diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index 0eb66176..79ebe18d 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -904,16 +904,17 @@ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str =
             getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/bias']))
 
 
-def _convert_openai_clip(state_dict, model):
+def _convert_openai_clip(state_dict, model, prefix='visual.'):
     out_dict = {}
     swaps = [
-        ('visual.', ''), ('conv1', 'patch_embed.proj'), ('positional_embedding', 'pos_embed'),
+        ('conv1', 'patch_embed.proj'), ('positional_embedding', 'pos_embed'),
         ('transformer.resblocks.', 'blocks.'), ('ln_pre', 'norm_pre'), ('ln_post', 'norm'), ('ln_', 'norm'),
         ('in_proj_', 'qkv.'), ('out_proj', 'proj'), ('mlp.c_fc', 'mlp.fc1'), ('mlp.c_proj', 'mlp.fc2'),
     ]
     for k, v in state_dict.items():
-        if not k.startswith('visual.'):
+        if not k.startswith(prefix):
             continue
+        k = k.replace(prefix, '')
         for sp in swaps:
             k = k.replace(sp[0], sp[1])
 
@@ -974,6 +975,8 @@ def checkpoint_filter_fn(
 
     if 'visual.class_embedding' in state_dict:
         return _convert_openai_clip(state_dict, model)
+    elif 'module.visual.class_embedding' in state_dict:
+        return _convert_openai_clip(state_dict, model, prefix='module.visual.')
 
     if "mask_token" in state_dict:
         state_dict = _convert_dinov2(state_dict, model)
@@ -1416,6 +1419,10 @@ default_cfgs = generate_default_cfgs({
         hf_hub_id='laion/CLIP-ViT-B-16-DataComp.XL-s13B-b90K',
         hf_hub_filename='open_clip_pytorch_model.bin',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
+    'vit_base_patch16_clip_224.dfn2b': _cfg(
+        hf_hub_id='apple/DFN2B-CLIP-ViT-B-16',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
     'vit_large_patch14_clip_224.laion2b': _cfg(
         hf_hub_id='laion/CLIP-ViT-L-14-laion2B-s32B-b82K',
         hf_hub_filename='open_clip_pytorch_model.bin',
@@ -1424,10 +1431,22 @@ default_cfgs = generate_default_cfgs({
         hf_hub_id='laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K',
         hf_hub_filename='open_clip_pytorch_model.bin',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
+    'vit_large_patch14_clip_224.dfn2b': _cfg(
+        hf_hub_id='apple/DFN2B-CLIP-ViT-L-14',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
     'vit_huge_patch14_clip_224.laion2b': _cfg(
         hf_hub_id='laion/CLIP-ViT-H-14-laion2B-s32B-b79K',
         hf_hub_filename='open_clip_pytorch_model.bin',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
+    'vit_huge_patch14_clip_224.dfn5b': _cfg(
+        hf_hub_id='apple/DFN5B-CLIP-ViT-H-14',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
+    'vit_huge_patch14_clip_378.dfn5b': _cfg(
+        hf_hub_id='apple/DFN5B-CLIP-ViT-H-14-378',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
     'vit_giant_patch14_clip_224.laion2b': _cfg(
         hf_hub_id='laion/CLIP-ViT-g-14-laion2B-s12B-b42K',
         hf_hub_filename='open_clip_pytorch_model.bin',
@@ -2026,6 +2045,16 @@ def vit_huge_patch14_clip_336(pretrained=False, **kwargs) -> VisionTransformer:
     return model
 
 
+@register_model
+def vit_huge_patch14_clip_378(pretrained=False, **kwargs) -> VisionTransformer:
+    """ ViT-Huge model (ViT-H/14) CLIP image tower @ 378x378
+    """
+    model_args = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
+    model = _create_vision_transformer(
+        'vit_huge_patch14_clip_378', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
 @register_model
 def vit_giant_patch14_clip_224(pretrained=False, **kwargs) -> VisionTransformer:
     """ ViT-Giant (little-g) model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560