Spaces:
Sleeping
Sleeping
File size: 4,978 Bytes
6d56d5a 6734637 6d56d5a 6734637 6d56d5a f6e532b 6734637 6d56d5a 6734637 f6e532b 6734637 f6e532b 6d56d5a 6734637 f6e532b 6734637 f6e532b 6734637 f6e532b 6734637 f6e532b 6734637 f6e532b 6734637 f6e532b 6734637 f6e532b 6734637 f6e532b 6734637 f6e532b 6734637 f6e532b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
import random
import gradio as gr
import numpy as np
import torch
# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
# model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
model_repo_id = "CompVis/stable-diffusion-v1-4"
model_dropdown = ['stabilityai/sdxl-turbo', 'CompVis/stable-diffusion-v1-4' ]
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
# pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
prompt,
negative_prompt,
randomize_seed,
width,
height,
model_repo_id=model_repo_id,
seed=42,
guidance_scale=7,
num_inference_steps=20,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)
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, seed
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image SemaSci Template")
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, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
# model_repo_id = gr.Text(
# label="Model Id",
# max_lines=1,
# placeholder="Choose model",
# visible=True,
# value=model_repo_id,
# )
model_repo_id = gr.Dropdown(
label="Model Id",
choices=model_dropdown,
info="Choose model",
visible=True,
allow_custom_value=True,
value=model_repo_id,
)
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=42,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024, # Replace with defaults that work for your model
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024, # Replace with defaults that work for your model
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.0, # Replace with defaults that work for your model
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=20, # Replace with defaults that work for your model
)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
randomize_seed,
width,
height,
model_repo_id,
seed,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()
|