#@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 import html from share_btn import community_icon_html, loading_icon_html, share_js api = HfApi() models_list = api.list_models(author="sd-concepts-library", sort="likes", direction=-1) 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 # 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 #if token cannot be loaded, skip it try: learned_token = load_learned_embed_in_clip(f"{model_id}/learned_embeds.bin", pipe.text_encoder, pipe.tokenizer, token_name) except: continue 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 assembleHTML(model): html_gallery = '' html_gallery = html_gallery+'''
''' cap = 0 for model in models: html_gallery = html_gallery+f'''
''' cap += 1 if(cap == 99): break html_gallery = html_gallery+'''
''' return html_gallery 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.images): # output_images.append(image) return images_list.images, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) # idetnical to `infer` function without gradio state updates for share btn def infer_examples(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 images_list.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: 550px; overflow-y: scroll; flex-direction: row}*/ #component-3{height: 760px; overflow: auto} #component-9{position: sticky;top: 0;align-self: flex-start;} @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; width: auto !important;} #prompt_area{margin-bottom: .75em} #prompt_area > div:first-child{flex: 1 3 auto} .animate-spin { animation: spin 1s linear infinite; } @keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } } #share-btn-container { display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; } #share-btn { all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; } #share-btn * { all: unset; } ''' examples = ["a in style", "a style mecha robot", "a piano being played by ", "Candid photo of , high resolution photo, trending on artstation, interior design"] 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('''

Stable Diffusion Conceptualizer

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 check out this notebook.

''') with gr.Row(): with gr.Column(): gr.Markdown(f"### Navigate the top 100 Textual-Inversion community trained concepts. Use 600+ from [The Library](https://huggingface.co/sd-concepts-library)") with gr.Row(): image_blocks = [] #for i, model in enumerate(models): with gr.Box().style(border=None): gr.HTML(assembleHTML(models)) #title_block(model["token"], model["id"]) #image_blocks.append(image_block(model["images"], model["concept_type"])) with gr.Column(): 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, elem_id="generated-gallery").style(grid=[2], height="512px") with gr.Row(): gr.HTML("

Prompting may not work as you are used to. objects may need the concept added at the end, styles may work better at the beginning. You can navigate on lexica.art to get inspired on prompts

") with gr.Row(): gr.Examples(examples=examples, fn=infer_examples, inputs=[text], outputs=infer_outputs, cache_examples=True) with gr.Group(elem_id="share-btn-container"): community_icon = gr.HTML(community_icon_html, visible=False) loading_icon = gr.HTML(loading_icon_html, visible=False) share_button = gr.Button("Share to community", elem_id="share-btn", visible=False) checkbox_states = {} inputs = [text] btn.click( infer, inputs=inputs, outputs=[infer_outputs, community_icon, loading_icon, share_button] ) share_button.click( None, [], [], _js=share_js, ) demo.queue(max_size=20).launch()