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#!/usr/bin/env python | |
import os | |
import random | |
import gradio as gr | |
import numpy as np | |
import PIL.Image | |
import torch | |
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = int(os.getenv('MAX_IMAGE_SIZE', '1024')) | |
SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret') | |
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') | |
if torch.cuda.is_available(): | |
unet = UNet2DConditionModel.from_pretrained( | |
"latent-consistency/lcm-ssd-1b", | |
torch_dtype=torch.float16, | |
variant="fp16" | |
) | |
pipe = DiffusionPipeline.from_pretrained( | |
"segmind/SSD-1B", | |
unet=unet, | |
torch_dtype=torch.float16, | |
variant="fp16" | |
) | |
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) | |
pipe.to(device) | |
else: | |
pipe = None | |
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
return seed | |
def generate(prompt: str, | |
negative_prompt: str = '', | |
use_negative_prompt: bool = False, | |
seed: int = 0, | |
width: int = 1024, | |
height: int = 1024, | |
guidance_scale: float = 1.0, | |
num_inference_steps: int = 6, | |
secret_token: str = '') -> PIL.Image.Image: | |
if secret_token != SECRET_TOKEN: | |
raise gr.Error( | |
f'Invalid secret token. Please fork the original space if you want to use it for yourself.') | |
generator = torch.Generator().manual_seed(seed) | |
if not use_negative_prompt: | |
negative_prompt = None # type: ignore | |
return pipe(prompt=prompt, | |
negative_prompt=negative_prompt, | |
width=width, | |
height=height, | |
guidance_scale=guidance_scale, | |
num_inference_steps=num_inference_steps, | |
generator=generator, | |
output_type='pil').images[0] | |
with gr.Blocks() as demo: | |
gr.HTML(""" | |
<div style="z-index: 100; position: fixed; top: 0px; right: 0px; left: 0px; bottom: 0px; width: 100%; height: 100%; background: white; display: flex; align-items: center; justify-content: center; color: black;"> | |
<div style="text-align: center; color: black;"> | |
<p style="color: black;">This space is a REST API to programmatically generate images using LCM-SSD-1B.</p> | |
<p style="color: black;">It is not meant to be directly used through a user interface, but using code and an access key.</p> | |
</div> | |
</div>""") | |
secret_token = gr.Text( | |
label='Secret Token', | |
max_lines=1, | |
placeholder='Enter your secret token', | |
) | |
prompt = gr.Text( | |
label='Prompt', | |
show_label=False, | |
max_lines=1, | |
placeholder='Enter your prompt', | |
container=False, | |
) | |
result = gr.Image(label='Result', show_label=False) | |
use_negative_prompt = gr.Checkbox(label='Use negative prompt', value=False) | |
negative_prompt = gr.Text( | |
label='Negative prompt', | |
max_lines=1, | |
placeholder='Enter a negative prompt', | |
visible=False, | |
) | |
seed = gr.Slider(label='Seed', | |
minimum=0, | |
maximum=MAX_SEED, | |
step=1, | |
value=0) | |
randomize_seed = gr.Checkbox(label='Randomize seed', value=True) | |
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, | |
) | |
guidance_scale = gr.Slider( | |
label='Guidance scale', | |
minimum=1, | |
maximum=20, | |
step=0.1, | |
value=1.0) | |
num_inference_steps = gr.Slider( | |
label='Number of inference steps', | |
minimum=2, | |
maximum=40, | |
step=1, | |
value=6) | |
use_negative_prompt.change( | |
fn=lambda x: gr.update(visible=x), | |
inputs=use_negative_prompt, | |
outputs=negative_prompt, | |
queue=False, | |
api_name=False, | |
) | |
inputs = [ | |
prompt, | |
negative_prompt, | |
use_negative_prompt, | |
seed, | |
width, | |
height, | |
guidance_scale, | |
num_inference_steps, | |
secret_token, | |
] | |
prompt.submit( | |
fn=randomize_seed_fn, | |
inputs=[seed, randomize_seed], | |
outputs=seed, | |
queue=False, | |
api_name=False, | |
).then( | |
fn=generate, | |
inputs=inputs, | |
outputs=result, | |
api_name='run', | |
) | |
demo.queue(max_size=6).launch() |