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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
from tqdm.notebook import tqdm
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 tqdm(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) |