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  1. .gitignore +1 -1
  2. app_hg.py +0 -2
  3. third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md +117 -0
  4. third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/config.json +28 -0
  5. third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/model.safetensors +3 -0
  6. weights/.gitattributes +35 -0
  7. weights/.huggingface/.gitignore +1 -0
  8. weights/.huggingface/download/.gitattributes.metadata +3 -0
  9. weights/.huggingface/download/README.md.metadata +3 -0
  10. weights/.huggingface/download/mvd_lite/.gitattributes.metadata +3 -0
  11. weights/.huggingface/download/mvd_lite/feature_extractor_clip/preprocessor_config.json.metadata +3 -0
  12. weights/.huggingface/download/mvd_lite/feature_extractor_vae/preprocessor_config.json.metadata +3 -0
  13. weights/.huggingface/download/mvd_lite/model_index.json.metadata +3 -0
  14. weights/.huggingface/download/mvd_lite/scheduler/scheduler_config.json.metadata +3 -0
  15. weights/.huggingface/download/mvd_lite/text_encoder/config.json.metadata +3 -0
  16. weights/.huggingface/download/mvd_lite/text_encoder/model.safetensors.metadata +3 -0
  17. weights/.huggingface/download/mvd_lite/tokenizer/merges.txt.metadata +3 -0
  18. weights/.huggingface/download/mvd_lite/tokenizer/special_tokens_map.json.metadata +3 -0
  19. weights/.huggingface/download/mvd_lite/tokenizer/tokenizer_config.json.metadata +3 -0
  20. weights/.huggingface/download/mvd_lite/tokenizer/vocab.json.metadata +3 -0
  21. weights/.huggingface/download/mvd_lite/unet/config.json.metadata +3 -0
  22. weights/.huggingface/download/mvd_lite/unet/diffusion_pytorch_model.safetensors.metadata +3 -0
  23. weights/.huggingface/download/mvd_lite/vae/config.json.metadata +3 -0
  24. weights/.huggingface/download/mvd_lite/vae/diffusion_pytorch_model.safetensors.metadata +3 -0
  25. weights/.huggingface/download/mvd_lite/vision_encoder/config.json.metadata +3 -0
  26. weights/.huggingface/download/mvd_lite/vision_encoder/model.safetensors.metadata +3 -0
  27. weights/.huggingface/download/mvd_lite/vision_encoder/preprocessor_config.json.metadata +3 -0
  28. weights/.huggingface/download/mvd_std/feature_extractor_vae/preprocessor_config.json.metadata +3 -0
  29. weights/.huggingface/download/mvd_std/model_index.json.metadata +3 -0
  30. weights/.huggingface/download/mvd_std/scheduler/scheduler_config.json.metadata +3 -0
  31. weights/.huggingface/download/mvd_std/uc_text_emb.pt.metadata +3 -0
  32. weights/.huggingface/download/mvd_std/uc_text_emb_2.pt.metadata +3 -0
  33. weights/.huggingface/download/mvd_std/unet/config.json.metadata +3 -0
  34. weights/.huggingface/download/mvd_std/unet/diffusion_pytorch_model.safetensors.metadata +3 -0
  35. weights/.huggingface/download/mvd_std/vae/config.json.metadata +3 -0
  36. weights/.huggingface/download/mvd_std/vae/diffusion_pytorch_model.safetensors.metadata +3 -0
  37. weights/.huggingface/download/mvd_std/vision_encoder/config.json.metadata +3 -0
  38. weights/.huggingface/download/mvd_std/vision_encoder/model.safetensors.metadata +3 -0
  39. weights/.huggingface/download/mvd_std/vision_encoder_2/config.json.metadata +3 -0
  40. weights/.huggingface/download/mvd_std/vision_encoder_2/model.safetensors.metadata +3 -0
  41. weights/.huggingface/download/mvd_std/vision_processor/preprocessor_config.json.metadata +3 -0
  42. weights/.huggingface/download/svrm/svrm.safetensors.metadata +3 -0
  43. weights/hunyuanDiT/.gitattributes +35 -0
  44. weights/hunyuanDiT/.huggingface/.gitignore +1 -0
  45. weights/hunyuanDiT/.huggingface/download/.gitattributes.metadata +3 -0
  46. weights/hunyuanDiT/.huggingface/download/README.md.metadata +3 -0
  47. weights/hunyuanDiT/.huggingface/download/model_index.json.metadata +3 -0
  48. weights/hunyuanDiT/.huggingface/download/scheduler/scheduler_config.json.metadata +3 -0
  49. weights/hunyuanDiT/.huggingface/download/text_encoder/config.json.metadata +3 -0
  50. weights/hunyuanDiT/.huggingface/download/text_encoder/model.safetensors.metadata +3 -0
.gitignore CHANGED
@@ -33,9 +33,9 @@
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  .codecc
34
 
35
  outputs
36
- weights
37
  .vscode/
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  inference.py
 
39
  # third_party/weights
40
  # third_party/dust3r
41
  # app_hg.py
 
33
  .codecc
34
 
35
  outputs
 
36
  .vscode/
37
  inference.py
38
+ # weights
39
  # third_party/weights
40
  # third_party/dust3r
41
  # app_hg.py
app_hg.py CHANGED
@@ -48,7 +48,6 @@ except Exception as err:
48
  check_bake_available()
49
  BAKE_AVAILEBLE = False
50
 
51
-
52
  warnings.simplefilter('ignore', category=UserWarning)
53
  warnings.simplefilter('ignore', category=FutureWarning)
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  warnings.simplefilter('ignore', category=DeprecationWarning)
@@ -85,7 +84,6 @@ def download_models():
85
  except Exception as e:
86
  print(f"Error downloading HunyuanDiT: {e}")
87
 
88
- # Download models before starting the app
89
  download_models()
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91
  ################################################################
 
48
  check_bake_available()
49
  BAKE_AVAILEBLE = False
50
 
 
51
  warnings.simplefilter('ignore', category=UserWarning)
52
  warnings.simplefilter('ignore', category=FutureWarning)
53
  warnings.simplefilter('ignore', category=DeprecationWarning)
 
84
  except Exception as e:
85
  print(f"Error downloading HunyuanDiT: {e}")
86
 
 
87
  download_models()
88
 
89
  ################################################################
third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md ADDED
@@ -0,0 +1,117 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - vision
4
+ ---
5
+
6
+ ## DUSt3R
7
+
8
+ # Model info
9
+
10
+ Project page: https://dust3r.europe.naverlabs.com/
11
+
12
+ # How to use
13
+
14
+ Here's how to load the model (after [installing](https://github.com/naver/dust3r?tab=readme-ov-file#installation) the dust3r package):
15
+
16
+ ```python
17
+ from dust3r.model import AsymmetricCroCo3DStereo
18
+ import torch
19
+
20
+ model = AsymmetricCroCo3DStereo.from_pretrained("nielsr/DUSt3R_ViTLarge_BaseDecoder_512_dpt")
21
+
22
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
23
+ model.to(device)
24
+ ```
25
+
26
+ Next, one can run inference as follows:
27
+
28
+ ```
29
+ from dust3r.inference import inference
30
+ from dust3r.utils.image import load_images
31
+ from dust3r.image_pairs import make_pairs
32
+ from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
33
+
34
+ if __name__ == '__main__':
35
+ batch_size = 1
36
+ schedule = 'cosine'
37
+ lr = 0.01
38
+ niter = 300
39
+
40
+ # load_images can take a list of images or a directory
41
+ images = load_images(['croco/assets/Chateau1.png', 'croco/assets/Chateau2.png'], size=512)
42
+ pairs = make_pairs(images, scene_graph='complete', prefilter=None, symmetrize=True)
43
+ output = inference(pairs, model, device, batch_size=batch_size)
44
+
45
+ # at this stage, you have the raw dust3r predictions
46
+ view1, pred1 = output['view1'], output['pred1']
47
+ view2, pred2 = output['view2'], output['pred2']
48
+ # here, view1, pred1, view2, pred2 are dicts of lists of len(2)
49
+ # -> because we symmetrize we have (im1, im2) and (im2, im1) pairs
50
+ # in each view you have:
51
+ # an integer image identifier: view1['idx'] and view2['idx']
52
+ # the img: view1['img'] and view2['img']
53
+ # the image shape: view1['true_shape'] and view2['true_shape']
54
+ # an instance string output by the dataloader: view1['instance'] and view2['instance']
55
+ # pred1 and pred2 contains the confidence values: pred1['conf'] and pred2['conf']
56
+ # pred1 contains 3D points for view1['img'] in view1['img'] space: pred1['pts3d']
57
+ # pred2 contains 3D points for view2['img'] in view1['img'] space: pred2['pts3d_in_other_view']
58
+
59
+ # next we'll use the global_aligner to align the predictions
60
+ # depending on your task, you may be fine with the raw output and not need it
61
+ # with only two input images, you could use GlobalAlignerMode.PairViewer: it would just convert the output
62
+ # if using GlobalAlignerMode.PairViewer, no need to run compute_global_alignment
63
+ scene = global_aligner(output, device=device, mode=GlobalAlignerMode.PointCloudOptimizer)
64
+ loss = scene.compute_global_alignment(init="mst", niter=niter, schedule=schedule, lr=lr)
65
+
66
+ # retrieve useful values from scene:
67
+ imgs = scene.imgs
68
+ focals = scene.get_focals()
69
+ poses = scene.get_im_poses()
70
+ pts3d = scene.get_pts3d()
71
+ confidence_masks = scene.get_masks()
72
+
73
+ # visualize reconstruction
74
+ scene.show()
75
+
76
+ # find 2D-2D matches between the two images
77
+ from dust3r.utils.geometry import find_reciprocal_matches, xy_grid
78
+ pts2d_list, pts3d_list = [], []
79
+ for i in range(2):
80
+ conf_i = confidence_masks[i].cpu().numpy()
81
+ pts2d_list.append(xy_grid(*imgs[i].shape[:2][::-1])[conf_i]) # imgs[i].shape[:2] = (H, W)
82
+ pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i])
83
+ reciprocal_in_P2, nn2_in_P1, num_matches = find_reciprocal_matches(*pts3d_list)
84
+ print(f'found {num_matches} matches')
85
+ matches_im1 = pts2d_list[1][reciprocal_in_P2]
86
+ matches_im0 = pts2d_list[0][nn2_in_P1][reciprocal_in_P2]
87
+
88
+ # visualize a few matches
89
+ import numpy as np
90
+ from matplotlib import pyplot as pl
91
+ n_viz = 10
92
+ match_idx_to_viz = np.round(np.linspace(0, num_matches-1, n_viz)).astype(int)
93
+ viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz]
94
+
95
+ H0, W0, H1, W1 = *imgs[0].shape[:2], *imgs[1].shape[:2]
96
+ img0 = np.pad(imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
97
+ img1 = np.pad(imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
98
+ img = np.concatenate((img0, img1), axis=1)
99
+ pl.figure()
100
+ pl.imshow(img)
101
+ cmap = pl.get_cmap('jet')
102
+ for i in range(n_viz):
103
+ (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T
104
+ pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)
105
+ pl.show(block=True)
106
+
107
+ ```
108
+
109
+ ### BibTeX entry and citation info
110
+
111
+ ```bibtex
112
+ @journal{dust3r2023,
113
+ title={{DUSt3R: Geometric 3D Vision Made Easy}},
114
+ author={{Wang, Shuzhe and Leroy, Vincent and Cabon, Yohann and Chidlovskii, Boris and Revaud Jerome}},
115
+ journal={arXiv preprint 2312.14132},
116
+ year={2023}}
117
+ ```
third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/config.json ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "output_mode": "pts3d",
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+ "head_type": "dpt",
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+ "depth_mode": [
5
+ "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",
15
+ "landscape_only": false,
16
+ "patch_embed_cls": "PatchEmbedDust3R",
17
+ "enc_depth": 24,
18
+ "dec_depth": 12,
19
+ "enc_embed_dim": 1024,
20
+ "dec_embed_dim": 768,
21
+ "enc_num_heads": 16,
22
+ "dec_num_heads": 12,
23
+ "pos_embed": "RoPE100",
24
+ "img_size": [
25
+ 512,
26
+ 512
27
+ ]
28
+ }
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