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app.py

@@ -168,8 +168,10 @@ print("[Echo-Infinity] Pipeline ready.")
 # Inference function
 # ----------------------------------------------------------------------------
 from einops import rearrange
-from torchvision.io import write_video
+import imageio
+import numpy as np
 import random
+import gradio as gr
 
 
 def _estimate_duration(prompt: str, num_frames: int = 21, *args, **kwargs):
@@ -198,8 +200,6 @@ def generate(
         seed: Random seed for reproducibility.
         num_frames: Number of frames to generate (must be divisible by 3).
     """
-    import gradio as gr
-
     if not prompt or not prompt.strip():
         return None, "Please enter a text prompt."
 
@@ -234,10 +234,14 @@ def generate(
     video = 255.0 * current_video
     pipeline.vae.model.clear_cache()
 
-    # Save to temp file
+    # Save to temp file using imageio (torchvision.io.write_video not available)
+    frames = video[0].numpy().astype(np.uint8)  # [T, H, W, C]
     tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
     tmp.close()
-    write_video(tmp.name, video[0], fps=16)
+    writer = imageio.get_writer(tmp.name, fps=16, codec="libx264")
+    for frame in frames:
+        writer.append_data(frame)
+    writer.close()
 
     return tmp.name, f"Generated {num_frames} frames from seed {seed}."
 
@@ -245,8 +249,6 @@ def generate(
 # ----------------------------------------------------------------------------
 # Gradio UI
 # ----------------------------------------------------------------------------
-import gradio as gr
-
 CSS = """
 #col-container { max-width: 1100px; margin: 0 auto; }
 .dark .gradio-container { color: var(--body-text-color); }