History

Nathan Lambert 7c5fef81e0 Add UNet 1d for RL model for planning + colab (#105 ) * re-add RL model code * match model forward api * add register_to_config, pass training tests * fix tests, update forward outputs * remove unused code, some comments * add to docs * remove extra embedding code * unify time embedding * remove conv1d output sequential * remove sequential from conv1dblock * style and deleting duplicated code * clean files * remove unused variables * clean variables * add 1d resnet block structure for downsample * rename as unet1d * fix renaming * rename files * add get_block(...) api * unify args for model1d like model2d * minor cleaning * fix docs * improve 1d resnet blocks * fix tests, remove permuts * fix style * add output activation * rename flax blocks file * Add Value Function and corresponding example script to Diffuser implementation (#884) * valuefunction code * start example scripts * missing imports * bug fixes and placeholder example script * add value function scheduler * load value function from hub and get best actions in example * very close to working example * larger batch size for planning * more tests * merge unet1d changes * wandb for debugging, use newer models * success! * turns out we just need more diffusion steps * run on modal * merge and code cleanup * use same api for rl model * fix variance type * wrong normalization function * add tests * style * style and quality * edits based on comments * style and quality * remove unused var * hack unet1d into a value function * add pipeline * fix arg order * add pipeline to core library * community pipeline * fix couple shape bugs * style * Apply suggestions from code review Co-authored-by: Nathan Lambert <nathan@huggingface.co> * update post merge of scripts * add mdiblock / outblock architecture * Pipeline cleanup (#947) * valuefunction code * start example scripts * missing imports * bug fixes and placeholder example script * add value function scheduler * load value function from hub and get best actions in example * very close to working example * larger batch size for planning * more tests * merge unet1d changes * wandb for debugging, use newer models * success! * turns out we just need more diffusion steps * run on modal * merge and code cleanup * use same api for rl model * fix variance type * wrong normalization function * add tests * style * style and quality * edits based on comments * style and quality * remove unused var * hack unet1d into a value function * add pipeline * fix arg order * add pipeline to core library * community pipeline * fix couple shape bugs * style * Apply suggestions from code review * clean up comments * convert older script to using pipeline and add readme * rename scripts * style, update tests * delete unet rl model file * remove imports in src Co-authored-by: Nathan Lambert <nathan@huggingface.co> * Update src/diffusers/models/unet_1d_blocks.py * Update tests/test_models_unet.py * RL Cleanup v2 (#965) * valuefunction code * start example scripts * missing imports * bug fixes and placeholder example script * add value function scheduler * load value function from hub and get best actions in example * very close to working example * larger batch size for planning * more tests * merge unet1d changes * wandb for debugging, use newer models * success! * turns out we just need more diffusion steps * run on modal * merge and code cleanup * use same api for rl model * fix variance type * wrong normalization function * add tests * style * style and quality * edits based on comments * style and quality * remove unused var * hack unet1d into a value function * add pipeline * fix arg order * add pipeline to core library * community pipeline * fix couple shape bugs * style * Apply suggestions from code review * clean up comments * convert older script to using pipeline and add readme * rename scripts * style, update tests * delete unet rl model file * remove imports in src * add specific vf block and update tests * style * Update tests/test_models_unet.py Co-authored-by: Nathan Lambert <nathan@huggingface.co> * fix quality in tests * fix quality style, split test file * fix checks / tests * make timesteps closer to main * unify block API * unify forward api * delete lines in examples * style * examples style * all tests pass * make style * make dance_diff test pass * Refactoring RL PR (#1200) * init file changes * add import utils * finish cleaning files, imports * remove import flags * clean examples * fix imports, tests for merge * update readmes * hotfix for tests * quality * fix some tests * change defaults * more mps test fixes * unet1d defaults * do not default import experimental * defaults for tests * fix tests * fix-copies * fix * changes per Patrik's comments (#1285) * changes per Patrik's comments * update conversion script * fix renaming * skip more mps tests * last test fix * Update examples/rl/README.md Co-authored-by: Ben Glickenhaus <benglickenhaus@gmail.com>		2022-11-14 13:48:48 -08:00
..
README.md	Add UNet 1d for RL model for planning + colab (#105 )	2022-11-14 13:48:48 -08:00
run_diffuser_gen_trajectories.py	Add UNet 1d for RL model for planning + colab (#105 )	2022-11-14 13:48:48 -08:00
run_diffuser_locomotion.py	Add UNet 1d for RL model for planning + colab (#105 )	2022-11-14 13:48:48 -08:00

README.md

Overview

These examples show how to run (Diffuser)[https://arxiv.org/abs/2205.09991] in Diffusers. There are four scripts,

run_diffuser_locomotion.py to sample actions and run them in the environment,
and run_diffuser_gen_trajectories.py to just sample actions from the pre-trained diffusion model.

You will need some RL specific requirements to run the examples:

pip install -f https://download.pytorch.org/whl/torch_stable.html \
                free-mujoco-py \
                einops \
                gym==0.24.1 \
                protobuf==3.20.1 \
                git+https://github.com/rail-berkeley/d4rl.git \
                mediapy \
                Pillow==9.0.0