How can I use*.safetensorsmodles indiffuserslibrary?#2105 Yiximailopened this issueJan 25, 2023· 3 comments I found a really great model a few days ago. Then I wanted to use it on a pure API server (without webui or other Gradio interface) ...
) If you want to load a local single file ckpt/safetensors file, you can refer to this document: https://www.iopaint.com/models#load-ckptsafetensors.Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment ...
(encode, batched=True) # Format the dataset to PyTorch tensors imdb_data.set_format(type='torch', columns=['input_ids', 'attention_ mask', 'label'])With our dataset loaded up, we can run some training code to update our BERT model on our labeled data:# Define the model model = ...
It is possible to fine-tune either a schnell or dev model, but we recommend training the dev model. dev has a more limited license for use, but it is also far more powerful in terms of prompt understanding, spelling, and object composition compared to schnell. schnell however should be fa...
self.model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL)) File “/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py”, line 1468, in load_state_dict load(self) File “/usr/local/lib/python3.6/dist-packages/torch/nn/modules...
Copy the llama.cpp file from the repository to your working directory. Edit the llama.cpp file and modify the main() function to load the model and generate a response: #include "transformer.h"int main() { std::string prompt = "What is the meaning of life?";std::string response = ...
After the class prediction, the image goes throughlinear regressionto find the exact tensor (container of numeric data returned by the regressor of the object). Regression is performed using open-source platforms such asDarknet,TensorFlow, orPyTorch. The final output of the object recognition algori...
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model download --url https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/resolve/main/512-inpainting-ema.safetensors --relative-path models/checkpoints" ) .run_commands( # As of this writing, main is broken, so using this fork's fix "git clone -b fix/custom_module_init ...
filename = 'model.onnx' model = onnx.load(filename, load_external_data=False) load_external_data_for_model(model, 'external_data_folderl/') onnx.checker.check_model(model) Then I used this model to do inference, although the model folder was only 1.1GB (i.e., the meaning is t...