feat: add context window ksampler
This commit is contained in:
Fillip
@@ -141,6 +141,7 @@ from .nodes.image.FL_SaveWebpImages import FL_SaveWebPImage
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# KSAMPLERS NODES
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from .nodes.ksamplers.FL_KsamplerBasic import FL_KsamplerBasic
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from .nodes.ksamplers.FL_KsamplerContextWindow import FL_KsamplerContextWindow
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from .nodes.ksamplers.FL_KsamplerPlus import FL_KsamplerPlus
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from .nodes.ksamplers.FL_KsamplerPlusV2 import FL_KsamplerPlusV2
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from .nodes.ksamplers.FL_KsamplerSigma import FL_KsamplerSigma
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@@ -310,6 +311,7 @@ NODE_CLASS_MAPPINGS = {
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"FL_KsamplerPlus": FL_KsamplerPlus,
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"FL_KsamplerPlusV2": FL_KsamplerPlusV2,
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"FL_KsamplerBasic": FL_KsamplerBasic,
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"FL_KsamplerContextWindow": FL_KsamplerContextWindow,
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"FL_KsamplerSigma": FL_KsamplerSigma,
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"FL_KsamplerSEG_Regions": FL_KsamplerSEG_Regions,
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"FL_KsamplerSEG_Captioner": FL_KsamplerSEG_Captioner,
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@@ -510,6 +512,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"FL_KsamplerPlus": "FL KSampler Plus",
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"FL_KsamplerPlusV2": "FL KSampler Plus V2",
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"FL_KsamplerBasic": "FL KSampler Basic",
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"FL_KsamplerContextWindow": "FL Context Window KSampler",
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"FL_KsamplerSigma": "FL KSampler Sigma",
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"FL_KsamplerSEG_Regions": "FL KSampler SEG Regions",
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"FL_KsamplerSEG_Captioner": "FL KSampler SEG Captioner",
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@@ -0,0 +1,357 @@
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import logging
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import time
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import torch
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import comfy.context_windows
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import comfy.samplers
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from comfy_execution.utils import get_executing_context
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from nodes import VAEDecode, VAEEncode, common_ksampler
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CONTEXT_SCHEDULES = [
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comfy.context_windows.ContextSchedules.STATIC_STANDARD,
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comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
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comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
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comfy.context_windows.ContextSchedules.BATCHED,
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]
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FUSE_METHODS = comfy.context_windows.ContextFuseMethods.LIST_STATIC
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TEMPORAL_UNITS = [
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"video_frames_4n_plus_1",
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"latent_frames",
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]
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class FLSafeIndexListContextHandler(comfy.context_windows.IndexListContextHandler):
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def __init__(self, *args, node_id=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.node_id = str(node_id) if node_id is not None else None
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self._total_steps = 1
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self._last_total_windows = 1
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self._last_progress_value = 0
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self._last_event_at = 0.0
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def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
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sample_sigmas = model_options.get("transformer_options", {}).get("sample_sigmas")
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if sample_sigmas is None:
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return
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self._total_steps = max(1, int(sample_sigmas.numel()) - 1)
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current_timestep = timestep[0].to(device=sample_sigmas.device, dtype=sample_sigmas.dtype)
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mask = torch.isclose(sample_sigmas, current_timestep, rtol=0.0001)
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matches = torch.nonzero(mask)
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if torch.numel(matches) == 0:
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return
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self._step = int(matches[0].item())
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def get_context_windows(self, model, x_in: torch.Tensor, model_options: dict):
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context_windows = super().get_context_windows(model, x_in, model_options)
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self._last_total_windows = max(1, len(context_windows))
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return context_windows
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def combine_context_window_results(
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self,
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x_in: torch.Tensor,
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sub_conds_out,
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sub_conds,
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window,
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window_idx: int,
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total_windows: int,
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timestep: torch.Tensor,
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conds_final,
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counts_final,
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biases_final,
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):
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result = super().combine_context_window_results(
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x_in,
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sub_conds_out,
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sub_conds,
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window,
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window_idx,
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total_windows,
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timestep,
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conds_final,
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counts_final,
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biases_final,
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)
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self._emit_progress(window_idx, max(1, total_windows), window)
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return result
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def emit_done(self):
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if self.node_id is None:
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return
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max_value = max(1, self._total_steps * self._last_total_windows)
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self._send_event(
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{
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"node": self.node_id,
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"status": "done",
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"value": max_value,
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"max": max_value,
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"step": self._total_steps,
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"total_steps": self._total_steps,
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"window_index": self._last_total_windows,
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"total_windows": self._last_total_windows,
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"window": [],
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}
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)
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def _emit_progress(self, window_idx: int, total_windows: int, window):
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if self.node_id is None:
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return
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max_value = max(1, self._total_steps * total_windows)
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raw_value = min(max_value, self._step * total_windows + window_idx + 1)
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value = max(self._last_progress_value, raw_value)
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self._last_progress_value = value
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now = time.monotonic()
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if now - self._last_event_at < 0.25 and value < max_value:
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return
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self._last_event_at = now
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self._send_event(
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{
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"node": self.node_id,
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"status": "running",
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"value": value,
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"max": max_value,
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"step": min(self._step + 1, self._total_steps),
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"total_steps": self._total_steps,
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"window_index": window_idx + 1,
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"total_windows": total_windows,
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"window": list(getattr(window, "index_list", [])),
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}
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)
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@staticmethod
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def _send_event(payload: dict):
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try:
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from server import PromptServer
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PromptServer.instance.send_sync("fl_context_window_progress", payload)
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except Exception as e:
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logging.debug(f"[FL_KsamplerContextWindow] progress event send failed: {e}")
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class FL_KsamplerContextWindow:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("MODEL",),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"latent_image": ("LATENT",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"context_length": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4}),
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"context_overlap": ("INT", {"default": 30, "min": 0, "max": 10000}),
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"context_schedule": (CONTEXT_SCHEDULES, {"default": comfy.context_windows.ContextSchedules.STATIC_STANDARD}),
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"context_stride": ("INT", {"default": 1, "min": 1, "max": 10000}),
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"fuse_method": (FUSE_METHODS, {"default": comfy.context_windows.ContextFuseMethods.PYRAMID}),
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"temporal_unit": (TEMPORAL_UNITS, {"default": "video_frames_4n_plus_1"}),
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"closed_loop": ("BOOLEAN", {"default": False, "advanced": True}),
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"freenoise": ("BOOLEAN", {"default": False, "advanced": True}),
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"causal_window_fix": ("BOOLEAN", {"default": True, "advanced": True}),
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"temporal_dim": ("INT", {"default": 2, "min": 0, "max": 5, "advanced": True}),
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"cond_retain_index_list": ("STRING", {"default": "", "multiline": False, "advanced": True}),
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"split_conds_to_windows": ("BOOLEAN", {"default": False, "advanced": True}),
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},
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"optional": {
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"vae": ("VAE",),
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"image": ("IMAGE",),
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},
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"hidden": {"unique_id": "UNIQUE_ID"},
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}
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RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "IMAGE", "STRING")
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RETURN_NAMES = ("model", "positive", "negative", "latent", "vae", "image", "debug_info")
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FUNCTION = "sample"
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CATEGORY = "🏵️Fill Nodes/Ksamplers"
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def sample(
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self,
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model,
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positive,
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negative,
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latent_image,
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seed,
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steps,
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cfg,
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sampler_name,
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scheduler,
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denoise,
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context_length,
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context_overlap,
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context_schedule,
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context_stride,
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fuse_method,
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temporal_unit,
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vae=None,
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image=None,
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closed_loop=False,
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freenoise=False,
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causal_window_fix=True,
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temporal_dim=2,
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cond_retain_index_list="",
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split_conds_to_windows=False,
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unique_id=None,
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):
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try:
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node_id = unique_id
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if node_id is None:
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context = get_executing_context()
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if context is not None:
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node_id = context.node_id
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if image is not None:
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if vae is None:
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raise ValueError("FL_KsamplerContextWindow: image input requires a VAE.")
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latent_image = VAEEncode().encode(vae, image)[0]
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if latent_image is None or "samples" not in latent_image:
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raise ValueError("FL_KsamplerContextWindow: latent_image must contain samples.")
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samples = latent_image["samples"]
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if not isinstance(samples, torch.Tensor):
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raise ValueError("FL_KsamplerContextWindow: nested tensor latents are not supported.")
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if temporal_dim >= samples.ndim:
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raise ValueError(
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"FL_KsamplerContextWindow: temporal_dim is outside the latent sample shape. "
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f"Got temporal_dim={temporal_dim}, shape={tuple(samples.shape)}."
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)
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if samples.ndim == 4 and temporal_dim != 0:
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raise ValueError(
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"FL_KsamplerContextWindow: expected a 5D video latent [B, C, T, H, W]. "
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"For 4D latents, set temporal_dim=0 only if you intentionally want to window over batch."
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)
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latent_context_length, latent_context_overlap = self._convert_context_units(
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context_length,
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context_overlap,
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temporal_unit,
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)
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self._validate_context(latent_context_length, latent_context_overlap)
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context_model = model.clone()
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context_model.model_options["context_handler"] = FLSafeIndexListContextHandler(
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context_schedule=comfy.context_windows.get_matching_context_schedule(context_schedule),
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fuse_method=comfy.context_windows.get_matching_fuse_method(fuse_method),
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context_length=latent_context_length,
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context_overlap=latent_context_overlap,
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context_stride=context_stride,
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closed_loop=closed_loop,
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dim=temporal_dim,
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freenoise=freenoise,
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cond_retain_index_list=cond_retain_index_list,
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split_conds_to_windows=split_conds_to_windows,
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causal_window_fix=causal_window_fix,
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node_id=node_id,
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)
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context_handler = context_model.model_options["context_handler"]
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comfy.context_windows.create_prepare_sampling_wrapper(context_model)
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if freenoise:
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comfy.context_windows.create_sampler_sample_wrapper(context_model)
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sampled = common_ksampler(
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context_model,
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seed,
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steps,
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cfg,
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sampler_name,
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scheduler,
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positive,
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negative,
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latent_image,
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denoise=denoise,
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)[0]
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context_handler.emit_done()
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output_image = None
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if vae is not None:
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output_image = VAEDecode().decode(vae, sampled)[0]
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debug_info = self._debug_info(
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samples=samples,
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temporal_dim=temporal_dim,
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context_length=context_length,
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context_overlap=context_overlap,
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latent_context_length=latent_context_length,
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latent_context_overlap=latent_context_overlap,
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context_schedule=context_schedule,
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context_stride=context_stride,
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closed_loop=closed_loop,
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fuse_method=fuse_method,
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freenoise=freenoise,
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causal_window_fix=causal_window_fix,
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temporal_unit=temporal_unit,
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)
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return (model, positive, negative, sampled, vae, output_image, debug_info)
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except Exception as e:
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logging.error(f"Error in FL_KsamplerContextWindow: {e}")
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raise
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@staticmethod
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def _convert_context_units(context_length, context_overlap, temporal_unit):
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if temporal_unit == "latent_frames":
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return int(context_length), int(context_overlap)
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if temporal_unit == "video_frames_4n_plus_1":
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latent_length = max(((int(context_length) - 1) // 4) + 1, 1)
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latent_overlap = max(((int(context_overlap) - 1) // 4) + 1, 0) if context_overlap > 0 else 0
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return latent_length, latent_overlap
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raise ValueError(f"FL_KsamplerContextWindow: unknown temporal_unit '{temporal_unit}'.")
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@staticmethod
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def _validate_context(context_length, context_overlap):
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if context_length < 1:
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raise ValueError("FL_KsamplerContextWindow: context_length must be at least 1.")
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if context_overlap < 0:
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raise ValueError("FL_KsamplerContextWindow: context_overlap cannot be negative.")
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if context_overlap >= context_length:
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raise ValueError("FL_KsamplerContextWindow: context_overlap must be smaller than context_length.")
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@staticmethod
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def _debug_info(
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samples,
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temporal_dim,
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context_length,
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context_overlap,
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latent_context_length,
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latent_context_overlap,
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context_schedule,
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context_stride,
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closed_loop,
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fuse_method,
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freenoise,
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causal_window_fix,
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temporal_unit,
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):
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total_temporal = samples.shape[temporal_dim]
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context_active = total_temporal > latent_context_length
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return (
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"FL Context Window KSampler\n"
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f"- latent_shape: {tuple(samples.shape)}\n"
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f"- temporal_dim: {temporal_dim}\n"
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f"- total_temporal_length: {total_temporal}\n"
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f"- temporal_unit: {temporal_unit}\n"
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f"- requested_context_length: {context_length}\n"
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f"- requested_context_overlap: {context_overlap}\n"
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f"- effective_latent_context_length: {latent_context_length}\n"
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f"- effective_latent_context_overlap: {latent_context_overlap}\n"
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f"- context_schedule: {context_schedule}\n"
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f"- context_stride: {context_stride}\n"
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f"- closed_loop: {closed_loop}\n"
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f"- fuse_method: {fuse_method}\n"
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f"- freenoise: {freenoise}\n"
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f"- causal_window_fix: {causal_window_fix}\n"
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f"- context_active: {context_active}"
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)
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+1
-1
@@ -1,7 +1,7 @@
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[project]
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name = "comfyui_fill-nodes"
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description = "Fill-Nodes is a versatile collection of custom nodes for ComfyUI that extends functionality across multiple domains. Features include advanced image processing (pixelation, slicing, masking), visual effects generation (glitch, halftone, pixel art), comprehensive file handling (PDF creation/extraction, Google Drive integration), AI model interfaces (GPT, DALL-E, Hugging Face), utility nodes for workflow enhancement, and specialized tools for video processing, captioning, and batch operations. The pack provides both practical workflow solutions and creative tools within a unified node collection."
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version = "2.7.6"
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version = "2.7.7"
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license = "LICENSE"
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dependencies = ["librosa", "sounddevice", "glitch_this", "PyOpenGL", "glfw", "scipy>=1.13.1", "requests", "aiohttp", "moviepy", "matplotlib", "reportlab", "openai", "PyPDF2", "pdf2image", "PyMuPDF", "reportlab", "PyPDF2", "ollama", "kornia", "opencv-python", "gdown", "open_clip_torch", "google-genai"]
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@@ -0,0 +1,198 @@
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import { app } from "../../../../scripts/app.js";
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import { api } from "../../../../scripts/api.js";
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const STYLES = `
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.flks-context-widget {
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background: #17181c;
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border: 1px solid #2a2d34;
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border-radius: 8px;
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color: #f4f4f5;
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display: flex;
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flex-direction: column;
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font-family: Inter, -apple-system, BlinkMacSystemFont, sans-serif;
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gap: 8px;
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min-height: 104px;
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padding: 10px;
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box-sizing: border-box;
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}
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.flks-context-widget * { box-sizing: border-box; }
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.flks-context-header {
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align-items: center;
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display: flex;
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justify-content: space-between;
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gap: 8px;
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}
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.flks-context-title {
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font-size: 11px;
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font-weight: 650;
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line-height: 1.2;
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}
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.flks-context-badge {
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background: #06b6d4;
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border-radius: 999px;
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color: white;
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font-size: 10px;
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font-variant-numeric: tabular-nums;
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font-weight: 700;
|
||||
line-height: 1;
|
||||
padding: 4px 7px;
|
||||
white-space: nowrap;
|
||||
}
|
||||
.flks-context-bar {
|
||||
background: #27272a;
|
||||
border-radius: 999px;
|
||||
height: 9px;
|
||||
overflow: hidden;
|
||||
width: 100%;
|
||||
}
|
||||
.flks-context-fill {
|
||||
background: linear-gradient(90deg, #06b6d4, #22c55e);
|
||||
height: 100%;
|
||||
transition: width 120ms linear;
|
||||
width: 0%;
|
||||
}
|
||||
.flks-context-meta {
|
||||
color: #cbd5e1;
|
||||
display: grid;
|
||||
gap: 4px;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
font-size: 10px;
|
||||
font-variant-numeric: tabular-nums;
|
||||
line-height: 1.25;
|
||||
}
|
||||
.flks-context-window {
|
||||
color: #94a3b8;
|
||||
font-size: 10px;
|
||||
line-height: 1.25;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
`;
|
||||
|
||||
class ContextWindowProgressWidget {
|
||||
constructor({ container }) {
|
||||
this.container = container;
|
||||
this.injectStyles();
|
||||
this.element = document.createElement("div");
|
||||
this.element.className = "flks-context-widget";
|
||||
this.element.innerHTML = `
|
||||
<div class="flks-context-header">
|
||||
<span class="flks-context-title">Context Windows</span>
|
||||
<span class="flks-context-badge" data-role="percent">idle</span>
|
||||
</div>
|
||||
<div class="flks-context-bar">
|
||||
<div class="flks-context-fill" data-role="fill"></div>
|
||||
</div>
|
||||
<div class="flks-context-meta">
|
||||
<span data-role="step">step - / -</span>
|
||||
<span data-role="window">window - / -</span>
|
||||
</div>
|
||||
<div class="flks-context-window" data-role="indices">Run to see context-window progress.</div>
|
||||
`;
|
||||
this.percentEl = this.element.querySelector('[data-role="percent"]');
|
||||
this.fillEl = this.element.querySelector('[data-role="fill"]');
|
||||
this.stepEl = this.element.querySelector('[data-role="step"]');
|
||||
this.windowEl = this.element.querySelector('[data-role="window"]');
|
||||
this.indicesEl = this.element.querySelector('[data-role="indices"]');
|
||||
this.container.appendChild(this.element);
|
||||
}
|
||||
|
||||
injectStyles() {
|
||||
const id = "flks-context-window-styles";
|
||||
if (document.getElementById(id)) return;
|
||||
const style = document.createElement("style");
|
||||
style.id = id;
|
||||
style.textContent = STYLES;
|
||||
document.head.appendChild(style);
|
||||
}
|
||||
|
||||
reset() {
|
||||
this.percentEl.textContent = "0%";
|
||||
this.fillEl.style.width = "0%";
|
||||
this.stepEl.textContent = "step 0 / -";
|
||||
this.windowEl.textContent = "window 0 / -";
|
||||
this.indicesEl.textContent = "Waiting for first context window...";
|
||||
}
|
||||
|
||||
update(detail) {
|
||||
const value = Number(detail.value || 0);
|
||||
const max = Math.max(1, Number(detail.max || 1));
|
||||
const pct = Math.max(0, Math.min(100, (value / max) * 100));
|
||||
this.percentEl.textContent = detail.status === "done" ? "done" : `${pct.toFixed(1)}%`;
|
||||
this.fillEl.style.width = `${pct}%`;
|
||||
this.stepEl.textContent = `step ${detail.step ?? "-"} / ${detail.total_steps ?? "-"}`;
|
||||
this.windowEl.textContent = `window ${detail.window_index ?? "-"} / ${detail.total_windows ?? "-"}`;
|
||||
|
||||
const indices = Array.isArray(detail.window) ? detail.window : [];
|
||||
if (indices.length) {
|
||||
const first = indices[0];
|
||||
const last = indices[indices.length - 1];
|
||||
this.indicesEl.textContent = `latent frames ${first}-${last} (${indices.length})`;
|
||||
} else if (detail.status === "done") {
|
||||
this.indicesEl.textContent = "Sampling completed.";
|
||||
}
|
||||
}
|
||||
|
||||
dispose() {
|
||||
this.element?.remove();
|
||||
}
|
||||
}
|
||||
|
||||
const INSTANCES = new Map();
|
||||
|
||||
app.registerExtension({
|
||||
name: "ComfyUI.FL_KsamplerContextWindow",
|
||||
nodeCreated(node) {
|
||||
const comfyClass = (node.constructor && node.constructor.comfyClass) || "";
|
||||
if (comfyClass !== "FL_KsamplerContextWindow") return;
|
||||
|
||||
const container = document.createElement("div");
|
||||
container.style.width = "100%";
|
||||
container.style.minHeight = "104px";
|
||||
|
||||
const widget = node.addDOMWidget(
|
||||
"context_progress",
|
||||
"flks-context-window-progress",
|
||||
container,
|
||||
{
|
||||
getMinHeight: () => 130,
|
||||
hideOnZoom: false,
|
||||
serialize: false,
|
||||
}
|
||||
);
|
||||
|
||||
const [oldW, oldH] = node.size;
|
||||
node.setSize([Math.max(oldW, 330), Math.max(oldH, 730)]);
|
||||
|
||||
setTimeout(() => {
|
||||
const inst = new ContextWindowProgressWidget({ container });
|
||||
INSTANCES.set(node.id, inst);
|
||||
}, 50);
|
||||
|
||||
widget.onRemove = () => {
|
||||
const inst = INSTANCES.get(node.id);
|
||||
if (inst) {
|
||||
inst.dispose();
|
||||
INSTANCES.delete(node.id);
|
||||
}
|
||||
};
|
||||
},
|
||||
});
|
||||
|
||||
api.addEventListener("executing", (event) => {
|
||||
const detail = event.detail;
|
||||
if (!detail || !detail.node) return;
|
||||
const nodeId = parseInt(detail.node, 10);
|
||||
const inst = INSTANCES.get(nodeId);
|
||||
if (inst) inst.reset();
|
||||
});
|
||||
|
||||
api.addEventListener("fl_context_window_progress", (event) => {
|
||||
const detail = event.detail;
|
||||
if (!detail) return;
|
||||
const nodeId = parseInt(detail.node, 10);
|
||||
const inst = INSTANCES.get(nodeId);
|
||||
if (!inst) return;
|
||||
inst.update(detail);
|
||||
});
|
||||
Reference in New Issue
Block a user