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# Search models on Civitai and Hugging Face
The [auto_diffusers](https://github.com/suzukimain/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](https://pypi.org/project/auto-diffusers/)
## Installation
Before running the scripts, make sure to install the library's training dependencies:
> [!IMPORTANT]
> 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.
```bash
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.
```bash
!wget https://raw.githubusercontent.com/suzukimain/auto_diffusers/refs/heads/master/src/auto_diffusers/pipeline_easy.py
```
## Load from Civitai
```python
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
```python
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
```python
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
> [!TIP]
> **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. <br>(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. <br>(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
> [!TIP]
> **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.<br>● `single_file` to search for `single file checkpoint` <br>●`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. <br>● `single_file` to search for `single file checkpoint` <br>●`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. |
|