mirror of
https://github.com/vladmandic/automatic
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CalamitousFelicitousness
8c884c1e02
One UNET override cannot serve dual-transformer arches: ideogram4 conditional/unconditional and wan combined-stage experts need separate files, and previously a single override landed on both experts. - sd_unet_secondary option with per-slot tracking, consumed-state sync, arch-change reset, and incompatible-override fallback - dropdown renders beside the primary, follows it into quicksettings, and is visible only for dual-transformer model types - ideogram4 native single-file spec with a quant-aware fused-qkv converter; such converters run before comfy_quant detection via TransformerSpec.converter_handles_quant - quicksettings render in configured order (sort keyed on the option object and always fell back to alphabetical) - post-load dtype warning skips quantized transformers
63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
"""Ideogram 4 native loader spec.
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Kept import-light: only the converter and spec live here so the native
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loader can import them without pulling the pipeline or the qwen patch.
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"""
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import re
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import torch
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import diffusers
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from pipelines.native_transformer import TransformerSpec
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QKV_TARGETS = ("to_q", "to_k", "to_v")
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QKV_RE = re.compile(r"^(layers\.\d+\.attention)\.qkv\.(.+)$")
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OUT_RE = re.compile(r"^(layers\.\d+\.attention)\.o\.(.+)$")
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def convert_ideogram4_transformer_checkpoint(state_dict: dict, **kwargs) -> dict: # pylint: disable=unused-argument
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"""Fused community layout to ``Ideogram4Transformer2DModel`` layout.
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``layers.N.attention.qkv.<suffix>`` splits into ``to_q/to_k/to_v.<suffix>``:
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row-stacked tensors (weight, row-wise scale, bias) are dim-0 sliced into
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thirds, non-row-stacked sidecars (markers, scalar scales) copied verbatim
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to all three. ``attention.o.<suffix>`` renames to ``attention.to_out.0.<suffix>``,
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everything else passes through. Returns a new dict; the input (which may
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be the cached state dict) is not mutated.
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"""
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converted: dict = {}
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for key, value in state_dict.items():
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fused_match = QKV_RE.match(key)
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if fused_match is not None:
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prefix, suffix = fused_match.group(1), fused_match.group(2)
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fused_weight = state_dict.get(f"{prefix}.qkv.weight")
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fused_rows = fused_weight.shape[0] if torch.is_tensor(fused_weight) and fused_weight.ndim >= 1 else None
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row_stacked = (
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suffix != "comfy_quant"
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and torch.is_tensor(value) and value.ndim >= 1
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and fused_rows is not None and value.shape[0] == fused_rows and fused_rows % 3 == 0
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)
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if row_stacked:
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third = fused_rows // 3
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for i, target in enumerate(QKV_TARGETS):
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converted[f"{prefix}.{target}.{suffix}"] = value[i * third:(i + 1) * third]
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else:
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for target in QKV_TARGETS:
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converted[f"{prefix}.{target}.{suffix}"] = value
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continue
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out_match = OUT_RE.match(key)
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if out_match is not None:
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converted[f"{out_match.group(1)}.to_out.0.{out_match.group(2)}"] = value
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continue
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converted[key] = value
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return converted
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IDEOGRAM4_SPEC = TransformerSpec(
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cls=diffusers.Ideogram4Transformer2DModel,
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converter=convert_ideogram4_transformer_checkpoint,
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converter_handles_quant=True,
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)
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