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
import pandas as pd
import re
def rank_score(repo_str):
p_list = re.findall(r"[-_xlv\d]+" ,repo_str.split("/")[-1])
xl_in_str = any(map(lambda x: "xl" in x, p_list))
v_in_str = any(map(lambda x: "v" in x and
any(map(lambda y:
any(map(lambda z: y.startswith(z), "0123456789"))
,x.split("v")))
, p_list))
stable_in_str = repo_str.split("/")[-1].lower().startswith("stable")
score = sum(map(lambda t2: t2[0] * t2[1] ,(zip(*[[stable_in_str, xl_in_str, v_in_str], [1000, 100, 10]]))))
#return p_list, xl_in_str, v_in_str, stable_in_str, score
return score
def shorten_by(repo_list, by = None):
if by == "user":
return sorted(
pd.DataFrame(pd.Series(repo_list).map(lambda x: (x.split("/")[0], x)).values.tolist()).groupby(0)[1].apply(list).map(lambda x:
sorted(x, key = rank_score, reverse = True)[0]).values.tolist(),
key = rank_score, reverse = True
)
if by == "model":
return sorted(repo_list, key = lambda x: rank_score(x), reverse = True)
return repo_list
'''
dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts")
prompt_df = dataset["train"].to_pandas()
'''
prompt_df = pd.read_csv("Stable-Diffusion-Prompts.csv")
DEFAULT_MODEL = "stabilityai/stable-diffusion-2-1"
#DEFAULT_PROMPT = "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"
DEFAULT_PROMPT = "house"
def get_samples():
prompt_list = prompt_df.sample(n = 10)["Prompt"].map(lambda x: x).values.tolist()
return prompt_list
def update_models(models_rank_by = "model"):
client = InferenceClient()
models = client.list_deployed_models()
list_models = models["text-to-image"]
if hasattr(models_rank_by, "value"):
list_models = shorten_by(list_models, models_rank_by.value)
else:
list_models = shorten_by(list_models, models_rank_by)
return gr.Dropdown.update(choices=list_models)
def update_prompts():
return gr.Dropdown.update(choices=get_samples())
def get_params(request: gr.Request, models_rank_by):
params = request.query_params
ip = request.client.host
req = {"params": params,
"ip": ip}
return update_models(models_rank_by), update_prompts()
'''
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):
print("call {} {} one time".format(current_model, prompt))
'''
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 = DEFAULT_MODEL
if type(prompt) != type(""):
prompt = DEFAULT_PROMPT
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 = '![]()
'
gr.Markdown(
f"""🐦 {favicon} AII Diffusion
"""
)
gr.Markdown(
f"""May not stable, But have many choices.
"""
)
with gr.Row(elem_id="prompt-container"):
with gr.Column():
btn_refresh = gr.Button(value="Click to get current deployed models and newly Prompt candidates")
models_rank_by = gr.Radio(choices=["model", "user"],
value="model", label="Models ranked by", elem_id="rank_radio")
list_models = update_models(models_rank_by)
list_prompts = get_samples()
#btn_refresh.click(None, js="window.location.reload()")
current_model = gr.Dropdown(label="Current Model", choices=list_models, value=DEFAULT_MODEL,
info = "default model: {}".format(DEFAULT_MODEL)
)
with gr.Row("prompt-container"):
text_prompt = gr.Textbox(label="Input Prompt", placeholder=DEFAULT_PROMPT,
value = DEFAULT_PROMPT,
lines=2, elem_id="prompt-text-input")
text_button = gr.Button("Manualy input Generate", variant='primary', elem_id="gen-button")
with gr.Row("prompt-container"):
select_prompt = gr.Dropdown(label="Prompt selected", choices=list_prompts,
value = DEFAULT_PROMPT,
info = "default prompt: {}".format(DEFAULT_PROMPT)
)
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)
btn_refresh.click(update_models, models_rank_by, current_model)
btn_refresh.click(update_prompts, None, select_prompt)
models_rank_by.change(update_models, models_rank_by, current_model)
demo.load(get_params, models_rank_by, [current_model, select_prompt])
demo.launch(show_api=False)