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" 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="a cute dog", 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 = "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", 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)