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186aa49 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 | import torch
from ...configuration_utils import FrozenDict
from ...models.autoencoders.autoencoder_cosmos3_audio import Cosmos3AVAEAudioTokenizer
from ...models.autoencoders.autoencoder_kl_wan import AutoencoderKLWan
from ...utils import logging
from ...video_processor import VideoProcessor
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import Cosmos3OmniModularPipeline
logger = logging.get_logger(__name__)
class Cosmos3VideoDecodeStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Decodes denoised vision latents into video outputs."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKLWan),
ComponentSpec(
"video_processor",
VideoProcessor,
config=FrozenDict({"vae_scale_factor": 16, "resample": "bilinear"}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("latents", required=True, description="Denoised vision latents to decode."),
InputParam.template("output_type", default="pil"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam.template("videos")]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
if block_state.output_type == "latent":
block_state.videos = block_state.latents
else:
in_dtype = block_state.latents.dtype
vae_dtype = components.vae.dtype
mean = components._vae_latents_mean.to(device=block_state.latents.device, dtype=vae_dtype)
inv_std = components._vae_latents_inv_std.to(device=block_state.latents.device, dtype=vae_dtype)
z_raw = block_state.latents.to(vae_dtype) / inv_std.view(1, -1, 1, 1, 1) + mean.view(1, -1, 1, 1, 1)
decoded = components.vae.decode(z_raw).sample.to(in_dtype)
block_state.videos = components.video_processor.postprocess_video(
decoded, output_type=block_state.output_type
)[0]
if components.requires_safety_checker and block_state.output_type != "latent":
if getattr(components, "safety_checker", None) is None:
raise ValueError(
"Cosmos3 requires a safety checker by default. Call `pipe.enable_safety_checker()` to load it "
"(or pass your own), or opt out explicitly with `pipe.disable_safety_checker()`."
)
block_state.videos = components._apply_video_safety_check(
block_state.videos, output_type=block_state.output_type, device=device
)
self.set_block_state(state, block_state)
return components, state
class Cosmos3SoundDecodeStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return "Decodes sound latents into waveform output."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("sound_tokenizer", Cosmos3AVAEAudioTokenizer)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="sound_latents",
type_hint=torch.Tensor,
required=True,
description="Denoised sound latents to decode.",
)
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("sound", type_hint=torch.Tensor, description="Generated waveform."),
OutputParam("sampling_rate", type_hint=int, description="Sample rate of the generated waveform in Hz."),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
if components.sound_tokenizer is None:
raise ValueError("Sound decoding requires a sound-capable checkpoint with a sound_tokenizer.")
block_state.sound = components.decode_sound(block_state.sound_latents)
block_state.sampling_rate = int(components.sound_tokenizer.config.sampling_rate)
self.set_block_state(state, block_state)
return components, state
class Cosmos3TransferDecodeChunkStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return (
"Decodes one transfer chunk's latents to pixels (float32, clamped to [-1, 1]), records it as the "
"autoregressive seed for the next chunk, and appends it to output_chunks (dropping the overlap that "
"later chunks share with the previous chunk's conditioning frames)."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("vae", AutoencoderKLWan)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="latents",
type_hint=torch.Tensor,
required=True,
description="Denoised target latents for this chunk.",
),
InputParam(name="chunk_id", type_hint=int, default=0, description="Index of the current chunk."),
InputParam(
name="current_conditional_frames",
type_hint=int,
required=True,
description="Number of pixel frames this chunk reused from the previous chunk.",
),
InputParam(
name="output_chunks",
type_hint=list[torch.Tensor],
required=True,
description="Decoded pixel chunks accumulated so far.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"previous_output",
type_hint=torch.Tensor,
description="Decoded pixels of this chunk, used to seed the next chunk.",
),
OutputParam(
"output_chunks",
type_hint=list[torch.Tensor],
description="Decoded pixel chunks accumulated so far (with this chunk appended).",
),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
latents = block_state.latents
vae_dtype = components.vae.dtype
mean = components._vae_latents_mean.to(device=latents.device, dtype=vae_dtype)
inv_std = components._vae_latents_inv_std.to(device=latents.device, dtype=vae_dtype)
z_raw = latents.to(vae_dtype) / inv_std.view(1, -1, 1, 1, 1) + mean.view(1, -1, 1, 1, 1)
output_video = components.vae.decode(z_raw).sample.to(torch.float32).clamp(-1, 1)
block_state.previous_output = output_video
chunk = (
output_video if block_state.chunk_id == 0 else output_video[:, :, block_state.current_conditional_frames :]
)
block_state.output_chunks = [*block_state.output_chunks, chunk]
self.set_block_state(state, block_state)
return components, state
class Cosmos3TransferStitchStep(ModularPipelineBlocks):
model_name = "cosmos3-omni"
@property
def description(self) -> str:
return (
"Concatenates the decoded transfer chunks along time, truncates to total_frames, and post-processes to "
"the requested output type. Transfer produces no audio, so sound / sampling_rate are None."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKLWan),
ComponentSpec(
"video_processor",
VideoProcessor,
config=FrozenDict({"vae_scale_factor": 16, "resample": "bilinear"}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="output_chunks",
type_hint=list[torch.Tensor],
required=True,
description="Decoded pixel chunks to stitch together.",
),
InputParam(
name="total_frames", type_hint=int, required=True, description="Total number of output frames to keep."
),
InputParam.template("output_type", default="pil"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("videos", description="The generated transfer video."),
OutputParam("sound", description="Always None for transfer (no audio)."),
OutputParam("sampling_rate", description="Always None for transfer (no audio)."),
]
@torch.no_grad()
def __call__(self, components: Cosmos3OmniModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
decoded = torch.cat(block_state.output_chunks, dim=2)[:, :, : block_state.total_frames]
block_state.videos = components.video_processor.postprocess_video(
decoded, output_type=block_state.output_type
)[0]
if components.requires_safety_checker and block_state.output_type != "latent":
if getattr(components, "safety_checker", None) is None:
raise ValueError(
"Cosmos3 requires a safety checker by default. Call `pipe.enable_safety_checker()` to load it "
"(or pass your own), or opt out explicitly with `pipe.disable_safety_checker()`."
)
block_state.videos = components._apply_video_safety_check(
block_state.videos, output_type=block_state.output_type, device=device
)
block_state.sound = None
block_state.sampling_rate = None
self.set_block_state(state, block_state)
return components, state
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