Instructions to use GM717/InstinctWAM-Wan22-5B-chip-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use GM717/InstinctWAM-Wan22-5B-chip-lora with PEFT:
Task type is invalid.
- Wan2.2
How to use GM717/InstinctWAM-Wan22-5B-chip-lora 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
- Diffusers
How to use GM717/InstinctWAM-Wan22-5B-chip-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.2-TI2V-5B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("GM717/InstinctWAM-Wan22-5B-chip-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33027274480709dded5ee5c02bedebfb336d7449c0f7ac289566ce3ee15d598f
- Size of remote file:
- 323 MB
- SHA256:
- 6db7d368e76294c384c6f484f14f8bc4a1215758a6cd9259a1eaac32b0516320
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