Datasets:

ppppssss

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ou aren't part of it already

!curl -X POST -H 'Authorization: Bearer '$hf_token -H 'Content-Type: application/json' https://huggingface.co/organizations/sd-concepts-library/share/VcLXJtzwwxnHYCkNMLpSJCdnNFZHQwWywv

images_upload = os.listdir("my_concept")

image_string = ""

repo_id = f"sd-concepts-library/{slugify(name_of_your_concept)}"

for i, image in enumerate(images_upload):

image_string = f'''{image_string}

'''

if(what_to_teach == "style"):

what_to_teach_article = f"a `{what_to_teach}`"

else:#@title Save your newly created concept to the [library of concepts](https://huggingface.co/sd-concepts-library)?

save_concept_to_public_library = True #@param {type:"boolean"}

name_of_your_concept = "tzuyu m" #@param {type:"string"}

#@markdown `hf_token_write`: leave blank if you logged in with a token with `write access` in the [Initial Setup](#scrollTo=KbzZ9xe6dWwf). If not, [go to your tokens settings and create a write access token](https://huggingface.co/settings/tokens)

hf_token_write = "" #@param {type:"string"}

if(save_concept_to_public_library):

from slugify import slugify

from huggingface_hub import HfApi, HfFolder, CommitOperationAdd

from huggingface_hub import create_repo

repo_id = f"sd-concepts-library/{slugify(name_of_your_concept)}"

output_dir = hyperparameters["output_dir"]

if(not hf_token_write):

with open(HfFolder.path_token, 'r') as fin: hf_token = fin.read();

else:

hf_token = hf_token_write

#Join the Concepts Library organization if y

what_to_teach_article = f"an `{what_to_teach}`"

readme_text = f'''---

license: mit

---

### {name_of_your_concept} on Stable Diffusion

This is the `{placeholder_token}` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).

Here is the new concept you will be able to use as {what_to_teach_article}:

{image_string}

'''

#Save the readme to a file

readme_file = open("README.md", "w")

readme_file.write(readme_text)

readme_file.close()

#Save the token identifier to a file

text_file = open("token_identifier.txt", "w")

text_file.write(placeholder_token)

text_file.close()

#Save the type of teached thing to a file

type_file = open("type_of_concept.txt","w")

type_file.write(what_to_teach)

type_file.close()

operations = [

CommitOperationAdd(path_in_repo="learned_embeds.bin", path_or_fileobj=f"{output_dir}/learned_embeds.bin"),

CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"),

CommitOperationAdd(path_in_repo="type_of_concept.txt", path_or_fileobj="type_of_concept.txt"),

CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="README.md"),

]

create_repo(repo_id,private=True, token=hf_token)

api = HfApi()

api.create_commit(

repo_id=repo_id,

operations=operations,

commit_message=f"Upload the concept {name_of_your_concept} embeds and token",

token=hf_token

)

api.upload_folder(

folder_path=save_path,

path_in_repo="concept_images",

repo_id=repo_id,

token=hf_token

)