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import numpy as np
import torch
from ...models.transformers.transformer_cosmos3 import Cosmos3OmniTransformer
from ...pipelines.cosmos.pipeline_cosmos3_omni import _EMBODIMENT_TO_DOMAIN_ID, CosmosActionCondition
from ...schedulers import FlowMatchEulerDiscreteScheduler, UniPCMultistepScheduler
from ...utils.torch_utils import randn_tensor
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, ConfigSpec, InputParam, OutputParam
from .modular_pipeline import Cosmos3OmniModularPipeline
class Cosmos3PrepareTextSegmentsStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Builds cond/uncond text segments before denoising."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", Cosmos3OmniTransformer)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(name="cond_input_ids", required=True, description="Token IDs for the conditional prompt."),
InputParam(name="uncond_input_ids", required=True, description="Token IDs for the unconditional prompt."),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"cond_text_segment",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Conditional text segment for the denoiser.",
),
OutputParam(
"uncond_text_segment",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Unconditional text segment for the denoiser.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
block_state.cond_text_segment = components._prepare_text_segment(block_state.cond_input_ids, device=device)
block_state.uncond_text_segment = components._prepare_text_segment(block_state.uncond_input_ids, device=device)
self.set_block_state(state, block_state)
return components, state
class Cosmos3VisionPrepareLatentsStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Prepares noisy vision latents and the vision conditioning mask."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", Cosmos3OmniTransformer)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="x0_tokens_vision",
type_hint=torch.Tensor,
default=None,
description="Vision latents encoded from the conditioning image or video.",
),
InputParam(
name="vision_condition_frames",
type_hint=list[int],
default=None,
description="Latent-frame indexes fixed by visual conditioning.",
),
InputParam(name="num_frames", type_hint=int, required=True, description="Number of frames to generate."),
InputParam(
name="height", type_hint=int, required=True, description="Height of the generated video in pixels."
),
InputParam(
name="width", type_hint=int, required=True, description="Width of the generated video in pixels."
),
InputParam(name="fps", type_hint=float, default=24.0, description="Frame rate of the generated video."),
InputParam(
name="latents",
type_hint=torch.Tensor,
default=None,
description="Pre-generated noisy vision latents.",
),
InputParam.template("generator"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("latents", type_hint=torch.Tensor, description="Noisy vision latents for denoising."),
OutputParam("fps_vision", type_hint=float, description="Frame rate used to pack vision latents."),
OutputParam(
"vision_condition_mask",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Mask marking conditioned vision latent frames.",
),
OutputParam(
"vision_condition_indexes_for_pack",
type_hint=list[int],
description="Indexes of conditioned vision latent frames.",
),
OutputParam(
"vision_conditioning_latents",
type_hint=torch.Tensor,
description="Clean encoded vision latents used to re-anchor image conditioning each step.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
dtype = components.transformer.dtype
x0_tokens_vision = block_state.x0_tokens_vision
if x0_tokens_vision is None:
if block_state.num_frames < 1:
raise ValueError(f"num_frames must be >= 1, got {block_state.num_frames}.")
sf_spatial = components.vae_scale_factor_spatial
if block_state.height % sf_spatial != 0 or block_state.width % sf_spatial != 0:
raise ValueError(
f"height and width must be multiples of {sf_spatial}, got ({block_state.height}, {block_state.width})."
)
latent_shape = (
1,
components.num_channels_latents,
(block_state.num_frames - 1) // components.vae_scale_factor_temporal + 1,
block_state.height // sf_spatial,
block_state.width // sf_spatial,
)
x0_tokens_vision = torch.zeros(latent_shape, device=device, dtype=torch.float32)
else:
x0_tokens_vision = x0_tokens_vision.to(device=device, dtype=torch.float32)
block_state.fps_vision = float(block_state.fps)
condition_frames = block_state.vision_condition_frames or []
block_state.vision_condition_mask = torch.zeros((x0_tokens_vision.shape[2], 1, 1), device=device, dtype=dtype)
for frame_idx in condition_frames:
if 0 <= frame_idx < block_state.vision_condition_mask.shape[0]:
block_state.vision_condition_mask[frame_idx, 0, 0] = 1.0
if block_state.latents is None:
pure_noise = randn_tensor(
tuple(x0_tokens_vision.shape), generator=block_state.generator, device=device, dtype=dtype
)
block_state.latents = (
block_state.vision_condition_mask * x0_tokens_vision.to(device=device, dtype=dtype)
+ (1.0 - block_state.vision_condition_mask) * pure_noise
)
else:
block_state.latents = block_state.latents.to(device=device, dtype=dtype)
vision_condition_indexes = torch.nonzero(
block_state.vision_condition_mask[:, 0, 0] > 0, as_tuple=False
).flatten()
block_state.vision_condition_indexes_for_pack = [int(idx.item()) for idx in vision_condition_indexes]
block_state.vision_conditioning_latents = x0_tokens_vision
self.set_block_state(state, block_state)
return components, state
class Cosmos3SoundPrepareLatentsStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Prepares noisy sound latents and the sound conditioning mask."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("transformer", Cosmos3OmniTransformer),
ComponentSpec("scheduler", UniPCMultistepScheduler),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(name="num_frames", type_hint=int, required=True, description="Number of frames to generate."),
InputParam(name="fps", type_hint=float, default=24.0, description="Frame rate of the generated video."),
InputParam(
name="sound_latents",
type_hint=torch.Tensor,
default=None,
description="Pre-generated noisy sound latents.",
),
InputParam.template("generator"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("sound_latents", type_hint=torch.Tensor, description="Noisy sound latents for denoising."),
OutputParam("fps_sound", type_hint=float, description="Frame rate of the sound latent sequence."),
OutputParam(
"sound_condition_mask",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Mask marking conditioned sound latent frames.",
),
OutputParam("sound_scheduler", description="Scheduler used to update sound latents."),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
dtype = components.transformer.dtype
if not components.transformer.config.sound_gen:
raise ValueError("Sound generation requires a transformer trained with sound_gen=True.")
sound_dim = components.transformer.config.sound_dim
block_state.fps_sound = float(components.transformer.config.sound_latent_fps)
n_audio_samples = int(block_state.num_frames / block_state.fps * components.sound_sampling_rate)
hop_size = components.sound_hop_size
t_sound = (n_audio_samples + hop_size - 1) // hop_size
x0_tokens_sound = torch.zeros(sound_dim, t_sound, device=device, dtype=dtype)
block_state.sound_condition_mask = torch.zeros((x0_tokens_sound.shape[1], 1), device=device, dtype=dtype)
if block_state.sound_latents is None:
pure_noise = randn_tensor(
tuple(x0_tokens_sound.shape), generator=block_state.generator, device=device, dtype=dtype
)
block_state.sound_latents = (
block_state.sound_condition_mask.T * x0_tokens_sound
+ (1.0 - block_state.sound_condition_mask.T) * pure_noise
)
else:
block_state.sound_latents = block_state.sound_latents.to(device=device, dtype=dtype)
block_state.sound_scheduler = copy.deepcopy(components.scheduler)
self.set_block_state(state, block_state)
return components, state
class Cosmos3ActionPrepareLatentsStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Prepares noisy action latents and the action conditioning mask."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("transformer", Cosmos3OmniTransformer),
ComponentSpec("scheduler", UniPCMultistepScheduler),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="action",
type_hint=CosmosActionCondition,
required=True,
description="Action-conditioning metadata.",
),
InputParam(
name="action_condition_frame_indexes",
type_hint=list[int],
default=None,
description="Action-frame indexes fixed by action conditioning.",
),
InputParam(
name="action_latents",
type_hint=torch.Tensor,
default=None,
description="Pre-generated noisy action latents.",
),
InputParam.template("generator"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("action_latents", type_hint=torch.Tensor, description="Noisy action latents for denoising."),
OutputParam(
"action_condition_mask",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Mask marking conditioned action latent frames.",
),
OutputParam(
"action_domain_ids",
type_hint=list[torch.Tensor],
kwargs_type="denoiser_input_fields",
description="Embodiment domain IDs for action conditioning.",
),
OutputParam(
"raw_action_dim_resolved",
type_hint=int,
kwargs_type="denoiser_input_fields",
description="Unpadded action-vector dimension.",
),
OutputParam("action_scheduler", description="Scheduler used to update action latents."),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
dtype = components.transformer.dtype
action = block_state.action
if not components.transformer.config.action_gen:
raise ValueError("action requires a transformer trained with action_gen=True.")
block_state.raw_action_dim_resolved = int(action.raw_action_dim) if action.raw_action_dim is not None else None
if (
block_state.raw_action_dim_resolved is not None
and block_state.raw_action_dim_resolved > components.transformer.config.action_dim
):
raise ValueError(
f"raw_action_dim={block_state.raw_action_dim_resolved} exceeds the model action_dim="
f"{components.transformer.config.action_dim}."
)
action_chunk_size = action.chunk_size
action_dim = components.transformer.action_dim
if action.mode == "forward_dynamics":
raw_actions = action.raw_actions
if raw_actions is None:
raise ValueError("action_mode='forward_dynamics' requires an action tensor.")
raw_actions = raw_actions.to(device=device, dtype=dtype)
if raw_actions.shape[-1] > action_dim:
raise ValueError(
f"Cosmos3 action dimension {raw_actions.shape[-1]} exceeds model action_dim={action_dim}."
)
if raw_actions.shape[0] < action_chunk_size:
raw_actions = torch.cat(
[raw_actions, raw_actions[-1:].expand(action_chunk_size - raw_actions.shape[0], -1)],
dim=0,
)
raw_actions = raw_actions[:action_chunk_size]
if raw_actions.shape[-1] < action_dim:
action_padding = torch.zeros(
raw_actions.shape[0],
action_dim - raw_actions.shape[-1],
dtype=raw_actions.dtype,
device=raw_actions.device,
)
raw_actions = torch.cat([raw_actions, action_padding], dim=-1)
x0_tokens_action = raw_actions
else:
x0_tokens_action = torch.zeros(action_chunk_size, action_dim, device=device, dtype=dtype)
if action.domain_name not in _EMBODIMENT_TO_DOMAIN_ID:
raise ValueError(
f"Unknown Cosmos3 action domain_name={action.domain_name!r}; expected one of {sorted(_EMBODIMENT_TO_DOMAIN_ID)}."
)
block_state.action_domain_ids = [
torch.tensor([_EMBODIMENT_TO_DOMAIN_ID[action.domain_name]], dtype=torch.long, device=device)
]
condition_frames = block_state.action_condition_frame_indexes or []
block_state.action_condition_mask = torch.zeros((x0_tokens_action.shape[0], 1), device=device, dtype=dtype)
for frame_idx in condition_frames:
if 0 <= frame_idx < block_state.action_condition_mask.shape[0]:
block_state.action_condition_mask[frame_idx, 0] = 1.0
if block_state.action_latents is None:
pure_noise = randn_tensor(
tuple(x0_tokens_action.shape), generator=block_state.generator, device=device, dtype=dtype
)
block_state.action_latents = (
block_state.action_condition_mask * x0_tokens_action
+ (1.0 - block_state.action_condition_mask) * pure_noise
)
if block_state.raw_action_dim_resolved is not None:
block_state.action_latents[:, block_state.raw_action_dim_resolved :] = 0
else:
block_state.action_latents = block_state.action_latents.to(device=device, dtype=dtype)
block_state.action_scheduler = copy.deepcopy(components.scheduler)
self.set_block_state(state, block_state)
return components, state
class Cosmos3VisionPackSequenceStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Builds separate cond/uncond vision sequence segments."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", Cosmos3OmniTransformer)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="cond_text_segment", type_hint=dict, required=True, description="Conditional text segment."
),
InputParam(
name="uncond_text_segment",
type_hint=dict,
required=True,
description="Unconditional text segment.",
),
InputParam(
name="latents", type_hint=torch.Tensor, required=True, description="Noisy vision latents to pack."
),
InputParam(
name="fps_vision",
type_hint=float,
required=True,
description="Frame rate used to pack vision latents.",
),
InputParam(
name="vision_condition_indexes_for_pack",
type_hint=list[int],
required=True,
description="Indexes of conditioned vision latent frames.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"cond_vision_segment",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Conditional vision segment for the denoiser.",
),
OutputParam(
"uncond_vision_segment",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Unconditional vision segment for the denoiser.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
has_image_condition = bool(block_state.vision_condition_indexes_for_pack)
block_state.cond_vision_segment = components._prepare_vision_segment(
input_vision_tokens=block_state.latents,
has_image_condition=has_image_condition,
mrope_offset=block_state.cond_text_segment["vision_start_temporal_offset"],
vision_fps=block_state.fps_vision,
curr=block_state.cond_text_segment["und_len"],
device=device,
condition_frame_indexes=block_state.vision_condition_indexes_for_pack,
)
block_state.uncond_vision_segment = components._prepare_vision_segment(
input_vision_tokens=block_state.latents,
has_image_condition=has_image_condition,
mrope_offset=block_state.uncond_text_segment["vision_start_temporal_offset"],
vision_fps=block_state.fps_vision,
curr=block_state.uncond_text_segment["und_len"],
device=device,
condition_frame_indexes=block_state.vision_condition_indexes_for_pack,
)
self.set_block_state(state, block_state)
return components, state
class Cosmos3SoundPackSequenceStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Builds separate cond/uncond sound sequence segments."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", Cosmos3OmniTransformer)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="cond_text_segment", type_hint=dict, required=True, description="Conditional text segment."
),
InputParam(
name="uncond_text_segment",
type_hint=dict,
required=True,
description="Unconditional text segment.",
),
InputParam(
name="cond_sequence_length",
type_hint=int,
required=True,
description="Conditional multimodal sequence length.",
),
InputParam(
name="uncond_sequence_length",
type_hint=int,
required=True,
description="Unconditional multimodal sequence length.",
),
InputParam(
name="sound_latents", type_hint=torch.Tensor, required=True, description="Noisy sound latents to pack."
),
InputParam(
name="fps_sound",
type_hint=float,
required=True,
description="Frame rate of the sound latent sequence.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"cond_sound_segment",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Conditional sound segment for the denoiser.",
),
OutputParam(
"uncond_sound_segment",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Unconditional sound segment for the denoiser.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
block_state.cond_sound_segment = components._prepare_sound_segment(
input_sound_tokens=block_state.sound_latents,
mrope_offset=block_state.cond_text_segment["vision_start_temporal_offset"],
sound_fps=block_state.fps_sound,
curr=block_state.cond_sequence_length,
device=device,
)
block_state.uncond_sound_segment = components._prepare_sound_segment(
input_sound_tokens=block_state.sound_latents,
mrope_offset=block_state.uncond_text_segment["vision_start_temporal_offset"],
sound_fps=block_state.fps_sound,
curr=block_state.uncond_sequence_length,
device=device,
)
self.set_block_state(state, block_state)
return components, state
class Cosmos3ActionPackSequenceStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Builds separate cond/uncond action sequence segments."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", Cosmos3OmniTransformer)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="cond_text_segment", type_hint=dict, required=True, description="Conditional text segment."
),
InputParam(
name="uncond_text_segment",
type_hint=dict,
required=True,
description="Unconditional text segment.",
),
InputParam(
name="cond_sequence_length",
type_hint=int,
required=True,
description="Conditional multimodal sequence length.",
),
InputParam(
name="uncond_sequence_length",
type_hint=int,
required=True,
description="Unconditional multimodal sequence length.",
),
InputParam(
name="action_latents",
type_hint=torch.Tensor,
required=True,
description="Noisy action latents to pack.",
),
InputParam(
name="action_condition_frame_indexes",
type_hint=list[int],
default=None,
description="Action-frame indexes fixed by action conditioning.",
),
InputParam(
name="fps_vision",
type_hint=float,
required=True,
description="Frame rate used to pack vision latents.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"cond_action_segment",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Conditional action segment for the denoiser.",
),
OutputParam(
"uncond_action_segment",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Unconditional action segment for the denoiser.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
block_state.cond_action_segment = components._prepare_action_segment(
input_action_tokens=block_state.action_latents,
condition_frame_indexes=block_state.action_condition_frame_indexes,
mrope_offset=block_state.cond_text_segment["vision_start_temporal_offset"],
action_fps=block_state.fps_vision,
curr=block_state.cond_sequence_length,
device=device,
)
block_state.uncond_action_segment = components._prepare_action_segment(
input_action_tokens=block_state.action_latents,
condition_frame_indexes=block_state.action_condition_frame_indexes,
mrope_offset=block_state.uncond_text_segment["vision_start_temporal_offset"],
action_fps=block_state.fps_vision,
curr=block_state.uncond_sequence_length,
device=device,
)
self.set_block_state(state, block_state)
return components, state
class Cosmos3VisionDenoiseInputStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Assembles text and vision sequence metadata for the denoising loop."
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="cond_text_segment", type_hint=dict, required=True, description="Conditional text segment."
),
InputParam(
name="uncond_text_segment",
type_hint=dict,
required=True,
description="Unconditional text segment.",
),
InputParam(
name="cond_vision_segment", type_hint=dict, required=True, description="Conditional vision segment."
),
InputParam(
name="uncond_vision_segment",
type_hint=dict,
required=True,
description="Unconditional vision segment.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"cond_position_ids",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Conditional multimodal RoPE position IDs.",
),
OutputParam(
"uncond_position_ids",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Unconditional multimodal RoPE position IDs.",
),
OutputParam(
"cond_sequence_length",
type_hint=int,
kwargs_type="denoiser_input_fields",
description="Conditional multimodal sequence length.",
),
OutputParam(
"uncond_sequence_length",
type_hint=int,
kwargs_type="denoiser_input_fields",
description="Unconditional multimodal sequence length.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.cond_position_ids = torch.cat(
[
block_state.cond_text_segment["text_mrope_ids"],
block_state.cond_vision_segment["vision_mrope_ids"],
],
dim=1,
)
block_state.uncond_position_ids = torch.cat(
[
block_state.uncond_text_segment["text_mrope_ids"],
block_state.uncond_vision_segment["vision_mrope_ids"],
],
dim=1,
)
block_state.cond_sequence_length = (
block_state.cond_text_segment["und_len"] + block_state.cond_vision_segment["num_vision_tokens"]
)
block_state.uncond_sequence_length = (
block_state.uncond_text_segment["und_len"] + block_state.uncond_vision_segment["num_vision_tokens"]
)
self.set_block_state(state, block_state)
return components, state
class Cosmos3SoundDenoiseInputStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Appends sound sequence metadata to the denoising-loop inputs."
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="cond_position_ids",
type_hint=torch.Tensor,
required=True,
description="Conditional multimodal RoPE position IDs.",
),
InputParam(
name="uncond_position_ids",
type_hint=torch.Tensor,
required=True,
description="Unconditional multimodal RoPE position IDs.",
),
InputParam(
name="cond_sequence_length",
type_hint=int,
required=True,
description="Conditional multimodal sequence length.",
),
InputParam(
name="uncond_sequence_length",
type_hint=int,
required=True,
description="Unconditional multimodal sequence length.",
),
InputParam(
name="cond_sound_segment", type_hint=dict, required=True, description="Conditional sound segment."
),
InputParam(
name="uncond_sound_segment",
type_hint=dict,
required=True,
description="Unconditional sound segment.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"cond_position_ids",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Conditional multimodal RoPE position IDs.",
),
OutputParam(
"uncond_position_ids",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Unconditional multimodal RoPE position IDs.",
),
OutputParam(
"cond_sequence_length",
type_hint=int,
kwargs_type="denoiser_input_fields",
description="Conditional multimodal sequence length.",
),
OutputParam(
"uncond_sequence_length",
type_hint=int,
kwargs_type="denoiser_input_fields",
description="Unconditional multimodal sequence length.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.cond_position_ids = torch.cat(
[block_state.cond_position_ids, block_state.cond_sound_segment["sound_mrope_ids"]], dim=1
)
block_state.uncond_position_ids = torch.cat(
[block_state.uncond_position_ids, block_state.uncond_sound_segment["sound_mrope_ids"]], dim=1
)
block_state.cond_sequence_length += block_state.cond_sound_segment["sound_len"]
block_state.uncond_sequence_length += block_state.uncond_sound_segment["sound_len"]
self.set_block_state(state, block_state)
return components, state
class Cosmos3ActionDenoiseInputStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Appends action sequence metadata to the denoising-loop inputs."
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="cond_position_ids",
type_hint=torch.Tensor,
required=True,
description="Conditional multimodal RoPE position IDs.",
),
InputParam(
name="uncond_position_ids",
type_hint=torch.Tensor,
required=True,
description="Unconditional multimodal RoPE position IDs.",
),
InputParam(
name="cond_sequence_length",
type_hint=int,
required=True,
description="Conditional multimodal sequence length.",
),
InputParam(
name="uncond_sequence_length",
type_hint=int,
required=True,
description="Unconditional multimodal sequence length.",
),
InputParam(
name="cond_action_segment", type_hint=dict, required=True, description="Conditional action segment."
),
InputParam(
name="uncond_action_segment",
type_hint=dict,
required=True,
description="Unconditional action segment.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"cond_position_ids",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Conditional multimodal RoPE position IDs.",
),
OutputParam(
"uncond_position_ids",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Unconditional multimodal RoPE position IDs.",
),
OutputParam(
"cond_sequence_length",
type_hint=int,
kwargs_type="denoiser_input_fields",
description="Conditional multimodal sequence length.",
),
OutputParam(
"uncond_sequence_length",
type_hint=int,
kwargs_type="denoiser_input_fields",
description="Unconditional multimodal sequence length.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.cond_position_ids = torch.cat(
[block_state.cond_position_ids, block_state.cond_action_segment["action_mrope_ids"]], dim=1
)
block_state.uncond_position_ids = torch.cat(
[block_state.uncond_position_ids, block_state.uncond_action_segment["action_mrope_ids"]], dim=1
)
block_state.cond_sequence_length += block_state.cond_action_segment["action_len"]
block_state.uncond_sequence_length += block_state.uncond_action_segment["action_len"]
self.set_block_state(state, block_state)
return components, state
class Cosmos3SetTimestepsStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Initializes scheduler timesteps."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", UniPCMultistepScheduler)]
@property
def expected_configs(self) -> list[ConfigSpec]:
return [ConfigSpec(name="use_native_flow_schedule", default=False)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("num_inference_steps", required=True),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("timesteps", type_hint=torch.Tensor, description="Scheduler timesteps for denoising."),
OutputParam("num_warmup_steps", type_hint=int, description="Number of scheduler warmup steps."),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
if components.config.use_native_flow_schedule:
sigmas = np.linspace(
1.0 - 1.0 / components.scheduler.config.num_train_timesteps,
0.0,
block_state.num_inference_steps + 1,
)[:-1]
components.scheduler.set_timesteps(block_state.num_inference_steps, device=device, sigmas=sigmas)
else:
components.scheduler.set_timesteps(block_state.num_inference_steps, device=device)
block_state.timesteps = components.scheduler.timesteps
block_state.num_warmup_steps = (
len(block_state.timesteps) - block_state.num_inference_steps * components.scheduler.order
)
self.set_block_state(state, block_state)
return components, state
class Cosmos3TransferPrepareLatentsStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return (
"Per-chunk transfer latent prep: takes the clean target latents encoded by "
"Cosmos3TransferChunkVaeEncoderStep and builds the noisy target latents, velocity mask, condition latents "
"and conditioned-frame indexes for this chunk."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", Cosmos3OmniTransformer)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="x0_tokens_vision",
type_hint=torch.Tensor,
required=True,
description="Clean target vision latents encoded from the seeded target frames.",
),
InputParam(
name="current_conditional_frames",
type_hint=int,
required=True,
description="Number of pixel frames used to seed this chunk's target.",
),
InputParam.template("generator"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("latents", type_hint=torch.Tensor, description="Noisy target latents for this chunk."),
OutputParam(
"velocity_mask",
type_hint=torch.Tensor,
description="Mask that zeroes the velocity on conditioned (clean) latent frames.",
),
OutputParam(
"condition_latents",
type_hint=torch.Tensor,
description="Clean target latents on the conditioned frames (the autoregressive seed).",
),
OutputParam(
"target_condition_indexes",
type_hint=list[int],
description="Latent-frame indexes fixed by the chunk's conditioning.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
dtype = components.transformer.dtype
tcf = components.vae_scale_factor_temporal
target_x0 = block_state.x0_tokens_vision.to(device=device)
current_conditional_frames = block_state.current_conditional_frames
# Build the noisy target latents + conditioning mask from the clean target latents.
latent_t = target_x0.shape[2]
condition_mask = torch.zeros((latent_t, 1, 1), device=device, dtype=dtype)
latent_condition_frames = 0
if current_conditional_frames > 0:
latent_condition_frames = (current_conditional_frames - 1) // tcf + 1
condition_mask[:latent_condition_frames] = 1.0
noise = randn_tensor(tuple(target_x0.shape), generator=block_state.generator, device=device, dtype=dtype)
block_state.latents = condition_mask * target_x0 + (1.0 - condition_mask) * noise
block_state.velocity_mask = 1.0 - condition_mask
block_state.condition_latents = condition_mask * target_x0
block_state.target_condition_indexes = list(range(latent_condition_frames))
self.set_block_state(state, block_state)
return components, state
class Cosmos3TransferPackSequenceStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return (
"Pre-packs the three transfer CFG sequence variants: cond_full / uncond_full carry every control item, "
"the no-control branch drops them (only [text, target]) so the control axis can be amplified."
)
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="cond_text_segment", type_hint=dict, required=True, description="Conditional text segment."
),
InputParam(
name="uncond_text_segment", type_hint=dict, required=True, description="Unconditional text segment."
),
InputParam(
name="control_latents",
type_hint=list[torch.Tensor],
required=True,
description="Clean control latents for this chunk, one per hint in canonical order.",
),
InputParam(
name="latents",
type_hint=torch.Tensor,
required=True,
description="Noisy target latents for this chunk.",
),
InputParam(
name="target_condition_indexes",
type_hint=list[int],
required=True,
description="Latent-frame indexes fixed by the chunk's conditioning.",
),
InputParam(name="fps", type_hint=float, default=24.0, description="Frame rate of the generated video."),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"cond_full_static",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Conditional [control..., target] transfer sequence carrying every control item.",
),
OutputParam(
"cond_no_control_static",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Conditional [target] transfer sequence with the control items dropped.",
),
OutputParam(
"uncond_full_static",
type_hint=dict,
kwargs_type="denoiser_input_fields",
description="Unconditional [control..., target] transfer sequence for text CFG.",
),
OutputParam(
"num_noisy_vision_tokens",
type_hint=int,
description="Number of noisy target vision tokens denoised each step.",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
num_hints = len(block_state.control_latents)
def _vision_pack(text_segment: dict, include_controls: bool) -> dict:
if include_controls:
vision_items = [*block_state.control_latents, block_state.latents]
condition_indexes = [None] * num_hints + [block_state.target_condition_indexes]
clean_flags = [True] * num_hints + [False]
else:
vision_items = [block_state.latents]
condition_indexes = [block_state.target_condition_indexes]
clean_flags = [False]
# Transfer packs [ctrl_1, ..., ctrl_N, target] into one vision segment
mrope_offset = text_segment["vision_start_temporal_offset"]
item_curr = text_segment["und_len"]
token_shapes = []
sequence_index_parts = []
mse_loss_index_parts = []
noisy_frame_indexes_per_item = []
mrope_id_parts = []
num_vision_tokens = 0
num_noisy_vision_tokens = 0
for item, item_condition, is_clean in zip(vision_items, condition_indexes, clean_flags):
latent_t = item.shape[2]
if is_clean:
frame_condition = list(range(latent_t))
else:
frame_condition = item_condition if item_condition is not None else []
item_segment = components._prepare_vision_segment(
input_vision_tokens=item,
has_image_condition=False,
mrope_offset=mrope_offset,
vision_fps=block_state.fps,
curr=item_curr,
device=device,
condition_frame_indexes=frame_condition,
)
token_shapes.extend(item_segment["vision_token_shapes"])
sequence_index_parts.append(item_segment["vision_sequence_indexes"])
mse_loss_index_parts.append(item_segment["vision_mse_loss_indexes"])
noisy_frame_indexes_per_item.extend(item_segment["vision_noisy_frame_indexes"])
mrope_id_parts.append(item_segment["vision_mrope_ids"])
num_vision_tokens += item_segment["num_vision_tokens"]
num_noisy_vision_tokens += item_segment["num_noisy_vision_tokens"]
item_curr += item_segment["num_vision_tokens"]
vision_segment = {
"vision_token_shapes": token_shapes,
"vision_sequence_indexes": torch.cat(sequence_index_parts, dim=0),
"vision_mse_loss_indexes": torch.cat(mse_loss_index_parts, dim=0),
"vision_noisy_frame_indexes": noisy_frame_indexes_per_item,
"vision_mrope_ids": torch.cat(mrope_id_parts, dim=1),
"num_vision_tokens": num_vision_tokens,
"num_noisy_vision_tokens": num_noisy_vision_tokens,
}
return {
**text_segment,
**vision_segment,
"position_ids": torch.cat([text_segment["text_mrope_ids"], vision_segment["vision_mrope_ids"]], dim=1),
"sequence_length": text_segment["und_len"] + vision_segment["num_vision_tokens"],
}
block_state.cond_full_static = _vision_pack(block_state.cond_text_segment, include_controls=True)
block_state.cond_no_control_static = _vision_pack(block_state.cond_text_segment, include_controls=False)
block_state.uncond_full_static = _vision_pack(block_state.uncond_text_segment, include_controls=True)
block_state.num_noisy_vision_tokens = block_state.cond_full_static["num_noisy_vision_tokens"]
self.set_block_state(state, block_state)
return components, state
class Cosmos3TransferSetTimestepsStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return (
"Resets the scheduler and computes timesteps for a single transfer chunk. UniPCMultistepScheduler keeps "
"per-step state on the instance, so it is reset per chunk (each autoregressive chunk is a full denoise)."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", UniPCMultistepScheduler)]
@property
def inputs(self) -> list[InputParam]:
return [InputParam.template("num_inference_steps", required=True)]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("timesteps", type_hint=torch.Tensor, description="Scheduler timesteps for this chunk."),
OutputParam(
"num_warmup_steps", type_hint=int, description="Number of scheduler warmup steps for this chunk."
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
components.scheduler.set_timesteps(block_state.num_inference_steps, device=device)
block_state.timesteps = components.scheduler.timesteps
block_state.num_warmup_steps = (
len(block_state.timesteps) - block_state.num_inference_steps * components.scheduler.order
)
self.set_block_state(state, block_state)
return components, state
class Cosmos3DistilledSetTimestepsStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Initializes the fixed distilled sampling schedule from the pipeline's `distilled_sigmas` config."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def expected_configs(self) -> list[ConfigSpec]:
return [
ConfigSpec(name="is_distilled", default=True),
ConfigSpec(name="distilled_sigmas", default=None),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("num_inference_steps", required=False, default=None),
InputParam(
name="guidance_scale",
type_hint=float,
default=None,
description=(
"Unused for distilled checkpoints; classifier-free guidance is baked into the weights and the "
"scale is forced to 1.0. Passing a value other than 1.0 raises an error."
),
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("timesteps", type_hint=torch.Tensor, description="Scheduler timesteps for denoising."),
OutputParam("num_warmup_steps", type_hint=int, description="Number of scheduler warmup steps."),
OutputParam(
"num_inference_steps",
type_hint=int,
description="Resolved number of denoising steps (fixed by the distilled schedule).",
),
OutputParam(
name="guidance_scale",
type_hint=float,
description="Resolved classifier-free guidance scale (always 1.0 for distilled checkpoints).",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
sigmas = components.config.distilled_sigmas
if not sigmas:
raise ValueError(
"Cosmos3DistilledSetTimestepsStep requires the pipeline config `distilled_sigmas` to be set "
"(populated from the distilled checkpoint's `modular_model_index.json`). Load a distilled Cosmos3 "
"checkpoint or use `Cosmos3OmniModularPipeline` for base checkpoints."
)
sigmas = [float(s) for s in sigmas]
distilled_steps = len(sigmas)
if block_state.num_inference_steps is not None and block_state.num_inference_steps != distilled_steps:
raise ValueError(
"This is a distilled checkpoint; the step count is fixed by the pipeline's "
f"`distilled_sigmas` config ({distilled_steps} steps). "
f"`num_inference_steps` must be {distilled_steps} or left unset (got {block_state.num_inference_steps})."
)
if block_state.guidance_scale is not None and block_state.guidance_scale != 1.0:
raise ValueError(
"This is a distilled checkpoint; classifier-free guidance is baked into the weights. "
f"`guidance_scale` must be 1.0 or left unset (got {block_state.guidance_scale})."
)
components.scheduler.set_timesteps(sigmas=sigmas, device=device)
block_state.num_inference_steps = distilled_steps
block_state.guidance_scale = 1.0
block_state.timesteps = components.scheduler.timesteps
block_state.num_warmup_steps = len(block_state.timesteps) - distilled_steps * components.scheduler.order
self.set_block_state(state, block_state)
return components, state
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