app.py file

app.py

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+import gradio as gr
+import spaces
+from diffusers import StableDiffusionPipeline, AutoencoderKL
+import os
+import torch
+from PIL import Image
+import random
+
+# SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
+
+# Constants
+repo = "IDKiro/sdxs-512-0.9"
+
+
+# Ensure model and scheduler are initialized in GPU-enabled function
+if torch.cuda.is_available():
+    weight_type = torch.float32 
+    pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=weight_type)
+
+    # pipe.vae = AutoencoderKL.from_pretrained("IDKiro/sdxs-512-0.9/vae_large")     # use original VAE
+    pipe.to("cuda")
+
+# Function 
+@spaces.GPU(enable_queue=True)
+def generate_image(prompt):  
+    seed  =  random.randint(-100000,100000)
+
+    results =  pipe(
+                prompt, 
+                num_inference_steps=1, 
+                guidance_scale=0,
+                generator=torch.Generator(device="cuda").manual_seed(seed)
+            )
+    return results.images[0]
+
+
+
+# Gradio Interface
+description = """
+This demo utilizes the SDXLS model
+"""
+
+with gr.Blocks(css="style.css") as demo:
+    gr.HTML("<h1><center>Text-to-Image with SDXS ⚡</center></h1>")
+    gr.Markdown(description)
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Textbox(label='Enter your prompt (English)', scale=8, value="portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour")
+            num_inference_steps = gr.Slider(label="num_inference_steps", value=1,interactive=True)
+            submit = gr.Button(scale=1, variant='primary')
+    img = gr.Image(label='SDXS Generated Image')
+
+    prompt.submit(fn=generate_image,
+                 inputs=[prompt],
+                 outputs=img,
+                 )
+    submit.click(fn=generate_image,
+                 inputs=[prompt],
+                 outputs=img,
+                 )
+    
+demo.queue().launch()