https://github.com/openai/guided-diffusion

https://github.com/openai/guided-diffusion

Last synced: 5 days ago
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• Host: GitHub
• URL: https://github.com/openai/guided-diffusion
• Owner: openai
• License: mit
• Created: 2021-04-27T20:27:52.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-07-02T11:26:50.000Z (7 months ago)
• Last Synced: 2025-01-14T12:12:17.388Z (12 days ago)
• Language: Python
• Size: 27.3 KB
• Stars: 6,465
• Watchers: 140
• Forks: 838
• Open Issues: 105
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-vision-language-pretraining - [code - guided-diffusion) [[code]](https://github.com/nerdyrodent/CLIP-Guided-Diffusion) [[code]](https://github.com/crowsonkb/v-diffusion-pytorch) (CLIP-related)

README

        
# guided-diffusion

This is the codebase for [Diffusion Models Beat GANS on Image Synthesis](http://arxiv.org/abs/2105.05233).

This repository is based on [openai/improved-diffusion](https://github.com/openai/improved-diffusion), with modifications for classifier conditioning and architecture improvements.

# Download pre-trained models

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.

Here are the download links for each model checkpoint:

 * 64x64 classifier: [64x64_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/64x64_classifier.pt)

 * 64x64 diffusion: [64x64_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/64x64_diffusion.pt)

 * 128x128 classifier: [128x128_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/128x128_classifier.pt)

 * 128x128 diffusion: [128x128_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/128x128_diffusion.pt)

 * 256x256 classifier: [256x256_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_classifier.pt)

 * 256x256 diffusion: [256x256_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion.pt)

 * 256x256 diffusion (not class conditional): [256x256_diffusion_uncond.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt)

 * 512x512 classifier: [512x512_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/512x512_classifier.pt)

 * 512x512 diffusion: [512x512_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/512x512_diffusion.pt)

 * 64x64 -> 256x256 upsampler: [64_256_upsampler.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/64_256_upsampler.pt)

 * 128x128 -> 512x512 upsampler: [128_512_upsampler.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/128_512_upsampler.pt)

 * LSUN bedroom: [lsun_bedroom.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_bedroom.pt)

 * LSUN cat: [lsun_cat.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_cat.pt)

 * LSUN horse: [lsun_horse.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_horse.pt)

 * LSUN horse (no dropout): [lsun_horse_nodropout.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_horse_nodropout.pt)

# Sampling from pre-trained models

To sample from these models, you can use the `classifier_sample.py`, `image_sample.py`, and `super_res_sample.py` scripts.

Here, we provide flags for sampling from all of these models.

We assume that you have downloaded the relevant model checkpoints into a folder called `models/`.

For these examples, we will generate 100 samples with batch size 4. Feel free to change these values.

```

SAMPLE_FLAGS="--batch_size 4 --num_samples 100 --timestep_respacing 250"

```

## Classifier guidance

Note for these sampling runs that you can set `--classifier_scale 0` to sample from the base diffusion model.

You may also use the `image_sample.py` script instead of `classifier_sample.py` in that case.

 * 64x64 model:

```

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"

python classifier_sample.py $MODEL_FLAGS --classifier_scale 1.0 --classifier_path models/64x64_classifier.pt --classifier_depth 4 --model_path models/64x64_diffusion.pt $SAMPLE_FLAGS

```

 * 128x128 model:

```

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"

python classifier_sample.py $MODEL_FLAGS --classifier_scale 0.5 --classifier_path models/128x128_classifier.pt --model_path models/128x128_diffusion.pt $SAMPLE_FLAGS

```

 * 256x256 model:

```

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"

python classifier_sample.py $MODEL_FLAGS --classifier_scale 1.0 --classifier_path models/256x256_classifier.pt --model_path models/256x256_diffusion.pt $SAMPLE_FLAGS

```

 * 256x256 model (unconditional):

```

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"

python classifier_sample.py $MODEL_FLAGS --classifier_scale 10.0 --classifier_path models/256x256_classifier.pt --model_path models/256x256_diffusion_uncond.pt $SAMPLE_FLAGS

```

 * 512x512 model:

```

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"

python classifier_sample.py $MODEL_FLAGS --classifier_scale 4.0 --classifier_path models/512x512_classifier.pt --model_path models/512x512_diffusion.pt $SAMPLE_FLAGS

```

## Upsampling

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.

 * 64 -> 256:

```

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"

python super_res_sample.py $MODEL_FLAGS --model_path models/64_256_upsampler.pt --base_samples 64_samples.npz $SAMPLE_FLAGS

```

 * 128 -> 512:

```

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"

python super_res_sample.py $MODEL_FLAGS --model_path models/128_512_upsampler.pt $SAMPLE_FLAGS --base_samples 128_samples.npz

```

## LSUN models

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:

```

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"

python image_sample.py $MODEL_FLAGS --model_path models/lsun_bedroom.pt $SAMPLE_FLAGS

```

You can sample from `lsun_horse_nodropout.pt` by changing the dropout flag:

```

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"

python image_sample.py $MODEL_FLAGS --model_path models/lsun_horse_nodropout.pt $SAMPLE_FLAGS

```

Note that for these models, the best samples result from using 1000 timesteps:

```

SAMPLE_FLAGS="--batch_size 4 --num_samples 100 --timestep_respacing 1000"

```

# Results

This table summarizes our ImageNet results for pure guided diffusion models:

| Dataset          | FID  | Precision | Recall |

|------------------|------|-----------|--------|

| ImageNet 64x64   | 2.07 | 0.74      | 0.63   |

| ImageNet 128x128 | 2.97 | 0.78      | 0.59   |

| ImageNet 256x256 | 4.59 | 0.82      | 0.52   |

| ImageNet 512x512 | 7.72 | 0.87      | 0.42   |

This table shows the best results for high resolutions when using upsampling and guidance together:

| Dataset          | FID  | Precision | Recall |

|------------------|------|-----------|--------|

| ImageNet 256x256 | 3.94 | 0.83      | 0.53   |

| ImageNet 512x512 | 3.85 | 0.84      | 0.53   |

Finally, here are the unguided results on individual LSUN classes:

| Dataset      | FID  | Precision | Recall |

|--------------|------|-----------|--------|

| LSUN Bedroom | 1.90 | 0.66      | 0.51   |

| LSUN Cat     | 5.57 | 0.63      | 0.52   |

| LSUN Horse   | 2.57 | 0.71      | 0.55   |

# Training models

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:

```

mpiexec -n N python scripts/classifier_train.py --data_dir path/to/imagenet $TRAIN_FLAGS $CLASSIFIER_FLAGS

```

Make sure to divide the batch size in `TRAIN_FLAGS` by the number of MPI processes you are using.

Here are flags for training the 128x128 classifier. You can modify these for training classifiers at other resolutions:

```sh

TRAIN_FLAGS="--iterations 300000 --anneal_lr True --batch_size 256 --lr 3e-4 --save_interval 10000 --weight_decay 0.05"

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"

```

For sampling from a 128x128 classifier-guided model, 25 step DDIM:

```sh

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"

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"

SAMPLE_FLAGS="--batch_size 4 --num_samples 50000 --timestep_respacing ddim25 --use_ddim True"

mpiexec -n N python scripts/classifier_sample.py \

    --model_path /path/to/model.pt \

    --classifier_path path/to/classifier.pt \

    $MODEL_FLAGS $CLASSIFIER_FLAGS $SAMPLE_FLAGS

```

To sample for 250 timesteps without DDIM, replace `--timestep_respacing ddim25` to `--timestep_respacing 250`, and replace `--use_ddim True` with `--use_ddim False`.