Spaces:
Running
Running
File size: 9,211 Bytes
983d072 b9e7f35 4abec4b a0a2137 552f93e a0a2137 983d072 508a796 04a682a 508a796 4abec4b 552f93e 4abec4b 08a7509 7134d92 08a7509 8d68db5 7134d92 08a7509 4abec4b 08a7509 e4046eb 08a7509 4abec4b 08a7509 7134d92 08a7509 7b9ab38 7134d92 4abec4b 08a7509 9837774 4abec4b 08a7509 b9e7f35 4abec4b fbebe9f 4abec4b b9e7f35 4abec4b b9e7f35 d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee d633848 544b4ee 9ce81f8 9837774 08a7509 b583b60 08a7509 b9e7f35 08a7509 0368d4d 42b655c 0368d4d 59af101 08a7509 552f93e 08a7509 b9e7f35 08a7509 b9e7f35 59af101 b9e7f35 552f93e 2144105 6d9770b a0a2137 59c5d79 a0a2137 552f93e 8b6ff0e 08a7509 552f93e 04a682a b9e7f35 08a7509 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 |
import gradio as gr
import requests
import io
import random
import os
from PIL import Image
from huggingface_hub import InferenceApi, InferenceClient
from datasets import load_dataset
dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts")
prompt_df = dataset["train"].to_pandas()
def get_samples():
prompt_list = prompt_df.sample(n = 10)["Prompt"].map(lambda x: x).values.tolist()
return prompt_list
def get_params(request: gr.Request):
params = request.query_params
ip = request.client.host
req = {"params": params,
"ip": ip}
client = InferenceClient()
models = client.list_deployed_models()
list_models = models["text-to-image"]
return list_models
client = InferenceClient()
models = client.list_deployed_models()
list_models = models["text-to-image"]
list_prompts = get_samples()
'''
list_models = [
"SDXL-1.0",
"SD-1.5",
"OpenJourney-V4",
"Anything-V4",
"Disney-Pixar-Cartoon",
"Pixel-Art-XL",
"Dalle-3-XL",
"Midjourney-V4-XL",
]
'''
def generate_txt2img(current_model, prompt, is_negative=False, image_style="None style", steps=50, cfg_scale=7,
seed=None):
'''
if current_model == "SD-1.5":
API_URL = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5"
elif current_model == "SDXL-1.0":
API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
elif current_model == "OpenJourney-V4":
API_URL = "https://api-inference.huggingface.co/models/prompthero/openjourney"
elif current_model == "Anything-V4":
API_URL = "https://api-inference.huggingface.co/models/xyn-ai/anything-v4.0"
elif current_model == "Disney-Pixar-Cartoon":
API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/disney-pixar-cartoon"
elif current_model == "Pixel-Art-XL":
API_URL = "https://api-inference.huggingface.co/models/nerijs/pixel-art-xl"
elif current_model == "Dalle-3-XL":
API_URL = "https://api-inference.huggingface.co/models/openskyml/dalle-3-xl"
elif current_model == "Midjourney-V4-XL":
API_URL = "https://api-inference.huggingface.co/models/openskyml/midjourney-v4-xl"
'''
API_TOKEN = os.environ.get("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
if type(current_model) != type(""):
current_model = "stabilityai/stable-diffusion-2-1"
api = InferenceApi(current_model)
api.headers = headers
if image_style == "None style":
payload = {
"inputs": prompt + ", 8k",
"is_negative": is_negative,
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed is not None else random.randint(-1, 2147483647)
}
elif image_style == "Cinematic":
payload = {
"inputs": prompt + ", realistic, detailed, textured, skin, hair, eyes, by Alex Huguet, Mike Hill, Ian Spriggs, JaeCheol Park, Marek Denko",
"is_negative": is_negative + ", abstract, cartoon, stylized",
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed is not None else random.randint(-1, 2147483647)
}
elif image_style == "Digital Art":
payload = {
"inputs": prompt + ", faded , vintage , nostalgic , by Jose Villa , Elizabeth Messina , Ryan Brenizer , Jonas Peterson , Jasmine Star",
"is_negative": is_negative + ", sharp , modern , bright",
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed is not None else random.randint(-1, 2147483647)
}
elif image_style == "Portrait":
payload = {
"inputs": prompt + ", soft light, sharp, exposure blend, medium shot, bokeh, (hdr:1.4), high contrast, (cinematic, teal and orange:0.85), (muted colors, dim colors, soothing tones:1.3), low saturation, (hyperdetailed:1.2), (noir:0.4), (natural skin texture, hyperrealism, soft light, sharp:1.2)",
"is_negative": is_negative,
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed is not None else random.randint(-1, 2147483647)
}
#image_bytes = requests.post(API_URL, headers=headers, json=payload).content
image = api(data = payload)
return image
'''
image = Image.open(io.BytesIO(image_bytes))
return image
'''
css = """
/* General Container Styles */
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
max-width: 730px !important;
margin: auto;
padding-top: 1.5rem;
}
/* Button Styles */
.gr-button {
color: white;
border-color: black;
background: black;
white-space: nowrap;
}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
/* Footer Styles */
.footer, .dark .footer {
margin-bottom: 45px;
margin-top: 35px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer > p, .dark .footer > p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer > p {
background: #0b0f19;
}
/* Share Button Styles */
#share-btn-container {
padding: 0 0.5rem !important;
background-color: #000000;
justify-content: center;
align-items: center;
border-radius: 9999px !important;
max-width: 13rem;
margin-left: auto;
}
#share-btn-container:hover {
background-color: #060606;
}
#share-btn {
all: initial;
color: #ffffff;
font-weight: 600;
cursor: pointer;
font-family: 'IBM Plex Sans', sans-serif;
margin-left: 0.5rem !important;
padding: 0.5rem !important;
right: 0;
}
/* Animation Styles */
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from { transform: rotate(0deg); }
to { transform: rotate(360deg); }
}
/* Other Styles */
#gallery {
min-height: 22rem;
margin-bottom: 15px;
margin-left: auto;
margin-right: auto;
border-bottom-right-radius: .5rem !important;
border-bottom-left-radius: .5rem !important;
}
"""
with gr.Blocks(css=css) as demo:
#with gr.Blocks() as demo:
favicon = '<img src="" width="48px" style="display: inline">'
gr.Markdown(
f"""<h1><center>🐦 {favicon} AII Diffusion</center></h1>
"""
)
with gr.Row(elem_id="prompt-container"):
with gr.Column():
btn_refresh = gr.Button(value="Click Refresh to get current deployed models")
btn_refresh.click(None, js="window.location.reload()")
current_model = gr.Dropdown(label="Current Model", choices=list_models, value="stabilityai/stable-diffusion-2-1")
with gr.Row(elem_id="prompt-container"):
with gr.Row():
text_prompt = gr.Textbox(label="Prompt", placeholder="a cute dog",
value = "1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt",
lines=2, elem_id="prompt-text-input")
text_button = gr.Button("Manualy input Generate", variant='primary', elem_id="gen-button")
with gr.Row():
select_prompt = gr.Dropdown(label="prompt selected", choices=list_prompts, value=list_prompts[1])
select_button = gr.Button("Select Prompt Generate", variant='primary', elem_id="gen-button")
with gr.Row():
image_output = gr.Image(type="pil", label="Output Image", elem_id="gallery")
with gr.Accordion("Advanced settings", open=False):
negative_prompt = gr.Textbox(label="Negative Prompt", value="text, blurry, fuzziness", lines=1, elem_id="negative-prompt-text-input")
image_style = gr.Dropdown(label="Style", choices=["None style", "Cinematic", "Digital Art", "Portrait"], value="Portrait", allow_custom_value=False)
'''
with gr.Row():
with gr.Column():
exps = gr.Examples(
get_samples(),
inputs = text_prompt,
label = "Prompt Examples",
elem_id = "Examples"
)
'''
text_button.click(generate_txt2img, inputs=[current_model, text_prompt, negative_prompt, image_style], outputs=image_output)
select_button.click(generate_txt2img, inputs=[current_model, select_prompt, negative_prompt, image_style], outputs=image_output)
demo.load(get_params, None, current_model)
demo.launch(show_api=False) |