debug 13

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 from diffusers import DiffusionPipeline
+
 # import torch
 # from diffusers import DDPMScheduler, UNet2DModel
 # from PIL import Image
@@ -9,6 +10,7 @@ from diffusers import DiffusionPipeline
 pipeline = DiffusionPipeline.from_pretrained("google/ddpm-celebahq-256")
 # pipeline.to("cuda")
 
+
 def erzeuge(prompt):
     return pipeline(prompt).images  # [0]
 
@@ -33,11 +35,3 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     demo.launch()
-
-
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-
-
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-

app1.py

@@ -1,28 +0,0 @@
-# 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()