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* Better deprecation message

* Better deprecation message

* Better doc string

* Fixes

* fix more

* fix more

* Improve __getattr__

* correct more

* fix more

* fix

* Improve more

* more improvements

* fix more

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* make style

* Fix all rest & add tests & remove old deprecation fns

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Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

Make sure correct timesteps are chosen for img2img

Fix img2img processor with safety checker

fix breaking change

* fix default

* add guess mode (WIP)

* fix uncond/cond order

* support guidance_scale=1.0 and batch != 1

* remove magic coeff

* add docstring

* add intergration test

* add document to controlnet.mdx

* made the comments a bit more explanatory

* fix table

Allow stable diffusion attend and excite pipeline to work with any size output image. Re: #2476, #2603

Throw deprecation warning

* fix progress bar issue in pipeline_text_to_video_zero.py. Copy scheduler after first backward

* fix tensor loading in test_text_to_video_zero.py

* make style && make quality

* Fix a bug of pano when not doing CFG

* enhance code quality

* apply formatting.

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

* fix: norm group test for UNet3D.

* refactor text-to-video zero docs.

* add support for prompt embeds to SD ONNX pipeline

* fix up the pipeline copies

* add prompt embeds param to other ONNX pipelines

* fix up prompt embeds param for SD upscaling ONNX pipeline

* add missing type annotations to ONNX pipes

* fix pipeline __setattr__

* add test

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* add use_memory_efficient params placeholder

* test

* add memory efficient attention jax

* add memory efficient attention jax

* newline

* forgot dot

* Rename use_memory_efficient

* Keep dtype last.

* Actually use key_chunk_size

* Rename symbol

* Apply style

* Rename use_memory_efficient

* Keep dtype last

* Pass `use_memory_efficient_attention` in `from_pretrained`

* Move JAX memory efficient attention to attention_flax.

* Simple test.

* style

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Co-authored-by: muhammad_hanif <muhammad_hanif@sofcograha.co.id>
Co-authored-by: MuhHanif <48muhhanif@gmail.com>

* Update index.mdx

* Edit docs & add HF space link

* Only change equation numbers in comments

* add: first draft for a better LoRA enabler.

* make fix-copies.

* feat: backward compatibility.

* add: entry to the docs.

* add: tests.

* fix: docs.

* fix: norm group test for UNet3D.

* feat: add support for flat dicts.

* add depcrcation message instead of warning.

add AttnAddedKVProcessor2_0 block

add group norm type to attention processor cross attention norm

This lets the cross attention norm use both a group norm block and a
layer norm block.

The group norm operates along the channels dimension
and requires input shape (batch size, channels, *) where as the layer norm with a single
`normalized_shape` dimension only operates over the least significant
dimension i.e. (*, channels).

The channels we want to normalize are the hidden dimension of the encoder hidden states.

By convention, the encoder hidden states are always passed as (batch size, sequence
length, hidden states).

This means the layer norm can operate on the tensor without modification, but the group
norm requires flipping the last two dimensions to operate on (batch size, hidden states, sequence length).

All existing attention processors will have the same logic and we can
consolidate it in a helper function `prepare_encoder_hidden_states`

prepare_encoder_hidden_states -> norm_encoder_hidden_states re: @patrickvonplaten

move norm_cross defined check to outside norm_encoder_hidden_states

add missing attn.norm_cross check

* unet time embedding activation function

* typo act_fn -> time_embedding_act_fn

* flatten conditional

* add only cross attention to simple attention blocks

* add test for only_cross_attention re: @patrickvonplaten

* mid_block_only_cross_attention better default

allow mid_block_only_cross_attention to default to
`only_cross_attention` when `only_cross_attention` is given
as a single boolean

When doing generation manually and using guidance_scale as a static
argument.

* [Config] Fix config prints and save, load

* Only use potential nn.Modules for dtype and device

* Correct vae image processor

* make sure in_channels is not accessed directly

* make sure in channels is only accessed via config

* Make sure schedulers only access config attributes

* Make sure to access config in SAG

* Fix vae processor and make style

* add tests

* uP

* make style

* Fix more naming issues

* Final fix with vae config

* change more

* add TextToVideoZeroPipeline and CrossFrameAttnProcessor

* add docs for text-to-video zero

* add teaser image for text-to-video zero docs

* Fix review changes. Add Documentation. Add test

* clean up the codes in pipeline_text_to_video.py. Add descriptive comments and docstrings

* make style && make quality

* make fix-copies

* make requested changes to docs. use huggingface server links for resources, delete res folder

* make style && make quality && make fix-copies

* make style && make quality

* Apply suggestions from code review

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

`encoder_hid_dim` provides an additional projection for the input `encoder_hidden_states` from `encoder_hidden_dim` to `cross_attention_dim`

Update the K-Diffusion SD pipeline, to allow calling it with only prompt_embeds (instead of always requiring a prompt) (#2962)

* [Pipeline download] Improve pipeline download for index and passed components

* correct

* add more tests

* up

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

* [2884]: Fix cross_attention_kwargs in StableDiffusionImg2ImgPipeline

* [Build Fix]

* [Build Fix]

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Co-authored-by: njindal <njindal@adobe.com>