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| import gradio as gr | |
| import pandas as pd | |
| import requests | |
| import os | |
| import numpy as np | |
| import re | |
| from tqdm import tqdm | |
| from time import sleep | |
| from PIL import Image | |
| import requests | |
| from io import BytesIO | |
| from datasets import Dataset, load_dataset | |
| import json | |
| import cv2 | |
| import pathlib | |
| import imagehash | |
| API_TOKEN = os.environ.get("HF_READ_TOKEN") | |
| MAX_MODEL_NUM = 300 | |
| ''' | |
| Yntec digplay | |
| ''' | |
| hf_civitai_image_info_dataset = load_dataset("svjack/hf_civitai_image_info_v0", token = API_TOKEN) | |
| hf_civitai_image_info_df = hf_civitai_image_info_dataset["train"].to_pandas() | |
| def gen_interface(model_name, max_times = 3): | |
| times = 0 | |
| gr_model_interface = None | |
| while gr_model_interface is None and times < max_times: | |
| try: | |
| gr_model_interface = gr.load("models/{}".format(model_name),live=True,preprocess = False) | |
| except: | |
| print("error {} times {}".format(model_name, times)) | |
| sleep(2) | |
| times += 1 | |
| return gr_model_interface | |
| #gr_model_interface.title | |
| def toImgOpenCV(imgPIL): # Conver imgPIL to imgOpenCV | |
| i = np.array(imgPIL) # After mapping from PIL to numpy : [R,G,B,A] | |
| # numpy Image Channel system: [B,G,R,A] | |
| red = i[:,:,0].copy(); i[:,:,0] = i[:,:,2].copy(); i[:,:,2] = red; | |
| return i; | |
| def toImgPIL(imgOpenCV): return Image.fromarray(cv2.cvtColor(imgOpenCV, cv2.COLOR_BGR2RGB)); | |
| def jpg_val_to_img(jpg_bytes): | |
| img_buf = np.frombuffer(jpg_bytes, np.uint8) | |
| img = cv2.imdecode(img_buf, cv2.IMREAD_UNCHANGED) | |
| return toImgPIL(img) | |
| model_list = hf_civitai_image_info_df["hf_repo_id"].drop_duplicates().values.tolist() | |
| model_interface_list = [] | |
| for model_name in tqdm(model_list): | |
| gr_model_interface = gen_interface(model_name) | |
| if gr_model_interface is not None: | |
| model_interface_list.append(gr_model_interface) | |
| if len(model_interface_list) >= MAX_MODEL_NUM: | |
| break | |
| print("load model num : {}".format(len(model_interface_list))) | |
| def get_civitai_iframe(url, width = 1400, height = 768, as_html = True, visible = False): | |
| html= ''' | |
| <div style="justify-content: center; display: flex;"> | |
| <iframe | |
| src="{}" | |
| frameborder="0" | |
| width="{}" | |
| height="{}" | |
| ></iframe> | |
| </div> | |
| '''.format(url, width, height) | |
| if as_html: | |
| html = gr.HTML(html, visible = visible) | |
| return html | |
| def get_info_by_interface(gr_interface, model_interface_list = model_interface_list): | |
| #### out: (gr_interface, civitai_url, civitai_info) | |
| if hasattr(gr_interface, "app"): | |
| civitai_url = hf_civitai_image_info_df[ | |
| hf_civitai_image_info_df["hf_repo_id"] == gr_interface.title | |
| ]["civital_url"].iloc[0] | |
| civitai_info = hf_civitai_image_info_df[ | |
| hf_civitai_image_info_df["hf_repo_id"] == gr_interface.title | |
| ][["prompt", "image"]].values.tolist() | |
| return gr_interface ,civitai_url, civitai_info | |
| else: | |
| civitai_url = hf_civitai_image_info_df[ | |
| hf_civitai_image_info_df["hf_repo_id"] == gr_interface | |
| ]["civital_url"].iloc[0] | |
| civitai_info = hf_civitai_image_info_df[ | |
| hf_civitai_image_info_df["hf_repo_id"] == gr_interface | |
| ][["prompt", "image"]].values.tolist() | |
| return list(filter(lambda x:x.title == gr_interface, model_interface_list))[0] ,civitai_url, civitai_info | |
| def read_image_from_url(url): | |
| response = requests.get(url) | |
| img = Image.open(BytesIO(response.content)) | |
| return img | |
| def image_click(images, evt: gr.SelectData, gr_interface_value, | |
| ): | |
| img_selected = images[evt.index] | |
| #print(img_selected) | |
| im_data = img_selected["name"] | |
| im = Image.open(im_data) | |
| im_hash = imagehash.average_hash( | |
| im, hash_size = 1024 | |
| ) | |
| min_diff = int(1e10) | |
| #print(-1) | |
| repo_card_im_dict = dict( | |
| get_info_by_interface(gr_interface_value)[2] | |
| ) | |
| min_repo_name = "" | |
| for idx ,(repo_name, repo_card_image) in enumerate(repo_card_im_dict.items()): | |
| repo_img = jpg_val_to_img(repo_card_image["bytes"]) | |
| repo_img_hash = imagehash.average_hash( | |
| repo_img, hash_size = 1024 | |
| ) | |
| diff = im_hash - repo_img_hash | |
| if diff < min_diff: | |
| min_diff = diff | |
| min_repo_name = repo_name | |
| #print(idx) | |
| prompt = min_repo_name | |
| return prompt | |
| #return prompt, im | |
| def try_repo_act_func(civitai_url, show_civitai_button): | |
| repo_html_iframe_hide = get_civitai_iframe(civitai_url, visible = True if show_civitai_button == "Show Civitai Page" else False) | |
| return repo_html_iframe_hide, gr.Button("Hide Civitai Page" if show_civitai_button == "Show Civitai Page" else "Show Civitai Page") | |
| with gr.Blocks( | |
| css = ''' | |
| .header img { | |
| float: middle; | |
| width: 33px; | |
| height: 33px; | |
| } | |
| .header h1 { | |
| top: 18px; | |
| left: 10px; | |
| } | |
| ''' | |
| ) as demo: | |
| gr.HTML( | |
| ''' | |
| <center> | |
| <div class="header"> | |
| <h1 class = "logo"> <img src="https://huggingface.co/spaces/svjack/Civitai-Stable-Diffusion-HF/resolve/main/civitai_logo.webp" alt="logo" /> 🤗 Civitai Model on Huggingface </h1> | |
| </center> | |
| ''' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| hf_model_dropdown = gr.Dropdown(label = "🤗 Hf model", | |
| choices=sorted(map(lambda x: x.title, model_interface_list)), | |
| value=sorted(map(lambda x: x.title, model_interface_list))[0],) | |
| with gr.Column(): | |
| with gr.Row(): | |
| civitai_prompt = gr.Textbox(label = "🤭 Civitai Prompt (Click from 👉 right gallery to get them, and You can edit ✍️ yourself) Don't use them to generate NSFW content, such as porns. 😬👎 Or you will get ❌👀", | |
| interactive = True, | |
| ) | |
| gen_button = gr.Button(label = "Generate") | |
| hf_image = gr.Image(label = "🤭 Image generate by 🤗 Huggingface", height = 768) | |
| with gr.Column(): | |
| civitai_info_gallery = gr.Gallery( | |
| pd.Series( | |
| get_info_by_interface(hf_model_dropdown.value)[2] | |
| ).sample(n = min(len(get_info_by_interface(hf_model_dropdown.value)[2]), 30)).map(lambda t2: t2[1]).map(lambda x: x["bytes"]).map(jpg_val_to_img).values.tolist(), | |
| height = 1024, | |
| label = "🖱️👇 ➡️ 👈 Civitai image samples", | |
| object_fit = "contain" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| try_repo_button = gr.Button("Show Civitai Page") | |
| civitai_iframe_html = get_civitai_iframe( | |
| get_info_by_interface(hf_model_dropdown.value)[1] | |
| ) | |
| hf_model_dropdown.change( | |
| lambda x: pd.Series( | |
| get_info_by_interface(x)[2] | |
| ).sample(n = min(len(get_info_by_interface(x)[2]), 30)).map(lambda t2: t2[1]).map(lambda x: x["bytes"]).map(jpg_val_to_img).values.tolist(), | |
| hf_model_dropdown, | |
| civitai_info_gallery | |
| ) | |
| hf_model_dropdown.change( | |
| lambda _: (gr.Button("Show Civitai Page"), gr.HTML(visible = False)), | |
| None, | |
| [try_repo_button, civitai_iframe_html] | |
| ) | |
| civitai_info_gallery.select( | |
| image_click, | |
| [civitai_info_gallery, hf_model_dropdown], | |
| civitai_prompt | |
| ) | |
| gen_button.click(lambda hf_model_name, text_prompt: | |
| get_info_by_interface(hf_model_name)[0](text_prompt), | |
| [hf_model_dropdown, civitai_prompt], | |
| hf_image | |
| ) | |
| try_repo_button.click( | |
| lambda hf_model_name, button: try_repo_act_func( | |
| get_info_by_interface(hf_model_name)[1] | |
| , button), | |
| [hf_model_dropdown, try_repo_button], | |
| [civitai_iframe_html, try_repo_button] | |
| ) | |
| demo.launch(show_api = False) | |