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Remove Helios include-history-in-output plumbing

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qqingzheng 2026-03-10 19:54:05 +08:00
parent c25df83b8a
commit 86c0755ee2
1 changed files with 3 additions and 43 deletions
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46
comfy_extras/nodes_helios.py
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@ -540,7 +540,6 @@ class HeliosImageToVideo(io.ComfyNode):
io.Float.Input("image_noise_sigma_min", default=0.111, min=0.0, max=1.0, step=0.0001, round=False, advanced=True), io.Float.Input("image_noise_sigma_min", default=0.111, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
io.Float.Input("image_noise_sigma_max", default=0.135, min=0.0, max=1.0, step=0.0001, round=False, advanced=True), io.Float.Input("image_noise_sigma_max", default=0.135, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
io.Int.Input("noise_seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, advanced=True), io.Int.Input("noise_seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, advanced=True),
io.Boolean.Input("include_history_in_output", default=False, advanced=True),
], ],
outputs=[ outputs=[
io.Conditioning.Output(display_name="positive"), io.Conditioning.Output(display_name="positive"),
@ -567,7 +566,6 @@ class HeliosImageToVideo(io.ComfyNode):
image_noise_sigma_min=0.111, image_noise_sigma_min=0.111,
image_noise_sigma_max=0.135, image_noise_sigma_max=0.135,
noise_seed=0, noise_seed=0,
include_history_in_output=False,
) -> io.NodeOutput: ) -> io.NodeOutput:
video_noise_sigma_min = 0.111 video_noise_sigma_min = 0.111
video_noise_sigma_max = 0.135 video_noise_sigma_max = 0.135
@ -637,7 +635,6 @@ class HeliosImageToVideo(io.ComfyNode):
"helios_history_valid_mask": history_valid_mask, "helios_history_valid_mask": history_valid_mask,
"helios_num_frames": int(length), "helios_num_frames": int(length),
"helios_noise_gen_state": noise_gen_state, "helios_noise_gen_state": noise_gen_state,
"helios_include_history_in_output": _strict_bool(include_history_in_output, default=False),
}, },
) )
@ -743,7 +740,6 @@ class HeliosVideoToVideo(io.ComfyNode):
io.Float.Input("video_noise_sigma_min", default=0.111, min=0.0, max=1.0, step=0.0001, round=False, advanced=True), io.Float.Input("video_noise_sigma_min", default=0.111, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
io.Float.Input("video_noise_sigma_max", default=0.135, min=0.0, max=1.0, step=0.0001, round=False, advanced=True), io.Float.Input("video_noise_sigma_max", default=0.135, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
io.Int.Input("noise_seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, advanced=True), io.Int.Input("noise_seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, advanced=True),
io.Boolean.Input("include_history_in_output", default=True, advanced=True),
], ],
outputs=[ outputs=[
io.Conditioning.Output(display_name="positive"), io.Conditioning.Output(display_name="positive"),
@ -770,7 +766,6 @@ class HeliosVideoToVideo(io.ComfyNode):
video_noise_sigma_min=0.111, video_noise_sigma_min=0.111,
video_noise_sigma_max=0.135, video_noise_sigma_max=0.135,
noise_seed=0, noise_seed=0,
include_history_in_output=True,
) -> io.NodeOutput: ) -> io.NodeOutput:
spacial_scale = vae.spacial_compression_encode() spacial_scale = vae.spacial_compression_encode()
latent_channels = vae.latent_channels latent_channels = vae.latent_channels
@ -869,8 +864,6 @@ class HeliosVideoToVideo(io.ComfyNode):
"helios_history_valid_mask": history_valid_mask, "helios_history_valid_mask": history_valid_mask,
"helios_num_frames": int(length), "helios_num_frames": int(length),
"helios_noise_gen_state": noise_gen_state, "helios_noise_gen_state": noise_gen_state,
# Keep initial history segment and generated chunks together in sampler output.
"helios_include_history_in_output": _strict_bool(include_history_in_output, default=True),
}, },
) )
@ -1011,13 +1004,11 @@ class HeliosPyramidSampler(io.ComfyNode):
history_valid_mask = latent.get("helios_history_valid_mask", None) history_valid_mask = latent.get("helios_history_valid_mask", None)
if history_valid_mask is None: if history_valid_mask is None:
raise ValueError("Helios sampler requires `helios_history_valid_mask` in latent input.") raise ValueError("Helios sampler requires `helios_history_valid_mask` in latent input.")
history_full = None
history_from_latent_applied = False history_from_latent_applied = False
if image_latent_prefix is not None: if image_latent_prefix is not None:
image_latent_prefix = model.model.process_latent_in(image_latent_prefix) image_latent_prefix = model.model.process_latent_in(image_latent_prefix)
if "helios_history_latent" in latent: if "helios_history_latent" in latent:
history_in = _process_latent_in_preserve_zero_frames(model, latent["helios_history_latent"], valid_mask=history_valid_mask) history_in = _process_latent_in_preserve_zero_frames(model, latent["helios_history_latent"], valid_mask=history_valid_mask)
history_full = history_in
positive, negative = _set_helios_history_values( positive, negative = _set_helios_history_values(
positive, positive,
negative, negative,
@ -1068,25 +1059,7 @@ class HeliosPyramidSampler(io.ComfyNode):
if image_latent_prefix is not None: if image_latent_prefix is not None:
image_latent_prefix = image_latent_prefix.to(device=target_device, dtype=torch.float32) image_latent_prefix = image_latent_prefix.to(device=target_device, dtype=torch.float32)
history_output = history_full if history_full is not None else rolling_history # Always return only newly generated chunks; input history is used only for conditioning.
if "helios_history_latent_output" in latent:
history_output = _process_latent_in_preserve_zero_frames(
model,
latent["helios_history_latent_output"],
valid_mask=history_valid_mask,
)
history_output = history_output.to(device=target_device, dtype=torch.float32)
if history_valid_mask is not None:
if not torch.is_tensor(history_valid_mask):
history_valid_mask = torch.tensor(history_valid_mask, device=target_device)
history_valid_mask = history_valid_mask.to(device=target_device)
if history_valid_mask.ndim == 2:
initial_generated_latent_frames = int(history_valid_mask.any(dim=0).sum().item())
else:
initial_generated_latent_frames = int(history_valid_mask.reshape(-1).sum().item())
else:
initial_generated_latent_frames = 0
total_generated_latent_frames = initial_generated_latent_frames
for chunk_idx in range(chunk_count): for chunk_idx in range(chunk_count):
# Prepare initial latent for this chunk # Prepare initial latent for this chunk
@ -1268,14 +1241,8 @@ class HeliosPyramidSampler(io.ComfyNode):
rolling_history = torch.cat([rolling_history, stage_latent.to(rolling_history.device, rolling_history.dtype)], dim=2) rolling_history = torch.cat([rolling_history, stage_latent.to(rolling_history.device, rolling_history.dtype)], dim=2)
keep_hist = max(1, sum(history_sizes_list)) keep_hist = max(1, sum(history_sizes_list))
rolling_history = rolling_history[:, :, -keep_hist:] rolling_history = rolling_history[:, :, -keep_hist:]
total_generated_latent_frames += stage_latent.shape[2]
history_output = torch.cat([history_output, stage_latent.to(history_output.device, history_output.dtype)], dim=2)
include_history_in_output = _strict_bool(latent.get("helios_include_history_in_output", False), default=False) if len(generated_chunks) > 0:
if include_history_in_output and history_output is not None:
keep_t = max(0, int(total_generated_latent_frames))
stage_latent = history_output[:, :, -keep_t:] if keep_t > 0 else history_output[:, :, :0]
elif len(generated_chunks) > 0:
stage_latent = torch.cat(generated_chunks, dim=2) stage_latent = torch.cat(generated_chunks, dim=2)
else: else:
stage_latent = torch.zeros((b, c, 0, h, w), device=target_device, dtype=torch.float32) stage_latent = torch.zeros((b, c, 0, h, w), device=target_device, dtype=torch.float32)
@ -1288,7 +1255,6 @@ class HeliosPyramidSampler(io.ComfyNode):
out["helios_chunk_count"] = int(len(generated_chunks)) out["helios_chunk_count"] = int(len(generated_chunks))
out["helios_window_num_frames"] = int(window_num_frames) out["helios_window_num_frames"] = int(window_num_frames)
out["helios_num_frames"] = int(num_frames) out["helios_num_frames"] = int(num_frames)
out["helios_prefix_latent_frames"] = int(initial_generated_latent_frames if include_history_in_output else 0)
if "x0" in x0_output: if "x0" in x0_output:
x0_out = model.model.process_latent_out(x0_output["x0"].cpu()) x0_out = model.model.process_latent_out(x0_output["x0"].cpu())
@ -1321,7 +1287,6 @@ class HeliosVAEDecode(io.ComfyNode):
helios_chunk_decode = bool(samples.get("helios_chunk_decode", False)) helios_chunk_decode = bool(samples.get("helios_chunk_decode", False))
helios_chunk_latent_frames = int(samples.get("helios_chunk_latent_frames", 0) or 0) helios_chunk_latent_frames = int(samples.get("helios_chunk_latent_frames", 0) or 0)
helios_prefix_latent_frames = int(samples.get("helios_prefix_latent_frames", 0) or 0)
if ( if (
helios_chunk_decode helios_chunk_decode
@ -1330,12 +1295,7 @@ class HeliosVAEDecode(io.ComfyNode):
and latent.shape[2] > 0 and latent.shape[2] > 0
): ):
decoded_chunks = [] decoded_chunks = []
prefix_t = max(0, min(helios_prefix_latent_frames, latent.shape[2])) body = latent
if prefix_t > 0:
decoded_chunks.append(vae.decode(latent[:, :, :prefix_t]))
body = latent[:, :, prefix_t:]
for start in range(0, body.shape[2], helios_chunk_latent_frames): for start in range(0, body.shape[2], helios_chunk_latent_frames):
chunk = body[:, :, start:start + helios_chunk_latent_frames] chunk = body[:, :, start:start + helios_chunk_latent_frames]
if chunk.shape[2] == 0: if chunk.shape[2] == 0: