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#!/usr/bin/env python | |
import gradio as gr | |
import PIL.Image | |
import torch | |
import torchvision.transforms.functional as TF | |
from model import Model | |
from utils import ( | |
DEFAULT_STYLE_NAME, | |
MAX_SEED, | |
STYLE_NAMES, | |
apply_style, | |
randomize_seed_fn, | |
) | |
def create_demo(model: Model) -> gr.Blocks: | |
def run( | |
image: PIL.Image.Image, | |
prompt: str, | |
negative_prompt: str, | |
style_name: str = DEFAULT_STYLE_NAME, | |
num_steps: int = 25, | |
guidance_scale: float = 5, | |
adapter_conditioning_scale: float = 0.8, | |
adapter_conditioning_factor: float = 0.8, | |
seed: int = 0, | |
progress=gr.Progress(track_tqdm=True), | |
) -> PIL.Image.Image: | |
image = image.convert("RGB") | |
image = TF.to_tensor(image) > 0.5 | |
image = TF.to_pil_image(image.to(torch.float32)) | |
prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) | |
return model.run( | |
image=image, | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
adapter_name="sketch", | |
num_inference_steps=num_steps, | |
guidance_scale=guidance_scale, | |
adapter_conditioning_scale=adapter_conditioning_scale, | |
adapter_conditioning_factor=adapter_conditioning_factor, | |
seed=seed, | |
apply_preprocess=False, | |
)[1] | |
with gr.Blocks() as demo: | |
with gr.Row(): | |
with gr.Column(): | |
with gr.Group(): | |
image = gr.Image( | |
source="canvas", | |
tool="sketch", | |
type="pil", | |
image_mode="L", | |
invert_colors=True, | |
shape=(1024, 1024), | |
brush_radius=4, | |
height=600, | |
) | |
prompt = gr.Textbox(label="Prompt") | |
run_button = gr.Button("Run") | |
with gr.Accordion("Advanced options", open=False): | |
style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) | |
negative_prompt = gr.Textbox(label="Negative prompt") | |
num_steps = gr.Slider( | |
label="Number of steps", | |
minimum=1, | |
maximum=50, | |
step=1, | |
value=25, | |
) | |
guidance_scale = gr.Slider( | |
label="Guidance scale", | |
minimum=0.1, | |
maximum=10.0, | |
step=0.1, | |
value=5, | |
) | |
adapter_conditioning_scale = gr.Slider( | |
label="Adapter conditioning scale", | |
minimum=0.5, | |
maximum=1, | |
step=0.1, | |
value=0.8, | |
) | |
adapter_conditioning_factor = gr.Slider( | |
label="Adapter conditioning factor", | |
info="Fraction of timesteps for which adapter should be applied", | |
minimum=0.5, | |
maximum=1, | |
step=0.1, | |
value=0.8, | |
) | |
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.Image(label="Result", height=600) | |
inputs = [ | |
image, | |
prompt, | |
negative_prompt, | |
style, | |
num_steps, | |
guidance_scale, | |
adapter_conditioning_scale, | |
adapter_conditioning_factor, | |
seed, | |
] | |
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=False, | |
) | |
return demo | |
if __name__ == "__main__": | |
model = Model("sketch") | |
demo = create_demo(model) | |
demo.queue(max_size=20).launch() | |