Delete app.py

app.py

@@ -1,26 +0,0 @@
-from diffusers import DiffusionPipeline
-import torch
-import PIL.Image
-import gradio as gr
-import random
-import numpy as np
-
-pipeline = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128")
-
-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="A DDPM scheduler and UNet model trained (from this <a href=\"https://huggingface.co/anton-l/ddpm-butterflies-128\">checkpoint</a>) on a subset of the <a href=\"https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset\">Smithsonian Butterflies</a> dataset for unconditional image generation.",
-).queue().launch()