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LTX2: unified conditioning mechanism, flash-attn 3 support, IC-LoRA validations
FSDP2 LoRA training (LTX 2.3 verified)
update FSDP2 documentation
support fused projections across more models
add context parallel plans for more model families
add more config examples centred around 8xH100 for LTX 2.3
LTX 2.3 context-parallel fixes for uneven sequence lengths
krea2: CREPA self-flow, LayerSync, TREAD, block swap, cfg-zero star, skip layer guidance, and flowmap generation (AnyFlow)
cog: add a script to deploy multiple versions in sequence for multigpu support
…sion manifest is registered
cloud: update replicate deploy script to push using cog to ensure version manifest is registered
cloud: multigpu training options via Replicate
cloud: fix UI settings default to actually show the tab by default
cloud: update configuration wizard to show correct hardware type selector
…anges fix --disable_tf32 in newer torch api checks
…w-matching training Add a `timestep_sampling_offset` option to per-dataset config. This offsets the pre-sigmoid normal sample in the default logit-normal flow-matching schedule, letting users shift the noise distribution per dataset based on semantic granularity. Negative offset -> lower-noise timesteps (detail), positive -> higher-noise (structure). Default 0.0 = stock behavior. Based on: The Quadratic Geometry of Flow Matching: Semantic Granularity Alignment for Text-to-Image Synthesis (arXiv:2603.10785).
Unify naming across code, config, docs, tests, and WebUI template to match the PR description and align with noise_offset convention. No behavioral change.
feat: add per-dataset timestep_sampling_offset for semantic-aware flow-matching training
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
metal-flash-attention integration (fp32, int8, int4)
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