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, progress=gr.Progress(track_tqdm=True)):
    pipe.to("cuda")
    output = pipe(
        prompt=prompt,
        # negative_prompt=negative_prompt,
        height=720,
        width=1280,
        num_frames=1,
        num_inference_steps=28,
        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"),
        # gr.Slider(label="Width", minimum=256, maximum=2048, step=8, value=1024),
        # gr.Slider(label="Height", minimum=256, maximum=2048, step=8, value=1024),
        # gr.Textbox(label="Lora ID", placeholder="Optional"),
        # gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Lora Scale", value=1)
    ],
    outputs=gr.Image(label="output"),
)

iface.launch()