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#no oom?
import gradio as gr
import numpy as np
import random
# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
targets = {"pussy", "boobs", "breasts", "vagina", "penis", "sex", "oral", "anal", "butt", "ass"}
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "John6666/spicy-realism-nsfw-mix-v30-sdxl" # Replace to the model you would like to use
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
print(pipe)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
from PIL import Image
def safe_infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_steps):
clean = ''.join(c for c in prompt.lower() if c.isalnum() or c.isspace())
if any(word in clean for word in targets):
print("Found at least one banned word!")
blank = Image.new("RGB", (width, height), (0, 0, 0))
return blank, seed
return infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_steps)
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image Gradio Template")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
value = "(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn, (breasts:3), (nipple:3), (nipples:3), (boobs:3), (butt:3), (ass:3), (butthole:3), (sex:3), (fetish:3), (pussy:3), (vagina:3), (porn:3), (hentai:3), (explicit:3)",
visible=False,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=64,
maximum=MAX_IMAGE_SIZE,
step=32,
value=384, # Replace with defaults that work for your model
)
height = gr.Slider(
label="Height",
minimum=64,
maximum=MAX_IMAGE_SIZE,
step=32,
value=384, # Replace with defaults that work for your model
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=20.0,
step=0.1,
value=3.6, # Replace with defaults that work for your model
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=80,
step=1,
value=15, # Replace with defaults that work for your model
)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=safe_infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch() |