| 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() |
