digital-garbageandClaude Opus 4.8
e1801fdb3e
VELOCITY_BIAS_MEMORY is an in-process dict, so reset_session (which only removed on-disk enhancement files via clear_refinement_data) left stale good-trajectory memory behind — rescue and velocity bias would keep steering toward the previous session's content. Add clear_velocity_bias_memory(key) in samplers.py (drops global averages + prompt clusters for that key) and call it from clear_refinement_data. Key matches: refine_v2 and the velocity key default both resolve to the same effective refinement key. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
1984 lines
90 KiB
Python
1984 lines
90 KiB
Python
import math
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import torch
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import comfy.k_diffusion.sampling as k_diffusion_sampling
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import comfy.model_sampling
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import comfy.nested_tensor
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import comfy.sample
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import comfy.samplers
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import comfy.utils
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MOTION_PULSE_MODES = ["off", "balanced", "aggressive", "custom"]
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VELOCITY_BIAS_MODES = ["off", "capture", "apply", "capture_and_apply"]
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VELOCITY_BIAS_TARGETS = (0.90, 0.80)
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VELOCITY_BIAS_MEMORY = {}
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# Prompt-conditioned rescue clustering. The good-trajectory memory keeps, per
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# velocity key, a set of clusters each tagged with a pooled+normalized prompt
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# signature. Capture merges into the nearest cluster (or starts a new one);
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# rescue builds its reference from clusters whose prompt is similar enough to the
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# current run, so it steers toward what was good *for this kind of prompt* rather
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# than a prompt-blind global average.
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RESCUE_CLUSTER_MERGE_SIM = 0.92 # capture: merge into a cluster at/above this cosine
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RESCUE_CLUSTER_MAX = 8 # cap clusters per key (evict lowest-count); each holds a full-shape direction
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RESCUE_USE_SIM = 0.5 # rescue: only use clusters at/above this cosine
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RESCUE_MIN_COUNT = 2 # rescue: need this many matched captures to act
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def _normalize_sig(sig):
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"""Pooled prompt vector -> CPU float unit vector, or None."""
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if not isinstance(sig, torch.Tensor) or sig.numel() == 0:
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return None
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s = sig.detach().float().reshape(-1).cpu()
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n = s.norm()
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if not torch.isfinite(n) or float(n) <= 1e-8:
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return None
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return s / n
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def _update_prompt_clusters(slot, prompt_sig, direction):
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"""Capture-side: fold (prompt_sig, direction) into the slot's prompt clusters."""
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if prompt_sig is None:
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return
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clusters = slot.setdefault("clusters", [])
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best_i, best_cos = -1, -1.0
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for i, c in enumerate(clusters):
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cs = c.get("sig")
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if not isinstance(cs, torch.Tensor) or cs.shape != prompt_sig.shape:
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continue
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cos = float(cs @ prompt_sig)
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if cos > best_cos:
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best_cos, best_i = cos, i
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if best_i >= 0 and best_cos >= RESCUE_CLUSTER_MERGE_SIM:
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c = clusters[best_i]
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n = int(c.get("count", 1))
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if isinstance(c.get("direction"), torch.Tensor) and c["direction"].shape == direction.shape:
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c["direction"] = (c["direction"] * n + direction) / float(n + 1)
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merged = c["sig"] * n + prompt_sig
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mn = merged.norm().clamp_min(1e-8)
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c["sig"] = merged / mn
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c["count"] = min(n + 1, 256)
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else:
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clusters.append({"sig": prompt_sig.clone(), "direction": direction.clone(), "count": 1})
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if len(clusters) > RESCUE_CLUSTER_MAX:
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clusters.sort(key=lambda c: int(c.get("count", 0)), reverse=True)
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del clusters[RESCUE_CLUSTER_MAX:]
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def _select_prompt_direction(slot, prompt_sig, x_shape):
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"""Rescue-side: similarity-weighted good direction over prompt-relevant clusters.
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Returns None when there is no confident prompt-matching reference (caller then
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skips rescue rather than steering toward a prompt-blind average)."""
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clusters = slot.get("clusters") if isinstance(slot, dict) else None
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if not clusters or prompt_sig is None:
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return None
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num = None
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total_w = 0.0
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matched = 0
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for c in clusters:
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cs = c.get("sig")
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d = c.get("direction")
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if not isinstance(cs, torch.Tensor) or cs.shape != prompt_sig.shape:
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continue
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if not isinstance(d, torch.Tensor) or tuple(d.shape) != tuple(x_shape):
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continue
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cos = float(cs @ prompt_sig)
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if cos < RESCUE_USE_SIM:
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continue
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contrib = d * cos
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num = contrib if num is None else num + contrib
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total_w += cos
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matched += int(c.get("count", 1))
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if num is None or total_w <= 0.0 or matched < RESCUE_MIN_COUNT:
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return None
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return num / total_w
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def _sigma_fn(t):
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return t.neg().exp()
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def _t_fn(sigma):
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return sigma.log().neg()
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def _hybrid_ode_step(model, x, sigma, sigma_next, s_in, extra_args, correction_blend, denoised=None):
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if denoised is None:
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denoised = model(x, sigma * s_in, **extra_args)
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if sigma_next == 0:
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return denoised, denoised
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d = k_diffusion_sampling.to_d(x, sigma, denoised)
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dt = sigma_next - sigma
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x_euler = x + d * dt
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if correction_blend <= 0.0:
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return x_euler, denoised
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t = _t_fn(sigma)
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t_next = _t_fn(sigma_next)
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h = t_next - t
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r = 0.5
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s = t + r * h
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x_mid = (_sigma_fn(s) / _sigma_fn(t)) * x - torch.expm1(-h * r) * denoised
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denoised_mid = model(x_mid, _sigma_fn(s) * s_in, **extra_args)
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x_2s = (_sigma_fn(t_next) / _sigma_fn(t)) * x - torch.expm1(-h) * denoised_mid
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if correction_blend >= 1.0:
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return x_2s, denoised
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return x_euler.lerp(x_2s, correction_blend), denoised
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def _apply_motion_pulse(x, sigma, sigma_next, pulse_noise, noise_sampler):
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if pulse_noise <= 0.0 or sigma_next >= sigma:
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return x
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sigma_delta_sq = max(0.0, float(sigma * sigma - sigma_next * sigma_next))
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if sigma_delta_sq <= 0.0:
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return x
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sigma_delta = math.sqrt(sigma_delta_sq)
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return x + noise_sampler(sigma, sigma_next) * (pulse_noise * sigma_delta)
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def clear_velocity_bias_memory(refinement_key):
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"""Drop all stored velocity-bias / rescue trajectory memory (global averages and
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prompt clusters) for a key. Called on Studio session reset so rescue and velocity
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bias start from a clean slate instead of steering toward last session's content."""
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norm = str(refinement_key or "default").strip() or "default"
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removed = [k for k in list(VELOCITY_BIAS_MEMORY)
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if isinstance(k, tuple) and k and k[0] == norm]
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for k in removed:
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VELOCITY_BIAS_MEMORY.pop(k, None)
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if removed:
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print(f"[FunPack rescue] cleared {len(removed)} velocity-bias memory bucket(s) "
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f"for key '{norm}' (session reset)")
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return len(removed)
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def _velocity_bias_enabled(mode, action):
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mode = (mode or "off").lower()
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if mode == "capture_and_apply":
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return action in {"capture", "apply"}
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return mode == action
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def _velocity_bias_key(refinement_key, aspect_bucket, target, x):
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shape = tuple(int(item) for item in getattr(x, "shape", ()))
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key = str(refinement_key or "default").strip() or "default"
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aspect = str(aspect_bucket or "any").strip() or "any"
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return (key, aspect, f"{float(target):.2f}", shape)
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def _sigma_ratio(sigmas, sigma):
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try:
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start = float(sigmas[0].item())
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current = float(sigma.item())
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except Exception:
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return None
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if start <= 0.0:
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return None
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return current / start
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def _velocity_bias_target(sigmas, sigma):
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ratio = _sigma_ratio(sigmas, sigma)
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if ratio is None:
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return None
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target = min(VELOCITY_BIAS_TARGETS, key=lambda item: abs(float(item) - ratio))
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return target if abs(float(target) - ratio) <= 0.065 else None
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def _capture_velocity_bias(refinement_key, aspect_bucket, target, x, sigma, denoised, prompt_sig=None):
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if target is None:
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return
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try:
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direction = k_diffusion_sampling.to_d(x, sigma, denoised).detach().float().cpu()
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except Exception:
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return
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key = _velocity_bias_key(refinement_key, aspect_bucket, target, x)
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slot = VELOCITY_BIAS_MEMORY.setdefault(key, {"count": 0, "direction": None})
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count = int(slot.get("count", 0))
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previous = slot.get("direction")
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if count <= 0 or not isinstance(previous, torch.Tensor) or tuple(previous.shape) != tuple(direction.shape):
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slot["direction"] = direction
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slot["count"] = 1
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else:
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slot["direction"] = (previous * count + direction) / float(count + 1)
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slot["count"] = min(count + 1, 256)
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# Prompt-conditioned clusters (used by rescue); global average above stays
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# intact for velocity-bias apply.
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_update_prompt_clusters(slot, prompt_sig, direction)
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def _apply_velocity_bias(x, refinement_key, aspect_bucket, target, strength):
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if target is None or strength <= 0.0:
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return x
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key = _velocity_bias_key(refinement_key, aspect_bucket, target, x)
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slot = VELOCITY_BIAS_MEMORY.get(key)
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if not isinstance(slot, dict) or not isinstance(slot.get("direction"), torch.Tensor):
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return x
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direction = slot["direction"]
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if tuple(direction.shape) != tuple(x.shape):
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return x
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try:
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direction = direction.to(device=x.device, dtype=x.dtype)
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delta = direction * max(0.0, min(0.35, float(strength)))
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max_delta = x.detach().float().norm().clamp_min(1e-8) * 0.045
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delta_norm = delta.detach().float().norm().clamp_min(1e-8)
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if delta_norm > max_delta:
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delta = delta * (max_delta / delta_norm).to(device=x.device, dtype=x.dtype)
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return x + delta
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except Exception:
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return x
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# Per-run rescue logging state, reset at the start of each sampler call.
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_RESCUE_LOG = {"warned_no_memory": False, "warned_no_prompt_match": False, "fired": 0}
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def _rescue_denoised(denoised, x, sigma, refinement_key, aspect_bucket, target, threshold, strength, prompt_sig=None):
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"""Reactive in-flight rescue.
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Detects when this step's trajectory direction has diverged from the learned
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good-trajectory memory (the same averaged ``to_d`` captured by velocity bias) by
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more than ``threshold`` (divergence = 1 - cosine). When it has, the denoised
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prediction is rotated back toward the good direction by ``strength``, preserving
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its magnitude (no energy injected). Read-only on the memory. Returns ``denoised``
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unchanged when there is no reference yet or the trajectory is still on-manifold.
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"""
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if target is None or strength <= 0.0:
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return denoised
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sig = float(sigma.item()) if isinstance(sigma, torch.Tensor) else float(sigma)
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if sig <= 1e-6:
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return denoised
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key = _velocity_bias_key(refinement_key, aspect_bucket, target, x)
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slot = VELOCITY_BIAS_MEMORY.get(key)
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if not isinstance(slot, dict) or not isinstance(slot.get("direction"), torch.Tensor):
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if not _RESCUE_LOG["warned_no_memory"]:
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print("[FunPack rescue] no good-trajectory memory for this key/aspect yet — "
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"run with velocity_bias_mode=capture first. Rescue is a no-op until then.")
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_RESCUE_LOG["warned_no_memory"] = True
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return denoised
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if prompt_sig is not None:
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# Prompt-aware: steer only toward good trajectories captured for similar
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# prompts. No confident prompt match -> skip (don't fall back to the
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# prompt-blind average, which could fight a legitimately different prompt).
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g = _select_prompt_direction(slot, prompt_sig, x.shape)
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if g is None:
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if not _RESCUE_LOG["warned_no_prompt_match"]:
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print("[FunPack rescue] no prompt-matching good trajectory yet for this prompt — "
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"capture a few good runs with a similar prompt. Skipping (prompt-aware mode).")
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_RESCUE_LOG["warned_no_prompt_match"] = True
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return denoised
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else:
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# No prompt signature available (e.g. standalone sampler node): fall back
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# to the prompt-blind global average.
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g = slot["direction"]
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if tuple(g.shape) != tuple(x.shape):
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return denoised
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try:
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g = g.to(device=x.device, dtype=torch.float32)
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d = k_diffusion_sampling.to_d(x, sigma, denoised).detach().float()
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d_norm = d.norm().clamp_min(1e-8)
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g_norm = g.norm().clamp_min(1e-8)
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cos = float((d.flatten() @ g.flatten()) / (d_norm * g_norm))
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divergence = 1.0 - cos
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if divergence <= float(threshold):
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return denoised
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w = max(0.0, min(0.5, float(strength)))
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target_unit = (1.0 - w) * (d / d_norm) + w * (g / g_norm)
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tu_norm = target_unit.norm().clamp_min(1e-8)
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d_corr = d_norm * (target_unit / tu_norm)
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denoised_corr = (x.float() - sig * d_corr).to(dtype=denoised.dtype, device=denoised.device)
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_RESCUE_LOG["fired"] += 1
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if _RESCUE_LOG["fired"] <= 8:
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print(f"[FunPack rescue] sigma~{sig:.3f} divergence={divergence:.3f} > "
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f"thr={float(threshold):.3f} -> steered toward good trajectory (w={w:.2f})")
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return denoised_corr
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except Exception as e:
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print(f"[FunPack rescue] skipped: {e}")
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return denoised
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def _find_schedule_anchor_index(sigmas, total_steps, schedule_progress):
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if sigmas is None or total_steps <= 1:
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return 0
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schedule_progress = max(0.0, min(1.0, schedule_progress))
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return min(total_steps - 1, max(0, int(round(schedule_progress * max(0, total_steps - 1)))))
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def _resolve_motion_pulse_options(motion_pulse_mode, motion_pulse_start_pct,
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motion_pulse_count, motion_pulse_spacing_pct,
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motion_pulse_strength):
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mode = (motion_pulse_mode or "off").lower()
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if mode not in MOTION_PULSE_MODES:
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mode = "off"
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if mode == "off":
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return {
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"enabled": False,
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"start_pct": 0.30,
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"count": 0,
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"spacing_pct": 0.22,
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"strength": 0.0,
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"noise": 0.0,
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}
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start_pct = 0.30 if motion_pulse_start_pct is None else float(motion_pulse_start_pct)
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spacing_pct = 0.22 if motion_pulse_spacing_pct is None else float(motion_pulse_spacing_pct)
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strength = 0.85 if motion_pulse_strength is None else float(motion_pulse_strength)
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count = 2 if motion_pulse_count is None else int(motion_pulse_count)
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start_pct = max(0.02, min(0.90, start_pct))
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spacing_pct = max(0.04, min(0.45, spacing_pct))
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strength = max(0.0, min(1.0, strength))
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count = max(1, min(6, count))
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if mode == "balanced":
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count = min(count, 1)
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strength = 0.55 if motion_pulse_strength is None else min(strength, 0.70)
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elif mode == "aggressive":
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count = max(2, count)
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strength = max(strength, 0.85)
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noise = 0.10 + strength * 0.55
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return {
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"enabled": True,
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"start_pct": start_pct,
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"count": count,
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"spacing_pct": spacing_pct,
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"strength": strength,
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"noise": max(0.0, min(0.80, noise)),
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}
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def _get_late_start_index(total_steps, high_quality_pct):
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high_quality_pct = max(0.0, min(1.0, float(high_quality_pct)))
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late_steps = max(1, int(math.ceil(total_steps * high_quality_pct))) if high_quality_pct > 0.0 else 0
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return max(0, total_steps - late_steps)
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def _build_motion_pulse_steps(sigmas, total_steps, high_quality_pct,
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motion_pulse_mode, motion_pulse_start_pct,
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motion_pulse_count, motion_pulse_spacing_pct,
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motion_pulse_strength):
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options = _resolve_motion_pulse_options(
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motion_pulse_mode,
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motion_pulse_start_pct,
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motion_pulse_count,
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motion_pulse_spacing_pct,
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motion_pulse_strength,
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)
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if not options["enabled"] or total_steps <= 2:
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return [], options
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late_start = _get_late_start_index(total_steps, high_quality_pct)
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latest_anchor_pct = max(0.04, min(0.92, (late_start - 1) / max(1, total_steps - 1)))
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pulse_steps = []
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used_anchors = set()
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for pulse_index in range(options["count"]):
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trigger_pct = options["start_pct"] + options["spacing_pct"] * pulse_index
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if trigger_pct >= latest_anchor_pct:
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break
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anchor_index = _find_schedule_anchor_index(sigmas, total_steps, trigger_pct)
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anchor_index = min(total_steps - 1, max(0, anchor_index))
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if anchor_index in used_anchors or anchor_index >= late_start:
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continue
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used_anchors.add(anchor_index)
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pulse_steps.append({
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"step_index": anchor_index,
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"noise": options["noise"],
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})
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return pulse_steps, options
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def _prepare_dynamic_sigmas(sigmas, high_quality_pct, motion_pulse_mode="off",
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motion_pulse_start_pct=0.30, motion_pulse_count=2,
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motion_pulse_spacing_pct=0.22, motion_pulse_strength=0.85):
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if sigmas is None or not isinstance(sigmas, torch.Tensor):
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return None, None, [], 0.0
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base_sigmas = sigmas.detach().clone()
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total_steps = max(0, int(base_sigmas.shape[0]) - 1)
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if total_steps <= 0:
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return base_sigmas, None, [], 0.0
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late_start = _get_late_start_index(total_steps, high_quality_pct)
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quality_sigma_start = None
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if late_start < base_sigmas.shape[0]:
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quality_sigma_start = float(base_sigmas[late_start].item())
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pulse_steps, motion_pulse_options = _build_motion_pulse_steps(
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base_sigmas,
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total_steps,
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high_quality_pct,
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motion_pulse_mode,
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motion_pulse_start_pct,
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motion_pulse_count,
|
||
motion_pulse_spacing_pct,
|
||
motion_pulse_strength,
|
||
)
|
||
return base_sigmas, quality_sigma_start, pulse_steps, motion_pulse_options["noise"]
|
||
|
||
|
||
def _order2_ancestral_denoised(denoised, prev_denoised, h, prev_h):
|
||
"""
|
||
Linear extrapolation of the denoised estimate using the previous step's value.
|
||
Equivalent to the DPM-Solver++ 2M approach applied to the ancestral phase.
|
||
Gives second-order accuracy at zero extra model-call cost.
|
||
"""
|
||
if prev_denoised is None or prev_h is None or prev_h < 1e-7 or h < 1e-7:
|
||
return denoised
|
||
r = max(0.25, min(4.0, prev_h / h))
|
||
c1 = 1.0 + 0.5 / r
|
||
c2 = 0.5 / r
|
||
try:
|
||
extrap = c1 * denoised - c2 * prev_denoised.to(device=denoised.device, dtype=denoised.dtype)
|
||
return extrap
|
||
except Exception:
|
||
return denoised
|
||
|
||
|
||
def _sample_const_rf_full(model, x, sigmas, extra_args, callback, disable,
|
||
eta, s_noise, eta_final, high_quality_pct, correction_blend,
|
||
motion_pulse_mode, motion_pulse_start_pct, motion_pulse_count,
|
||
motion_pulse_spacing_pct, motion_pulse_strength,
|
||
motion_pulse_noise, motion_pulse_steps,
|
||
velocity_bias_mode, velocity_bias_strength,
|
||
velocity_refinement_key, velocity_aspect_bucket,
|
||
rescue_mode, rescue_threshold, rescue_strength, rescue_prompt_sig):
|
||
"""Full-feature rectified-flow sampler for CONST models (LTXAV).
|
||
|
||
Rectified-flow-correct port of the hybrid sampler so its features actually run on
|
||
LTXAV (which stock comfy routes to euler_ancestral_RF, bypassing the custom loop):
|
||
- early phase: ancestral RF euler with eta decay, AB2 (order-2) denoised
|
||
extrapolation, and anti-stiffness motion pulses
|
||
- late (quality) phase: deterministic Heun corrector — the RF-correct 2nd order;
|
||
DPM++ 2S is eps-only and intentionally not used here — with the hybrid's
|
||
progressive correction_blend schedule
|
||
- velocity-bias capture/apply and reactive rescue around every model eval
|
||
With motion/velocity/rescue off AND high_quality_pct=0 it reduces to stock
|
||
euler_ancestral_RF. The base RF step matches comfy.sample_euler_ancestral_RF.
|
||
"""
|
||
extra_args = {} if extra_args is None else extra_args
|
||
seed = extra_args.get("seed", None)
|
||
noise_sampler = k_diffusion_sampling.default_noise_sampler(x, seed=seed)
|
||
try:
|
||
s_noise = s_noise * getattr(
|
||
model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
|
||
except Exception:
|
||
pass
|
||
|
||
total_steps = max(0, len(sigmas) - 1)
|
||
if total_steps <= 0:
|
||
return x
|
||
|
||
high_quality_pct = max(0.0, min(1.0, float(high_quality_pct)))
|
||
correction_blend = max(0.0, min(1.0, float(correction_blend)))
|
||
eta_final = max(0.0, min(float(eta), float(eta_final)))
|
||
|
||
if not motion_pulse_steps:
|
||
_, _, motion_pulse_steps, motion_pulse_noise = _prepare_dynamic_sigmas(
|
||
sigmas, high_quality_pct, motion_pulse_mode, motion_pulse_start_pct,
|
||
motion_pulse_count, motion_pulse_spacing_pct, motion_pulse_strength)
|
||
motion_pulse_noise = max(0.0, float(motion_pulse_noise or 0.0))
|
||
motion_step_noise = {
|
||
int(item.get("step_index", -1)): max(0.0, float(item.get("noise", motion_pulse_noise)))
|
||
for item in (motion_pulse_steps or []) if isinstance(item, dict)
|
||
}
|
||
|
||
late_start = _get_late_start_index(total_steps, high_quality_pct)
|
||
quality_sigma_start = float(sigmas[late_start].item()) if late_start < sigmas.shape[0] else None
|
||
num_quality_steps = total_steps - late_start
|
||
|
||
s_in = x.new_ones([x.shape[0]])
|
||
prev_denoised = None
|
||
prev_h = None
|
||
quality_step_index = 0
|
||
|
||
for i in comfy.utils.model_trange(total_steps, disable=disable):
|
||
sigma = sigmas[i]
|
||
sigma_next = sigmas[i + 1]
|
||
in_quality = quality_sigma_start is not None and float(sigma.item()) <= quality_sigma_start
|
||
velocity_target = _velocity_bias_target(sigmas, sigma)
|
||
|
||
if not in_quality:
|
||
pulse = motion_step_noise.get(int(i), 0.0)
|
||
if pulse > 0.0:
|
||
x = _apply_motion_pulse(x, sigma, sigma_next, pulse, noise_sampler)
|
||
prev_denoised = None
|
||
prev_h = None
|
||
|
||
if _velocity_bias_enabled(velocity_bias_mode, "apply"):
|
||
x = _apply_velocity_bias(x, velocity_refinement_key, velocity_aspect_bucket, velocity_target, velocity_bias_strength)
|
||
|
||
denoised = model(x, sigma * s_in, **extra_args)
|
||
|
||
if _velocity_bias_enabled(velocity_bias_mode, "capture"):
|
||
_capture_velocity_bias(velocity_refinement_key, velocity_aspect_bucket, velocity_target, x, sigma, denoised, prompt_sig=rescue_prompt_sig)
|
||
if rescue_mode and velocity_target is not None:
|
||
denoised = _rescue_denoised(
|
||
denoised, x, sigma, velocity_refinement_key, velocity_aspect_bucket,
|
||
velocity_target, rescue_threshold, rescue_strength, prompt_sig=rescue_prompt_sig,
|
||
)
|
||
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigma, 'sigma_hat': sigma, 'denoised': denoised})
|
||
|
||
h = float((sigma - sigma_next).abs().item())
|
||
# AB2 (order-2) extrapolation on the x0/denoised sequence — RF-valid.
|
||
if prev_denoised is not None and prev_h is not None and prev_h > 1e-7 and h > 1e-7:
|
||
r = max(0.1, min(5.0, h / prev_h))
|
||
try:
|
||
denoised_eff = (1.0 + r / 2.0) * denoised - (r / 2.0) * prev_denoised.to(device=denoised.device, dtype=denoised.dtype)
|
||
except Exception:
|
||
denoised_eff = denoised
|
||
else:
|
||
denoised_eff = denoised
|
||
prev_denoised = denoised.detach()
|
||
prev_h = h
|
||
|
||
if sigma_next == 0:
|
||
x = denoised_eff
|
||
continue
|
||
|
||
if in_quality:
|
||
# Deterministic Heun corrector (RF-correct 2nd order). Progressive blend:
|
||
# first half of quality steps = euler, second half = full Heun, matching
|
||
# the hybrid sampler's correction schedule. Note: to_d-based euler is
|
||
# identical to the RF flow update, so this is consistent with the early phase.
|
||
if num_quality_steps <= 1:
|
||
effective_blend = correction_blend
|
||
else:
|
||
effective_blend = 0.0 if quality_step_index < (num_quality_steps // 2) else correction_blend
|
||
dt = sigma_next - sigma
|
||
d1 = k_diffusion_sampling.to_d(x, sigma, denoised_eff)
|
||
if effective_blend > 0.0:
|
||
x_pred = x + d1 * dt
|
||
denoised_pred = model(x_pred, sigma_next * s_in, **extra_args)
|
||
d2 = k_diffusion_sampling.to_d(x_pred, sigma_next, denoised_pred)
|
||
d_use = d1 + effective_blend * ((d1 + d2) * 0.5 - d1)
|
||
else:
|
||
d_use = d1
|
||
x = x + d_use * dt
|
||
# Heun changed x with a corrected direction; invalidate AB2 history.
|
||
prev_denoised = None
|
||
prev_h = None
|
||
quality_step_index += 1
|
||
else:
|
||
# Early phase: ancestral RF euler (matches sample_euler_ancestral_RF), with
|
||
# eta decay toward the quality boundary, using the AB2 denoised estimate.
|
||
if quality_sigma_start is not None and quality_sigma_start > 0.0 and eta_final < eta:
|
||
sv = float(sigma.item())
|
||
proximity = min(1.0, max(0.0, quality_sigma_start / max(sv, 1e-8)))
|
||
effective_eta = eta_final + (eta - eta_final) * (1.0 - proximity)
|
||
else:
|
||
effective_eta = eta
|
||
downstep_ratio = 1 + (sigma_next / sigma - 1) * effective_eta
|
||
sigma_down = sigma_next * downstep_ratio
|
||
alpha_ip1 = 1 - sigma_next
|
||
alpha_down = 1 - sigma_down
|
||
sigma_down_i_ratio = sigma_down / sigma
|
||
x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised_eff
|
||
if effective_eta > 0:
|
||
renoise_coeff = (sigma_next ** 2 - sigma_down ** 2 * alpha_ip1 ** 2 / alpha_down ** 2) ** 0.5
|
||
x = (alpha_ip1 / alpha_down) * x + noise_sampler(sigma, sigma_next) * s_noise * renoise_coeff
|
||
|
||
return x
|
||
|
||
|
||
def sample_funpack_hybrid_euler_2s(model, x, sigmas, extra_args=None, callback=None,
|
||
disable=None, eta=1.0, s_noise=1.0,
|
||
high_quality_pct=0.35, correction_blend=1.0,
|
||
quality_sigma_start=None,
|
||
motion_pulse_mode="off",
|
||
motion_pulse_start_pct=0.30,
|
||
motion_pulse_count=2,
|
||
motion_pulse_spacing_pct=0.22,
|
||
motion_pulse_strength=0.85,
|
||
motion_pulse_noise=0.0,
|
||
motion_pulse_steps=None,
|
||
velocity_bias_mode="off",
|
||
velocity_bias_strength=0.0,
|
||
velocity_refinement_key="default",
|
||
velocity_aspect_bucket="any",
|
||
rescue_mode=False,
|
||
rescue_threshold=0.15,
|
||
rescue_strength=0.2,
|
||
rescue_prompt_sig=None,
|
||
eta_final=1.0):
|
||
"""
|
||
Hybrid sampler:
|
||
- Early schedule: Euler ancestral with order-2 denoised extrapolation for
|
||
motion/anatomy buildup. Order-2 reuses the previous step's denoised to
|
||
extrapolate the score direction, giving DPM-Solver++ 2M accuracy at zero
|
||
extra model-call cost.
|
||
- Late schedule: deterministic DPM-Solver++(2S) ODE refinement for detail,
|
||
with progressive correction_blend — first half of quality steps use single-
|
||
eval Euler ODE, second half use the full configured 2S correction. This
|
||
cuts quality-phase model calls by roughly half while preserving the 2S
|
||
benefit where sigma is lowest and it matters most.
|
||
- Eta decay: when eta_final < eta, ancestral noise strength decays toward
|
||
eta_final as sigma approaches the quality boundary, giving a cleaner
|
||
transition into deterministic refinement.
|
||
- Motion pulses: optional monotonic noise kicks before the late quality phase.
|
||
"""
|
||
# Normalize velocity/rescue params up front — both the CONST (rectified-flow)
|
||
# branch and the main hybrid loop rely on them.
|
||
velocity_bias_mode = (velocity_bias_mode or "off").lower()
|
||
if velocity_bias_mode not in VELOCITY_BIAS_MODES:
|
||
velocity_bias_mode = "off"
|
||
velocity_bias_strength = max(0.0, min(0.35, float(velocity_bias_strength or 0.0)))
|
||
rescue_mode = bool(rescue_mode)
|
||
rescue_threshold = max(0.0, min(1.0, float(rescue_threshold or 0.0)))
|
||
rescue_strength = max(0.0, min(0.5, float(rescue_strength or 0.0)))
|
||
rescue_prompt_sig = _normalize_sig(rescue_prompt_sig) # capture also uses this
|
||
if rescue_mode:
|
||
_RESCUE_LOG["warned_no_memory"] = False
|
||
_RESCUE_LOG["warned_no_prompt_match"] = False
|
||
_RESCUE_LOG["fired"] = 0
|
||
|
||
if bool(rescue_mode):
|
||
# One-shot startup diagnostic so it is unambiguous why rescue does or does
|
||
# not fire: confirms the option reached the sampler, whether this model
|
||
# bypasses the custom loop (CONST), and which steps are eligible.
|
||
try:
|
||
_is_const = isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST)
|
||
except Exception:
|
||
_is_const = "unknown"
|
||
try:
|
||
_ts = max(0, len(sigmas) - 1)
|
||
_elig = [(i, round(float(sigmas[i].item()), 4)) for i in range(_ts)
|
||
if _velocity_bias_target(sigmas, sigmas[i]) is not None]
|
||
except Exception:
|
||
_elig = []
|
||
print(f"[FunPack rescue] requested — strength={float(rescue_strength)}, "
|
||
f"threshold={float(rescue_threshold)}, "
|
||
f"prompt_sig={'yes' if isinstance(rescue_prompt_sig, torch.Tensor) else 'no'}, "
|
||
f"model_is_CONST={_is_const}, eligible_steps={_elig}")
|
||
if _is_const is True:
|
||
print("[FunPack rescue] CONST model -> using rectified-flow rescue path (velocity/rescue active).")
|
||
if not _elig:
|
||
print("[FunPack rescue] WARNING: no sigma step matches a velocity target "
|
||
"(normalized ~0.90/0.80 +/-0.065) -> rescue/capture cannot fire on this schedule.")
|
||
|
||
if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
|
||
# CONST (rectified-flow, e.g. LTXAV): stock comfy routes to euler_ancestral_RF,
|
||
# bypassing the entire custom loop. Run the RF-correct full-feature port so the
|
||
# hybrid sampler's behavior (motion pulses, order-2, quality correction, eta
|
||
# decay, velocity bias, rescue) actually applies on LTXAV.
|
||
return _sample_const_rf_full(
|
||
model, x, sigmas, extra_args, callback, disable, eta, s_noise, eta_final,
|
||
high_quality_pct, correction_blend,
|
||
motion_pulse_mode, motion_pulse_start_pct, motion_pulse_count,
|
||
motion_pulse_spacing_pct, motion_pulse_strength,
|
||
motion_pulse_noise, motion_pulse_steps,
|
||
velocity_bias_mode, velocity_bias_strength,
|
||
velocity_refinement_key, velocity_aspect_bucket,
|
||
rescue_mode, rescue_threshold, rescue_strength, rescue_prompt_sig,
|
||
)
|
||
|
||
extra_args = {} if extra_args is None else extra_args
|
||
seed = extra_args.get("seed", None)
|
||
noise_sampler = k_diffusion_sampling.default_noise_sampler(x, seed=seed)
|
||
|
||
total_steps = max(0, len(sigmas) - 1)
|
||
if total_steps <= 0:
|
||
return x
|
||
|
||
high_quality_pct = max(0.0, min(1.0, float(high_quality_pct)))
|
||
correction_blend = max(0.0, min(1.0, float(correction_blend)))
|
||
eta_final = max(0.0, min(float(eta), float(eta_final)))
|
||
|
||
if not motion_pulse_steps:
|
||
_, _, motion_pulse_steps, computed_motion_pulse_noise = _prepare_dynamic_sigmas(
|
||
sigmas,
|
||
high_quality_pct,
|
||
motion_pulse_mode,
|
||
motion_pulse_start_pct,
|
||
motion_pulse_count,
|
||
motion_pulse_spacing_pct,
|
||
motion_pulse_strength,
|
||
)
|
||
motion_pulse_noise = computed_motion_pulse_noise
|
||
motion_pulse_noise = max(0.0, float(motion_pulse_noise))
|
||
motion_step_noise = {
|
||
int(item.get("step_index", -1)): max(0.0, float(item.get("noise", motion_pulse_noise)))
|
||
for item in (motion_pulse_steps or [])
|
||
if isinstance(item, dict)
|
||
}
|
||
|
||
s_in = x.new_ones([x.shape[0]])
|
||
callback_step = 0
|
||
|
||
# Resolve quality phase boundary
|
||
late_start = _get_late_start_index(total_steps, high_quality_pct)
|
||
if quality_sigma_start is None:
|
||
if late_start < sigmas.shape[0]:
|
||
quality_sigma_start = float(sigmas[late_start].item())
|
||
else:
|
||
quality_sigma_start = float(quality_sigma_start)
|
||
|
||
num_quality_steps = total_steps - late_start
|
||
|
||
# Order-2 ancestral state
|
||
prev_denoised = None
|
||
prev_h = None
|
||
quality_step_index = 0
|
||
|
||
for i in comfy.utils.model_trange(total_steps, disable=disable):
|
||
sigma = sigmas[i]
|
||
sigma_next = sigmas[i + 1]
|
||
in_quality_phase = quality_sigma_start is not None and float(sigma.item()) <= quality_sigma_start
|
||
# Velocity-bias capture and rescue must run wherever a velocity target lands,
|
||
# regardless of phase. With short distilled schedules the only target step
|
||
# (norm sigma ~0.9) often falls inside the quality phase, so gating these to
|
||
# the early branch alone would silently disable them.
|
||
velocity_target = _velocity_bias_target(sigmas, sigma)
|
||
|
||
if not in_quality_phase:
|
||
# Adaptive eta: decay from eta toward eta_final as sigma
|
||
# approaches the quality boundary.
|
||
if quality_sigma_start is not None and quality_sigma_start > 0.0 and eta_final < eta:
|
||
sigma_val = float(sigma.item())
|
||
proximity = min(1.0, max(0.0, quality_sigma_start / max(sigma_val, 1e-8)))
|
||
effective_eta = eta_final + (eta - eta_final) * (1.0 - proximity)
|
||
else:
|
||
effective_eta = eta
|
||
|
||
pulse_noise = motion_step_noise.get(int(i), 0.0)
|
||
if pulse_noise > 0.0:
|
||
x = _apply_motion_pulse(x, sigma, sigma_next, pulse_noise, noise_sampler)
|
||
# Pulse modifies x; previous denoised is no longer a valid
|
||
# second-order estimate for the next step.
|
||
prev_denoised = None
|
||
prev_h = None
|
||
|
||
if _velocity_bias_enabled(velocity_bias_mode, "apply"):
|
||
x = _apply_velocity_bias(x, velocity_refinement_key, velocity_aspect_bucket, velocity_target, velocity_bias_strength)
|
||
denoised = model(x, sigma * s_in, **extra_args)
|
||
if _velocity_bias_enabled(velocity_bias_mode, "capture"):
|
||
_capture_velocity_bias(velocity_refinement_key, velocity_aspect_bucket, velocity_target, x, sigma, denoised, prompt_sig=rescue_prompt_sig)
|
||
if rescue_mode and velocity_target is not None:
|
||
denoised = _rescue_denoised(
|
||
denoised, x, sigma, velocity_refinement_key, velocity_aspect_bucket,
|
||
velocity_target, rescue_threshold, rescue_strength, prompt_sig=rescue_prompt_sig,
|
||
)
|
||
|
||
if callback is not None:
|
||
callback({
|
||
"x": x,
|
||
"i": callback_step,
|
||
"sigma": sigma,
|
||
"sigma_hat": sigma,
|
||
"denoised": denoised,
|
||
})
|
||
callback_step += 1
|
||
|
||
h = float((sigma - sigma_next).abs().item())
|
||
denoised_eff = _order2_ancestral_denoised(denoised, prev_denoised, h, prev_h)
|
||
|
||
sigma_down, sigma_up = k_diffusion_sampling.get_ancestral_step(sigma, sigma_next, eta=effective_eta)
|
||
if sigma_down == 0:
|
||
x = denoised_eff
|
||
else:
|
||
d = k_diffusion_sampling.to_d(x, sigma, denoised_eff)
|
||
dt = sigma_down - sigma
|
||
x = x + d * dt
|
||
if sigma_next > 0 and effective_eta > 0 and s_noise > 0:
|
||
x = x + noise_sampler(sigma, sigma_next) * s_noise * sigma_up
|
||
|
||
prev_denoised = denoised.detach()
|
||
prev_h = h
|
||
|
||
else:
|
||
# Quality phase: progressive correction_blend.
|
||
# First half of quality steps use blend=0 (single-eval Euler ODE),
|
||
# second half use the configured blend (full 2S correction).
|
||
# 2S matters most at the lowest sigmas, so this concentrates the
|
||
# expensive second model call where it has the most impact.
|
||
if num_quality_steps <= 1:
|
||
effective_blend = correction_blend
|
||
else:
|
||
mid_quality = num_quality_steps // 2
|
||
effective_blend = 0.0 if quality_step_index < mid_quality else correction_blend
|
||
|
||
if _velocity_bias_enabled(velocity_bias_mode, "apply"):
|
||
x = _apply_velocity_bias(x, velocity_refinement_key, velocity_aspect_bucket, velocity_target, velocity_bias_strength)
|
||
denoised = model(x, sigma * s_in, **extra_args)
|
||
if _velocity_bias_enabled(velocity_bias_mode, "capture"):
|
||
_capture_velocity_bias(velocity_refinement_key, velocity_aspect_bucket, velocity_target, x, sigma, denoised, prompt_sig=rescue_prompt_sig)
|
||
if rescue_mode and velocity_target is not None:
|
||
denoised = _rescue_denoised(
|
||
denoised, x, sigma, velocity_refinement_key, velocity_aspect_bucket,
|
||
velocity_target, rescue_threshold, rescue_strength, prompt_sig=rescue_prompt_sig,
|
||
)
|
||
|
||
if callback is not None:
|
||
callback({
|
||
"x": x,
|
||
"i": callback_step,
|
||
"sigma": sigma,
|
||
"sigma_hat": sigma,
|
||
"denoised": denoised,
|
||
})
|
||
callback_step += 1
|
||
|
||
x, _ = _hybrid_ode_step(
|
||
model,
|
||
x,
|
||
sigma,
|
||
sigma_next,
|
||
s_in,
|
||
extra_args,
|
||
effective_blend,
|
||
denoised=denoised,
|
||
)
|
||
quality_step_index += 1
|
||
|
||
return x
|
||
|
||
|
||
class FunPackHybridEuler2SSampler:
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"eta": ("FLOAT", {
|
||
"default": 1.0,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.01,
|
||
"tooltip": "Ancestral stochasticity at the start of sampling. Keep at 1.0 for classic ancestral behaviour."
|
||
}),
|
||
"eta_final": ("FLOAT", {
|
||
"default": 1.0,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.01,
|
||
"tooltip": "Eta value at the quality phase boundary. When below eta, ancestral noise decays linearly toward this value as sigma approaches the quality phase. Lower values give a cleaner hand-off into deterministic refinement. Set equal to eta to disable decay."
|
||
}),
|
||
"s_noise": ("FLOAT", {
|
||
"default": 1.0,
|
||
"min": 0.0,
|
||
"max": 10.0,
|
||
"step": 0.01,
|
||
"tooltip": "Noise scale for ancestral noise injection."
|
||
}),
|
||
"high_quality_pct": ("FLOAT", {
|
||
"default": 0.35,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.01,
|
||
"tooltip": "Fraction of late denoise steps that enter the quality phase. The first half of quality steps use single-eval Euler ODE; the second half use the full 2S correction."
|
||
}),
|
||
"correction_blend": ("FLOAT", {
|
||
"default": 1.0,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.01,
|
||
"tooltip": "Blend between Euler ODE (0.0) and 2S correction (1.0) for the second half of quality-phase steps."
|
||
}),
|
||
"motion_pulse_mode": (MOTION_PULSE_MODES, {
|
||
"default": "off",
|
||
"tooltip": "Adds early/mid anti-stiffness motion pulses. Off preserves legacy sampler behavior."
|
||
}),
|
||
"motion_pulse_start_pct": ("FLOAT", {
|
||
"default": 0.30,
|
||
"min": 0.0,
|
||
"max": 0.95,
|
||
"step": 0.01,
|
||
"tooltip": "Sampling progress where the first anti-stiffness pulse is applied."
|
||
}),
|
||
"motion_pulse_count": ("INT", {
|
||
"default": 2,
|
||
"min": 1,
|
||
"max": 6,
|
||
"step": 1,
|
||
"tooltip": "How many early/mid motion pulses to request before the late quality phase."
|
||
}),
|
||
"motion_pulse_spacing_pct": ("FLOAT", {
|
||
"default": 0.22,
|
||
"min": 0.04,
|
||
"max": 0.45,
|
||
"step": 0.01,
|
||
"tooltip": "Progress spacing between motion pulses."
|
||
}),
|
||
"motion_pulse_strength": ("FLOAT", {
|
||
"default": 0.85,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.01,
|
||
"tooltip": "How strongly motion pulses add monotonic noise kicks. Higher values push harder against stale image references."
|
||
}),
|
||
"velocity_bias_mode": (VELOCITY_BIAS_MODES, {
|
||
"default": "off",
|
||
"tooltip": "Experimental: capture/apply averaged early model velocity around normalized sigma 0.9 and 0.8. Off preserves legacy sampler behavior."
|
||
}),
|
||
"velocity_bias_strength": ("FLOAT", {
|
||
"default": 0.0,
|
||
"min": 0.0,
|
||
"max": 0.35,
|
||
"step": 0.01,
|
||
"tooltip": "Experimental strength for applying captured early velocity bias. Keep low; 0 disables the applied delta."
|
||
}),
|
||
"velocity_refinement_key": ("STRING", {
|
||
"default": "default",
|
||
"multiline": False,
|
||
"tooltip": "Memory key used to capture/apply early velocity bias."
|
||
}),
|
||
"velocity_aspect_bucket": ("STRING", {
|
||
"default": "any",
|
||
"multiline": False,
|
||
"tooltip": "Optional aspect bucket such as landscape, portrait, square, ultrawide, or vertical."
|
||
}),
|
||
"rescue_mode": ("BOOLEAN", {
|
||
"default": False,
|
||
"tooltip": "Reactive in-flight rescue. When a step's trajectory diverges from the learned good-trajectory memory (the same memory velocity_bias captures), steer the prediction back toward it on that step only. Requires that memory to exist first: run with velocity_bias_mode=capture under the same velocity_refinement_key/aspect to build it. Read-only on the memory; a no-op until it exists."
|
||
}),
|
||
"rescue_threshold": ("FLOAT", {
|
||
"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.01,
|
||
"tooltip": "Divergence (1 - cosine to the good direction) above which rescue fires for that step. Lower = corrects more eagerly. 0.10-0.20 typical; raise toward 0.4+ to only catch severe drift."
|
||
}),
|
||
"rescue_strength": ("FLOAT", {
|
||
"default": 0.2, "min": 0.0, "max": 0.5, "step": 0.01,
|
||
"tooltip": "How far to rotate the diverging trajectory back toward the good direction when triggered (magnitude preserved, no energy injected). Keep moderate; 0.5 fully snaps to the good direction."
|
||
}),
|
||
},
|
||
"optional": {
|
||
"sigmas": ("SIGMAS",),
|
||
}
|
||
}
|
||
|
||
RETURN_TYPES = ("SAMPLER", "SIGMAS")
|
||
RETURN_NAMES = ("sampler", "sigmas")
|
||
FUNCTION = "get_sampler"
|
||
CATEGORY = "FunPack/Sampling"
|
||
DESCRIPTION = (
|
||
"Hybrid sampler: early Euler ancestral with order-2 denoised extrapolation for motion, "
|
||
"late DPM-Solver++(2S) ODE for quality with progressive correction blending. "
|
||
"Optional eta decay, anti-stiffness motion pulses, velocity bias, and reactive rescue. "
|
||
"On rectified-flow models (CONST, e.g. LTXAV) it runs an RF-correct port of the same "
|
||
"features (Heun corrector in place of 2S) instead of falling back to plain euler-ancestral."
|
||
)
|
||
|
||
def get_sampler(self, eta, s_noise, high_quality_pct, correction_blend,
|
||
motion_pulse_mode="off", motion_pulse_start_pct=0.30,
|
||
motion_pulse_count=2, motion_pulse_spacing_pct=0.22,
|
||
motion_pulse_strength=0.85, velocity_bias_mode="off",
|
||
velocity_bias_strength=0.0, velocity_refinement_key="default",
|
||
velocity_aspect_bucket="any", rescue_mode=False,
|
||
rescue_threshold=0.15, rescue_strength=0.2, rescue_prompt_sig=None,
|
||
sigmas=None, eta_final=1.0):
|
||
prepared_sigmas, quality_sigma_start, motion_pulse_steps, motion_pulse_noise = _prepare_dynamic_sigmas(
|
||
sigmas,
|
||
high_quality_pct,
|
||
motion_pulse_mode,
|
||
motion_pulse_start_pct,
|
||
motion_pulse_count,
|
||
motion_pulse_spacing_pct,
|
||
motion_pulse_strength,
|
||
)
|
||
sampler = comfy.samplers.KSAMPLER(
|
||
sample_funpack_hybrid_euler_2s,
|
||
extra_options={
|
||
"eta": eta,
|
||
"s_noise": s_noise,
|
||
"high_quality_pct": high_quality_pct,
|
||
"correction_blend": correction_blend,
|
||
"quality_sigma_start": quality_sigma_start,
|
||
"motion_pulse_mode": motion_pulse_mode,
|
||
"motion_pulse_start_pct": motion_pulse_start_pct,
|
||
"motion_pulse_count": motion_pulse_count,
|
||
"motion_pulse_spacing_pct": motion_pulse_spacing_pct,
|
||
"motion_pulse_strength": motion_pulse_strength,
|
||
"motion_pulse_noise": motion_pulse_noise,
|
||
"motion_pulse_steps": motion_pulse_steps,
|
||
"velocity_bias_mode": velocity_bias_mode,
|
||
"velocity_bias_strength": velocity_bias_strength,
|
||
"velocity_refinement_key": velocity_refinement_key,
|
||
"velocity_aspect_bucket": velocity_aspect_bucket,
|
||
"rescue_mode": rescue_mode,
|
||
"rescue_threshold": rescue_threshold,
|
||
"rescue_strength": rescue_strength,
|
||
"rescue_prompt_sig": rescue_prompt_sig,
|
||
"eta_final": eta_final,
|
||
}
|
||
)
|
||
return (sampler, prepared_sigmas)
|
||
|
||
|
||
def sample_funpack_distilled_flow(model, x, sigmas, extra_args=None, callback=None,
|
||
disable=None, order=2, s_noise=0.0,
|
||
final_correction_steps=1):
|
||
"""
|
||
ODE sampler for distilled few-step video models (e.g. LTX2.3 distilled LoRA).
|
||
|
||
- Adams-Bashforth 2-step multistep (order=2): extrapolates the denoised
|
||
direction from two consecutive steps for second-order accuracy at zero
|
||
extra model-call cost. Reduces discretisation error across the large
|
||
sigma jumps typical of 4–8 step distilled schedules.
|
||
- Heun predictor-corrector on final steps: calls the model a second time
|
||
at sigma_next to correct the update direction. Significantly improves
|
||
sharpness and detail in the steps that define the final output.
|
||
- Optional s_noise: tiny ancestral-style noise injection for diversity.
|
||
Default 0 = fully deterministic ODE (recommended for distilled models).
|
||
"""
|
||
extra_args = {} if extra_args is None else extra_args
|
||
seed = extra_args.get("seed", None)
|
||
noise_sampler = k_diffusion_sampling.default_noise_sampler(x, seed=seed)
|
||
|
||
total_steps = max(0, len(sigmas) - 1)
|
||
if total_steps <= 0:
|
||
return x
|
||
|
||
order = max(1, min(2, int(order)))
|
||
s_noise = max(0.0, min(0.5, float(s_noise)))
|
||
final_correction_steps = max(0, min(total_steps // 2, int(final_correction_steps)))
|
||
correction_start_idx = total_steps - final_correction_steps
|
||
|
||
s_in = x.new_ones([x.shape[0]])
|
||
prev_denoised = None
|
||
prev_h = None
|
||
|
||
for i in comfy.utils.model_trange(total_steps, disable=disable):
|
||
sigma = sigmas[i]
|
||
sigma_next = sigmas[i + 1]
|
||
|
||
denoised = model(x, sigma * s_in, **extra_args)
|
||
|
||
if callback is not None:
|
||
callback({"x": x, "i": i, "sigma": sigma, "sigma_hat": sigma, "denoised": denoised})
|
||
|
||
h = float((sigma - sigma_next).abs().item())
|
||
|
||
# Adams-Bashforth 2-step multistep correction.
|
||
# Coefficients for variable step sizes: r = h_current / h_previous.
|
||
# denoised_eff = (1 + r/2) * denoised - (r/2) * prev_denoised
|
||
if order >= 2 and prev_denoised is not None and prev_h is not None and prev_h > 1e-7 and h > 1e-7:
|
||
r = max(0.1, min(5.0, h / prev_h))
|
||
try:
|
||
denoised_eff = (1.0 + r / 2.0) * denoised - (r / 2.0) * prev_denoised.to(device=denoised.device, dtype=denoised.dtype)
|
||
except Exception:
|
||
denoised_eff = denoised
|
||
else:
|
||
denoised_eff = denoised
|
||
|
||
# Store current denoised for the next step's multistep correction.
|
||
# Reset after a Heun step since x was updated with a corrected direction.
|
||
prev_denoised = denoised.detach()
|
||
prev_h = h
|
||
|
||
if sigma_next == 0:
|
||
x = denoised_eff
|
||
continue
|
||
|
||
dt = sigma_next - sigma # negative: sigmas decrease
|
||
|
||
if i >= correction_start_idx:
|
||
# Heun predictor-corrector.
|
||
# Predictor: Euler step using the (multistep-corrected) denoised.
|
||
d1 = k_diffusion_sampling.to_d(x, sigma, denoised_eff)
|
||
x_pred = x + d1 * dt
|
||
# Corrector: evaluate model at the predicted x and sigma_next.
|
||
denoised_pred = model(x_pred, sigma_next * s_in, **extra_args)
|
||
d2 = k_diffusion_sampling.to_d(x_pred, sigma_next, denoised_pred)
|
||
x = x + (d1 + d2) / 2.0 * dt
|
||
# Heun updates x differently; invalidate multistep history.
|
||
prev_denoised = None
|
||
prev_h = None
|
||
else:
|
||
d = k_diffusion_sampling.to_d(x, sigma, denoised_eff)
|
||
x = x + d * dt
|
||
if s_noise > 0.0:
|
||
sigma_up = math.sqrt(max(0.0, float(sigma.item()) ** 2 - float(sigma_next.item()) ** 2))
|
||
if sigma_up > 0.0:
|
||
x = x + noise_sampler(sigma, sigma_next) * s_noise * sigma_up
|
||
|
||
return x
|
||
|
||
|
||
class FunPackDistilledFlowSampler:
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"order": ("INT", {
|
||
"default": 2,
|
||
"min": 1,
|
||
"max": 2,
|
||
"step": 1,
|
||
"tooltip": "Multistep order. 1 = standard Euler ODE. 2 = Adams-Bashforth 2-step: extrapolates the denoised direction from two consecutive steps for better accuracy at no extra model-call cost.",
|
||
}),
|
||
"final_correction_steps": ("INT", {
|
||
"default": 1,
|
||
"min": 0,
|
||
"max": 3,
|
||
"step": 1,
|
||
"tooltip": "Number of final steps that use a Heun predictor-corrector pass. Each costs one extra model call but significantly improves final-step detail. 1 is usually enough for 8-step runs.",
|
||
}),
|
||
"s_noise": ("FLOAT", {
|
||
"default": 0.0,
|
||
"min": 0.0,
|
||
"max": 0.50,
|
||
"step": 0.01,
|
||
"tooltip": "Optional stochastic noise for diversity. 0 = fully deterministic ODE (recommended). Small values (0.05–0.15) add variation without strongly disrupting the distilled trajectory.",
|
||
}),
|
||
},
|
||
"optional": {
|
||
"sigmas": ("SIGMAS",),
|
||
}
|
||
}
|
||
|
||
RETURN_TYPES = ("SAMPLER", "SIGMAS")
|
||
RETURN_NAMES = ("sampler", "sigmas")
|
||
FUNCTION = "get_sampler"
|
||
CATEGORY = "FunPack/Sampling"
|
||
DESCRIPTION = (
|
||
"ODE sampler for distilled few-step video models (e.g. LTX2.3 distilled LoRA). "
|
||
"Adams-Bashforth 2-step multistep for better trajectory accuracy across large sigma jumps, "
|
||
"Heun predictor-corrector on final steps for quality, and optional controlled noise for diversity."
|
||
)
|
||
|
||
def get_sampler(self, order=2, final_correction_steps=1, s_noise=0.0, sigmas=None):
|
||
prepared_sigmas = sigmas.detach().clone() if isinstance(sigmas, torch.Tensor) else sigmas
|
||
sampler = comfy.samplers.KSAMPLER(
|
||
sample_funpack_distilled_flow,
|
||
extra_options={
|
||
"order": order,
|
||
"final_correction_steps": final_correction_steps,
|
||
"s_noise": s_noise,
|
||
}
|
||
)
|
||
return (sampler, prepared_sigmas)
|
||
|
||
|
||
class FunPackLTXAVSceneChainSampler:
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"model": ("MODEL",),
|
||
"vae": ("VAE",),
|
||
"positive": ("CONDITIONING",),
|
||
"negative": ("CONDITIONING",),
|
||
"sampler": ("SAMPLER",),
|
||
"sigmas": ("SIGMAS",),
|
||
"seed": ("INT", {"default": 1, "min": 0, "max": 0xffffffffffffffff}),
|
||
"latent_template": ("LATENT",),
|
||
"num_frames_per_scene": ("INT", {"default": 97, "min": 1, "max": 4096, "step": 8}),
|
||
"frame_overlap": ("INT", {
|
||
"default": 16, "min": 0, "max": 512, "step": 8,
|
||
"tooltip": "Pixel frames copied from the previous scene into the next chunk and preserved during denoising. 0 disables overlap blending entirely. WARNING: combining frame_overlap=0 with carry_i2v_guides=True is confirmed to produce bad results — use only for testing.",
|
||
}),
|
||
"cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1}),
|
||
"max_scenes": ("INT", {"default": 8, "min": 1, "step": 1}),
|
||
"use_same_seed": ("BOOLEAN", {
|
||
"default": False,
|
||
"tooltip": "Use the first provided scene seed for every scene. Off uses per-scene metadata seeds or seed + scene index.",
|
||
}),
|
||
"carry_i2v_guides": ("BOOLEAN", {
|
||
"default": False,
|
||
"tooltip": "Carry protected frames from latent_template noise_mask into each continuation chunk as a style guide.",
|
||
}),
|
||
"mid_scene_guide": ("BOOLEAN", {
|
||
"default": False,
|
||
"tooltip": "Experimental: append the middle frame of the previous scene as a guide for the current scene via LTX guide attention. Helps maintain character positioning across scenes.",
|
||
}),
|
||
"mid_scene_guide_strength": ("FLOAT", {
|
||
"default": 0.25, "min": 0.25, "max": 0.5, "step": 0.05,
|
||
"tooltip": "Guide attention strength for mid-scene anchor. 0.25 is the minimum — below that audio degrades and character appearance drifts. Above 0.35 causes spatial conflicts when scene composition shifts.",
|
||
}),
|
||
"embed_guidance": ("BOOLEAN", {
|
||
"default": False,
|
||
"tooltip": "Apply the Refiner's learned quality direction at each denoising step, not just once before sampling. Requires refinement_key_input and enough liked generations to have a direction. Adds ~20-30% inference overhead.",
|
||
}),
|
||
"embed_guidance_strength": ("FLOAT", {
|
||
"default": 0.02, "min": 0.005, "max": 0.1, "step": 0.005,
|
||
"tooltip": "Per-step nudge strength toward the liked conditioning direction. Keep small — the direction is applied at every step so it compounds. 0.01-0.03 is typical.",
|
||
}),
|
||
"transition_duration": ("INT", {
|
||
"default": 16, "min": 0, "max": 128, "step": 2,
|
||
"tooltip": "Extra pixel frames of fade beyond the blend zone on each side of a scene boundary. 0 = disable all transition effects.",
|
||
}),
|
||
},
|
||
"optional": {
|
||
"decode_tile_size": ("INT", {
|
||
"default": 0, "min": 0, "max": 4096, "step": 64,
|
||
"tooltip": "Tile size for VAE decode (0 = no tiling). Set to e.g. 512 if decode OOMs.",
|
||
}),
|
||
"refinement_key_input": ("STRING", {
|
||
"default": "",
|
||
"multiline": False,
|
||
"forceInput": True,
|
||
"tooltip": "Connect to the same refinement key as your V2 Refiner. When wired, the sampler writes carry_i2v_guides, frame_overlap, and scene count into the refinement state so the Refiner can reason about what changed between rated runs.",
|
||
}),
|
||
},
|
||
"hidden": {
|
||
"unique_id": "UNIQUE_ID",
|
||
"prompt": "PROMPT",
|
||
},
|
||
}
|
||
|
||
RETURN_TYPES = ("LATENT", "IMAGE", "STRING", "INT", "STRING", "STRING")
|
||
RETURN_NAMES = ("latent", "images", "status", "scene_count", "scene_report", "scene_boundaries")
|
||
FUNCTION = "sample"
|
||
CATEGORY = "FunPack/Sampling"
|
||
DESCRIPTION = (
|
||
"Samples multi-entry scene conditioning as a smooth LTXV/LTXAV continuation chain. "
|
||
"Use with FunPack Studio split-by-transitions output."
|
||
)
|
||
|
||
def _is_nested(self, samples):
|
||
return bool(getattr(samples, "is_nested", False))
|
||
|
||
def _clone_value(self, value):
|
||
if isinstance(value, torch.Tensor):
|
||
return value.detach().clone()
|
||
if self._is_nested(value):
|
||
return comfy.nested_tensor.NestedTensor([t.detach().clone() for t in value.unbind()])
|
||
return value
|
||
|
||
def _clone_latent(self, latent):
|
||
if not isinstance(latent, dict) or "samples" not in latent:
|
||
raise ValueError("latent_template must be a LATENT dict with samples.")
|
||
return {key: self._clone_value(value) for key, value in latent.items()}
|
||
|
||
def _tensor_frames(self, tensor):
|
||
if not isinstance(tensor, torch.Tensor) or tensor.dim() < 3:
|
||
raise ValueError("Scene chain latents must have a time dimension at index 2.")
|
||
return int(tensor.shape[2])
|
||
|
||
def _latent_tensors(self, latent):
|
||
samples = latent.get("samples")
|
||
if self._is_nested(samples):
|
||
tensors = list(samples.unbind())
|
||
if not tensors:
|
||
raise ValueError("Nested latent has no tensors.")
|
||
return tensors
|
||
if isinstance(samples, torch.Tensor):
|
||
return [samples]
|
||
raise ValueError("Scene chain sampler requires tensor or nested tensor latent samples.")
|
||
|
||
def _latent_masks(self, latent, count):
|
||
masks = latent.get("noise_mask")
|
||
if masks is None:
|
||
return [None] * count
|
||
if self._is_nested(masks):
|
||
out = list(masks.unbind())
|
||
else:
|
||
out = [masks]
|
||
while len(out) < count:
|
||
out.append(None)
|
||
return out[:count]
|
||
|
||
def _time_scale(self, vae):
|
||
scale = getattr(vae, "downscale_index_formula", None)
|
||
if isinstance(scale, (list, tuple)) and scale:
|
||
try:
|
||
return max(1, int(scale[0]))
|
||
except Exception:
|
||
return 1
|
||
return 1
|
||
|
||
def _expected_latent_frames(self, pixel_frames, time_scale):
|
||
return ((max(1, int(pixel_frames)) - 1) // max(1, int(time_scale))) + 1
|
||
|
||
def _validate_template_length(self, latent_template, num_frames_per_scene, time_scale):
|
||
video_frames = self._tensor_frames(self._latent_tensors(latent_template)[0])
|
||
expected = self._expected_latent_frames(num_frames_per_scene, time_scale)
|
||
if video_frames != expected:
|
||
raise ValueError(
|
||
f"latent_template has {video_frames} video latent frames, expected {expected} "
|
||
f"from num_frames_per_scene={num_frames_per_scene} and time scale={time_scale}."
|
||
)
|
||
return video_frames
|
||
|
||
def _overlap_frames(self, latent_template, frame_overlap, time_scale):
|
||
video_frames = self._tensor_frames(self._latent_tensors(latent_template)[0])
|
||
overlap = self._expected_latent_frames(frame_overlap + 1, time_scale) - 1
|
||
if frame_overlap <= 0:
|
||
overlap = 0
|
||
if overlap >= video_frames:
|
||
raise ValueError("frame_overlap must be smaller than the latent scene length.")
|
||
return max(0, int(overlap))
|
||
|
||
def _derived_overlap(self, video_overlap, video_frames, tensor_frames):
|
||
if video_overlap <= 0:
|
||
return 0
|
||
ratio = tensor_frames / max(1, video_frames)
|
||
overlap = int(round(video_overlap * ratio))
|
||
return max(1, min(tensor_frames - 1, overlap))
|
||
|
||
def _replace_start(self, target, source_tail, overlap):
|
||
if overlap <= 0:
|
||
return target
|
||
target = target.clone()
|
||
source_tail = source_tail.to(device=target.device, dtype=target.dtype)
|
||
target[:, :, :overlap] = source_tail
|
||
return target
|
||
|
||
def _tail(self, tensor, overlap):
|
||
if overlap <= 0:
|
||
return tensor[:, :, :0]
|
||
return tensor[:, :, -overlap:]
|
||
|
||
def _time_slice(self, tensor, start, end):
|
||
slices = [slice(None)] * tensor.dim()
|
||
slices[2] = slice(start, end)
|
||
return tensor[tuple(slices)]
|
||
|
||
def _set_time_slice(self, tensor, start, end, value):
|
||
slices = [slice(None)] * tensor.dim()
|
||
slices[2] = slice(start, end)
|
||
tensor[tuple(slices)] = value
|
||
return tensor
|
||
|
||
def _expand_mask_like(self, mask, target):
|
||
if mask.shape[0] != target.shape[0] or mask.shape[2] != target.shape[2]:
|
||
raise ValueError("Guide mask batch/time dimensions must match target mask.")
|
||
shape = list(mask.shape)
|
||
while len(shape) < target.dim():
|
||
shape.append(1)
|
||
mask = mask.reshape(shape)
|
||
expand_shape = list(target.shape)
|
||
for dim in range(target.dim()):
|
||
if dim == 2:
|
||
continue
|
||
if mask.shape[dim] not in (1, target.shape[dim]):
|
||
raise ValueError("Guide mask dimensions are not broadcastable to target mask.")
|
||
expand_shape[dim] = target.shape[dim]
|
||
return mask.expand(expand_shape)
|
||
|
||
def _make_mask_tensor(self, tensor, overlap):
|
||
mask = torch.ones_like(tensor)
|
||
if overlap > 0:
|
||
mask[:, :, :overlap] = 0
|
||
return mask
|
||
|
||
def _protected_prefix_frames(self, template_mask, tensor_frames):
|
||
if template_mask is None or not isinstance(template_mask, torch.Tensor) or template_mask.dim() < 3:
|
||
return 0
|
||
dims = [dim for dim in range(template_mask.dim()) if dim != 2]
|
||
per_frame = template_mask.float().mean(dim=dims).flatten()
|
||
limit = min(int(tensor_frames), int(per_frame.numel()))
|
||
count = 0
|
||
for value in per_frame[:limit]:
|
||
if float(value) >= 0.999:
|
||
break
|
||
count += 1
|
||
return count
|
||
|
||
def _build_continuation_chunk(self, template, previous, video_overlap):
|
||
chunk = self._clone_latent(template)
|
||
chunk_tensors = self._latent_tensors(chunk)
|
||
previous_tensors = self._latent_tensors(previous)
|
||
if len(chunk_tensors) != len(previous_tensors):
|
||
raise ValueError("Previous output and latent_template must have the same latent structure.")
|
||
|
||
video_frames = self._tensor_frames(chunk_tensors[0])
|
||
out_tensors = []
|
||
mask_tensors = []
|
||
for index, tensor in enumerate(chunk_tensors):
|
||
tensor_frames = self._tensor_frames(tensor)
|
||
overlap = video_overlap if index == 0 else self._derived_overlap(video_overlap, video_frames, tensor_frames)
|
||
prev_tail = self._tail(previous_tensors[index], overlap)
|
||
out_tensor = self._replace_start(tensor, prev_tail, overlap)
|
||
mask_tensor = self._make_mask_tensor(tensor, overlap)
|
||
out_tensors.append(out_tensor)
|
||
mask_tensors.append(mask_tensor)
|
||
|
||
if self._is_nested(chunk.get("samples")):
|
||
chunk["samples"] = comfy.nested_tensor.NestedTensor(out_tensors)
|
||
chunk["noise_mask"] = comfy.nested_tensor.NestedTensor(mask_tensors)
|
||
else:
|
||
chunk["samples"] = out_tensors[0]
|
||
chunk["noise_mask"] = mask_tensors[0]
|
||
return chunk
|
||
|
||
def _condition_with_values(self, conditioning, values):
|
||
out = []
|
||
for cond, meta in conditioning:
|
||
new_meta = dict(meta) if isinstance(meta, dict) else {}
|
||
for key, value in values.items():
|
||
if value is None:
|
||
new_meta.pop(key, None)
|
||
else:
|
||
new_meta[key] = value
|
||
out.append((cond, new_meta))
|
||
return out
|
||
|
||
def _conditioning_value(self, conditioning, key):
|
||
for item in conditioning or []:
|
||
if isinstance(item, (list, tuple)) and len(item) >= 2 and isinstance(item[1], dict) and key in item[1]:
|
||
return item[1][key]
|
||
return None
|
||
|
||
|
||
def _prepend_soft_continuation(self, chunk, previous, mask_value=0.4, n_frames=4):
|
||
chunk_tensors = self._latent_tensors(chunk)
|
||
previous_tensors = self._latent_tensors(previous)
|
||
if not chunk_tensors or not previous_tensors:
|
||
return chunk, 0
|
||
prev_len = self._tensor_frames(previous_tensors[0])
|
||
count = min(n_frames, prev_len)
|
||
if count <= 0:
|
||
return chunk, 0
|
||
soft_frames = self._tail(previous_tensors[0], count).to(
|
||
device=chunk_tensors[0].device, dtype=chunk_tensors[0].dtype,
|
||
)
|
||
out_tensors = list(chunk_tensors)
|
||
out_masks = self._latent_masks(chunk, len(out_tensors))
|
||
if out_masks[0] is None:
|
||
out_masks[0] = torch.ones_like(out_tensors[0])
|
||
soft_mask = torch.full_like(soft_frames, mask_value)
|
||
out_tensors[0] = torch.cat([soft_frames, out_tensors[0]], dim=2)
|
||
out_masks[0] = torch.cat([soft_mask, out_masks[0].to(soft_mask.device, soft_mask.dtype)], dim=2)
|
||
if self._is_nested(chunk.get("samples")):
|
||
chunk["samples"] = comfy.nested_tensor.NestedTensor(out_tensors)
|
||
chunk["noise_mask"] = comfy.nested_tensor.NestedTensor(out_masks)
|
||
else:
|
||
chunk["samples"] = out_tensors[0]
|
||
chunk["noise_mask"] = out_masks[0]
|
||
return chunk, count
|
||
|
||
def _append_i2v_guides(self, chunk, template, positive, negative):
|
||
chunk_tensors = self._latent_tensors(chunk)
|
||
template_tensors = self._latent_tensors(template)
|
||
template_masks = self._latent_masks(template, len(template_tensors))
|
||
if not chunk_tensors or not template_tensors:
|
||
return chunk, positive, negative, 0
|
||
|
||
video_mask = template_masks[0]
|
||
protected = self._protected_prefix_frames(video_mask, self._tensor_frames(template_tensors[0]))
|
||
if protected <= 0:
|
||
return chunk, positive, negative, 0
|
||
|
||
guide = self._time_slice(template_tensors[0], 0, protected).to(
|
||
device=chunk_tensors[0].device, dtype=chunk_tensors[0].dtype,
|
||
)
|
||
guide_mask = self._time_slice(video_mask, 0, protected).to(
|
||
device=chunk_tensors[0].device, dtype=chunk_tensors[0].dtype,
|
||
)
|
||
|
||
out_tensors = list(chunk_tensors)
|
||
out_masks = self._latent_masks(chunk, len(out_tensors))
|
||
if out_masks[0] is None:
|
||
out_masks[0] = torch.ones_like(out_tensors[0])
|
||
target_mask = self._time_slice(out_masks[0], 0, protected).to(guide_mask.device, guide_mask.dtype)
|
||
guide_mask = self._expand_mask_like(guide_mask, target_mask)
|
||
# Prepend so guide is temporal pos 0, overlap frames follow at 1, 9, 17... — no conflict.
|
||
out_tensors[0] = torch.cat([guide, out_tensors[0]], dim=2)
|
||
out_masks[0] = torch.cat([guide_mask, out_masks[0].to(guide_mask.device, guide_mask.dtype)], dim=2)
|
||
|
||
if self._is_nested(chunk.get("samples")):
|
||
chunk["samples"] = comfy.nested_tensor.NestedTensor(out_tensors)
|
||
chunk["noise_mask"] = comfy.nested_tensor.NestedTensor(out_masks)
|
||
else:
|
||
chunk["samples"] = out_tensors[0]
|
||
chunk["noise_mask"] = out_masks[0]
|
||
|
||
return chunk, positive, negative, protected
|
||
|
||
def _crop_video_tail(self, latent, count):
|
||
if count <= 0:
|
||
return latent
|
||
result = self._clone_latent(latent)
|
||
tensors = self._latent_tensors(result)
|
||
tensors[0] = tensors[0][:, :, :-count]
|
||
if self._is_nested(result.get("samples")):
|
||
result["samples"] = comfy.nested_tensor.NestedTensor(tensors)
|
||
else:
|
||
result["samples"] = tensors[0]
|
||
return result
|
||
|
||
def _crop_video_head(self, latent, count):
|
||
if count <= 0:
|
||
return latent
|
||
result = self._clone_latent(latent)
|
||
tensors = self._latent_tensors(result)
|
||
tensors[0] = tensors[0][:, :, count:]
|
||
if self._is_nested(result.get("samples")):
|
||
result["samples"] = comfy.nested_tensor.NestedTensor(tensors)
|
||
else:
|
||
result["samples"] = tensors[0]
|
||
return result
|
||
|
||
def _blend_tensors(self, left, right, overlap):
|
||
if overlap <= 0:
|
||
return torch.cat([left, right], dim=2)
|
||
if left.shape[:2] != right.shape[:2] or left.shape[3:] != right.shape[3:]:
|
||
raise ValueError("Cannot blend scene latents with different non-time dimensions.")
|
||
alpha = torch.linspace(1.0, 0.0, overlap + 2, device=left.device, dtype=left.dtype)[1:-1]
|
||
shape = [1] * left.dim()
|
||
shape[2] = overlap
|
||
alpha = alpha.reshape(shape)
|
||
blended = alpha * left[:, :, -overlap:] + (1.0 - alpha) * right[:, :, :overlap].to(left.device, left.dtype)
|
||
return torch.cat([left[:, :, :-overlap], blended, right[:, :, overlap:].to(left.device, left.dtype)], dim=2)
|
||
|
||
def _blend_latents(self, previous, current, video_overlap):
|
||
result = self._clone_latent(previous)
|
||
previous_tensors = self._latent_tensors(previous)
|
||
current_tensors = self._latent_tensors(current)
|
||
if len(previous_tensors) != len(current_tensors):
|
||
raise ValueError("Cannot blend different latent structures.")
|
||
|
||
video_frames = self._tensor_frames(current_tensors[0])
|
||
blended_tensors = []
|
||
for index, tensor in enumerate(current_tensors):
|
||
tensor_frames = self._tensor_frames(tensor)
|
||
overlap = video_overlap if index == 0 else self._derived_overlap(video_overlap, video_frames, tensor_frames)
|
||
blended_tensors.append(self._blend_tensors(previous_tensors[index], tensor, overlap))
|
||
|
||
if self._is_nested(previous.get("samples")):
|
||
result["samples"] = comfy.nested_tensor.NestedTensor(blended_tensors)
|
||
else:
|
||
result["samples"] = blended_tensors[0]
|
||
result.pop("noise_mask", None)
|
||
return result
|
||
|
||
def _sample_chunk(self, model, sampler, sigmas, seed, cfg, positive, negative, latent):
|
||
if sampler is None:
|
||
raise ValueError("sampler input is required.")
|
||
if not isinstance(sigmas, torch.Tensor):
|
||
raise ValueError("sigmas input must be a SIGMAS tensor.")
|
||
latent = self._clone_latent(latent)
|
||
samples = latent["samples"]
|
||
noise = comfy.sample.prepare_noise(samples, int(seed))
|
||
sampled = comfy.sample.sample_custom(
|
||
model, noise, float(cfg), sampler, sigmas, positive, negative, samples,
|
||
noise_mask=latent.get("noise_mask"), seed=int(seed),
|
||
)
|
||
latent["samples"] = sampled
|
||
latent.pop("noise_mask", None)
|
||
return latent
|
||
|
||
def _output_connected(self, prompt, unique_id, output_index):
|
||
"""Return True if the given output slot index is wired to any downstream node."""
|
||
if not prompt or not unique_id:
|
||
return True # can't tell — assume connected
|
||
uid = str(unique_id)
|
||
for node_data in prompt.values():
|
||
for v in node_data.get("inputs", {}).values():
|
||
if isinstance(v, list) and len(v) == 2 and str(v[0]) == uid and v[1] == output_index:
|
||
return True
|
||
return False
|
||
|
||
def _blur_pixel_frame(self, frame, sigma):
|
||
"""Box-blur approximation on a single [H, W, C] pixel frame."""
|
||
if sigma <= 0:
|
||
return frame
|
||
k = max(3, int(sigma * 3) | 1)
|
||
pad = k // 2
|
||
x = frame.permute(2, 0, 1).unsqueeze(0).float()
|
||
x = torch.nn.functional.pad(x, (pad, pad, pad, pad), mode="reflect")
|
||
x = torch.nn.functional.avg_pool2d(x, k, stride=1, padding=0)
|
||
return x.squeeze(0).permute(1, 2, 0).to(dtype=frame.dtype)
|
||
|
||
def _apply_effect_on_pixels(self, frames, effect, center, half):
|
||
"""Apply one visual transition effect in-place on a [N, H, W, C] float tensor.
|
||
|
||
Simple V-ramp centered at the seam: full effect at center, linear ramp
|
||
back to no-effect at ±half frames. transition_duration controls half.
|
||
"""
|
||
n = frames.shape[0]
|
||
center = max(half, min(n - half - 1, center))
|
||
if effect == "fade_to_black":
|
||
for i in range(max(0, center - half), min(n, center + half)):
|
||
brightness = abs(i - center) / max(1, half)
|
||
frames[i] = frames[i] * brightness
|
||
elif effect == "crossfade":
|
||
orig = frames.clone()
|
||
for k in range(1, half + 1):
|
||
alpha = k / (half + 1)
|
||
pre_i, post_i = center - k, center + k - 1
|
||
if 0 <= pre_i < n and 0 <= post_i < n:
|
||
frames[pre_i] = (1 - alpha) * orig[pre_i] + alpha * orig[post_i]
|
||
frames[post_i] = (1 - alpha) * orig[post_i] + alpha * orig[pre_i]
|
||
elif effect == "blur_out_in":
|
||
for i in range(max(0, center - half), min(n, center + half)):
|
||
ramp = abs(i - center) / max(1, half)
|
||
sigma = 8.0 * (1.0 - ramp)
|
||
if sigma > 0:
|
||
frames[i] = self._blur_pixel_frame(frames[i], sigma)
|
||
|
||
def _apply_transitions_pixel(self, decoded, boundary_entries, transition_duration):
|
||
"""Apply visual transitions on a full decoded [N, H, W, C] frame tensor."""
|
||
active = [e for e in boundary_entries if e.get("effect") and e["effect"] != "none"]
|
||
if not active:
|
||
return decoded
|
||
frames = decoded.clone().float()
|
||
half = max(1, transition_duration // 2)
|
||
for entry in active:
|
||
self._apply_effect_on_pixels(frames, entry["effect"], int(entry["pixel_frame"]), half)
|
||
return frames.clamp(0.0, 1.0).to(dtype=decoded.dtype)
|
||
|
||
def _scene_text(self, scene_conditioning, index):
|
||
if (
|
||
isinstance(scene_conditioning, (list, tuple))
|
||
and len(scene_conditioning) >= 2
|
||
and isinstance(scene_conditioning[1], dict)
|
||
):
|
||
text = str(scene_conditioning[1].get("funpack_scene_text", "") or "").strip()
|
||
if text:
|
||
return text
|
||
return f"Scene {index + 1}"
|
||
|
||
def _scene_seed(self, scene_conditioning):
|
||
if (
|
||
isinstance(scene_conditioning, (list, tuple))
|
||
and len(scene_conditioning) >= 2
|
||
and isinstance(scene_conditioning[1], dict)
|
||
):
|
||
try:
|
||
return int(scene_conditioning[1].get("funpack_scene_seed"))
|
||
except (TypeError, ValueError):
|
||
return None
|
||
return None
|
||
|
||
def _scene_transition_effect(self, scene_conditioning):
|
||
if (
|
||
isinstance(scene_conditioning, (list, tuple))
|
||
and len(scene_conditioning) >= 2
|
||
and isinstance(scene_conditioning[1], dict)
|
||
):
|
||
effect = str(scene_conditioning[1].get("funpack_transition_effect") or "none")
|
||
return None if effect == "none" else effect
|
||
return None
|
||
|
||
def _decode_last_frame(self, latent, vae):
|
||
try:
|
||
tensors = self._latent_tensors(latent)
|
||
if not tensors:
|
||
return None
|
||
n = self._tensor_frames(tensors[0])
|
||
if n < 1:
|
||
return None
|
||
last = self._time_slice(tensors[0], n - 1, None)
|
||
decoded = vae.decode(last)
|
||
if decoded is None:
|
||
return None
|
||
if decoded.dim() == 5:
|
||
decoded = decoded[:, 0]
|
||
elif decoded.dim() == 3:
|
||
decoded = decoded.unsqueeze(0)
|
||
return decoded.clamp(0.0, 1.0)
|
||
except Exception:
|
||
return None
|
||
|
||
def _load_value_function(self, refinement_key):
|
||
"""Load the online value function if trained and ready."""
|
||
try:
|
||
try:
|
||
from .value_function import OnlineValueFunction
|
||
from .conditioning import refinement_state_path
|
||
except ImportError:
|
||
from value_function import OnlineValueFunction
|
||
from conditioning import refinement_state_path
|
||
import os as _os
|
||
path = refinement_state_path(refinement_key, "value_fn", prefix="refine_v2", extension="pt")
|
||
if not _os.path.exists(path):
|
||
return None
|
||
with torch.inference_mode(False):
|
||
vf = OnlineValueFunction.load(path)
|
||
return vf if vf.is_ready() else None
|
||
except Exception:
|
||
return None
|
||
|
||
def _load_liked_direction(self, refinement_key):
|
||
"""Read the liked conditioning direction from the Refiner's state file."""
|
||
try:
|
||
try:
|
||
from .conditioning import refinement_state_path, serializable_to_tensor
|
||
except ImportError:
|
||
from conditioning import refinement_state_path, serializable_to_tensor
|
||
import json as _json
|
||
path = refinement_state_path(refinement_key, "clip", prefix="refine_v2")
|
||
with open(path, "r", encoding="utf-8") as f:
|
||
state = _json.load(f)
|
||
global_state = state.get("global", state) # liked_dir lives under state["global"]
|
||
liked_dir_slot = global_state.get("liked_dir", {})
|
||
if int(liked_dir_slot.get("direction_count", 0)) < 3:
|
||
return None
|
||
raw = liked_dir_slot.get("direction")
|
||
if raw is None:
|
||
return None
|
||
return serializable_to_tensor(raw)
|
||
except Exception:
|
||
return None
|
||
|
||
def _build_embed_guidance_wrapper(self, model, liked_dir, strength, value_fn=None):
|
||
"""Register a model_function_wrapper that nudges conditioning toward the
|
||
liked quality direction at each denoising step. Uses value function gradient
|
||
when available, falls back to the fixed liked direction otherwise."""
|
||
old_wrapper = model.model_options.get("model_function_wrapper")
|
||
fixed_dir = torch.nn.functional.normalize(liked_dir.float(), dim=-1)
|
||
|
||
def _embed_wrapper(apply_fn, args, _ew=old_wrapper, _fixed=fixed_dir, _vf=value_fn, _s=strength):
|
||
c = args.get("c") or {}
|
||
cond = c.get("c_crossattn")
|
||
if cond is not None:
|
||
ts = args.get("timestep")
|
||
try:
|
||
sigma = float(ts.max().item()) if ts is not None else 1.0
|
||
except Exception:
|
||
sigma = 1.0
|
||
scale = max(0.0, 1.0 - sigma * 2.0)
|
||
if scale > 0:
|
||
if _vf is not None:
|
||
try:
|
||
grad = _vf.gradient(cond)
|
||
d = torch.nn.functional.normalize(grad.float(), dim=-1).to(cond.dtype)
|
||
except Exception as _e:
|
||
print(f"[FunPackSceneChain] embed_guidance: value function gradient failed ({_e}), using fixed direction")
|
||
d = _fixed.to(cond.device, cond.dtype).expand_as(cond)
|
||
else:
|
||
d = _fixed.to(cond.device, cond.dtype).expand_as(cond)
|
||
new_c = dict(c)
|
||
new_c["c_crossattn"] = cond + (_s * scale) * d
|
||
args = dict(args)
|
||
args["c"] = new_c
|
||
if _ew is not None:
|
||
return _ew(apply_fn, args)
|
||
return apply_fn(args["input"], args["timestep"], **args.get("c", {}))
|
||
|
||
model.model_options["model_function_wrapper"] = _embed_wrapper
|
||
return old_wrapper
|
||
|
||
def _append_mid_scene_guide(self, chunk, previous_output, positive, negative, vae, strength):
|
||
"""Append the middle frame of the previous scene as a guide for the current chunk
|
||
using LTX's guide attention mechanism (keyframe_idxs + guide_attention_entries).
|
||
Audio-safe: appends only to the video tensor, guide tokens influence denoising
|
||
through attention weights rather than overwriting hidden states."""
|
||
try:
|
||
from comfy_extras.nodes_lt import LTXVAddGuide, _append_guide_attention_entry
|
||
except ImportError:
|
||
return chunk, positive, negative, 0
|
||
|
||
prev_tensors = self._latent_tensors(previous_output)
|
||
chunk_tensors = self._latent_tensors(chunk)
|
||
if not prev_tensors or not chunk_tensors:
|
||
return chunk, positive, negative, 0
|
||
|
||
# Middle frame of previous scene as guide source
|
||
F_prev = self._tensor_frames(prev_tensors[0])
|
||
guide_frame = self._time_slice(prev_tensors[0], F_prev // 2, F_prev // 2 + 1)
|
||
guide_frame = guide_frame.to(device=chunk_tensors[0].device, dtype=chunk_tensors[0].dtype)
|
||
|
||
# Target temporal position: middle of current chunk in pixel space
|
||
F_chunk = self._tensor_frames(chunk_tensors[0])
|
||
mid_chunk = F_chunk // 2
|
||
scale_factors = getattr(vae, 'downscale_index_formula', [8, 8, 8])
|
||
time_scale = int(scale_factors[0]) if hasattr(scale_factors, '__getitem__') else 8
|
||
causal_fix = (mid_chunk == 0)
|
||
pixel_frame_idx = 0 if causal_fix else 1 + (mid_chunk - 1) * time_scale
|
||
|
||
# Add keyframe positional indices to conditioning
|
||
positive = LTXVAddGuide.add_keyframe_index(
|
||
positive, pixel_frame_idx, guide_frame, scale_factors, causal_fix=causal_fix
|
||
)
|
||
negative = LTXVAddGuide.add_keyframe_index(
|
||
negative, pixel_frame_idx, guide_frame, scale_factors, causal_fix=causal_fix
|
||
)
|
||
|
||
# Register guide attention entry (strength controls how strongly noisy frames
|
||
# attend to the guide tokens vs. ignoring them)
|
||
guide_latent_shape = [guide_frame.shape[2], guide_frame.shape[3], guide_frame.shape[4]]
|
||
pre_filter_count = guide_frame.shape[2] * guide_frame.shape[3] * guide_frame.shape[4]
|
||
positive, negative = _append_guide_attention_entry(
|
||
positive, negative, pre_filter_count, guide_latent_shape, strength=float(strength)
|
||
)
|
||
|
||
# Append guide frame to video tensor (mask = 1-strength, partially pinned)
|
||
result = self._clone_latent(chunk)
|
||
tensors = self._latent_tensors(result)
|
||
masks = self._latent_masks(result, len(tensors))
|
||
if masks[0] is None:
|
||
masks[0] = torch.ones(
|
||
tensors[0].shape[0], 1, tensors[0].shape[2], 1, 1,
|
||
dtype=torch.float32, device=tensors[0].device,
|
||
)
|
||
guide_mask = torch.full_like(masks[0][:, :, :1], max(0.0, 1.0 - float(strength)))
|
||
tensors[0] = torch.cat([tensors[0], guide_frame], dim=2)
|
||
masks[0] = torch.cat([masks[0], guide_mask.to(masks[0].device, masks[0].dtype)], dim=2)
|
||
|
||
if self._is_nested(result.get("samples")):
|
||
result["samples"] = comfy.nested_tensor.NestedTensor(tensors)
|
||
result["noise_mask"] = comfy.nested_tensor.NestedTensor(masks)
|
||
else:
|
||
result["samples"] = tensors[0]
|
||
result["noise_mask"] = masks[0]
|
||
|
||
return result, positive, negative, 1
|
||
|
||
|
||
def sample(self, model, vae, positive, negative, sampler, sigmas, seed, latent_template,
|
||
num_frames_per_scene, frame_overlap, cfg, max_scenes, use_same_seed=False,
|
||
carry_i2v_guides=False,
|
||
mid_scene_guide=False, mid_scene_guide_strength=0.4,
|
||
embed_guidance=False, embed_guidance_strength=0.02,
|
||
transition_duration=16, decode_tile_size=0,
|
||
refinement_key_input="", unique_id=None, prompt=None):
|
||
if not isinstance(positive, list) or not positive:
|
||
raise ValueError("positive conditioning must contain at least one scene entry.")
|
||
if negative is None:
|
||
negative = []
|
||
|
||
max_scene_count = max(1, int(max_scenes))
|
||
scene_conditionings = positive[:max_scene_count]
|
||
scene_count = len(scene_conditionings)
|
||
time_scale = self._time_scale(vae)
|
||
video_frames = self._validate_template_length(latent_template, num_frames_per_scene, time_scale)
|
||
video_overlap = self._overlap_frames(latent_template, frame_overlap, time_scale)
|
||
|
||
output = None
|
||
report_lines = []
|
||
carried_guide_frames = 0
|
||
boundary_entries = []
|
||
cumulative_latent_frames = 0
|
||
|
||
# Load liked direction once for embed_guidance
|
||
_liked_dir = None
|
||
_value_fn = None
|
||
if embed_guidance and refinement_key_input:
|
||
_liked_dir = self._load_liked_direction(refinement_key_input)
|
||
if _liked_dir is None:
|
||
print("[FunPackSceneChain] embed_guidance: no liked direction found (need 3+ liked generations)")
|
||
else:
|
||
_value_fn = self._load_value_function(refinement_key_input)
|
||
if _value_fn:
|
||
ready = _value_fn.is_ready()
|
||
mode = f"value function ({_value_fn.n_trained} samples, ascent {'on' if ready else 'pending'})"
|
||
else:
|
||
mode = "fixed direction"
|
||
print(f"[FunPackSceneChain] embed_guidance: active via {mode}, strength={embed_guidance_strength}")
|
||
|
||
first_scene_seed = self._scene_seed(scene_conditionings[0])
|
||
if first_scene_seed is None:
|
||
first_scene_seed = int(seed)
|
||
for scene_index, scene_cond in enumerate(scene_conditionings):
|
||
scene_positive = [scene_cond]
|
||
scene_negative = negative
|
||
|
||
provided_seed = self._scene_seed(scene_cond)
|
||
if use_same_seed:
|
||
scene_seed = first_scene_seed
|
||
else:
|
||
scene_seed = provided_seed if provided_seed is not None else int(seed) + scene_index
|
||
carried = 0
|
||
soft_carried = 0
|
||
guide_tail = 0
|
||
if output is None:
|
||
chunk = self._clone_latent(latent_template)
|
||
else:
|
||
# Record boundary before blending
|
||
effect = self._scene_transition_effect(scene_cond)
|
||
if effect and transition_duration > 0:
|
||
boundary_latent = cumulative_latent_frames
|
||
boundary_pixel = int((boundary_latent - 1) * time_scale + 1) if time_scale > 1 else boundary_latent
|
||
boundary_entries.append({
|
||
"boundary_latent": boundary_latent,
|
||
"pixel_frame": max(0, boundary_pixel),
|
||
"effect": effect,
|
||
})
|
||
chunk = self._build_continuation_chunk(latent_template, output, video_overlap)
|
||
if video_overlap == 0:
|
||
chunk, soft_carried = self._prepend_soft_continuation(chunk, output)
|
||
if carry_i2v_guides:
|
||
chunk, scene_positive, scene_negative, carried = self._append_i2v_guides(
|
||
chunk, latent_template, scene_positive, scene_negative,
|
||
)
|
||
carried_guide_frames = max(carried_guide_frames, carried)
|
||
if mid_scene_guide:
|
||
chunk, scene_positive, scene_negative, guide_tail = self._append_mid_scene_guide(
|
||
chunk, output, scene_positive, scene_negative, vae, mid_scene_guide_strength,
|
||
)
|
||
else:
|
||
guide_tail = 0
|
||
|
||
if embed_guidance and _value_fn is not None and _value_fn.is_ready():
|
||
orig_cond, orig_extra = scene_positive[0][0], scene_positive[0][1]
|
||
ascended = _value_fn.ascend(orig_cond)
|
||
scene_positive = [[ascended, orig_extra]] + list(scene_positive[1:])
|
||
if embed_guidance and _liked_dir is not None:
|
||
_eg_old_wrapper = self._build_embed_guidance_wrapper(model, _liked_dir, embed_guidance_strength, value_fn=_value_fn)
|
||
sampled = self._sample_chunk(
|
||
model, sampler, sigmas, scene_seed, cfg, scene_positive, scene_negative, chunk,
|
||
)
|
||
if embed_guidance and _liked_dir is not None:
|
||
if _eg_old_wrapper is not None:
|
||
model.model_options["model_function_wrapper"] = _eg_old_wrapper
|
||
elif "model_function_wrapper" in model.model_options:
|
||
del model.model_options["model_function_wrapper"]
|
||
if carried + soft_carried > 0:
|
||
sampled = self._crop_video_head(sampled, carried + soft_carried)
|
||
if guide_tail > 0:
|
||
sampled = self._crop_video_tail(sampled, guide_tail)
|
||
output = sampled if output is None else self._blend_latents(output, sampled, video_overlap)
|
||
cumulative_latent_frames = self._tensor_frames(self._latent_tensors(output)[0])
|
||
report_lines.append(f"Scene {scene_index + 1}: seed={scene_seed}, text={self._scene_text(scene_cond, scene_index)}")
|
||
|
||
del scene_cond, scene_positive, scene_negative, scene_conditionings, chunk, sampled
|
||
|
||
# RETURN_TYPES slot indices: 0=latent, 1=images, 2=status...
|
||
# Sampling is fully complete. Latent is untouched and returned as-is.
|
||
# IMAGES: decode the whole latent in one pass, then apply transition effects.
|
||
want_image = self._output_connected(prompt, unique_id, 1)
|
||
|
||
images = None
|
||
if want_image:
|
||
video_tensor = self._latent_tensors(output)[0]
|
||
if decode_tile_size > 0:
|
||
try:
|
||
decoded = vae.decode_tiled(video_tensor, tile_x=decode_tile_size // 8, tile_y=decode_tile_size // 8)
|
||
except Exception:
|
||
decoded = vae.decode(video_tensor)
|
||
else:
|
||
decoded = vae.decode(video_tensor)
|
||
if decoded.dim() == 5:
|
||
b, t, h, w, c = decoded.shape
|
||
decoded = decoded.reshape(b * t, h, w, c)
|
||
images = self._apply_transitions_pixel(decoded, boundary_entries, transition_duration)
|
||
|
||
if images is None:
|
||
images = torch.zeros(1, 8, 8, 3)
|
||
|
||
import json as _json
|
||
final_frames = self._tensor_frames(self._latent_tensors(output)[0])
|
||
status = (
|
||
f"Scene chain complete: {scene_count} scene(s), "
|
||
f"template={video_frames} latent frames, overlap={video_overlap}, output={final_frames}"
|
||
)
|
||
if carry_i2v_guides and carried_guide_frames > 0:
|
||
status += f", i2v guide tokens={carried_guide_frames} latent frame(s)"
|
||
boundaries_out = [{"pixel_frame": e["pixel_frame"], "effect": e["effect"]} for e in boundary_entries]
|
||
if refinement_key_input:
|
||
try:
|
||
try:
|
||
from .conditioning import update_refinement_sampler_context
|
||
except ImportError:
|
||
from conditioning import update_refinement_sampler_context
|
||
update_refinement_sampler_context(refinement_key_input, {
|
||
"carry_i2v_guides": bool(carry_i2v_guides),
|
||
"frame_overlap": int(frame_overlap),
|
||
"transitions_enabled": scene_count > 1,
|
||
})
|
||
except Exception as e:
|
||
print(f"[FunPackLTXAVSceneChainSampler] Failed to write sampler context: {e}")
|
||
|
||
return (output, images, status, scene_count, "\n".join(report_lines), _json.dumps(boundaries_out))
|