Instructions to use TheBaldDudeCo/CineForge-Wan-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TheBaldDudeCo/CineForge-Wan-Models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("TheBaldDudeCo/CineForge-Wan-Models", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Wan2.2
How to use TheBaldDudeCo/CineForge-Wan-Models with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| # Validation record | |
| ## Current state | |
| - Four core files uploaded: passed | |
| - Remote byte sizes: passed | |
| - Remote SHA-256 identifiers: passed | |
| - Native load: passed on NVIDIA GeForce RTX 4070 (12 GB) | |
| - End-to-end generation: passed without ComfyUI | |
| - Progress telemetry: passed at both Wan expert steps | |
| - Output validation: passed; five finite 256x256 frames exported to a decodable MP4 | |
| - Clean-machine install: not passed | |
| The pack is a release candidate. It must still pass the clean-machine installer test before stable designation. | |
| ## 2026-08-14 native smoke test | |
| - Pipeline: Wan 2.2 I2V A14B high-noise + low-noise experts | |
| - Runtime: CineForge native PyTorch/Diffusers loader; ComfyUI absent | |
| - GPU: NVIDIA GeForce RTX 4070, 12 GB VRAM | |
| - Resolution / frames / steps: 256x256 / 5 / 2 diagnostic steps | |
| - Scheduler: UniPC flow-prediction scheduler, flow shift 3.0 | |
| - Seed: 42 | |
| - Latents: finite after both expert stages | |
| - Frames: finite, range 0.0-1.0, standard deviation 0.081485 | |
| - MP4: exported successfully; 19,970 bytes | |
| - Wall time: 231.1 seconds | |
| ## Evidence required | |
| - CineForge commit and model repository commit | |
| - Windows, NVIDIA driver, GPU, VRAM, RAM, and disk usage | |
| - input image hash and non-sensitive prompt fixture | |
| - seed, resolution, frame count, frame rate, and inference settings | |
| - wall time, peak VRAM, and peak system RAM | |
| - output video hash and media probe results | |
| - startup and shutdown logs with secrets and private paths removed | |