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import gradio as gr | |
import numpy as np | |
import random | |
import spaces #[uncomment to use ZeroGPU] | |
from diffusers import DiffusionPipeline , DPMSolverMultistepScheduler | |
import torch | |
from huggingface_hub import login | |
import os | |
a=os.getenv('key_for_man_asshole') | |
login(token=a ) | |
use_karras_sigmas=True | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model_repo_id = "black-forest-labs/FLUX.1-dev" # 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) | |
pipe.load_lora_weights("artificiallover0/man_asshole") | |
pipe.fuse_lora() | |
pipe = pipe.to(device) | |
print(pipe.scheduler.compatibles) | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 1024 | |
#[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 = [ | |
"""a naked hairy man kneeling on all fours with his dirty feet facing the viewer | |
dirty feet, big hands holding his butt | |
he is crowd surfing | |
the crowd has their hands on him passing him forward | |
he legs rest on their shoulders | |
people below him are cheering | |
big erect penis""", | |
"""Photograph of a plus-sized ginger man kneeling with his butt facing the viewer. he has a large belly | |
he has a hairy butt, big low hanging testicles and dirty unwashed bare feet | |
high quality, fashion photography | |
he is eating corn from a metal trough | |
in a barn | |
he is eating from the metal bin like a pig""", | |
"""Photograph of a huge muscle man kneeling with his butt facing the viewer. | |
he has a hairy butt, big low hanging testicles and dirty unwashed bare feet | |
high quality, fashion photography | |
he is laying face down on a red leather fainting couch""", | |
""" | |
8k resolution, ultra detailed, 1 chico , Alone, huge muscle man | |
, man in mechanic naked, ultra detailed piernas gorditas y peludas, futurist, | |
with his ass in a sexual pose, asshole , military hat, looking over shoulder, | |
bending down, back towards viewer, big body, fit body, not fat, gigantic buttocks, | |
looking over shoulder, bending down, | |
back towards viewer whole body, sexy round hairy ass butt, thigh show, Super detailed""", | |
"""a chunky naked plumber kneeling under a sink holding a wrench and fixing a metal pipe under the sink. | |
he looking over shoulder, bending down, back towards viewer, big body, | |
His exposed anus and big testicles are the focus of the image, rear view, | |
facing away from viewer, ass in viewers face, greasy, (huge:1.9) muscle man , | |
(huge:1.9) hairy back, leather boots, plumbers crack, ((dirty sweatpants pulled down:1.9)) view from below""", | |
] | |
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, | |
lines=5, | |
) | |
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", | |
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=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, # Replace with defaults that work for your model | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, # Replace with defaults that work for your model | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance scale", | |
minimum=0.0, | |
maximum=10.0, | |
step=0.1, | |
value=7.0, # Replace with defaults that work for your model | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=1, | |
maximum=50, | |
step=1, | |
value=30, # Replace with defaults that work for your model | |
) | |
gr.Examples(examples=examples, inputs=[prompt]) | |
gr.on( | |
triggers=[run_button.click, prompt.submit], | |
fn=infer, | |
inputs=[ | |
prompt, | |
negative_prompt, | |
seed, | |
randomize_seed, | |
width, | |
height, | |
guidance_scale, | |
num_inference_steps, | |
], | |
outputs=[result, seed], | |
) | |
if __name__ == "__main__": | |
demo.launch() | |