diff --git a/.gitignore b/.gitignore
index 4cf2853ee486a98c18868a367b42e25f9d1b9382..a854e73dd40c598b133e01d97d8ff6e4deea82c9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -33,9 +33,9 @@
.codecc
outputs
-weights
.vscode/
inference.py
+# weights
# third_party/weights
# third_party/dust3r
# app_hg.py
diff --git a/app_hg.py b/app_hg.py
index 59e78ac2c8511e5be3cb0ad9e69c4d72c646b5b8..4dc5add07c3d151ff9982f73bd9c11e5adac3db3 100644
--- a/app_hg.py
+++ b/app_hg.py
@@ -48,7 +48,6 @@ except Exception as err:
check_bake_available()
BAKE_AVAILEBLE = False
-
warnings.simplefilter('ignore', category=UserWarning)
warnings.simplefilter('ignore', category=FutureWarning)
warnings.simplefilter('ignore', category=DeprecationWarning)
@@ -85,7 +84,6 @@ def download_models():
except Exception as e:
print(f"Error downloading HunyuanDiT: {e}")
-# Download models before starting the app
download_models()
################################################################
diff --git a/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md b/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ac44d459bfaa06e5c92687313c2f1661dd6de37d
--- /dev/null
+++ b/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md
@@ -0,0 +1,117 @@
+---
+tags:
+- vision
+---
+
+## DUSt3R
+
+# Model info
+
+Project page: https://dust3r.europe.naverlabs.com/
+
+# How to use
+
+Here's how to load the model (after [installing](https://github.com/naver/dust3r?tab=readme-ov-file#installation) the dust3r package):
+
+```python
+from dust3r.model import AsymmetricCroCo3DStereo
+import torch
+
+model = AsymmetricCroCo3DStereo.from_pretrained("nielsr/DUSt3R_ViTLarge_BaseDecoder_512_dpt")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+```
+
+Next, one can run inference as follows:
+
+```
+from dust3r.inference import inference
+from dust3r.utils.image import load_images
+from dust3r.image_pairs import make_pairs
+from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
+
+if __name__ == '__main__':
+ batch_size = 1
+ schedule = 'cosine'
+ lr = 0.01
+ niter = 300
+
+ # load_images can take a list of images or a directory
+ images = load_images(['croco/assets/Chateau1.png', 'croco/assets/Chateau2.png'], size=512)
+ pairs = make_pairs(images, scene_graph='complete', prefilter=None, symmetrize=True)
+ output = inference(pairs, model, device, batch_size=batch_size)
+
+ # at this stage, you have the raw dust3r predictions
+ view1, pred1 = output['view1'], output['pred1']
+ view2, pred2 = output['view2'], output['pred2']
+ # here, view1, pred1, view2, pred2 are dicts of lists of len(2)
+ # -> because we symmetrize we have (im1, im2) and (im2, im1) pairs
+ # in each view you have:
+ # an integer image identifier: view1['idx'] and view2['idx']
+ # the img: view1['img'] and view2['img']
+ # the image shape: view1['true_shape'] and view2['true_shape']
+ # an instance string output by the dataloader: view1['instance'] and view2['instance']
+ # pred1 and pred2 contains the confidence values: pred1['conf'] and pred2['conf']
+ # pred1 contains 3D points for view1['img'] in view1['img'] space: pred1['pts3d']
+ # pred2 contains 3D points for view2['img'] in view1['img'] space: pred2['pts3d_in_other_view']
+
+ # next we'll use the global_aligner to align the predictions
+ # depending on your task, you may be fine with the raw output and not need it
+ # with only two input images, you could use GlobalAlignerMode.PairViewer: it would just convert the output
+ # if using GlobalAlignerMode.PairViewer, no need to run compute_global_alignment
+ scene = global_aligner(output, device=device, mode=GlobalAlignerMode.PointCloudOptimizer)
+ loss = scene.compute_global_alignment(init="mst", niter=niter, schedule=schedule, lr=lr)
+
+ # retrieve useful values from scene:
+ imgs = scene.imgs
+ focals = scene.get_focals()
+ poses = scene.get_im_poses()
+ pts3d = scene.get_pts3d()
+ confidence_masks = scene.get_masks()
+
+ # visualize reconstruction
+ scene.show()
+
+ # find 2D-2D matches between the two images
+ from dust3r.utils.geometry import find_reciprocal_matches, xy_grid
+ pts2d_list, pts3d_list = [], []
+ for i in range(2):
+ conf_i = confidence_masks[i].cpu().numpy()
+ pts2d_list.append(xy_grid(*imgs[i].shape[:2][::-1])[conf_i]) # imgs[i].shape[:2] = (H, W)
+ pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i])
+ reciprocal_in_P2, nn2_in_P1, num_matches = find_reciprocal_matches(*pts3d_list)
+ print(f'found {num_matches} matches')
+ matches_im1 = pts2d_list[1][reciprocal_in_P2]
+ matches_im0 = pts2d_list[0][nn2_in_P1][reciprocal_in_P2]
+
+ # visualize a few matches
+ import numpy as np
+ from matplotlib import pyplot as pl
+ n_viz = 10
+ match_idx_to_viz = np.round(np.linspace(0, num_matches-1, n_viz)).astype(int)
+ viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz]
+
+ H0, W0, H1, W1 = *imgs[0].shape[:2], *imgs[1].shape[:2]
+ img0 = np.pad(imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
+ img1 = np.pad(imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
+ img = np.concatenate((img0, img1), axis=1)
+ pl.figure()
+ pl.imshow(img)
+ cmap = pl.get_cmap('jet')
+ for i in range(n_viz):
+ (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T
+ pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)
+ pl.show(block=True)
+
+```
+
+### BibTeX entry and citation info
+
+```bibtex
+@journal{dust3r2023,
+title={{DUSt3R: Geometric 3D Vision Made Easy}},
+author={{Wang, Shuzhe and Leroy, Vincent and Cabon, Yohann and Chidlovskii, Boris and Revaud Jerome}},
+journal={arXiv preprint 2312.14132},
+year={2023}}
+```
\ No newline at end of file
diff --git a/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/config.json b/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..068a82af1fd355b65536180b484b2251b514ee08
--- /dev/null
+++ b/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/config.json
@@ -0,0 +1,28 @@
+{
+ "output_mode": "pts3d",
+ "head_type": "dpt",
+ "depth_mode": [
+ "exp",
+ -Infinity,
+ Infinity
+ ],
+ "conf_mode": [
+ "exp",
+ 1,
+ Infinity
+ ],
+ "freeze": "none",
+ "landscape_only": false,
+ "patch_embed_cls": "PatchEmbedDust3R",
+ "enc_depth": 24,
+ "dec_depth": 12,
+ "enc_embed_dim": 1024,
+ "dec_embed_dim": 768,
+ "enc_num_heads": 16,
+ "dec_num_heads": 12,
+ "pos_embed": "RoPE100",
+ "img_size": [
+ 512,
+ 512
+ ]
+}
\ No newline at end of file
diff --git a/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/model.safetensors b/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/model.safetensors
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--- /dev/null
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diff --git a/weights/.gitattributes b/weights/.gitattributes
new file mode 100644
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--- /dev/null
+++ b/weights/.gitattributes
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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
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diff --git a/weights/hunyuanDiT/README.md b/weights/hunyuanDiT/README.md
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+---
+license: other
+license_name: tencent-hunyuan-community
+license_link: https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/LICENSE.txt
+language:
+- en
+---
+
+
+
+
+
+
+# Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
+# 混元-DiT: 具有细粒度中文理解的多分辨率Diffusion Transformer
+
+[[Arxiv]](https://arxiv.org/abs/2405.08748) [[project page]](https://dit.hunyuan.tencent.com/) [[github]](https://github.com/Tencent/HunyuanDiT)
+
+This repo contains the distilled Hunyuan-DiT in 🤗 [Diffusers](https://github.com/huggingface/diffusers) format.
+
+It supports 25-step text-to-image generation.
+
+## Dependency
+Please install PyTorch first, following the instruction in [https://pytorch.org](https://pytorch.org)
+
+Install the latest version of transformers with `pip`:
+```
+pip install --upgrade transformers
+```
+
+Then install the latest github version of 🤗 Diffusers with `pip`:
+```
+pip install git+https://github.com/huggingface/diffusers.git
+```
+
+## Example Usage with 🤗 Diffusers
+```py
+import torch
+from diffusers import HunyuanDiTPipeline
+
+pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled", torch_dtype=torch.float16)
+pipe.to("cuda")
+
+# You may also use English prompt as HunyuanDiT supports both English and Chinese
+# prompt = "An astronaut riding a horse"
+prompt = "一个宇航员在骑马"
+image = pipe(prompt).images[0]
+```
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/646b0bbdec9a61e871799339/xoO_-5N7eZ-aCt4KpBYY6.png)
+
+## 📈 Comparisons
+In order to comprehensively compare the generation capabilities of HunyuanDiT and other models, we constructed a 4-dimensional test set, including Text-Image Consistency, Excluding AI Artifacts, Subject Clarity, Aesthetic. More than 50 professional evaluators performs the evaluation.
+
+
+
+
+
+ Model | Open Source | Text-Image Consistency (%) | Excluding AI Artifacts (%) | Subject Clarity (%) | Aesthetics (%) | Overall (%) |
+
+
+
+
+ SDXL | ✔ | 64.3 | 60.6 | 91.1 | 76.3 | 42.7 |
+
+
+ PixArt-α | ✔ | 68.3 | 60.9 | 93.2 | 77.5 | 45.5 |
+
+
+ Playground 2.5 | ✔ | 71.9 | 70.8 | 94.9 | 83.3 | 54.3 |
+
+
+
+ SD 3 | ✘ | 77.1 | 69.3 | 94.6 | 82.5 | 56.7 |
+
+
+
+ MidJourney v6 | ✘ | 73.5 | 80.2 | 93.5 | 87.2 | 63.3 |
+
+
+ DALL-E 3 | ✘ | 83.9 | 80.3 | 96.5 | 89.4 | 71.0 |
+
+
+ Hunyuan-DiT | ✔ | 74.2 | 74.3 | 95.4 | 86.6 | 59.0 |
+
+
+
+
+
+## 🎥 Visualization
+
+* **Chinese Elements**
+
+
+
+
+* **Long Text Input**
+
+
+
+
+
+
+
+## 🔥🔥🔥 Tencent Hunyuan Bot
+
+Welcome to [Tencent Hunyuan Bot](https://hunyuan.tencent.com/bot/chat), where you can explore our innovative products in multi-round conversation!
\ No newline at end of file
diff --git a/weights/hunyuanDiT/model_index.json b/weights/hunyuanDiT/model_index.json
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+{
+ "_class_name": "HunyuanDiTPipeline",
+ "_diffusers_version": "0.29.0",
+ "feature_extractor": [
+ null,
+ null
+ ],
+ "requires_safety_checker": true,
+ "safety_checker": [
+ null,
+ null
+ ],
+ "scheduler": [
+ "diffusers",
+ "DDPMScheduler"
+ ],
+ "text_encoder": [
+ "transformers",
+ "BertModel"
+ ],
+ "text_encoder_2": [
+ "transformers",
+ "T5EncoderModel"
+ ],
+ "tokenizer": [
+ "transformers",
+ "BertTokenizer"
+ ],
+ "tokenizer_2": [
+ "transformers",
+ "T5Tokenizer"
+ ],
+ "transformer": [
+ "diffusers",
+ "HunyuanDiT2DModel"
+ ],
+ "vae": [
+ "diffusers",
+ "AutoencoderKL"
+ ]
+}
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+{
+ "_class_name": "DDPMScheduler",
+ "_diffusers_version": "0.29.0",
+ "beta_end": 0.03,
+ "beta_schedule": "scaled_linear",
+ "beta_start": 0.00085,
+ "clip_sample": false,
+ "clip_sample_range": 1.0,
+ "dynamic_thresholding_ratio": 0.995,
+ "num_train_timesteps": 1000,
+ "prediction_type": "v_prediction",
+ "rescale_betas_zero_snr": false,
+ "sample_max_value": 1.0,
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+ "skip_prk_steps": true,
+ "steps_offset": 1,
+ "thresholding": false,
+ "timestep_spacing": "leading",
+ "trained_betas": null,
+ "variance_type": "fixed_small"
+}
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+ "transformers_version": "4.41.1",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 47020
+}
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diff --git a/weights/hunyuanDiT/tokenizer/special_tokens_map.json b/weights/hunyuanDiT/tokenizer/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bbecc17cabbcbd3112c14d6982b51403b264bfa
--- /dev/null
+++ b/weights/hunyuanDiT/tokenizer/special_tokens_map.json
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+{
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diff --git a/weights/hunyuanDiT/tokenizer/tokenizer_config.json b/weights/hunyuanDiT/tokenizer/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c57897ebb275499b3c6d5284f1187860f86741e6
--- /dev/null
+++ b/weights/hunyuanDiT/tokenizer/tokenizer_config.json
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diff --git a/weights/hunyuanDiT/tokenizer/vocab.txt b/weights/hunyuanDiT/tokenizer/vocab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6246906805d02aca01714c71e4c8d77b69a7a131
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..ae0c5be6f35217e51c4c000fd325d8de0294e99c
--- /dev/null
+++ b/weights/mvd_lite/tokenizer/special_tokens_map.json
@@ -0,0 +1,24 @@
+{
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+ "content": "<|startoftext|>",
+ "lstrip": false,
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+ "rstrip": false,
+ "single_word": false
+ }
+}
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new file mode 100644
index 0000000000000000000000000000000000000000..bd2abe19377557ff5771584921f9b65fa041fef0
--- /dev/null
+++ b/weights/mvd_lite/tokenizer/tokenizer_config.json
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+ }
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+ "bos_token": "<|startoftext|>",
+ "clean_up_tokenization_spaces": true,
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+ "eos_token": "<|endoftext|>",
+ "errors": "replace",
+ "model_max_length": 77,
+ "pad_token": "!",
+ "tokenizer_class": "CLIPTokenizer",
+ "unk_token": "<|endoftext|>"
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new file mode 100644
index 0000000000000000000000000000000000000000..469be27c5c010538f845f518c4f5e8574c78f7c8
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