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app.py

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+import gradio as gr
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline
+import random
+
+model = "hakurei/waifu-diffusion"
+device = "cpu"
+
+pipe = StableDiffusionPipeline.from_pretrained(model, torch_dtype=torch.float32)
+pipe = pipe.to(device)
+
+block = gr.Blocks(css=".container { max-width: 800px; margin: auto; }")
+
+def infer(prompt, width, height, nums, steps, guidance_scale, seed):
+    print(prompt)
+    print(width, height, nums, steps, guidance_scale, seed)
+
+    if prompt is not None and prompt != "":
+        if seed is None or seed == '' or seed == -1:
+            seed = int(random.randrange(4294967294))
+        generator = torch.Generator(device).manual_seed(seed)
+        images = pipe([prompt] * nums, height=height, width=width, num_inference_steps=steps, generator=generator, guidance_scale=guidance_scale )["sample"]
+    return images
+
+# with block as demo:
+def run():
+    _app = gr.Interface(
+        fn=infer,
+        title="Waifu Generator",
+        inputs=[
+            gr.Textbox(label="prompt"),
+            gr.Slider(512, 1024, 512, step=64, label="width"),
+            gr.Slider(512, 1024, 512, step=64, label="height"),
+            gr.Slider(1, 4, 1, step=1, label="Number of Images"),
+            gr.Slider(10, 150, step=1, value=50,
+                      label="num_inference_steps:\n"
+                            "The number of denoising steps. More de-scaling steps usually result in a higher quality image, but will slow down inference."),
+            gr.Slider(0, 20, 7.5, step=0.5,
+                      label="guidance_scale:\n" +
+                            "A higher boot ratio encourages the generation of images that are closely related to text \"hints\", often at the expense of reduced image quality"),
+            gr.Textbox(label="Random seed",
+                       placeholder="Random Seed",
+                       lines=1),
+        ],
+        outputs=[
+            gr.Gallery(label="Generated images")
+        ])
+
+    return _app
+
+app = run()
+app.launch(debug=True)