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import gradio as gr
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
from datasets import load_dataset
from PIL import Image
import re
import os
auth_token = os.getenv("auth_token")
model_id = "CompVis/stable-diffusion-v1-4"
device = "cpu"
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=auth_token, revision="fp16", torch_dtype=torch.float16)
pipe = pipe.to(device)
def infer(prompt, samples, steps, scale, seed):
generator = torch.Generator(device=device).manual_seed(seed)
images_list = pipe(
[prompt] * samples,
num_inference_steps=steps,
guidance_scale=scale,
generator=generator,
)
images = []
safe_image = Image.open(r"unsafe.png")
for i, image in enumerate(images_list["sample"]):
if(images_list["nsfw_content_detected"][i]):
images.append(safe_image)
else:
images.append(image)
return images
block = gr.Blocks()
with block:
with gr.Group():
with gr.Box():
with gr.Row().style(mobile_collapse=False, equal_height=True):
text = gr.Textbox(
label="Enter your prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
btn = gr.Button("Generate image").style(
margin=False,
rounded=(False, True, True, False),
)
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
).style(grid=[2], height="auto")
advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
with gr.Row(elem_id="advanced-options"):
samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
scale = gr.Slider(
label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=2147483647,
step=1,
randomize=True,
)
text.submit(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
btn.click(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
advanced_button.click(
None,
[],
text,
)
block.launch() |