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add more variables (probably useless)

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Daniel Martinek
2026-04-03 11:35:24 +02:00
parent cd28d6a9fe
commit 1a47e37e5a
2 changed files with 101 additions and 9 deletions
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README.md
+18 -4
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@@ -436,10 +436,20 @@ The following variables are available in `Expression`.
| `v` | tensor | Value tensor in attention hooks; fallback placeholder otherwise. |
| `heads` | float | Number of attention heads (attention hooks only, else `0`). |
| `dim_head` | float | Per-head channel size (`q.shape[-1] / heads`) when available. |
| `cond_side` | string | CFG side: `positive`, `negative`, `mixed`, or `unknown`. |
| `cond_index` | float | CFG side index: `0` (positive), `1` (negative), `-1` (mixed/unknown). |
| `is_positive` | float (0/1) | `1` for positive conditioning pass, else `0`. |
| `is_negative` | float (0/1) | `1` for negative conditioning pass, else `0`. |
| `activations_shape` | list | Raw shape from transformer context (typically `[B, C, H, W]` in UNet attention). Empty list if unavailable. |
| `activation_b` | float | Batch dimension from `activations_shape[0]` (or `-1`). |
| `activation_c` | float | Channel dimension from `activations_shape[1]` (or `-1`). |
| `activation_h` | float | Height dimension from `activations_shape[2]` (or `-1`). |
| `activation_w` | float | Width dimension from `activations_shape[3]` (or `-1`). |
| `attn_mode` | string | Unified attention mode: `self`, `cross`, or `unknown`. Prefer this over `attn1`/`attn2` for non-UNet models. |
| `is_self_attention` | float (0/1) | `1` when the active attention is self-attention. |
| `is_cross_attention` | float (0/1) | `1` when the active attention is cross-attention. |
| `has_context` | float (0/1) | `1` when attention context appears to be present. |
| `query_tokens` | float | Query sequence length. |
| `context_tokens` | float | Context sequence length. |
| `value_tokens` | float | Value sequence length. |
| `activation_rank` | float | Rank of `activations_shape` (`4` for image-like, `5` for video-like tensors). |
| `activation_t` | float | Temporal dimension for video-like activations (`-1` if unavailable). |
#### Practical notes
@@ -449,3 +459,7 @@ The following variables are available in `Expression`.
- For timestep-begin hooking use `layer_x=0` and filter by `block_name=="time_emb"` (or `layer_key=="unet.time_emb.0"`).
- For model-edge hooks filter by `hook_kind=="model_begin"` or `hook_kind=="model_end"`.
- For model-agnostic expressions, prefer guard variables: `has_qkv`, `is_dit`, `is_unet_block`, `is_attn1`, `is_attn2`.
- `attn2` is only a hook label, not guaranteed to mean real cross-attention.
- Use `is_cross_attention` only as an inferred relation from runtime metadata.
- Use `is_attn2_hook` when you need to know the hook path, and `attention_relation` when you need the semantic relation.
more_math/SelectiveGuiderMathNode.py
+83 -5
View File
@@ -102,12 +102,9 @@ class SelectiveGuiderMathNode(io.ComfyNode):
# Fallback heuristika: self-attn má obvykle stejný zdroj pro q/k/v
if isinstance(q, torch.Tensor) and isinstance(k, torch.Tensor) and isinstance(v, torch.Tensor):
if q.shape == k.shape == v.shape:
if (q.data_ptr() == k.data_ptr()) and (k.data_ptr() == v.data_ptr()):
return "attn1"
# stejný tvar bývá často pořád self-attn
if (q.data_ptr() == k.data_ptr()) and (k.data_ptr() == v.data_ptr()):
return "attn1"
return "attn2"
return "attn_unknown"
return "attn_unknown"
@@ -140,18 +137,23 @@ class SelectiveGuiderMathNode(io.ComfyNode):
return ("unknown", -1)
def run_expr(inp: torch.Tensor, extra_vars: dict | None = None) -> torch.Tensor:
# v run_expr(...) rozšiř výchozí proměnné:
variables = {
"inp": inp,
"sample": inp,
"hook_kind": "unknown",
"hook_domain": "unknown",
"attn_kind": "none",
"attn_mode": "unknown",
"transformer_index": -1.0,
"is_dit": 0.0,
"is_unet_block": 0.0,
"is_time_emb": 0.0,
"is_attn1": 0.0,
"is_attn2": 0.0,
"is_self_attention": 0.0,
"is_cross_attention": 0.0,
"has_context": 0.0,
"block_name": "",
"layer_key": "",
"layer_id": -1.0,
@@ -164,10 +166,23 @@ class SelectiveGuiderMathNode(io.ComfyNode):
"v": inp,
"heads": 0.0,
"dim_head": 0.0,
"query_tokens": -1.0,
"context_tokens": -1.0,
"value_tokens": -1.0,
"activations_shape": [],
"activation_rank": -1.0,
"activation_b": -1.0,
"activation_c": -1.0,
"activation_t": -1.0,
"activation_h": -1.0,
"activation_w": -1.0,
"cond_side": "unknown",
"cond_index": -1.0,
"is_positive": 0.0,
"is_negative": 0.0,
"is_attn1_hook": 0.0,
"is_attn2_hook": 0.0,
"attention_relation": "unknown",
} | generate_dim_variables(inp)
if extra_vars:
@@ -176,6 +191,55 @@ class SelectiveGuiderMathNode(io.ComfyNode):
for kf, vf in F.items():
variables[kf] = vf if vf is not None else 0.0
act_shape = variables.get("activations_shape", [])
if isinstance(act_shape, (list, tuple)):
variables["activation_rank"] = float(len(act_shape))
if len(act_shape) >= 1:
variables["activation_b"] = float(act_shape[0])
if len(act_shape) >= 2:
variables["activation_c"] = float(act_shape[1])
if len(act_shape) == 4:
variables["activation_h"] = float(act_shape[2])
variables["activation_w"] = float(act_shape[3])
elif len(act_shape) >= 5:
variables["activation_t"] = float(act_shape[2])
variables["activation_h"] = float(act_shape[3])
variables["activation_w"] = float(act_shape[4])
qv = variables.get("q", inp)
kv = variables.get("k", inp)
vv = variables.get("v", inp)
if variables.get("attn_kind") == "attn1":
variables["is_attn1_hook"] = 1.0
elif variables.get("attn_kind") == "attn2":
variables["is_attn2_hook"] = 1.0
if hasattr(qv, "shape") and len(qv.shape) >= 2:
variables["query_tokens"] = float(qv.shape[-2])
if hasattr(kv, "shape") and len(kv.shape) >= 2:
variables["context_tokens"] = float(kv.shape[-2])
variables["has_context"] = 1.0
if hasattr(vv, "shape") and len(vv.shape) >= 2:
variables["value_tokens"] = float(vv.shape[-2])
qt = variables["query_tokens"]
kt = variables["context_tokens"]
if variables["has_context"] == 1.0 and qt > 0 and kt > 0:
if qt != kt:
variables["is_cross_attention"] = 1.0
variables["is_self_attention"] = 0.0
variables["attention_relation"] = "cross"
else:
variables["is_cross_attention"] = 0.0
variables["is_self_attention"] = 1.0
variables["attention_relation"] = "self"
else:
variables["is_cross_attention"] = 0.0
variables["is_self_attention"] = 0.0
variables["attention_relation"] = "unknown"
visitor = UnifiedMathVisitor(variables, inp.shape, inp.device, state_storage=stack)
out = visitor.visit(tree)
return as_tensor(out, inp.shape).to(inp.device)
@@ -270,6 +334,14 @@ class SelectiveGuiderMathNode(io.ComfyNode):
logging.warning(f"[SelectiveGuiderMathNode] {attn_kind.upper()} HIT stage={stage} layer_id={layer_id} side={side}")
hit_flags[attn_kind] = True
# v register_attn -> attn_override(...), před `meta = { ... }` přidej ---
act_shape = transformer_options.get("activations_shape", [])
if not isinstance(act_shape, (list, tuple)):
act_shape = []
act_h = float(act_shape[2]) if len(act_shape) >= 4 else -1.0
act_w = float(act_shape[3]) if len(act_shape) >= 4 else -1.0
meta = {
"hook_kind": attn_kind,
"hook_domain": "attention",
@@ -294,6 +366,12 @@ class SelectiveGuiderMathNode(io.ComfyNode):
"cond_index": float(idx_side),
"is_positive": 1.0 if side == "positive" else 0.0,
"is_negative": 1.0 if side == "negative" else 0.0,
} | {
"activations_shape": list(act_shape),
"activation_b": float(act_shape[0]) if len(act_shape) > 0 else -1.0,
"activation_c": float(act_shape[1]) if len(act_shape) > 1 else -1.0,
"activation_h": float(act_shape[2]) if len(act_shape) > 2 else -1.0,
"activation_w": float(act_shape[3]) if len(act_shape) > 3 else -1.0,
}
out = original_attn(q, k, v, heads, **kwargs)