From 41580696c7fcaa470877ab3cc7789ba96036124d Mon Sep 17 00:00:00 2001 From: Mert <101130780+mertalev@users.noreply.github.com> Date: Wed, 31 Jul 2024 00:34:45 -0400 Subject: [PATCH] feat(ml): add more search models (#11468) * update export code * add uuid glob, sort model names * add new models to ml, sort names * add new models to server, sort by dims and name * typo in name * update export dependencies * onnx save function * format --- machine-learning/ann/ann.py | 4 +- machine-learning/app/models/constants.py | 53 +- machine-learning/export/conda-lock.yml | 6426 +++++++++++--------- machine-learning/export/env.yaml | 6 +- machine-learning/export/models/mclip.py | 22 +- machine-learning/export/models/openclip.py | 23 +- machine-learning/export/models/optimize.py | 21 +- machine-learning/export/run.py | 115 +- server/src/constants.ts | 57 +- 9 files changed, 3804 insertions(+), 2923 deletions(-) diff --git a/machine-learning/ann/ann.py b/machine-learning/ann/ann.py index a6667d50fb..21f7022a5c 100644 --- a/machine-learning/ann/ann.py +++ b/machine-learning/ann/ann.py @@ -65,7 +65,7 @@ class Ann(metaclass=_Singleton): self.input_shapes: dict[int, tuple[tuple[int], ...]] = {} self.ann: int | None = None self.new() - + if self.tuning_file is not None: # make sure tuning file exists (without clearing contents) # once filled, the tuning file reduces the cost/time of the first @@ -105,7 +105,7 @@ class Ann(metaclass=_Singleton): raise ValueError("model_path must be a file with extension .armnn, .tflite or .onnx") if not exists(model_path): raise ValueError("model_path must point to an existing file!") - + save_cached_network = False if cached_network_path is not None and not exists(cached_network_path): save_cached_network = True diff --git a/machine-learning/app/models/constants.py b/machine-learning/app/models/constants.py index c51dd3b66d..338a481594 100644 --- a/machine-learning/app/models/constants.py +++ b/machine-learning/app/models/constants.py @@ -2,53 +2,64 @@ from app.config import clean_name from app.schemas import ModelSource _OPENCLIP_MODELS = { - "RN50__openai", - "RN50__yfcc15m", - "RN50__cc12m", "RN101__openai", "RN101__yfcc15m", - "RN50x4__openai", + "RN50__cc12m", + "RN50__openai", + "RN50__yfcc15m", "RN50x16__openai", + "RN50x4__openai", "RN50x64__openai", - "ViT-B-32__openai", + "ViT-B-16-SigLIP-256__webli", + "ViT-B-16-SigLIP-384__webli", + "ViT-B-16-SigLIP-512__webli", + "ViT-B-16-SigLIP-i18n-256__webli", + "ViT-B-16-SigLIP__webli", + "ViT-B-16-plus-240__laion400m_e31", + "ViT-B-16-plus-240__laion400m_e32", + "ViT-B-16__laion400m_e31", + "ViT-B-16__laion400m_e32", + "ViT-B-16__openai", + "ViT-B-32__laion2b-s34b-b79k", "ViT-B-32__laion2b_e16", "ViT-B-32__laion400m_e31", "ViT-B-32__laion400m_e32", - "ViT-B-32__laion2b-s34b-b79k", - "ViT-B-16__openai", - "ViT-B-16__laion400m_e31", - "ViT-B-16__laion400m_e32", - "ViT-B-16-plus-240__laion400m_e31", - "ViT-B-16-plus-240__laion400m_e32", - "ViT-L-14__openai", + "ViT-B-32__openai", + "ViT-H-14-378-quickgelu__dfn5b", + "ViT-H-14-quickgelu__dfn5b", + "ViT-H-14__laion2b-s32b-b79k", + "ViT-L-14-336__openai", + "ViT-L-14-quickgelu__dfn2b", + "ViT-L-14__laion2b-s32b-b82k", "ViT-L-14__laion400m_e31", "ViT-L-14__laion400m_e32", - "ViT-L-14__laion2b-s32b-b82k", - "ViT-L-14-336__openai", - "ViT-H-14__laion2b-s32b-b79k", + "ViT-L-14__openai", + "ViT-L-16-SigLIP-256__webli", + "ViT-L-16-SigLIP-384__webli", + "ViT-SO400M-14-SigLIP-384__webli", "ViT-g-14__laion2b-s12b-b42k", - "ViT-L-14-quickgelu__dfn2b", - "ViT-H-14-quickgelu__dfn5b", - "ViT-H-14-378-quickgelu__dfn5b", + "XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k", "XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k", + "nllb-clip-base-siglip__mrl", "nllb-clip-base-siglip__v1", + "nllb-clip-large-siglip__mrl", "nllb-clip-large-siglip__v1", } _MCLIP_MODELS = { "LABSE-Vit-L-14", - "XLM-Roberta-Large-Vit-B-32", "XLM-Roberta-Large-Vit-B-16Plus", + "XLM-Roberta-Large-Vit-B-32", "XLM-Roberta-Large-Vit-L-14", } _INSIGHTFACE_MODELS = { "antelopev2", - "buffalo_l", - "buffalo_m", "buffalo_s", + "buffalo_m", + "buffalo_l", } diff --git a/machine-learning/export/conda-lock.yml b/machine-learning/export/conda-lock.yml index 12dc35778e..17578746de 100644 --- a/machine-learning/export/conda-lock.yml +++ b/machine-learning/export/conda-lock.yml @@ -5,7 +5,7 @@ # available, unless you explicitly update the lock file. # # Install this environment as "YOURENV" with: -# conda-lock install -n YOURENV --file conda-lock.yml +# conda-lock install -n YOURENV conda-lock.yml # To update a single package to the latest version compatible with the version constraints in the source: # conda-lock lock --lockfile conda-lock.yml --update PACKAGE # To re-solve the entire environment, e.g. after changing a version constraint in the source file: @@ -13,13 +13,13 @@ version: 1 metadata: content_hash: - linux-64: d42204fa0b65bc7acfc3edfff2d027fb2c395335b1de603909316cf7971a602a + linux-64: 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multilingual-clip @@ -3506,15 +4314,15 @@ package: sha256: b9acf95b8309c85a0db5e9c88c5f1b400687e08d72408c460731ae31e71dc73a category: main optional: false -- name: onnx-simplifier - version: 0.4.35 +- name: onnxsim + version: 0.4.36 manager: pip platform: linux-64 dependencies: onnx: '*' rich: '*' - url: https://files.pythonhosted.org/packages/5f/b3/b953aa4d877661c66e626fdd57830b4e217b45b0139ba6d5e0f38a663b1c/onnx_simplifier-0.4.35-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl + url: https://files.pythonhosted.org/packages/db/94/22aab761b3d416bce02020d9ca98dc692427c2717b0325952e30ce41f83b/onnxsim-0.4.36-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl hash: - sha256: 4986c4272440e719d0652c3bbecba5d842ae2ddb7de7804f23c781761e11cc8a + sha256: fa7596e6b806ed19077f7652788a50ee576c172b4d16d421f0593aef1a6fa4c4 category: main optional: false diff --git a/machine-learning/export/env.yaml b/machine-learning/export/env.yaml index f7144812d0..27fb72098e 100644 --- a/machine-learning/export/env.yaml +++ b/machine-learning/export/env.yaml @@ -2,7 +2,7 @@ name: base channels: - conda-forge - nvidia - - pytorch-nightly + - pytorch platforms: - linux-64 dependencies: @@ -13,7 +13,7 @@ dependencies: - orjson==3.* - pip - python==3.11.* - - pytorch + - pytorch>=2.3 - rich==13.* - safetensors==0.* - setuptools==68.* @@ -21,5 +21,5 @@ dependencies: - transformers==4.* - pip: - multilingual-clip - - onnx-simplifier + - onnxsim category: main diff --git a/machine-learning/export/models/mclip.py b/machine-learning/export/models/mclip.py index 565539016a..06324e490d 100644 --- a/machine-learning/export/models/mclip.py +++ b/machine-learning/export/models/mclip.py @@ -1,3 +1,4 @@ +import os import tempfile import warnings from pathlib import Path @@ -8,7 +9,6 @@ from transformers import AutoTokenizer from .openclip import OpenCLIPModelConfig from .openclip import to_onnx as openclip_to_onnx -from .optimize import optimize from .util import get_model_path _MCLIP_TO_OPENCLIP = { @@ -23,18 +23,20 @@ def to_onnx( model_name: str, output_dir_visual: Path | str, output_dir_textual: Path | str, -) -> None: +) -> tuple[Path, Path]: textual_path = get_model_path(output_dir_textual) with tempfile.TemporaryDirectory() as tmpdir: - model = MultilingualCLIP.from_pretrained(model_name, cache_dir=tmpdir) + model = MultilingualCLIP.from_pretrained(model_name, cache_dir=os.environ.get("CACHE_DIR", tmpdir)) AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual) + model.eval() for param in model.parameters(): param.requires_grad_(False) export_text_encoder(model, textual_path) - openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual) - optimize(textual_path) + visual_path, _ = openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual) + assert visual_path is not None, "Visual model export failed" + return visual_path, textual_path def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> None: @@ -58,10 +60,10 @@ def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> Non args, output_path.as_posix(), input_names=["input_ids", "attention_mask"], - output_names=["text_embedding"], + output_names=["embedding"], opset_version=17, - dynamic_axes={ - "input_ids": {0: "batch_size", 1: "sequence_length"}, - "attention_mask": {0: "batch_size", 1: "sequence_length"}, - }, + # dynamic_axes={ + # "input_ids": {0: "batch_size", 1: "sequence_length"}, + # "attention_mask": {0: "batch_size", 1: "sequence_length"}, + # }, ) diff --git a/machine-learning/export/models/openclip.py b/machine-learning/export/models/openclip.py index d5d2b3ef5d..68a4b90353 100644 --- a/machine-learning/export/models/openclip.py +++ b/machine-learning/export/models/openclip.py @@ -1,3 +1,4 @@ +import os import tempfile import warnings from dataclasses import dataclass, field @@ -7,7 +8,6 @@ import open_clip import torch from transformers import AutoTokenizer -from .optimize import optimize from .util import get_model_path, save_config @@ -23,25 +23,28 @@ class OpenCLIPModelConfig: if open_clip_cfg is None: raise ValueError(f"Unknown model {self.name}") self.image_size = open_clip_cfg["vision_cfg"]["image_size"] - self.sequence_length = open_clip_cfg["text_cfg"]["context_length"] + self.sequence_length = open_clip_cfg["text_cfg"].get("context_length", 77) def to_onnx( model_cfg: OpenCLIPModelConfig, output_dir_visual: Path | str | None = None, output_dir_textual: Path | str | None = None, -) -> None: +) -> tuple[Path | None, Path | None]: + visual_path = None + textual_path = None with tempfile.TemporaryDirectory() as tmpdir: model = open_clip.create_model( model_cfg.name, pretrained=model_cfg.pretrained, jit=False, - cache_dir=tmpdir, + cache_dir=os.environ.get("CACHE_DIR", tmpdir), require_pretrained=True, ) text_vision_cfg = open_clip.get_model_config(model_cfg.name) + model.eval() for param in model.parameters(): param.requires_grad_(False) @@ -53,8 +56,6 @@ def to_onnx( save_config(text_vision_cfg, output_dir_visual.parent / "config.json") export_image_encoder(model, model_cfg, visual_path) - optimize(visual_path) - if output_dir_textual is not None: output_dir_textual = Path(output_dir_textual) textual_path = get_model_path(output_dir_textual) @@ -62,7 +63,7 @@ def to_onnx( tokenizer_name = text_vision_cfg["text_cfg"].get("hf_tokenizer_name", "openai/clip-vit-base-patch32") AutoTokenizer.from_pretrained(tokenizer_name).save_pretrained(output_dir_textual) export_text_encoder(model, model_cfg, textual_path) - optimize(textual_path) + return visual_path, textual_path def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None: @@ -83,9 +84,9 @@ def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, args, output_path.as_posix(), input_names=["image"], - output_names=["image_embedding"], + output_names=["embedding"], opset_version=17, - dynamic_axes={"image": {0: "batch_size"}}, + # dynamic_axes={"image": {0: "batch_size"}}, ) @@ -107,7 +108,7 @@ def export_text_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, o args, output_path.as_posix(), input_names=["text"], - output_names=["text_embedding"], + output_names=["embedding"], opset_version=17, - dynamic_axes={"text": {0: "batch_size"}}, + # dynamic_axes={"text": {0: "batch_size"}}, ) diff --git a/machine-learning/export/models/optimize.py b/machine-learning/export/models/optimize.py index b0ef1ee60a..48b0c67634 100644 --- a/machine-learning/export/models/optimize.py +++ b/machine-learning/export/models/optimize.py @@ -5,13 +5,26 @@ import onnxruntime as ort import onnxsim +def save_onnx(model: onnx.ModelProto, output_path: Path | str) -> None: + try: + onnx.save(model, output_path) + except ValueError as e: + if "The proto size is larger than the 2 GB limit." in str(e): + onnx.save(model, output_path, save_as_external_data=True, size_threshold=1_000_000) + else: + raise e + + def optimize_onnxsim(model_path: Path | str, output_path: Path | str) -> None: model_path = Path(model_path) output_path = Path(output_path) model = onnx.load(model_path.as_posix()) - model, check = onnxsim.simplify(model, skip_shape_inference=True) + model, check = onnxsim.simplify(model) assert check, "Simplified ONNX model could not be validated" - onnx.save(model, output_path.as_posix()) + for file in model_path.parent.iterdir(): + if file.name.startswith("Constant") or "onnx" in file.name or file.suffix == ".weight": + file.unlink() + save_onnx(model, output_path) def optimize_ort( @@ -33,6 +46,4 @@ def optimize(model_path: Path | str) -> None: model_path = Path(model_path) optimize_ort(model_path, model_path) - # onnxsim serializes large models as a blob, which uses much more memory when loading the model at runtime - if not any(file.name.startswith("Constant") for file in model_path.parent.iterdir()): - optimize_onnxsim(model_path, model_path) + optimize_onnxsim(model_path, model_path) diff --git a/machine-learning/export/run.py b/machine-learning/export/run.py index 5ce32189e2..3edb5644e3 100644 --- a/machine-learning/export/run.py +++ b/machine-learning/export/run.py @@ -3,74 +3,111 @@ import os from pathlib import Path from tempfile import TemporaryDirectory -from huggingface_hub import create_repo, login, upload_folder +import torch +from huggingface_hub import create_repo, upload_folder from models import mclip, openclip +from models.optimize import optimize from rich.progress import Progress models = [ - "RN50::openai", - "RN50::yfcc15m", - "RN50::cc12m", + "M-CLIP/LABSE-Vit-L-14", + "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus", + "M-CLIP/XLM-Roberta-Large-Vit-B-32", + "M-CLIP/XLM-Roberta-Large-Vit-L-14", "RN101::openai", "RN101::yfcc15m", - "RN50x4::openai", + "RN50::cc12m", + "RN50::openai", + "RN50::yfcc15m", "RN50x16::openai", + "RN50x4::openai", "RN50x64::openai", - "ViT-B-32::openai", + "ViT-B-16-SigLIP-256::webli", + "ViT-B-16-SigLIP-384::webli", + "ViT-B-16-SigLIP-512::webli", + "ViT-B-16-SigLIP-i18n-256::webli", + "ViT-B-16-SigLIP::webli", + "ViT-B-16-plus-240::laion400m_e31", + "ViT-B-16-plus-240::laion400m_e32", + "ViT-B-16::laion400m_e31", + "ViT-B-16::laion400m_e32", + "ViT-B-16::openai", + "ViT-B-32::laion2b-s34b-b79k", "ViT-B-32::laion2b_e16", "ViT-B-32::laion400m_e31", "ViT-B-32::laion400m_e32", - "ViT-B-32::laion2b-s34b-b79k", - "ViT-B-16::openai", - "ViT-B-16::laion400m_e31", - "ViT-B-16::laion400m_e32", - "ViT-B-16-plus-240::laion400m_e31", - "ViT-B-16-plus-240::laion400m_e32", - "ViT-L-14::openai", + "ViT-B-32::openai", + "ViT-H-14-378-quickgelu::dfn5b", + "ViT-H-14-quickgelu::dfn5b", + "ViT-H-14::laion2b-s32b-b79k", + "ViT-L-14-336::openai", + "ViT-L-14-quickgelu::dfn2b", + "ViT-L-14::laion2b-s32b-b82k", "ViT-L-14::laion400m_e31", "ViT-L-14::laion400m_e32", - "ViT-L-14::laion2b-s32b-b82k", - "ViT-L-14-336::openai", - "ViT-H-14::laion2b-s32b-b79k", + "ViT-L-14::openai", + "ViT-L-16-SigLIP-256::webli", + "ViT-L-16-SigLIP-384::webli", + "ViT-SO400M-14-SigLIP-384::webli", "ViT-g-14::laion2b-s12b-b42k", - "M-CLIP/LABSE-Vit-L-14", - "M-CLIP/XLM-Roberta-Large-Vit-B-32", - "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus", - "M-CLIP/XLM-Roberta-Large-Vit-L-14", + "nllb-clip-base-siglip::mrl", + "nllb-clip-base-siglip::v1", + "nllb-clip-large-siglip::mrl", + "nllb-clip-large-siglip::v1", + "xlm-roberta-base-ViT-B-32::laion5b_s13b_b90k", + "xlm-roberta-large-ViT-H-14::frozen_laion5b_s13b_b90k", ] -login(token=os.environ["HF_AUTH_TOKEN"]) +# glob to delete old UUID blobs when reuploading models +uuid_char = "[a-fA-F0-9]" +uuid_glob = uuid_char * 8 + "-" + uuid_char * 4 + "-" + uuid_char * 4 + "-" + uuid_char * 4 + "-" + uuid_char * 12 + +# remote repo files to be deleted before uploading +# deletion is in the same commit as the upload, so it's atomic +delete_patterns = ["**/*onnx*", "**/Constant*", "**/*.weight", "**/*.bias", f"**/{uuid_glob}"] with Progress() as progress: - task1 = progress.add_task("[green]Exporting models...", total=len(models)) - task2 = progress.add_task("[yellow]Uploading models...", total=len(models)) - + task = progress.add_task("[green]Exporting models...", total=len(models)) + token = os.environ.get("HF_AUTH_TOKEN") + torch.backends.mha.set_fastpath_enabled(False) with TemporaryDirectory() as tmp: tmpdir = Path(tmp) for model in models: model_name = model.split("/")[-1].replace("::", "__") + hf_model_name = model_name.replace("xlm-roberta-large", "XLM-Roberta-Large") + hf_model_name = model_name.replace("xlm-roberta-base", "XLM-Roberta-Base") config_path = tmpdir / model_name / "config.json" - def upload() -> None: - progress.update(task2, description=f"[yellow]Uploading {model_name}") - repo_id = f"immich-app/{model_name}" - - create_repo(repo_id, exist_ok=True) - upload_folder(repo_id=repo_id, folder_path=tmpdir / model_name) - progress.update(task2, advance=1) - def export() -> None: - progress.update(task1, description=f"[green]Exporting {model_name}") - visual_dir = tmpdir / model_name / "visual" - textual_dir = tmpdir / model_name / "textual" + progress.update(task, description=f"[green]Exporting {hf_model_name}") + visual_dir = tmpdir / hf_model_name / "visual" + textual_dir = tmpdir / hf_model_name / "textual" if model.startswith("M-CLIP"): - mclip.to_onnx(model, visual_dir, textual_dir) + visual_path, textual_path = mclip.to_onnx(model, visual_dir, textual_dir) else: name, _, pretrained = model_name.partition("__") - openclip.to_onnx(openclip.OpenCLIPModelConfig(name, pretrained), visual_dir, textual_dir) + config = openclip.OpenCLIPModelConfig(name, pretrained) + visual_path, textual_path = openclip.to_onnx(config, visual_dir, textual_dir) + progress.update(task, description=f"[green]Optimizing {hf_model_name} (visual)") + optimize(visual_path) + progress.update(task, description=f"[green]Optimizing {hf_model_name} (textual)") + optimize(textual_path) - progress.update(task1, advance=1) gc.collect() + def upload() -> None: + progress.update(task, description=f"[yellow]Uploading {hf_model_name}") + repo_id = f"immich-app/{hf_model_name}" + + create_repo(repo_id, exist_ok=True) + upload_folder( + repo_id=repo_id, + folder_path=tmpdir / hf_model_name, + delete_patterns=delete_patterns, + token=token, + ) + export() - upload() + if token is not None: + upload() + progress.update(task, advance=1) diff --git a/server/src/constants.ts b/server/src/constants.ts index 0d1d992992..422fa21a1b 100644 --- a/server/src/constants.ts +++ b/server/src/constants.ts @@ -93,39 +93,50 @@ export const supportedPresetTokens = [ type ModelInfo = { dimSize: number }; export const CLIP_MODEL_INFO: Record = { - RN50__openai: { dimSize: 1024 }, - RN50__yfcc15m: { dimSize: 1024 }, - RN50__cc12m: { dimSize: 1024 }, RN101__openai: { dimSize: 512 }, RN101__yfcc15m: { dimSize: 512 }, - RN50x4__openai: { dimSize: 640 }, - RN50x16__openai: { dimSize: 768 }, - RN50x64__openai: { dimSize: 1024 }, - 'ViT-B-32__openai': { dimSize: 512 }, + 'ViT-B-16__laion400m_e31': { dimSize: 512 }, + 'ViT-B-16__laion400m_e32': { dimSize: 512 }, + 'ViT-B-16__openai': { dimSize: 512 }, + 'ViT-B-32__laion2b-s34b-b79k': { dimSize: 512 }, 'ViT-B-32__laion2b_e16': { dimSize: 512 }, 'ViT-B-32__laion400m_e31': { dimSize: 512 }, 'ViT-B-32__laion400m_e32': { dimSize: 512 }, - 'ViT-B-32__laion2b-s34b-b79k': { dimSize: 512 }, - 'ViT-B-16__openai': { dimSize: 512 }, - 'ViT-B-16__laion400m_e31': { dimSize: 512 }, - 'ViT-B-16__laion400m_e32': { dimSize: 512 }, + 'ViT-B-32__openai': { dimSize: 512 }, + 'XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k': { dimSize: 512 }, + 'XLM-Roberta-Large-Vit-B-32': { dimSize: 512 }, + RN50x4__openai: { dimSize: 640 }, 'ViT-B-16-plus-240__laion400m_e31': { dimSize: 640 }, 'ViT-B-16-plus-240__laion400m_e32': { dimSize: 640 }, - 'ViT-L-14__openai': { dimSize: 768 }, - 'ViT-L-14__laion400m_e31': { dimSize: 768 }, - 'ViT-L-14__laion400m_e32': { dimSize: 768 }, - 'ViT-L-14__laion2b-s32b-b82k': { dimSize: 768 }, + 'XLM-Roberta-Large-Vit-B-16Plus': { dimSize: 640 }, + 'LABSE-Vit-L-14': { dimSize: 768 }, + RN50x16__openai: { dimSize: 768 }, + 'ViT-B-16-SigLIP-256__webli': { dimSize: 768 }, + 'ViT-B-16-SigLIP-384__webli': { dimSize: 768 }, + 'ViT-B-16-SigLIP-512__webli': { dimSize: 768 }, + 'ViT-B-16-SigLIP-i18n-256__webli': { dimSize: 768 }, + 'ViT-B-16-SigLIP__webli': { dimSize: 768 }, 'ViT-L-14-336__openai': { dimSize: 768 }, 'ViT-L-14-quickgelu__dfn2b': { dimSize: 768 }, - 'ViT-H-14__laion2b-s32b-b79k': { dimSize: 1024 }, - 'ViT-H-14-quickgelu__dfn5b': { dimSize: 1024 }, - 'ViT-H-14-378-quickgelu__dfn5b': { dimSize: 1024 }, - 'ViT-g-14__laion2b-s12b-b42k': { dimSize: 1024 }, - 'LABSE-Vit-L-14': { dimSize: 768 }, - 'XLM-Roberta-Large-Vit-B-32': { dimSize: 512 }, - 'XLM-Roberta-Large-Vit-B-16Plus': { dimSize: 640 }, + 'ViT-L-14__laion2b-s32b-b82k': { dimSize: 768 }, + 'ViT-L-14__laion400m_e31': { dimSize: 768 }, + 'ViT-L-14__laion400m_e32': { dimSize: 768 }, + 'ViT-L-14__openai': { dimSize: 768 }, 'XLM-Roberta-Large-Vit-L-14': { dimSize: 768 }, - 'XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k': { dimSize: 1024 }, + 'nllb-clip-base-siglip__mrl': { dimSize: 768 }, 'nllb-clip-base-siglip__v1': { dimSize: 768 }, + RN50__cc12m: { dimSize: 1024 }, + RN50__openai: { dimSize: 1024 }, + RN50__yfcc15m: { dimSize: 1024 }, + RN50x64__openai: { dimSize: 1024 }, + 'ViT-H-14-378-quickgelu__dfn5b': { dimSize: 1024 }, + 'ViT-H-14-quickgelu__dfn5b': { dimSize: 1024 }, + 'ViT-H-14__laion2b-s32b-b79k': { dimSize: 1024 }, + 'ViT-L-16-SigLIP-256__webli': { dimSize: 1024 }, + 'ViT-L-16-SigLIP-384__webli': { dimSize: 1024 }, + 'ViT-g-14__laion2b-s12b-b42k': { dimSize: 1024 }, + 'XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k': { dimSize: 1024 }, + 'ViT-SO400M-14-SigLIP-384__webli': { dimSize: 1152 }, + 'nllb-clip-large-siglip__mrl': { dimSize: 1152 }, 'nllb-clip-large-siglip__v1': { dimSize: 1152 }, };