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import os
import gradio as gr
from pathlib import Path
from diffusers import StableDiffusionPipeline
from PIL import Image
from huggingface_hub import notebook_login
#if not (Path.home()/'.huggingface'/'token').exists():
#token = os.environ.get("HUGGING_FACE_HUB_TOKEN")
token = "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"
import torch, logging
logging.disable(logging.WARNING)
torch.cuda.empty_cache()
torch.manual_seed(3407)
from torch import autocast
from contextlib import nullcontext
from diffusers import StableDiffusionPipeline
model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda" if torch.cuda.is_available() else "cpu"
context = autocast if device == "cuda" else nullcontext
pipe = StableDiffusionPipeline.from_pretrained(model_id,use_auth_token=token).to(device)
def infer(prompt,samples):
with context(device):
images = pipe(samples*[prompt], guidance_scale=7.5).images
return images
demo = gr.Blocks()
with demo:
text = gr.Textbox(lines=7,placeholder="Enter your prompt to generate a background image... something like - Photorealistic scenery of bookshelf in a room")
samples = gr.Slider(label="Number of Images", minimum=1, maximum=5, value=2, step=1)
btn = gr.Button("Generate images",variant="primary").style(
margin=False,
rounded=(False, True, True, False),
)
gallery = gr.Gallery(label="Generated images", show_label=True).style(grid=(1, 3), height="auto")
text.submit(infer, inputs=[text, samples], outputs=gallery)
btn.click(infer, inputs=[text, samples], outputs=gallery, show_progress=True, status_tracker=None)
demo.launch()
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