digital-garbageandClaude Opus 5
ab9cbdb556
Ported from v4, minus two mechanisms that are no longer needed. The LoRA loader takes one LoRA and chains, so the FlexibleOptionalInputType dict subclass that lied to ComfyUI's validator has nothing left to do. The CLIP loader uses numbered slots like core's DualCLIPLoader instead of a JSON array in a string widget. Autogrow looked like the right answer for the encoder list and is not: it sets force_input on any widget input, so a list of combos renders as sockets and the file picker stops being a picker. Every core use of it is a connection type. GGUF and SLA are deliberately not here. Both are capabilities rather than loader options, and both belong in modules of their own. The tests follow what the loaders PRODUCE rather than stopping at the call: the attention override is carried into comfy's real dispatch and checked against every backend this machine registers, because an override that only fails at the first attention call fails at sampling, not at load. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
"""Shared loading options.
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Not a module: it announces nothing and ships no node. `registry.scan` only walks
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two levels of DIRECTORIES, so a plain file at the domain level is a helper the
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loaders import and nothing else.
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These options are on the loader node rather than in a settings payload, and the
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line is worth stating: a loader widget describes the FILE and how it is read,
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which the loader cannot be handed by anyone else. Feature knobs -- strengths,
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sigma windows, anything a modifier owns -- are settings, and never appear here.
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"""
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import logging
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import torch
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# Named exactly as core's UNETLoader names them where they overlap, so a value
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# copied from a stock workflow means the same thing here.
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WEIGHT_DTYPES = ["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"]
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COMPUTE_DTYPES = ["default", "fp16", "bf16", "fp32"]
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VAE_DTYPES = ["bfloat16", "float16", "float32"]
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_BY_NAME = {
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"fp16": torch.float16,
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"bf16": torch.bfloat16,
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"fp32": torch.float32,
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"float16": torch.float16,
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"bfloat16": torch.bfloat16,
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"float32": torch.float32,
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"fp8_e4m3fn": getattr(torch, "float8_e4m3fn", None),
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"fp8_e5m2": getattr(torch, "float8_e5m2", None),
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# fp8_e4m3fn plus comfy's fp8 matmul path; see weight_model_options.
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"fp8_e4m3fn_fast": getattr(torch, "float8_e4m3fn", None),
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}
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def dtype_of(name):
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"""The torch dtype a choice names, or None for 'default'/an unsupported build."""
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return _BY_NAME.get(str(name or "default"))
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def weight_model_options(weight_dtype):
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"""comfy `model_options` for a weight dtype. The mapping core's UNETLoader uses."""
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options = {}
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dtype = dtype_of(weight_dtype)
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if dtype is not None:
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options["dtype"] = dtype
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if weight_dtype == "fp8_e4m3fn_fast":
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options["fp8_optimizations"] = True
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return options
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def attention_choices():
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"""Backends this ComfyUI can actually run.
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Read from ComfyUI's own registry rather than a private import, so the list
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offers exactly what the machine has -- sage where SageAttention is installed,
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flash where flash-attn is -- and never lies about the rest. 'default' leaves
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whatever ComfyUI itself selected from its launch flags.
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"""
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names = []
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try:
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from comfy.ldm.modules.attention import REGISTERED_ATTENTION_FUNCTIONS
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names = sorted(REGISTERED_ATTENTION_FUNCTIONS.keys())
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except Exception: # noqa: BLE001 -- older ComfyUI
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pass
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return ["default"] + names
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def attention_override(name):
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"""A transformer_options override routing every attention call to `name`.
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ComfyUI wraps each implementation with `wrap_attn`, which hands the override
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the original function plus the call's arguments -- so calling the WRAPPED
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replacement would re-enter that machinery. The unwrapped one is what gets
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called.
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"""
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if not name or name == "default":
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return None
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try:
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from comfy.ldm.modules.attention import get_attention_function
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except ImportError:
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logging.warning("[FunPack] this ComfyUI has no attention registry; leaving attention alone")
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return None
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chosen = get_attention_function(name, None)
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if chosen is None:
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logging.warning("[FunPack] attention backend %r is not available; leaving attention alone", name)
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return None
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inner = getattr(chosen, "__wrapped__", chosen)
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def override(_func, *args, **kwargs):
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return inner(*args, **kwargs)
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return override
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def set_fp16_accumulation(enabled):
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"""torch's fp16 accumulation switch, or None where the build has no such knob."""
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matmul = getattr(getattr(torch.backends, "cuda", None), "matmul", None)
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if matmul is None or not hasattr(matmul, "allow_fp16_accumulation"):
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return None
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matmul.allow_fp16_accumulation = bool(enabled)
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return bool(enabled)
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