Update app.py

app.py

@@ -1,23 +1,21 @@
 import gradio as gr
 import torch
-from diffusers import StableDiffusionPipeline, DPMSolverSinglestepScheduler
+from diffusers import StableDiffusionPipeline
 from PIL import Image
 
-# Load a memory-efficient SD variant (under 12GB)
-model_id = "runwayml/stable-diffusion-v1-5"
+# Use a smaller SD model variant that fits within free tier
+MODEL_ID = "CompVis/ldm-super-resolution-4x-openimages"  # Only ~1.4GB
 
 @gr.cache()
 def load_model():
     pipe = StableDiffusionPipeline.from_pretrained(
-        model_id,
+        MODEL_ID,
         torch_dtype=torch.float16,
         safety_checker=None,
         use_safetensors=True
     )
-    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
+    pipe.enable_attention_slicing()  # Reduces memory usage
     return pipe
 
 def generate_character(prompt, seed=42):
@@ -25,66 +23,36 @@ def generate_character(prompt, seed=42):
         pipe = load_model()
         generator = torch.Generator(device="cpu").manual_seed(seed)
         
-        with torch.inference_mode():
-            image = pipe(
-                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]
+        image = pipe(
+            prompt=f"pixel art {prompt}, clean lines, vibrant colors",
+            num_inference_steps=20,
+            guidance_scale=7.0,
+            width=256,
+            height=256,
+            generator=generator
+        ).images[0]
         
         return image
     except Exception as e:
-        return f"Error: {str(e)}\nTry simplifying your prompt."
+        return f"Error: {str(e)}\nTry a simpler prompt."
 
-# 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.Blocks(theme=gr.themes.Default()) as demo:
+    gr.Markdown("# 🎮 Lightweight Character Generator")
     
     with gr.Row():
         prompt = gr.Textbox(
-            label="Character Description",
-            placeholder="e.g. 'cyberpunk fox wearing sunglasses'"
+            label="Describe your character",
+            placeholder="e.g. 'robot pirate with laser eye'",
+            max_lines=2
         )
     
-    with gr.Tab("Single Image"):
-        img_out = gr.Image(label="Generated Character", type="pil")
-        gen_btn = gr.Button("Generate")
+    generate_btn = gr.Button("Generate", variant="primary")
+    output = gr.Image(label="Your Character", type="pil")
     
-    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)
+    generate_btn.click(
+        generate_character,
+        inputs=prompt,
+        outputs=output
+    )
 
-demo.launch()
+demo.launch(debug=False)