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feat(wan): run Wan 2.2 attention on SageAttention when enabled - #9665
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New `attention_backend: auto | sage` setting; with `sage`, eligible PyTorch SDPA calls inside a denoising scope run on SageAttention 2.2 and every other call keeps SDPA unchanged. Each GPU's first SageAttention call is checked against SDPA and a failing GPU falls back for the session; no model family enters the scope yet. Adds the SageAttention docs page and the regenerated config schema.
With `attention_backend: sage`, FLUX.1 denoising, including ControlNets inside the loop, enters the SageAttention scope. RTX 4090, FLUX.1 dev FP8: 16.1 s → 14.0 s at 1024², 84.2 s → 54.8 s at 2048², peak VRAM unchanged; a blind review judged no image worse.
With `attention_backend: sage`, FLUX.2 denoising enters the SageAttention scope. RTX 4090, FLUX.2 Klein 9B FP8: 15.6 s → 15.1 s at 1024², 27.1 s → 23.1 s at 2048², peak VRAM unchanged; a blind review judged no image worse.
With `attention_backend: sage`, Z-Image denoising enters the SageAttention scope; Z-Image Control masks its attention and keeps SDPA. RTX 4090, Z-Image FP8: 10.4 s → 10.0 s at 1024², 33.7 s → 25.9 s at 2048², peak VRAM unchanged; a blind review judged no image worse.
With `attention_backend: sage`, Qwen-Image denoising enters the SageAttention scope. RTX 4090, Qwen-Image 2512 GGUF Q4_K_M: 78.8 s → 73.5 s at 1024², 208.9 s → 150.3 s at 2048², peak VRAM unchanged; a blind review judged no image worse.
With `attention_backend: sage`, Krea-2 denoising enters the SageAttention scope unless INVOKE_KREA2_SDPA_BACKEND pins a PyTorch kernel. RTX 4090, Krea-2 Turbo GGUF Q4_K_M: 11.5 s → 11.0 s at 1024², 42.7 s → 36.2 s at 2048², peak VRAM unchanged; a blind review judged no image worse.
With `attention_backend: sage`, SDXL denoising and tiled multi-diffusion enter the SageAttention scope; SD1.5 and SD2 do not. RTX 4090: 4.4 s → 4.2 s at 1024², 19.6 s → 16.1 s at 2048², peak VRAM unchanged; a blind review judged no image worse.
With `attention_backend: sage`, LTX-2 denoising enters the SageAttention scope; video self-attention qualifies, while the masked text cross-attention and the short audio attention keep SDPA. RTX 4090, LTX-2.5 distilled int8 at 1248×704, 121 frames: 72.0 s → 63.4 s, peak VRAM unchanged, no added flicker.
With `attention_backend: sage`, Wan 2.2 image and video denoising enter the SageAttention scope. RTX 4090, Wan 2.2 TI2V-5B GGUF Q8 at 1280×704, 121 frames: 380.9 s → 247.9 s, peak VRAM unchanged, no added flicker.
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…ent SageAttention failures sm86 now runs the Triton kernel upstream's sageattn picks there, and sm87 gets none; the Windows build's CUDA routing for both is unexplained and unmeasured here. Out-of-memory and Triton cache errors fall back to SDPA for that call (three retire the device), and the first-call check runs per precision and head size. The kernel runs with the tensors' device current, so a single-device install on cuda:1 uses SageAttention too.
An out-of-memory error now moves only the rest of that generation to SDPA, logged once, instead of counting toward retiring the device; out-of-memory is recognised by the shared is_oom_error. Only Triton cache failures in a row retire a device, and a success resets the count.
# Conflicts: # invokeai/frontend/api/openapi.json
# Conflicts: # invokeai/frontend/api/openapi.json # tests/test_config.py
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Note
Stacked PR 8 of 8: merge after #9664. It targets
main, so until the PRs before it merge, its diff also shows their commits and the merges that carry them forward. This PR's own change is one commit,cd6e58c52d.Summary
With
attention_backend: sage, Wan 2.2 denoising ("Denoise - Wan 2.2" and "Denoise Video - Wan 2.2") enters the SageAttention scope from #9657, so its eligible attention calls run on SageAttention. Everything else stays on PyTorch SDPA, and the defaultautois unchanged. The docs page lists Wan 2.2 under Supported models.Related Issues / Discussions
Stacked on #9657 (SageAttention backend) and #9664, the PR before it. Closes #9529, which asks for SageAttention to speed up Wan 2.2. That report is Wan 2.2 A14B on an RTX 5090 Laptop GPU (compute capability 12.0, per-warp FP8 kernel); neither was measured here.
QA Instructions
Wan 2.2 TI2V-5B GGUF Q8 through "Denoise Video - Wan 2.2", model loaded, three arms per seed as described in #9657:
sageattn()'s default K smoothing; the selection feat(attention): add opt-in SageAttention backend for diffusion models #9657 ships is faster per call.To try it: install SageAttention as the docs page from #9657 describes, set
attention_backend: sage, restart and generate with Wan 2.2. The first generation logsSageAttention ... serves diffusion-model attention on cuda:0 (...)with the GPU and kernel name, and withlog_level: debugevery generation logs how many calls ran on SageAttention and why the others did not. Withattention_backend: autothe output is bit-identical tomain.Checks: ruff clean;
pytest tests/app/invocationspasses with the whole stack applied (2090 passed). No call-site test was added: this change only enters the scope, whose behavior #9657 tests.Review
No material findings for this call site. Remaining: the image invocation and A14B are unmeasured.
Compatibility / Rollout
Merge after #9664. No API or persisted-state change; only
attention_backend: sagebehaves differently.Checklist
What's Newcopy (if doing a release after this PR)