Create app.py

app.py

@@ -0,0 +1,68 @@
+import gradio as gr
+
+import os
+
+from PIL import Image
+import random
+
+import torch
+from diffusers import StableDiffusionPipeline, AutoencoderKL
+
+def gen_seed():
+    random_data = os.urandom(3)
+    seed = int.from_bytes(random_data, byteorder="big")
+    return seed
+
+repo = "IDKiro/sdxs-512-0.9"
+weight_type = torch.float32     # or float16
+
+# Load model.
+pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=weight_type)
+
+# use original VAE
+# pipe.vae = AutoencoderKL.from_pretrained("IDKiro/sdxs-512-0.9/vae_large")
+ 
+#pipe.to("cuda")
+
+prompt = "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour"
+
+def sdxs_run(prompt, steps, guidance, seed):
+    # Ensure using 1 inference step and CFG set to 0.
+    image = pipe(
+        prompt, 
+        num_inference_steps=steps, 
+        guidance_scale=guidance,
+        generator=torch.Generator(device="cpu").manual_seed(seed)
+    ).images[0]
+    return image
+
+#image.save("output.png")
+
+def update_seed(rand, seed):
+    if rand:
+        return gen_seed()
+    else:
+        return seed
+
+desc = """# SDXS CPU Test Space
+Just a quick test. Model is `sdxs-512-0.9` for txt2img.
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown(desc)
+    with gr.Group():
+        with gr.Row():
+            img = gr.Image(label='SDXS Generated Image')
+        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")
+            with gr.Accordion("More options", open=False):
+                steps = gr.Slider(label="Number of steps", value=1, minimum=1, maximum=20, step=1)
+                guidance = gr.Slider(label="Guidance", value=0, minimum=0, maximum=2, step=0.1)
+                seed = gr.Slider(label="Seed", minimum=20, maximum=100000000, step=1, randomize=True)
+                rand = gr.Checkbox(label="Randomize Seed After Generation?", value=True)
+        with gr.Row():
+            submit = gr.Button(scale=1, variant='primary')
+            #clear = gr.ClearButton(components=[])
+        submit.click(fn=sdxs_run, inputs=[prompt, steps, guidance, seed], outputs=img).then(fn=update_seed, inputs=[rand, seed], outputs=seed)
+
+demo.queue(max_size=20).launch()