app.py

import torch
from diffusers import UniPCMultistepScheduler
from diffusers import WanPipeline, AutoencoderKLWan  # Use Wan-specific VAE
from diffusers.models import UNetSpatioTemporalConditionModel
from transformers import T5EncoderModel, T5Tokenizer

from PIL import Image
import numpy as np

import gradio as gr
import spaces

model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
flow_shift = 5.0  # 5.0 for 720P, 3.0 for 480P
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)


@spaces.GPU()
def generate(prompt, negative_prompt, width=1280, height=720, num_inference_steps=50, progress=gr.Progress(track_tqdm=True)):
    pipe.to("cuda")
    output = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=height,
        width=width,
        num_frames=1,
        num_inference_steps=num_inference_steps,
        guidance_scale=5.0,
    )
    image = output.frames[0][0]
    image = (image * 255).astype(np.uint8)
    return Image.fromarray(image)

iface = gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(label="Input prompt"),
    ],
    additional_inputs = [
        gr.Textbox(label="Negative prompt", value = ""),
        gr.Slider(label="Width", minimum=480, maximum=1280, step=16, value=1024),
        gr.Slider(label="Height", minimum=480, maximum=1280, step=16, value=1024),
        gr.Slider(minimum=1, maximum=60, step=1, label="Inference Steps", value=28)
    ],
    outputs=gr.Image(label="output"),
)

iface.launch()