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from diffusers import LDMPipeline
import torch
import PIL.Image
import gradio as gr
import random
import numpy as np
pipeline = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
def predict(steps, seed):
generator = torch.manual_seed(seed)
for i in range(1,steps):
yield pipeline(generator=generator, num_inference_steps=i)["sample"][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=[256,256], type="pil", elem_id="output_image"),
css="#output_image{width: 256px}",
title="ldm-celebahq-256 - 🧨 diffusers library",
description="This Spaces contains an unconditional Latent Diffusion process for the <a href=\"https://huggingface.co/CompVis/ldm-celebahq-256\">ldm-celebahq-256</a> face generator model by <a href=\"https://huggingface.co/CompVis\">CompVis</a> using the <a href=\"https://github.com/huggingface/diffusers\">diffusers library</a>. The goal of this demo is to showcase the diffusers library capabilities. If you want the state-of-the-art experience with Latent Diffusion text-to-image check out the <a href=\"https://huggingface.co/spaces/multimodalart/latentdiffusion\">main Spaces</a>.",
).queue().launch(debug=True) |