File size: 4,076 Bytes
bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 bb4a0a3 83b3367 |
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 |
import gradio as gr
import numpy as np
import random
#from diffusers import DiffusionPipeline
from diffusers import StableDiffusionXLPipeline
import torch
import spaces
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1216
#pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe = StableDiffusionXLPipeline.from_pretrained(
#"yodayo-ai/kivotos-xl-2.0",
"Laxhar/noobai-XL-1.0",
torch_dtype=torch.float16,
use_safetensors=True,
custom_pipeline="lpw_stable_diffusion_xl",
add_watermarker=False,
variant="fp16"
)
pipe.to('cuda')
prompt = "1girl, solo, upper body, v, smile, looking at viewer, outdoors, night, masterpiece, best quality, very aesthetic, absurdres"
negative_prompt = "nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt = prompt+", masterpiece, best quality, very aesthetic, absurdres",
negative_prompt = negative_prompt,
guidance_scale = guidance_scale,
num_inference_steps = num_inference_steps,
width = width,
height = height,
generator = generator
).images[0]
return image
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Text-to-Image Demo
using [noobai XL 1.0](https://huggingface.co/Laxhar/noobai-XL-1.0)
""")
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 Settings", open=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=832,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1216,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=20.0,
step=0.1,
value=7,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=28,
step=1,
value=28,
)
run_button.click(
fn = infer,
inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result]
)
demo.queue().launch()
|