#!/usr/bin/env python
from __future__ import annotations
import os
import random
import gradio as gr
import numpy as np
import PIL.Image
import torch
from diffusers import DiffusionPipeline
DESCRIPTION = 'This space is an API service meant to be used by VideoChain and VideoQuest.\nWant to use this space for yourself? Please use the original code: [https://huggingface.co/spaces/hysts/SD-XL](https://huggingface.co/spaces/hysts/SD-XL)'
if not torch.cuda.is_available():
DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = int(os.getenv('MAX_IMAGE_SIZE', '1024'))
USE_TORCH_COMPILE = os.getenv('USE_TORCH_COMPILE') == '1'
ENABLE_CPU_OFFLOAD = os.getenv('ENABLE_CPU_OFFLOAD') == '1'
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():
pipe = DiffusionPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-base-1.0',
torch_dtype=torch.float16,
use_safetensors=True,
variant='fp16')
refiner = DiffusionPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-refiner-1.0',
torch_dtype=torch.float16,
use_safetensors=True,
variant='fp16')
if ENABLE_CPU_OFFLOAD:
pipe.enable_model_cpu_offload()
refiner.enable_model_cpu_offload()
else:
pipe.to(device)
refiner.to(device)
if USE_TORCH_COMPILE:
pipe.unet = torch.compile(pipe.unet,
mode='reduce-overhead',
fullgraph=True)
else:
pipe = None
refiner = 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 = '',
prompt_2: str = '',
negative_prompt_2: str = '',
use_negative_prompt: bool = False,
use_prompt_2: bool = False,
use_negative_prompt_2: 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 = 50,
num_inference_steps_refiner: int = 50,
apply_refiner: bool = False,
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
if not use_prompt_2:
prompt_2 = None # type: ignore
if not use_negative_prompt_2:
negative_prompt_2 = None # type: ignore
if not apply_refiner:
return pipe(prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
guidance_scale=guidance_scale_base,
num_inference_steps=num_inference_steps_base,
generator=generator,
output_type='pil').images[0]
else:
latents = pipe(prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
guidance_scale=guidance_scale_base,
num_inference_steps=num_inference_steps_base,
generator=generator,
output_type='latent').images
image = refiner(prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
guidance_scale=guidance_scale_refiner,
num_inference_steps=num_inference_steps_refiner,
image=latents,
generator=generator).images[0]
return image
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Box():
with gr.Row():
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,
)
run_button = gr.Button('Run', scale=0)
result = gr.Image(label='Result', show_label=False)
with gr.Accordion('Advanced options', open=False):
with gr.Row():
use_negative_prompt = gr.Checkbox(label='Use negative prompt',
value=False)
use_prompt_2 = gr.Checkbox(label='Use prompt 2', value=False)
use_negative_prompt_2 = gr.Checkbox(
label='Use negative prompt 2', value=False)
negative_prompt = gr.Text(
label='Negative prompt',
max_lines=1,
placeholder='Enter a negative prompt',
visible=False,
)
prompt_2 = gr.Text(
label='Prompt 2',
max_lines=1,
placeholder='Enter your prompt',
visible=False,
)
negative_prompt_2 = gr.Text(
label='Negative prompt 2',
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)
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)
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=50)
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=50)
use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt,
outputs=negative_prompt,
queue=False,
api_name=False,
)
use_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_prompt_2,
outputs=prompt_2,
queue=False,
api_name=False,
)
use_negative_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt_2,
outputs=negative_prompt_2,
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,
)
inputs = [
prompt,
negative_prompt,
prompt_2,
negative_prompt_2,
use_negative_prompt,
use_prompt_2,
use_negative_prompt_2,
seed,
width,
height,
guidance_scale_base,
guidance_scale_refiner,
num_inference_steps_base,
num_inference_steps_refiner,
apply_refiner,
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',
)
negative_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=False,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=inputs,
outputs=result,
api_name=False,
)
demo.queue(max_size=6).launch()