app.py

#!/usr/bin/env python
"""
Gradio demo for Wan2.1 First-Last-Frame-to-Video (FLF2V)
Author: GeradeHouse
"""

import numpy as np
import torch
import gradio as gr
from diffusers import WanImageToVideoPipeline, AutoencoderKLWan
from diffusers.utils import export_to_video
from transformers import CLIPVisionModel, CLIPImageProcessor
from PIL import Image
import torchvision.transforms.functional as TF

# ---------------------------------------------------------------------
# CONFIG ----------------------------------------------------------------
MODEL_ID       = "Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers"  # or switch to 1.3B
DTYPE          = torch.float16                            # or bfloat16
MAX_AREA       = 1280 * 720                                # ≤720p
DEFAULT_FRAMES = 81                                        # ~5s @16 fps
# ----------------------------------------------------------------------

def load_pipeline():
    """Lazy‐load & configure the pipeline once per process."""
    # 1) load the CLIP image encoder (full-precision)
    image_encoder = CLIPVisionModel.from_pretrained(
        MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32
    )
    # 2) load the VAE (half-precision)
    vae = AutoencoderKLWan.from_pretrained(
        MODEL_ID, subfolder="vae", torch_dtype=DTYPE
    )
    # 3) load the video pipeline
    pipe = WanImageToVideoPipeline.from_pretrained(
        MODEL_ID,
        vae=vae,
        image_encoder=image_encoder,
        torch_dtype=DTYPE,
    )

    # 4) override the processor with the fast Rust implementation
    pipe.image_processor = CLIPImageProcessor.from_pretrained(
        MODEL_ID, subfolder="image_processor", use_fast=True
    )

    # 5) memory helpers (offload UNet to CPU as needed)
    # pipe.enable_model_cpu_offload()
    # (Removed pipe.vae.enable_slicing() — not supported on AutoencoderKLWan)

    return pipe.to("cuda" if torch.cuda.is_available() else "cpu")

PIPE = load_pipeline()

# ----------------------------------------------------------------------
# UTILS ----------------------------------------------------------------
def aspect_resize(img: Image.Image, max_area=MAX_AREA):
    """Resize while keeping aspect & respecting patch multiples."""
    ar = img.height / img.width
    mod = PIPE.vae_scale_factor_spatial * PIPE.transformer.config.patch_size[1]
    h = round(np.sqrt(max_area * ar)) // mod * mod
    w = round(np.sqrt(max_area / ar)) // mod * mod
    return img.resize((w, h), Image.LANCZOS), h, w

def center_crop_resize(img: Image.Image, h, w):
    """Center‐crop & resize to H×W."""
    ratio = max(w / img.width, h / img.height)
    img = img.resize(
        (round(img.width * ratio), round(img.height * ratio)),
        Image.LANCZOS
    )
    return TF.center_crop(img, [h, w])

# ----------------------------------------------------------------------
# GENERATE --------------------------------------------------------------
def generate(first_frame, last_frame, prompt, negative_prompt, steps,
             guidance, num_frames, seed, fps):

    # seed handling
    if seed == -1:
        seed = torch.seed()
    gen = torch.Generator(device=PIPE.device).manual_seed(seed)

    # preprocess frames
    first_frame, h, w = aspect_resize(first_frame)
    if last_frame.size != first_frame.size:
        last_frame = center_crop_resize(last_frame, h, w)

    # run the pipeline
    output = PIPE(
        image=first_frame,
        last_image=last_frame,
        prompt=prompt,
        negative_prompt=negative_prompt or None,
        height=h,
        width=w,
        num_frames=num_frames,
        num_inference_steps=steps,
        guidance_scale=guidance,
        generator=gen,
    )
    frames = output.frames[0]  # list of PIL Image

    # export to MP4
    video_path = export_to_video(frames, fps=fps)
    return video_path, seed

# ----------------------------------------------------------------------
# UI --------------------------------------------------------------------
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("## Wan 2.1 FLF2V – First & Last Frame → Video")

    with gr.Row():
        first_img = gr.Image(label="First frame", type="pil")
        last_img  = gr.Image(label="Last frame",  type="pil")

    prompt         = gr.Textbox(label="Prompt", placeholder="A blue bird takes off…")
    negative       = gr.Textbox(label="Negative prompt (optional)", placeholder="ugly, blurry")

    with gr.Accordion("Advanced parameters", open=False):
        steps      = gr.Slider(10, 50, value=30, step=1, label="Sampling steps")
        guidance   = gr.Slider(0.0, 10.0, value=5.5, step=0.1, label="Guidance scale")
        num_frames = gr.Slider(16, 129, value=DEFAULT_FRAMES, step=1, label="Frames")
        fps        = gr.Slider(4, 30, value=16, step=1, label="FPS (export)")
        seed       = gr.Number(value=-1, precision=0, label="Seed (-1 = random)")

    run_btn   = gr.Button("Generate")
    video     = gr.Video(label="Result (.mp4)")
    used_seed = gr.Number(label="Seed used", interactive=False)

    run_btn.click(
        fn=generate,
        inputs=[first_img, last_img, prompt, negative, steps, guidance, num_frames, seed, fps],
        outputs=[video, used_seed]
    )

if __name__ == "__main__":
    demo.launch()