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Running
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A10G
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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 MAX_SEED, randomize_seed_fn, styles, style_names, apply_style
SKETCH_ADAPTER_NAME = "TencentARC/t2i-adapter-sketch-sdxl-1.0"
default_style_name = "Photographic"
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,
cond_tau: 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_ADAPTER_NAME,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
adapter_conditioning_scale=adapter_conditioning_scale,
cond_tau=cond_tau,
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(choices=style_names, value=default_style_name, label="Style")
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,
)
cond_tau = gr.Slider(
label="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,
cond_tau,
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_ADAPTER_NAME)
demo = create_demo(model)
demo.queue(max_size=20).launch()
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