543 lines
24 KiB
Python
543 lines
24 KiB
Python
import torch
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from .base.control_base import ControlNodeBase
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import comfy.model_management
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import comfy.samplers
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import random
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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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# Handle both batched and single sigmas
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sigma = args["sigma"]
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if torch.is_tensor(sigma):
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# For batched sampling, use first sigma in batch
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# This is safe because we process in order and track seen sigmas
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sigma = sigma[0].item() if len(sigma.shape) > 0 else sigma.item()
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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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#NOTE: totally and utterly experimental. No theoretical backing whatsoever.
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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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"context_size": ("INT", {
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"default": 4,
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"min": 1,
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"max": 32,
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"step": 1,
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"tooltip": "Number of past conditionings to keep in context. Higher values = smoother transitions but more memory usage."
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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, context_size=4, 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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"pos_context": [], # Store past positive conditionings
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"neg_context": [] # Store past negative conditionings
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})
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# Extract conditioning tensors
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current_pos_cond = positive[0][0]
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current_neg_cond = negative[0][0]
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# Update context queues
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if len(state["pos_context"]) >= context_size:
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state["pos_context"].pop(0)
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state["neg_context"].pop(0)
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state["pos_context"].append(current_pos_cond.detach().clone())
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state["neg_context"].append(current_neg_cond.detach().clone())
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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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state["stock_noise"] = current_neg_cond.detach().clone()
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elif cfg_type == "self":
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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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# Use entire context for smoother transitions
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pos_context = torch.stack(state["pos_context"], dim=0)
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neg_context = torch.stack(state["neg_context"], dim=0)
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# Calculate weighted residuals across context
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weights = torch.linspace(0.5, 1.0, len(state["pos_context"]), device=current_pos_cond.device)
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pos_residual = (current_pos_cond - pos_context) * weights.view(-1, 1, 1)
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neg_residual = (current_neg_cond - neg_context) * weights.view(-1, 1, 1)
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# Average residuals
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pos_residual = pos_residual.mean(dim=0)
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neg_residual = neg_residual.mean(dim=0)
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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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class StreamCrossAttention(ControlNodeBase):
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"""Implements optimized cross attention with KV-cache for real-time generation
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Paper reference: StreamDiffusion Section 3.5 "Pre-computation"
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- Pre-computes and caches prompt embeddings
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- Stores Key-Value pairs for reuse with static prompts
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- Only recomputes KV pairs when prompt changes
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Additional optimizations beyond paper:
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- QK normalization for better numerical stability
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- Rotary position embeddings (RoPE) for improved temporal consistency
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- Configurable context window size for memory/quality tradeoff
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"""
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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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"qk_norm": ("BOOLEAN", {
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"default": True,
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"tooltip": "Additional optimization: Whether to apply layer normalization to query and key tensors"
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}),
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"use_rope": ("BOOLEAN", {
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"default": True,
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"tooltip": "Additional optimization: Whether to use rotary position embeddings for better temporal consistency"
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}),
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"context_size": ("INT", {
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"default": 4,
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"min": 1,
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"max": 32,
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"step": 1,
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"tooltip": "Additional optimization: Maximum number of past frames to keep in context. Higher values use more memory but may improve temporal consistency."
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}),
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"use_kv_cache": ("BOOLEAN", {
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"default": True,
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"tooltip": "Paper Section 3.5: Whether to cache key-value pairs for static prompts to avoid recomputation"
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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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self.last_model_hash = None
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self.cross_attention_hook = None
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def update(self, model, always_execute=True, qk_norm=True, use_rope=True, context_size=4, use_kv_cache=True):
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print(f"[StreamCrossAttention] Initializing with qk_norm={qk_norm}, use_rope={use_rope}, context_size={context_size}, use_kv_cache={use_kv_cache}")
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# Get state with defaults
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state = self.get_state({
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"qk_norm": qk_norm, # Additional optimization
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"use_rope": use_rope, # Additional optimization
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"context_size": context_size, # Additional optimization
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"use_kv_cache": use_kv_cache, # From paper Section 3.5
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"workflow_count": 0,
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"context_queue": [], # Additional: Store past context tensors for temporal consistency
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"kv_cache": {}, # From paper Section 3.5: Cache KV pairs for each prompt
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"last_prompt_embeds": None, # From paper Section 3.5: For cache validation
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})
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def cross_attention_forward(module, x, context=None, mask=None, value=None):
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q = module.to_q(x)
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context = x if context is None else context
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# Paper Section 3.5: KV Caching Logic
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cache_hit = False
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if state["use_kv_cache"] and state["last_prompt_embeds"] is not None:
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# Compare current context with cached prompt embeddings
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if torch.allclose(context, state["last_prompt_embeds"], rtol=1e-5, atol=1e-5):
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cache_hit = True
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k, v = state["kv_cache"].get(module, (None, None))
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if k is not None and v is not None:
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print("[StreamCrossAttention] Using cached KV pairs")
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if not cache_hit:
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# Additional optimization: Temporal context management
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if len(state["context_queue"]) >= state["context_size"]:
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state["context_queue"].pop(0)
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state["context_queue"].append(context.detach().clone())
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# Additional optimization: Use past context for temporal consistency
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full_context = torch.cat(state["context_queue"], dim=1)
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# Generate k/v for full context
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k = module.to_k(full_context)
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v = value if value is not None else module.to_v(full_context)
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# Paper Section 3.5: Cache KV pairs for static prompts
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if state["use_kv_cache"]:
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state["last_prompt_embeds"] = context.detach().clone()
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state["kv_cache"][module] = (k.detach().clone(), v.detach().clone())
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# Additional optimization: QK normalization
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if state["qk_norm"]:
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q_norm = torch.nn.LayerNorm(q.shape[-1], device=q.device, dtype=q.dtype)
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k_norm = torch.nn.LayerNorm(k.shape[-1], device=k.device, dtype=k.dtype)
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q = q_norm(q)
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k = k_norm(k)
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# Additional optimization: Rotary position embeddings
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if state["use_rope"]:
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# Calculate position embeddings
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batch_size = q.shape[0]
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seq_len = q.shape[1]
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full_seq_len = k.shape[1] # Use full context length for k/v
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dim = q.shape[2]
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# Create position indices for q and k separately
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q_position = torch.arange(seq_len, device=q.device).unsqueeze(0).unsqueeze(-1)
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k_position = torch.arange(full_seq_len, device=k.device).unsqueeze(0).unsqueeze(-1)
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q_position = q_position.repeat(batch_size, 1, dim//2)
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k_position = k_position.repeat(batch_size, 1, dim//2)
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# Calculate frequencies
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freq = 10000.0 ** (-torch.arange(0, dim//2, 2, device=q.device) / dim)
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freq = freq.repeat((dim + 1) // 2)[:dim//2]
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# Calculate rotation angles
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q_theta = q_position * freq
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k_theta = k_position * freq
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# Apply rotations to q
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q_cos = torch.cos(q_theta)
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q_sin = torch.sin(q_theta)
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q_reshaped = q.view(*q.shape[:-1], -1, 2)
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q_out = torch.cat([
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q_reshaped[..., 0] * q_cos - q_reshaped[..., 1] * q_sin,
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q_reshaped[..., 0] * q_sin + q_reshaped[..., 1] * q_cos
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], dim=-1)
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# Apply rotations to k
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k_cos = torch.cos(k_theta)
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k_sin = torch.sin(k_theta)
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k_reshaped = k.view(*k.shape[:-1], -1, 2)
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k_out = torch.cat([
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k_reshaped[..., 0] * k_cos - k_reshaped[..., 1] * k_sin,
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k_reshaped[..., 0] * k_sin + k_reshaped[..., 1] * k_cos
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], dim=-1)
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|
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q = q_out
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k = k_out
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|
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# Standard attention computation with memory-efficient access pattern
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batch_size = q.shape[0]
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q_seq_len = q.shape[1]
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k_seq_len = k.shape[1]
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head_dim = q.shape[-1] // module.heads
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|
|
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# Reshape for multi-head attention
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q = q.view(batch_size, q_seq_len, module.heads, head_dim)
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k = k.view(batch_size, k_seq_len, module.heads, head_dim)
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v = v.view(batch_size, k_seq_len, module.heads, head_dim)
|
|
|
|
# Transpose for attention computation
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|
q = q.transpose(1, 2)
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|
k = k.transpose(1, 2)
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|
v = v.transpose(1, 2)
|
|
|
|
# Compute attention scores
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|
scale = head_dim ** -0.5
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|
scores = torch.matmul(q, k.transpose(-2, -1)) * scale
|
|
|
|
if mask is not None:
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scores = scores + mask
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|
|
|
# Apply attention
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|
attn = torch.softmax(scores, dim=-1)
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|
out = torch.matmul(attn, v)
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|
|
|
# Reshape back
|
|
out = out.transpose(1, 2).contiguous()
|
|
out = out.view(batch_size, q_seq_len, -1)
|
|
|
|
# Project back to original dimension
|
|
out = module.to_out[0](out)
|
|
|
|
return out
|
|
|
|
def hook_cross_attention(module, input, output):
|
|
if isinstance(module, torch.nn.Module) and hasattr(module, "to_q"):
|
|
# Store original forward
|
|
if not hasattr(module, "_original_forward"):
|
|
module._original_forward = module.forward
|
|
# Replace with our optimized version
|
|
module.forward = lambda *args, **kwargs: cross_attention_forward(module, *args, **kwargs)
|
|
return output
|
|
|
|
# Only set up hooks if model has changed
|
|
model_hash = hash(str(model))
|
|
if model_hash != self.last_model_hash:
|
|
m = model.clone()
|
|
|
|
# Remove old hooks if they exist
|
|
if self.cross_attention_hook is not None:
|
|
self.cross_attention_hook.remove()
|
|
|
|
# Register hook for cross attention modules
|
|
def register_hooks(module):
|
|
if isinstance(module, torch.nn.Module) and hasattr(module, "to_q"):
|
|
self.cross_attention_hook = module.register_forward_hook(hook_cross_attention)
|
|
|
|
m.model.apply(register_hooks)
|
|
self.last_model_hash = model_hash
|
|
return (m,)
|
|
|
|
return (model,)
|
|
|
|
|
|
|
|
|