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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 | |
import html | |
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 | |
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 assembleHTML(model): | |
html_gallery = '' | |
html_gallery = html_gallery+''' | |
<div class="flex gr-gap gr-form-gap row gap-4 w-full flex-wrap" id="main_row"> | |
''' | |
for model in models: | |
html_gallery = html_gallery+f''' | |
<div class="gr-block gr-box relative w-full overflow-hidden border-solid border border-gray-200 gr-panel"> | |
<div class="output-markdown gr-prose" style="max-width: 100%;"> | |
<h3> | |
<a href="https://huggingface.co/{model["id"]}" target="_blank"> | |
<code>{html.escape(model["token"])}</code> | |
</a> | |
</h3> | |
</div> | |
<div id="gallery" class="gr-block gr-box relative w-full overflow-hidden border-solid border border-gray-200"> | |
<div class="wrap svelte-17ttdjv opacity-0"></div> | |
<div class="absolute left-0 top-0 py-1 px-2 rounded-br-lg shadow-sm text-xs text-gray-500 flex items-center pointer-events-none bg-white z-20 border-b border-r border-gray-100 dark:bg-gray-900"> | |
<span class="mr-2 h-[12px] w-[12px] opacity-80"> | |
<svg xmlns="http://www.w3.org/2000/svg" width="100%" height="100%" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather feather-image"> | |
<rect x="3" y="3" width="18" height="18" rx="2" ry="2"></rect> | |
<circle cx="8.5" cy="8.5" r="1.5"></circle> | |
<polyline points="21 15 16 10 5 21"></polyline> | |
</svg> | |
</span> {model["concept_type"]} | |
</div> | |
<div class="overflow-y-auto h-full p-2" style="position: relative;"> | |
<div class="grid gap-2 grid-cols-2 sm:grid-cols-2 md:grid-cols-2 lg:grid-cols-2 xl:grid-cols-2 2xl:grid-cols-2 svelte-1g9btlg pt-6"> | |
''' | |
for image in model["images"]: | |
html_gallery = html_gallery + f''' | |
<button class="gallery-item svelte-1g9btlg"> | |
<img alt="" loading="lazy" class="h-full w-full overflow-hidden object-contain" src="file/{image}"> | |
</button> | |
''' | |
html_gallery = html_gallery+''' | |
</div> | |
<iframe style="display: block; position: absolute; top: 0; left: 0; width: 100%; height: 100%; overflow: hidden; border: 0; opacity: 0; pointer-events: none; z-index: -1;" aria-hidden="true" tabindex="-1" src="about:blank"></iframe> | |
</div> | |
</div> | |
</div> | |
''' | |
html_gallery = html_gallery+''' | |
</div> | |
''' | |
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["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: 550px; 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 <line-art> style mecha robot", "a piano being played by <bonzi>", "Candid photo of <cheburashka>, 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(''' | |
<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(): | |
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.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, <code>styles</code> may work better at the beginning. You can navigate on <a href='https://lexica.art'>lexica.art</a> to get inspired on prompts</p>") | |
with gr.Row(): | |
gr.Examples(examples=examples, fn=infer, inputs=[text], outputs=infer_outputs, cache_examples=True) | |
checkbox_states = {} | |
inputs = [text] | |
btn.click( | |
infer, | |
inputs=inputs, | |
outputs=infer_outputs | |
) | |
demo.queue(max_size=25).launch() |