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