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Running
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A10G
Running
on
A10G
#!/usr/bin/env python | |
import gradio as gr | |
import PIL.Image | |
from model import ADAPTER_NAMES, Model | |
from utils import MAX_SEED, randomize_seed_fn | |
style_list = [ | |
{ | |
"name": "Cinematic", | |
"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", | |
"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", | |
}, | |
{ | |
"name": "3D Model", | |
"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", | |
"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", | |
}, | |
{ | |
"name": "Anime", | |
"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", | |
"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", | |
}, | |
{ | |
"name": "Digital Art", | |
"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", | |
"negative_prompt": "photo, photorealistic, realism, ugly", | |
}, | |
{ | |
"name": "Photographic", | |
"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", | |
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", | |
}, | |
{ | |
"name": "Pixel art", | |
"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", | |
"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", | |
}, | |
{ | |
"name": "Fantasy art", | |
"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", | |
"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", | |
}, | |
] | |
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} | |
default_style_name = "Photographic" | |
default_style = styles[default_style_name] | |
style_names = list(styles.keys()) | |
def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]: | |
p, n = styles.get(style_name, default_style) | |
return p.replace("{prompt}", positive), n + negative | |
def create_demo(model: Model) -> gr.Blocks: | |
def run( | |
image: PIL.Image.Image, | |
prompt: str, | |
negative_prompt: str, | |
style_name: str = default_style_name, | |
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]: | |
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, | |
cond_tau=cond_tau, | |
seed=seed, | |
apply_preprocess=apply_preprocess, | |
) | |
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", 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", | |
) | |
style = gr.Dropdown(choices=style_names, value=default_style_name, label="Style") | |
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, | |
style, | |
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", | |
) | |
return demo | |
if __name__ == "__main__": | |
model = Model(ADAPTER_NAMES[0]) | |
demo = create_demo(model) | |
demo.queue(max_size=20).launch() | |