---
library_name: diffusers
tags:
- fp8
- safetensors
- lora
- low-rank
- diffusion
- converted-by-gradio
---

# FP8 Model with Low-Rank LoRA

- **Source**: `https://huggingface.co/Kijai/WanVideo_comfy`
- **File**: `Wan2_1_VAE_bf16.safetensors`
- **FP8 Format**: `E5M2`
- **LoRA Rank**: 32
- **LoRA File**: `Wan2_1_VAE_bf16-lora-r32.safetensors`

## Usage (Inference)

```python
from safetensors.torch import load_file
import torch

# Load FP8 model
fp8_state = load_file("Wan2_1_VAE_bf16-fp8-e5m2.safetensors")
lora_state = load_file("Wan2_1_VAE_bf16-lora-r32.safetensors")

# Reconstruct approximate original weights
reconstructed = {}
for key in fp8_state:
    if f"lora_A.{key}" in lora_state and f"lora_B.{key}" in lora_state:
        A = lora_state[f"lora_A.{key}"].to(torch.float32)
        B = lora_state[f"lora_B.{key}"].to(torch.float32)
        lora_weight = B @ A  # (rank, out) @ (in, rank) -> (out, in)
        fp8_weight = fp8_state[key].to(torch.float32)
        reconstructed[key] = fp8_weight + lora_weight
    else:
        reconstructed[key] = fp8_state[key].to(torch.float32)
```

> Requires PyTorch ≥ 2.1 for FP8 support.