Jesse1981 commited on
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Create app.py

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  1. app.py +26 -0
app.py ADDED
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+ from diffusers import LDMPipeline
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+ import torch
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+ import PIL.Image
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+ import gradio as gr
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+ import random
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+ import numpy as np
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+
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+ pipeline = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
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+
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+ def predict(steps, seed):
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+ generator = torch.manual_seed(seed)
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+ for i in range(1,steps):
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+ yield pipeline(generator=generator, num_inference_steps=i)["sample"][0]
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+
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+ random_seed = random.randint(0, 2147483647)
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+ gr.Interface(
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+ predict,
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+ inputs=[
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+ gr.inputs.Slider(1, 100, label='Inference Steps', default=5, step=1),
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+ gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
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+ ],
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+ outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"),
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+ css="#output_image{width: 256px}",
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+ title="ldm-celebahq-256 - 🧨 diffusers library",
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+ 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>.",
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+ ).queue().launch()