mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +03:00
7c5fef81e0
* 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>
78 lines
2.2 KiB
Plaintext
78 lines
2.2 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
|
the License. You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
|
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
|
specific language governing permissions and limitations under the License.
|
|
-->
|
|
|
|
# Models
|
|
|
|
Diffusers contains pretrained models for popular algorithms and modules for creating the next set of diffusion models.
|
|
The primary function of these models is to denoise an input sample, by modeling the distribution $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$.
|
|
The models are built on the base class ['ModelMixin'] that is a `torch.nn.module` with basic functionality for saving and loading models both locally and from the HuggingFace hub.
|
|
|
|
## ModelMixin
|
|
[[autodoc]] ModelMixin
|
|
|
|
## UNet2DOutput
|
|
[[autodoc]] models.unet_2d.UNet2DOutput
|
|
|
|
## UNet2DModel
|
|
[[autodoc]] UNet2DModel
|
|
|
|
## UNet1DOutput
|
|
[[autodoc]] models.unet_1d.UNet1DOutput
|
|
|
|
## UNet1DModel
|
|
[[autodoc]] UNet1DModel
|
|
|
|
## UNet2DConditionOutput
|
|
[[autodoc]] models.unet_2d_condition.UNet2DConditionOutput
|
|
|
|
## UNet2DConditionModel
|
|
[[autodoc]] UNet2DConditionModel
|
|
|
|
## DecoderOutput
|
|
[[autodoc]] models.vae.DecoderOutput
|
|
|
|
## VQEncoderOutput
|
|
[[autodoc]] models.vae.VQEncoderOutput
|
|
|
|
## VQModel
|
|
[[autodoc]] VQModel
|
|
|
|
## AutoencoderKLOutput
|
|
[[autodoc]] models.vae.AutoencoderKLOutput
|
|
|
|
## AutoencoderKL
|
|
[[autodoc]] AutoencoderKL
|
|
|
|
## Transformer2DModel
|
|
[[autodoc]] Transformer2DModel
|
|
|
|
## Transformer2DModelOutput
|
|
[[autodoc]] models.attention.Transformer2DModelOutput
|
|
|
|
## FlaxModelMixin
|
|
[[autodoc]] FlaxModelMixin
|
|
|
|
## FlaxUNet2DConditionOutput
|
|
[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionOutput
|
|
|
|
## FlaxUNet2DConditionModel
|
|
[[autodoc]] FlaxUNet2DConditionModel
|
|
|
|
## FlaxDecoderOutput
|
|
[[autodoc]] models.vae_flax.FlaxDecoderOutput
|
|
|
|
## FlaxAutoencoderKLOutput
|
|
[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
|
|
|
|
## FlaxAutoencoderKL
|
|
[[autodoc]] FlaxAutoencoderKL
|