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from comfy_api.latest import io
from .prompt_relay import (
get_raw_tokenizer,
map_token_indices,
build_segments,
create_mask_fn,
distribute_segment_lengths,
)
from .patches import detect_model_type, apply_patches
from .advanced_options import PromptRelayAdvancedOptions, RelayOptions
log = logging.getLogger(__name__)
def _convert_to_latent_lengths(pixel_lengths, temporal_stride, latent_frames):
"""Convert pixel-space segment lengths to integer latent-space lengths using the
largest-remainder method. Targets the full `latent_frames` when the pixel sum looks
like full coverage (within one stride of latent_frames * stride). Otherwise targets
round(total_pixel / temporal_stride) so partial-coverage timelines stay partial.
"""
if not pixel_lengths:
return []
total_pixel = sum(pixel_lengths)
if total_pixel <= 0:
return [1] * len(pixel_lengths)
naive_total = max(1, round(total_pixel / temporal_stride))
target_total = min(latent_frames, naive_total)
# Within one frame of full → user clearly intended full coverage; pin to latent_frames.
if target_total >= latent_frames - 1:
target_total = latent_frames
exact = [p * target_total / total_pixel for p in pixel_lengths]
result = [int(e) for e in exact]
diff = target_total - sum(result)
if diff > 0:
order = sorted(range(len(exact)), key=lambda i: -(exact[i] - int(exact[i])))
for k in range(diff):
result[order[k % len(order)]] += 1
# Ensure every segment has ≥ 1 latent frame (steal from the largest if needed).
for i in range(len(result)):
if result[i] < 1:
max_idx = max(range(len(result)), key=lambda j: result[j])
if result[max_idx] > 1:
result[max_idx] -= 1
result[i] = 1
return result
def _encode_relay(model, clip, latent, global_prompt, local_prompts, segment_lengths, epsilon, relay_options=None):
for name, val in (("global_prompt", global_prompt),
("local_prompts", local_prompts),
("segment_lengths", segment_lengths)):
if val is None:
raise ValueError(
f"PromptRelay: '{name}' arrived as None. "
"Likely causes: a stale workflow JSON saved with null, the timeline "
"editor's web extension failing to load, or an upstream node returning None. "
"Set the field to an empty string or fix the upstream connection."
)
locals_list = [p.strip() for p in local_prompts.split("|") if p.strip()]
if not locals_list:
raise ValueError("At least one local prompt is required (separate with |)")
arch, patch_size, temporal_stride = detect_model_type(model)
samples = latent["samples"]
latent_frames = samples.shape[2]
tokens_per_frame = (samples.shape[3] // patch_size[1]) * (samples.shape[4] // patch_size[2])
parsed_lengths = None
if segment_lengths.strip():
pixel_lengths = [int(x.strip()) for x in segment_lengths.split(",") if x.strip()]
parsed_lengths = _convert_to_latent_lengths(pixel_lengths, temporal_stride, latent_frames)
raw_tokenizer = get_raw_tokenizer(clip)
full_prompt, token_ranges = map_token_indices(raw_tokenizer, global_prompt, locals_list)
log.info("[PromptRelay] Global: tokens [0:%d] (%d tokens)", token_ranges[0][0], token_ranges[0][0])
for i, (s, e) in enumerate(token_ranges):
log.info("[PromptRelay] Segment %d: tokens [%d:%d] (%d tokens)", i, s, e, e - s)
conditioning = clip.encode_from_tokens_scheduled(clip.tokenize(full_prompt))
effective_lengths = distribute_segment_lengths(len(locals_list), latent_frames, parsed_lengths)
log.info(
"[PromptRelay] Latent: %d frames, %d tokens/frame, segments: %s",
latent_frames, tokens_per_frame, effective_lengths,
)
q_token_idx = build_segments(token_ranges, effective_lengths, epsilon, relay_options)
mask_fn = create_mask_fn(q_token_idx, tokens_per_frame, latent_frames)
patched = model.clone()
apply_patches(patched, arch, mask_fn)
return patched, conditioning
class PromptRelayEncode(io.ComfyNode):
"""Encodes temporal local prompts and patches the model for Prompt Relay."""
@classmethod
def define_schema(cls):
return io.Schema(
node_id="PromptRelayEncode",
display_name="Prompt Relay Encode",
category="conditioning/prompt_relay",
description=(
"Encodes a global prompt combined with temporal local prompts and patches the model "
"for Prompt Relay temporal control. Local prompts are separated by |. "
"Use a standard CLIPTextEncode for the negative prompt."
),
inputs=[
io.Model.Input("model"),
io.Clip.Input("clip"),
io.Latent.Input("latent", tooltip="Empty latent video — dimensions are read from its shape."),
io.String.Input(
"global_prompt", multiline=True, default="",
tooltip="Conditions the entire video. Anchors persistent characters, objects, and scene context.",
),
io.String.Input(
"local_prompts", multiline=True, default="",
tooltip="Ordered prompts for each temporal segment, separated by |",
),
io.String.Input(
"segment_lengths", default="",
tooltip="Comma-separated pixel space frame counts per segment. Leave empty to auto-distribute evenly.",
),
io.Float.Input(
"epsilon", default=1e-3, min=1e-6, max=0.99, step=1e-4,
tooltip="Penalty decay parameter. Values below ~0.1 all produce sharp boundaries (paper default 0.001). For softer transitions, try 0.5 or higher.",
),
RelayOptions.Input(
"relay_options", optional=True,
tooltip="Optional advanced per-stream tuning. Connect a Prompt Relay Advanced Options node.",
),
],
outputs=[
io.Model.Output(display_name="model"),
io.Conditioning.Output(display_name="positive"),
],
)
@classmethod
def execute(cls, model, clip, latent, global_prompt, local_prompts, segment_lengths, epsilon, relay_options=None) -> io.NodeOutput:
patched, conditioning = _encode_relay(
model, clip, latent, global_prompt, local_prompts, segment_lengths, epsilon, relay_options,
)
return io.NodeOutput(patched, conditioning)
class PromptRelayEncodeTimeline(io.ComfyNode):
"""WYSIWYG timeline variant — segments and lengths come from a visual editor in the node UI."""
@classmethod
def define_schema(cls):
return io.Schema(
node_id="PromptRelayEncodeTimeline",
display_name="Prompt Relay Encode (Timeline)",
category="conditioning/prompt_relay",
description=(
"Same as Prompt Relay Encode, but local prompts and segment lengths are edited "
"visually as draggable blocks on a timeline. The max_frames input only sets the "
"timeline scale (pixel space) — actual frame count is still read from the latent."
),
inputs=[
io.Model.Input("model"),
io.Clip.Input("clip"),
io.Latent.Input("latent", tooltip="Empty latent video — dimensions are read from its shape."),
io.String.Input(
"global_prompt", multiline=True, default="",
tooltip="Conditions the entire video. Anchors persistent characters, objects, and scene context.",
),
io.Int.Input(
"max_frames", default=129, min=1, max=10000, step=1,
tooltip="Total timeline length in pixel-space frames. Used by the editor for visual scale only.",
),
io.String.Input(
"timeline_data", default="",
tooltip="JSON state of the timeline editor (auto-managed; do not edit by hand).",
),
io.String.Input(
"local_prompts", multiline=True, default="",
tooltip="Auto-populated from the timeline editor.",
),
io.String.Input(
"segment_lengths", default="",
tooltip="Auto-populated from the timeline editor (pixel-space frame counts).",
),
io.Float.Input(
"epsilon", default=1e-3, min=1e-6, max=0.99, step=1e-4,
tooltip="Penalty decay parameter. Values below ~0.1 all produce sharp boundaries (paper default 0.001). For softer transitions, try 0.5 or higher.",
),
io.Float.Input(
"fps", default=24.0, min=0.1, max=240.0, step=0.1, optional=True,
tooltip="Frames per second — only affects how time is displayed in the timeline editor when time_units is set to 'seconds'.",
),
io.Combo.Input(
"time_units", options=["frames", "seconds"], default="frames", optional=True,
tooltip="Display the ruler, segment ranges, length input, and total in frames or seconds. Internal storage is always pixel-space frames.",
),
RelayOptions.Input(
"relay_options", optional=True,
tooltip="Optional advanced per-stream tuning. Connect a Prompt Relay Advanced Options node.",
),
],
outputs=[
io.Model.Output(display_name="model"),
io.Conditioning.Output(display_name="positive"),
],
)
@classmethod
def execute(cls, model, clip, latent, global_prompt, max_frames, timeline_data, local_prompts, segment_lengths, epsilon, fps=24.0, time_units="frames", relay_options=None) -> io.NodeOutput:
patched, conditioning = _encode_relay(
model, clip, latent, global_prompt, local_prompts, segment_lengths, epsilon, relay_options,
)
return io.NodeOutput(patched, conditioning)
NODE_CLASS_MAPPINGS = {
"PromptRelayEncode": PromptRelayEncode,
"PromptRelayEncodeTimeline": PromptRelayEncodeTimeline,
"PromptRelayAdvancedOptions": PromptRelayAdvancedOptions,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PromptRelayEncode": "Prompt Relay Encode",
"PromptRelayEncodeTimeline": "Prompt Relay Encode (Timeline)",
"PromptRelayAdvancedOptions": "Prompt Relay Advanced Options",
}
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