Update app.py

app.py

@@ -1,55 +1,90 @@
 import gradio as gr
 import torch
+from diffusers import StableDiffusionPipeline, DPMSolverSinglestepScheduler
 from PIL import Image
 
-# We'll use the smallest available pipeline
-from diffusers import DiffusionPipeline
+# Load a memory-efficient SD variant (under 12GB)
+model_id = "runwayml/stable-diffusion-v1-5"
 
-# Load model (cached after first run)
 @gr.cache()
 def load_model():
-    return DiffusionPipeline.from_pretrained(
-        "OFA-Sys/small-stable-diffusion-v0",
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id,
         torch_dtype=torch.float16,
         safety_checker=None,
         use_safetensors=True
-    ).to("cpu")
+    )
+    pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config)
+    pipe = pipe.to("cpu")
+    pipe.enable_attention_slicing()  # Reduces memory by 30%
+    pipe.enable_model_cpu_offload()  # Only loads needed components
+    return pipe
 
-def generate_character(description, seed=42):
+def generate_character(prompt, seed=42):
     try:
         pipe = load_model()
+        generator = torch.Generator(device="cpu").manual_seed(seed)
         
-        torch.manual_seed(seed)
         with torch.inference_mode():
             image = pipe(
-                prompt=f"pixel art character, {description}",
-                num_inference_steps=15,
-                guidance_scale=7.0,
-                width=256,
-                height=256
+                prompt=f"cartoon character {prompt}, vibrant colors, clean lines",
+                negative_prompt="blurry, deformed, ugly",
+                num_inference_steps=20,
+                guidance_scale=7.5,
+                width=512,
+                height=512,
+                generator=generator
             ).images[0]
         
         return image
     except Exception as e:
-        return f"Error: {str(e)}\nTry a simpler description."
+        return f"Error: {str(e)}\nTry simplifying your prompt."
 
-with gr.Blocks(title="Lightweight Character Generator") as demo:
-    gr.Markdown("# 🎨 Tiny Character Creator")
+# Animation through img2img
+def generate_animation(prompt, frames=3):
+    base_image = generate_character(prompt)
+    if isinstance(base_image, str):  # If error
+        return base_image
+    
+    images = [base_image]
+    pipe = load_model()
+    
+    for i in range(1, frames):
+        result = pipe(
+            prompt=prompt,
+            image=images[-1],
+            strength=0.3,  # Small changes per frame
+            generator=torch.Generator().manual_seed(i)
+        )
+        images.append(result.images[0])
+    
+    images[0].save(
+        "animation.gif",
+        save_all=True,
+        append_images=images[1:],
+        duration=500,
+        loop=0
+    )
+    return "animation.gif"
+
+with gr.Blocks(theme=gr.themes.Base()) as demo:
+    gr.Markdown("# 🎬 Character Animator (12GB Optimized)")
     
     with gr.Row():
-        desc = gr.Textbox(
-            label="Describe your character",
-            placeholder="e.g., 'green alien with one eye'",
-            max_lines=2
+        prompt = gr.Textbox(
+            label="Character Description",
+            placeholder="e.g. 'cyberpunk fox wearing sunglasses'"
         )
     
-    generate_btn = gr.Button("Generate Character")
-    output = gr.Image(label="Your Character", shape=(256, 256))
+    with gr.Tab("Single Image"):
+        img_out = gr.Image(label="Generated Character", type="pil")
+        gen_btn = gr.Button("Generate")
     
-    generate_btn.click(
-        generate_character,
-        inputs=desc,
-        outputs=output
-    )
+    with gr.Tab("Animation"):
+        anim_out = gr.Image(label="Animation", format="gif")
+        anim_btn = gr.Button("Create Animation (3 frames)")
+    
+    gen_btn.click(generate_character, inputs=prompt, outputs=img_out)
+    anim_btn.click(generate_animation, inputs=prompt, outputs=anim_out)
 
-demo.launch(debug=False)
+demo.launch()