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Runtime error
| from diffusers import DiffusionPipeline | |
| import torch | |
| import PIL.Image | |
| import gradio as gr | |
| import random | |
| import numpy as np | |
| pipeline = DiffusionPipeline.from_pretrained("SeyedAli/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.Slider(1, 100, label='Inference Steps', default=5, step=1), | |
| gr.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/SeyedAli/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() |