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
| license: apache-2.0 | |
| library_name: diffusers | |
| pipeline_tag: image-to-video | |
| tags: | |
| - wan | |
| - wan2.2 | |
| - image-to-video | |
| - video-generation | |
| - cineforge | |
| - fp8 | |
| # CineForge Wan Models | |
| This repository is the model distribution channel for CineForge, a standalone local Windows application for Wan video generation. | |
| ## Publication status | |
| **Native generation validated; Desktop 0.5.0 release-candidate pack.** | |
| The four core Wan 2.2 I2V A14B scaled-FP8 components are published with provenance, exact byte sizes, and SHA-256 checksums. CineForge's standalone native loader and a real high/low-expert I2V generation were validated on 2026-08-14 without ComfyUI. The repository also includes the pinned scheduler, tokenizer, and architecture configs required by the Desktop loader. | |
| ## Intended pack contents | |
| - Wan 2.2 I2V A14B high-noise expert | |
| - Wan 2.2 I2V A14B low-noise expert | |
| - Wan-compatible UMT5 text encoder | |
| - Wan VAE | |
| - CineForge pack manifest and checksums | |
| - upstream and derivative license/notice files | |
| - reproducible conversion and validation records | |
| Optional third-party acceleration LoRAs are excluded from the core pack until their independent origin and license are documented. | |
| ## Upstream and provenance | |
| - Wan 2.2 project: https://github.com/Wan-Video/Wan2.2 | |
| - Official Wan models: https://huggingface.co/Wan-AI | |
| - Split FP8 source candidates used on the development workstation: | |
| - https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged | |
| - https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged | |
| Wan 2.2 is published by the Wan Team under Apache 2.0. Any repackaged or converted artifact must retain the applicable notices and must be verified independently before redistribution. | |
| ## Compatibility | |
| | Pack | CineForge | State | Notes | | |
| |---|---|---|---| | |
| | Wan 2.2 I2V A14B scaled-FP8 | 0.5.0 | Release candidate | Native load, two-expert generation, live step telemetry, finite decoded frames, MP4 export, and Desktop installer auto-download flow verified on RTX 4070. Wider hardware validation remains. | | |
| The word `supported` is reserved for packs that load without ComfyUI, complete deterministic generation, provide live progress telemetry, export a decodable video, and pass a clean-machine installation test. | |
| ## Application | |
| CineForge source and releases: https://github.com/thebalddudeco/CineForge | |
| The public CineForge Desktop installer automatically downloads this pinned model pack into the user's selected **CineForge Library**. Users do not browse for individual Wan files manually; setup resumes interrupted transfers and verifies every component by file size and SHA-256 before the pack is accepted. | |
| ## Independence notice | |
| CineForge is an independent project and is not affiliated with or endorsed by Alibaba, the Wan Team, Hugging Face, or Comfy Org. | |