* fix(torchao): route root-level tensors around safetensors reconstruction
Models with parameters at the root of the module tree (e.g. Wan's
`scale_shift_table`) crashed torchao's `unflatten_tensor_state_dict`
when loading serialized checkpoints, since flattened tensor names are
assumed to carry a module prefix. Filter such tensors and their
metadata entries out of the reconstruction and merge them back
unchanged.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(bnb): defer 8-bit weights split from their SCB stats across shards
Sharded serialization can place an 8-bit weight and its `SCB`
statistics in different shard files, in which case the shard-by-shard
loader failed with "Missing quantization component `SCB`". Hold the
incomplete half of the pair back until its counterpart arrives with a
later shard, mirroring the torchao pending mechanism, and disable
parallel shard loading for prequantized 8-bit checkpoints.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* [tests] extend model-level quantization tester mixins
Migrate remaining model-level coverage from tests/quantization into the
tester mixins so it runs for every wired model:
- base: buffer-placement assertions in the device-map test, and an
opt-in sharded-serialization test enabled by setting
`sharded_serialization_config`
- bnb: serialization across all configs (sharded included), dtype
assignment and adapter training for 8-bit, device moves preserving
the memory footprint, corrupted-state-dict loading error, and a fixed
modules-to-not-convert test (BitsAndBytesConfig only exposes
llm_int8_skip_modules; the old test passed an unsupported kwarg and
only survived by being skipped)
- torchao: custom device maps with cpu/disk offload, generalized from
the Flux-specific test
- gguf: the diffusers-format single-file loading path, wired into the
Flux model tests
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* [tests] fix model-level quantization test wiring
- SD3.5: the quantized testers reused the random-init dummy inputs
(4 latent channels, fp32) while the tiny Hub checkpoint has
in_channels=8 and the quantizers load the model in half precision;
give them matching inputs and relax the 4-bit memory expectation for
the tiny checkpoint.
- QwenImage / Flux2: the quantized testers had no Hub checkpoint wired
at all, so every test errored; point them at
hf-internal-testing/tiny-qwenimage-pipe and tiny-flux2 with matching
inputs.
- NucleusMoE: no tiny checkpoint exists on the Hub yet; comment the
testers out like the LTX ones.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* [tests] split tests/quantization into pipeline-level and backend-level tiers
Pipeline-level quantization tests (pipeline quality slices, cpu offload,
LoRA loading, compile, PipelineQuantizationConfig) move to
tests/pipelines/testing_utils/quantization.py, marked per backend so the
nightly CI can select them with `pytest -m`. tests/quantization keeps
only backend-level tests that fit neither tier: config validation,
utility warnings, and GGUF CUDA kernel correctness. Tests already
covered by the model-level mixins are dropped.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* [ci] select nightly quantization tests by marker across test tiers
Each backend job now runs `pytest -m <marker>` over tests/models,
tests/quantization, and tests/pipelines/testing_utils/quantization.py,
giving the model-level mixin tests a nightly home with the backend
dependencies installed. The torchao job additionally installs mslk.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: sayak@huggingface.co <sayak@ip-10-53-90-45.ec2.internal>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* Reduce FLUX int8 test peak memory with sequential offload
* Gate FLUX int8 slow test with require_big_accelerator
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Remove JAX/Flax support
Deletes all Flax models, schedulers, pipelines, and loading utilities,
the from_flax loading path, flax/jax packaging extras, Flax CI filters,
Flax training examples, and Flax content in translated docs.
Hub compatibility is preserved: Flax class names in old model_index.json
files are still stripped to their PyTorch equivalents, and *.msgpack
files in repos are still ignored during download.
* Drop Flax class-name remapping from model_index.json loading
Flax-saved checkpoint metadata is no longer recognized: class names like
FlaxStableDiffusionPipeline in model_index.json now fail to resolve
instead of being remapped to their PyTorch equivalents. *.msgpack files
in hub repos are still ignored during download.
* Restore Flax training examples
The example scripts are standalone and still work with diffusers<=0.39.x.
A warning note is added to each Flax README section pointing out the
version requirement now that the library itself no longer ships Flax.
* update
* Repoint scheduler default-config tests off the Flax fixture
* Repoint example training tests off the Flax fixture
* up
* up
* quantization level refactors.
* style
* up
* up
* use mixins and remove stale attributes
* fixture for the deprecated pipelines
* remove qkv
* remove lergacy for attention processor.
* use pytorch tensors exclusively.
* rejig dodgy test.
* improve how base pipeline output fixture is used.
* rename params.
* up
* use pipeline load api for ip adapter.
* move to flux specific.
* redesign config for caching
* remove eval.
* up
* up
* add a todo note on check_qkv_fusion_matches_attn_procs_length
* nan test in layerwise casting
* proper skipping
* skip properly.
* up
* up
