#@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+'''
<div class="flex gr-gap gr-form-gap row gap-4 w-full flex-wrap" id="main_row">
'''
cap = 0
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>
'''
cap += 1
if(cap == 99):
break
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, 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 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}*/
#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 <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(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,
full_width=False,
)
btn = gr.Button("Run",elem_id="run_btn").style(
margin=False,
rounded=(False, True, True, False),
full_width=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("<p style=\"font-size: 95%;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_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()