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import os | |
import random | |
import uuid | |
import gradio as gr | |
import numpy as np | |
from PIL import Image | |
import torch | |
from transformers import DiffusionModel, DiffusionImageProcessor | |
DESCRIPTION = """ | |
# Image Generator | |
""" | |
def save_image(img): | |
unique_name = str(uuid.uuid4()) + ".png" | |
img.save(unique_name) | |
return unique_name | |
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
return seed | |
MAX_SEED = np.iinfo(np.int32).max | |
if not torch.cuda.is_available(): | |
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>" | |
def generate_image(text): | |
model = DiffusionModel.from_pretrained("stability/stable-diffusion-xl-base-1.0") | |
processor = DiffusionImageProcessor.from_model(model) | |
inputs = processor(text, return_tensors="pt") | |
with torch.no_grad(): | |
result = processor.generate(**inputs) | |
return result[0] | |
def post_process_images(paths): | |
return [gr.outputs.Image(path) for path in paths] | |
def post_process_seed(seed): | |
return seed | |
examples = [ | |
"Newton and Einstein sitting together and thinking about gravity and space", | |
"an astronaut riding a horse in space", | |
"a cartoon of a boy playing with a tiger", | |
"neon holography crystal cat", | |
"a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone", | |
"a cute robot artist painting on an easel,concept art", | |
"a cat eating a piece of cheese", | |
] | |
css = ''' | |
.gradio-container{max-width: 560px !important} | |
h1{text-align:center} | |
footer { | |
visibility: hidden | |
} | |
''' | |
with gr.Blocks(css=css, theme="pseudolab/huggingface-korea-theme") as demo: | |
gr.Markdown(DESCRIPTION) | |
gr.DuplicateButton( | |
value="Duplicate Space for private use", | |
elem_id="duplicate-button", | |
visible=False, | |
) | |
with gr.Group(): | |
with gr.Row(): | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
container=False, | |
) | |
run_button = gr.Button("Run", scale=0) | |
result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False) | |
with gr.Accordion("Advanced options", open=False): | |
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) | |
negative_prompt = gr.Text( | |
label="Negative prompt", | |
lines=4, | |
max_lines=6, | |
value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""", | |
placeholder="Enter a negative prompt", | |
visible=True, | |
) | |
seed = gr.Slider( | |
label="Seed", | |
minimum=0, | |
maximum=MAX_SEED, | |
step=1, | |
value=0, | |
visible=True | |
) | |
randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
with gr.Row(visible=True): | |
width = gr.Slider( | |
label="Width", | |
minimum=512, | |
maximum=2048, | |
step=8, | |
value=1024, | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=512, | |
maximum=2048, | |
step=8, | |
value=1024, | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance Scale", | |
minimum=0.1, | |
maximum=20.0, | |
step=0.1, | |
value=6, | |
) | |
gr.Examples( | |
examples=examples, | |
inputs=prompt, | |
outputs=[result, seed], | |
fn=generate_image, | |
cache_examples=False, | |
output_hooks=[post_process_images, post_process_seed] | |
) | |
use_negative_prompt.change( | |
fn=lambda x: gr.update(visible=x), | |
inputs=use_negative_prompt, | |
outputs=negative_prompt, | |
api_name=False, | |
) | |
gr.on( | |
triggers=[ | |
prompt.submit, | |
negative_prompt.submit, | |
run_button.click, | |
], | |
fn=generate_image, | |
inputs=[ | |
prompt, | |
negative_prompt, | |
use_negative_prompt, | |
seed, | |
width, | |
height, | |
guidance_scale, | |
randomize_seed, | |
], | |
outputs=[result, seed], | |
api_name="run", | |
) | |
if __name__ == "__main__": | |
demo.queue(max_size=20).launch(show_api=False, debug=False) | |