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Cataloged from microsoft/Mage-ViT
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Mage-ViT is the visual encoder at the core of Mage-VL. It is a Codec-ViT built primarily for video, where a single image is simply the degenerate one-frame case. Mage-ViT follows a codec-aligned sparsity principle: visual tokens should be spent where a video codec spends bits, since codec bit-allocation is a natural proxy for spatio-temporal importance. On a shared 16Γ16 patch grid it keeps every anchor (I-frame) patch and only the motion-salient predicted (P-frame) patches, while a shared 3D rotary position encoding preserves spatio-temporal structure even after large fractions of the grid are dropped.
This repository is the ViT-pre-trained checkpoint only β it has not gone through the joint VLM training with the language model. Use it as a data-efficient, codec-native visual encoder, or as a drop-in ViT for your own multimodal training.
A 24-layer pre-norm Vision Transformer trunk (hidden size 1024, 16 attention heads, GELU MLP at 4Γ expansion) processes the variable-length token sequence produced by the codec-driven patchifier, followed by a multi-head attention pooling head. The standalone encoder in this repo consumes pixel_values (and optional patch_positions); codec-driven patch selection is applied upstream in the Mage-VL data pipeline.
Pre-training is a two-stage, from-scratch recipe in bf16: (1) variable-resolution image pre-training (224β448), then (2) joint image + video pre-training (video at resolution 256, 64 frames per clip, 4096-token budget).
Mage-ViT follows the Mage-VL runtime:
Attention is dispatched internally to sdpa (default) β flash_attention_2 β eager, so flash-attn is optional and the model runs on CPU or GPU out of the box.
Pass attn_implementation="flash_attention_2" (or "sdpa" / "eager") to from_pretrained to pick the attention backend.
Stack the preprocessed frames into [B, C, T, H, W] and pass a patch_positions tensor of shape [B, T * tokens_per_frame, 3] giving the (t, h, w) grid coordinate of every patch:
Released under the MIT License.
pip install "transformers>=5.7" torch torchvision pillow# optional, for the fastest GPU attention path:# pip install flash-attn --no-build-isolationimport torchfrom PIL import Imagefrom transformers import AutoModel, AutoImageProcessor
model = AutoModel.from_pretrained( "microsoft/Mage-ViT", trust_remote_code=True).to("cuda").eval()processor = AutoImageProcessor.from_pretrained( "microsoft/Mage-ViT", trust_remote_code=True)
image = Image.open("your_image.jpg")pixel_values = processor(images=image, return_tensors="pt")["pixel_values"].to("cuda")
with torch.no_grad(): out = model(pixel_values) # pixel_values: [B, 3, H, W]
patch_features = out.last_hidden_state # [B, num_patches, 1024]pooled_feature = out.pooler_output # [B, 1024]import torchfrom PIL import Image
PATCH = 16
def build_patch_positions(num_frames, target_frames, grid_h, grid_w, device="cuda"): # temporal index for each frame, spread across the target timeline t = torch.linspace(0, target_frames - 1, num_frames, device=device).long() t = t.repeat_interleave(grid_h * grid_w) # [T * H * W] h = torch.arange(grid_h, device=device).repeat_interleave(grid_w).repeat(num_frames) w = torch.arange(grid_w, device=device).repeat(grid_h).repeat(num_frames) return torch.stack([t, h, w], dim=-1).unsqueeze(0) # [1, T*H*W, 3]
frames = [Image.open(f"frame_{i}.jpg") for i in range(16)] # your sampled framespv = processor(images=frames, return_tensors="pt")["pixel_values"] # [T, C, H, W]video = pv.unsqueeze(0).permute(0, 2, 1, 3, 4).to("cuda") # [1, C, T, H, W]
gh, gw = video.shape[-2] // PATCH, video.shape[-1] // PATCHpatch_positions = build_patch_positions(num_frames=16, target_frames=64, grid_h=gh, grid_w=gw)
with torch.no_grad(): out = model(video, patch_positions=patch_positions)