perf: Tier 1 hot-path allocation elimination & text encoder stutter fix

[forkni]

Summary

Stacked on #6 — merge that PR first.

Systematic hot-path allocation elimination targeting the inference loop, plus correction of text encoder VRAM offloading strategy based on real-world testing.

Tier 1 Optimizations (~1.5–4ms/frame saved, ~300+ allocations/frame eliminated on SDXL 4-step)

• KV clone pre-alloc: _curr_key_buf/_curr_value_buf with .copy_() in CachedSTAttnProcessor2_0 — extends ce0d51c from PR feat: FP8 quantization & TensorRT build infrastructure #6, now also caches transposed KV buffers for the cache path
• Pre-computed scheduler tensors: _alpha_next, _beta_next, _init_noise_rotated computed once in prepare() — eliminates 5 mallocs + 8 kernel launches per frame
• Pre-allocated buffers: _combined_latent_buf, _cfg_latent_buf/_cfg_t_buf, _unet_kwargs dict
• In-place ops: stock_noise[0:1].copy_() replaces torch.concat
• Dummy ControlNet tensors: _cached_dummy_controlnet_tensors cached in unet_engine.py, zero-alloc reuse
• Async output transfer: Pinned memory + CUDA event in td_manager.py (eliminates 1–3ms GPU→CPU sync stall per frame)
• CUDA env tuning: PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128,expandable_segments:True, CUDA_MODULE_LOADING=LAZY, cudnn.benchmark

Text Encoder VRAM Offload Removal

Supersedes text encoder CPU offloading from PR #5. Real-world testing showed prompt-change stuttering (GPU→CPU→GPU + torch.cuda.empty_cache() on every prompt update):

• _offload_text_encoders() / _reload_text_encoders() → no-ops (text encoders remain on GPU)
• _force_offload_text_encoders() / _force_reload_text_encoders() preserved for engine building / FP8 quantization only
• Skip re-encode when prompt is identical to last call
• FPS EMA seeded from first measurement (no slow ramp-up from 0.0)

Files Modified

File	Changes
pipeline.py	Pre-allocated buffers, pre-computed tensors, in-place ops
attention_processors.py	KV + cached-KV buffer pre-alloc with _use_prealloc flag
unet_engine.py	Dummy ControlNet tensor cache
stream_parameter_updater.py	Logical cache window (no runtime resize race)
wrapper.py	Text encoder offload→no-op, force variants, identical prompt skip
td_manager.py	Async GPU→CPU transfer, FPS EMA seeding
td_main.py / demo/main.py	CUDA/PyTorch env var tuning before import torch

Impact

Metric	Before	After
Per-frame CUDA allocs (SDXL 4-step)	~300+	~0
GPU→CPU output transfer	Sync stall 1–3ms	Async, overlapped
Text encoder reload per prompt	Always	Skip if identical
FPS EMA initial value	0.0 (slow ramp)	Seeded from first frame

Test plan

• Run img2img inference 60s at 512×512: no regression, no stutter on prompt change
• VRAM monitor: text encoders remain on GPU throughout inference
• update_prompt() with same prompt: confirm skip (no re-encode in logs)
• SDXL 4-step: confirm 0 per-frame .clone() calls in hot path
• 1000 frames: torch.allclose(atol=0, rtol=0) vs pre-patch baseline

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