Spaces:
Sleeping
Sleeping
File size: 6,091 Bytes
631675c d8d52ea fc6b4b7 631675c cde0bea 5093d68 68be1e4 5093d68 fc6b4b7 631675c 5093d68 631675c 0b8ff32 6e1d375 631675c 0b8ff32 631675c d8d52ea 631675c 2221b2d 1c39bbc 5d454e2 1c39bbc 5ac140b 631675c d80b8c6 631675c d80b8c6 631675c 2bbeeda 631675c 2bbeeda 631675c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
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
@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 = [
"""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()
|