diff --git a/MOFA-Video-Traj/README.md b/MOFA-Video-Traj/README.md deleted file mode 100644 index 4f256cdb68a167b5459ad629c3b33b954cae3a4f..0000000000000000000000000000000000000000 --- a/MOFA-Video-Traj/README.md +++ /dev/null @@ -1,42 +0,0 @@ -## Environment Setup - -`pip install -r requirements.txt` - -## Download checkpoints - -1. Download the pretrained checkpoints of [SVD_xt](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1) from huggingface to `./ckpts`. - -2. Download the checkpoint of [MOFA-Adapter](https://huggingface.co/MyNiuuu/MOFA-Video-Traj) from huggingface to `./ckpts`. - -3. Download the checkpoint of CMP from [here](https://huggingface.co/MyNiuuu/MOFA-Video-Traj/blob/main/models/cmp/experiments/semiauto_annot/resnet50_vip%2Bmpii_liteflow/checkpoints/ckpt_iter_42000.pth.tar) and put it into `./models/cmp/experiments/semiauto_annot/resnet50_vip+mpii_liteflow/checkpoints`. - -The final structure of checkpoints should be: - - -```text -./ckpts/ -|-- controlnet -| |-- config.json -| `-- diffusion_pytorch_model.safetensors -|-- stable-video-diffusion-img2vid-xt-1-1 -| |-- feature_extractor -| |-- ... -| |-- image_encoder -| |-- ... -| |-- scheduler -| |-- ... -| |-- unet -| |-- ... -| |-- unet_ch9 -| |-- ... -| |-- vae -| |-- ... -| |-- svd_xt_1_1.safetensors -| `-- model_index.json -``` - -## Run Gradio Demo - -`python run_gradio.py` - -Please refer to the instructions on the gradio interface during the inference process. \ No newline at end of file diff --git a/README.md b/README.md index 63d2a81e4e93bb762cd0832e58295414ea81eee1..4f256cdb68a167b5459ad629c3b33b954cae3a4f 100644 --- a/README.md +++ b/README.md @@ -1,85 +1,42 @@ +## Environment Setup +`pip install -r requirements.txt` +## Download checkpoints +1. Download the pretrained checkpoints of [SVD_xt](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1) from huggingface to `./ckpts`. -
-

- đŸĻ„ī¸ MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model -

-     -
- Muyao Niu 1,2   - Xiaodong Cun2,*   - Xintao Wang2   - Yong Zhang2  
- Ying Shan2   - Yinqiang Zheng1,*   -
-
- 1 The University of Tokyo   2 Tencent AI Lab   * Corresponding Author   -
-
+2. Download the checkpoint of [MOFA-Adapter](https://huggingface.co/MyNiuuu/MOFA-Video-Traj) from huggingface to `./ckpts`. ---- +3. Download the checkpoint of CMP from [here](https://huggingface.co/MyNiuuu/MOFA-Video-Traj/blob/main/models/cmp/experiments/semiauto_annot/resnet50_vip%2Bmpii_liteflow/checkpoints/ckpt_iter_42000.pth.tar) and put it into `./models/cmp/experiments/semiauto_annot/resnet50_vip+mpii_liteflow/checkpoints`. -
-Check the gallery of our project page for many visual results! -
+The final structure of checkpoints should be: - - -## New Features/Updates đŸ”ĨđŸ”ĨđŸ”Ĩ - -We have released the Gradio inference code and the checkpoints for trajectory-based image animation! Please refer to `./MOFA-Video-Traj/README.md` for instructions. - - -## 📰 CODE RELEASE -- [x] (2024.05.31) Gradio demo and checkpoints for trajectory-based image animation -- [ ] Training scripts for trajectory-based image animation -- [ ] Inference scripts and checkpoints for keypoint-based facial image animation -- [ ] Training scripts for keypoint-based facial image animation -- [ ] Inference Gradio demo for hybrid image animation - - -## Introduction - -
-

- TL;DR: Image 🏞ī¸ + Hybrid Controls 🕹ī¸ = Videos đŸŽŦđŸŋ -

-
- -
- -
- -We introduce MOFA-Video, a method designed to adapt motions from different domains to the frozen Video Diffusion Model. By employing sparse-to-dense (S2D) motion generation and flow-based motion adaptation, MOFA-Video can effectively animate a single image using various types of control signals, including trajectories, keypoint sequences, AND their combinations. - -

- -

- -During the training stage, we generate sparse control signals through sparse motion sampling and then train different MOFA-Adapters to generate video via pre-trained SVD. During the inference stage, different MOFA-Adapters can be combined to jointly control the frozen SVD. - - -## đŸ’Ģ Trajectory-based Image Animation - -### Inference - -Our inference demo is based on Gradio. Please refer to `./MOFA-Video-Traj/README.md` for instructions. - - -## Citation -``` -@article{niu2024mofa, - title={MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model}, - author={Niu, Muyao and Cun, Xiaodong and Wang, Xintao and Zhang, Yong and Shan, Ying and Zheng, Yinqiang}, - journal={arXiv preprint arXiv:2405.20222}, - year={2024} -} +```text +./ckpts/ +|-- controlnet +| |-- config.json +| `-- diffusion_pytorch_model.safetensors +|-- stable-video-diffusion-img2vid-xt-1-1 +| |-- feature_extractor +| |-- ... +| |-- image_encoder +| |-- ... +| |-- scheduler +| |-- ... +| |-- unet +| |-- ... +| |-- unet_ch9 +| |-- ... +| |-- vae +| |-- ... +| |-- svd_xt_1_1.safetensors +| `-- model_index.json ``` -## Acknowledgements -We sincerely appreciate the code release of the following projects: [DragNUWA](https://arxiv.org/abs/2308.08089), [SadTalker](https://github.com/OpenTalker/SadTalker), [AniPortrait](https://github.com/Zejun-Yang/AniPortrait), [Diffusers](https://github.com/huggingface/diffusers), [SVD_Xtend](https://github.com/pixeli99/SVD_Xtend), [Conditional-Motion-Propagation](https://github.com/XiaohangZhan/conditional-motion-propagation), and [Unimatch](https://github.com/autonomousvision/unimatch). +## Run Gradio Demo + +`python run_gradio.py` +Please refer to the instructions on the gradio interface during the inference process. \ No newline at end of file diff --git a/MOFA-Video-Traj/run_gradio.py b/app.py similarity index 98% rename from MOFA-Video-Traj/run_gradio.py rename to app.py index 5a5f290133ea15db1dca5af21d93cc3e92e4a05b..bcde271ccd36aafafc39d2412a0b3c77380e1548 100644 --- a/MOFA-Video-Traj/run_gradio.py +++ b/app.py @@ -28,6 +28,7 @@ from diffusers.utils.import_utils import is_xformers_available from utils.flow_viz import flow_to_image from utils.utils import split_filename, image2arr, image2pil, ensure_dirname +from huggingface_hub import login, hf_hub_download, snapshot_download output_dir_video = "./outputs/videos" @@ -85,7 +86,12 @@ def get_sparseflow_and_mask_forward( return s_flow, mask - +def download_models(ckpts_path): + try: + snapshot_download(repo_id="vdo/stable-video-diffusion-img2vid-xt-1-1", local_dir=ckpts_path, cache_dir=ckpts_path) + snapshot_download(repo_id="MyNiuuu/MOFA-Video-Traj", local_dir=ckpts_path, cache_dir=ckpts_path, allow_patterns=["ckpts/controlnet/*"]) + except (Exception, BaseException) as error: + print(error) def init_models(pretrained_model_name_or_path, resume_from_checkpoint, weight_dtype, device='cuda', enable_xformers_memory_efficient_attention=False, allow_tf32=False): @@ -216,11 +222,14 @@ class Drag: def __init__(self, device, height, width, model_length): self.device = device + ckpts_dir = "ckpts/" svd_ckpt = "ckpts/stable-video-diffusion-img2vid-xt-1-1" mofa_ckpt = "ckpts/controlnet" self.device = 'cuda' self.weight_dtype = torch.float16 + + download_models(ckpts_dir) self.pipeline, self.cmp = init_models( svd_ckpt, @@ -631,6 +640,10 @@ class Drag: return hint_path, outputs_path, flows_path, outputs_mp4_path, flows_mp4_path +# Download checkpoints to the right place + + + with gr.Blocks() as demo: gr.Markdown("""

MOFA-Video


""") @@ -828,4 +841,5 @@ with gr.Blocks() as demo: run_button.click(DragNUWA_net.run, [first_frame_path, tracking_points, inference_batch_size, motion_brush_mask, motion_brush_viz, ctrl_scale], [hint_image, output_video, output_flow, output_video_mp4, output_flow_mp4]) - demo.launch(server_name="0.0.0.0", debug=True, server_port=80) + demo.launch() + # demo.launch(server_name="0.0.0.0", debug=True, server_port=80) diff --git a/assets/images/README.md b/assets/images/README.md deleted file mode 100644 index e845566c06f9bf557d35e8292c37cf05d97a9769..0000000000000000000000000000000000000000 --- a/assets/images/README.md +++ /dev/null @@ -1 +0,0 @@ -README diff --git a/assets/images/project-mofa.png b/assets/images/project-mofa.png deleted file mode 100644 index 980e854de69d5df386a0998219c15cc1546145b5..0000000000000000000000000000000000000000 Binary files a/assets/images/project-mofa.png and /dev/null differ diff --git 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