Update app.py

app.py

@@ -4,75 +4,70 @@ Gradio demo for Wan2.1 FLF2V – First & Last Frame → Video
 """
 
 import os
-import torch
+# Persist HF cache on /mnt/data so it survives across launches
+os.environ["HF_HOME"] = "/mnt/data/huggingface"
+
 import numpy as np
+import torch
 import gradio as gr
 from diffusers import WanImageToVideoPipeline, AutoencoderKLWan
-from transformers import CLIPProcessor, CLIPVisionModel
 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"
-DTYPE           = torch.float16                # switch to torch.bfloat16 if you have AMP-friendly GPU
-MAX_AREA        = 1280 * 720                   # ≤ 720p
-DEFAULT_FRAMES  = 81                            # ~5s @ 16fps
+MODEL_ID       = "Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers"
+DTYPE          = torch.float16
+MAX_AREA       = 1280 * 720
+DEFAULT_FRAMES = 81
 
 # ----------------------------------------------------------------------
-# PIPELINE LOADING (once)
+# PIPELINE LOADING
 # ----------------------------------------------------------------------
 def load_pipeline():
-    # 1) image encoder in fp32 for stability
+    # 1) load CLIP image encoder in full precision
     image_encoder = CLIPVisionModel.from_pretrained(
         MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32
     )
-    # 2) VAE in reduced precision
+    # 2) load VAE in reduced precision
     vae = AutoencoderKLWan.from_pretrained(
         MODEL_ID, subfolder="vae", torch_dtype=DTYPE
     )
-    # 3) use the unified CLIPProcessor (inherits ProcessorMixin) in fast mode
-    processor = CLIPProcessor.from_pretrained(MODEL_ID, use_fast=True)
-
-    # 4) assemble pipeline, overriding the default processor
+    # 3) load the WanImageToVideo pipeline, balanced across GPU/CPU
     pipe = WanImageToVideoPipeline.from_pretrained(
         MODEL_ID,
         vae=vae,
         image_encoder=image_encoder,
-        processor=processor,
         torch_dtype=DTYPE,
+        device_map="balanced",            # auto-offload large modules to CPU
     )
-
-    # 5) offload to CPU / reduce footprint
+    # 4) reduce VAE peaks & enable CPU offload for everything else
+    pipe.enable_vae_slicing()
     pipe.enable_model_cpu_offload()
-
-    # 6) safe VAE slicing if available
-    try:
-        pipe.vae.enable_slicing()
-    except (AttributeError, TypeError):
-        pass
-
     return pipe
 
-pipe = load_pipeline()
+# create once, at import time
+PIPE = load_pipeline()
 
 # ----------------------------------------------------------------------
-# IMAGE RESIZING HELPERS
+# IMAGE PREPROCESSING UTILS
 # ----------------------------------------------------------------------
 def aspect_resize(img: Image.Image, max_area=MAX_AREA):
     ar  = img.height / img.width
-    # align to VAE & transformer patch grid
-    mod = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+    # ensure multiple of patch size
+    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 = img.resize(
+        (round(img.width * ratio), round(img.height * ratio)),
+        Image.LANCZOS
     )
     return TF.center_crop(img, [h, w])
 
@@ -83,49 +78,55 @@ def generate(
     first_frame: Image.Image,
     last_frame:  Image.Image,
     prompt:      str,
-    negative_prompt: str,
+    negative:    str,
     steps:       int,
     guidance:    float,
     num_frames:  int,
     seed:        int,
     fps:         int,
+    progress=    gr.Progress()
 ):
-    # randomize seed if requested
+    # seed
     if seed == -1:
         seed = torch.seed()
-    gen = torch.Generator(device=pipe.device).manual_seed(seed)
+    gen = torch.Generator(device=PIPE.device).manual_seed(seed)
 
-    # preprocess inputs
+    # initial progress
+    progress(0, steps, desc="Preprocessing images")
+
+    # resize / crop
     first_frame, h, w = aspect_resize(first_frame)
     if last_frame.size != first_frame.size:
         last_frame = center_crop_resize(last_frame, h, w)
 
-    # set up streaming progress
-    progress = gr.Progress(track_tqdm=True)
+    # callback to update progress bar on each denoising step
+    def progress_callback(step, timestep, latents):
+        progress(step, steps, desc=f"Inference step {step}/{steps}")
 
-    # run the pipeline, streaming progress every step
-    result = pipe(
+    # run the pipeline (streams progress via callback)
+    result = PIPE(
         image=first_frame,
         last_image=last_frame,
         prompt=prompt,
-        negative_prompt=negative_prompt or None,
+        negative_prompt=negative or None,
         height=h,
         width=w,
         num_frames=num_frames,
         num_inference_steps=steps,
         guidance_scale=guidance,
         generator=gen,
-        callback=progress,
-        callback_steps=1,
+        callback=progress_callback,
     )
 
-    # export to video and return path + seed used
-    frames     = result.frames[0]
+    # assemble and export to video
+    frames     = result.frames[0]  # list of PIL images
     video_path = export_to_video(frames, fps=fps)
+
+    # return video and seed used (Gradio will auto-download the .mp4)
     return video_path, seed
 
 # ----------------------------------------------------------------------
-# GRADIO APP
+# GRADIO UI
 # ----------------------------------------------------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("## Wan 2.1 FLF2V – First & Last Frame → Video")
@@ -134,26 +135,26 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         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")
+    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, label="Sampling steps")
+        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, label="Frames")
-        fps        = gr.Slider(4, 30, value=16, label="FPS (export)")
-        seed_input = gr.Number(value=-1, precision=0, label="Seed (-1 = random)")
+        num_frames = gr.Slider(16, 129, value=DEFAULT_FRAMES, step=1, label="Number of 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_out   = gr.Video(label="Result (.mp4)")
-    used_seed   = gr.Number(label="Seed used", interactive=False)
+    run_btn   = gr.Button("Generate")
+    video_out = 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_input, fps],
-        outputs=[video_out, used_seed],
-        show_progress=True,             # hook into Gradio’s built-in progress UI
+        inputs=[ first_img, last_img, prompt, negative,
+                 steps, guidance, num_frames, seed, fps ],
+        outputs=[ video_out, used_seed ]
     )
 
-    demo.queue()                       # serialize GPU calls
-    demo.launch()
+# no special queue args needed
+demo.launch()