Update app.py

app.py

@@ -41,14 +41,22 @@ DEFAULT_FPS = 24.0
 DEFAULT_DURATION = 5.0
 DEFAULT_RESOLUTION = 768
 
+
+def make_temp_file(suffix: str) -> str:
+    f = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
+    path = f.name
+    f.close()
+    return path
+
+
 # ---------------------------------------------------------------------------
 # Core processing function
 # ---------------------------------------------------------------------------
 
 @GPU(duration=300)
 def generate(
-    guide_video_path: str,
-    face_image: Image.Image,
+    guide_video_path: str | None,
+    face_image: Image.Image | None,
     prompt: str,
     duration: float,
     fps: float,
@@ -56,7 +64,7 @@ def generate(
     seed: int,
     hf_token: str = "",
     progress: gr.Progress = gr.Progress(track_tqdm=True),
-) -> tuple[str, str]:
+) -> tuple[str | None, str]:
     """
     Full head-swap pipeline:
       1. Load + resize guide video frames
@@ -70,159 +78,5 @@ def generate(
     global _pipeline_state
 
     # ---- validate inputs early ----
-    if guide_video_path is None:
-        return "", "Please upload a guide video."
-    if face_image is None:
-        return "", "Please upload a reference face image."
-    if not prompt.strip():
-        return "", "Please enter a text prompt."
-
-    # ---- lazy model load ----
-    if _pipeline_state is None:
-        from pipeline import load_pipeline
-        progress(0, desc="Loading models (first run only — ~5 min)…")
-        _pipeline_state = load_pipeline(
-            token=hf_token.strip() or None,
-            progress_cb=lambda msg: progress(0, desc=msg),
-        )
-
-    progress(0.05, desc="Loading guide video…")
-    frames, source_fps = load_video_frames(guide_video_path)
-    if len(frames) == 0:
-        return "", "Could not read frames from the guide video."
-
-    # ---- extract audio before we do anything else ----
-    audio_tmp = tempfile.mktemp(suffix=".wav")
-    has_audio = extract_audio(guide_video_path, audio_tmp)
-
-    # ---- resize frames ----
-    progress(0.10, desc="Resizing frames…")
-    orig_h, orig_w = frames.shape[1], frames.shape[2]
-    target_w, target_h = compute_target_size(orig_w, orig_h, DEFAULT_RESOLUTION)
-    frames = resize_frames(frames, target_w, target_h)
-
-    # ---- trim / pad to requested duration ----
-    n_frames = frames_for_duration(fps, duration)
-    if len(frames) >= n_frames:
-        frames = frames[:n_frames]
-    else:
-        # loop last frame
-        pad = np.stack([frames[-1]] * (n_frames - len(frames)))
-        frames = np.concatenate([frames, pad], axis=0)
-
-    # ---- compose chroma strip ----
-    progress(0.15, desc="Compositing reference face strip…")
-    composed = compose_frames(
-        frames,
-        face_image,
-        region_position="left",
-        region_size_px=REGION_SIZE,
-    )
-
-    # ---- run diffusion ----
-    progress(0.20, desc="Running LTX-2.3 diffusion…")
-    from pipeline import run_inference
-    generated = run_inference(
-        _pipeline_state,
-        composed,
-        prompt=prompt,
-        fps=fps,
-        lora_strength=lora_strength,
-        seed=int(seed),
-        progress_cb=lambda msg: progress(0.20, desc=msg),
-    )
-
-    # ---- crop face strip from output ----
-    progress(0.90, desc="Cropping reserved region…")
-    cropped = crop_reserved_region(
-        generated,
-        region_position="left",
-        region_size_px=REGION_SIZE,
-        output_size=(target_w, target_h),
-    )
-
-    # ---- save output video with audio ----
-    progress(0.95, desc="Encoding output video…")
-    out_path = tempfile.mktemp(suffix=".mp4")
-    save_video(
-        cropped,
-        fps=fps,
-        output_path=out_path,
-        audio_path=audio_tmp if has_audio else None,
-        audio_duration=duration,
-    )
-
-    progress(1.0, desc="Done.")
-    return out_path, "Generation complete."
-
-
-# ---------------------------------------------------------------------------
-# Gradio UI
-# ---------------------------------------------------------------------------
-
-DESCRIPTION = """
-# BFS — Best Face Swap Video
-
-Swap the identity in any video using the **V3 persistent-template** technique.
-The reference face is placed in a green chroma side-strip that persists across
-all frames, giving the model continuous identity conditioning throughout generation.
-
-**Prompt format:**
-```
-head_swap:
-FACE: Female, fair skin, ~25 years old, long wavy auburn hair, green eyes…
-ACTION: A person in a grey hoodie walks toward the camera indoors…
-```
-"""
-
-EXAMPLES: list[list] = [
-    # [guide_video, face_image, prompt, duration, fps, lora_strength, seed]
-]
-
-with gr.Blocks(title="BFS — Best Face Swap Video") as demo:
-    gr.Markdown(DESCRIPTION)
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            guide_video = gr.Video(label="Guide Video", sources=["upload"])
-            face_image   = gr.Image(label="Reference Face", type="pil", sources=["upload"])
-            prompt       = gr.Textbox(
-                label="Text Prompt",
-                placeholder="head_swap:\nFACE: ...\nACTION: ...",
-                lines=6,
-            )
-
-            with gr.Accordion("Parameters", open=False):
-                duration       = gr.Slider(1, 15, value=DEFAULT_DURATION, step=0.5,  label="Duration (seconds)")
-                fps            = gr.Slider(8, 30,  value=DEFAULT_FPS,      step=1.0,  label="FPS")
-                lora_strength  = gr.Slider(0.5, 1.5, value=1.2,            step=0.05, label="Face Swap Strength")
-                seed           = gr.Number(value=42, label="Seed", precision=0)
-                hf_token       = gr.Textbox(
-                    label="HF Token (optional)",
-                    type="password",
-                    placeholder="hf_…  — only needed if the Space owner's token has no access to a gated model",
-                )
-
-            run_btn = gr.Button("Generate", variant="primary")
-
-        with gr.Column(scale=1):
-            output_video  = gr.Video(label="Result", interactive=False)
-            status_text   = gr.Textbox(label="Status", interactive=False)
-
-    run_btn.click(
-        fn=generate,
-        inputs=[guide_video, face_image, prompt, duration, fps, lora_strength, seed, hf_token],
-        outputs=[output_video, status_text],
-        api_name=False,
-    )
-
-    gr.Markdown("""
----
-**Hardware:** A100 80 GB GPU required.
-**Model:** [Alissonerdx/BFS-Best-Face-Swap-Video](https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap-Video) · Built on [LTX-2.3](https://huggingface.co/Lightricks/LTX-2.3)
-**License:** For research and professional VFX use only. You must have explicit consent for any likeness you process.
-""")
-
-
-if __name__ == "__main__":
-    demo.launch()
+    if not guide_video_path:
+        return None, "Please up