Add app to spaces

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline, AutoencoderKL
+
+
+title = "Text-to-image Generator"
+description = """
+This Space uses the sdxs-512-0.9 model which has the ability to generate high quality images in the faction of the time of previous methods.
+
+This Space demos the model on an inexpensive CPU, where it can  generate images in just a couple of seconds. When on a GPU this model can generate up to 100 images per second. 
+
+Model: https://huggingface.co/IDKiro/sdxs-512-0.9\n
+Paper: https://arxiv.org/pdf/2403.16627.pdf
+
+
+"""
+
+
+def generate_image(prompt):
+
+    repo = "IDKiro/sdxs-512-0.9"
+    weight_type = torch.float32
+
+    # Load model.
+    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") # add this in only for gpu inference
+
+    # Ensure using the same inference steps as the loaded model and CFG set to 0.
+    image = pipe(
+        prompt, 
+        num_inference_steps=1, 
+        guidance_scale=0,
+        generator=torch.Generator(device="cpu")  # change to 'cuda' for gpu inference
+    ).images[0]
+
+    return image
+
+
+# Build the Gradio interface
+iface_generate_image = gr.Interface(
+    fn=generate_image,
+    title=title, 
+    description=description,
+    inputs=[
+        gr.Textbox(label="Text Prompt", placeholder="Type your prompt here..."),
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    allow_flagging="never",
+)
+
+# start interface
+iface_generate_image.launch()

requirements.txt

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+gradio==4.23.0
+torch==2.2.1
+diffusers==0.27.2
+transformers==4.39.1
+accelerate==0.27.2