custom_diffusers/examples/model_search/README.md

Search models on Civitai and Hugging Face

The auto_diffusers library provides additional functionalities to Diffusers such as searching for models on Civitai and the Hugging Face Hub. Please refer to the original library here

Installation

Before running the scripts, make sure to install the library's training dependencies:

To make sure you can successfully run the latest versions of the example scripts, we highly recommend installing from source and keeping the installation up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment.

git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .

Set up the pipeline. You can also cd to this folder and run it.

!wget https://raw.githubusercontent.com/suzukimain/auto_diffusers/refs/heads/master/src/auto_diffusers/pipeline_easy.py

Load from Civitai

from pipeline_easy import (
    EasyPipelineForText2Image,
    EasyPipelineForImage2Image,
    EasyPipelineForInpainting,
)

# Text-to-Image
pipeline = EasyPipelineForText2Image.from_civitai(
    "search_word",
    base_model="SD 1.5",
).to("cuda")


# Image-to-Image
pipeline = EasyPipelineForImage2Image.from_civitai(
    "search_word",
    base_model="SD 1.5",
).to("cuda")


# Inpainting
pipeline = EasyPipelineForInpainting.from_civitai(
    "search_word",
    base_model="SD 1.5",
).to("cuda")

Load from Hugging Face

from pipeline_easy import (
    EasyPipelineForText2Image,
    EasyPipelineForImage2Image,
    EasyPipelineForInpainting,
)

# Text-to-Image
pipeline = EasyPipelineForText2Image.from_huggingface(
    "search_word",
    checkpoint_format="diffusers",
).to("cuda")


# Image-to-Image
pipeline = EasyPipelineForImage2Image.from_huggingface(
    "search_word",
    checkpoint_format="diffusers",
).to("cuda")


# Inpainting
pipeline = EasyPipelineForInpainting.from_huggingface(
    "search_word",
    checkpoint_format="diffusers",
).to("cuda")

Search Civitai and Huggingface

from pipeline_easy import (
    search_huggingface,
    search_civitai,
) 

# Search Lora
Lora = search_civitai(
    "Keyword_to_search_Lora",
    model_type="LORA",
    base_model = "SD 1.5",
    download=True,
    )
# Load Lora into the pipeline.
pipeline.load_lora_weights(Lora)


# Search TextualInversion
TextualInversion = search_civitai(
    "EasyNegative",
    model_type="TextualInversion",
    base_model = "SD 1.5",
    download=True
)
# Load TextualInversion into the pipeline.
pipeline.load_textual_inversion(TextualInversion, token="EasyNegative")

Search Civitai

If an error occurs, insert the token and run again.

EasyPipeline.from_civitai parameters

Name	Type	Default	Description
search_word	string, Path	ー	The search query string. Can be a keyword, Civitai URL, local directory or file path.
model_type	string	Checkpoint	The type of model to search for. (for example Checkpoint, TextualInversion, Controlnet, LORA, Hypernetwork, AestheticGradient, Poses)
base_model	string	None	Trained model tag (for example SD 1.5, SD 3.5, SDXL 1.0)
torch_dtype	string, torch.dtype	None	Override the default torch.dtype and load the model with another dtype.
force_download	bool	False	Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
cache_dir	string, Path	None	Path to the folder where cached files are stored.
resume	bool	False	Whether to resume an incomplete download.
token	string	None	API token for Civitai authentication.

search_civitai parameters

Name	Type	Default	Description
search_word	string, Path	ー	The search query string. Can be a keyword, Civitai URL, local directory or file path.
model_type	string	Checkpoint	The type of model to search for. (for example Checkpoint, TextualInversion, Controlnet, LORA, Hypernetwork, AestheticGradient, Poses)
base_model	string	None	Trained model tag (for example SD 1.5, SD 3.5, SDXL 1.0)
download	bool	False	Whether to download the model.
force_download	bool	False	Whether to force the download if the model already exists.
cache_dir	string, Path	None	Path to the folder where cached files are stored.
resume	bool	False	Whether to resume an incomplete download.
token	string	None	API token for Civitai authentication.
include_params	bool	False	Whether to include parameters in the returned data.
skip_error	bool	False	Whether to skip errors and return None.

Search Huggingface

If an error occurs, insert the token and run again.

EasyPipeline.from_huggingface parameters

Name	Type	Default	Description
search_word	string, Path	ー	The search query string. Can be a keyword, Hugging Face URL, local directory or file path, or a Hugging Face path (<creator>/<repo>).
checkpoint_format	string	single_file	The format of the model checkpoint. ● single_file to search for single file checkpoint ●diffusers to search for multifolder diffusers format checkpoint
torch_dtype	string, torch.dtype	None	Override the default torch.dtype and load the model with another dtype.
force_download	bool	False	Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
cache_dir	string, Path	None	Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used.
token	string, bool	None	The token to use as HTTP bearer authorization for remote files.

search_huggingface parameters

Name	Type	Default	Description
search_word	string, Path	ー	The search query string. Can be a keyword, Hugging Face URL, local directory or file path, or a Hugging Face path (<creator>/<repo>).
checkpoint_format	string	single_file	The format of the model checkpoint. ● single_file to search for single file checkpoint ●diffusers to search for multifolder diffusers format checkpoint
pipeline_tag	string	None	Tag to filter models by pipeline.
download	bool	False	Whether to download the model.
force_download	bool	False	Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
cache_dir	string, Path	None	Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used.
token	string, bool	None	The token to use as HTTP bearer authorization for remote files.
include_params	bool	False	Whether to include parameters in the returned data.
skip_error	bool	False	Whether to skip errors and return None.