Create app.py

app.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python
+"""
+Gradio demo for Wan2.1 First-Last-Frame-to-Video (FLF2V)
+Author: <your-handle>
+"""
+
+import os, tempfile, numpy as np, torch, gradio as gr
+from diffusers import WanImageToVideoPipeline, AutoencoderKLWan
+from diffusers.utils import export_to_video
+from transformers import CLIPVisionModel
+from PIL import Image
+import torchvision.transforms.functional as TF
+
+# ---------------------------------------------------------------------
+# CONFIG ----------------------------------------------------------------
+MODEL_ID = "Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers"  # switch to 1.3B if needed
+DTYPE = torch.float16                                 # or torch.bfloat16 on AMP-friendly GPUs
+MAX_AREA = 1280 * 720                                 # keep ≤ 720 p
+DEFAULT_FRAMES = 81                                   # ≈ 5 s at 16 fps
+# ----------------------------------------------------------------------
+
+def load_pipeline():
+    """Lazy-load the huge model once per process."""
+    image_encoder = CLIPVisionModel.from_pretrained(
+        MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32
+    )
+    vae = AutoencoderKLWan.from_pretrained(
+        MODEL_ID, subfolder="vae", torch_dtype=DTYPE
+    )
+    pipe = WanImageToVideoPipeline.from_pretrained(
+        MODEL_ID,
+        vae=vae,
+        image_encoder=image_encoder,
+        torch_dtype=DTYPE,
+    )
+
+    # memory helpers for ≤ 24 GB cards / HF T4-medium
+    pipe.enable_model_cpu_offload()          # paged UNet blocks
+    pipe.enable_vae_slicing()                # reduces VAE RAM spikes
+    # Optional (needs xformers): pipe.enable_xformers_memory_efficient_attention()
+    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 respecting model patch size (multiple of 8*transformer patch)."""
+    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):
+    ratio = max(w / img.width, h / img.height)
+    img = img.resize((round(img.width * ratio), round(img.height * ratio)), Image.LANCZOS)
+    img = TF.center_crop(img, [h, w])
+    return img
+
+# ----------------------------------------------------------------------
+# GENERATE --------------------------------------------------------------
+def generate(first_frame, last_frame, prompt, negative_prompt, steps,
+             guidance, num_frames, seed, fps):
+
+    if seed == -1:
+        seed = torch.seed()
+    generator = torch.Generator(device=PIPE.device).manual_seed(seed)
+
+    first_frame, h, w = aspect_resize(first_frame)
+    if last_frame.size != first_frame.size:
+        last_frame = center_crop_resize(last_frame, h, w)
+
+    out = 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=generator,
+    ).frames[0]  # list[pillow]
+
+    video_path = export_to_video(out, 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()