Update app.py

app.py

@@ -1,66 +1,75 @@
 import gradio as gr
 import numpy as np
 import random
-#import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
 
+# Set the device based on availability
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
 
+# Use the ByteDance/AnimateDiff-Lightning model
+model_repo_id = "ByteDance/AnimateDiff-Lightning" 
+
+# Set the torch dtype based on available hardware
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
+# Load the pipeline from the pretrained model repository
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
+# Maximum values for seed and image size
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-#@spaces.GPU #[uncomment to use ZeroGPU]
+# Define the inference function
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
 
+    # Randomize seed if the checkbox is selected
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    # Generate the animation using the pipeline
+    animation = pipe(
+        prompt=prompt, 
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale, 
+        num_inference_steps=num_inference_steps, 
+        width=width, 
+        height=height,
+        generator=generator
+    ).images[0]  # Assuming the model generates images in the `.images` property
     
-    return image, seed
+    return animation, seed
 
+# Sample prompts for the UI
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "A cat playing with a ball in a garden",
+    "A dancing astronaut in space",
+    "A flying dragon in the sky at sunset",
 ]
 
-css="""
+# Define CSS for styling
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 640px;
 }
 """
 
+# Build the Gradio UI
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # Text-to-Image Gradio Template
+        # AnimateDiff Lightning Model Text-to-Animation
         """)
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -68,10 +77,9 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
             run_button = gr.Button("Run", scale=0)
         
-        result = gr.Image(label="Result", show_label=False)
+        result = gr.Image(label="Generated Animation", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
             
@@ -79,7 +87,7 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                visible=True,
             )
             
             seed = gr.Slider(
@@ -93,50 +101,43 @@ with gr.Blocks(css=css) as demo:
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,
                 )
             
             with gr.Row():
-                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
+                    value=7.5,
                 )
-                
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2, #Replace with defaults that work for your model
+                    value=30,
                 )
-        
+
+        # Example prompts for user selection
         gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+            examples=examples,
+            inputs=[prompt]
         )
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
-    )
 
-demo.queue().launch()
+    # Create an API endpoint for the model
+    demo.api(fn=infer, inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], outputs=[result, seed])
+    demo.launch()