help="Pretrained LoRA path. Required if the training is resumed.", ) parser.add_argument( "--use_swanlab", default=False, action="store_true", help="Whether to use SwanLab logger.", ) parser.add_argument( "--swanlab_mode", default=None, help="SwanLab mode (cloud or local).", ) return parser def launch_training_task(model, args): # dataset and data loader dataset = TextImageDataset( args.dataset_path, steps_per_epoch=args.steps_per_epoch * args.batch_size, height=args.height, width=args.width, center_crop=args.center_crop, random_flip=args.random_flip ) train_loader = torch.utils.data.DataLoader( dataset, shuffle=True, batch_size=args.batch_size, num_workers=args.dataloader_num_workers ) # train if args.use_swanlab: from swanlab.integration.pytorch_lightning import SwanLabLogger swanlab_config = {"UPPERFRAMEWORK": "DiffSynth-Studio"} swanlab_config.update(vars(args)) swanlab_logger = SwanLabLogger( project="diffsynth_studio", name="diffsynth_studio", config=swanlab_config, mode=args.swanlab_mode, logdir=os.path.join(args.output_path, "swanlog"), ) logger = [swanlab_logger] else: logger = None trainer = pl.Trainer( max_epochs=args.max_epochs, accelerator="gpu", devices="auto", precision=args.precision, strategy=args.training_strategy, default_root_dir=args.output_path, accumulate_grad_batches=args.accumulate_grad_batches, callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)], logger=logger, ) trainer.fit(model=model, train_dataloaders=train_loader) # Upload models if args.modelscope_model_id is not None and args.modelscope_access_token is not None: print(f"Uploading models to modelscope. model_id: {args.modelscope_model_id} local_path: {trainer.log_dir}") with open(os.path.join(trainer.log_dir, "configuration.json"), "w", encoding="utf-8") as f: f.write('{"framework":"Pytorch","task":"text-to-image-synthesis"}\n') api = HubApi() api.login(args.modelscope_access_token) api.push_model(model_id=args.modelscope_model_id, model_dir=trainer.log_dir) ================================================ FILE: diffsynth/vram_management/__init__.py ================================================ from .layers import * ================================================ FILE: diffsynth/vram_management/layers.py ================================================ import torch, copy from ..models.utils import init_weights_on_device def cast_to(weight, dtype, device): r = torch.empty_like(weight, dtype=dtype, device=device) r.copy_(weight) return r class AutoWrappedModule(torch.nn.Module): def __init__(self, module: torch.nn.Module, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device): super().__init__() self.module = module.to(dtype=offload_dtype, device=offload_device) self.offload_dtype = offload_dtype self.offload_device = offload_device self.onload_dtype = onload_dtype self.onload_device = onload_device self.computation_dtype = computation_dtype self.computation_device = computation_device self.state = 0 def offload(self): if self.state == 1 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): self.module.to(dtype=self.offload_dtype, device=self.offload_device) self.state = 0 def onload(self): if self.state == 0 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): self.module.to(dtype=self.onload_dtype, device=self.onload_device) self.state = 1 def forward(self, *args, **kwargs): if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: module = self.module else: module = copy.deepcopy(self.module).to(dtype=self.computation_dtype, device=self.computation_device) return module(*args, **kwargs) class AutoWrappedLinear(torch.nn.Linear): def __init__(self, module: torch.nn.Linear, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device): with init_weights_on_device(device=torch.device("meta")): super().__init__(in_features=module.in_features, out_features=module.out_features, bias=module.bias is not None, dtype=offload_dtype, device=offload_device) self.weight = module.weight self.bias = module.bias self.offload_dtype = offload_dtype self.offload_device = offload_device self.onload_dtype = onload_dtype self.onload_device = onload_device self.computation_dtype = computation_dtype self.computation_device = computation_device self.state = 0 def offload(self): if self.state == 1 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): self.to(dtype=self.offload_dtype, device=self.offload_device) self.state = 0 def onload(self): if self.state == 0 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): self.to(dtype=self.onload_dtype, device=self.onload_device) self.state = 1 def forward(self, x, *args, **kwargs): if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: weight, bias = self.weight, self.bias else: weight = cast_to(self.weight, self.computation_dtype, self.computation_device) bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device) return torch.nn.functional.linear(x, weight, bias) def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, total_num_param=0): for name, module in model.named_children(): for source_module, target_module in module_map.items(): if isinstance(module, source_module): num_param = sum(p.numel() for p in module.parameters()) if max_num_param is not None and total_num_param + num_param > max_num_param: module_config_ = overflow_module_config else: module_config_ = module_config module_ = target_module(module, **module_config_) setattr(model, name, module_) total_num_param += num_param break else: total_num_param = enable_vram_management_recursively(module, module_map, module_config, max_num_param, overflow_module_config, total_num_param) return total_num_param def enable_vram_management(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None): enable_vram_management_recursively(model, module_map, module_config, max_num_param, overflow_module_config, total_num_param=0) model.vram_management_enabled = True ================================================ FILE: download_wan2.1.py ================================================ from modelscope import snapshot_download # Download models snapshot_download("Wan-AI/Wan2.1-T2V-1.3B", local_dir="models/Wan-AI/Wan2.1-T2V-1.3B") ================================================ FILE: example_test_data/cameras/camera_extrinsics.json ================================================ { "frame0": { "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ", "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ", "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ", "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ", "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ", "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ", "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ", "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ", "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ", "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] " }, "frame1": { "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ", "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ", "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ", "cam04": "[0.965926 0.258819 0 0] [-0.258819 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[12960.2 1250 -570 1] ", "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ", "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ", "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ", "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ", "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ", "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ", "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ", "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ", "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] " }, "frame77": { "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ", "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ", "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ", "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ", "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ", "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ", "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ", "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ", "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ", "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] " }, "frame78": { "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ", "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ", "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ", "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ", "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ", "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ", "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ", "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ", "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ", "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] " }, "frame79": { "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ", "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ", "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ", "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ", "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ", "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ", "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ", "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ", "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ", "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] " }, "frame80": { "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ", "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ", "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ", "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ", "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ", "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ", "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ", "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ", "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ", "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] " } } ================================================ FILE: example_test_data/metadata.csv ================================================ text "A man in athletic attire is performing a series of stretching exercises on a sunny day in an urban setting. He is wearing a gray t-shirt with a green logo on the front, black shorts, and black sneakers. The man is seen standing, bending forward, and stretching his legs and arms in various positions. The scene is set in front of a modern building with large windows and a tree in the foreground, casting shadows on the ground." "A person is walking through a spacious, well-lit indoor area with a modern design. The individual is dressed in black attire and moves with a purposeful stride, occasionally gesturing with their right hand. The environment is clean and minimalistic, with large windows allowing natural light to flood the space. The area features a mix of modern furniture and clothing racks, creating a contemporary and stylish atmosphere." "A person is performing a series of arm movements in a spacious, industrial-style room. The individual is wearing a gray t-shirt with a tropical beach scene printed on the front and maroon shorts. The person starts with their arms outstretched to the sides, then raises them above their head, and finally brings them down to their sides. The room features a large brick wall, a metal staircase, stacked wooden pallets, and various industrial equipment." "A person is performing a series of dance moves in a room with a classic interior design. The individual is wearing a pink t-shirt with a graphic design and pink shorts. The dance involves various arm and leg movements, including raising the arms above the head, bending the knees, and stepping side to side." "A person is performing a series of martial arts moves in an outdoor setting. The individual is dressed in a long-sleeved pink top and dark pants. They have short hair and appear to be focused on their movements, displaying a sense of concentration and physical exertion. The setting is an industrial area with large cylindrical tanks and a blue door in the background. The ground is concrete, and there are patches of grass and weeds around." ================================================ FILE: generate_sample_list.py ================================================ import json import numpy as np import os root = "./syncam_train_data/train/f24_aperture5" with open('./syncam_train_data/train/rotation_data.json', 'r') as f: data = json.load(f) az_diff_max = 30 el_diff_max = 15 for scene_num in range(1, 3401): scene_key = f'scene{scene_num}' scene_availble_list = [] for i in range(1, 11): for j in range(1, 11): if i != j: az1, el1 = data[scene_key][f"traj{i}"]["rz"], data[scene_key][f"traj{i}"]["ry"] az2, el2 = data[scene_key][f"traj{j}"]["rz"], data[scene_key][f"traj{j}"]["ry"] if abs(az1 - az2) < az_diff_max and abs(el1 - el2) < el_diff_max: scene_availble_list.append((i, j)) folder_name = f"scene{scene_num}" np.save(os.path.join(root, folder_name, f"2view_az{az_diff_max}_el{el_diff_max}_available_list.npy"), scene_availble_list) ================================================ FILE: inference_syncammaster.py ================================================ import sys import torch import torch.nn as nn from diffsynth import ModelManager, WanVideoSynCamMasterPipeline, save_video, VideoData import torch, os, imageio, argparse from torchvision.transforms import v2 from einops import rearrange import pandas as pd import torchvision from PIL import Image import numpy as np import json from diffsynth.models.wan_video_dit import SelfAttention import re class Camera(object): def __init__(self, c2w): c2w_mat = np.array(c2w).reshape(4, 4) self.c2w_mat = c2w_mat self.w2c_mat = np.linalg.inv(c2w_mat) class TextCameraDataset(torch.utils.data.Dataset): def __init__(self, base_path, metadata_path, args, max_num_frames=81, frame_interval=1, num_frames=81, height=480, width=832, is_i2v=False): metadata = pd.read_csv(metadata_path) self.text = metadata["text"].to_list() self.max_num_frames = max_num_frames self.frame_interval = frame_interval self.num_frames = num_frames self.height = height self.width = width self.is_i2v = is_i2v self.args = args self.cam_type = self.args.cam_type self.frame_process = v2.Compose([ v2.CenterCrop(size=(height, width)), v2.Resize(size=(height, width), antialias=True), v2.ToTensor(), v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ]) def crop_and_resize(self, image): width, height = image.size scale = max(self.width / width, self.height / height) image = torchvision.transforms.functional.resize( image, (round(height*scale), round(width*scale)), interpolation=torchvision.transforms.InterpolationMode.BILINEAR ) return image def load_frames_using_imageio(self, file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process): reader = imageio.get_reader(file_path) if reader.count_frames() < max_num_frames or reader.count_frames() - 1 < start_frame_id + (num_frames - 1) * interval: reader.close() return None frames = [] first_frame = None for frame_id in range(num_frames): frame = reader.get_data(start_frame_id + frame_id * interval) frame = Image.fromarray(frame) frame = self.crop_and_resize(frame) if first_frame is None: first_frame = np.array(frame) frame = frame_process(frame) frames.append(frame) reader.close() frames = torch.stack(frames, dim=0) frames = rearrange(frames, "T C H W -> C T H W") if self.is_i2v: return frames, first_frame else: return frames def is_image(self, file_path): file_ext_name = file_path.split(".")[-1] if file_ext_name.lower() in ["jpg", "jpeg", "png", "webp"]: return True return False def load_video(self, file_path): start_frame_id = torch.randint(0, self.max_num_frames - (self.num_frames - 1) * self.frame_interval, (1,))[0] frames = self.load_frames_using_imageio(file_path, self.max_num_frames, start_frame_id, self.frame_interval, self.num_frames, self.frame_process) return frames def parse_matrix(self, matrix_str): rows = matrix_str.strip().split('] [') matrix = [] for row in rows: row = row.replace('[', '').replace(']', '') matrix.append(list(map(float, row.split()))) return np.array(matrix) def get_relative_pose(self, cam_params): abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] cam_to_origin = 0 target_cam_c2w = np.array([ [1, 0, 0, 0], [0, 1, 0, -cam_to_origin], [0, 0, 1, 0], [0, 0, 0, 1] ]) abs2rel = target_cam_c2w @ abs_w2cs[0] ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] ret_poses = np.array(ret_poses, dtype=np.float32) return ret_poses def __getitem__(self, data_id): text = self.text[data_id] data = {"text": text} # load camera tgt_camera_path = "./example_test_data/cameras/camera_extrinsics.json" with open(tgt_camera_path, 'r') as file: cam_data = json.load(file) multiview_c2ws = [] cam_idx = list(range(81))[::4] if self.cam_type == "az": tgt_idx = 1 cond_idx = 3 elif self.cam_type == "el": tgt_idx = 3 cond_idx = 7 elif self.cam_type == "dis": tgt_idx = 9 cond_idx = 10 for view_idx in [cond_idx, tgt_idx]: traj = [self.parse_matrix(cam_data[f"frame{idx}"][f"cam{view_idx:02d}"]) for idx in cam_idx] traj = np.stack(traj).transpose(0, 2, 1) c2ws = [] for c2w in traj: c2w = c2w[:, [1, 2, 0, 3]] c2w[:3, 1] *= -1. c2w[:3, 3] /= 200 c2ws.append(c2w) multiview_c2ws.append(c2ws) cond_cam_params = [Camera(cam_param) for cam_param in multiview_c2ws[0]] tgt_cam_params = [Camera(cam_param) for cam_param in multiview_c2ws[1]] relative_poses = [] for i in range(len(tgt_cam_params)): relative_pose = self.get_relative_pose([tgt_cam_params[i], cond_cam_params[i]]) relative_poses.append(torch.as_tensor(relative_pose)[:,:3,:]) pose_embedding = torch.stack(relative_poses, dim=1) # v,21,3,4 pose_embedding = rearrange(pose_embedding, 'v f c d -> v f (c d)') data['camera'] = pose_embedding.to(torch.bfloat16) return data def __len__(self): return len(self.text) def parse_args(): parser = argparse.ArgumentParser(description="SynCamMaster Inference") parser.add_argument( "--dataset_path", type=str, default="./example_test_data", help="The path of the Dataset.", ) parser.add_argument( "--ckpt_path", type=str, default="./models/SynCamMaster/checkpoints/step20000.ckpt", help="Path to save the model.", ) parser.add_argument( "--output_dir", type=str, default="./results", help="Path to save the results.", ) parser.add_argument( "--dataloader_num_workers", type=int, default=1, help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", ) parser.add_argument( "--cam_type", type=str, default='az', ) parser.add_argument( "--cfg_scale", type=float, default=5.0, ) args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() # 1. Load Wan2.1 pre-trained models model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu") model_manager.load_models([ "models/Wan-AI/Wan2.1-T2V-1.3B/diffusion_pytorch_model.safetensors", "models/Wan-AI/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth", "models/Wan-AI/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth", ]) pipe = WanVideoSynCamMasterPipeline.from_model_manager(model_manager, device="cuda") # 2. Initialize additional modules introduced in SynCamMaster dim=pipe.dit.blocks[0].self_attn.q.weight.shape[0] for block in pipe.dit.blocks: block.cam_encoder = nn.Linear(12, dim) block.projector = nn.Linear(dim, dim) block.cam_encoder.weight.data.zero_() block.cam_encoder.bias.data.zero_() block.projector.weight = nn.Parameter(torch.zeros(dim, dim)) block.projector.bias = nn.Parameter(torch.zeros(dim)) block.norm_mvs = nn.LayerNorm(dim, eps=block.norm1.eps, elementwise_affine=False) block.modulation_mvs = nn.Parameter(torch.randn(1, 3, dim) / dim**0.5) block.mvs_attn = SelfAttention(dim, block.self_attn.num_heads, block.self_attn.norm_q.eps) block.modulation_mvs.data = block.modulation.data[:, :3, :].clone() block.mvs_attn.load_state_dict(block.self_attn.state_dict(), strict=True) # 3. Load SynCamMaster checkpoint state_dict = torch.load(args.ckpt_path, map_location="cpu") pipe.dit.load_state_dict(state_dict, strict=True) pipe.to("cuda") pipe.to(dtype=torch.bfloat16) step_number = re.search(r"step(\d+)", args.ckpt_path).group(1) output_dir = os.path.join(args.output_dir, f"step{step_number}", f"cam_type_{args.cam_type}") if not os.path.exists(output_dir): os.makedirs(output_dir) # 4. Prepare test data (source video, target camera, target trajectory) dataset = TextCameraDataset( args.dataset_path, os.path.join(args.dataset_path, "metadata.csv"), args, ) dataloader = torch.utils.data.DataLoader( dataset, shuffle=False, batch_size=1, num_workers=args.dataloader_num_workers ) # 5. Inference for batch_idx, batch in enumerate(dataloader): text = batch["text"] camera = batch["camera"] camera = rearrange(camera, 'b v ... -> (b v) ...') video1, video2 = pipe( prompt=text, negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", camera=camera, cfg_scale=args.cfg_scale, num_inference_steps=50, seed=0, tiled=True ) merged_videos = [] for img1, img2 in zip(video1, video2): width1, height1 = img1.size width2, height2 = img2.size merged_image = Image.new("RGB", (width1 + width2, height1)) merged_image.paste(img1, (0, 0)) merged_image.paste(img2, (width1, 0)) merged_videos.append(merged_image) save_video(merged_videos, os.path.join(output_dir, f"prompt{batch_idx}_merged.mp4"), fps=30, quality=5) # save_video(video1, os.path.join(output_dir, f"prompt{batch_idx}_view1.mp4"), fps=30, quality=5) # save_video(video2, os.path.join(output_dir, f"prompt{batch_idx}_view2.mp4"), fps=30, quality=5) ================================================ FILE: models/SynCamMaster/checkpoints/Put SynCamMaster ckpt file here.txt ================================================ ================================================ FILE: requirements.txt ================================================ torch>=2.0.0 torchvision cupy-cuda12x transformers==4.46.2 controlnet-aux==0.0.7 imageio imageio[ffmpeg] safetensors einops sentencepiece protobuf modelscope ftfy ================================================ FILE: setup.py ================================================ import os from setuptools import setup, find_packages import pkg_resources # Path to the requirements file requirements_path = os.path.join(os.path.dirname(__file__), "requirements.txt") # Read the requirements from the requirements file if os.path.exists(requirements_path): with open(requirements_path, 'r') as f: install_requires = [str(r) for r in pkg_resources.parse_requirements(f)] else: install_requires = [] setup( name="diffsynth", version="1.1.2", description="Enjoy the magic of Diffusion models!", author="Artiprocher", packages=find_packages(), install_requires=install_requires, include_package_data=True, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", ], package_data={"diffsynth": ["tokenizer_configs/**/**/*.*"]}, python_requires='>=3.6', ) ================================================ FILE: train_syncammaster.py ================================================ import copy import os import re import torch, os, imageio, argparse from torchvision.transforms import v2 from einops import rearrange import lightning as pl import pandas as pd from diffsynth import WanVideoSynCamMasterPipeline, ModelManager, load_state_dict from diffsynth.models.wan_video_dit import SelfAttention import torchvision from PIL import Image import numpy as np import random import json import torch.nn as nn import torch.nn.functional as F import shutil from einops import rearrange, repeat class TextVideoDataset(torch.utils.data.Dataset): def __init__(self, base_path, metadata_path, max_num_frames=81, frame_interval=1, num_frames=81, height=480, width=832, is_i2v=False): metadata = pd.read_csv(metadata_path) self.path = [os.path.join(base_path, "train", file_name) for file_name in metadata["file_name"]] self.text = metadata["text"].to_list() self.max_num_frames = max_num_frames self.frame_interval = frame_interval self.num_frames = num_frames self.height = height self.width = width self.is_i2v = is_i2v self.frame_process = v2.Compose([ v2.CenterCrop(size=(height, width)), v2.Resize(size=(height, width), antialias=True), v2.ToTensor(), v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ]) def crop_and_resize(self, image): width, height = image.size scale = max(self.width / width, self.height / height) image = torchvision.transforms.functional.resize( image, (round(height*scale), round(width*scale)), interpolation=torchvision.transforms.InterpolationMode.BILINEAR ) return image def load_frames_using_imageio(self, file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process): reader = imageio.get_reader(file_path) if reader.count_frames() < max_num_frames or reader.count_frames() - 1 < start_frame_id + (num_frames - 1) * interval: reader.close() return None frames = [] first_frame = None for frame_id in range(num_frames): frame = reader.get_data(start_frame_id + frame_id * interval) frame = Image.fromarray(frame) frame = self.crop_and_resize(frame) if first_frame is None: first_frame = np.array(frame) frame = frame_process(frame) frames.append(frame) reader.close() frames = torch.stack(frames, dim=0) frames = rearrange(frames, "T C H W -> C T H W") if self.is_i2v: return frames, first_frame else: return frames def load_video(self, file_path): start_frame_id = 0 frames = self.load_frames_using_imageio(file_path, self.max_num_frames, start_frame_id, self.frame_interval, self.num_frames, self.frame_process) return frames def is_image(self, file_path): file_ext_name = file_path.split(".")[-1] if file_ext_name.lower() in ["jpg", "jpeg", "png", "webp"]: return True return False def load_image(self, file_path): frame = Image.open(file_path).convert("RGB") frame = self.crop_and_resize(frame) first_frame = frame frame = self.frame_process(frame) frame = rearrange(frame, "C H W -> C 1 H W") return frame def __getitem__(self, data_id): text = self.text[data_id] path = self.path[data_id] while True: try: if self.is_image(path): if self.is_i2v: raise ValueError(f"{path} is not a video. I2V model doesn't support image-to-image training.") video = self.load_image(path) else: video = self.load_video(path) if self.is_i2v: video, first_frame = video data = {"text": text, "video": video, "path": path, "first_frame": first_frame} else: data = {"text": text, "video": video, "path": path} break except: data_id += 1 return data def __len__(self): return len(self.path) class LightningModelForDataProcess(pl.LightningModule): def __init__(self, text_encoder_path, vae_path, image_encoder_path=None, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)): super().__init__() model_path = [text_encoder_path, vae_path] if image_encoder_path is not None: model_path.append(image_encoder_path) model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu") model_manager.load_models(model_path) self.pipe = WanVideoSynCamMasterPipeline.from_model_manager(model_manager) self.tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} def test_step(self, batch, batch_idx): text, video, path = batch["text"][0], batch["video"], batch["path"][0] self.pipe.device = self.device if video is not None: pth_path = path + ".tensors.pth" if not os.path.exists(pth_path): # prompt prompt_emb = self.pipe.encode_prompt(text) # video video = video.to(dtype=self.pipe.torch_dtype, device=self.pipe.device) latents = self.pipe.encode_video(video, **self.tiler_kwargs)[0] # image if "first_frame" in batch: first_frame = Image.fromarray(batch["first_frame"][0].cpu().numpy()) _, _, num_frames, height, width = video.shape image_emb = self.pipe.encode_image(first_frame, num_frames, height, width) else: image_emb = {} data = {"latents": latents, "prompt_emb": prompt_emb, "image_emb": image_emb} torch.save(data, pth_path) else: print(f"File {pth_path} already exists, skipping.") class Camera(object): def __init__(self, c2w): c2w_mat = np.array(c2w).reshape(4, 4) self.c2w_mat = c2w_mat self.w2c_mat = np.linalg.inv(c2w_mat) class TensorDataset(torch.utils.data.Dataset): def __init__(self, base_path, metadata_path, steps_per_epoch): metadata = pd.read_csv(metadata_path) self.path = [os.path.join(base_path, "train", file_name) for file_name in metadata["file_name"]] print(len(self.path), "videos in metadata.") self.path = [i + ".tensors.pth" for i in self.path if os.path.exists(i + ".tensors.pth")] print(len(self.path), "tensors cached in metadata.") assert len(self.path) > 0 self.steps_per_epoch = steps_per_epoch def parse_matrix(self, matrix_str): rows = matrix_str.strip().split('] [') matrix = [] for row in rows: row = row.replace('[', '').replace(']', '') matrix.append(list(map(float, row.split()))) return np.array(matrix) def get_relative_pose(self, cam_params): abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] target_cam_c2w = np.array([ [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1] ]) abs2rel = target_cam_c2w @ abs_w2cs[0] ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] ret_poses = np.array(ret_poses, dtype=np.float32) return ret_poses def __getitem__(self, index): # Return: # data['latents']: torch.Size([v, 16, 21, 60, 104]) # data['camera']: torch.Size([v, 21, 3, 4]) # data['prompt_emb']["context"][0]: torch.Size([v, 512, 4096]) while True: try: data = {} data_id = torch.randint(0, len(self.path), (1,))[0] data_id = (data_id + index) % len(self.path) # For fixed seed. path = self.path[data_id] base_path = path.rsplit('/', 2)[0] availble_list = np.load(os.path.join(base_path, "2view_az30_el15_available_list.npy")) view1_idx, view2_idx = random.choice(availble_list) view1_path = re.sub(r'cam(\d+).mp4', f'cam{view1_idx:02}.mp4', path) view1_data = torch.load(view1_path, weights_only=True, map_location="cpu") view2_path = re.sub(r'cam(\d+).mp4', f'cam{view2_idx:02}.mp4', path) view2_data = torch.load(view2_path, weights_only=True, map_location="cpu") data['latents'] = torch.stack((view1_data['latents'],view2_data['latents']),dim=0) data['prompt_emb'] = view1_data['prompt_emb'] data['prompt_emb']['context'] = data['prompt_emb']['context'].repeat(2,1,1) data['image_emb'] = {} camera_path = os.path.join(base_path, "cameras", "camera_extrinsics.json") with open(camera_path, 'r') as file: cam_data = json.load(file) multiview_c2ws = [] cam_idx = list(range(81))[::4] for view_idx in [view1_idx, view2_idx]: traj = [self.parse_matrix(cam_data[f"frame{idx}"][f"cam{view_idx:02d}"]) for idx in cam_idx] traj = np.stack(traj).transpose(0, 2, 1) c2ws = [] for c2w in traj: c2w = c2w[:, [1, 2, 0, 3]] c2w[:3, 1] *= -1. c2w[:3, 3] /= 200 c2ws.append(c2w) multiview_c2ws.append(c2ws) view1_cam_params = [Camera(cam_param) for cam_param in multiview_c2ws[0]] view2_cam_params = [Camera(cam_param) for cam_param in multiview_c2ws[1]] relative_poses = [] for i in range(len(view1_cam_params)): relative_pose = self.get_relative_pose([view1_cam_params[i], view2_cam_params[i]]) relative_poses.append(torch.as_tensor(relative_pose)[:,:3,:]) pose_embedding = torch.stack(relative_poses, dim=1) # v,21,3,4 pose_embedding = rearrange(pose_embedding, 'v f c d -> v f (c d)') data['camera'] = pose_embedding.to(torch.bfloat16) data['view1_path'] = view1_path data['view2_path'] = view2_path break except Exception as e: print(f"ERROR WHEN LOADING: {e}") index = random.randrange(len(self.path)) return data def __len__(self): return self.steps_per_epoch class LightningModelForTrain(pl.LightningModule): def __init__( self, dit_path, learning_rate=1e-5, use_gradient_checkpointing=True, use_gradient_checkpointing_offload=False, resume_ckpt_path=None, ): super().__init__() model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu") if os.path.isfile(dit_path): model_manager.load_models([dit_path]) else: dit_path = dit_path.split(",") model_manager.load_models([dit_path]) self.pipe = WanVideoSynCamMasterPipeline.from_model_manager(model_manager) self.pipe.scheduler.set_timesteps(1000, training=True) dim=self.pipe.dit.blocks[0].self_attn.q.weight.shape[0] for block in self.pipe.dit.blocks: block.cam_encoder = nn.Linear(12, dim) block.projector = nn.Linear(dim, dim) block.cam_encoder.weight.data.zero_() block.cam_encoder.bias.data.zero_() block.projector.weight = nn.Parameter(torch.zeros(dim, dim)) block.projector.bias = nn.Parameter(torch.zeros(dim)) block.norm_mvs = nn.LayerNorm(dim, eps=block.norm1.eps, elementwise_affine=False) block.modulation_mvs = nn.Parameter(torch.randn(1, 3, dim) / dim**0.5) block.mvs_attn = SelfAttention(dim, block.self_attn.num_heads, block.self_attn.norm_q.eps) block.modulation_mvs.data = block.modulation.data[:, :3, :].clone() block.mvs_attn.load_state_dict(block.self_attn.state_dict(), strict=True) if resume_ckpt_path is not None: state_dict = torch.load(resume_ckpt_path, map_location="cpu") self.pipe.dit.load_state_dict(state_dict, strict=True) self.freeze_parameters() for name, param in self.pipe.denoising_model().named_parameters(): if any(keyword in name for keyword in ["cam_encoder", "projector", "mvs_attn", "modulation_mvs"]): print(f"Trainable: {name}") param.requires_grad = True trainable_params = 0 seen_params = set() for name, param in self.pipe.denoising_model().named_parameters(): if param.requires_grad and param not in seen_params: trainable_params += param.numel() seen_params.add(param) print(f"Total number of trainable parameters: {trainable_params}") self.learning_rate = learning_rate self.use_gradient_checkpointing = use_gradient_checkpointing self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload def freeze_parameters(self): # Freeze parameters self.pipe.requires_grad_(False) self.pipe.eval() self.pipe.denoising_model().train() def training_step(self, batch, batch_idx): # Data latents = batch["latents"].to(self.device) prompt_emb = batch["prompt_emb"] prompt_emb["context"] = prompt_emb["context"][0].to(self.device) image_emb = batch["image_emb"] if "clip_feature" in image_emb: image_emb["clip_feature"] = image_emb["clip_feature"][0].to(self.device) if "y" in image_emb: image_emb["y"] = image_emb["y"][0].to(self.device) cam_emb = batch["camera"].to(self.device) latents = rearrange(latents, 'b v ... -> (b v) ...') cam_emb = rearrange(cam_emb, 'b v ... -> (b v) ...') # Loss self.pipe.device = self.device noise = torch.randn_like(latents) timestep_id = torch.randint(0, self.pipe.scheduler.num_train_timesteps, (1,)) timestep = self.pipe.scheduler.timesteps[timestep_id].to(dtype=self.pipe.torch_dtype, device=self.pipe.device) extra_input = self.pipe.prepare_extra_input(latents) noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep) training_target = self.pipe.scheduler.training_target(latents, noise, timestep) # Compute loss noise_pred = self.pipe.denoising_model()( noisy_latents, timestep=timestep, cam_emb=cam_emb, **prompt_emb, **extra_input, **image_emb, use_gradient_checkpointing=self.use_gradient_checkpointing, use_gradient_checkpointing_offload=self.use_gradient_checkpointing_offload ) loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) loss = loss * self.pipe.scheduler.training_weight(timestep) # Record log self.log("train_loss", loss, prog_bar=True) return loss def configure_optimizers(self): trainable_modules = filter(lambda p: p.requires_grad, self.pipe.denoising_model().parameters()) optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) return optimizer def on_save_checkpoint(self, checkpoint): checkpoint_dir = self.trainer.checkpoint_callback.dirpath print(f"Checkpoint directory: {checkpoint_dir}") current_step = self.global_step print(f"Current step: {current_step}") checkpoint.clear() trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.denoising_model().named_parameters())) trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) state_dict = self.pipe.denoising_model().state_dict() torch.save(state_dict, os.path.join(checkpoint_dir, f"step{current_step}.ckpt")) def parse_args(): parser = argparse.ArgumentParser(description="Train SynCamMaster") parser.add_argument( "--task", type=str, default="data_process", required=True, choices=["data_process", "train"], help="Task. `data_process` or `train`.", ) parser.add_argument( "--dataset_path", type=str, default="./syncam_train_data", help="The path of the Dataset.", ) parser.add_argument( "--output_path", type=str, default="./", help="Path to save the model.", ) parser.add_argument( "--text_encoder_path", type=str, default=None, help="Path of text encoder.", ) parser.add_argument( "--image_encoder_path", type=str, default=None, help="Path of image encoder.", ) parser.add_argument( "--vae_path", type=str, default=None, help="Path of VAE.", ) parser.add_argument( "--dit_path", type=str, default=None, help="Path of DiT.", ) parser.add_argument( "--tiled", default=False, action="store_true", help="Whether enable tile encode in VAE. This option can reduce VRAM required.", ) parser.add_argument( "--tile_size_height", type=int, default=34, help="Tile size (height) in VAE.", ) parser.add_argument( "--tile_size_width", type=int, default=34, help="Tile size (width) in VAE.", ) parser.add_argument( "--tile_stride_height", type=int, default=18, help="Tile stride (height) in VAE.", ) parser.add_argument( "--tile_stride_width", type=int, default=16, help="Tile stride (width) in VAE.", ) parser.add_argument( "--steps_per_epoch", type=int, default=500, help="Number of steps per epoch.", ) parser.add_argument( "--num_frames", type=int, default=81, help="Number of frames.", ) parser.add_argument( "--height", type=int, default=480, help="Image height.", ) parser.add_argument( "--width", type=int, default=832, help="Image width.", ) parser.add_argument( "--dataloader_num_workers", type=int, default=4, help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", ) parser.add_argument( "--learning_rate", type=float, default=1e-5, help="Learning rate.", ) parser.add_argument( "--accumulate_grad_batches", type=int, default=1, help="The number of batches in gradient accumulation.", ) parser.add_argument( "--max_epochs", type=int, default=1, help="Number of epochs.", ) parser.add_argument( "--training_strategy", type=str, default="deepspeed_stage_1", choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"], help="Training strategy", ) parser.add_argument( "--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.", ) parser.add_argument( "--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to use gradient checkpointing offload.", ) parser.add_argument( "--use_swanlab", default=False, action="store_true", help="Whether to use SwanLab logger.", ) parser.add_argument( "--swanlab_mode", default=None, help="SwanLab mode (cloud or local).", ) parser.add_argument( "--metadata_file_name", type=str, default="metadata.csv", ) parser.add_argument( "--resume_ckpt_path", type=str, default=None, ) args = parser.parse_args() return args def data_process(args): dataset = TextVideoDataset( args.dataset_path, os.path.join(args.dataset_path, args.metadata_file_name), max_num_frames=args.num_frames, frame_interval=1, num_frames=args.num_frames, height=args.height, width=args.width, is_i2v=args.image_encoder_path is not None ) dataloader = torch.utils.data.DataLoader( dataset, shuffle=False, batch_size=1, num_workers=args.dataloader_num_workers ) model = LightningModelForDataProcess( text_encoder_path=args.text_encoder_path, image_encoder_path=args.image_encoder_path, vae_path=args.vae_path, tiled=args.tiled, tile_size=(args.tile_size_height, args.tile_size_width), tile_stride=(args.tile_stride_height, args.tile_stride_width), ) trainer = pl.Trainer( accelerator="gpu", devices="auto", default_root_dir=args.output_path, ) trainer.test(model, dataloader) def train(args): dataset = TensorDataset( args.dataset_path, os.path.join(args.dataset_path, "metadata.csv"), steps_per_epoch=args.steps_per_epoch, ) dataloader = torch.utils.data.DataLoader( dataset, shuffle=True, batch_size=1, num_workers=args.dataloader_num_workers ) model = LightningModelForTrain( dit_path=args.dit_path, learning_rate=args.learning_rate, use_gradient_checkpointing=args.use_gradient_checkpointing, use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, resume_ckpt_path=args.resume_ckpt_path, ) if args.use_swanlab: from swanlab.integration.pytorch_lightning import SwanLabLogger swanlab_config = {"UPPERFRAMEWORK": "DiffSynth-Studio"} swanlab_config.update(vars(args)) swanlab_logger = SwanLabLogger( project="wan", name="wan", config=swanlab_config, mode=args.swanlab_mode, logdir=os.path.join(args.output_path, "swanlog"), ) logger = [swanlab_logger] else: logger = None trainer = pl.Trainer( max_epochs=args.max_epochs, accelerator="gpu", devices="auto", precision="bf16", strategy=args.training_strategy, default_root_dir=args.output_path, accumulate_grad_batches=args.accumulate_grad_batches, callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)], logger=logger, ) trainer.fit(model, dataloader) if __name__ == '__main__': args = parse_args() os.makedirs(os.path.join(args.output_path, "checkpoints"), exist_ok=True) if args.task == "data_process": data_process(args) elif args.task == "train": train(args) ================================================ FILE: vis_cam.py ================================================ import argparse import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib.patches import Patch from mpl_toolkits.mplot3d.art3d import Poly3DCollection import json class CameraPoseVisualizer: def __init__(self, xlim, ylim, zlim): self.fig = plt.figure(figsize=(18, 7)) self.ax = self.fig.add_subplot(projection='3d') self.plotly_data = None self.ax.set_aspect("auto") self.ax.set_xlim(xlim) self.ax.set_ylim(ylim) self.ax.set_zlim(zlim) self.ax.set_xlabel('x') self.ax.set_ylabel('y') self.ax.set_zlabel('z') print('initialize camera pose visualizer') def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=9/16, base_xval=1, zval=3): vertex_std = np.array([[0, 0, 0, 1], [base_xval, -base_xval * hw_ratio, zval, 1], [base_xval, base_xval * hw_ratio, zval, 1], [-base_xval, base_xval * hw_ratio, zval, 1], [-base_xval, -base_xval * hw_ratio, zval, 1]]) vertex_transformed = vertex_std @ extrinsic.T meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]], [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]], [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]], [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]], [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]] color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map) self.ax.add_collection3d( Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.35)) def customize_legend(self, list_label): list_handle = [] for idx, label in enumerate(list_label): color = plt.cm.viridis(idx / len(list_label)) patch = Patch(color=color, label=label) list_handle.append(patch) plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle) def colorbar(self, max_frame_length): cmap = mpl.cm.rainbow norm = mpl.colors.Normalize(vmin=0, vmax=max_frame_length) self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical', label='Frame Number') def show(self): plt.title('Extrinsic Parameters') plt.savefig('extrinsic_parameters.jpg', format='jpg', dpi=300) plt.show() def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--pose_file_path', default='./example_test_data/cameras/camera_extrinsics.json', type=str, help='the path of the pose file') parser.add_argument('--hw_ratio', default=9/16, type=float, help='the height over width of the film plane') parser.add_argument('--base_xval', type=float, default=0.08) parser.add_argument('--zval', type=float, default=0.15) parser.add_argument('--x_min', type=float, default=-2) parser.add_argument('--x_max', type=float, default=2) parser.add_argument('--y_min', type=float, default=-2) parser.add_argument('--y_max', type=float, default=2) parser.add_argument('--z_min', type=float, default=-1.) parser.add_argument('--z_max', type=float, default=1) return parser.parse_args() def get_c2w(w2cs, transform_matrix, relative_c2w=True): if relative_c2w: target_cam_c2w = np.array([ [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1] ]) abs2rel = target_cam_c2w @ w2cs[0] ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]] else: ret_poses = [np.linalg.inv(w2c) for w2c in w2cs] ret_poses = [transform_matrix @ x for x in ret_poses] return np.array(ret_poses, dtype=np.float32) def parse_matrix(matrix_str): rows = matrix_str.strip().split('] [') matrix = [] for row in rows: row = row.replace('[', '').replace(']', '') if len((list(map(float, row.split())))) == 3: matrix.append((list(map(float, row.split()))) +[0.]) else: matrix.append(list(map(float, row.split()))) return np.array(matrix) if __name__ == '__main__': args = get_args() with open(args.pose_file_path, 'r') as file: data = json.load(file) cameras = [parse_matrix(data[f"frame0"][f"cam{cam_idx:02d}"]) for cam_idx in range(1, 11)] cameras = np.transpose(np.stack(cameras), (0, 2, 1)) w2cs = [] for cam in cameras: if cam.shape[0] == 3: cam = np.vstack((cam, np.array([[0, 0, 0, 1]]))) cam = cam[:, [1, 2, 0, 3]] cam[:3, 1] *= -1. w2cs.append(np.linalg.inv(cam)) transform_matrix = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]) c2ws = get_c2w(w2cs, transform_matrix, True) scale = max(max(abs(c2w[:3, 3])) for c2w in c2ws) if scale > 1e-3: # otherwise, pan or tilt for c2w in c2ws: c2w[:3, 3] /= scale visualizer = CameraPoseVisualizer([args.x_min, args.x_max], [args.y_min, args.y_max], [args.z_min, args.z_max]) for frame_idx, c2w in enumerate(c2ws): visualizer.extrinsic2pyramid(c2w, frame_idx / len(cameras), hw_ratio=args.hw_ratio, base_xval=args.base_xval, zval=(args.zval)) visualizer.colorbar(len(cameras)) visualizer.show()