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.gitattributes

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-*.arrow filter=lfs diff=lfs merge=lfs -text
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-*.ckpt filter=lfs diff=lfs merge=lfs -text
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 *.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
 *.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*.ckpt filter=lfs diff=lfs merge=lfs -text

.gitignore

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-# Ignore everything in this directory
-*
-# Except this file
-!.gitignore
+# Allow model weights
+!*.pt
+!*.ckpt
+!*.safetensors

LICENSE.md

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+**LICENSE**  
+Add whichever license you intend (MIT is common for baseline weights).
+
+```bash
+git add README.md LICENSE

README.md

@@ -1,3 +1,21 @@
----
-license: mit
----
+# Locality Diffusion Baselines
+
+This repository contains pretrained **baseline model weights** used in:
+
+*Locality in Image Diffusion Models Emerges from Data Statistics*  
+Artem Lukoianov et al., NeurIPS 2025.
+
+## Structure
+
+- `models/baseline_unet/` – baseline UNet models on multiple datasets
+- `models/wiener/` – Wiener models
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="ottogin/locality-diffusion-baselines",
+    filename="models/baseline_unet/cifar10/ckpt_epoch_200.pt"
+)

models/baseline_unet/afhq/ckpt_epoch_200.pt

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+size 320613872

models/baseline_unet/afhq/config.yaml

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+T: 1000
+attn: []
+batch_size: 80
+beta_1: 0.0001
+beta_T: 0.02
+channel: 128
+channel_mult:
+- 1
+- 2
+- 3
+- 4
+checkpoint_freq: 20
+dataset_name: afhq
+dataset_root: data/
+device: cuda:0
+dropout: 0.15
+epoch: 200
+eval_random_seed: 42
+grad_clip: 1.0
+img_size: 64
+in_channels: 3
+lr: 0.0001
+model_type: unet
+multiplier: 2.0
+nrow: 8
+num_res_blocks: 2
+out_channels: 3
+random_seed: 42
+sample_freq: 20
+sampledImgName: SampledNoGuidenceImgs.png
+sampledNoisyImgName: NoisyNoGuidenceImgs.png
+sampled_dir: ./SampledImgs/
+save_weight_dir: ./Checkpoints/
+subset_size: -1
+test_load_weight: ckpt_199_.pt
+training_load_weight: null
+use_wandb: true

models/baseline_unet/celeba_hq/ckpt_epoch_200.pt

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+size 320613872

models/baseline_unet/celeba_hq/config.yaml

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+T: 1000
+attn: []
+batch_size: 80
+beta_1: 0.0001
+beta_T: 0.02
+channel: 128
+channel_mult:
+- 1
+- 2
+- 3
+- 4
+checkpoint_freq: 20
+dataset_name: celeba_hq
+dataset_root: data/
+device: cuda:0
+dropout: 0.15
+epoch: 200
+eval_random_seed: 42
+grad_clip: 1.0
+img_size: 64
+in_channels: 3
+lr: 0.0001
+model_type: unet
+multiplier: 2.0
+nrow: 8
+num_res_blocks: 2
+out_channels: 3
+random_seed: 42
+sample_freq: 20
+sampledImgName: SampledNoGuidenceImgs.png
+sampledNoisyImgName: NoisyNoGuidenceImgs.png
+sampled_dir: ./SampledImgs/
+save_weight_dir: ./Checkpoints/
+subset_size: -1
+test_load_weight: ckpt_199_.pt
+training_load_weight: null
+use_wandb: true

models/baseline_unet/cifar10/ckpt_epoch_200.pt

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+size 320613461

models/baseline_unet/cifar10/config.yaml

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+T: 1000
+attn: []
+batch_size: 80
+beta_1: 0.0001
+beta_T: 0.02
+channel: 128
+channel_mult:
+- 1
+- 2
+- 3
+- 4
+checkpoint_freq: 20
+dataset_root: data/
+device: cuda:6
+dropout: 0.15
+epoch: 200
+eval_random_seed: 42
+grad_clip: 1.0
+img_size: 32
+lr: 0.0001
+multiplier: 2.0
+nrow: 8
+num_res_blocks: 2
+random_seed: 42
+sample_freq: 20
+sampledImgName: SampledNoGuidenceImgs.png
+sampledNoisyImgName: NoisyNoGuidenceImgs.png
+sampled_dir: ./SampledImgs/
+save_weight_dir: ./Checkpoints/
+subset_size: -1
+test_load_weight: ckpt_199_.pt
+training_load_weight: null
+use_wandb: true

models/baseline_unet/fashion_mnist/ckpt_epoch_200.pt

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models/baseline_unet/fashion_mnist/config.yaml

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+T: 1000
+attn: []
+batch_size: 80
+beta_1: 0.0001
+beta_T: 0.02
+channel: 64
+channel_mult:
+- 1
+- 2
+- 2
+checkpoint_freq: 20
+dataset_name: fashion_mnist
+dataset_root: data/
+device: cuda:0
+dropout: 0.15
+epoch: 200
+eval_random_seed: 42
+grad_clip: 1.0
+img_size: 28
+in_channels: 1
+lr: 0.0001
+model_type: unet
+multiplier: 2.0
+nrow: 8
+num_res_blocks: 2
+out_channels: 1
+random_seed: 42
+sample_freq: 20
+sampledImgName: SampledNoGuidenceImgs.png
+sampledNoisyImgName: NoisyNoGuidenceImgs.png
+sampled_dir: ./SampledImgs/
+save_weight_dir: ./Checkpoints/
+subset_size: -1
+test_load_weight: ckpt_199_.pt
+training_load_weight: null
+use_wandb: true

models/baseline_unet/mnist/ckpt_epoch_200.pt

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models/baseline_unet/mnist/config.yaml

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+T: 1000
+attn: []
+batch_size: 80
+beta_1: 0.0001
+beta_T: 0.02
+channel: 64
+channel_mult:
+- 1
+- 2
+- 2
+checkpoint_freq: 20
+dataset_name: mnist
+dataset_root: data/
+device: cuda:0
+dropout: 0.15
+epoch: 200
+eval_random_seed: 42
+grad_clip: 1.0
+img_size: 28
+in_channels: 1
+lr: 0.0001
+model_type: unet
+multiplier: 2.0
+nrow: 8
+num_res_blocks: 2
+out_channels: 1
+random_seed: 42
+sample_freq: 20
+sampledImgName: SampledNoGuidenceImgs.png
+sampledNoisyImgName: NoisyNoGuidenceImgs.png
+sampled_dir: ./SampledImgs/
+save_weight_dir: ./Checkpoints/
+subset_size: -1
+test_load_weight: ckpt_199_.pt
+training_load_weight: null
+use_wandb: true