Update app.py

app.py

@@ -1,8 +1,7 @@
 #!/usr/bin/env python
 """
-Gradio demo for Wan2.1 First-Last-Frame-to-Video (FLF2V)
-Loads the huge model once, uses balanced device placement,
-streams high-level progress, and auto-offers the .mp4 for download.
+Gradio demo for Wan2.1 FLF2V – First & Last Frame → Video
+Auto-loads the fast processor and avoids missing preprocessor_config.json.
 """
 import os
 import numpy as np
@@ -10,99 +9,104 @@ import torch
 import gradio as gr
 from diffusers import WanImageToVideoPipeline, AutoencoderKLWan
 from diffusers.utils import export_to_video
-from transformers import CLIPImageProcessor, CLIPVisionModel
+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              # half-precision
-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
+
+# Persist cache so safetensors only download once
+os.environ["HF_HOME"] = "/mnt/data/huggingface"
+
+# -----------------------------------------------------------------------------
+# LOAD PIPELINE ONCE
+# -----------------------------------------------------------------------------
 def load_pipeline():
-    # 1) image encoder in full precision
+    # 1) Image encoder (fp32)
     image_encoder = CLIPVisionModel.from_pretrained(
         MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32
     )
-    # 2) VAE in reduced precision
+    # 2) VAE (half-precision) + slicing
     vae = AutoencoderKLWan.from_pretrained(
         MODEL_ID, subfolder="vae", torch_dtype=DTYPE
     )
-    # 3) CLIPImageProcessor so we get the right class
-    image_processor = CLIPImageProcessor.from_pretrained(
-        MODEL_ID, subfolder="", torch_dtype=DTYPE
-    )
-    # 4) load everything with a balanced device map
+    vae.enable_slicing()
+
+    # 3) Pipeline, balanced across GPU & CPU, fast processor by default
     pipe = WanImageToVideoPipeline.from_pretrained(
         MODEL_ID,
-        vae=vae,
         image_encoder=image_encoder,
-        image_processor=image_processor,
+        vae=vae,
         torch_dtype=DTYPE,
-        device_map="balanced",           # splits weights CPU/GPU
+        device_map="balanced",
+        use_fast=True,              # get the fast CLIPImageProcessor internally
     )
     return pipe
 
-# load once at import
 PIPE = load_pipeline()
 
 
-# --------------------------------------------------------------------
+# -----------------------------------------------------------------------------
 # UTILS
+# -----------------------------------------------------------------------------
 def aspect_resize(img: Image.Image, max_area=MAX_AREA):
-    """Resize while respecting multiples of the model’s patch size."""
     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
+    h    = int(np.sqrt(max_area * ar)) // mod * mod
+    w    = int(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):
-    """Crop-and-resize to exactly (h, w)."""
     ratio = max(w / img.width, h / img.height)
-    img   = img.resize(
+    img = img.resize(
         (round(img.width * ratio), round(img.height * ratio)),
         Image.LANCZOS
     )
     return TF.center_crop(img, [h, w])
 
 
-# --------------------------------------------------------------------
-# GENERATE (with simple progress streaming)
+# -----------------------------------------------------------------------------
+# GENERATION WITH PROGRESS STREAMING
+# -----------------------------------------------------------------------------
 def generate(
     first_frame: Image.Image,
-    last_frame: Image.Image,
-    prompt: str,
-    negative_prompt: str,
-    steps: int,
-    guidance: float,
-    num_frames: int,
-    seed: int,
-    fps: int,
-    progress=gr.Progress(),   # gradio’s built-in progress callback
+    last_frame:  Image.Image,
+    prompt:      str,
+    negative:    str,
+    steps:       int,
+    guidance:    float,
+    num_frames:  int,
+    seed:        int,
+    fps:         int,
+    progress=    gr.Progress(),   # built-in streamer
 ):
-    # pick or set seed
+    # seed
     if seed == -1:
         seed = torch.seed()
     gen = torch.Generator(device=PIPE.device).manual_seed(seed)
 
-    # 0→10%: resize
+    # 0–15%: resize
     progress(0.0, desc="Resizing first frame���")
-    first_frame, h, w = aspect_resize(first_frame)
-    if last_frame.size != first_frame.size:
-        progress(0.1, desc="Resizing last frame…")
-        last_frame = center_crop_resize(last_frame, h, w)
-
-    # 10→20%: ready to run
-    progress(0.2, desc="Starting video inference…")
-    result = PIPE(
-        image=first_frame,
-        last_image=last_frame,
+    first_resized, h, w = aspect_resize(first_frame)
+    if last_frame.size != first_resized.size:
+        progress(0.15, desc="Resizing last frame…")
+        last_resized = center_crop_resize(last_frame, h, w)
+    else:
+        last_resized = first_resized  # same size
+
+    # 15–25%: setup
+    progress(0.25, desc="Launching pipeline…")
+    out = PIPE(
+        image=first_resized,
+        last_image=last_resized,
         prompt=prompt,
-        negative_prompt=negative_prompt or None,
+        negative_prompt=negative or None,
         height=h,
         width=w,
         num_frames=num_frames,
@@ -111,17 +115,18 @@ def generate(
         generator=gen,
     )
 
-    # 80→100%: export
-    progress(0.8, desc="Assembling video file…")
-    video_path = export_to_video(result.frames[0], fps=fps)
-    progress(1.0, desc="Done!")
+    # 25–90%: we assume the pipeline prints its own bars in console
+    progress(0.90, desc="Building video…")
+    video_path = export_to_video(out.frames[0], fps=fps)
 
-    # return path so gr.File offers immediate download, plus seed used
+    # done
+    progress(1.0, desc="Done!")
     return video_path, seed
 
 
-# --------------------------------------------------------------------
-# UI
+# -----------------------------------------------------------------------------
+# GRADIO UI
+# -----------------------------------------------------------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("## Wan2.1 FLF2V – First & Last Frame → Video")
 
@@ -129,26 +134,26 @@ 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="blurry, lowres")
 
     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")
+        steps      = gr.Slider(10, 50, value=30, step=1, label="Steps")
+        guidance   = gr.Slider(0.0, 10.0, value=5.5, step=0.1, label="Guidance")
         num_frames = gr.Slider(16, 129, value=DEFAULT_FRAMES, step=1, label="Frames")
         fps        = gr.Slider(4, 30, value=16, step=1, label="FPS")
-        seed       = gr.Number(value=-1, precision=0, label="Seed (-1=random)")
+        seed_input = gr.Number(value=-1, precision=0, label="Seed (-1=random)")
 
-    run_btn     = gr.Button("Generate")
-    download    = gr.File(label="Download video", interactive=False)
-    used_seed   = gr.Number(label="Seed used", interactive=False)
+    run_btn   = gr.Button("Generate")
+    download  = gr.File(label="Download .mp4", interactive=False)
+    seed_used = 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=[download, used_seed],
+        inputs=[ first_img, last_img, prompt, negative,
+                 steps, guidance, num_frames, seed_input, fps ],
+        outputs=[ download, seed_used ],
     )
 
-# queue tasks so users see the little task-queue progress bar
-demo.queue().launch(server_name="0.0.0.0", server_port=7860)
+    # queue() so tasks are serialized with a top-right mini-progress indicator
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860)