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Running
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A10G
Running
on
A10G
#!/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() | |