update in the app.py

app.py

@@ -1,27 +1,50 @@
 import gradio as gr
 import torch
-from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusion3Pipeline
 
-# Load the Stable Diffusion model from Hugging Face
-model_id = "stabilityai/stable-diffusion-3.5-large"
+# Check if CUDA is available and set the device accordingly
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Initialize the pipeline
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipe = pipe.to(device)
+# Load the Stable Diffusion 3.5 Large model
+model_id = "stabilityai/stable-diffusion-3.5-large"
+pipe = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe.to(device)
 
-# Define a function to generate images
-def generate_image(prompt):
-    with torch.autocast(device):
-        image = pipe(prompt).images[0]
+# Define the image generation function
+def generate_image(prompt, negative_prompt, width, height, guidance_scale, num_inference_steps, seed):
+    generator = torch.manual_seed(seed) if seed else None
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator
+    ).images[0]
     return image
 
 # Set up the Gradio interface
-interface = gr.Interface(
-    fn=generate_image,
-    inputs=gr.Textbox(label="Enter your prompt"),
-    outputs=gr.Image(label="Generated Image"),
-    title="Stable Diffusion 3.5 Demo"
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# Stable Diffusion 3.5 Large Image Generator")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
+            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
+            width = gr.Slider(label="Width", minimum=512, maximum=1024, step=64, value=512)
+            height = gr.Slider(label="Height", minimum=512, maximum=1024, step=64, value=512)
+            guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=20.0, step=0.5, value=7.5)
+            num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, step=1, value=50)
+            seed = gr.Number(label="Seed", value=42, precision=0)
+            generate_button = gr.Button("Generate Image")
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image")
+
+    generate_button.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, width, height, guidance_scale, num_inference_steps, seed],
+        outputs=output_image
+    )
 
-interface.launch()
+if __name__ == "__main__":
+    demo.launch()