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Runtime error
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import gradio as gr
import torch
from diffusers import StableDiffusionPipeline # type: ignore
from PIL import Image
import os
auth_token = os.getenv("HF_TOKEN")
if not auth_token:
print(
"ERROR: No Hugging Face access token found.\n"
"Please define an environment variable 'auth_token' before running.\n"
"Example:\n"
" export HF_TOKEN=XXXXXXXX\n"
)
model_id = "CompVis/stable-diffusion-v1-4"
device = "cpu"
pipe = StableDiffusionPipeline.from_pretrained(
model_id, token=auth_token, variant="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( # type: ignore
[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"]): # type: ignore
if images_list["nsfw_content_detected"][i]: # type: ignore
images.append(safe_image)
else:
images.append(image)
return images
block = gr.Blocks()
with block:
with gr.Group():
with gr.Row():
text = gr.Textbox(
label="Enter your prompt",
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
btn = gr.Button("Generate image")
gallery = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery",
columns=[2],
)
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,
)
gr.on(
[text.submit, btn.click],
infer,
inputs=[text, samples, steps, scale, seed],
outputs=gallery,
)
advanced_button.click(
None,
[],
text,
)
block.launch()
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