Try stable diffusion

app.py

@@ -1,55 +1,32 @@
 import gradio as gr
 import torch
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-from PIL import Image
-import base64
-from io import BytesIO
-
-# You can change these:
-BASE_MODEL = "runwayml/stable-diffusion-v1-5"
-CONTROLNET_ID = "lllyasviel/sd-controlnet-canny"  # placeholder; change to a QR-focused ControlNet if you have one
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-controlnet = ControlNetModel.from_pretrained(
-    CONTROLNET_ID, torch_dtype=torch.float16 if device=="cuda" else torch.float32
-)
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    BASE_MODEL,
-    controlnet=controlnet,
-    torch_dtype=torch.float16 if device=="cuda" else torch.float32,
-    safety_checker=None
+from diffusers import StableDiffusionPipeline
+
+# Load Stable Diffusion (v1.5)
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+dtype = torch.float16 if device != "cpu" else torch.float32
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=dtype
+).to(device)
+
+def generate(prompt, steps, guidance, seed):
+    generator = torch.Generator(device=device).manual_seed(int(seed)) if seed != 0 else None
+    with torch.autocast(device):
+        image = pipe(prompt, num_inference_steps=steps, guidance_scale=guidance, generator=generator).images[0]
+    return image
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Textbox(label="Prompt", value="A fantasy castle at sunset"),
+        gr.Slider(10, 50, value=30, label="Steps"),
+        gr.Slider(1, 12, value=7.5, label="Guidance Scale"),
+        gr.Number(value=0, label="Seed (0 = random)")
+    ],
+    outputs=gr.Image()
 )
-pipe.to(device)
-
-def generate(prompt, control_image, guidance_scale=7.5, steps=30, seed=0):
-    print("API called:", type(control_image))
-    generator = torch.Generator(device=device).manual_seed(int(seed)) if seed else None
-    img = pipe(
-        prompt=prompt,
-        image=control_image,
-        num_inference_steps=int(steps),
-        guidance_scale=float(guidance_scale),
-        generator=generator
-    ).images[0]
-    return img
-
-with gr.Blocks() as demo:
-    gr.Markdown("# ControlNet Image Generator")
-    with gr.Row():
-        prompt = gr.Textbox(label="Prompt", value="A futuristic poster, high detail")
-        seed = gr.Number(label="Seed (0=random)", value=0)
-    with gr.Row():
-        control = gr.Image(type="pil", label="Control image (e.g., QR or edge map)")
-        steps = gr.Slider(10, 50, 30, step=1, label="Steps")
-        guidance = gr.Slider(1.0, 12.0, 7.5, step=0.1, label="Guidance scale")
-    out = gr.Image(label="Result")
-
-    btn = gr.Button("Generate")
-    btn.click(generate, [prompt, control, guidance, steps, seed], out)
-
-    # Enable simple API use
-    gr.Examples([], inputs=[prompt, control, guidance, steps, seed], outputs=out)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1,8 +1,6 @@
 torch
-diffusers>=0.27.0
+diffusers
 transformers
 accelerate
 safetensors
-controlnet-aux
-gradio>=4.0.0
-Pillow
+gradio