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README.md

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+---
+license: cc-by-nc-4.0
+tags:
+- normal-estimation
+- depth-estimation
+- diffusion
+- transparent-objects
+library_name: diffusers
+pipeline_tag: image-to-image
+---
+
+# TransNormal
+
+Surface normal estimation for transparent objects using diffusion models with DINOv3 semantic guidance.
+
+## Usage
+
+```python
+from transnormal import TransNormalPipeline, create_dino_encoder
+import torch
+
+# Load DINO encoder (download separately)
+dino_encoder = create_dino_encoder(
+    model_name="dinov3_vith16plus",
+    weights_path="path/to/dinov3_vith16plus",
+    projector_path="path/to/cross_attention_projector.pt",
+    device="cuda",
+    dtype=torch.bfloat16,
+)
+
+# Load pipeline
+pipe = TransNormalPipeline.from_pretrained(
+    "longxiang-ai/transnormal-v1",
+    dino_encoder=dino_encoder,
+    torch_dtype=torch.bfloat16,
+)
+pipe = pipe.to("cuda")
+
+# Inference
+normal_map = pipe("image.jpg", output_type="pil")
+```
+
+## Citation
+
+```bibtex
+@article{transnormal2025,
+  title={TransNormal: Dense Visual Semantics for Diffusion-based Transparent Object Normal Estimation},
+  author={Li, Mingwei and Fan, Hehe and Yang, Yi},
+  year={2025}
+}
+```
+
+## License
+
+CC BY-NC 4.0

cross_attention_projector.pt

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model_index.json

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+{
+  "_class_name": "TransNormalPipeline",
+  "_diffusers_version": "0.28.0",
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.8.0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "num_train_timesteps": 1000,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "trained_betas": null
+}

text_encoder/config.json

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+{
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+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1024,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 23,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.0.dev0",
+  "vocab_size": 49408
+}

text_encoder/model.fp16.safetensors

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tokenizer/special_tokens_map.json

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+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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tokenizer/tokenizer_config.json

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+  "add_prefix_space": false,
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+    "content": "<|startoftext|>",
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+  "tokenizer_class": "CLIPTokenizer",
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+    "normalized": true,
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+    "single_word": false
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unet/config.json

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+  "_diffusers_version": "0.35.2",
+  "_name_or_path": "/home/dataset-local/lmw/hf_models/lotus-normal-d-v1-1",
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