Update app.py

app.py

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+import torch
+from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter
+from diffusers.utils import export_to_gif
+import os
+import gc
+import gradio as gr
+
+# Environment setup
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
+gc.collect()
+torch.cuda.empty_cache()
+gc.collect()
+torch.cuda.empty_cache()
+
+# Load models and 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()
+
+# Hardcoded predefined prompts
+predefined_prompts = [
+    "640*480 pixels, high resolution, ultra realistic",
+    "bad quality, worse quality, low resolution"
+]
+
+def generate_gif(custom_prompt):
+    # Combine the predefined prompts with the custom prompt
+    prompt = custom_prompt + ", " + predefined_prompts[0]
+    negative_prompt = predefined_prompts[1]
+    
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_frames=32,
+        guidance_scale=2.0,
+        num_inference_steps=6,
+        generator=torch.Generator("cuda").manual_seed(0),
+    )
+    frames = output.frames[0]
+    export_to_gif(frames, "animatelcm.gif")
+    return "animatelcm.gif"
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Animate LCM GIF Generator")
+    custom_prompt_input = gr.Textbox(label="Custom Prompt", placeholder="Enter your custom prompt here...")
+    output_gif = gr.Image(label="Generated GIF")
+    generate_button = gr.Button("Generate GIF")
+    
+    generate_button.click(fn=generate_gif, inputs=custom_prompt_input, outputs=output_gif)
+
+# Launch the interface
+demo.launch()