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- 07 Feb, 2023 3 commits
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Patrick von Platen authored
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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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chavinlo authored
* Create convert_vae_pt_to_diffusers.py Just a simple script to convert VAE.pt files to diffusers format Tested with: https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/VAEs/orangemix.vae.pt * Update convert_vae_pt_to_diffusers.py Forgot to add the function call * make style --------- Co-authored-by:
chavinlo <example@example.com>
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- 23 Jan, 2023 1 commit
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cafe+ai — かふぇあい authored
* Safetensors loading in "convert_diffusers_to_original_stable_diffusion" Adds diffusers format saftetensors loading support * Fix import sort order: convert_diffusers_to_original_stable_diffusion.py Co-authored-by:
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- 20 Jan, 2023 1 commit
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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:
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- 17 Jan, 2023 1 commit
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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:
Suraj Patil <surajp815@gmail.com> * added copyright license headers * added example usage and toc * fix variable names asserts * remove comment * added docs * fix typo * upstream changes * set proper device for drop_ids * added initial dit pipeline test * update docs * fix imports * make fix-copies * isort * fix imports * get rid of more magic numbers * fix code when guidance is off * remove block_kwargs * cleanup script * removed to_2tuple * use FeedForward class instead of another MLP * style * work on mergint DiTBlock with BasicTransformerBlock * added missing final_dropout and args to BasicTransformerBlock * use norm from block * fix arg * remove unused arg * fix call to class_embedder * use timesteps * make style * attn_output gets multiplied * removed commented code * use Transformer2D * use self.is_input_patches * fix flags * fixed conversion to use Transformer2DModel * fixes for pipeline * remove dit.py * fix timesteps device * use randn_tensor and fix fp16 inf. * timesteps_emb already the right dtype * fix dit test class * fix test and style * fix norm2 usage in vq-diffusion * added author names to pipeline and lmagenet labels link * fix tests * use norm_type as string * rename dit to transformer * fix name * fix test * set norm_type = "layer" by default * fix tests * do not skip common tests * Update src/diffusers/models/attention.py Co-authored-by:
William Berman <WLBberman@gmail.com> Co-authored-by:
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- 16 Jan, 2023 2 commits
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Patrick von Platen authored
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蓝色的秋风 authored
fix: support diffusers to safetensors
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- 12 Jan, 2023 1 commit
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Katsuya authored
Fix unused upcast_attn flag in sd to diffusers script
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- 10 Jan, 2023 1 commit
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Patrick von Platen authored
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- 06 Jan, 2023 1 commit
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Patrick von Platen authored
* [Conversion] Make sure ema weights are extracted correctly * up * finish
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- 03 Jan, 2023 2 commits
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Patrick von Platen authored
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Patrick von Platen authored
* [Deterministic torch randn] Allow tensors to be generated on CPU * fix more * up * fix more * up * Update src/diffusers/utils/torch_utils.py Co-authored-by:
Anton Lozhkov <anton@huggingface.co> * Apply suggestions from code review * up * up * Apply suggestions from code review Co-authored-by:
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- 30 Dec, 2022 1 commit
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Patrick von Platen authored
* move files a bit * more refactors * fix more * more fixes * fix more onnx * make style * upload * fix * up * fix more * up again * up * small fix * Update src/diffusers/__init__.py Co-authored-by:
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- 28 Dec, 2022 1 commit
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Will Berman authored
* unCLIP image variation * remove prior comment re: @pcuenca * stable diffusion -> unCLIP re: @pcuenca * add copy froms re: @patil-suraj
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- 27 Dec, 2022 1 commit
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camenduru authored
* Device to use (e.g. cpu, cuda:0, cuda:1, etc.) * "cuda" if torch.cuda.is_available() else "cpu"
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- 19 Dec, 2022 1 commit
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Mikołaj Siedlarek authored
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- 18 Dec, 2022 1 commit
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Will Berman authored
* [wip] attention block updates * [wip] unCLIP unet decoder and super res * [wip] unCLIP prior transformer * [wip] scheduler changes * [wip] text proj utility class * [wip] UnCLIPPipeline * [wip] kakaobrain unCLIP convert script * [unCLIP pipeline] fixes re: @patrickvonplaten remove callbacks move denoising loops into call function * UNCLIPScheduler re: @patrickvonplaten Revert changes to DDPMScheduler. Make UNCLIPScheduler, a modified DDPM scheduler with changes to support karlo * mask -> attention_mask re: @patrickvonplaten * [DDPMScheduler] remove leftover change * [docs] PriorTransformer * [docs] UNet2DConditionModel and UNet2DModel * [nit] UNCLIPScheduler -> UnCLIPScheduler matches existing unclip naming better * [docs] SchedulingUnCLIP * [docs] UnCLIPTextProjModel * refactor * finish licenses * rename all to attention_mask and prep in models * more renaming * don't expose unused configs * final renaming fixes * remove x attn mask when not necessary * configure kakao script to use new class embedding config * fix copies * [tests] UnCLIPScheduler * finish x attn * finish * remove more * rename condition blocks * clean more * Apply suggestions from code review * up * fix * [tests] UnCLIPPipelineFastTests * remove unused imports * [tests] UnCLIPPipelineIntegrationTests * correct * make style Co-authored-by:
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- 13 Dec, 2022 1 commit
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apolinario authored
* Fix type checking remainders * Remove IS_V20_MODEL flag always being True Co-authored-by:apolinario <joaopaulo.passos+multimodal@gmail.com>
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- 12 Dec, 2022 3 commits
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Patrick von Platen authored
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Cyberes authored
handle missing global_step key and don't download config if it already exists
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lawfordp2017 authored
* Initial code for attempt at improving SD <--> diffusers conversions for v2.0 * Updates to support round-trip between orig. SD 2.0 and diffusers models * Corrected formatting to Black standard * Correcting import formatting * Fixed imports (properly this time) * add some corrections * remove inference files Co-authored-by:
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- 07 Dec, 2022 1 commit
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Patrick von Platen authored
* add paint by example * mkae loading possibel * up * Update src/diffusers/models/attention.py * up * finalize weight structure * make example work * make it work * up * up * fix * del * add * update * Apply suggestions from code review * correct transformer 2d * finish * up * up * up * up * fix * Apply suggestions from code review Co-authored-by:
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- 05 Dec, 2022 1 commit
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Patrick von Platen authored
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- 02 Dec, 2022 1 commit
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Patrick von Platen authored
* Conversion SD 2 * finish
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- 01 Dec, 2022 1 commit
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Anton Lozhkov authored
* Add an explicit `--image_size` to the conversion script * style
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- 25 Nov, 2022 2 commits
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Anton Lozhkov authored
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Patrick von Platen authored
up
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- 23 Nov, 2022 1 commit
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Patrick von Platen authored
* up * convert dual unet * revert dual attn * adapt for vd-official * test the full pipeline * mixed inference * mixed inference for text2img * add image prompting * fix clip norm * split text2img and img2img * fix format * refactor text2img * mega pipeline * add optimus * refactor image var * wip text_unet * text unet end to end * update tests * reshape * fix image to text * add some first docs * dual guided pipeline * fix token ratio * propose change * dual transformer as a native module * DualTransformer(nn.Module) * DualTransformer(nn.Module) * correct unconditional image * save-load with mega pipeline * remove image to text * up * uP * fix * up * final fix * remove_unused_weights * test updates * save progress * uP * fix dual prompts * some fixes * finish * style * finish renaming * up * fix * fix * fix * finish 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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- 15 Nov, 2022 1 commit
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Patrick von Platen authored
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- 14 Nov, 2022 2 commits
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Nathan Lambert authored
* 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:
Ben Glickenhaus <benglickenhaus@gmail.com>
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Patrick von Platen authored
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- 09 Nov, 2022 2 commits
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Patrick von Platen authored
up
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Duong A. Nguyen authored
fix script convert LDM
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- 08 Nov, 2022 1 commit
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Anton Lozhkov authored
* [ONNX] Improve ONNX scheduler compatibility, fix safety_checker * typo
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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:
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- 26 Oct, 2022 1 commit
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Patrick von Platen authored
CompVis -> diffusers script - allow converting from merged checkpoint to either EMA or non-EMA (#991) * improve script * up
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- 25 Oct, 2022 2 commits
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Patrick von Platen authored
* start * add more logic * Update src/diffusers/models/unet_2d_condition_flax.py * match weights * up * make model work * making class more general, fixing missed file rename * small fix * make new conversion work * up * finalize conversion * up * first batch of variable renamings * remove c and c_prev var names * add mid and out block structure * add pipeline * up * finish conversion * finish * upload * more fixes * Apply suggestions from code review * add attr * up * uP * up * finish tests * finish * uP * finish * fix test * up * naming consistency in tests * Apply suggestions from code review Co-authored-by:
Nathan Lambert <nol@berkeley.edu> Co-authored-by:
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SkyTNT authored
* [Onnx] support half-precision and fix bugs for onnx pipelines * Update convert_stable_diffusion_checkpoint_to_onnx.py * style * fix has_nsfw_concept * Update convert_stable_diffusion_checkpoint_to_onnx.py * fix style
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