Instructions to use InvokeAI/ip_adapter_sd_image_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InvokeAI/ip_adapter_sd_image_encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, CLIPVisionModelWithProjection tokenizer = AutoTokenizer.from_pretrained("InvokeAI/ip_adapter_sd_image_encoder") model = CLIPVisionModelWithProjection.from_pretrained("InvokeAI/ip_adapter_sd_image_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from InvokeAI/ip_adapter_sd_image_encoder: direct link, hf CLI and curl.
- Browser
- Download file 560 Bytes
-
https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder/resolve/main/config.json
- Command line
-
hf download hf://InvokeAI/ip_adapter_sd_image_encoder/config.json
-
curl -L -o config.json https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder/resolve/main/config.json
560 Bytes
| { | |
| "_name_or_path": "./image_encoder", | |
| "architectures": [ | |
| "CLIPVisionModelWithProjection" | |
| ], | |
| "attention_dropout": 0.0, | |
| "dropout": 0.0, | |
| "hidden_act": "gelu", | |
| "hidden_size": 1280, | |
| "image_size": 224, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5120, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "clip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 32, | |
| "patch_size": 14, | |
| "projection_dim": 1024, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.28.0.dev0" | |
| } | |