Spaces:
Sleeping
Sleeping
File size: 4,780 Bytes
6e5e1d5 13da042 6e5e1d5 88f16e0 6e5e1d5 1145832 6e5e1d5 f807c45 6e5e1d5 09fc56c 13da042 6e5e1d5 88f16e0 6e5e1d5 08f603c 6e5e1d5 08f603c 6e5e1d5 c186777 6e5e1d5 4993a5f 6e5e1d5 88f16e0 6e5e1d5 88f16e0 6e5e1d5 4993a5f 6e5e1d5 0691ff2 |
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 |
import gradio as gr
import numpy as np
import random
import spaces
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "tensorart/stable-diffusion-3.5-large-TurboX"
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.bfloat16
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype, use_fast=True)
pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_repo_id, subfolder="scheduler", shift=5)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU(duration=65)
def infer(
prompt,
negative_prompt="",
seed=42,
randomize_seed=False,
width=1024,
height=1024,
guidance_scale=1.5,
num_inference_steps=8,
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
css = """
body {
background: linear-gradient(135deg, #f9e2e6 0%, #e8f3fc 50%, #e2f9f2 100%);
background-attachment: fixed;
min-height: 100vh;
}
#col-container {
margin: 0 auto;
max-width: 640px;
background-color: rgba(255, 255, 255, 0.85);
border-radius: 16px;
box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
padding: 24px;
backdrop-filter: blur(10px);
}
.gradio-container {
background: transparent !important;
}
.gr-button-primary {
background: linear-gradient(90deg, #6b9dfc, #8c6bfc) !important;
border: none !important;
transition: all 0.3s ease;
}
.gr-button-primary:hover {
transform: translateY(-2px);
box-shadow: 0 5px 15px rgba(108, 99, 255, 0.3);
}
.gr-form {
border-radius: 12px;
background-color: rgba(255, 255, 255, 0.7);
}
.gr-accordion {
border-radius: 12px;
overflow: hidden;
}
h1 {
background: linear-gradient(90deg, #6b9dfc, #8c6bfc);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-weight: 800;
}
"""
with gr.Blocks(theme="apriel", css=css) as demo:
with gr.Column(elem_id="col-container"):
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt copied from the previous website",
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",
)
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=512,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=7.5,
step=0.1,
value=1.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=8,
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
"cartoon styled korean" + prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch(mcp_server=True) |