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- .gitignore +1 -1
- app_hg.py +0 -2
- third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md +117 -0
- third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/config.json +28 -0
- third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/model.safetensors +3 -0
- weights/.gitattributes +35 -0
- weights/.huggingface/.gitignore +1 -0
- weights/.huggingface/download/.gitattributes.metadata +3 -0
- weights/.huggingface/download/README.md.metadata +3 -0
- weights/.huggingface/download/mvd_lite/.gitattributes.metadata +3 -0
- weights/.huggingface/download/mvd_lite/feature_extractor_clip/preprocessor_config.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/feature_extractor_vae/preprocessor_config.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/model_index.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/scheduler/scheduler_config.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/text_encoder/config.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/text_encoder/model.safetensors.metadata +3 -0
- weights/.huggingface/download/mvd_lite/tokenizer/merges.txt.metadata +3 -0
- weights/.huggingface/download/mvd_lite/tokenizer/special_tokens_map.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/tokenizer/tokenizer_config.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/tokenizer/vocab.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/unet/config.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/unet/diffusion_pytorch_model.safetensors.metadata +3 -0
- weights/.huggingface/download/mvd_lite/vae/config.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/vae/diffusion_pytorch_model.safetensors.metadata +3 -0
- weights/.huggingface/download/mvd_lite/vision_encoder/config.json.metadata +3 -0
- weights/.huggingface/download/mvd_lite/vision_encoder/model.safetensors.metadata +3 -0
- weights/.huggingface/download/mvd_lite/vision_encoder/preprocessor_config.json.metadata +3 -0
- weights/.huggingface/download/mvd_std/feature_extractor_vae/preprocessor_config.json.metadata +3 -0
- weights/.huggingface/download/mvd_std/model_index.json.metadata +3 -0
- weights/.huggingface/download/mvd_std/scheduler/scheduler_config.json.metadata +3 -0
- weights/.huggingface/download/mvd_std/uc_text_emb.pt.metadata +3 -0
- weights/.huggingface/download/mvd_std/uc_text_emb_2.pt.metadata +3 -0
- weights/.huggingface/download/mvd_std/unet/config.json.metadata +3 -0
- weights/.huggingface/download/mvd_std/unet/diffusion_pytorch_model.safetensors.metadata +3 -0
- weights/.huggingface/download/mvd_std/vae/config.json.metadata +3 -0
- weights/.huggingface/download/mvd_std/vae/diffusion_pytorch_model.safetensors.metadata +3 -0
- weights/.huggingface/download/mvd_std/vision_encoder/config.json.metadata +3 -0
- weights/.huggingface/download/mvd_std/vision_encoder/model.safetensors.metadata +3 -0
- weights/.huggingface/download/mvd_std/vision_encoder_2/config.json.metadata +3 -0
- weights/.huggingface/download/mvd_std/vision_encoder_2/model.safetensors.metadata +3 -0
- weights/.huggingface/download/mvd_std/vision_processor/preprocessor_config.json.metadata +3 -0
- weights/.huggingface/download/svrm/svrm.safetensors.metadata +3 -0
- weights/hunyuanDiT/.gitattributes +35 -0
- weights/hunyuanDiT/.huggingface/.gitignore +1 -0
- weights/hunyuanDiT/.huggingface/download/.gitattributes.metadata +3 -0
- weights/hunyuanDiT/.huggingface/download/README.md.metadata +3 -0
- weights/hunyuanDiT/.huggingface/download/model_index.json.metadata +3 -0
- weights/hunyuanDiT/.huggingface/download/scheduler/scheduler_config.json.metadata +3 -0
- weights/hunyuanDiT/.huggingface/download/text_encoder/config.json.metadata +3 -0
- weights/hunyuanDiT/.huggingface/download/text_encoder/model.safetensors.metadata +3 -0
.gitignore
CHANGED
@@ -33,9 +33,9 @@
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.codecc
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outputs
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-
weights
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.vscode/
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inference.py
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# third_party/weights
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# third_party/dust3r
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# app_hg.py
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.codecc
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outputs
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.vscode/
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inference.py
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# weights
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# third_party/weights
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# third_party/dust3r
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# app_hg.py
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app_hg.py
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check_bake_available()
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BAKE_AVAILEBLE = False
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-
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warnings.simplefilter('ignore', category=UserWarning)
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warnings.simplefilter('ignore', category=FutureWarning)
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warnings.simplefilter('ignore', category=DeprecationWarning)
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except Exception as e:
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print(f"Error downloading HunyuanDiT: {e}")
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-
# Download models before starting the app
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download_models()
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################################################################
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check_bake_available()
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BAKE_AVAILEBLE = False
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warnings.simplefilter('ignore', category=UserWarning)
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warnings.simplefilter('ignore', category=FutureWarning)
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warnings.simplefilter('ignore', category=DeprecationWarning)
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except Exception as e:
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print(f"Error downloading HunyuanDiT: {e}")
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download_models()
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################################################################
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third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md
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---
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tags:
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- vision
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---
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## DUSt3R
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# Model info
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Project page: https://dust3r.europe.naverlabs.com/
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# How to use
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Here's how to load the model (after [installing](https://github.com/naver/dust3r?tab=readme-ov-file#installation) the dust3r package):
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```python
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from dust3r.model import AsymmetricCroCo3DStereo
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import torch
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model = AsymmetricCroCo3DStereo.from_pretrained("nielsr/DUSt3R_ViTLarge_BaseDecoder_512_dpt")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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```
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Next, one can run inference as follows:
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```
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from dust3r.inference import inference
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from dust3r.utils.image import load_images
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from dust3r.image_pairs import make_pairs
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from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
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if __name__ == '__main__':
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batch_size = 1
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schedule = 'cosine'
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lr = 0.01
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niter = 300
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# load_images can take a list of images or a directory
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images = load_images(['croco/assets/Chateau1.png', 'croco/assets/Chateau2.png'], size=512)
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pairs = make_pairs(images, scene_graph='complete', prefilter=None, symmetrize=True)
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output = inference(pairs, model, device, batch_size=batch_size)
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# at this stage, you have the raw dust3r predictions
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view1, pred1 = output['view1'], output['pred1']
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view2, pred2 = output['view2'], output['pred2']
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# here, view1, pred1, view2, pred2 are dicts of lists of len(2)
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# -> because we symmetrize we have (im1, im2) and (im2, im1) pairs
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# in each view you have:
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# an integer image identifier: view1['idx'] and view2['idx']
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# the img: view1['img'] and view2['img']
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# the image shape: view1['true_shape'] and view2['true_shape']
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# an instance string output by the dataloader: view1['instance'] and view2['instance']
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# pred1 and pred2 contains the confidence values: pred1['conf'] and pred2['conf']
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# pred1 contains 3D points for view1['img'] in view1['img'] space: pred1['pts3d']
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# pred2 contains 3D points for view2['img'] in view1['img'] space: pred2['pts3d_in_other_view']
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# next we'll use the global_aligner to align the predictions
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# depending on your task, you may be fine with the raw output and not need it
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# with only two input images, you could use GlobalAlignerMode.PairViewer: it would just convert the output
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# if using GlobalAlignerMode.PairViewer, no need to run compute_global_alignment
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scene = global_aligner(output, device=device, mode=GlobalAlignerMode.PointCloudOptimizer)
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loss = scene.compute_global_alignment(init="mst", niter=niter, schedule=schedule, lr=lr)
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# retrieve useful values from scene:
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imgs = scene.imgs
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focals = scene.get_focals()
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poses = scene.get_im_poses()
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pts3d = scene.get_pts3d()
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confidence_masks = scene.get_masks()
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# visualize reconstruction
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scene.show()
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# find 2D-2D matches between the two images
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from dust3r.utils.geometry import find_reciprocal_matches, xy_grid
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pts2d_list, pts3d_list = [], []
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for i in range(2):
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conf_i = confidence_masks[i].cpu().numpy()
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pts2d_list.append(xy_grid(*imgs[i].shape[:2][::-1])[conf_i]) # imgs[i].shape[:2] = (H, W)
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pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i])
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reciprocal_in_P2, nn2_in_P1, num_matches = find_reciprocal_matches(*pts3d_list)
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print(f'found {num_matches} matches')
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matches_im1 = pts2d_list[1][reciprocal_in_P2]
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matches_im0 = pts2d_list[0][nn2_in_P1][reciprocal_in_P2]
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# visualize a few matches
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import numpy as np
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from matplotlib import pyplot as pl
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n_viz = 10
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match_idx_to_viz = np.round(np.linspace(0, num_matches-1, n_viz)).astype(int)
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viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz]
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H0, W0, H1, W1 = *imgs[0].shape[:2], *imgs[1].shape[:2]
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img0 = np.pad(imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
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img1 = np.pad(imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
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img = np.concatenate((img0, img1), axis=1)
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pl.figure()
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pl.imshow(img)
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cmap = pl.get_cmap('jet')
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for i in range(n_viz):
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(x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T
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pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)
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pl.show(block=True)
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```
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### BibTeX entry and citation info
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```bibtex
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@journal{dust3r2023,
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title={{DUSt3R: Geometric 3D Vision Made Easy}},
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author={{Wang, Shuzhe and Leroy, Vincent and Cabon, Yohann and Chidlovskii, Boris and Revaud Jerome}},
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journal={arXiv preprint 2312.14132},
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year={2023}}
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```
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third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/config.json
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{
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"output_mode": "pts3d",
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"head_type": "dpt",
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"depth_mode": [
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"exp",
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-Infinity,
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Infinity
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],
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"conf_mode": [
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"exp",
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1,
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Infinity
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],
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"freeze": "none",
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"landscape_only": false,
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"patch_embed_cls": "PatchEmbedDust3R",
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"enc_depth": 24,
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"dec_depth": 12,
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"enc_embed_dim": 1024,
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"dec_embed_dim": 768,
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"enc_num_heads": 16,
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"dec_num_heads": 12,
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"pos_embed": "RoPE100",
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"img_size": [
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512,
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512
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]
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}
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third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:cdbd4c6d7e91df3f3dc3551a0aadc7983bc85ed9e02794fba633eb1ed10174b5
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size 2284790056
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weights/.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.npz filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tar filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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weights/.huggingface/.gitignore
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*
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weights/.huggingface/download/.gitattributes.metadata
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