595 lines
26 KiB
Python
595 lines
26 KiB
Python
import copy
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import torch
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import comfy.model_patcher
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from comfy_api.latest import io
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import comfy.hooks
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import logging
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import comfy.patcher_extension
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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from .helper_functions import checkLazyNew, generate_dim_variables, parse_expr, as_tensor
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from .Stack import MrmthStack
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from .ParseTree import MrmthParseTree
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from comfy.ldm.modules.attention import optimized_attention
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class SelectiveGuiderMathNode(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="mrmth_ag_SelectiveGuiderMathNode",
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category="More math",
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display_name="Selective guider math",
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inputs=[
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io.Guider.Input("V"),
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io.Autogrow.Input(
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id="F",
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template=io.Autogrow.TemplatePrefix(
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io.Float.Input("float", default=0.0, optional=True, lazy=True, force_input=True),
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prefix="F",
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min=1,
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max=50,
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),
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),
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io.MultiType.Input(
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io.String.Input("Expression", default="print([layer, layer_key, hook_kind, cond_side, activations_shape]); inp", multiline=False),
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types=[io.String, MrmthParseTree],
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),
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io.Int.Input("layer_x", default=0, min=-999, max=999),
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],
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outputs=[io.Guider.Output(), MrmthStack.Output()],
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)
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@classmethod
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def check_lazy_status(
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cls,
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V,
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F,
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Expression,
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layer_x=0,
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remember_stack=False,
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stack={},
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):
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return checkLazyNew(Expression, {}, F)
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@classmethod
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def execute(
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cls,
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V,
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F,
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Expression,
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layer_x=0,
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remember_stack=False,
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stack=None,
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):
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if V is None or not hasattr(V, "model_options"):
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raise ValueError("Vstupní guider musí mít model_options.")
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hook_target = "all"
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hook_when = "just_after_x"
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stack = stack if remember_stack else (copy.deepcopy(stack) if stack is not None else {})
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tree = parse_expr(Expression) if isinstance(Expression, str) else Expression
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MAX_HOOK_INDEX = 999
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hit_flags = {"attn1": False, "attn2": False, "dit": False, "attn_unknown": False, "unet": False}
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def resolve_attn_kind(transformer_options: dict, q: torch.Tensor | None = None, k: torch.Tensor | None = None, v: torch.Tensor | None = None) -> str:
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raw = transformer_options.get(
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"attn_name",
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transformer_options.get(
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"attn_type",
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transformer_options.get(
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"block_attn",
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transformer_options.get("transformer_index", None),
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),
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),
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)
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if isinstance(raw, str):
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r = raw.lower()
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if r in ("attn1", "self", "self_attn", "self_attention"):
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return "attn1"
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if r in ("attn2", "cross", "cross_attn", "cross_attention"):
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return "attn2"
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return "attn_unknown"
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if isinstance(raw, (int, float)):
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if int(raw) == 0:
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return "attn1"
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if int(raw) == 1:
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return "attn2"
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# Fallback heuristika: self-attn má obvykle stejný zdroj pro q/k/v
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if isinstance(q, torch.Tensor) and isinstance(k, torch.Tensor) and isinstance(v, torch.Tensor):
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if (q.data_ptr() == k.data_ptr()) and (k.data_ptr() == v.data_ptr()):
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return "attn1"
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return "attn_unknown"
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return "attn_unknown"
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def resolve_x(total_blocks: int | None) -> int:
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x = int(layer_x)
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if x < 0 and total_blocks is not None:
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x = total_blocks + x
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if total_blocks is not None:
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x = max(0, min(x, total_blocks - 1))
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return x
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def match_index(idx: int, total_blocks: int | None, stage: str, topts: dict, hook_kind: str) -> bool:
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x = resolve_x(total_blocks)
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return idx == x
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def target_indices():
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return [int(layer_x)]
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def side_from_cond_or_uncond(cond_or_uncond):
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if isinstance(cond_or_uncond, (list, tuple)) and len(cond_or_uncond) > 0:
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vals = list(cond_or_uncond)
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uniq = set(vals)
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if len(uniq) == 1:
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idx = int(vals[0])
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if idx == 0:
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return ("positive", 0)
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if idx == 1:
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return ("negative", 1)
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return ("mixed", -1)
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return ("unknown", -1)
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def run_expr(inp: torch.Tensor, extra_vars: dict | None = None) -> torch.Tensor:
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# v run_expr(...) rozšiř výchozí proměnné:
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variables = {
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"inp": inp,
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"sample": inp,
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"hook_kind": "unknown",
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"hook_domain": "unknown",
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"attn_kind": "none",
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"attn_mode": "unknown",
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"transformer_index": -1.0,
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"is_dit": 0.0,
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"is_unet_block": 0.0,
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"is_time_emb": 0.0,
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"is_attn1": 0.0,
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"is_attn2": 0.0,
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"is_self_attention": 0.0,
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"is_cross_attention": 0.0,
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"has_context": 0.0,
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"block_name": "",
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"layer_key": "",
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"layer_id": -1.0,
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"layer": -1.0,
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"i": -1.0,
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"total_blocks": -1.0,
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"has_qkv": 0.0,
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"q": inp,
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"k": inp,
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"v": inp,
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"heads": 0.0,
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"dim_head": 0.0,
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"query_tokens": -1.0,
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"context_tokens": -1.0,
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"value_tokens": -1.0,
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"activations_shape": [],
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"activation_rank": -1.0,
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"activation_b": -1.0,
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"activation_c": -1.0,
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"activation_t": -1.0,
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"activation_h": -1.0,
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"activation_w": -1.0,
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"cond_side": "unknown",
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"cond_index": -1.0,
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"is_positive": 0.0,
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"is_negative": 0.0,
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"is_attn1_hook": 0.0,
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"is_attn2_hook": 0.0,
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"attention_relation": "unknown",
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} | generate_dim_variables(inp)
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if extra_vars:
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variables.update(extra_vars)
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for kf, vf in F.items():
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variables[kf] = vf if vf is not None else 0.0
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act_shape = variables.get("activations_shape", [])
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if isinstance(act_shape, (list, tuple)):
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variables["activation_rank"] = float(len(act_shape))
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if len(act_shape) >= 1:
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variables["activation_b"] = float(act_shape[0])
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if len(act_shape) >= 2:
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variables["activation_c"] = float(act_shape[1])
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if len(act_shape) == 4:
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variables["activation_h"] = float(act_shape[2])
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variables["activation_w"] = float(act_shape[3])
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elif len(act_shape) >= 5:
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variables["activation_t"] = float(act_shape[2])
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variables["activation_h"] = float(act_shape[3])
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variables["activation_w"] = float(act_shape[4])
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qv = variables.get("q", inp)
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kv = variables.get("k", inp)
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vv = variables.get("v", inp)
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if variables.get("attn_kind") == "attn1":
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variables["is_attn1_hook"] = 1.0
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elif variables.get("attn_kind") == "attn2":
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variables["is_attn2_hook"] = 1.0
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if hasattr(qv, "shape") and len(qv.shape) >= 2:
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variables["query_tokens"] = float(qv.shape[-2])
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if hasattr(kv, "shape") and len(kv.shape) >= 2:
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variables["context_tokens"] = float(kv.shape[-2])
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variables["has_context"] = 1.0
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if hasattr(vv, "shape") and len(vv.shape) >= 2:
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variables["value_tokens"] = float(vv.shape[-2])
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qt = variables["query_tokens"]
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kt = variables["context_tokens"]
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if variables["has_context"] == 1.0 and qt > 0 and kt > 0:
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if qt != kt:
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variables["is_cross_attention"] = 1.0
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variables["is_self_attention"] = 0.0
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variables["attention_relation"] = "cross"
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else:
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variables["is_cross_attention"] = 0.0
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variables["is_self_attention"] = 1.0
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variables["attention_relation"] = "self"
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else:
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variables["is_cross_attention"] = 0.0
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variables["is_self_attention"] = 0.0
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variables["attention_relation"] = "unknown"
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visitor = UnifiedMathVisitor(variables, inp.shape, inp.device, state_storage=stack)
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out = visitor.visit(tree)
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return as_tensor(out, inp.shape).to(inp.device)
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def register_dit(patched_dict, forced_side=None):
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topts = patched_dict["transformer_options"]
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def dit_wrap(args, extra_args):
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idx = int(args.get("transformer_options", {}).get("block_index", -1))
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total_blocks = args.get("transformer_options", {}).get("total_blocks", None)
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if not match_index(idx, total_blocks, "dit", args.get("transformer_options", {}), "dit_block"):
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return extra_args["original_block"](args)
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if not hit_flags["dit"]:
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logging.warning(f"[SelectiveGuiderMathNode] DIT HIT idx={idx} total={total_blocks}")
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hit_flags["dit"] = True
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btype = args.get("transformer_options", {}).get("block_type", "dit")
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co = args.get("transformer_options", {}).get("cond_or_uncond", None)
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auto_side, auto_idx = side_from_cond_or_uncond(co)
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side = forced_side if forced_side is not None else auto_side
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idx_side = 0 if side == "positive" else (1 if side == "negative" else auto_idx)
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meta = {
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"hook_kind": "dit_block",
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"hook_domain": "diffusion",
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"attn_kind": "none",
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"is_dit": 1.0,
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"is_attn1": 0.0,
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"is_attn2": 0.0,
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"layer_id": float(idx),
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"layer": float(idx),
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"i": float(idx),
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"total_blocks": float(total_blocks) if total_blocks is not None else -1.0,
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"block_name": btype,
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"layer_key": f"dit.{btype}.{idx}",
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"has_qkv": 0.0,
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"cond_side": side,
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"cond_index": float(idx_side),
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"is_positive": 1.0 if side == "positive" else 0.0,
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"is_negative": 1.0 if side == "negative" else 0.0,
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}
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out = extra_args["original_block"](args)
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if isinstance(out, dict) and "img" in out:
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out2 = dict(out)
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out2["img"] = run_expr(out["img"], meta)
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return out2
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return out
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patches_replace = topts.get("patches_replace", {}).copy()
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dit_map = patches_replace.get("dit", {}).copy()
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for li in target_indices():
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dit_map[("double_block", li)] = dit_wrap
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dit_map[("single_block", li)] = dit_wrap
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patches_replace["dit"] = dit_map
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topts["patches_replace"] = patches_replace
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patched_dict["transformer_options"] = topts
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def register_attn(patched_dict, forced_side=None):
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topts = patched_dict["transformer_options"].copy()
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def attn_override(original_attn, q, k, v, heads, **kwargs):
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transformer_options = kwargs.get("transformer_options", {})
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attn_kind = resolve_attn_kind(transformer_options, q=q, k=k, v=v)
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t_index = int(transformer_options.get("transformer_index", -1))
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if hook_target == "attn1" and attn_kind != "attn1":
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return original_attn(q, k, v, heads, **kwargs)
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if hook_target == "attn2" and attn_kind != "attn2":
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return original_attn(q, k, v, heads, **kwargs)
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blk = transformer_options.get("block", None)
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if isinstance(blk, (tuple, list)) and len(blk) >= 2:
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stage = str(blk[0])
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layer_id = int(blk[1])
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else:
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stage = "unknown"
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layer_id = int(transformer_options.get("block_index", -1))
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idx = layer_id
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total_blocks = None
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if not match_index(idx, total_blocks, stage, transformer_options, attn_kind):
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return original_attn(q, k, v, heads, **kwargs)
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co = transformer_options.get("cond_or_uncond", None)
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auto_side, auto_idx = side_from_cond_or_uncond(co)
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side = forced_side if forced_side is not None else auto_side
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idx_side = 0 if side == "positive" else (1 if side == "negative" else auto_idx)
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if not hit_flags.get(attn_kind, False):
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logging.warning(f"[SelectiveGuiderMathNode] {attn_kind.upper()} HIT stage={stage} layer_id={layer_id} side={side}")
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hit_flags[attn_kind] = True
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# v register_attn -> attn_override(...), před `meta = { ... }` přidej ---
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act_shape = transformer_options.get("activations_shape", [])
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if not isinstance(act_shape, (list, tuple)):
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act_shape = []
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act_h = float(act_shape[2]) if len(act_shape) >= 4 else -1.0
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act_w = float(act_shape[3]) if len(act_shape) >= 4 else -1.0
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meta = {
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"hook_kind": attn_kind,
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"hook_domain": "attention",
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"attn_kind": attn_kind,
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"transformer_index": float(t_index),
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"is_dit": 0.0,
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"is_attn1": 1.0 if attn_kind == "attn1" else 0.0,
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"is_attn2": 1.0 if attn_kind == "attn2" else 0.0,
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"block_name": stage,
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"layer_id": float(layer_id),
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"layer": float(layer_id),
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"i": float(layer_id),
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"total_blocks": -1.0,
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"has_qkv": 1.0,
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"q": q,
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"k": k,
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"v": v,
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"heads": float(heads),
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"dim_head": float(q.shape[-1] // heads) if heads > 0 else 0.0,
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"layer_key": f"{stage}.{layer_id}.{attn_kind}.{t_index}",
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"cond_side": side,
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"cond_index": float(idx_side),
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"is_positive": 1.0 if side == "positive" else 0.0,
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"is_negative": 1.0 if side == "negative" else 0.0,
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} | {
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"activations_shape": list(act_shape),
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"activation_b": float(act_shape[0]) if len(act_shape) > 0 else -1.0,
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"activation_c": float(act_shape[1]) if len(act_shape) > 1 else -1.0,
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"activation_h": float(act_shape[2]) if len(act_shape) > 2 else -1.0,
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"activation_w": float(act_shape[3]) if len(act_shape) > 3 else -1.0,
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}
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out = original_attn(q, k, v, heads, **kwargs)
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return run_expr(out, meta)
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topts["optimized_attention_override"] = attn_override
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patched_dict["transformer_options"] = topts
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return patched_dict
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def register_unet_blocks(patched_dict, forced_side=None):
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topts = patched_dict["transformer_options"].copy()
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patches = topts.get("patches", {}).copy()
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def _parse_block(transformer_options: dict, fallback_stage: str):
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blk = transformer_options.get("block", None)
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if isinstance(blk, (tuple, list)) and len(blk) >= 2:
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return str(blk[0]), int(blk[1])
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return fallback_stage, int(transformer_options.get("block_index", -1))
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def _apply_block_expr(t: torch.Tensor, transformer_options: dict, stage: str, idx: int):
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if not match_index(idx, None, stage, transformer_options, "unet_block"):
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return t
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co = transformer_options.get("cond_or_uncond", None)
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auto_side, auto_idx = side_from_cond_or_uncond(co)
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side = forced_side if forced_side is not None else auto_side
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idx_side = 0 if side == "positive" else (1 if side == "negative" else auto_idx)
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if not hit_flags["unet"]:
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logging.warning(f"[SelectiveGuiderMathNode] UNET BLOCK HIT stage={stage} layer_id={idx} side={side}")
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hit_flags["unet"] = True
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meta = {
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"hook_kind": "unet_block",
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"hook_domain": "diffusion",
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"attn_kind": "none",
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"is_dit": 0.0,
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"is_unet_block": 1.0,
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"is_time_emb": 1.0 if stage == "time_emb" else 0.0,
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"is_attn1": 0.0,
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"is_attn2": 0.0,
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"block_name": stage,
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"layer_id": float(idx),
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"layer": float(idx),
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"i": float(idx),
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"total_blocks": -1.0,
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"has_qkv": 0.0,
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"layer_key": f"unet.{stage}.{idx}",
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"cond_side": side,
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"cond_index": float(idx_side),
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"is_positive": 1.0 if side == "positive" else 0.0,
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"is_negative": 1.0 if side == "negative" else 0.0,
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}
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return run_expr(t, meta)
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def input_block_patch(h, transformer_options):
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stage, idx = _parse_block(transformer_options, "input")
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return _apply_block_expr(h, transformer_options, stage, idx)
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def output_block_patch(h, hsp, transformer_options):
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stage, idx = _parse_block(transformer_options, "output")
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h2 = _apply_block_expr(h, transformer_options, stage, idx)
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return h2, hsp
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def middle_patch(x, extra_options):
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stage, idx = _parse_block(extra_options, "middle")
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return _apply_block_expr(x, extra_options, stage, idx)
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def emb_patch(emb, model_channels, transformer_options):
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# začátek timestep pipeline
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return _apply_block_expr(emb, transformer_options, "time_emb", 0)
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patches["input_block_patch"] = list(patches.get("input_block_patch", [])) + [input_block_patch]
|
|
patches["output_block_patch"] = list(patches.get("output_block_patch", [])) + [output_block_patch]
|
|
patches["middle_patch"] = list(patches.get("middle_patch", [])) + [middle_patch]
|
|
patches["emb_patch"] = list(patches.get("emb_patch", [])) + [emb_patch]
|
|
|
|
topts["patches"] = patches
|
|
patched_dict["transformer_options"] = topts
|
|
return patched_dict
|
|
|
|
def register_model_edges(patched_dict, forced_side=None):
|
|
topts = patched_dict["transformer_options"].copy()
|
|
wrappers = topts.get("wrappers", {}).copy()
|
|
wtype = comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL
|
|
bucket = wrappers.get(wtype, {}).copy()
|
|
key = "mrmth_model_edges"
|
|
|
|
def model_edges_wrapper(executor, x, *args, **kwargs):
|
|
# BEGIN (all the way at the beginning)
|
|
pre_meta = {
|
|
"hook_kind": "model_begin",
|
|
"hook_domain": "diffusion",
|
|
"attn_kind": "none",
|
|
"block_name": "model",
|
|
"layer_id": -1.0,
|
|
"layer": -1.0,
|
|
"i": -1.0,
|
|
"total_blocks": -1.0,
|
|
"has_qkv": 0.0,
|
|
"layer_key": "model.begin",
|
|
}
|
|
xin = run_expr(x, pre_meta) if isinstance(x, torch.Tensor) else x
|
|
|
|
out = executor(xin, *args, **kwargs)
|
|
|
|
# END (all the way at the end)
|
|
if isinstance(out, torch.Tensor):
|
|
post_meta = {
|
|
"hook_kind": "model_end",
|
|
"hook_domain": "diffusion",
|
|
"attn_kind": "none",
|
|
"block_name": "model",
|
|
"layer_id": -1.0,
|
|
"layer": -1.0,
|
|
"i": -1.0,
|
|
"total_blocks": -1.0,
|
|
"has_qkv": 0.0,
|
|
"layer_key": "model.end",
|
|
}
|
|
return run_expr(out, post_meta)
|
|
return out
|
|
|
|
bucket[key] = list(bucket.get(key, [])) + [model_edges_wrapper]
|
|
wrappers[wtype] = bucket
|
|
topts["wrappers"] = wrappers
|
|
patched_dict["transformer_options"] = topts
|
|
return patched_dict
|
|
|
|
def build_transformers_for_side(side_label=None):
|
|
p = comfy.model_patcher.create_model_options_clone(V.model_options)
|
|
p.setdefault("transformer_options", {})
|
|
if hook_target in ("all", "dit_block"):
|
|
register_dit(p, forced_side=side_label)
|
|
if hook_target in ("all", "unet_block"):
|
|
p = register_unet_blocks(p, forced_side=side_label)
|
|
if hook_target in ("all", "attn1", "attn2"):
|
|
p = register_attn(p, forced_side=side_label)
|
|
if hook_target in ("all", "unet_block"):
|
|
p = register_model_edges(p, forced_side=side_label)
|
|
return p["transformer_options"]
|
|
|
|
def attach_side_hook(side_name: str):
|
|
if not hasattr(V, "original_conds") or side_name not in V.original_conds:
|
|
return
|
|
tdict = build_transformers_for_side(side_name)
|
|
hook = comfy.hooks.TransformerOptionsHook(
|
|
transformers_dict=tdict,
|
|
hook_scope=comfy.hooks.EnumHookScope.HookedOnly,
|
|
)
|
|
hg = comfy.hooks.HookGroup()
|
|
hg.add(hook)
|
|
|
|
new_conds = []
|
|
for c in V.original_conds[side_name]:
|
|
cc = c.copy()
|
|
old = cc.get("hooks", None)
|
|
cc["hooks"] = comfy.hooks.HookGroup.combine_all_hooks([old, hg])
|
|
new_conds.append(cc)
|
|
V.original_conds[side_name] = new_conds
|
|
|
|
# po parse_expr(...)
|
|
if hasattr(V, "original_conds"):
|
|
if not hasattr(V, "_mrmth_base_original_conds"):
|
|
V._mrmth_base_original_conds = copy.deepcopy(V.original_conds)
|
|
else:
|
|
V.original_conds = copy.deepcopy(V._mrmth_base_original_conds)
|
|
|
|
if hasattr(V, "original_conds"):
|
|
attach_side_hook("positive")
|
|
attach_side_hook("negative")
|
|
logging.warning(f"[SelectiveGuiderMathNode] has original_conds: {list(V.original_conds.keys())}")
|
|
for side in ("positive", "negative"):
|
|
if side in V.original_conds:
|
|
cnt = sum(1 for c in V.original_conds[side] if c.get("hooks", None) is not None)
|
|
logging.warning(f"[SelectiveGuiderMathNode] side={side} conds_with_hooks={cnt}/{len(V.original_conds[side])}")
|
|
else:
|
|
patched = comfy.model_patcher.create_model_options_clone(V.model_options)
|
|
patched.setdefault("transformer_options", {})
|
|
if hook_target in ("all", "dit_block"):
|
|
register_dit(patched, forced_side=None)
|
|
if hook_target in ("all", "unet_block"):
|
|
patched = register_unet_blocks(patched, forced_side=None)
|
|
if hook_target in ("all", "attn1", "attn2"):
|
|
patched = register_attn(patched, forced_side=None)
|
|
V.model_options = patched
|
|
|
|
if hasattr(V, "model_patcher") and V.model_patcher is not None and not getattr(V, "_mrmth_debug_callbacks_added", False):
|
|
def _on_register_all_hook_patches(model_patcher, hooks, target_dict, model_options, registered):
|
|
try:
|
|
reg_len = len(registered) if registered is not None else 0
|
|
in_len = len(hooks) if hooks is not None else 0
|
|
tr_count = len(registered.get_type(comfy.hooks.EnumHookType.TransformerOptions)) if registered is not None else 0
|
|
logging.warning(f"[SelectiveGuiderMathNode] REGISTER hooks_in={in_len} registered={reg_len} transformer_registered={tr_count}")
|
|
if model_options is not None:
|
|
rh = model_options.get("registered_hooks", None)
|
|
logging.warning(f"[SelectiveGuiderMathNode] model_options.registered_hooks={'set' if rh is not None else 'None'}")
|
|
except Exception as e:
|
|
logging.warning(f"[SelectiveGuiderMathNode] REGISTER debug error: {e}")
|
|
|
|
def _on_apply_hooks(model_patcher, hooks):
|
|
try:
|
|
hl = len(hooks) if hooks is not None else 0
|
|
logging.warning(f"[SelectiveGuiderMathNode] APPLY hooks_len={hl}")
|
|
except Exception as e:
|
|
logging.warning(f"[SelectiveGuiderMathNode] APPLY debug error: {e}")
|
|
|
|
V.model_patcher.add_callback_with_key(
|
|
comfy.patcher_extension.CallbacksMP.ON_REGISTER_ALL_HOOK_PATCHES,
|
|
"mrmth_selective_debug",
|
|
_on_register_all_hook_patches,
|
|
)
|
|
V.model_patcher.add_callback_with_key(
|
|
comfy.patcher_extension.CallbacksMP.ON_APPLY_HOOKS,
|
|
"mrmth_selective_debug",
|
|
_on_apply_hooks,
|
|
)
|
|
V._mrmth_debug_callbacks_added = True
|
|
|
|
stack = stack if remember_stack else copy.deepcopy(stack)
|
|
return (V, stack) |