Update app.py

app.py

@@ -1,51 +1,230 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline, EulerDiscreteScheduler
+
+import spaces #[uncomment to use ZeroGPU]
+from diffusers import DiffusionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+model_repo_id = "ByteDance/SDXL-Lightning"  # Replace to the model you would like to use
 
-# Define model and checkpoint
-model_repo_id = "ByteDance/SDXL-Lightning"
-ckpt = "sdxl_lightning_4step_unet.safetensors"
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
 
-# Load the diffusion pipeline
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe.to(device)
-
-# Update the scheduler
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
 
+@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)
+
+    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,
-        num_inference_steps=num_inference_steps,
+        negative_prompt=negative_prompt,
         guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
         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
+                )
 
-@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
+                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.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__":
-    launch_demo()
+    demo.launch()