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| #@title Prepare the Concepts Library to be used | |
| import requests | |
| import os | |
| import gradio as gr | |
| import wget | |
| import torch | |
| from torch import autocast | |
| from diffusers import StableDiffusionPipeline | |
| from huggingface_hub import HfApi | |
| from transformers import CLIPTextModel, CLIPTokenizer | |
| api = HfApi() | |
| models_list = api.list_models(author="sd-concepts-library") | |
| models = [] | |
| pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True, revision="fp16", torch_dtype=torch.float16).to("cuda") | |
| def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None): | |
| loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu") | |
| # separate token and the embeds | |
| trained_token = list(loaded_learned_embeds.keys())[0] | |
| embeds = loaded_learned_embeds[trained_token] | |
| # cast to dtype of text_encoder | |
| dtype = text_encoder.get_input_embeddings().weight.dtype | |
| embeds.to(dtype) | |
| # add the token in tokenizer | |
| token = token if token is not None else trained_token | |
| num_added_tokens = tokenizer.add_tokens(token) | |
| i = 1 | |
| while(num_added_tokens == 0): | |
| print(f"The tokenizer already contains the token {token}.") | |
| token = f"{token[:-1]}-{i}>" | |
| print(f"Attempting to add the token {token}.") | |
| num_added_tokens = tokenizer.add_tokens(token) | |
| i+=1 | |
| # resize the token embeddings | |
| text_encoder.resize_token_embeddings(len(tokenizer)) | |
| # get the id for the token and assign the embeds | |
| token_id = tokenizer.convert_tokens_to_ids(token) | |
| text_encoder.get_input_embeddings().weight.data[token_id] = embeds | |
| return token | |
| print("Setting up the public library") | |
| for model in models_list: | |
| model_content = {} | |
| model_id = model.modelId | |
| model_content["id"] = model_id | |
| embeds_url = f"https://huggingface.co/{model_id}/resolve/main/learned_embeds.bin" | |
| os.makedirs(model_id,exist_ok = True) | |
| if not os.path.exists(f"{model_id}/learned_embeds.bin"): | |
| try: | |
| wget.download(embeds_url, out=model_id) | |
| except: | |
| continue | |
| token_identifier = f"https://huggingface.co/{model_id}/raw/main/token_identifier.txt" | |
| response = requests.get(token_identifier) | |
| token_name = response.text | |
| concept_type = f"https://huggingface.co/{model_id}/raw/main/type_of_concept.txt" | |
| response = requests.get(concept_type) | |
| concept_name = response.text | |
| model_content["concept_type"] = concept_name | |
| images = [] | |
| for i in range(4): | |
| url = f"https://huggingface.co/{model_id}/resolve/main/concept_images/{i}.jpeg" | |
| image_download = requests.get(url) | |
| url_code = image_download.status_code | |
| if(url_code == 200): | |
| file = open(f"{model_id}/{i}.jpeg", "wb") ## Creates the file for image | |
| file.write(image_download.content) ## Saves file content | |
| file.close() | |
| images.append(f"{model_id}/{i}.jpeg") | |
| model_content["images"] = images | |
| learned_token = load_learned_embed_in_clip(f"{model_id}/learned_embeds.bin", pipe.text_encoder, pipe.tokenizer, token_name) | |
| model_content["token"] = learned_token | |
| models.append(model_content) | |
| #@title Run the app to navigate around [the Library](https://huggingface.co/sd-concepts-library) | |
| #@markdown Click the `Running on public URL:` result to run the Gradio app | |
| SELECT_LABEL = "Select concept" | |
| def title_block(title, id): | |
| return gr.Markdown(f"### [`{title}`](https://huggingface.co/{id})") | |
| def image_block(image_list, concept_type): | |
| return gr.Gallery( | |
| label=concept_type, value=image_list, elem_id="gallery" | |
| ).style(grid=[2], height="auto") | |
| def checkbox_block(): | |
| checkbox = gr.Checkbox(label=SELECT_LABEL).style(container=False) | |
| return checkbox | |
| def infer(text): | |
| with autocast("cuda"): | |
| images_list = pipe( | |
| [text]*2, | |
| num_inference_steps=50, | |
| guidance_scale=7.5 | |
| ) | |
| output_images = [] | |
| for i, image in enumerate(images_list["sample"]): | |
| output_images.append(image) | |
| return output_images | |
| css = ''' | |
| .gradio-container {font-family: 'IBM Plex Sans', sans-serif} | |
| #top_title{margin-bottom: .5em} | |
| #top_title h2{margin-bottom: 0; text-align: center} | |
| #main_row{flex-wrap: wrap; gap: 1em; max-height: calc(100vh - 16em); overflow-y: scroll; flex-direction: row} | |
| @media (min-width: 768px){#main_row > div{flex: 1 1 32%; margin-left: 0 !important}} | |
| .gr-prose code::before, .gr-prose code::after {content: "" !important} | |
| ::-webkit-scrollbar {width: 10px} | |
| ::-webkit-scrollbar-track {background: #f1f1f1} | |
| ::-webkit-scrollbar-thumb {background: #888} | |
| ::-webkit-scrollbar-thumb:hover {background: #555} | |
| .gr-button {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; | |
| } | |
| #prompt_input{flex: 1 3 auto} | |
| #prompt_area{margin-bottom: .75em} | |
| #prompt_area > div:first-child{flex: 1 3 auto} | |
| ''' | |
| examples = ["a <cat-toy> in <madhubani-art> style", "a mecha robot in <line-art> style", "a piano being played by <bonzi>"] | |
| with gr.Blocks(css=css) as demo: | |
| state = gr.Variable({ | |
| 'selected': -1 | |
| }) | |
| state = {} | |
| def update_state(i): | |
| global checkbox_states | |
| if(checkbox_states[i]): | |
| checkbox_states[i] = False | |
| state[i] = False | |
| else: | |
| state[i] = True | |
| checkbox_states[i] = True | |
| gr.HTML(''' | |
| <div style="text-align: center; max-width: 720px; margin: 0 auto;"> | |
| <div | |
| style=" | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| " | |
| > | |
| <svg | |
| width="0.65em" | |
| height="0.65em" | |
| viewBox="0 0 115 115" | |
| fill="none" | |
| xmlns="http://www.w3.org/2000/svg" | |
| > | |
| <rect width="23" height="23" fill="white"></rect> | |
| <rect y="69" width="23" height="23" fill="white"></rect> | |
| <rect x="23" width="23" height="23" fill="#AEAEAE"></rect> | |
| <rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect> | |
| <rect x="46" width="23" height="23" fill="white"></rect> | |
| <rect x="46" y="69" width="23" height="23" fill="white"></rect> | |
| <rect x="69" width="23" height="23" fill="black"></rect> | |
| <rect x="69" y="69" width="23" height="23" fill="black"></rect> | |
| <rect x="92" width="23" height="23" fill="#D9D9D9"></rect> | |
| <rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect> | |
| <rect x="115" y="46" width="23" height="23" fill="white"></rect> | |
| <rect x="115" y="115" width="23" height="23" fill="white"></rect> | |
| <rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect> | |
| <rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect> | |
| <rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect> | |
| <rect x="92" y="69" width="23" height="23" fill="white"></rect> | |
| <rect x="69" y="46" width="23" height="23" fill="white"></rect> | |
| <rect x="69" y="115" width="23" height="23" fill="white"></rect> | |
| <rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect> | |
| <rect x="46" y="46" width="23" height="23" fill="black"></rect> | |
| <rect x="46" y="115" width="23" height="23" fill="black"></rect> | |
| <rect x="46" y="69" width="23" height="23" fill="black"></rect> | |
| <rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect> | |
| <rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect> | |
| <rect x="23" y="69" width="23" height="23" fill="black"></rect> | |
| </svg> | |
| <h1 style="font-weight: 900; margin-bottom: 7px;"> | |
| Stable Diffusion Conceptualizer | |
| </h1> | |
| </div> | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| Navigate through community created concepts and styles via Stable Diffusion Textual Inversion and pick yours for inference. | |
| To train your own concepts and contribute to the library <a style="text-decoration: underline" href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb">check out this notebook</a>. | |
| </p> | |
| </div> | |
| ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(''' | |
| ### Textual-Inversion trained [concepts library](https://huggingface.co/sd-concepts-library) navigator | |
| ''') | |
| with gr.Row(elem_id="main_row"): | |
| image_blocks = [] | |
| for i, model in enumerate(models): | |
| with gr.Box().style(border=None): | |
| title_block(model["token"], model["id"]) | |
| image_blocks.append(image_block(model["images"], model["concept_type"])) | |
| with gr.Box(): | |
| with gr.Row(elem_id="prompt_area").style(mobile_collapse=False, equal_height=True): | |
| text = gr.Textbox( | |
| label="Enter your prompt", placeholder="Enter your prompt", show_label=False, max_lines=1, elem_id="prompt_input" | |
| ).style( | |
| border=(True, False, True, True), | |
| rounded=(True, False, False, True), | |
| container=False | |
| ) | |
| btn = gr.Button("Run",elem_id="run_btn").style( | |
| margin=False, | |
| rounded=(False, True, True, False) | |
| ) | |
| with gr.Row().style(): | |
| infer_outputs = gr.Gallery(show_label=False).style(grid=[2], height="512px") | |
| with gr.Row(): | |
| gr.HTML("<p style=\"font-size: 85%;margin-top: .75em\">Prompting may not work as you are used to; <code>objects</code> may need the concept added at the end.</p>") | |
| with gr.Row(): | |
| gr.Examples(examples=examples, fn=infer, inputs=[text], outputs=infer_outputs, cache_examples=False) | |
| checkbox_states = {} | |
| inputs = [text] | |
| btn.click( | |
| infer, | |
| inputs=inputs, | |
| outputs=infer_outputs | |
| ) | |
| demo.launch(inline=False, debug=True) |