codemichaeld/wan2.1_2x_fp8_l02

FP8 Model with Precision Recovery

• Source: https://huggingface.co/spacepxl/Wan2.1-VAE-upscale2x
• File: Wan2.1_VAE_upscale2x_imageonly_real_v1.safetensors
• FP8 Format: E5M2
• Architecture: vae
• Precision Recovery Type: Correction Factors
• Precision Recovery File: Wan2.1_VAE_upscale2x_imageonly_real_v1-correction-vae.safetensors
• FP8 File: Wan2.1_VAE_upscale2x_imageonly_real_v1-fp8-e5m2.safetensors

Usage (Inference)

from safetensors.torch import load_file
import torch
# Load FP8 model
fp8_state = load_file("Wan2.1_VAE_upscale2x_imageonly_real_v1-fp8-e5m2.safetensors")
# Load precision recovery file
recovery_state = load_file("Wan2.1_VAE_upscale2x_imageonly_real_v1-correction-vae.safetensors") if "Wan2.1_VAE_upscale2x_imageonly_real_v1-correction-vae.safetensors" else {}
# Reconstruct high-precision weights
reconstructed = {}
for key in fp8_state:
    fp8_weight = fp8_state[key].to(torch.float32)
    if recovery_state:
        # For LoRA approach
        if "lora_A" in recovery_state:
            if f"lora_A.{key}" in recovery_state and f"lora_B.{key}" in recovery_state:
                A = recovery_state[f"lora_A.{key}"].to(torch.float32)
                B = recovery_state[f"lora_B.{key}"].to(torch.float32)
                lora_weight = B @ A
                reconstructed[key] = fp8_weight + lora_weight
            else:
                reconstructed[key] = fp8_weight
        # For correction factor approach
        elif f"correction.{key}" in recovery_state:
            correction = recovery_state[f"correction.{key}"].to(torch.float32)
            reconstructed[key] = fp8_weight + correction
        else:
            reconstructed[key] = fp8_weight
    else:
        reconstructed[key] = fp8_weight

Requires PyTorch ≥ 2.1 for FP8 support.