scheduling_ddpm: fix variance in the case of learned_range type.
In the case of learned_range variance type, there are missing logs
and exponent comparing to the theory (see "Improved Denoising Diffusion
Probabilistic Models" section 3.1 equation 15:
https://arxiv.org/pdf/2102.09672.pdf).
* Short doc on changing the scheduler in Flax.
* Apply fix from @patil-suraj
Co-authored-by: Suraj Patil <surajp815@gmail.com>
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Co-authored-by: Suraj Patil <surajp815@gmail.com>
-- This commit adopts `requests` in place of `wget` to fetch config `.yaml`
files as part of `load_pipeline_from_original_stable_diffusion_ckpt` API.
-- This was done because in Windows PowerShell one needs to explicitly ensure
that `wget` binary is part of the PATH variable. If not present, this leads
to the code not being able to download the `.yaml` config file.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
Co-authored-by: Abhishek Varma <abhishek@nod-labs.com>
* Section on using LoRA alpha / scale.
* Accept suggestion
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Clarify on merge.
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* make scaling factor cnfig arg of vae
* fix
* make flake happy
* fix ldm
* fix upscaler
* qualirty
* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* solve conflicts, addres some comments
* examples
* examples min version
* doc
* fix type
* typo
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* remove duplicate line
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Allow `UNet2DModel` to use arbitrary class embeddings.
We can currently use class conditioning in `UNet2DConditionModel`, but
not in `UNet2DModel`. However, `UNet2DConditionModel` requires text
conditioning too, which is unrelated to other types of conditioning.
This commit makes it possible for `UNet2DModel` to be conditioned on
entities other than timesteps. This is useful for training /
research purposes. We can currently train models to perform
unconditional image generation or text-to-image generation, but it's not
straightforward to train a model to perform class-conditioned image
generation, if text conditioning is not required.
We could potentiall use `UNet2DConditionModel` for class-conditioning
without text embeddings by using down/up blocks without
cross-conditioning. However:
- The mid block currently requires cross attention.
- We are required to provide `encoder_hidden_states` to `forward`.
* Style
* Align class conditioning, add docstring for `num_class_embeds`.
* Copy docstring to versatile_diffusion UNetFlatConditionModel
* make tests deterministic
* run slow tests
* prepare for testing
* finish
* refactor
* add print statements
* finish more
* correct some test failures
* more fixes
* set up to correct tests
* more corrections
* up
* fix more
* more prints
* add
* up
* up
* up
* uP
* uP
* more fixes
* uP
* up
* up
* up
* up
* fix more
* up
* up
* clean tests
* up
* up
* up
* more fixes
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* make
* correct
* finish
* finish
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* add text embeds to sd
* add text embeds to sd
* finish tests
* finish
* finish
* make style
* fix tests
* make style
* make style
* up
* better docs
* fix
* fix
* new try
* up
* up
* finish
Dudu Moshe