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- README.md +45 -0
- config.yaml +116 -0
- samples.png +3 -0
- t5.vocab +0 -0
- vis_model.pth +3 -0
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README.md
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---
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license: apple-ascl
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tags:
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- mdm
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---
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# Matryoshka Diffusion Models
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Matryoshka Diffusion Models was introduced in [the paper of the same name](https://huggingface.co/papers/2310.15111), by Jiatao Gu,Shuangfei Zhai, Yizhe Zhang, Josh Susskind, Navdeep Jaitly.
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This repository contains the **Flickr 256** checkpoint.
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![Generation Examples from the MDM repository](samples.png)
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### Highlights
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* This checkpoint was trained on a dataset of 50M text-image pairs collected from Flickr.
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* This model was trained using nested UNets at various resolutions, and generates images with a resolution of 256 × 256.
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* Despite training on relatively small datasets, MDMs show strong zero-shot capabilities of generating high-resolution images and videos.
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## Checkpoints
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| Model | Dataset | Resolution | Nested UNets |
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|---------------------------------------------------------|------------|-------------|--------------|
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| [mdm-flickr-64](https://hf.co/pcuenq/mdm-flickr-64) | Flickr 50M | 64 × 64 | ❎ |
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| [mdm-flickr-256](https://hf.co/pcuenq/mdm-flickr-256) | Flickr 50M | 256 × 256 | ✅ |
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| [mdm-flickr-1024](https://hf.co/pcuenq/mdm-flickr-1024) | Flickr 50M | 1024 × 1024 | ✅ |
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## How to Use
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Please, refer to the [original repository](https://github.com/apple/ml-mdm) for training and inference instructions.
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## Citation
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```
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@misc{gu2023matryoshkadiffusionmodels,
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title={Matryoshka Diffusion Models},
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author={Jiatao Gu and Shuangfei Zhai and Yizhe Zhang and Josh Susskind and Navdeep Jaitly},
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year={2023},
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eprint={2310.15111},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2310.15111},
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}
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```
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config.yaml
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name: cc12m_256x256
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dataset_config: configs/datasets/cc12m.yaml
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# sampler_arguments
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min_examples: 10000
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sample-dir: /mnt/data/samples
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# batch-size: 32
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sample_image_size: 256
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test_file_list: validation.tsv
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#reader-config-file: configs/datasets/reader_config_eval.yaml
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# shared_arguments
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output_dir: /mnt/data/outputs
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num_diffusion_steps: 1000
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reproject_signal: false
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model_output_scale: 0
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prediction_type: V_PREDICTION
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loss_target_type: DDPM
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schedule_type: DEEPFLOYD
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prediction_length: 129
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use_vdm_loss_weights: false
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use_double_loss: true
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no_use_residual: true
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num_training_steps: 1000000
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avg_lm_steps: 0
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categorical_conditioning: 0
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rescale_signal: 1
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schedule_shifted: true
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skip_normalization: true
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random_low_noise: true
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vocab_file: t5.vocab
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text_model: google/flan-t5-xl
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model: nested_unet
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vision_model: nested_unet
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#model_config-file: configs/models/model_config_nested256.yaml
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unet_config:
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attention_levels: []
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conditioning_feature_dim: -1
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conditioning_feature_proj_dim: -1
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freeze_inner_unet: false
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initialize_inner_with_pretrained: None
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inner_config:
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attention_levels: [1, 2]
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conditioning_feature_dim: -1
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conditioning_feature_proj_dim: 2048
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masked_cross_attention: 0
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micro_conditioning: scale:64
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nesting: true
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num_attention_layers: [0, 1, 5]
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num_lm_head_layers: 0
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num_resnets_per_resolution: [2, 2, 2]
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num_temporal_attention_layers: null
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resnet_config: {dropout: 0.0, num_channels: -1, num_groups_norm: 32, output_channels: -1,
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use_attention_ffn: true}
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resolution_channels: [256, 512, 768]
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skip_cond_emb: false
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skip_mid_blocks: false
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temporal_dim: null
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temporal_mode: false
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temporal_positional_encoding: false
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temporal_spatial_ds: false
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interp_conditioning: false
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masked_cross_attention: 1
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micro_conditioning: scale:256
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nesting: false
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num_attention_layers: [0, 0, 0]
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num_lm_head_layers: 0
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num_resnets_per_resolution: [2, 2, 1]
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num_temporal_attention_layers: null
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resnet_config: {dropout: 0.0, num_channels: -1, num_groups_norm: 32, output_channels: -1,
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use_attention_ffn: false}
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resolution_channels: [64, 128, 256]
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skip_cond_emb: true
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skip_inner_unet_input: false
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skip_mid_blocks: true
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skip_normalization: true
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temporal_dim: 1024
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temporal_mode: false
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temporal_positional_encoding: false
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temporal_spatial_ds: false
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reader_config:
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image_size: 256
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smaller_side_size: 256
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random_crop: false
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max_caption_length: -1
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max_token_length: 128
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reader_buffer_size: 2000
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shuffle_buffer_size: 2000
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append_eos: true
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num_readers: 2
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pad_to_max_length: false
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padding_token: <pad>
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prepad_bos: false
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prepad_caption_with_space: true
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random_crop: false
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#reader_buffer_size: 64
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#shuffle_buffer_size: 9600
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use_tokenizer_scores: true
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use_lm_mask: 1
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# torchmetrics_arguments:
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metrics: fid,clip
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# trainer_arguments:
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use_precomputed_text_embeddings: 0
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pretrained_vision_file: vis_model_256x256.pth
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#batch_size: 24
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mixed_ratio: '2:1'
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gradient_clip_norm: 2
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loss_factor: 1
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num_gradient_accumulations: 1
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warmup_steps: 10000
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# reader-config-file: configs/datasets/reader_config.yaml
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log_freq: 50
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save_freq: 5000
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lr: 5.0e-05
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fp16: 0
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samples.png
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Git LFS Details
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t5.vocab
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vis_model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:e227885c80e9fc634abf064b3aca4bb56625ec54c0b02e34f43423437716c526
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size 1906796858
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