debug

app.py

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+# from diffusers import DiffusionPipeline
+from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
+import torch
+import gradio as gr
+import random
+
+pipeline = DDPMPipeline.from_pretrained("google/ddpm-cat-256")
+# pipeline.to("cuda")
+
+def predict(steps, seed):
+    generator = torch.manual_seed(seed)
+    for i in range(1,steps):
+        yield pipeline(generator=generator, num_inference_steps=i).images[0]
+
+random_seed = random.randint(0, 2147483647)
+gr.Interface(
+    predict,
+    inputs=[
+        gr.inputs.Slider(1, 100, label='Inference Steps', default=5, step=1),
+        gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
+    ],
+    outputs=gr.Image(shape=[128,128], type="pil", elem_id="output_image"),
+    css="#output_image{width: 256px}",
+    title="Unconditional butterflies",
+    description="图片生成器",
+).queue().launch()

requirements.txt

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+diffusers==0.23.1
+gradio==4.4.1