Spaces:
Sleeping
Sleeping
File size: 8,858 Bytes
6af7294 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 |
import gradio as gr
import torch
from diffusers import StableDiffusionPipeline
from .utils.schedulers import SCHEDULER_LIST, get_scheduler_list
from .utils.prompt2prompt import generate
from .utils.device import get_device
from .download import get_share_js, community_icon_html, loading_icon_html, CSS
#--- create a download button that takes the output image from gradio and downloads it
TEXT2IMG_MODEL_LIST = {
"OpenJourney v4" : "prompthero/openjourney-v4",
"StableDiffusion 1.5" : "runwayml/stable-diffusion-v1-5",
"StableDiffusion 2.1" : "stabilityai/stable-diffusion-2-1",
"DreamLike 1.0" : "dreamlike-art/dreamlike-diffusion-1.0",
"DreamLike 2.0" : "dreamlike-art/dreamlike-photoreal-2.0",
"DreamShaper" : "Lykon/DreamShaper",
"NeverEnding-Dream" : "Lykon/NeverEnding-Dream"
}
class StableDiffusionText2ImageGenerator:
def __init__(self):
self.pipe = None
def load_model(
self,
model_path,
scheduler
):
model_path = TEXT2IMG_MODEL_LIST[model_path]
if self.pipe is None:
self.pipe = StableDiffusionPipeline.from_pretrained(
model_path, safety_checker=None, torch_dtype=torch.float32
)
device = get_device()
self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
self.pipe.to(device)
#self.pipe.enable_attention_slicing()
return self.pipe
def generate_image(
self,
model_path: str,
prompt: str,
negative_prompt: str,
num_images_per_prompt: int,
scheduler: str,
guidance_scale: int,
num_inference_step: int,
height: int,
width: int,
seed_generator=0,
):
print("model_path", model_path)
print("prompt", prompt)
print("negative_prompt", negative_prompt)
print("num_images_per_prompt", num_images_per_prompt)
print("scheduler", scheduler)
print("guidance_scale", guidance_scale)
print("num_inference_step", num_inference_step)
print("height", height)
print("width", width)
print("seed_generator", seed_generator)
pipe = self.load_model(
model_path=model_path,
scheduler=scheduler,
)
if seed_generator == 0:
random_seed = torch.randint(0, 1000000, (1,))
generator = torch.manual_seed(random_seed)
else:
generator = torch.manual_seed(seed_generator)
images = pipe(
prompt=prompt,
height=height,
width=width,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=num_inference_step,
guidance_scale=guidance_scale,
generator=generator,
).images
return images
def app(username : str = "admin"):
demo = gr.Blocks(css = CSS)
with demo:
with gr.Row():
with gr.Column():
text2image_prompt = gr.Textbox(
lines=1,
placeholder="Prompt",
show_label=False,
elem_id="prompt-text-input",
value=''
)
text2image_negative_prompt = gr.Textbox(
lines=1,
placeholder="Negative Prompt",
show_label=False,
elem_id = "negative-prompt-text-input",
value=''
)
# add button for generating a prompt from the prompt
text2image_prompt_generate_button = gr.Button(
label="Generate Prompt",
type="primary",
align="center",
value = "Generate Prompt"
)
# show a text box with the generated prompt
text2image_prompt_generated_prompt = gr.Textbox(
lines=1,
placeholder="Generated Prompt",
show_label=False,
)
with gr.Row():
with gr.Column():
text2image_model_path = gr.Dropdown(
choices=list(TEXT2IMG_MODEL_LIST.keys()),
value=list(TEXT2IMG_MODEL_LIST.keys())[0],
label="Text2Image Model Selection",
elem_id="model-dropdown",
)
text2image_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label="Guidance Scale",
elem_id = "guidance-scale-slider"
)
text2image_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Step",
elem_id = "num-inference-step-slider"
)
text2image_num_images_per_prompt = gr.Slider(
minimum=1,
maximum=30,
step=1,
value=1,
label="Number Of Images",
)
with gr.Row():
with gr.Column():
text2image_scheduler = gr.Dropdown(
choices=SCHEDULER_LIST,
value=SCHEDULER_LIST[0],
label="Scheduler",
elem_id="scheduler-dropdown",
)
text2image_size = gr.Slider(
minimum=128,
maximum=1280,
step=32,
value=512,
label="Image Size",
elem_id="image-size-slider",
)
text2image_seed_generator = gr.Slider(
label="Seed(0 for random)",
minimum=0,
maximum=1000000,
value=0,
elem_id="seed-slider",
)
text2image_predict = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery",
).style(grid=(1, 2), height='auto')
with gr.Group(elem_id="container-advanced-btns"):
with gr.Group(elem_id="share-btn-container"):
community_icon = gr.HTML(community_icon_html)
loading_icon = gr.HTML(loading_icon_html)
share_button = gr.Button("Save artwork", elem_id="share-btn")
text2image_predict.click(
fn=StableDiffusionText2ImageGenerator().generate_image,
inputs=[
text2image_model_path,
text2image_prompt,
text2image_negative_prompt,
text2image_num_images_per_prompt,
text2image_scheduler,
text2image_guidance_scale,
text2image_num_inference_step,
text2image_size,
text2image_size,
text2image_seed_generator,
],
outputs=output_image,
)
text2image_prompt_generate_button.click(
fn=generate,
inputs=[text2image_prompt],
outputs=[text2image_prompt_generated_prompt],
)
# share_button.click(
# None,
# [],
# [],
# _js=get_share_js(),
# )
# autoclik the share button
return demo |