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
File size: 890 Bytes
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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
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