Spaces:
Paused
Paused
File size: 5,024 Bytes
65fd06d 7db3ca3 65fd06d 7db3ca3 65fd06d 7db3ca3 65fd06d 7db3ca3 65fd06d 7db3ca3 65fd06d 7f11b82 65fd06d 7db3ca3 9269fd4 65fd06d 7db3ca3 65fd06d 108abb9 16c8f31 9269fd4 65fd06d 9269fd4 108abb9 9269fd4 65fd06d 8790f79 65fd06d 7db3ca3 65fd06d 8790f79 65fd06d 9269fd4 8790f79 7db3ca3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
#!/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'))
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) -> PIL.Image.Image:
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:
with gr.Box():
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.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)
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)
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,
)
with gr.Row():
guidance_scale = gr.Slider(
label='Guidance scale',
minimum=1,
maximum=20,
step=0.1,
value=5.0)
num_inference_steps = gr.Slider(
label='Number of inference steps',
minimum=2,
maximum=50,
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,
]
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() |