Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import random
+import torch
+from diffusers import DiffusionPipeline
+
+# Check for GPU availability
+device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Load your DiffusionPipeline model
+model_repo_id = "stabilityai/sdxl-turbo"
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+# Define the custom model inference function
+def custom_infer(
+    prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator,
+    ).images[0]
+
+    return image, seed
+
+
+# Gradio interface for custom model
+def custom_model_ui():
+    with gr.Blocks() as custom_demo:
+        gr.Markdown("## Custom Model: Stability AI SDXL")
+        with gr.Row():
+            prompt = gr.Text(label="Prompt")
+            run_button = gr.Button("Generate")
+
+        result = gr.Image(label="Generated Image")
+        negative_prompt = gr.Text(label="Negative Prompt", placeholder="Optional")
+        seed = gr.Slider(0, MAX_SEED, label="Seed", step=1, value=0)
+        randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+        width = gr.Slider(256, MAX_IMAGE_SIZE, step=32, value=1024, label="Width")
+        height = gr.Slider(256, MAX_IMAGE_SIZE, step=32, value=1024, label="Height")
+        guidance_scale = gr.Slider(0, 10, step=0.1, value=7.5, label="Guidance Scale")
+        num_inference_steps = gr.Slider(1, 50, step=1, value=30, label="Inference Steps")
+
+        run_button.click(
+            custom_infer,
+            inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+            outputs=[result, seed],
+        )
+
+    return custom_demo
+
+
+# Preloaded Gradio model
+def preloaded_model_ui():
+    with gr.Blocks() as preloaded_demo:
+        gr.Markdown("## Preloaded Model: ZB-Tech Text-to-Image")
+        preloaded_demo = gr.load("models/ZB-Tech/Text-to-Image")
+
+    return preloaded_demo
+
+
+# Combine both interfaces in tabs
+with gr.Blocks() as demo:
+    with gr.Tab("Custom Model"):
+        custom_ui = custom_model_ui()
+
+    with gr.Tab("Preloaded Model"):
+        preloaded_ui = preloaded_model_ui()
+
+if __name__ == "__main__":
+    demo.launch()