Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
Paper • 2403.14781 • Published • 15
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("fudan-generative-ai/champ", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Model Sources
- Repository: fudan-generative-ai/champ
- Paper: https://arxiv.org/abs/2403.14781
Directory Structure
./PROJECT_ROOT/
|-- champ # Champ Model
| |-- denoising_unet.pth
| |-- guidance_encoder_depth.pth
| |-- guidance_encoder_dwpose.pth
| |-- guidance_encoder_normal.pth
| |-- guidance_encoder_semantic_map.pth
| |-- reference_unet.pth
| `-- motion_module.pth
|-- image_encoder
| |-- config.json
| `-- pytorch_model.bin
|-- sd-vae-ft-mse
| |-- config.json
| |-- diffusion_pytorch_model.bin
| `-- diffusion_pytorch_model.safetensors
`-- stable-diffusion-v1-5
|-- feature_extractor
| `-- preprocessor_config.json
|-- model_index.json
|-- unet
| |-- config.json
| `-- diffusion_pytorch_model.bin
`-- v1-inference.yaml
Reference
Thanks to the following projects and authors:
- [1] runwayml. Stable Diffusion v1-5.
- [2] stabilityai. sd-vae-ft-mse
- [3] bdsqlsz. image_encoder
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