GitLab

Regarding the RunwayML path for V1.5 did change to stable-diffusion-v1-5/[stable-diffusion-v1-5/ stable-diffusion-inpainting] (#10476)

* Update pipeline_controlnet.py

* Update pipeline_controlnet_img2img.py

runwayml Take-down so change all from to this
stable-diffusion-v1-5/stable-diffusion-v1-5

* Update pipeline_controlnet_inpaint.py

* runwayml take-down make change to sd-legacy

* runwayml take-down make change to sd-legacy

* runwayml take-down make change to sd-legacy

* runwayml take-down make change to sd-legacy

* Update convert_blipdiffusion_to_diffusers.py

style change

* Add torch_xla and from_single_file to instruct-pix2pix

* StableDiffusionInstructPix2PixPipelineSingleFileSlowTests

* StableDiffusionInstructPix2PixPipelineSingleFileSlowTests

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* LEditsPP - examples, check height/width, add tiling/slicing

* make style

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Fix AutoPipeline `from_pipe` where source pipeline is missing target pipeline's optional components (#10400)

* Optional components in AutoPipeline

* missing_modules

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Co-authored-by: YiYi Xu <yixu310@gmail.com>

fix attribute adjustment for ltx.

* IP-Adapter support for `StableDiffusion3ControlNetPipeline`

* Update src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py
Co-authored-by: hlky <hlky@hlky.ac>

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Co-authored-by: hlky <hlky@hlky.ac>

SD3 pipelines hasattr

[Add] torch_xla support in pipeline_sana.py

* Add torch_xla support to pipeline_aura_flow.py

* make style

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Co-authored-by: hlky <hlky@hlky.ac>

* update

* make style

* update

* update

* update

* make style

* single file related changes

* update

* fix

* update single file urls and docs

* update

* fix

* fix the Positinoal Embedding bug in 2K model;

* Change the default model to the BF16 one for more stable training and output

* make style

* substract buffer size

* add compute_module_persistent_sizes

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Co-authored-by: yiyixuxu <yixu310@gmail.com>

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

update docs and example to use community weights

* flux controlnet inpaint config bug

* Update src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py

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Co-authored-by: yigitozgenc <yigit@quantuslabs.ai>
Co-authored-by: hlky <hlky@hlky.ac>

* Flux IP-Adapter

* test cfg

* make style

* temp remove copied from

* fix test

* fix test

* v2

* fix

* make style

* temp remove copied from

* Apply suggestions from code review
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* Move encoder_hid_proj to inside FluxTransformer2DModel

* merge

* separate encode_prompt, add copied from, image_encoder offload

* make

* fix test

* fix

* Update src/diffusers/pipelines/flux/pipeline_flux.py

* test_flux_prompt_embeds change not needed

* true_cfg -> true_cfg_scale

* fix merge conflict

* test_flux_ip_adapter_inference

* add fast test

* FluxIPAdapterMixin not test mixin

* Update pipeline_flux.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

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Co-authored-by: YiYi Xu <yixu310@gmail.com>

[BUG FIX] [Stable Audio Pipeline] Resolve torch.Tensor.new_zeros() TypeError in function prepare_latents caused by audio_vae_length (#10306)

[BUG FIX] [Stable Audio Pipeline] TypeError: new_zeros(): argument 'size' failed to unpack the object at pos 3 with error "type must be tuple of ints,but got float"

torch.Tensor.new_zeros() takes a single argument size (int...) – a list, tuple, or torch.Size of integers defining the shape of the output tensor.

in function prepare_latents:
audio_vae_length = self.transformer.config.sample_size * self.vae.hop_length
audio_shape = (batch_size // num_waveforms_per_prompt, audio_channels, audio_vae_length)
...
audio = initial_audio_waveforms.new_zeros(audio_shape)

audio_vae_length evaluates to float because self.transformer.config.sample_size returns a float
Co-authored-by: hlky <hlky@hlky.ac>

Update pipeline_hunyuan_video.py

docs: fix a mistake

* Added support for single IPAdapter on SD3.5 pipeline



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Co-authored-by: hlky <hlky@hlky.ac>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* Port UNet2DModel gradient checkpointing code from #6718.


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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Vincent Neemie <92559302+VincentNeemie@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
Co-authored-by: hlky <hlky@hlky.ac>

* 1217

* 1217

* 1217

* update

* reverse

* add test

* update test

* make style

* update

* make style

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Co-authored-by: Aryan <aryan@huggingface.co>

* Check correct model type is passed to `from_pretrained`

* Flax, skip scheduler

* test_wrong_model

* Fix for scheduler

* Update tests/pipelines/test_pipelines.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* EnumMeta

* Flax

* scheduler in expected types

* make

* type object 'CLIPTokenizer' has no attribute '_PipelineFastTests__name'

* support union

* fix typing in kandinsky

* make

* add LCMScheduler

* 'LCMScheduler' object has no attribute 'sigmas'

* tests for wrong scheduler

* make

* update

* warning

* tests

* Update src/diffusers/pipelines/pipeline_utils.py
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* import FlaxSchedulerMixin

* skip scheduler

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

update

* flux_control_inpaint - failing test_flux_different_prompts

* removing test_flux_different_prompts?

* fix style

* fix from PR comments

* fix style

* reducing guidance_scale in demo

* Update src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py
Co-authored-by: hlky <hlky@hlky.ac>

* make

* prepare_latents is not copied from

* update docs

* typos

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Co-authored-by: affromero <ubuntu@ip-172-31-17-146.ec2.internal>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: hlky <hlky@hlky.ac>

* Use `torch` in `get_2d_rotary_pos_embed`

* Add deprecation

fix: licensing header.

* feat: lora support for SANA.

* make fix-copies

* rename test class.

* attention_kwargs -> cross_attention_kwargs.

* Revert "attention_kwargs -> cross_attention_kwargs."

This reverts commit 23433bf9bccc12e0f2f55df26bae58a894e8b43b.

* exhaust 119 max line limit

* sana lora fine-tuning script.

* readme

* add a note about the supported models.

* Apply suggestions from code review
Co-authored-by: Aryan <aryan@huggingface.co>

* style

* docs for attention_kwargs.

* remove lora_scale from pag pipeline.

* copy fix

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Co-authored-by: Aryan <aryan@huggingface.co>

* update

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* update

* Update src/diffusers/models/transformers/transformer_mochi.py
Co-authored-by: Aryan <aryan@huggingface.co>

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Aryan <aryan@huggingface.co>

* add lora support for ltx

* add tests

* fix copied from comments

* update

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Use non-human subject in StableDiffusion3ControlNetPipeline example

* make style

* Add `dynamic_shifting` to SD3

* calculate_shift

* FlowMatchHeunDiscreteScheduler doesn't support mu

* Inpaint/img2img

* Update pipeline_controlnet.py

* make style

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Co-authored-by: hlky <hlky@hlky.ac>

* copy transformer

* copy vae

* copy pipeline

* make fix-copies

* refactor; make original code work with diffusers; test latents for comparison generated with this commit

* move rope into pipeline; remove flash attention; refactor

* begin conversion script

* make style

* refactor attention

* refactor

* refactor final layer

* their mlp -> our feedforward

* make style

* add docs

* refactor layer names

* refactor modulation

* cleanup

* refactor norms

* refactor activations

* refactor single blocks attention

* refactor attention processor

* make style

* cleanup a bit

* refactor double transformer block attention

* update mochi attn proc

* use diffusers attention implementation in all modules; checkpoint for all values matching original

* remove helper functions in vae

* refactor upsample

* refactor causal conv

* refactor resnet

* refactor

* refactor

* refactor

* grad checkpointing

* autoencoder test

* fix scaling factor

* refactor clip

* refactor llama text encoding

* add coauthor
Co-Authored-By: "Gregory D. Hunkins" <greg@ollano.com>

* refactor rope; diff: 0.14990234375; reason and fix: create rope grid on cpu and move to device

Note: The following line diverges from original behaviour. We create the grid on the device, whereas
original implementation creates it on CPU and then moves it to device. This results in numerical
differences in layerwise debugging outputs, but visually it is the same.

* use diffusers timesteps embedding; diff: 0.10205078125

* rename

* convert

* update

* add tests for transformer

* add pipeline tests; text encoder 2 is not optional

* fix attention implementation for torch

* add example

* update docs

* update docs

* apply suggestions from review

* refactor vae

* update

* Apply suggestions from code review
Co-authored-by: hlky <hlky@hlky.ac>

* Update src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
Co-authored-by: hlky <hlky@hlky.ac>

* Update src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
Co-authored-by: hlky <hlky@hlky.ac>

* make fix-copies

* update

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Co-authored-by: "Gregory D. Hunkins" <greg@ollano.com>
Co-authored-by: hlky <hlky@hlky.ac>