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# guided-diffusion |
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This is the codebase for [Diffusion Models Beat GANS on Image Synthesis](http://arxiv.org/abs/2105.05233). |
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This repository is based on [openai/improved-diffusion](https://github.com/openai/improved-diffusion), with modifications for classifier conditioning and architecture improvements. |
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# Download pre-trained models |
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We have released checkpoints for the main models in the paper. Before using these models, please review the corresponding [model card](model-card.md) to understand the intended use and limitations of these models. |
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Here are the download links for each model checkpoint: |
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* 64x64 classifier: [64x64_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/64x64_classifier.pt) |
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* 64x64 diffusion: [64x64_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/64x64_diffusion.pt) |
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* 128x128 classifier: [128x128_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/128x128_classifier.pt) |
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* 128x128 diffusion: [128x128_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/128x128_diffusion.pt) |
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* 256x256 classifier: [256x256_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_classifier.pt) |
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* 256x256 diffusion: [256x256_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion.pt) |
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* 256x256 diffusion (not class conditional): [256x256_diffusion_uncond.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt) |
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* 512x512 classifier: [512x512_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/512x512_classifier.pt) |
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* 512x512 diffusion: [512x512_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/512x512_diffusion.pt) |
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* 64x64 -> 256x256 upsampler: [64_256_upsampler.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/64_256_upsampler.pt) |
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* 128x128 -> 512x512 upsampler: [128_512_upsampler.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/128_512_upsampler.pt) |
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* LSUN bedroom: [lsun_bedroom.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_bedroom.pt) |
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* LSUN cat: [lsun_cat.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_cat.pt) |
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* LSUN horse: [lsun_horse.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_horse.pt) |
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* LSUN horse (no dropout): [lsun_horse_nodropout.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_horse_nodropout.pt) |
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# Sampling from pre-trained models |
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To sample from these models, you can use the `classifier_sample.py`, `image_sample.py`, and `super_res_sample.py` scripts. |
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Here, we provide flags for sampling from all of these models. |
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We assume that you have downloaded the relevant model checkpoints into a folder called `models/`. |
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For these examples, we will generate 100 samples with batch size 4. Feel free to change these values. |
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``` |
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SAMPLE_FLAGS="--batch_size 4 --num_samples 100 --timestep_respacing 250" |
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``` |
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## Classifier guidance |
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Note for these sampling runs that you can set `--classifier_scale 0` to sample from the base diffusion model. |
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You may also use the `image_sample.py` script instead of `classifier_sample.py` in that case. |
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* 64x64 model: |
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``` |
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MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --dropout 0.1 --image_size 64 --learn_sigma True --noise_schedule cosine --num_channels 192 --num_head_channels 64 --num_res_blocks 3 --resblock_updown True --use_new_attention_order True --use_fp16 True --use_scale_shift_norm True" |
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python classifier_sample.py $MODEL_FLAGS --classifier_scale 1.0 --classifier_path models/64x64_classifier.pt --model_path models/64x64_diffusion.pt $SAMPLE_FLAGS |
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``` |
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* 128x128 model: |
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``` |
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MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 128 --learn_sigma True --noise_schedule linear --num_channels 256 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True" |
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python classifier_sample.py $MODEL_FLAGS --classifier_scale 0.5 --classifier_path models/128x128_classifier.pt --model_path models/128x128_diffusion.pt $SAMPLE_FLAGS |
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``` |
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* 256x256 model: |
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``` |
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MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True" |
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python classifier_sample.py $MODEL_FLAGS --classifier_scale 1.0 --classifier_path models/256x256_classifier.pt --model_path models/256x256_diffusion.pt $SAMPLE_FLAGS |
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``` |
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* 256x256 model (unconditional): |
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``` |
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MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True" |
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python classifier_sample.py $MODEL_FLAGS --classifier_scale 10.0 --classifier_path models/256x256_classifier.pt --model_path models/256x256_diffusion.pt $SAMPLE_FLAGS |
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``` |
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* 512x512 model: |
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``` |
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MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 512 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 False --use_scale_shift_norm True" |
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python classifier_sample.py $MODEL_FLAGS --classifier_scale 4.0 --classifier_path models/512x512_classifier.pt --model_path models/512x512_diffusion.pt $SAMPLE_FLAGS |
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``` |
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## Upsampling |
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For these runs, we assume you have some base samples in a file `64_samples.npz` or `128_samples.npz` for the two respective models. |
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* 64 -> 256: |
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``` |
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MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --large_size 256 --small_size 64 --learn_sigma True --noise_schedule linear --num_channels 192 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True" |
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python super_res_sample.py $MODEL_FLAGS --model_path models/64_256_upsampler.pt --base_samples 64_samples.npz $SAMPLE_FLAGS |
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``` |
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* 128 -> 512: |
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``` |
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MODEL_FLAGS="--attention_resolutions 32,16 --class_cond True --diffusion_steps 1000 --large_size 512 --small_size 128 --learn_sigma True --noise_schedule linear --num_channels 192 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True" |
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python super_res_sample.py $MODEL_FLAGS --model_path models/128_512_upsampler.pt $SAMPLE_FLAGS --base_samples 128_samples.npz |
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``` |
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## LSUN models |
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These models are class-unconditional and correspond to a single LSUN class. Here, we show how to sample from `lsun_bedroom.pt`, but the other two LSUN checkpoints should work as well: |
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``` |
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MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True" |
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python image_sample.py $MODEL_FLAGS --model_path models/lsun_bedroom.pt $SAMPLE_FLAGS |
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``` |
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You can sample from `lsun_horse_nodropout.pt` by changing the dropout flag: |
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``` |
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MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.0 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True" |
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python image_sample.py $MODEL_FLAGS --model_path models/lsun_horse_nodropout.pt $SAMPLE_FLAGS |
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``` |
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Note that for these models, the best samples result from using 1000 timesteps: |
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``` |
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SAMPLE_FLAGS="--batch_size 4 --num_samples 100 --timestep_respacing 1000" |
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``` |
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# Results |
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This table summarizes our ImageNet results for pure guided diffusion models: |
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| Dataset | FID | Precision | Recall | |
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|------------------|------|-----------|--------| |
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| ImageNet 64x64 | 2.07 | 0.74 | 0.63 | |
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| ImageNet 128x128 | 2.97 | 0.78 | 0.59 | |
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| ImageNet 256x256 | 4.59 | 0.82 | 0.52 | |
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| ImageNet 512x512 | 7.72 | 0.87 | 0.42 | |
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This table shows the best results for high resolutions when using upsampling and guidance together: |
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| Dataset | FID | Precision | Recall | |
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|------------------|------|-----------|--------| |
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| ImageNet 256x256 | 3.94 | 0.83 | 0.53 | |
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| ImageNet 512x512 | 3.85 | 0.84 | 0.53 | |
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Finally, here are the unguided results on individual LSUN classes: |
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| Dataset | FID | Precision | Recall | |
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|--------------|------|-----------|--------| |
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| LSUN Bedroom | 1.90 | 0.66 | 0.51 | |
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| LSUN Cat | 5.57 | 0.63 | 0.52 | |
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| LSUN Horse | 2.57 | 0.71 | 0.55 | |
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# Training models |
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Training diffusion models is described in the [parent repository](https://github.com/openai/improved-diffusion). Training a classifier is similar. We assume you have put training hyperparameters into a `TRAIN_FLAGS` variable, and classifier hyperparameters into a `CLASSIFIER_FLAGS` variable. Then you can run: |
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``` |
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mpiexec -n N python scripts/classifier_train.py --data_dir path/to/imagenet $TRAIN_FLAGS $CLASSIFIER_FLAGS |
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``` |
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Make sure to divide the batch size in `TRAIN_FLAGS` by the number of MPI processes you are using. |
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Here are flags for training the 128x128 classifier. You can modify these for training classifiers at other resolutions: |
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```sh |
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TRAIN_FLAGS="--iterations 300000 --anneal_lr True --batch_size 256 --lr 3e-4 --save_interval 10000 --weight_decay 0.05" |
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CLASSIFIER_FLAGS="--image_size 128 --classifier_attention_resolutions 32,16,8 --classifier_depth 2 --classifier_width 128 --classifier_pool attention --classifier_resblock_updown True --classifier_use_scale_shift_norm True" |
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``` |
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For sampling from a 128x128 classifier-guided model, 25 step DDIM: |
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```sh |
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MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --image_size 128 --learn_sigma True --num_channels 256 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True" |
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CLASSIFIER_FLAGS="--image_size 128 --classifier_attention_resolutions 32,16,8 --classifier_depth 2 --classifier_width 128 --classifier_pool attention --classifier_resblock_updown True --classifier_use_scale_shift_norm True --classifier_scale 1.0 --classifier_use_fp16 True" |
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SAMPLE_FLAGS="--batch_size 4 --num_samples 50000 --timestep_respacing ddim25 --use_ddim True" |
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mpiexec -n N python scripts/classifier_sample.py \ |
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--model_path /path/to/model.pt \ |
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--classifier_path path/to/classifier.pt \ |
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$MODEL_FLAGS $CLASSIFIER_FLAGS $SAMPLE_FLAGS |
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``` |
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To sample for 250 timesteps without DDIM, replace `--timestep_respacing ddim25` to `--timestep_respacing 250`, and replace `--use_ddim True` with `--use_ddim False`. |
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