Update app.py

app.py

@@ -1,95 +1,55 @@
 import gradio as gr
 import torch
-from diffusers import StableDiffusionPipeline
 from PIL import Image
 
-# Tiny model that fits in free tier memory
-MODEL_NAME = "OFA-Sys/small-stable-diffusion-v0"
+# We'll use the smallest available pipeline
+from diffusers import DiffusionPipeline
 
-# Load model (will cache after first run)
+# Load model (cached after first run)
 @gr.cache()
 def load_model():
-    return StableDiffusionPipeline.from_pretrained(
-        MODEL_NAME,
+    return DiffusionPipeline.from_pretrained(
+        "OFA-Sys/small-stable-diffusion-v0",
         torch_dtype=torch.float16,
-        safety_checker=None
+        safety_checker=None,
+        use_safetensors=True
     ).to("cpu")
 
 def generate_character(description, seed=42):
     try:
         pipe = load_model()
         
-        # Reduce memory usage
         torch.manual_seed(seed)
         with torch.inference_mode():
             image = pipe(
                 prompt=f"pixel art character, {description}",
-                num_inference_steps=15,  # Fewer steps = less memory
+                num_inference_steps=15,
                 guidance_scale=7.0,
-                width=256,  # Smaller resolution
+                width=256,
                 height=256
             ).images[0]
         
         return image
     except Exception as e:
-        return f"Error: {str(e)}\nTry a simpler description or different words."
+        return f"Error: {str(e)}\nTry a simpler description."
 
-# Create simple animation effect by generating variations
-def generate_animation(description, frames=3):
-    images = []
-    for i in range(frames):
-        img = generate_character(description, seed=i)
-        if isinstance(img, str):  # If error returned
-            return img
-        images.append(img)
-    
-    # Create simple animation (GIF)
-    images[0].save(
-        "animation.gif",
-        save_all=True,
-        append_images=images[1:],
-        duration=500,
-        loop=0
-    )
-    return "animation.gif"
-
-# Minimal interface
-with gr.Blocks(title="Tiny Character Animator") as demo:
-    gr.Markdown("""
-    # 🎮 Tiny Character Animator
-    *Free-tier optimized for Hugging Face Spaces*
-    """)
+with gr.Blocks(title="Lightweight Character Generator") as demo:
+    gr.Markdown("# 🎨 Tiny Character Creator")
     
     with gr.Row():
         desc = gr.Textbox(
             label="Describe your character",
-            placeholder="e.g., 'blue robot with antennae'",
+            placeholder="e.g., 'green alien with one eye'",
             max_lines=2
         )
     
-    with gr.Row():
-        btn_still = gr.Button("Generate Still", variant="secondary")
-        btn_animate = gr.Button("Generate Animation", variant="primary")
+    generate_btn = gr.Button("Generate Character")
+    output = gr.Image(label="Your Character", shape=(256, 256))
     
-    with gr.Row():
-        output_still = gr.Image(label="Character", shape=(256, 256))
-        output_anim = gr.Image(label="Animation", format="gif", visible=False)
-    
-    # Button actions
-    btn_still.click(
+    generate_btn.click(
         generate_character,
         inputs=desc,
-        outputs=output_still
-    )
-    
-    btn_animate.click(
-        lambda: (gr.Image(visible=False), gr.Image(visible=True)),  # Toggle visibility
-        None,
-        [output_still, output_anim]
-    ).then(
-        generate_animation,
-        inputs=desc,
-        outputs=output_anim
+        outputs=output
     )
 
 demo.launch(debug=False)