Add Echo modular pipeline

[Gelercatty]

What does this PR do?

Adds official Diffusers Modular Pipeline support for Echo, a long-video model that generates synchronized video and audio with cross-shot memory.

This supersedes #13910 with a modular implementation that incorporates the earlier review feedback:

• adds the standalone diffusers.modular_pipelines.echo package and the public EchoModularPipeline / EchoBlocks APIs
• reuses existing lower-level Diffusers components without exposing LTX in Echo's public API
• keeps checkpoint conversion in scripts/ instead of adding an original-checkpoint runtime pipeline
• supports an optional first frame and up to seven paired image/audio memory slots
• supports variable-length memory audio and crops audio longer than 9.62 seconds to its highest-response window
• implements Echo's positive-only conditioning and stochastic 8-step DMD denoising workflow
• adds mixed-precision-safe audio decoding, documentation, and tests using a public tiny fixture

Official resources:

• Source implementation: https://github.com/jd-opensource/JoyAI-Echo/tree/echo-long-diffusers
• Model weights: https://huggingface.co/Echo-Team/JoyAI-Echo
• Tracking issue: [New Pipeline/Model] Add JoyAI-Echo multi-shot audio-video generation pipeline #13909

Closes#13909
Supersedes #13910

Testing

• make style
• make fix-copies
• make quality
• PYTHONPATH=src python -m pytest -q tests/modular_pipelines/echo/test_modular_pipeline_echo.py
  • 31 passed, 6 skipped

AI-assisted self-review

Codex was used to help review, refactor, test, and prepare this contribution. The final diff was reviewed using the repository's model-integration and self-review guidance.

The final self-review found no blocking correctness issues. Echo has its own public package and naming throughout; existing lower-level Diffusers component types are reused only as implementation dependencies. Memory-slot count, short-audio handling, variable audio lengths, response-aware 9.62-second cropping, deterministic DMD re-noising, mixed-precision decoding, loading, save/load, workflow, and memory behavior are covered by the test suite.

Validation scope: the final pass used the public Echo-Team/tiny-echo-modular-pipe fixture on CPU. A full production-checkpoint conversion and end-to-end GPU inference run was not repeated in this final pass.

Before submitting

• Did you use an AI agent (Claude Code, Codex, Cursor, etc.) to help with this PR?
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  • Did you run the self-review skill on the diff?
  • Did you share the final self-review notes in the PR description or a comment?
• Did you read the contributor guideline?
• Did you read our philosophy doc?
• Was this discussed/approved via a GitHub issue or the forum? See [New Pipeline/Model] Add JoyAI-Echo multi-shot audio-video generation pipeline #13909 and the maintainer discussion on [feat] Add JoyAI-Echo multi-shot audio-video generation pipeline #13910.
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?
• Are you the author (or part of the team) of the model/pipeline?

Who can review?

@asomoza@yiyixuxu

[HuggingFaceDocBuilderDev]

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[yiyixuxu]

thanks, I left some feedbacks

[Gelercatty]

@yiyixuxu Thanks for the review — all feedback is addressed in 64f423a and 679db3f.

• removed the echo_ prefix from leaf source filenames
• changed module-level helpers to explicit component arguments
• reduced text-encoder outputs and derived batch size/dtype downstream
• moved all assembled blocks to modular_blocks_echo.py
• split VAE-only encoding from transformer-specific packing/RoPE preparation
• removed VAE requirements from before-denoise blocks
• flattened EchoBlocks to text_encoder, vae_encoder, denoise, decode
• inlined the single-use _as_list helper
• added a behavior test that runs the VAE encoder standalone without a transformer

Validation:

• make fix-copies
• Echo-scoped Ruff check and format check
• forward/call docstring check
• AI contribution check
• pytest -q tests/modular_pipelines/echo/test_modular_pipeline_echo.py: 31 passed, 6 skipped

Self-review found no remaining blocking issue in this update. The repository-wide make quality is currently blocked by 52 pre-existing formatting differences outside the Echo files; all Echo-scoped quality checks pass.

Could you please take another look?

[yiyixuxu]

thanks!
i left some more comments

[yiyixuxu]

is it possible to make the audio latent and video latent consistent? i.e. both normalized in encoder and packed in before_denoise? see a bit more write up here #14730

[yiyixuxu]

Suggested change

	latent_height=block_state.height//components.vae_spatial_compression_ratio
	latent_width=block_state.width//components.vae_spatial_compression_ratio
	_, _, latent_height, latent_width=memory_video_latents[0].shape

[yiyixuxu]

Suggested change

	InputParam.template("height", default=512),
	InputParam.template("width", default=704),

I think we can derive from latent shape

[yiyixuxu]

can we have a dedicated step to expand all the inputs? like https://github.com/huggingface/diffusers/blob/main/src/diffusers/modular_pipelines/krea2/before_denoise.py#L46

[yiyixuxu]

ohh this is typically inside denoise.py (even though it is a multi-block) - sorry if I've confused you earlier

[yiyixuxu]

I think we can move the unpack to core denoise blocks (see #14730)