Spaces:
Runtime error
Runtime error
File size: 4,747 Bytes
f92c162 dcbae16 9e63d68 ce743f5 f92c162 9dcb09a dcbae16 9dcb09a ce743f5 f92c162 22231ac f92c162 ce743f5 f92c162 ce743f5 cf4f8fb ce743f5 f92c162 ce743f5 f92c162 ce743f5 f92c162 ce743f5 f92c162 ce743f5 f92c162 ce743f5 dcbae16 ce743f5 f92c162 |
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 |
import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
else:
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
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,
negative_prompt = negative_prompt,
guidance_scale = guidance_scale,
num_inference_steps = num_inference_steps,
width = width,
height = height,
generator = generator
).images[0]
return image
examples = [
["穿着黑色T恤衫，上面中文绿色大字写着“可图”", "image/test_ip.jpg", 0.5],
["一只可爱的小狗在奔跑", "image/test_ip2.png", 0.5]
]
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
css="""
#col-container {
margin: 0 auto;
max-width: 650px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Row():
gr.Markdown(f"""
# Kolors-IP-Adapter-Plus
Currently running on {power_device}.
""")
with gr.Row():
with gr.Column(elem_id="col-container"):
with gr.Row():
ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil")
with gr.Row():
ip_adapter_scale = gr.Slider(
label="Image influence scale",
info="Use 1 for creating variations",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.5,
)
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)
with gr.Accordion("Advanced Settings", open=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,
)
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=512,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=10,
maximum=50,
step=1,
value=25,
)
with gr.Column(elem_id="col-container"):
result = gr.Image(label="Result", show_label=False)
with gr.Row():
gr.Examples(
examples = examples,
inputs = [prompt, ip_adapter_image, ip_adapter_scale]
)
run_button.click(
fn = infer,
inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result]
)
demo.queue().launch() |