Create app_gpu.py

app_gpu.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline
+from PIL import Image
+
+# Load the model
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+pipe = DiffusionPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,  # Use float32 for CPU
+    use_safetensors=True
+)
+pipe.to("cuda")
+
+def generate_image(prompt, negative_prompt, size):
+    if not prompt:
+        prompt = "a beautiful landscape"
+    if not negative_prompt:
+        negative_prompt = ""
+        
+    width, height = map(int, size.split('x'))
+    generator = torch.Generator("cuda").manual_seed(42)
+
+    try:
+        result = pipe(prompt=prompt, height=height, width=width, negative_prompt=negative_prompt, generator=generator)
+        if result and hasattr(result, 'images') and len(result.images) > 0:
+            return result.images[0]
+        else:
+            print("Error: No images in the result or result is None")
+            return None
+    except Exception as e:
+        print(f"Error occurred: {e}")
+        return None
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Text to Image SDXL")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="Enter the prompt here...")
+            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter the negative prompt here...")
+            size = gr.Dropdown(choices=["512x512", "768x768", "1024x1024"], value="1024x1024", label="Size")
+            submit = gr.Button("Submit")
+        with gr.Column():
+            output = gr.Image(label="Output")
+
+    submit.click(generate_image, inputs=[prompt, negative_prompt, size], outputs=output)
+
+demo.launch()