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import os | |
import random | |
import gradio as gr | |
from PIL import Image | |
from gradio_client import Client | |
import numpy as np | |
DESCRIPTION = "# SDXL Pixelart" | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024")) | |
ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1" | |
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
return seed | |
def pixelate(input_file_path, pixel_size): | |
image = Image.open(input_file_path) | |
image = image.resize( | |
(image.size[0] // pixel_size, image.size[1] // pixel_size), | |
Image.NEAREST | |
) | |
image = image.resize( | |
(image.size[0] * pixel_size, image.size[1] * pixel_size), | |
Image.NEAREST | |
) | |
return image | |
def generate( | |
prompt: str, | |
additional_prompt: str = "", | |
negative_prompt: str = "", | |
use_negative_prompt: bool = False, | |
seed: int = 0, | |
width: int = 1024, | |
height: int = 1024, | |
guidance_scale_base: float = 5.0, | |
guidance_scale_refiner: float = 5.0, | |
num_inference_steps_base: int = 25, | |
num_inference_steps_refiner: int = 25, | |
apply_refiner: bool = False, | |
pixel_size: int = 16 | |
): | |
if additional_prompt != "": | |
additional_prompt += ", " | |
client = Client("hysts/SDXL") | |
result = client.predict( | |
prompt=additional_prompt+prompt, | |
negative_prompt=negative_prompt, | |
prompt_2="", | |
negative_prompt_2="", | |
use_negative_prompt=use_negative_prompt, | |
use_prompt_2=False, | |
use_negative_prompt_2=False, | |
seed=seed, | |
width=width, | |
height=height, | |
guidance_scale_base=guidance_scale_base, | |
guidance_scale_refiner=guidance_scale_refiner, | |
num_inference_steps_base=num_inference_steps_base, | |
num_inference_steps_refiner=num_inference_steps_refiner, | |
apply_refiner=apply_refiner, | |
api_name="/run", | |
) | |
image = pixelate(result, pixel_size) | |
return image | |
examples = [ | |
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8-bit", | |
"An astronaut riding a green horse, pixel art", | |
"City of Tokyo at night, retro, pixel art", | |
] | |
with gr.Blocks(css="style.css") as demo: | |
gr.Markdown(DESCRIPTION) | |
with gr.Group(): | |
with gr.Row(): | |
prompt = gr.Textbox( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
container=False, | |
) | |
run_button = gr.Button("Run", scale=0) | |
result = gr.Image(label="Result", show_label=False) | |
with gr.Accordion("Advanced options", open=False): | |
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) | |
negative_prompt = gr.Textbox( | |
label="Negative prompt", | |
max_lines=1, | |
placeholder="Enter a negative prompt", | |
visible=True, | |
value="(deformed eyes, nose, ears, nose), bad anatomy, ugly", | |
) | |
additional_prompt = gr.Textbox( | |
label="Additional prompt", | |
max_lines=1, | |
placeholder="Enter an additional prompt", | |
visible=True, | |
value="((pixelart)), ((retro illustration)), bit games, 8-bit illustration, pixelated", | |
) | |
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.Row(): | |
width = gr.Slider( | |
label="Width", | |
minimum=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, | |
) | |
apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER) | |
with gr.Row(): | |
guidance_scale_base = gr.Slider( | |
label="Guidance scale for base", | |
minimum=1, | |
maximum=20, | |
step=0.1, | |
value=5.0, | |
) | |
num_inference_steps_base = gr.Slider( | |
label="Number of inference steps for base", | |
minimum=10, | |
maximum=100, | |
step=1, | |
value=25, | |
) | |
with gr.Row(visible=False) as refiner_params: | |
guidance_scale_refiner = gr.Slider( | |
label="Guidance scale for refiner", | |
minimum=1, | |
maximum=20, | |
step=0.1, | |
value=5.0, | |
) | |
num_inference_steps_refiner = gr.Slider( | |
label="Number of inference steps for refiner", | |
minimum=10, | |
maximum=100, | |
step=1, | |
value=25, | |
) | |
pixel_size = gr.Slider( | |
label="Pixel size", | |
minimum=1, | |
maximum=64, | |
step=1, | |
value=16, | |
) | |
gr.Examples( | |
examples=examples, | |
inputs=prompt, | |
outputs=result, | |
fn=generate, | |
) | |
use_negative_prompt.change( | |
fn=lambda x: gr.update(visible=x), | |
inputs=use_negative_prompt, | |
outputs=negative_prompt, | |
queue=False, | |
api_name=False, | |
) | |
apply_refiner.change( | |
fn=lambda x: gr.update(visible=x), | |
inputs=apply_refiner, | |
outputs=refiner_params, | |
queue=False, | |
api_name=False, | |
) | |
gr.on( | |
triggers=[ | |
prompt.submit, | |
additional_prompt.submit, | |
negative_prompt.submit, | |
run_button.click, | |
], | |
fn=randomize_seed_fn, | |
inputs=[seed, randomize_seed], | |
outputs=seed, | |
queue=False, | |
api_name=False, | |
).then( | |
fn=generate, | |
inputs=[ | |
prompt, | |
additional_prompt, | |
negative_prompt, | |
use_negative_prompt, | |
seed, | |
width, | |
height, | |
guidance_scale_base, | |
guidance_scale_refiner, | |
num_inference_steps_base, | |
num_inference_steps_refiner, | |
apply_refiner, | |
pixel_size | |
], | |
outputs=result, | |
api_name="run", | |
) | |
if __name__ == "__main__": | |
demo.queue(max_size=20).launch() | |