Wan 2.1 FP8 model weights causing color issue - BF16 has no issue

This is how I load. By the way text to video doesnt have this. E.g. tested on WAN 2.1 14B Text-to-Video

I hope you can help me @Artiprocher

    elif model_choice == "14B_image_480p":
        clip_path = get_common_file(os.path.join("models", "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"),
                                    os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"))
        t5_path = get_common_file(os.path.join("models", "models_t5_umt5-xxl-enc-bf16.pth"),
                                  os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "models_t5_umt5-xxl-enc-bf16.pth"))
        vae_path = get_common_file(os.path.join("models", "Wan2.1_VAE.pth"),
                                  os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "Wan2.1_VAE.pth"))
        model_manager.load_models([clip_path], torch_dtype=torch.float32)
        model_manager.load_models(
            [
                [
                    os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "diffusion_pytorch_model-00001-of-00007.safetensors"),
                    os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "diffusion_pytorch_model-00002-of-00007.safetensors"),
                    os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "diffusion_pytorch_model-00003-of-00007.safetensors"),
                    os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "diffusion_pytorch_model-00004-of-00007.safetensors"),
                    os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "diffusion_pytorch_model-00005-of-00007.safetensors"),
                    os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "diffusion_pytorch_model-00006-of-00007.safetensors"),
                    os.path.join("models", "Wan-AI", "Wan2.1-I2V-14B-480P", "diffusion_pytorch_model-00007-of-00007.safetensors"),
                ],
                t5_path,
                vae_path,
            ],
            torch_dtype=torch.float8_e4m3fn,
        )
    pipe = WanVideoPipeline.from_model_manager(model_manager, torch_dtype=torch.bfloat16, device=device)
    try:
        num_persistent_val = int(num_persistent)
    except:
        print("[CMD] Warning: could not parse num_persistent value, defaulting to 6000000000")
        num_persistent_val = 6000000000
    print(f"num_persistent_val {num_persistent_val}")
    pipe.enable_vram_management(num_persistent_param_in_dit=num_persistent_val)
    print("[CMD] Model loaded successfully.")
    return pipe

Now I will show BF16 vs FP8

https://github.com/user-attachments/assets/60aaea12-3fbc-480e-b9f4-ba7d25827e57

https://github.com/user-attachments/assets/814d1318-416d-453b-8977-509017030c51

Reproduce the Wan 2.1 image-to-video result using the shown model-loading code, comparing FP8 with BF16 and the text-to-video case. Start by tracing WanVideoPipeline and model_manager precision handling, then identify where the color difference is introduced. Done means the FP8 image-to-video output no longer has the reported color issue without regressing BF16 or text-to-video behavior.