Update app.py

app.py

@@ -2,29 +2,142 @@ import gradio as gr
 import numpy as np
 import random
 from diffusers import DiffusionPipeline
-from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
 import torch
 import spaces
 
-pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash").to("cuda")
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-@spaces.GPU(duration=50)
-def generate_image(prompt, negative_prompt):
-    # Run the diffusion model to generate an image
-    output = pipe(prompt, negative_prompt, num_inference_steps=7, guidance_scale=3.5)
-    return output.images[0]
+if torch.cuda.is_available():
+    torch.cuda.max_memory_allocated(device=device)
+    pipe = DiffusionPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe = pipe.to(device)
+else: 
+    pipe = DiffusionPipeline.from_pretrained("sd-community/sdxl-flash", use_safetensors=True)
+    pipe = pipe.to(device)
 
-prompt = gr.Textbox(label = "Prompt", info = "Describe the subject, the background and the style of image", placeholder = "Describe what you want to see", lines = 2)
-negative_prompt = gr.Textbox(label = "Negative prompt", placeholder = "Describe what you do NOT want to see", value = "Ugly, malformed, noise, blur, watermark")
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
 
-gr_interface = gr.Interface(
-    fn=generate_image,
-    inputs=[prompt, negative_prompt],
-    outputs="image",
-    title="Real-time Image Generation with Diffusion",
-    description="Enter a prompt to generate an image",
-    theme="soft"
-)
+@spaces.GPU(duration=20,queue=False)
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
 
-# Launch the Gradio app
-gr_interface.launch()
+    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
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # Text-to-Image Gradio Template
+        Currently running on {power_device}.
+        """)
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+        
+        result = gr.Image(label="Result", 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",
+                value = "Ugly, malformed, noise, blur, watermark",
+            )
+            
+            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=512,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+            
+            with gr.Row():
+                
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=3.0,
+                )
+                
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=12,
+                    step=1,
+                    value=5,
+                )
+        
+        gr.Examples(
+            examples = examples,
+            inputs = [prompt]
+        )
+
+    run_button.click(
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result]
+    )
+
+demo.queue().launch()