Turbo-only simplification + DMD2 preset + SDXL TRT support by happyFish · Pull Request #2 · happyFish/scope-stream_diffusion_v1

happyFish · 2026-05-05T19:36:52Z

Summary

What started as a "trim the multi-model branch back to Turbo only" PR
has grown into a full SDXL/TRT enablement push. End-to-end: the plugin
now ships a tested SDXL + DMD2 path with dynamic-shape TRT acceleration
at 1024×1024.

What's in the dropdown now

stabilityai/sd-turbo — SD 1.5 1-step, eager + TRT (existing path).
stabilityai/sdxl-turbo — SDXL 1-step, eager + TRT (new in this PR).
dmd2-sdxl-1step — SDXL base + DMD2 distilled UNet, eager + TRT
(new in this PR). Preset entry handles the model assembly + the
[399] training timestep override DMD2 needs.

Verified perf on a 4090 at 1024×1024

Path	DMD2	SDXL-Turbo
Eager fp16 + xformers	~91 ms/frame	~91 ms/frame
TRT static (single resolution)	11 ms/frame	11 ms/frame
TRT dynamic 512-1024 (this PR)	13 ms/frame	14 ms/frame

Dynamic-shape engine handles 1024×1024, 1024×768 (landscape), and
768×1024 (portrait) with no rebuild — confirmed.

What's removed

Multi-step LCM-LoRA path (_attach_lcm_lora, _predict_x0_serial,
denoising_steps_num > 1 branches). All supported models are
1-step distillations now.
num_inference_steps and use_suggested_num_inference_steps schema
fields (dead at 1-step).
The Lykon/dreamshaper-*, stable-diffusion-v1-5/...,
stabilityai/stable-diffusion-xl-base-1.0 entries from the dropdown.
HANDOFF_TURBO_ONLY.md (committed early when scope was just Turbo).

What's added

Schema / pipeline (the easy half)

MODEL_PRESETS dict in pipeline.py as the extension point for
curated multi-piece recipes. DMD2 lives there as
(base_model, unet_swap, timesteps_override).
_load_preset() for unet_swap-shape recipes:
load SDXL base → download DMD2 UNet via hf_hub_download →
override pipe.unet.state_dict. DMD2's repo ships weights only
(no config.json), so from_pretrained doesn't work directly.
timesteps_override plumbed through _set_timesteps. DMD2-1step is
distilled at t=399 specifically; LCMScheduler's default 1-step
picks ~t=979 and gets garbage out.

TRT (the hard half)

UNetSDXL I/O spec + UNetSDXLExportWrapper — adds
text_embeds (1280) and time_ids (6) as named ONNX inputs so
SDXL's get_aug_embed doesn't crash on added_cond_kwargs=None
during export.
UNet2DConditionModelSDXLEngine runtime adapter feeding all 5
inputs.
compile_unet_sdxl skips polygraphy's ONNX optimizer (passes the
same path twice for raw + opt) — polygraphy OOMs on the ~5 GB SDXL
ONNX, and TRT's builder does its own graph optimization anyway.
ONNX export uses external_data=True (torch 2.9 name; was
use_external_data_format pre-2.5) so the >2 GB SDXL UNet ONNX
serializes correctly. Post-processes the raw export to consolidate
the ~1500 per-tensor sidecar files into one .weights blob —
pytorch's location-only entries trip TRT's WeightsContextMemoryMap
on certain initializers ("Failed to open file" on a file that exists).
build_unet_sdxl_engine + TRTUNetSDXLAdapter. Dynamic-shape build
over [512, 1024] on both axes, static batch=1 (guidance_scale=0
means inference never uses batch>1, dynamic batch would just double
workspace cost). Engine cache key encodes the resolution range so
in-range runs hit the same cached file.
_ensure_trt_unet accepts explicit image_height / image_width
args. _prepare_runtime_state (which sets self.height / self.width)
hasn't run yet when this method fires, so without explicit dims the
build sized for __init__ defaults (512×512) and mismatched at
inference. Pre-emptively setting self.{height,width} would block
dims_changed in _prepare_runtime_state and leave latent dims
stale — explicit-args is the cleanest cut.
During the build, VAE + text encoders are moved to CPU to free VRAM
for TRT's TACTIC_DRAM allocation. UNet stays on GPU (the ONNX tracer
needs it there).
_ConfigShim(sdxl=True) returns the SDXL cross_attention_dim=2048
and addition_time_embed_dim=256. The SDXL adapter also stubs
add_embedding.linear_1.in_features=2816 because the pipeline
introspects that surface to size add_time_ids.

Host-side prerequisites for TRT

These were discovered the hard way and aren't in the wheel install
flow (yet) — I left them as deployment notes:

LD_LIBRARY_PATH must include the venv's tensorrt_libs dir at
process exec time. TRT lazy-dlopens
libnvinfer_builder_resource_smXX.so.10.x during engine build.
That lib has SONAME do_not_link_against_* so the ldconfig cache
misses on filename lookup. ctypes preload from inside Python is
too late — the dynamic linker reads LD_LIBRARY_PATH at exec
time only.
/etc/ld.so.conf.d entry pointing at venv lib dirs covers cuDNN
sublibs but isn't sufficient on its own (per Add Daydream node page link to README #1).
/usr/local/lib symlinks help filename-by-filename dlopen for
the main libs but don't cover the SM-specific lazy-loaded ones.

Known limitations (deferred)

SDXL + ControlNet + TRT raises NotImplementedError. The
existing UNetWithControlInputs and ControlNet model specs are
SD-1.5-shaped (12 down residuals at the (320, 640, 1280, 1280)
pattern); SDXL has a different shape. Doable but ~500 LoC of new
TRT plumbing mirroring the existing SD 1.5 ControlNet path.
Static batch=1 for SDXL engines. Allowing dynamic batch >1 would
double TRT workspace cost; with guidance_scale=0 always being the
Turbo / DMD2 mode, no inference benefit.
Build envelope 512-1024 (not 256-1024). Wider envelopes blew the
build memory budget on a 24 GB card. Practical sweet spot.
DMD2 produces visibly weaker output than SDXL-Turbo on short
prompts ("the sun"). Both produce excellent output on descriptive
prompts (oil-painting style + lighting cues). Documented in the
schema description.

Test plan

python -m py_compile src/scope_streamdiffusion/*.py src/scope_streamdiffusion/_trt/*.py — clean
SDXL-Turbo eager 1024 — Turner-style sun → moon morph renders correctly
DMD2 eager 1024 — same morph, comparable output
SDXL-Turbo TRT (static + dynamic 512-1024) — 11/14 ms/frame steady, frames visually equivalent to eager
DMD2 TRT (static + dynamic 512-1024) — 11/13 ms/frame steady, frames visually equivalent to eager
Aspect-ratio test: same dynamic engine renders 1024×1024, 1024×768, 768×1024 cleanly
Hot-reload in a running Scope and pick each model from the dropdown
Confirm acceleration_mode='trt' on SDXL-Turbo / DMD2 in a real Scope session (not just standalone scripts)

🤖 Generated with Claude Code

Five fixes that together let LCM-LoRA'd SD1.5, SDXL, SDXL-Turbo, and Dreamshaper variants produce sharp deterministic txt2img output: 1. Auto-fuse the matching LCM LoRA (lcm-lora-sdv1-5 / lcm-lora-sdxl) for non-Turbo bases. The schema declared use_lcm_lora but nothing wired it up, so non-Turbo models were running LCMScheduler with un-distilled UNet weights and outputting yellow/black blobs. 2. Swap SDXL's stock VAE for madebyollin/sdxl-vae-fp16-fix on load. The stock VAE decodes NaN in fp16, so every SDXL frame was pure black. 3. SDXL conditioning (add_text_embeds, add_time_ids) now broadcasts to the current batch size when t_index_list has multiple entries. 4. Per-family default num_inference_steps: 1 for sd-turbo proper, 4 for everything else. Single-step at t=999 only converges for the model distilled for that exact regime; SDXL-Turbo / Dreamshaper-XL-Turbo / non-Turbo + LCM LoRA are blurry at 1 step and sharp at 4. Exposed as the "Inference Steps" UI slider with an "Auto Inference Steps" toggle to defer to per-family suggestion. 5. Two text-mode bugs in __call__: - Image-loopback was implicit ("video missing AND prev_image_result exists"), making each frame feed its previous output back as input and drift to over-saturated abstract patterns. Now opt-in only. - Input latent used unseeded torch.randn each call, so seed=42 still produced a different scene per frame. Now reuses the seeded init_noise[0:1] for stable, deterministic output. Verified across sd-turbo, SD1.5, Dreamshaper-8, SDXL-Turbo, SDXL-Base, and Dreamshaper-XL-v2-Turbo at 512 / 1024.

Limits the UI to the six model IDs verified to produce sharp output via this pipeline's auto-LCM-LoRA + fp16-fix-VAE plumbing. Also adds the field to the UI surface (was previously schema-only).

The schema field is named ``model_id_or_path`` and Scope's pipeline_manager merges schema defaults into __init__ kwargs by their declared name, but __init__ only read ``model_id`` — so picking a model in the UI was silently ignored and the default reloaded every time.

Scope routes model_id_or_path through setNodeParams (the runtime/kwargs path), not through pipeline/load. Previously __call__ ignored the incoming value, so picking a different model in the UI updated logs but left the original weights loaded. Detect a mismatch against self.model_id and reload the weights in place — re-attaching the LCM LoRA / fp16-fix VAE per family, freeing the old pipe first to avoid 2x VRAM, and invalidating prompt / timestep / noise caches so the next frame rebuilds against the new model.

…e field Use the same config/kwargs lookup path that strength, seed, etc. use, instead of a hand-rolled kwargs.get() ahead of the rest of __call__.

… channels StreamDiffusion's batch denoising emits one frame per __call__ but each frame is at a different t_index in the cycle (frame i -> t_index i mod N). Across video that smooths out; for a steady text prompt it shows up as N different denoising stages flashing one after another. Switch to sequential denoising (all N steps inside one __call__) when there's no video input and the schedule has >1 step, so each frame is one fully denoised image.

…'t flash channels" This reverts commit 8315c82.

Adds _predict_x0_serial as a sibling of _predict_x0_batch and routes to it when num_inference_steps > 1 in steady-prompt modes (no video input, or explicit image_loopback) with ControlNet off. Walks the full N-step LCM schedule inside one __call__, so each emitted frame is one fully denoised image instead of one slot of the rolling N-track buffer cycle that otherwise flashes N different attractors at the camera. The batch path still owns: - num_inference_steps == 1 (degenerates to one UNet call anyway, and it's the path SD-Turbo and the depth/scribble ControlNet pre-passes expect) - video input / v2v streams (where the buffer reuse trick actually amortises across consecutive related frames — its design point) - ControlNet streams (same reasoning) Routing decision is a single boolean (`use_serial`) computed alongside the other extracted params; the rest of __call__ branches on it exactly twice — once to skip auto-noising the encoded image (serial adds its own noise based on `strength`) and once to pick the predict function. Batch path is untouched.

Scope rebuilds the plugin instance on every graph edit, which clears the in-memory `_trt_*_built` flags and forces a per-engine deserialize/bind cycle (visible stalls of hundreds of ms to seconds, plus the rare full ONNX→TRT compile). Hold the built adapters at module scope keyed by the graph node id so the new instance can swap them straight back in. - New `_trt_cache.py`: `CachedTRTState` (cuda_stream, unet_adapter, unet_has_controlnet, cn_adapters dict, taesd_adapter) keyed by `node:<id>`, with signature `(model_id, height, width)` so a real config change still triggers a clean rebuild. - `pipeline.py`: read `node_id` from kwargs (Scope must pass it through; until that lands, falls back to `_anon_<model_id>` — correct for the single-SD-node case). At first `_ensure_trt_*` call, look up the cache; on hit, swap `self.unet` / `self.controlnet` / `self.vae` to the cached adapter and skip the build. On miss, build then write back.

Replaces Literal[256, 320, ...] tuple on width/height with a Resolution IntEnum and a `mode='before'` field_validator that coerces ints into enum members and raises a clear error listing all allowed values otherwise. Pipeline code already wraps width/height in `int()`, so behavior is unchanged. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

- Node-id-keyed TRT adapter cache so swapping/replacing graph nodes no longer wipes warm engines (25478b7) - Schema: width/height as Resolution IntEnum + field_validator (7c89f3d)

# Conflicts: # src/scope_streamdiffusion/pipeline.py # src/scope_streamdiffusion/schema.py

Trim model_id_or_path enum to stabilityai/sd-turbo and stabilityai/sdxl-turbo — both 1-step distillations. Drops Dreamshaper, SD 1.5 base, SDXL base, and the Dreamshaper SDXL Turbo variant: keeping the multi-step models meant carrying LCM LoRA fusion + a serial denoise path that we no longer need. Removes num_inference_steps and use_suggested_num_inference_steps fields: both are dead now that step count is fixed at 1 for every supported model. LoRA-based step distillation (Hyper-SD / Lightning) on arbitrary checkpoints is the better path forward — tracked separately, not in this change. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Now that the schema only allows SD-Turbo and SDXL-Turbo, the runtime can shed everything that existed to make non-Turbo models usable at low step counts: - self.sd_turbo flag (everything is Turbo now) and the per-family step-count branch in __call__ - _attach_lcm_lora() and its call sites in __init__ / _swap_model (LCM LoRA was only fused for non-Turbo SD 1.5 / SDXL bases) - _predict_x0_serial() and the use_serial branch in __call__ — serial denoise was added for steady-prompt txt2img / image-loopback on multi-step models; with 1-step Turbo it never fires - denoising_steps_num > 1 dead branches in _prepare_runtime_state and _predict_x0_batch (always 1 now) - num_inference_steps plumbing — pinned at 1 in __call__ Untouched: TRT engine swap, ControlNet handling, prompt transitions, RCFG, mask compositing, hot-swap between sd-turbo and sdxl-turbo, and the SDXL fp16-fix VAE swap. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Adds a curated 1-step model option that isn't a direct HuggingFace repo: SDXL-base with the DMD2-distilled UNet (tianweiy/DMD2) swapped in. DMD2 generally outperforms SDXL-Turbo on FID/CLIP per the paper, while staying on the same LCMScheduler at 1 step that all our existing TRT/runtime infra is built around. Introduces a MODEL_PRESETS dict at module scope as the extension point for future Turbo-class additions: - 'unet_swap' shape — base pipeline + distilled UNet checkpoint. Used here for DMD2; DMD2 retrained the UNet via distribution matching, so it ships as a UNet, not a LoRA. - Future shapes documented inline: 'lora' (Hyper-SD / SDXL-Lightning step-distillation LoRAs), 'scheduler' override, 'timesteps_override'. Hyper-SD-1step / Lightning-1step both need TCD / Euler schedulers, which require a `_set_timesteps` refactor (the current path calls LCM-specific `get_scalings_for_boundary_condition_discrete` and reads `scheduler.alphas_cumprod` directly). That refactor is out of scope for this PR. The fp16-fix VAE swap, TRT cache keying, hot-swap, and rolling-buffer denoise math are all untouched — DMD2's UNet is architecturally an SDXL UNet, so everything downstream of `_load_model` is identical. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Distilled-UNet repos like tianweiy/DMD2 ship weights only — no config.json — because the architecture is identical to the base UNet. UNet2DConditionModel.from_pretrained needs a config and bails with 'tianweiy/DMD2 does not appear to have a file named config.json'. Switch to: load the base SDXL pipeline (gets a correctly-configured UNet module), download the DMD2 checkpoint via hf_hub_download, then override the UNet's state_dict in place. Verified end-to-end with a 300-frame sun→moon morph render at fp16, no acceleration: 6 fps eager, output matches expected DMD2 quality. Same pattern works for SDXL-Lightning's 1-step UNet variant once the scheduler refactor lands. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

DMD2-1step is distilled at a single specific timestep. Letting LCMScheduler pick the default 1-step (~979, near pure-noise endpoint) feeds the model a timestep it was never trained on and produces garbage — visually a blurry monochrome blob with no recognizable features. Add a `timesteps_override` field to MODEL_PRESETS and have `_set_timesteps` honor it when present. With the override pinned at [399] (the DMD2 paper's documented training timestep for SDXL 1-step), the model produces clean photographic output: a recognizable sun / moon with proper composition, contrast, and detail. Same mechanism will land Hyper-SDXL-1step (timesteps=[800]) once the broader scheduler-class refactor on feat/scheduler-refactor catches up; this commit just gets DMD2 to a usable state. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Adds the missing SDXL-shaped TRT path so acceleration_mode='trt' works on SDXL-Turbo and DMD2-distilled UNets. Eager-only on SDXL was a pre-existing limitation: ONNX export crashed in get_aug_embed because the export wrapper passed added_cond_kwargs=None instead of the SDXL {text_embeds, time_ids} dict. End-to-end this commit: * UNetSDXL I/O spec — 5 inputs (sample, timestep, encoder_hidden_states, text_embeds dim=1280, time_ids dim=6) instead of SD 1.5's 3. * UNetSDXLExportWrapper — wraps the diffusers UNet so text_embeds/time_ids are positional args for ONNX trace, reconstructed into added_cond_kwargs at the inner forward. * UNet2DConditionModelSDXLEngine — runtime engine wrapper feeding all 5 named inputs to the TRT context. * compile_unet_sdxl — same shape as compile_unet but routes through the SDXL wrapper. Skips the polygraphy ONNX optimizer (passes the same path twice for raw + "opt") because polygraphy's optimizer OOMs on the ~5 GB SDXL ONNX; TRT's builder does its own graph optimization. * export_onnx — adds use_external_data flag (torch 2.9 param `external_data`) so SDXL UNet's >2 GB ONNX serializes correctly. Post-processes the raw export to consolidate ~1500 per-tensor sidecar files into one weights.bin: pytorch's per-tensor location-only entries trip TRT's WeightsContextMemoryMap on certain initializers ("Failed to open file"). * build_unet_sdxl_engine + TRTUNetSDXLAdapter — build/load. Engine is static-shape (build_dynamic_shape=False) and static-batch (max=1). SDXL's tactic exploration over a dynamic shape envelope OOMs even on 24 GB VRAM; static-shape collapses the search space enough to fit. Engine is only valid at the (h,w) it was built for — resolution changes will rebuild. * _ConfigShim — gains an `sdxl=True` mode returning the SDXL cross_attention_dim=2048 and addition_time_embed_dim=256 the pipeline reads to size add_time_ids. TRTUNetSDXLAdapter also fakes an `add_embedding.linear_1.in_features=2816` shim because the SDXL pipeline introspects that attribute on UNet. * pipeline._ensure_trt_unet — accepts explicit image_height/width args. Static engines need the *real* runtime dims at build time; self.height/self.width are still init defaults (512x512) when this method runs because _prepare_runtime_state hasn't executed yet. Pre-emptively setting self.{height,width} would block dims_changed in _prepare_runtime_state and leave self.latent_{height,width} at init defaults — engine and inference would mismatch in the other direction. * SDXL build flow moves VAE + text encoders to CPU during the TRT build to free VRAM for the builder's TACTIC_DRAM allocation, then moves them back. UNet stays on GPU (the ONNX tracer needs it there). Verified end-to-end on a 4090: - SDXL-Turbo @ 1024x1024: 91 ms/frame eager → 11 ms/frame TRT (8.3x) - DMD2-SDXL-1step @ 1024x1024: 91 ms/frame eager → 11 ms/frame TRT (8.3x) - Output is byte-different but visually equivalent to eager, confirming correct numerical behavior. Build-time prerequisites the wheel install model alone doesn't satisfy (documented in trt_engines.py header): - LD_LIBRARY_PATH must include the venv's tensorrt_libs at process exec time. Loader's lazy dlopen of the per-SM kernel library (libnvinfer_builder_resource_smXX.so.10.x) bypasses ldconfig because those libs have a do_not_link_against_* SONAME, so cache lookup by filename fails. ctypes preload from inside Python is too late — the dynamic linker reads LD_LIBRARY_PATH at exec time only. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Static-shape engines locked the build to a single (h, w) — any resolution or aspect-ratio change required a 5–10 min rebuild. Replaced with a dynamic-shape build over the [512, 1024] envelope on both axes. Same cached engine now serves any in-range resolution. Verified end-to-end: a single cached engine handles 1024x1024, 1024x768 (landscape), and 768x1024 (portrait) without a rebuild. Composition adapts to the aspect (wide horizon vs. tall cloud column). Trade-offs vs. static-shape: - Steady-state at the opt point (1024x1024): 11 ms/frame → 14 ms/frame. ~27% slowdown for the flexibility, expected. - Build memory: 512-1024 envelope on a 24 GB card with VAE+text-encoders on CPU during build → fits cleanly. Wider envelopes (256-1024) blew past the budget; 512-1024 is the practical sweet spot. - Engine size: ~5.2 GB on disk (similar to static). Cache key now encodes the resolution range (`unet_sdxl_b1-1_h512-1024_w512-1024`) instead of the opt point, so engines don't collide across resolution choices and any in-range run hits the same cached file. Static batch (max=1) is kept — guidance_scale=0 is the only mode for Turbo / DMD2, so dynamic batch would just double workspace cost for no inference benefit. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

- Update acceleration_mode description: actual measured speedup is 2-8x (was "~2-3x"), and SDXL engines have a different envelope (512-1024, batch=1) than SD 1.5 (256-1024, batch 1-4) due to the 24 GB build budget. Also call out the SDXL + ControlNet + TRT NotImplementedError so users hit it via doc rather than runtime surprise. - Remove HANDOFF_TURBO_ONLY.md. The PR scope expanded well past "Turbo-only simplification": now covers DMD2 preset, scheduler timestep override, full SDXL TRT path with dynamic shape. Earlier handoff text is misleading. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

happyFish and others added 21 commits April 24, 2026 17:03

feat: model_id_or_path as enum dropdown of tested models

633f427

Limits the UI to the six model IDs verified to produce sharp output via this pipeline's auto-LCM-LoRA + fp16-fix-VAE plumbing. Also adds the field to the UI surface (was previously schema-only).

refactor: read model_id_or_path via get_param like every other runtim…

c4151c8

…e field Use the same config/kwargs lookup path that strength, seed, etc. use, instead of a hand-rolled kwargs.get() ahead of the rest of __call__.

Revert "fix: sequential denoising in txt2img mode so multi-step doesn…

9fdbb7e

…'t flash channels" This reverts commit 8315c82.

Merge feat/node-keyed-trt-cache

705da78

- Node-id-keyed TRT adapter cache so swapping/replacing graph nodes no longer wipes warm engines (25478b7) - Schema: width/height as Resolution IntEnum + field_validator (7c89f3d)

Merge branch 'main' into sd-multi-model

d8d2fbd

# Conflicts: # src/scope_streamdiffusion/pipeline.py # src/scope_streamdiffusion/schema.py

docs: handoff notes for the Turbo-only simplification

cc82a98

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

happyFish changed the title ~~Multi-model dropdown + Turbo-only simplification~~ Turbo-only simplification + DMD2 preset + SDXL TRT support May 6, 2026

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Turbo-only simplification + DMD2 preset + SDXL TRT support#2

Turbo-only simplification + DMD2 preset + SDXL TRT support#2
happyFish wants to merge 21 commits intomainfrom
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happyFish commented May 5, 2026 •

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happyFish commented May 5, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Summary

What's in the dropdown now

Verified perf on a 4090 at 1024×1024

What's removed

What's added

Schema / pipeline (the easy half)

TRT (the hard half)

Host-side prerequisites for TRT

Known limitations (deferred)

Test plan

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happyFish commented May 5, 2026 •

edited

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