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‎examples/model_search/README.md‎

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# Search models on Civitai and Hugging Face
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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.
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Please refer to the original library [here](https://pypi.org/project/auto-diffusers/)
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## Installation
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Before running the scripts, make sure to install the library's training dependencies:
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> [!IMPORTANT]
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> 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.
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```bash
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git clone https://github.com/huggingface/diffusers
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cd diffusers
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pip install .
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```
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Set up the pipeline. You can also cd to this folder and run it.
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```bash
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!wget https://raw.githubusercontent.com/suzukimain/auto_diffusers/refs/heads/master/src/auto_diffusers/pipeline_easy.py
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```
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## Load from Civitai
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```python
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from pipeline_easy import (
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EasyPipelineForText2Image,
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EasyPipelineForImage2Image,
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EasyPipelineForInpainting,
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)
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# Text-to-Image
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pipeline = EasyPipelineForText2Image.from_civitai(
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"search_word",
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base_model="SD 1.5",
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).to("cuda")
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# Image-to-Image
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pipeline = EasyPipelineForImage2Image.from_civitai(
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"search_word",
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base_model="SD 1.5",
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).to("cuda")
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# Inpainting
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pipeline = EasyPipelineForInpainting.from_civitai(
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"search_word",
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base_model="SD 1.5",
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).to("cuda")
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```
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## Load from Hugging Face
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```python
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from pipeline_easy import (
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EasyPipelineForText2Image,
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EasyPipelineForImage2Image,
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EasyPipelineForInpainting,
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)
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# Text-to-Image
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pipeline = EasyPipelineForText2Image.from_huggingface(
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"search_word",
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checkpoint_format="diffusers",
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).to("cuda")
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# Image-to-Image
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pipeline = EasyPipelineForImage2Image.from_huggingface(
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"search_word",
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checkpoint_format="diffusers",
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).to("cuda")
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# Inpainting
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pipeline = EasyPipelineForInpainting.from_huggingface(
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"search_word",
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checkpoint_format="diffusers",
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).to("cuda")
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```
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## Search Civitai and Huggingface
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```python
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from pipeline_easy import (
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search_huggingface,
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search_civitai,
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)
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# Search Lora
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Lora = search_civitai(
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"Keyword_to_search_Lora",
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model_type="LORA",
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base_model = "SD 1.5",
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download=True,
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)
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# Load Lora into the pipeline.
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pipeline.load_lora_weights(Lora)
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# Search TextualInversion
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TextualInversion = search_civitai(
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"EasyNegative",
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model_type="TextualInversion",
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base_model = "SD 1.5",
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download=True
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)
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# Load TextualInversion into the pipeline.
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pipeline.load_textual_inversion(TextualInversion, token="EasyNegative")
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```
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### Search Civitai
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> [!TIP]
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> **If an error occurs, insert the `token` and run again.**
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#### `EasyPipeline.from_civitai` parameters
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| Name | Type | Default | Description |
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|:---------------:|:----------------------:|:-------------:|:-----------------------------------------------------------------------------------:|
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| search_word | string, Path | ー | The search query string. Can be a keyword, Civitai URL, local directory or file path. |
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| model_type | string | `Checkpoint` | The type of model to search for. <br>(for example `Checkpoint`, `TextualInversion`, `Controlnet`, `LORA`, `Hypernetwork`, `AestheticGradient`, `Poses`) |
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| base_model | string | None | Trained model tag (for example `SD 1.5`, `SD 3.5`, `SDXL 1.0`) |
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| torch_dtype | string, torch.dtype | None | Override the default `torch.dtype` and load the model with another dtype. |
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| 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. |
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| cache_dir | string, Path | None | Path to the folder where cached files are stored. |
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| resume | bool | False | Whether to resume an incomplete download. |
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| token | string | None | API token for Civitai authentication. |
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#### `search_civitai` parameters
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| Name | Type | Default | Description |
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|:---------------:|:--------------:|:-------------:|:-----------------------------------------------------------------------------------:|
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| search_word | string, Path | ー | The search query string. Can be a keyword, Civitai URL, local directory or file path. |
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| model_type | string | `Checkpoint` | The type of model to search for. <br>(for example `Checkpoint`, `TextualInversion`, `Controlnet`, `LORA`, `Hypernetwork`, `AestheticGradient`, `Poses`) |
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| base_model | string | None | Trained model tag (for example `SD 1.5`, `SD 3.5`, `SDXL 1.0`) |
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| download | bool | False | Whether to download the model. |
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| force_download | bool | False | Whether to force the download if the model already exists. |
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| cache_dir | string, Path | None | Path to the folder where cached files are stored. |
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| resume | bool | False | Whether to resume an incomplete download. |
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| token | string | None | API token for Civitai authentication. |
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| include_params | bool | False | Whether to include parameters in the returned data. |
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| skip_error | bool | False | Whether to skip errors and return None. |
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### Search Huggingface
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> [!TIP]
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> **If an error occurs, insert the `token` and run again.**
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#### `EasyPipeline.from_huggingface` parameters
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| Name | Type | Default | Description |
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|:---------------------:|:-------------------:|:--------------:|:----------------------------------------------------------------:|
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| 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>`). |
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| 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` |
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| torch_dtype | string, torch.dtype | None | Override the default `torch.dtype` and load the model with another dtype. |
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| 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. |
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| cache_dir | string, Path | None | Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. |
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| token | string, bool | None | The token to use as HTTP bearer authorization for remote files. |
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#### `search_huggingface` parameters
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| Name | Type | Default | Description |
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|:---------------------:|:-------------------:|:--------------:|:----------------------------------------------------------------:|
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| 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>`). |
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| 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` |
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| pipeline_tag | string | None | Tag to filter models by pipeline. |
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| download | bool | False | Whether to download the model. |
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| 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. |
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| cache_dir | string, Path | None | Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. |
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| token | string, bool | None | The token to use as HTTP bearer authorization for remote files. |
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| include_params | bool | False | Whether to include parameters in the returned data. |
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| skip_error | bool | False | Whether to skip errors and return None. |

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