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| 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) |