295 lines
13 KiB
Python
295 lines
13 KiB
Python
import torch
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from .base.control_base import ControlNodeBase
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class StreamCFG(ControlNodeBase):
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"""Implements CFG approaches for temporal consistency between workflow runs"""
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "update"
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CATEGORY = "real-time/sampling"
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@classmethod
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def INPUT_TYPES(cls):
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inputs = super().INPUT_TYPES()
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inputs["required"].update({
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"model": ("MODEL",),
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"cfg_type": (["self", "full", "initialize"], {
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"default": "self",
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"tooltip": "Type of CFG to use: full (standard), self (memory efficient), or initialize (memory efficient with initialization)"
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}),
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"residual_scale": ("FLOAT", {
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"default": 0.4,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Scale factor for residual (higher = more temporal consistency)"
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}),
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"delta": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 5.0,
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"step": 0.1,
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"tooltip": "Delta parameter for self/initialize CFG types"
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}),
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})
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return inputs
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def __init__(self):
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super().__init__()
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# Store the last model to detect when we need to reapply the hook
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self.last_model_hash = None
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self.post_cfg_function = None
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def update(self, model, always_execute=True, cfg_type="self", residual_scale=0.4, delta=1.0):
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print(f"[StreamCFG] Initializing with cfg_type={cfg_type}, residual_scale={residual_scale}, delta={delta}")
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# Get state with defaults
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state = self.get_state({
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"last_uncond": None, # Store last workflow's unconditioned prediction
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"initialized": False,
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"cfg_type": cfg_type,
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"residual_scale": residual_scale,
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"delta": delta,
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"workflow_count": 0, # Track number of workflow runs
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"current_sigmas": None, # Track sigmas for this workflow
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"seen_sigmas": set(), # Track which sigmas we've seen this workflow
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"is_last_step": False, # Track if we're on the last step
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"alpha_prod_t": None, # Store alpha values for proper scaling
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"beta_prod_t": None, # Store beta values for proper scaling
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})
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def post_cfg_function(args):
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# Extract info
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denoised = args["denoised"]
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cond = args["cond"]
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uncond = args["uncond"]
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cond_denoised = args["cond_denoised"]
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uncond_denoised = args["uncond_denoised"]
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cond_scale = args["cond_scale"]
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sigma = args["sigma"].item() if torch.is_tensor(args["sigma"]) else args["sigma"]
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# Get step info from model options
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model_options = args["model_options"]
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sample_sigmas = model_options["transformer_options"].get("sample_sigmas", None)
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# Update current sigmas if needed
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if sample_sigmas is not None and state["current_sigmas"] is None:
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# Filter out the trailing 0.0 if present
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sigmas = [s.item() for s in sample_sigmas]
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if sigmas[-1] == 0.0:
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sigmas = sigmas[:-1]
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state["current_sigmas"] = sigmas
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state["seen_sigmas"] = set()
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# Calculate alpha and beta values for proper scaling
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alphas = [1.0 / (1.0 + s**2) for s in sigmas]
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state["alpha_prod_t"] = torch.tensor(alphas, device=denoised.device, dtype=denoised.dtype)
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state["beta_prod_t"] = torch.sqrt(1 - state["alpha_prod_t"])
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# Track this sigma
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state["seen_sigmas"].add(sigma)
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# Check if this is the last step
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state["is_last_step"] = False
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if state["current_sigmas"] is not None:
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# It's the last step if we've seen all sigmas
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is_last_step = len(state["seen_sigmas"]) >= len(state["current_sigmas"])
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# Or if this is the smallest sigma in the sequence
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if not is_last_step and sigma == min(state["current_sigmas"]):
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is_last_step = True
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state["is_last_step"] = is_last_step
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# First workflow case
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if state["last_uncond"] is None:
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if state["is_last_step"]:
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state["last_uncond"] = uncond_denoised.detach().clone()
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state["workflow_count"] += 1
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state["current_sigmas"] = None # Reset for next workflow
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if cfg_type == "initialize":
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state["initialized"] = True
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self.set_state(state)
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return denoised
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# Handle different CFG types for subsequent workflows
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if cfg_type == "full":
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result = denoised
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elif cfg_type == "initialize" and not state["initialized"]:
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result = denoised
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if state["is_last_step"]:
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state["initialized"] = True
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self.set_state(state)
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else: # self or initialized initialize
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# Get current step index
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current_idx = len(state["seen_sigmas"]) - 1
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# Scale last prediction with proper alpha/beta values
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noise_pred_uncond = state["last_uncond"] * delta
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# Apply CFG with scaled prediction
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result = noise_pred_uncond + cond_scale * (cond_denoised - noise_pred_uncond)
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# Store last prediction if this is the last step
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if state["is_last_step"]:
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# Calculate properly scaled residual
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scaled_noise = state["beta_prod_t"][current_idx] * state["last_uncond"]
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delta_x = uncond_denoised - scaled_noise
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# Scale delta_x with next step's alpha/beta
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if current_idx < len(state["current_sigmas"]) - 1:
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alpha_next = state["alpha_prod_t"][current_idx + 1]
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beta_next = state["beta_prod_t"][current_idx + 1]
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else:
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alpha_next = torch.ones_like(state["alpha_prod_t"][0])
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beta_next = torch.ones_like(state["beta_prod_t"][0])
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delta_x = alpha_next * delta_x / beta_next
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# Update stored prediction with scaled residual
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final_update = uncond_denoised + residual_scale * delta_x
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state["last_uncond"] = final_update
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state["workflow_count"] += 1
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state["current_sigmas"] = None # Reset for next workflow
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self.set_state(state)
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return result
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# Store function reference to prevent garbage collection
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self.post_cfg_function = post_cfg_function
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# Only set up post CFG function if model has changed
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model_hash = hash(str(model))
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if model_hash != self.last_model_hash:
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m = model.clone()
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m.model_options = m.model_options.copy()
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m.model_options["sampler_post_cfg_function"] = [self.post_cfg_function]
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self.last_model_hash = model_hash
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return (m,)
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# Make sure our function is still in the list
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if not any(f is self.post_cfg_function for f in model.model_options.get("sampler_post_cfg_function", [])):
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m = model.clone()
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m.model_options = m.model_options.copy()
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m.model_options["sampler_post_cfg_function"] = [self.post_cfg_function]
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return (m,)
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return (model,)
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class StreamConditioning(ControlNodeBase):
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"""Applies Residual CFG to conditioning for improved temporal consistency with different CFG types"""
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#NOTE: experimental
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"cfg_type": (["full", "self", "initialize"], {
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"default": "full",
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"tooltip": "Type of CFG to use: full (standard), self (memory efficient), or initialize (memory efficient with initialization)"
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}),
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"residual_scale": ("FLOAT", {
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"default": 0.4,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Scale factor for residual conditioning (higher = more temporal consistency)"
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}),
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"delta": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 5.0,
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"step": 0.1,
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"tooltip": "Delta parameter for self/initialize CFG types"
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}),
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"always_execute": ("BOOLEAN", {
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"default": False,
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}),
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}
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}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
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RETURN_NAMES = ("positive", "negative")
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FUNCTION = "update"
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CATEGORY = "real-time/control/utility"
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def __init__(self):
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super().__init__()
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def update(self, positive, negative, cfg_type="full", residual_scale=0.4, delta=1.0, always_execute=False):
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# Get state with defaults
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state = self.get_state({
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"prev_positive": None,
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"prev_negative": None,
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"stock_noise": None, # For self/initialize CFG
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"initialized": False # For initialize CFG
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})
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# Extract conditioning tensors
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current_pos_cond = positive[0][0] # Assuming standard ComfyUI conditioning format
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current_neg_cond = negative[0][0]
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# First frame case
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if state["prev_positive"] is None:
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state["prev_positive"] = current_pos_cond.detach().clone()
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state["prev_negative"] = current_neg_cond.detach().clone()
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if cfg_type == "initialize":
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# For initialize, we use the first negative as our stock noise
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state["stock_noise"] = current_neg_cond.detach().clone()
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elif cfg_type == "self":
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# For self, we start with a scaled version of the negative
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state["stock_noise"] = current_neg_cond.detach().clone() * delta
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self.set_state(state)
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return (positive, negative)
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# Handle different CFG types
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if cfg_type == "full":
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# Standard R-CFG with full negative conditioning
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pos_residual = current_pos_cond - state["prev_positive"]
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neg_residual = current_neg_cond - state["prev_negative"]
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blended_pos = current_pos_cond + residual_scale * pos_residual
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blended_neg = current_neg_cond + residual_scale * neg_residual
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# Update state
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state["prev_positive"] = current_pos_cond.detach().clone()
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state["prev_negative"] = current_neg_cond.detach().clone()
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# Reconstruct conditioning format
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positive_out = [[blended_pos, positive[0][1]]]
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negative_out = [[blended_neg, negative[0][1]]]
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else: # self or initialize
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# Calculate residual for positive conditioning
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pos_residual = current_pos_cond - state["prev_positive"]
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blended_pos = current_pos_cond + residual_scale * pos_residual
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# Update stock noise based on current prediction
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if cfg_type == "initialize" and not state["initialized"]:
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# First prediction for initialize type
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state["stock_noise"] = current_neg_cond.detach().clone()
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state["initialized"] = True
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else:
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# Update stock noise with temporal consistency
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stock_residual = current_neg_cond - state["stock_noise"]
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state["stock_noise"] = current_neg_cond + residual_scale * stock_residual
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# Scale stock noise by delta
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scaled_stock = state["stock_noise"] * delta
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# Update state
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state["prev_positive"] = current_pos_cond.detach().clone()
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state["prev_negative"] = scaled_stock.detach().clone()
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# Reconstruct conditioning format
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positive_out = [[blended_pos, positive[0][1]]]
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negative_out = [[scaled_stock, negative[0][1]]]
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self.set_state(state)
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return (positive_out, negative_out)
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