Spaces:
Sleeping
Sleeping
File size: 7,376 Bytes
8f873ac 130100d 8f873ac 130100d 8f873ac 3494400 74ca97e 130100d 8f873ac 3494400 8f873ac 376f4dc 8f873ac 74ca97e 3494400 8f873ac 376f4dc 8f873ac 130100d 2123cc0 130100d 2123cc0 130100d 2483fe6 2123cc0 130100d 2483fe6 2123cc0 130100d 2483fe6 2123cc0 130100d 2483fe6 2123cc0 130100d 2483fe6 2123cc0 130100d 8f873ac 2483fe6 8f873ac 246c984 8f873ac 5df7af3 8f873ac 376f4dc 8f873ac 376f4dc 8f873ac 130100d 2123cc0 130100d 8f873ac a28ba9d 8f873ac 376f4dc 8f873ac |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 |
#!/usr/bin/env python
import os
import gradio as gr
import PIL.Image
from diffusers.utils import load_image
from model import ADAPTER_NAMES, Model
from utils import (
DEFAULT_STYLE_NAME,
MAX_SEED,
STYLE_NAMES,
apply_style,
randomize_seed_fn,
)
CACHE_EXAMPLES = os.environ.get("CACHE_EXAMPLES") == "1"
def create_demo(model: Model) -> gr.Blocks:
def run(
image: PIL.Image.Image,
prompt: str,
negative_prompt: str,
adapter_name: str,
style_name: str = DEFAULT_STYLE_NAME,
num_inference_steps: int = 30,
guidance_scale: float = 5.0,
adapter_conditioning_scale: float = 1.0,
adapter_conditioning_factor: float = 1.0,
seed: int = 0,
apply_preprocess: bool = True,
progress=gr.Progress(track_tqdm=True),
) -> list[PIL.Image.Image]:
prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
return model.run(
image=image,
prompt=prompt,
negative_prompt=negative_prompt,
adapter_name=adapter_name,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
adapter_conditioning_scale=adapter_conditioning_scale,
adapter_conditioning_factor=adapter_conditioning_factor,
seed=seed,
apply_preprocess=apply_preprocess,
)
def process_example(
image_url: str,
prompt: str,
adapter_name: str,
guidance_scale: float,
adapter_conditioning_scale: float,
seed: int,
apply_preprocess: bool,
) -> list[PIL.Image.Image]:
image = load_image(image_url)
return run(
image=image,
prompt=prompt,
negative_prompt="",
adapter_name=adapter_name,
guidance_scale=guidance_scale,
adapter_conditioning_scale=adapter_conditioning_scale,
seed=seed,
apply_preprocess=apply_preprocess,
)
examples = [
[
"assets/org_canny.jpg",
"Mystical fairy in real, magic, 4k picture, high quality",
"canny",
5.0,
1.0,
0,
True,
],
[
"assets/org_sketch.png",
"a robot, mount fuji in the background, 4k photo, highly detailed",
"sketch",
5.0,
1.0,
0,
True,
],
[
"assets/org_lin.jpg",
"Ice dragon roar, 4k photo",
"lineart",
7.5,
0.8,
0,
True,
],
[
"assets/org_mid.jpg",
"A photo of a room, 4k photo, highly detailed",
"depth-midas",
5.0,
1.0,
0,
True,
],
[
"assets/org_zoe.jpg",
"A photo of a orchid, 4k photo, highly detailed",
"depth-zoe",
5.0,
1.0,
0,
True,
],
]
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
with gr.Group():
image = gr.Image(label="Input image", type="pil", height=600)
prompt = gr.Textbox(label="Prompt")
adapter_name = gr.Dropdown(label="Adapter name", choices=ADAPTER_NAMES, value=ADAPTER_NAMES[0])
run_button = gr.Button("Run")
with gr.Accordion("Advanced options", open=False):
apply_preprocess = gr.Checkbox(label="Apply preprocess", value=True)
negative_prompt = gr.Textbox(label="Negative prompt")
style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
num_inference_steps = gr.Slider(
label="Number of steps",
minimum=1,
maximum=Model.MAX_NUM_INFERENCE_STEPS,
step=1,
value=25,
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.1,
maximum=30.0,
step=0.1,
value=5.0,
)
adapter_conditioning_scale = gr.Slider(
label="Adapter conditioning scale",
minimum=0.5,
maximum=1,
step=0.1,
value=1.0,
)
adapter_conditioning_factor = gr.Slider(
label="Adapter conditioning factor",
info="Fraction of timesteps for which adapter should be applied",
minimum=0.5,
maximum=1.0,
step=0.1,
value=1.0,
)
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.Column():
result = gr.Gallery(label="Result", columns=2, height=600, object_fit="scale-down", show_label=False)
gr.Examples(
examples=examples,
inputs=[
image,
prompt,
adapter_name,
guidance_scale,
adapter_conditioning_scale,
seed,
apply_preprocess,
],
outputs=result,
fn=process_example,
cache_examples=CACHE_EXAMPLES,
)
inputs = [
image,
prompt,
negative_prompt,
adapter_name,
style,
num_inference_steps,
guidance_scale,
adapter_conditioning_scale,
adapter_conditioning_factor,
seed,
apply_preprocess,
]
prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=run,
inputs=inputs,
outputs=result,
api_name=False,
)
negative_prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=run,
inputs=inputs,
outputs=result,
api_name=False,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=run,
inputs=inputs,
outputs=result,
api_name="run",
)
return demo
if __name__ == "__main__":
model = Model(ADAPTER_NAMES[0])
demo = create_demo(model)
demo.queue(max_size=20).launch()
|