app.py

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 = '<img src="" width="48px" style="display: inline">'
    gr.Markdown(
        f"""<h1><center>🐦 {favicon} AII Diffusion</center></h1>
            """
    )
    gr.Markdown(
        f"""<h2><center>May not stable, But have many choices.</center></h2>
            """
    )
    
    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)