xiaolibai-sys
164896ed78
- nvfp4: _quantization_metadata -> comfy_quant synthesis, SlotEntry extra params (block_scale), dual-scale LoRA fold, orig_dtype fixup - LoRA: plain-mode block LoRAs no longer silently dropped; folded on GPU after weights land - loader: parallel positioned reads, pipelined group load, no per-tensor clone, header-only dtype probes - block swap: read_block intra-block parallel + disk_workers node option; async D2H write-back on transfer stream; lazy evict_all skips wasted write-back - arch: split models/loader.py out of wan_model.py; single utils/keys.py; LoRA folding consolidated in utils/lora.py; global utils/log.py ([BerniniR][Tag] format, BERNINI_LOG_LEVEL)
44 lines
2.1 KiB
Python
44 lines
2.1 KiB
Python
"""ComfyUI-BerniniR: Custom node package for the Bernini-R 1.3B/14B video model.
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Features:
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- Model loading with separate LoRA-aware torch.compile node
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- Temporal context window tiling for long video generation
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- Enhanced KSampler with native context window support
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- Seven-mode guidance family: CFG, APG, RAAG, S2, Z2, STG_A, STG_R
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- Prompt embedding with task presets, system prompts, and CLIP caching
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- In-context conditioning for video editing
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- Five attention backends: SageAttention 3, SageAttention, FlashAttention, xFormers, SDPA
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- TeaCache acceleration and GPU<->CPU block swap
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Registered nodes (19):
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BerniniR_ModelLoader — Load diffusion model
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BerniniR_CompileModel — Apply torch.compile after LoRA loading
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BerniniR_CLIPLoader — Load Wan text encoder
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BerniniR_VAELoader — Load Bernini-R VAE
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BerniniR_Conditioning — Create latents + attach in-context references
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BerniniR_KSampler — Enhanced sampler with context window support
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BerniniR_TeaCacheArgs — TeaCache block-skipping args
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BerniniR_ContextWindow — Configure temporal context windows
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BerniniR_PromptEmbedding — Task-aware prompt embedding & CLIP encoding
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BerniniR_VAEDecode — VAE decode with color matching
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BerniniR_VAEEncode — VAE encode with tiling
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BerniniR_ApplyNAG — Normalized Attention Guidance for detail enhancement
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BerniniR_GuidanceStrengthSchedule — Per-step guidance scale curve
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BerniniR_DualExpertSampler — High/low-noise model switching per step
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BerniniR_BlockSwapArgs — Configure GPU<->CPU block swapping
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BerniniR_GuidanceConfig — Select guidance mode & parameters
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BerniniR_SegmentSchedule — Per-segment schedule
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BerniniR_LoadLoRA — Append LoRA spec (inline merge)
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BerniniR_AttentionConfig — Select attention backend
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"""
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from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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from .utils.log import get_logger as _get_logger
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logger = _get_logger("Init")
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logger.info("ComfyUI-BerniniR node package loaded successfully.")
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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