initial

app.py

@@ -1,72 +1,57 @@
 import gradio as gr
+import spaces
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from diffusers import StableDiffusionXLPipeline
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+dtype = torch.float16
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+repo = "stabilityai/stable-diffusion-xl-base-1.0"
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    repo,
+    torch_dtype=dtype,
+    use_safetensors=True
+).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),
-):
+
+@spaces.GPU
+def infer(prompt, negative_prompt, seed, randomize_seed, 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)
+    generator = torch.Generator(device=device).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,
+        generator=generator
     ).images[0]
 
     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",
+    "A cozy Scandinavian living room, soft light, natural wood, white tones",
+    "A futuristic cityscape at night with flying cars",
+    "A magical forest with glowing mushrooms and fairies"
 ]
 
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 580px;
 }
 """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown(f"""
+        # Generate images [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+        Generate high-quality images with Stability AI's flagship SDXL base model.
+        """)
 
         with gr.Row():
             prompt = gr.Text(
@@ -76,8 +61,7 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
+            run_button = gr.Button("Run", scale=0)
 
         result = gr.Image(label="Result", show_label=False)
 
@@ -86,9 +70,7 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
             )
-
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -96,59 +78,34 @@ with gr.Blocks(css=css) as demo:
                 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.Examples(examples=examples, inputs=[prompt])
+            guidance_scale = gr.Slider(
+                label="Guidance scale",
+                minimum=0.0,
+                maximum=20.0,
+                step=0.1,
+                value=7.5,
+            )
+
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=50,
+                step=1,
+                value=30,
+            )
+
+        gr.Examples(
+            examples=examples,
+            inputs=[prompt]
+        )
+
     gr.on(
-        triggers=[run_button.click, prompt.submit],
+        triggers=[run_button.click, prompt.submit, negative_prompt.submit],
         fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+        inputs=[prompt, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps],
+        outputs=[result, seed]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()