- 25 Jan, 2023 1 commit
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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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- 06 Dec, 2022 1 commit
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Anton Lozhkov authored
* [WIP] Standardize fast pipeline tests with PipelineTestMixin * refactor the sd tests a bit * add more common tests * add xformers * add progressbar test * cleanup * upd fp16 * CycleDiffusionPipelineFastTests * DanceDiffusionPipelineFastTests * AltDiffusionPipelineFastTests * StableDiffusion2PipelineFastTests * StableDiffusion2InpaintPipelineFastTests * StableDiffusionImageVariationPipelineFastTests * StableDiffusionImg2ImgPipelineFastTests * StableDiffusionInpaintPipelineFastTests * remove unused mixins * quality * add missing inits * try to fix mps tests * fix mps tests * add mps warmups * skip for some pipelines * style * Update tests/test_pipelines_common.py Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by:
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- 16 Nov, 2022 1 commit
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Will Berman authored
* vq diffusion classifier free sampling * correct * uP Co-authored-by:
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- 03 Nov, 2022 1 commit
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Will Berman authored
* Changes for VQ-diffusion VQVAE Add specify dimension of embeddings to VQModel: `VQModel` will by default set the dimension of embeddings to the number of latent channels. The VQ-diffusion VQVAE has a smaller embedding dimension, 128, than number of latent channels, 256. Add AttnDownEncoderBlock2D and AttnUpDecoderBlock2D to the up and down unet block helpers. VQ-diffusion's VQVAE uses those two block types. * Changes for VQ-diffusion transformer Modify attention.py so SpatialTransformer can be used for VQ-diffusion's transformer. SpatialTransformer: - Can now operate over discrete inputs (classes of vector embeddings) as well as continuous. - `in_channels` was made optional in the constructor so two locations where it was passed as a positional arg were moved to kwargs - modified forward pass to take optional timestep embeddings ImagePositionalEmbeddings: - added to provide positional embeddings to discrete inputs for latent pixels BasicTransformerBlock: - norm layers were made configurable so that the VQ-diffusion could use AdaLayerNorm with timestep embeddings - modified forward pass to take optional timestep embeddings CrossAttention: - now may optionally take a bias parameter for its query, key, and value linear layers FeedForward: - Internal layers are now configurable ApproximateGELU: - Activation function in VQ-diffusion's feedforward layer AdaLayerNorm: - Norm layer modified to incorporate timestep embeddings * Add VQ-diffusion scheduler * Add VQ-diffusion pipeline * Add VQ-diffusion convert script to diffusers * Add VQ-diffusion dummy objects * Add VQ-diffusion markdown docs * Add VQ-diffusion tests * some renaming * some fixes * more renaming * correct * fix typo * correct weights * finalize * fix tests * Apply suggestions from code review Co-authored-by:
Anton Lozhkov <aglozhkov@gmail.com> * Apply suggestions from code review Co-authored-by:
Pedro Cuenca <pedro@huggingface.co> * finish * finish * up Co-authored-by:
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