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Running
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Zero
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#!/usr/bin/env python
import os
import gradio as gr
import numpy as np
import PIL
import base64
import io
import torch
# SSD-1B
#from diffusers import LCMScheduler, AutoPipelineForText2Image
# SDXL
from diffusers import UNet2DConditionModel, DiffusionPipeline, 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():
#pipe = AutoPipelineForText2Image.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float16, variant="fp16")
#pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
#pipe.to("cuda")
# load and fuse
#pipe.load_lora_weights("latent-consistency/lcm-lora-ssd-1b")
#pipe.fuse_lora()
unet = UNet2DConditionModel.from_pretrained("latent-consistency/lcm-sdxl", torch_dtype=torch.float16, variant="fp16")
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')
else:
pipe = None
def generate(prompt: str,
negative_prompt: str = '',
seed: int = 0,
width: int = 1024,
height: int = 1024,
guidance_scale: float = 0.0,
num_inference_steps: int = 4,
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)
image = 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]
return image
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 LoRA 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)
negative_prompt = gr.Text(
label='Negative prompt',
max_lines=1,
placeholder='Enter a negative prompt',
visible=True,
)
seed = gr.Slider(label='Seed',
minimum=0,
maximum=MAX_SEED,
step=1,
value=0)
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=0,
maximum=2,
step=0.1,
value=0.0)
num_inference_steps = gr.Slider(
label='Number of inference steps',
minimum=1,
maximum=8,
step=1,
value=4)
inputs = [
prompt,
negative_prompt,
seed,
width,
height,
guidance_scale,
num_inference_steps,
secret_token,
]
prompt.submit(
fn=generate,
inputs=inputs,
outputs=result,
api_name='run',
)
demo.queue(max_size=32).launch() |