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Commits on May 29, 2026

• AR Diffusion sampler for hackable_diffusion

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Commits on May 28, 2026

• Fix expected error message in sampling_test due to treemap change.

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Commits on May 26, 2026

• Refactor SkipConnection, Downsampling, and Resampling to callables.

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Commits on May 22, 2026

• Refactor RoPE position types to callable dataclasses.

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• Refactor embedding merging in ConditioningEncoder to use callable dataclasses.

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Commits on May 21, 2026

• Introduce RMS-Norm for attention normalization

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• Update DiT blocks

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• Add option to add bias in the attention

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• Allow DiT to be more configurable using FLUX / MMDiT / Classic DiT blocks

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• Remove SwiGLU but introduce Feedforward with SwiGLU

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• Implement attention dropout

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• Introduce Ada RMS norm which does not use shift

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Commits on May 20, 2026

• Add SwiGLU to mlp_blocks.

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Commits on May 19, 2026

• Remove .copy() calls from NestedSelfConditioningDiffusionNetwork

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• Fix serialization issue in discrete corruption process.

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Commits on May 18, 2026

• Add trailing dimension checks

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• Use separate norm for MLP and attention in DiT

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• In NormalizationLayerFactory, move from property to a method

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Commits on May 13, 2026

• Type hint for multi-modal/nested.

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• Add NestedSelfConditioningDiffusionNetwork for multi-modal discrete self-conditioning.

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• Extend use of routing dataclass to weights.

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Commits on May 12, 2026

• Remove cast from diffusion network and conditioning encoder (Potentially breaking change)

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• Rename utils to jax_helpers.

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• Relax `ConditioningEmbedding` by removing `Conditioning Mechanism`

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Commits on May 11, 2026

• Use eager execution for is_masking property.

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Commits on May 8, 2026

• Add diagram to discrete_step_sampler.py docstring illustrating routing and planning flow.

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• * Modify concatenate to stack logic in _sample_routing to be safe against dimension misspecification (which assumes a trailing dimension of 1)

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Commits on May 7, 2026

• Update documentation for routing and planner architecture.

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• Add GreedyPlanner matching.

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Commits on May 6, 2026

• Factor out candidate generation and post-processing in discrete samplers to reduce boilerplate. This makes the `update` methods focus purely on computing the routing probabilities.

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• Unify discrete samplers with 3-way routing representation.

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• Cleanup *_step_sampler.py - fix some interfaces and use keyword arguments consistently

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• Unify MARK comment casing to Title Case

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• Fix logSNR docstring errors in gaussian.py

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• Cleanup time_scheduling - Drop unused class and split tests.

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