323 lines
13 KiB
Python
323 lines
13 KiB
Python
import torch
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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from .helper_functions import (
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checkLazyNew,
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generate_dim_variables,
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getIndexTensorAlongDim,
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parse_expr,
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make_zero_like,
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as_tensor,
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get_v_variable,
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get_f_variable,
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LazyVariableDict,
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)
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from comfy_api.latest import io
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import comfy.sampler_helpers
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import comfy.model_patcher
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import comfy.utils
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import comfy.hooks
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import comfy.samplers
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from .Stack import MrmthStack
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from .ParseTree import MrmthParseTree
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import copy
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class GuiderMathNode(io.ComfyNode):
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"""
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Enables math expressions on Guiders (sampler inputs) with autogrow support.
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"""
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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_GuiderMathNode",
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category="More math",
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display_name="Guider math",
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inputs=[
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io.Autogrow.Input(id="V", template=io.Autogrow.TemplatePrefix(io.Guider.Input("values"), prefix="V", min=1, max=50)),
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io.Autogrow.Input(id="F", template=io.Autogrow.TemplatePrefix(io.Float.Input("float", default=0.0, optional=True, lazy=True, force_input=True), prefix="F", min=1, max=50)),
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io.MultiType.Input(
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io.String.Input("Expression", default="V0", multiline=False),
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types=[io.String,MrmthParseTree],
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tooltip="Expression to apply on input guiders. Aliases: a=G0, b=G1, c=G2, d=G3, w=F0, x=F1, y=F2, z=F3. Context: steps, current_step",
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),
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io.MultiType.Input(
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io.String.Input("Expression1", default="sample", multiline=False),
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types=[io.String,MrmthParseTree],
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tooltip="Expression to apply after generation finishes.",
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),
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io.Boolean.Input(
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id="remember_stack",
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default=False,
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display_name="Remember stack across batch",
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tooltip=(
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"If enabled, stack is copied at output leading to changes being remembered during batch operations (node runs multiple times in sucession). If disabled each batch gets it's own copy of the stack."
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),
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),
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MrmthStack.Input(id="stack", tooltip="Access stack between nodes",optional=True)
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],
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outputs=[
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io.Guider.Output(),
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MrmthStack.Output()
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],
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)
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@classmethod
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def check_lazy_status(cls, Expression,Expression1, V, F,remember_stack=True,stack={}):
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d = checkLazyNew(Expression,V,F)
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b = checkLazyNew(Expression1,V,F)
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return d|b
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@classmethod
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def execute(cls, V, F, Expression, Expression1,remember_stack = True, stack=None):
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stack = stack if remember_stack else (copy.deepcopy(stack) if stack is not None else {})
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guider = MathGuider(V, F, Expression, Expression1, stack);
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stack = stack if remember_stack else copy.deepcopy(stack)
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return (guider, stack)
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class MathGuider:
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def __init__(self, V, F, expression,expression1,stack):
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self.V = V
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self.F = F
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self.expression = expression
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if isinstance(expression,str):
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self.tree = parse_expr(expression)
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else:
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self.tree = expression
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if isinstance(expression1,str):
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self.tree1 = parse_expr(expression1)
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else:
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self.tree1 = expression1
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self.inner_model = None # Will be set during sample
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self.sigmas = None
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self.current_step = 0
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self.steps = 0
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self.stck = stack
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@property
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def model_patcher(self):
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for v in self.V.values():
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if v is not None and hasattr(v, "model_patcher"):
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return v.model_patcher
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return None
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@property
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def cfg(self):
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"""Delegate cfg to the primary guider."""
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for v in self.V.values():
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if v is not None and hasattr(v, "cfg"):
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return v.cfg
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return 1.0 # Default value if no guider has cfg
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@cfg.setter
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def cfg(self, value):
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"""Set cfg on all guiders that support it."""
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for v in self.V.values():
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if v is not None and hasattr(v, "cfg"):
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v.cfg = value
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def __call__(self, x, sigma, model_options={}, seed=None):
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lazy_g_results = {}
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zero_cache = [None]
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def get_zero_cache():
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if zero_cache[0] is None:
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zero_cache[0] = torch.zeros_like(x)
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return zero_cache[0]
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def make_lazy(guider_obj):
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def lazy_eval():
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if guider_obj is not None:
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return guider_obj(x, sigma, model_options=model_options, seed=seed)
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return get_zero_cache()
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lazy_eval.is_lazy_var = True
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return lazy_eval
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for k, guider in self.V.items():
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lazy_g_results[k] = make_lazy(guider)
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eval_samples, variables = self.setVars(x, sigma, seed, lazy_g_results)
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visitor = UnifiedMathVisitor(variables, eval_samples.shape,eval_samples.device,state_storage=self.stck)
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result_tensor = visitor.visit(self.tree)
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self.current_step = self.current_step + 1;
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return as_tensor(result_tensor, eval_samples.shape).to(x.device)
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def setVars(self, x, sigma, seed, g_results):
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eval_samples = x
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ndim = eval_samples.ndim
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batch_dim = 0
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channel_dim = 1
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height_dim = 2
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width_dim = 3
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time_dim = None
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if ndim >= 5:
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time_dim = 2
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channel_dim = 1
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height_dim = 3
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width_dim = 4
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frame_count = eval_samples.shape[time_dim] if time_dim is not None else 1
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def make_lazy_var(value_fn):
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value_fn.is_lazy_var = True
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return value_fn
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variables = LazyVariableDict()
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variables.update({
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"w": self.F.get("F0", 0.0),
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"x": self.F.get("F1", 0.0),
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"y": self.F.get("F2", 0.0),
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"z": self.F.get("F3", 0.0),
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"B": getIndexTensorAlongDim(eval_samples, batch_dim),
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"batch": getIndexTensorAlongDim(eval_samples, batch_dim),
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"W": float(eval_samples.shape[width_dim]) if width_dim < ndim else 0.0,
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"width": float(eval_samples.shape[width_dim]) if width_dim < ndim else 0.0,
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"H": float(eval_samples.shape[height_dim]) if height_dim < ndim else 0.0,
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"height": float(eval_samples.shape[height_dim]) if height_dim < ndim else 0.0,
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"T": float(frame_count),
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"batch_count": float(eval_samples.shape[0]),
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"N": float(eval_samples.shape[channel_dim]) if channel_dim < ndim else 0.0,
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"channel_count": float(eval_samples.shape[channel_dim]) if channel_dim < ndim else 0.0,
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"sigma": sigma.item() if isinstance(sigma,torch.Tensor) else sigma,
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"seed": seed if seed is not None else 0,
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"steps": self.steps,
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"current_step": self.current_step,
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"sample": x
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})
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if g_results is not None:
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for k, func in g_results.items():
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variables[k] = func
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variables["a"] = make_lazy_var(lambda: variables["V0"] if "V0" in g_results else make_zero_like(eval_samples))
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variables["b"] = make_lazy_var(lambda: variables["V1"] if "V1" in g_results else make_zero_like(eval_samples))
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variables["c"] = make_lazy_var(lambda: variables["V2"] if "V2" in g_results else make_zero_like(eval_samples))
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variables["d"] = make_lazy_var(lambda: variables["V3"] if "V3" in g_results else make_zero_like(eval_samples))
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def lazy_stacked_v():
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evaluated_g_results = {k: variables[k] for k in g_results.keys()}
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v_stacked, v_cnt = get_v_variable(evaluated_g_results)
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return v_stacked
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variables["V"] = make_lazy_var(lazy_stacked_v)
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def lazy_v_cnt():
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evaluated_g_results = {k: variables[k] for k in g_results.keys()}
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v_stacked, v_cnt = get_v_variable(evaluated_g_results)
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return float(v_cnt)
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variables["Vcnt"] = make_lazy_var(lazy_v_cnt)
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variables["V_count"] = variables["Vcnt"]
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for k, v in self.F.items():
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variables[k] = v if v is not None else 0.0
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f_stacked, f_cnt = get_f_variable(self.F)
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if f_stacked is not None:
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variables["F"] = f_stacked
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variables["Fcnt"] = float(f_cnt)
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variables["F_count"] = float(f_cnt)
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variables.update(generate_dim_variables(eval_samples))
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return eval_samples,variables
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def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
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self.sigmas = sigmas
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self.steps = len(sigmas)
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if sigmas.shape[-1] == 0:
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return latent_image
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active_guiders = [g for g in self.V.values() if g is not None]
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if not active_guiders:
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return latent_image
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primary_guider = active_guiders[0]
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cleanup_items = []
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try:
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for guider in active_guiders:
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if hasattr(guider, "original_conds"):
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guider.conds = {}
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for k in guider.original_conds:
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guider.conds[k] = list(map(lambda a: a.copy(), guider.original_conds[k]))
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if hasattr(comfy.samplers, "preprocess_conds_hooks") and hasattr(guider, "conds"):
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comfy.samplers.preprocess_conds_hooks(guider.conds)
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if hasattr(guider, "model_patcher"):
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guider._orig_model_options = guider.model_options
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guider.model_options = comfy.model_patcher.create_model_options_clone(guider.model_options)
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comfy.sampler_helpers.prepare_model_patcher(guider.model_patcher, guider.conds, guider.model_options)
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if hasattr(comfy.samplers, "filter_registered_hooks_on_conds"):
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comfy.samplers.filter_registered_hooks_on_conds(guider.conds, guider.model_options)
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guider.inner_model, guider.conds, guider.loaded_models = comfy.sampler_helpers.prepare_sampling(
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guider.model_patcher, noise.shape, guider.conds, guider.model_options
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)
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cleanup_items.append(guider)
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device = primary_guider.model_patcher.load_device
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noise = noise.to(device)
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latent_image = latent_image.to(device)
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sigmas = sigmas.to(device)
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for guider in active_guiders:
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if hasattr(guider, "model_options"):
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comfy.samplers.cast_to_load_options(guider.model_options, device=device, dtype=guider.model_patcher.model_dtype())
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patchers = set(g.model_patcher for g in active_guiders if hasattr(g, "model_patcher"))
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for p in patchers:
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p.pre_run()
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try:
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if latent_image is not None and torch.count_nonzero(latent_image) > 0:
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latent_image = primary_guider.inner_model.process_latent_in(latent_image)
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for guider in active_guiders:
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if hasattr(guider, "inner_model") and hasattr(guider, "conds"):
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guider.conds = comfy.samplers.process_conds(
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guider.inner_model, noise, guider.conds, device, latent_image, denoise_mask, seed
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)
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self.inner_model = primary_guider.inner_model
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extra_model_options = comfy.model_patcher.create_model_options_clone(primary_guider.model_options)
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extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas
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extra_args = {"model_options": extra_model_options, "seed": seed}
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output = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
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eval_samples, variables = self.setVars(output, 0.0, seed, None)
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variables["noise"] = noise
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variables["previous_sample"] = latent_image
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visitor = UnifiedMathVisitor(variables, eval_samples.shape,state_storage=self.stck)
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output = visitor.visit(self.tree1)
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if hasattr(primary_guider.inner_model, "process_latent_out"):
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output = primary_guider.inner_model.process_latent_out(output.to(torch.float32))
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return output
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finally:
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for p in patchers:
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p.cleanup()
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finally:
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for guider in cleanup_items:
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if hasattr(guider, "model_patcher") and hasattr(guider, "loaded_models"):
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comfy.sampler_helpers.cleanup_models(guider.conds, guider.loaded_models)
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if hasattr(guider, "_orig_model_options"):
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comfy.samplers.cast_to_load_options(guider.model_options, device=guider.model_patcher.offload_device)
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guider.model_options = guider._orig_model_options
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guider.model_patcher.restore_hook_patches()
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del guider.inner_model
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del guider.loaded_models
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del guider.conds
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if hasattr(guider, "_orig_model_options"): del guider._orig_model_options
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self.inner_model = None
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