On ROCm, host-to-device DMA from mmap'd safetensors pages stalls ~1s per
copy, so weights move to the GPU at ~27 MB/s instead of ~28 GB/s. With
offload enabled this re-copies weights every forward, so generation
appears to hang.
Adds `diffusers_disable_mmap` (Settings > Model Loading), off by default,
which makes diffusers read shards into anonymous memory instead. Costs
peak RAM equal to the model size, so it is opt-in.
SD3.5-large on RX 9070 (gfx1201), same prompt and steps:
off: no image after 120s
on: image in 20s
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The offload section interleaved shared and mode-specific options. The
page now reads top down: general options, an Offload Overrides section
holding the exclusions shared by balanced and group offload, then one
tuning section per mode. Use torch streams becomes Prefetch with streams
and Record torch streams becomes Overlap stream transfers, the section
headers carry hints, and the hints reflect live behavior, including
dropping the model-reload badge from options that apply on change.
Group offload is applied per component through one engine shared by
regular and modular pipelines. Each component carries a config
signature: re-application with unchanged settings is a no-op instead
of raising before the first forward or silently keeping a stale
config, and changed settings remove the hooks and reapply. Switching
offload modes cleans up the previous mode's hooks in both directions.
- text encoders always offload at leaf level without streams, so
their weights are never held in pinned host memory
- the vae never takes group hooks and stays resident: the hooks are
forward-scoped, while pipelines enter through encode/decode and
tiled calls re-enter per tile
- new pin offload memory option: disabled pins one group at a time
instead of holding the whole module in non-pageable memory, and
modules larger than half of system memory degrade automatically
- record stream is clamped to stream mode; the standalone
combination is rejected upstream
The secondary unet loads from the networks panel toggle or the settings
page; companion ordering and dynamic visibility pushes in ui_settings.py
are not worth their footprint for one setting.
One UNET override cannot serve dual-transformer arches: ideogram4
conditional/unconditional and wan combined-stage experts need separate
files, and previously a single override landed on both experts.
- sd_unet_secondary option with per-slot tracking, consumed-state sync,
arch-change reset, and incompatible-override fallback
- dropdown renders beside the primary, follows it into quicksettings,
and is visible only for dual-transformer model types
- ideogram4 native single-file spec with a quant-aware fused-qkv
converter; such converters run before comfy_quant detection via
TransformerSpec.converter_handles_quant
- quicksettings render in configured order (sort keyed on the option
object and always fell back to alphabetical)
- post-load dtype warning skips quantized transformers
Wan 2.2 A14B ships a per-model boundary_ratio (0.9 I2V, 0.875 T2V) that selects the high- or low-noise expert per step. The video and base-model image loaders both load the shipped value; the slider override is applied at generation time in set_pipeline_args, the one point both paths pass through before invoking the pipeline.
The denoising loop reads config.boundary_ratio each call, so tuning takes effect with no reload for video and base-model images alike. The slider defaults to -1, meaning use the model's value; 0 to 1 set the boundary explicitly. Single-expert stages stay load-time because they drop a transformer to free VRAM.
schedulers_sigma is a hidden OptionInfo registration; the visible control is the sampler-accordion dropdown. Its choices still listed the k-diffusion set, including polyexponential (which the diffusers backend does not support) and omitting betas, lambdas, and flowmatch. Align the list with what sd_samplers_diffusers accepts and the dropdown offers. The list feeds warn-only validation, so this clears a dead value and spurious debug logs, not user-facing behavior.
Vladimir Mandic
Christopher McGee
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Pablo Hellmann