Update app.py

app.py

@@ -1,230 +1,51 @@
 import gradio as gr
 import numpy as np
 import random
-
-import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, EulerDiscreteScheduler
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/stable-diffusion-xl-base-1.0"  # Replace to the model you would like to use
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+# Define model and checkpoint
+model_repo_id = "ByteDance/SDXL-Lightning"
+ckpt = "sdxl_lightning_4step_unet.safetensors"
 
+# Load the diffusion pipeline
 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
+pipe.to(device)
 
+# Update the scheduler
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
 
-@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+MAX_SEED = np.iinfo(np.int32).max
 
-    generator = torch.Generator().manual_seed(seed)
 
+def generate_and_display(prompt, num_inference_steps=10, guidance_scale=0):
+    generator = torch.Generator(device).manual_seed(random.randint(0, MAX_SEED))
     image = pipe(
         prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
+        guidance_scale=guidance_scale,
         generator=generator,
     ).images[0]
+    return image
 
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-/* General Styles */
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-    font-family: 'Arial', sans-serif;
-    color: #333;
-    background-color: #f0f4f8; /* Light gray background for better contrast */
-    border-radius: 15px;
-    padding: 20px;
-}
-
-#header {
-    text-align: center;
-    color: #1f5f99; /* Veshup Blue */
-}
-
-#title {
-    font-size: 36px;
-    font-weight: bold;
-    margin-bottom: 10px;
-}
-
-#subtitle {
-    font-size: 18px;
-    color: #555;
-    margin-bottom: 30px;
-}
-
-.gradio-button {
-    background-color: #1f5f99;
-    color: white;
-    font-weight: bold;
-    border-radius: 8px;
-}
-
-.gradio-button:hover {
-    background-color: #155b89;
-}
-
-.gradio-slider {
-    width: 100%;
-}
-
-.gradio-checkbox label {
-    font-weight: normal;
-}
-
-.gradio-markdown {
-    font-size: 16px;
-    line-height: 1.6;
-}
-
-/* Dark Mode adjustments for browser default theme */
-@media (prefers-color-scheme: dark) {
-    #col-container {
-        background-color: #2e2e2e; /* Dark background for dark mode */
-        color: #e0e0e0; /* Light text for dark mode */
-    }
-
-    #header {
-        color: #a5c4f6; /* Lighter blue for dark mode */
-    }
-
-    .gradio-button {
-        background-color: #4f89b0;
-    }
-
-    .gradio-button:hover {
-        background-color: #3a6a8b;
-    }
-
-    .gradio-slider,
-    .gradio-checkbox {
-        background-color: #444; /* Darker elements in dark mode */
-    }
-
-    .gradio-markdown {
-        color: #d1d1d1; /* Lighter text for markdown */
-    }
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("<div id='header'><h1 id='title'>Veginator: Veshup's Image Generation AI</h1><p id='subtitle'>Create stunning images with just a prompt. Powered by cutting-edge AI technology.</p></div>")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Your Creative Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt here...",
-                container=False,
-            )
-
-            run_button = gr.Button("Generate Image", scale=0, variant="primary", elem_classes="gradio-button")
-
-        result = gr.Image(label="Generated Image", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative Prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt if needed",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
 
-                num_inference_steps = gr.Slider(
-                    label="Number of Inference Steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
+@gr.Interface(
+    fn=generate_and_display,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Enter your creative prompt here"),
+        gr.Slider(1, 50, value=10, label="Number of Inference Steps"),
+        gr.Slider(0.0, 10.0, value=7.5, label="Guidance Scale"),
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="Veshon: Your Creative AI Assistant",
+    description="Generate stunning visuals effortlessly with cutting-edge technology!",
+)
+def launch_demo():
+    gr.launch(server_name="0.0.0.0", server_port=8080)  # Use a specified port for local testing
 
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
 
 if __name__ == "__main__":
-    demo.launch()
+    launch_demo()