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- 08 Feb, 2023 1 commit
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Patrick von Platen authored
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- 07 Feb, 2023 4 commits
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Patrick von Platen authored
* before running make style * remove left overs from flake8 * finish * make fix-copies * final fix * more fixes
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YiYi Xu authored
* Modify UNet2DConditionModel - allow skipping mid_block - adding a norm_group_size argument so that we can set the `num_groups` for group norm using `num_channels//norm_group_size` - allow user to set dimension for the timestep embedding (`time_embed_dim`) - the kernel_size for `conv_in` and `conv_out` is now configurable - add random fourier feature layer (`GaussianFourierProjection`) for `time_proj` - allow user to add the time and class embeddings before passing through the projection layer together - `time_embedding(t_emb + class_label))` - added 2 arguments `attn1_types` and `attn2_types` * currently we have argument `only_cross_attention`: when it's set to `True`, we will have a to the `BasicTransformerBlock` block with 2 cross-attention , otherwise we get a self-attention followed by a cross-attention; in k-upscaler, we need to have blocks that include just one cross-attention, or self-attention -> cross-attention; so I added `attn1_types` and `attn2_types` to the unet's argument list to allow user specify the attention types for the 2 positions in each block; note that I stil kept the `only_cross_attention` argument for unet for easy configuration, but it will be converted to `attn1_type` and `attn2_type` when passing down to the down blocks - the position of downsample layer and upsample layer is now configurable - in k-upscaler unet, there is only one skip connection per each up/down block (instead of each layer in stable diffusion unet), added `skip_freq = "block"` to support this use case - if user passes attention_mask to unet, it will prepare the mask and pass a flag to cross attention processer to skip the `prepare_attention_mask` step inside cross attention block add up/down blocks for k-upscaler modify CrossAttention class - make the `dropout` layer in `to_out` optional - `use_conv_proj` - use conv instead of linear for all projection layers (i.e. `to_q`, `to_k`, `to_v`, `to_out`) whenever possible. note that when it's used to do cross attention, to_k, to_v has to be linear because the `encoder_hidden_states` is not 2d - `cross_attention_norm` - add an optional layernorm on encoder_hidden_states - `attention_dropout`: add an optional dropout on attention score adapt BasicTransformerBlock - add an ada groupnorm layer to conditioning attention input with timestep embedding - allow skipping the FeedForward layer in between the attentions - replaced the only_cross_attention argument with attn1_type and attn2_type for more flexible configuration update timestep embedding: add new act_fn gelu and an optional act_2 modified ResnetBlock2D - refactored with AdaGroupNorm class (the timestep scale shift normalization) - add `mid_channel` argument - allow the first conv to have a different output dimension from the second conv - add option to use input AdaGroupNorm on the input instead of groupnorm - add options to add a dropout layer after each conv - allow user to set the bias in conv_shortcut (needed for k-upscaler) - add gelu adding conversion script for k-upscaler unet add pipeline * fix attention mask * fix a typo * fix a bug * make sure model can be used with GPU * make pipeline work with fp16 * fix an error in BasicTransfomerBlock * make style * fix typo * some more fixes * uP * up * correct more * some clean-up * clean time proj * up * uP * more changes * remove the upcast_attention=True from unet config * remove attn1_types, attn2_types etc * fix * revert incorrect changes up/down samplers * make style * remove outdated files * Apply suggestions from code review * attention refactor * refactor cross attention * Apply suggestions from code review * update * up * update * Apply suggestions from code review * finish * Update src/diffusers/models/cross_attention.py * more fixes * up * up * up * finish * more corrections of conversion state * act_2 -> act_2_fn * remove dropout_after_conv from ResnetBlock2D * make style * simplify KAttentionBlock * add fast test for latent upscaler pipeline * add slow test * slow test fp16 * make style * add doc string for pipeline_stable_diffusion_latent_upscale * add api doc page for latent upscaler pipeline * deprecate attention mask * clean up embeddings * simplify resnet * up * clean up resnet * up * correct more * up * up * improve a bit more * correct more * more clean-ups * Update docs/source/en/api/pipelines/stable_diffusion/latent_upscale.mdx Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> * Update docs/source/en/api/pipelines/stable_diffusion/latent_upscale.mdx Co-authored-by:
Pedro Cuenca <pedro@huggingface.co> * Update docs/source/en/api/pipelines/stable_diffusion/latent_upscale.mdx Co-authored-by:
yiyixuxu <yixu@yis-macbook-pro.lan> Co-authored-by:
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Patrick von Platen authored
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Jorge C. Gomes authored
* Fixes prompt input checks in img2img Allows providing prompt_embeds instead of the prompt, which is not currently possible as the first check fails. This becomes the same as the function found in https://github.com/huggingface/diffusers/blob/8267c7844504b55366525169187767ef92d1f499/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L393 * Continues the fix This also needs to be fixed. Becomes consistent with https://github.com/huggingface/diffusers/blob/8267c7844504b55366525169187767ef92d1f499/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L558 I've now tested this implementation, and it produces the expected results.
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- 06 Feb, 2023 1 commit
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nickkolok authored
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- 04 Feb, 2023 1 commit
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Pedro Cuenca authored
* Show error when loading safety_checker `from_flax` * fix style
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- 03 Feb, 2023 5 commits
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Robin Hutmacher authored
* Fix typo in StableDiffusionInpaintPipeline * Add embedded prompt handling --------- Co-authored-by: -
Patrick von Platen authored
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Justin Merrell authored
Flagged images would be set to the blank image instead of the original image that contained the NSF concept for optional viewing. Co-authored-by: -
Will Berman authored
* negative_prompt typo * fix
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dymil authored
* Fix timestep dtype in legacy inpaint This matches the structure in the text2img, img2img, and inpaint ONNX pipelines * Fix style in dtype patch
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- 31 Jan, 2023 3 commits
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Abhishek Varma authored
-- 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:
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Patrick von Platen authored
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1lint authored
* fix legacy inpaint noise and resize mask tensor * updated legacy inpaint pipe test expected_slice
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- 27 Jan, 2023 3 commits
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Will Berman authored
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Patrick von Platen authored
Don't call the Hub if
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Patrick von Platen authored
* [LoRA] All to use in inference with pipeline * [LoRA] allow cross attention kwargs passed to pipeline * finish
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- 26 Jan, 2023 4 commits
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Will Berman authored
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Will Berman authored
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Suraj Patil authored
* 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:
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Pedro Cuenca authored
* 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
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- 25 Jan, 2023 5 commits
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Patrick von Platen authored
* [Bump version] 0.13 * Bump model up * up
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Patrick von Platen authored
* 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:
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Patrick von Platen authored
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Patrick von Platen authored
* 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
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Suraj Patil authored
update example for pix2pix
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- 24 Jan, 2023 2 commits
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Takuma Mori authored
* allow passing op to xFormers attention original code by @patil-suraj huggingface/diffusers@ae0cc0b71f28c0f2c5c27026b18f1bea98b505f1 * correct style by `make style` * add attention_op arg documents * add usage example to docstring Co-authored-by:
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Patrick von Platen authored
* [Paint by example] Fix paint by example * fix more * final fix
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- 21 Jan, 2023 1 commit
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Patrick von Platen authored
* [From pretrained] Don't download .safetensors files if safetensors is not available * tests * tests * up
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- 20 Jan, 2023 2 commits
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Damian Stewart authored
* convert __main__ to a function call and call it * add missing type hint * make style check pass * move loading to src/diffusers Co-authored-by: -
Suraj Patil authored
* being pix2pix * ifx * cfg image_latents * fix some docstr * fix * fix * hack * fix * Apply suggestions from code review Co-authored-by:
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- 19 Jan, 2023 2 commits
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Patrick von Platen authored
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Joqsan authored
fix typos and minor redundancies Co-authored-by:
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- 18 Jan, 2023 1 commit
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Patrick von Platen authored
* [Lora] first upload * add first lora version * upload * more * first training * up * correct * improve * finish loaders and inference * up * up * fix more * up * finish more * finish more * up * up * change year * revert year change * Change lines * Add cloneofsimo as co-author. Co-authored-by:
Simo Ryu <cloneofsimo@gmail.com> * finish * fix docs * Apply suggestions from code review Co-authored-by:
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- 17 Jan, 2023 3 commits
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Kashif Rasul authored
* added dit model * import * initial pipeline * initial convert script * initial pipeline * make style * raise valueerror * single function * rename classes * use DDIMScheduler * timesteps embedder * samples to cpu * fix var names * fix numpy type * use timesteps class for proj * fix typo * fix arg name * flip_sin_to_cos and better var names * fix C shape cal * make style * remove unused imports * cleanup * add back patch_size * initial dit doc * typo * Update docs/source/api/pipelines/dit.mdx Co-authored-by:
William Berman <WLBberman@gmail.com> Co-authored-by:
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Pedro Cuenca authored
* Check k-diffusion version is at least 0.0.12 * make style
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Jerry Jiarui XU authored
* [Flax] Add Flax inpainting impl * fixed copies, add README.md * fixed README.md * add test * format * update README.md
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- 16 Jan, 2023 2 commits
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William Dalheim authored
* pndmpipeline uses pndmscheduler * formatted pipeline_pndm
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Will Berman authored
re: https://github.com/huggingface/diffusers/issues/1857 We relax some of the checks to deal with unclip reproducibility issues. Mainly by checking the average pixel difference (measured w/in 0-255) instead of the max pixel difference (measured w/in 0-1). - [x] add mixin to UnCLIPPipelineFastTests - [x] add mixin to UnCLIPImageVariationPipelineFastTests - [x] Move UnCLIPPipeline flags in mixin to base class - [x] Small MPS fixes for F.pad and F.interpolate - [x] Made test unCLIP model's dimensions smaller to run tests faster
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