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Currently there is no way to add load_lora_weights in deployment
hub = {
'HF_MODEL_ID': 'black-forest-labs/FLUX.1-dev',
'HF_TASK':'text-to-image',
'HF_TOKEN':'TOKEN'
}
# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
env=hub, # configuration for loading model from Hub
role=role, # IAM role with permissions to create an endpoint
transformers_version="4.26", # Transformers version used
pytorch_version="1.13", # PyTorch version used
py_version='py39', # Python version used
)
# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
initial_instance_count=1,
instance_type="ml.g4dn.xlarge"
)
Maybe in hub, there could be a new env var as "HF_LORA_MODEL"
Currently there is no way to add
load_lora_weights
in deploymentMaybe in hub, there could be a new env var as "HF_LORA_MODEL"
Similar implementation present in here aws-samples/sagemaker-stablediffusion-quick-kit@bd37fe9...2d1c43b
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