File size: 8,526 Bytes
7d311fc |
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 |
import gradio as gr
from fetch import get_values
from dotenv import load_dotenv
load_dotenv()
import prodia
import requests
import random
from datetime import datetime
import os
prodia_key = os.getenv('PRODIA_X_KEY', None)
if prodia_key is None:
print("Please set PRODIA_X_KEY in .env, closing...")
exit()
client = prodia.Client(api_key=prodia_key)
def process_input_text2img(prompt, negative_prompt, steps, cfg_scale, number, seed, model, sampler, aspect_ratio, upscale, save=False):
images = []
for image in range(number):
result = client.txt2img(prompt=prompt, negative_prompt=negative_prompt, model=model, sampler=sampler,
steps=steps, cfg_scale=cfg_scale, seed=seed, aspect_ratio=aspect_ratio, upscale=upscale)
images.append(result.url)
if save:
date = datetime.now()
if not os.path.isdir(f'./outputs/{date.year}-{date.month}-{date.day}'):
os.mkdir(f'./outputs/{date.year}-{date.month}-{date.day}')
img_data = requests.get(result.url).content
with open(f"./outputs/{date.year}-{date.month}-{date.day}/{random.randint(1, 10000000000000)}_{result.seed}.png", "wb") as f:
f.write(img_data)
return images
def process_input_img2img(init, prompt, negative_prompt, steps, cfg_scale, number, seed, model, sampler, ds, upscale, save):
images = []
for image in range(number):
result = client.img2img(imageUrl=init, prompt=prompt, negative_prompt=negative_prompt, model=model, sampler=sampler,
steps=steps, cfg_scale=cfg_scale, seed=seed, denoising_strength=ds, upscale=upscale)
images.append(result.url)
if save:
date = datetime.now()
if not os.path.isdir(f'./outputs/{date.year}-{date.month}-{date.day}'):
os.mkdir(f'./outputs/{date.year}-{date.month}-{date.day}')
img_data = requests.get(result.url).content
with open(f"./outputs/{date.year}-{date.month}-{date.day}/{random.randint(1, 10000000000000)}_{result.seed}.png", "wb") as f:
f.write(img_data)
return images
"""
def process_input_control(init, prompt, negative_prompt, steps, cfg_scale, number, seed, model, control_model, sampler):
images = []
for image in range(number):
result = client.controlnet(imageUrl=init, prompt=prompt, negative_prompt=negative_prompt, model=model, sampler=sampler,
steps=steps, cfg_scale=cfg_scale, seed=seed, controlnet_model=control_model)
images.append(result.url)
return images
"""
theme = "Base"
with gr.Blocks(theme=theme) as demo:
gr.Markdown("""
# Stable Diffusion Demo
<h3></h3>
🚀 This space generates images by text with many settings!
⏰️ Generation on average lasts 15-25 seconds!
👥️️ This demo was created by OpenskyML and 4COM!
""")
gr.Image("banner.png", elem_id="banner-image", show_label=False, show_download_button=False, show_share_button=False)
gr.DuplicateButton(value="Duplicate space for private use")
with gr.Tab(label="txt2img"):
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", lines=2, placeholder="beautiful cat, 8k")
negative = gr.Textbox(label="Negative Prompt", lines=3, value="text, blurry, fuzziness", placeholder="Add words you don't want to show up in your art...")
with gr.Row():
steps = gr.Slider(label="Steps", value=30, step=1, maximum=50, minimum=5, interactive=True)
cfg = gr.Slider(label="CFG Scale", maximum=20, minimum=1, value=7, interactive=True, info="Recommended 7 CFG Scale")
with gr.Row():
num = gr.Slider(label="Number of images", value=2, step=1, maximum=4, minimum=1, interactive=True)
seed = gr.Slider(label="Seed", value=-1, step=1, minimum=-1, maximum=4294967295, interactive=True, info="""'-1' is a random seed""")
with gr.Row():
model = gr.Dropdown(label="Model", choices=get_values()[0], value="v1-5-pruned-emaonly.ckpt [81761151]", interactive=True)
sampler = gr.Dropdown(label="Sampler", choices=get_values()[1], value="DPM++ SDE Karras", interactive=True)
with gr.Row():
ar = gr.Radio(label="Aspect Ratio", choices=["square", "portrait", "landscape"], value="square", interactive=True)
with gr.Column():
upscale = gr.Checkbox(label="upscale", value=True, interactive=True, info="""'True' recommended, improves image quality""")
with gr.Row():
run_btn = gr.Button("Generate", variant="primary")
with gr.Column():
result_image = gr.Gallery(label="Result Image(s)")
gr.Examples(
examples=[
["A high tech solarpunk utopia in the Amazon rainforest"],
["A pikachu fine dining with a view to the Eiffel Tower"],
["A mecha robot in a favela in expressionist style"],
["an insect robot preparing a delicious meal"],
["A small cabin on top of a snowy mountain in the style of Disney, artstation"]
],
inputs=[prompt],
cache_examples=False,
)
run_btn.click(
process_input_text2img,
inputs=[
prompt,
negative,
steps,
cfg,
num,
seed,
model,
sampler,
ar,
upscale
],
outputs=[result_image],
)
with gr.Tab(label="img2img"):
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", lines=2, placeholder="beautiful cat, 8k")
with gr.Row():
negative = gr.Textbox(label="Negative Prompt", lines=3, placeholder="Add words you don't want to show up in your art...")
init_image = gr.Textbox(label="Init Image Url", lines=3, placeholder="https://cdn.openai.com/API/images/guides/image_generation_simple.webp")
with gr.Row():
steps = gr.Slider(label="Steps", value=30, step=1, maximum=50, minimum=1, interactive=True)
cfg = gr.Slider(label="CFG Scale", maximum=20, minimum=1, value=7, interactive=True, info="Recommended 7 CFG Scale")
with gr.Row():
num = gr.Slider(label="Number of images", value=2, step=1, maximum=4, minimum=1, interactive=True)
seed = gr.Slider(label="Seed", value=-1, step=1, minimum=-1, maximum=4294967295, interactive=True, info="""'-1' is a random seed""")
with gr.Row():
model = gr.Dropdown(label="Model", choices=get_values()[0], value="v1-5-pruned-emaonly.ckpt [81761151]", interactive=True)
sampler = gr.Dropdown(label="Sampler", choices=get_values()[1], value="DPM++ 2M Karras", interactive=True)
with gr.Row():
ds = gr.Slider(label="Denoising strength", maximum=0.9, minimum=0.1, value=0.5, interactive=True)
with gr.Column():
upscale = gr.Checkbox(label="upscale", value=True, interactive=True, info="""'True' recommended, improves image quality""")
with gr.Row():
run_btn = gr.Button("Generate", variant="primary")
with gr.Column():
result_image = gr.Gallery(label="Result Image(s)")
run_btn.click(
process_input_img2img,
inputs=[
init_image,
prompt,
negative,
steps,
cfg,
num,
seed,
model,
sampler,
ds,
upscale
],
outputs=[result_image],
)
with gr.Tab(label="Gallery"):
gr.load("nateraw/stable_diffusion_gallery", src="spaces")
with gr.Tab(label="License"):
gr.load("4com/4com-license", src="spaces")
if __name__ == "__main__":
demo.launch(show_api=True) |