Update app.py

app.py

@@ -1,110 +1,110 @@
-from flask import Flask, request, jsonify, send_from_directory
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-from diffusers import DiffusionPipeline
-import torch
-import imageio
-import uuid
-import os
-
-app = Flask(__name__)
-
-# Load models once
-modelscope_model = pipeline(Tasks.text_to_video_synthesis, model='damo-vilab/modelscope-text-to-video-synthesis')
-zeroscope_model = DiffusionPipeline.from_pretrained(
-    "cerspense/zeroscope_v2_576w",
-    torch_dtype=torch.float16
-).to("cuda")
-
-# Output directory (for temporary files)
-os.makedirs("output", exist_ok=True)
-
-# Serve the generated video dynamically (no permanent storage)
-@app.route('/generate-video', methods=['POST'])
-def generate_video():
-    data = request.get_json()
-    
-    model_type = data.get("model", "").lower()
-    if not model_type:
-        return jsonify({"error": "Model is required: 'modelscope' or 'zeroscope'"}), 400
-
-    params = data.get("params", {})
-    prompt = params.get("prompt")
-    if not prompt:
-        return jsonify({"error": "Prompt is required in 'params'"}), 400
-
-    # Common settings
-    seed = params.get("seed", 42)
-    output_filename = f"{uuid.uuid4().hex}.mp4"
-    output_path = os.path.join("output", output_filename)
-
-    try:
-        if model_type == "modelscope":
-            # Extract ModelScope-specific parameters
-            result = modelscope_model({
-                "text": prompt,
-                "output_path": output_path,
-                "seed": seed,
-                "fps": params.get("fps", 8),
-                "num_frames": params.get("num_frames", 16),
-                "batch_size": params.get("batch_size", 1),
-                "decode_audio": params.get("decode_audio", False),
-                "resolution": params.get("resolution", "512x512")
-            })
-            video_path = result["output_path"]
-
-        elif model_type == "zeroscope":
-            # Extract Zeroscope-specific parameters
-            inference_steps = params.get("num_inference_steps", 25)
-            height = params.get("height", 320)
-            width = params.get("width", 576)
-            fps = params.get("fps", 8)
-
-            generator = torch.manual_seed(seed)
-
-            with torch.autocast("cuda"):
-                output = zeroscope_model(
-                    prompt,
-                    num_inference_steps=inference_steps,
-                    height=height,
-                    width=width,
-                    generator=generator
-                )
-                video_frames = output.frames
-
-            # Save as video
-            imageio.mimsave(output_path, video_frames, fps=fps)
-            video_path = output_path
-
-        else:
-            return jsonify({"error": f"Unsupported model type: {model_type}"}), 400
-
-        # Return a URL to the generated video file
-        video_url = f"/download-video/{output_filename}"
-
-        return jsonify({
-            "success": True,
-            "model": model_type,
-            "video_url": video_url,
-            "filename": output_filename
-        })
-
-    except Exception as e:
-        return jsonify({
-            "success": False,
-            "error": str(e)
-        }), 500
-
-
-# Endpoint to download the generated video
-@app.route('/download-video/<filename>', methods=['GET'])
-def download_video(filename):
-    try:
-        return send_from_directory("output", filename, as_attachment=True)
-    except FileNotFoundError:
-        return jsonify({"error": "Video file not found"}), 404
-
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=int(os.environ.get("PORT", 7860)))
-
+from flask import Flask, request, jsonify, send_from_directory
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from diffusers import DiffusionPipeline
+import torch
+import imageio
+import uuid
+import os
+
+app = Flask(__name__)
+
+# Load models once
+modelscope_model = pipeline(Tasks.text_to_video_synthesis, model='damo-vilab/modelscope-text-to-video-synthesis')
+zeroscope_model = DiffusionPipeline.from_pretrained(
+    "cerspense/zeroscope_v2_576w",
+    torch_dtype=torch.float16
+).to("cuda")
+
+# Output directory (for temporary files)
+os.makedirs("output", exist_ok=True)
+
+# Serve the generated video dynamically (no permanent storage)
+@app.route('/generate-video', methods=['POST'])
+def generate_video():
+    data = request.get_json()
+    
+    model_type = data.get("model", "").lower()
+    if not model_type:
+        return jsonify({"error": "Model is required: 'modelscope' or 'zeroscope'"}), 400
+
+    params = data.get("params", {})
+    prompt = params.get("prompt")
+    if not prompt:
+        return jsonify({"error": "Prompt is required in 'params'"}), 400
+
+    # Common settings
+    seed = params.get("seed", 42)
+    output_filename = f"{uuid.uuid4().hex}.mp4"
+    output_path = os.path.join("output", output_filename)
+
+    try:
+        if model_type == "modelscope":
+            # Extract ModelScope-specific parameters
+            result = modelscope_model({
+                "text": prompt,
+                "output_path": output_path,
+                "seed": seed,
+                "fps": params.get("fps", 8),
+                "num_frames": params.get("num_frames", 16),
+                "batch_size": params.get("batch_size", 1),
+                "decode_audio": params.get("decode_audio", False),
+                "resolution": params.get("resolution", "512x512")
+            })
+            video_path = result["output_path"]
+
+        elif model_type == "zeroscope":
+            # Extract Zeroscope-specific parameters
+            inference_steps = params.get("num_inference_steps", 25)
+            height = params.get("height", 320)
+            width = params.get("width", 576)
+            fps = params.get("fps", 8)
+
+            generator = torch.manual_seed(seed)
+
+            with torch.autocast("cuda"):
+                output = zeroscope_model(
+                    prompt,
+                    num_inference_steps=inference_steps,
+                    height=height,
+                    width=width,
+                    generator=generator
+                )
+                video_frames = output.frames
+
+            # Save as video
+            imageio.mimsave(output_path, video_frames, fps=fps)
+            video_path = output_path
+
+        else:
+            return jsonify({"error": f"Unsupported model type: {model_type}"}), 400
+
+        # Return a URL to the generated video file
+        video_url = f"/download-video/{output_filename}"
+
+        return jsonify({
+            "success": True,
+            "model": model_type,
+            "video_url": video_url,
+            "filename": output_filename
+        })
+
+    except Exception as e:
+        return jsonify({
+            "success": False,
+            "error": str(e)
+        }), 500
+
+
+# Endpoint to download the generated video
+@app.route('/download-video/<filename>', methods=['GET'])
+def download_video(filename):
+    try:
+        return send_from_directory("output", filename, as_attachment=True)
+    except FileNotFoundError:
+        return jsonify({"error": "Video file not found"}), 404
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)
+