- 13 Feb, 2023 11 commits
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Will Berman authored
* github issue forum link * Update .github/ISSUE_TEMPLATE/config.yml Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> --------- Co-authored-by:
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Will Berman authored
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Patrick von Platen authored
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Patrick von Platen authored
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Patrick von Platen authored
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Patrick von Platen authored
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Plat authored
fix typo
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bddppq authored
* Fix running LoRA with xformers * support disabling xformers * reformat * Add test
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Giada Pistilli authored
* add ethical guidelines * update file name * edit file name * update toctree * Update docs/source/en/conceptual/ethical_guidelines.mdx * Update docs/source/en/conceptual/ethical_guidelines.mdx --------- Co-authored-by: -
Patrick von Platen authored
* [Tests] Refactor push tests * correct
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Naga Sai Abhinay authored
* UnCLIP Text Interpolation Pipeline * Formatter fixes * Changes based on feedback * Formatting fix * Formatting fix * isort formatting fix(?) * Remove duplicate code * Formatting fix * Refactor __call__ and change example in readme. * Update examples/community/unclip_text_interpolation.py Refactor to linter formatting Co-authored-by:
Will Berman <wlbberman@gmail.com> --------- Co-authored-by:
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- 10 Feb, 2023 5 commits
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Will Berman authored
* convert ckpt script docstring fixes * Update src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py Co-authored-by:
Pedro Cuenca <pedro@huggingface.co> * Update src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py Co-authored-by:
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Will Berman authored
* remove ddpm test_full_inference * style
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Patrick von Platen authored
* correct some * Apply suggestions from code review * correct * Update tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py * Final
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Patrick von Platen authored
add fast tests
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Patrick von Platen authored
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- 09 Feb, 2023 3 commits
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erkams authored
* [LoRA] Freezing the model weights Freeze the model weights since we don't need to calculate grads for them. * Apply suggestions from code review Co-authored-by:
Suraj Patil <surajp815@gmail.com>
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Patrick von Platen authored
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Ben Evans authored
Resolves ValueError: `num_inference_steps`: 1000 cannot be larger than `self.config.train_timesteps`: 50 as the unet model trained with this scheduler can only handle maximal 50 timesteps.
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- 08 Feb, 2023 6 commits
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Will Berman authored
Co-authored-by: -
Patrick von Platen authored
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Patrick von Platen authored
* [Examples] Test all examples on CPU * add * correct * Apply suggestions from code review
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Chenguo Lin authored
* EMA: fix `state_dict()` & add `cur_decay_value` * EMA: fix a bug in `load_state_dict()` 'float' object (`state_dict["power"]`) has no attribute 'get'. * del train_unconditional_ort.py
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Isamu Isozaki authored
* Quality check and adding tokenizer * Adapted stable diffusion to mixed precision+finished up style fixes * Fixed based on patrick's review * Fixed oom from number of validation images * Removed unnecessary np.array conversion --------- Co-authored-by: -
Patrick von Platen authored
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- 07 Feb, 2023 13 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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Patrick von Platen authored
* better accelerated saving * up * finish * finish * uP * up * up * fix * Apply suggestions from code review * correct ema * Remove @ * up * Apply suggestions from code review Co-authored-by:
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Pedro Cuenca authored
* mps cross-attention hack: don't crash on fp16 * Make conversion explicit.
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wfng92 authored
* Fix torchvision.transforms and transforms function naming clash * Update unconditional script for onnx * Apply suggestions from code review Co-authored-by:
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Patrick von Platen authored
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wfng92 authored
* Add center crop and horizontal flip to args * Update command to use center crop and random flip * Add center crop and horizontal flip to args * Update command to use center crop and random flip
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Patrick von Platen authored
* [Examples] Remove datasets important that is not needed * remove from lora tambien
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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:
yiyixuxu <yixu@yis-macbook-pro.lan> Co-authored-by:
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Patrick von Platen authored
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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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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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Fazzie-Maqianli authored
fix colossalai dreambooth
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- 06 Feb, 2023 2 commits
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Pedro Cuenca authored
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nickkolok authored
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