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[community] Load Models from Sources like
Civitai into Existing Pip…
…elines (#9986) * Added example of model search. * Combine processing into one file * Add parameters for base model * Bug Fixes * bug fix * Create README.md * Update search_for_civitai_and_HF.py * Create requirements.txt * bug fix * Update README.md * bug fix * Correction of typos * Update examples/model_search/README.md Co-authored-by: Steven Liu <[email protected]> * Update examples/model_search/README.md Co-authored-by: Steven Liu <[email protected]> * Update examples/model_search/README.md Co-authored-by: Steven Liu <[email protected]> * Update examples/model_search/README.md Co-authored-by: Steven Liu <[email protected]> * Update examples/model_search/README.md Co-authored-by: Steven Liu <[email protected]> * Update examples/model_search/README.md Co-authored-by: Steven Liu <[email protected]> * apply the changes * Replace search_for_civitai_and_HF.py with pipeline_easy.py * Update examples/model_search/README.md Co-authored-by: Steven Liu <[email protected]> * Update examples/model_search/README.md Co-authored-by: Steven Liu <[email protected]> * Update examples/model_search/README.md Co-authored-by: Steven Liu <[email protected]> * Update README.md * Organize the table of parameters * Update README.md * Update README.md * Update README.md * make style * Fixing the style of pipeline * Fix pipeline style * fix --------- Co-authored-by: Steven Liu <[email protected]>
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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. | ||
| 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] | ||
| > 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 | ||
| ``` | ||
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| ## Load from Civitai | ||
| ```python | ||
| from pipeline_easy import ( | ||
| EasyPipelineForText2Image, | ||
| EasyPipelineForImage2Image, | ||
| EasyPipelineForInpainting, | ||
| ) | ||
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| # Text-to-Image | ||
| pipeline = EasyPipelineForText2Image.from_civitai( | ||
| "search_word", | ||
| base_model="SD 1.5", | ||
| ).to("cuda") | ||
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| # Image-to-Image | ||
| pipeline = EasyPipelineForImage2Image.from_civitai( | ||
| "search_word", | ||
| base_model="SD 1.5", | ||
| ).to("cuda") | ||
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| # Inpainting | ||
| pipeline = EasyPipelineForInpainting.from_civitai( | ||
| "search_word", | ||
| base_model="SD 1.5", | ||
| ).to("cuda") | ||
| ``` | ||
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| ## Load from Hugging Face | ||
| ```python | ||
| from pipeline_easy import ( | ||
| EasyPipelineForText2Image, | ||
| EasyPipelineForImage2Image, | ||
| EasyPipelineForInpainting, | ||
| ) | ||
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| # Text-to-Image | ||
| pipeline = EasyPipelineForText2Image.from_huggingface( | ||
| "search_word", | ||
| checkpoint_format="diffusers", | ||
| ).to("cuda") | ||
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| # Image-to-Image | ||
| pipeline = EasyPipelineForImage2Image.from_huggingface( | ||
| "search_word", | ||
| checkpoint_format="diffusers", | ||
| ).to("cuda") | ||
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| # Inpainting | ||
| pipeline = EasyPipelineForInpainting.from_huggingface( | ||
| "search_word", | ||
| checkpoint_format="diffusers", | ||
| ).to("cuda") | ||
| ``` | ||
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| ## Search Civitai and Huggingface | ||
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| ```python | ||
| from pipeline_easy import ( | ||
| search_huggingface, | ||
| search_civitai, | ||
| ) | ||
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| # 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) | ||
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| # 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") | ||
| ``` | ||
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| ### Search Civitai | ||
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| > [!TIP] | ||
| > **If an error occurs, insert the `token` and run again.** | ||
| #### `EasyPipeline.from_civitai` parameters | ||
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| | 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. | | ||
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| #### `search_civitai` parameters | ||
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| | 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. | | ||
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| ### Search Huggingface | ||
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| > [!TIP] | ||
| > **If an error occurs, insert the `token` and run again.** | ||
| #### `EasyPipeline.from_huggingface` parameters | ||
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| | 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. | | ||
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| #### `search_huggingface` parameters | ||
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| | 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. | |
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