Update app.py

app.py

@@ -1,36 +1,47 @@
 import torch
-import gradio as gr
-from diffusers import StableDiffusionXLPipeline
-
-MODEL_ID = "stabilityai/sdxl-turbo"
-
-pipe = StableDiffusionXLPipeline.from_pretrained(
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+
+# ----------------------------
+# Settings
+# ----------------------------
+MODEL_ID = "stabilityai/stable-diffusion-1.5-turbo"  # Turbo model for CPU speed
+DEVICE = "cpu"  # CPU only
+DTYPE = torch.float32  # CPU-friendly
+
+PROMPT = "A cute cat wearing sunglasses, digital art"
+WIDTH = 768
+HEIGHT = 768
+NUM_STEPS = 1       # Fastest possible
+GUIDANCE_SCALE = 0  # Disable guidance for speed
+
+# ----------------------------
+# Load pipeline
+# ----------------------------
+pipe = StableDiffusionPipeline.from_pretrained(
     MODEL_ID,
-    torch_dtype=torch.float32,
+    torch_dtype=DTYPE,
+    safety_checker=None,    # Optional, disable for speed
+    feature_extractor=None  # Optional, disable for speed
 )
-pipe.to("cpu")
-
-pipe.enable_attention_slicing()
-
-def generate(prompt):
-    image = pipe(
-        prompt=prompt,
-        num_inference_steps=4,
-        guidance_scale=0.0,
-    ).images[0]
-    return image
-
-with gr.Blocks() as demo:
-    gr.Markdown("## 🖼️ Image Generator (CPU Safe)")
-
-    prompt = gr.Textbox(
-        label="Prompt",
-        placeholder="A cinematic cyberpunk portrait"
-    )
-
-    btn = gr.Button("Generate")
-    out = gr.Image()
-
-    btn.click(generate, prompt, out)
-
-demo.launch()
+pipe.to(DEVICE)
+
+# ----------------------------
+# Generate image
+# ----------------------------
+generator = torch.Generator(device=DEVICE).manual_seed(42)  # Optional seed
+
+image: Image.Image = pipe(
+    prompt=PROMPT,
+    height=HEIGHT,
+    width=WIDTH,
+    num_inference_steps=NUM_STEPS,
+    guidance_scale=GUIDANCE_SCALE,
+    generator=generator
+).images[0]
+
+# ----------------------------
+# Save or show
+# ----------------------------
+image.save("output.png")
+image.show()