Spaces:
Runtime error
Runtime error
File size: 4,843 Bytes
2b755c2 c453122 2b755c2 c453122 bb7edb9 c453122 bb7edb9 c453122 2b755c2 c628a76 2b755c2 3c50bd9 2b755c2 3c50bd9 2b755c2 3c50bd9 2b755c2 bb7edb9 2b755c2 c453122 2b755c2 c453122 2b755c2 c453122 2b755c2 |
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 |
#!/usr/bin/env python
import os
import random
import gradio as gr
import numpy as np
import PIL.Image
import torch
from model import ADAPTER_NAMES, Model
DESCRIPTION = "# T2I-Adapter-SDXL"
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
MAX_SEED = np.iinfo(np.int32).max
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
model = Model(ADAPTER_NAMES[0])
def run(
image: PIL.Image.Image,
prompt: str,
negative_prompt: str,
adapter_name: str,
num_inference_steps: int = 30,
guidance_scale: float = 5.0,
adapter_conditioning_scale: float = 1.0,
cond_tau: float = 1.0,
seed: int = 0,
apply_preprocess: bool = True,
progress=gr.Progress(track_tqdm=True),
) -> list[PIL.Image.Image]:
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,
cond_tau=cond_tau,
seed=seed,
apply_preprocess=apply_preprocess,
)
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_id="duplicate-button",
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
)
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", 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",
value="anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
)
num_inference_steps = gr.Slider(
label="Number of steps",
minimum=1,
maximum=Model.MAX_NUM_INFERENCE_STEPS,
step=1,
value=30,
)
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,
)
cond_tau = gr.Slider(
label="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)
inputs = [
image,
prompt,
negative_prompt,
adapter_name,
num_inference_steps,
guidance_scale,
adapter_conditioning_scale,
cond_tau,
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",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()
|