Instructions to use codemichaeld/wan1.3b_cf_FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codemichaeld/wan1.3b_cf_FP8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("codemichaeld/wan1.3b_cf_FP8", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| library_name: diffusers | |
| tags: | |
| - fp8 | |
| - safetensors | |
| - converted-by-gradio | |
| # FP8 Model Conversion | |
| - **Source**: `https://huggingface.co/TalmajM/causal_forcing_framewise_ComfyUI_repackaged` | |
| - **Original File(s)**: `causal_forcing-framewise.safetensors` | |
| - **Original Format**: `safetensors` | |
| - **FP8 Format**: `E5M2` | |
| - **FP8 File**: `causal_forcing-framewise-fp8-e5m2.safetensors` | |
| ## Usage | |
| ```python | |
| from safetensors.torch import load_file | |
| import torch | |
| # Load FP8 model | |
| fp8_state = load_file("causal_forcing-framewise-fp8-e5m2.safetensors") | |
| # Convert tensors back to float32 for computation (auto-converted by PyTorch) | |
| model.load_state_dict(fp8_state) | |
| ``` | |
| > **Note**: FP8 tensors are automatically converted to float32 when loaded in PyTorch. | |
| > Requires PyTorch ≥ 2.1 for FP8 support. | |
| ## Statistics | |
| - **Total tensors**: 825 | |
| - **Converted to FP8**: 825 | |
| - **Skipped (non-float)**: 0 | |