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#!/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",
},
{
"name": "Neonpunk",
"prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured"
},
{
"name": "Manga",
"prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style"
},
]
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,
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,
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="",
)
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,
adapter_name,
style,
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()
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