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app.py

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+import torch
+from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter
+from diffusers.utils import export_to_video
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import base64
+import tempfile
+import os
+import threading
+
+app = Flask(__name__)
+CORS(app)
+
+pipe = None
+app.config['temp_response'] = None
+app.config['generation_thread'] = None
+
+def download_pipeline():
+    global pipe
+    try:
+        print('Downloading the model weights')
+        # Download and initialize the animation pipeline
+        adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM", torch_dtype=torch.float16)
+        pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16)
+        pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
+        pipe.load_lora_weights("wangfuyun/AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm-lora")
+        pipe.set_adapters(["lcm-lora"], [0.8])
+        pipe.enable_vae_slicing()
+        pipe.enable_model_cpu_offload()
+        return True
+    except Exception as e:
+        print(f"Error downloading pipeline: {e}")
+        return False
+
+
+def generate_and_export_animation(prompt):
+    global pipe
+
+    # Ensure the animation pipeline is initialized
+    if pipe is None:
+        if not download_pipeline():
+            return None, "Failed to initialize animation pipeline"
+
+    try:
+        # Generate animation frames
+        print('Generating Video frames')
+        output = pipe(
+            prompt=prompt,
+            negative_prompt="bad quality, worse quality, low resolution, blur",
+            num_frames=16,
+            guidance_scale=2.0,
+            num_inference_steps=6
+        )
+
+        # Export frames to a temporary video file
+        with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_file:
+            temp_video_path = temp_file.name
+            print('temp_video_path', temp_video_path)
+            export_to_video(output.frames[0], temp_video_path)
+
+        with open(temp_video_path, 'rb') as video_file:
+            video_binary = video_file.read()
+
+        video_base64 = base64.b64encode(video_binary).decode('utf-8')
+        os.remove(temp_video_path)
+        response_data = {'video_base64': '','status':None}
+        response_data['video_base64'] = video_base64
+        return response_data
+
+    except Exception as e:
+        print(f"Error generating animation: {e}")
+        return None, "Failed to generate animation"
+
+def background(prompt):
+    with app.app_context():
+        temp_response = generate_and_export_animation(prompt)
+        json_content = temp_response.get_json()
+        app.config['temp_response'] = json_content
+
+@app.route('/run', methods=['POST'])
+def handle_animation_request():
+
+    prompt = request.form.get('prompt')
+    if prompt:
+        generation_thread = threading.Thread(target=background, args=(prompt,))
+        app.config['generation_thread'] = generation_thread
+        generation_thread.start()
+        response_data = {"message": "Video generation started", "process_id": generation_thread.ident}
+    
+        return jsonify(response_data)
+    else:
+      return jsonify({"message": "Please provide a valid text prompt."}), 400
+
+@app.route('/status', methods=['GET'])
+def check_animation_status():
+    process_id = request.args.get('process_id',None)
+    
+    if process_id:
+        generation_thread = app.config.get('generation_thread')
+        if generation_thread and generation_thread.is_alive():
+            return jsonify({"status": "in_progress"}), 200
+        elif app.config.get('temp_response'):
+            app.config['temp_response']['status'] = 'completed'
+            final_response = app.config['temp_response']
+            return jsonify(final_response)
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+flask
+flask_cors
+diffusers
+peft
+torch
+gunicorn