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2107900 3235938 2107900 3235938 2107900 3235938 2107900 3235938 2107900 | 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 | # Copyright 2026 OpenMOSS and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0. See the LICENSE file in this repository.
import numpy as np
import torch
from diffusers.configuration_utils import FrozenDict
from diffusers.modular_pipelines import ModularPipelineBlocks, PipelineState
from diffusers.modular_pipelines.modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.utils.torch_utils import randn_tensor
from .transformer_moss_image import MOSSImageTransformer
# VAE spatial downsampling (8) times the latent patch size (2)
SPATIAL_COMPRESSION = 16
# 32 VAE channels packed in 2x2 patches
LATENT_CHANNELS = 128
class MOSSImageTextInputStep(ModularPipelineBlocks):
model_name = "moss-image"
@property
def description(self) -> str:
return (
"Input step that determines `batch_size` and `dtype`, and repeats each prompt's conditioning "
"`num_images_per_prompt` times."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", MOSSImageTransformer)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("num_images_per_prompt"),
InputParam(
"prompt_embeds",
required=True,
type_hint=list[torch.Tensor],
description="Text-encoder states, one tensor per prompt. Can be generated in the text_encoder step.",
),
InputParam(
"negative_prompt_embeds",
type_hint=list[torch.Tensor],
description="Negative text-encoder states, one tensor per prompt. Can be generated in the "
"text_encoder step.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"batch_size",
type_hint=int,
description="Number of prompts; the model batch is `batch_size * num_images_per_prompt`.",
),
OutputParam("dtype", type_hint=torch.dtype, description="Dtype of the transformer inputs."),
OutputParam(
"prompt_embeds",
type_hint=list[torch.Tensor],
kwargs_type="denoiser_input_fields",
description="Text-encoder states repeated per image.",
),
OutputParam(
"negative_prompt_embeds",
type_hint=list[torch.Tensor],
kwargs_type="denoiser_input_fields",
description="Negative text-encoder states repeated per image.",
),
]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
negative_prompt_embeds = block_state.negative_prompt_embeds
if negative_prompt_embeds is not None and len(negative_prompt_embeds) != len(block_state.prompt_embeds):
raise ValueError(
f"`negative_prompt_embeds` ({len(negative_prompt_embeds)}) and `prompt_embeds` "
f"({len(block_state.prompt_embeds)}) must hold one tensor per prompt"
)
block_state.batch_size = len(block_state.prompt_embeds)
block_state.dtype = components.transformer.dtype
def repeat(embeds):
if embeds is None:
return None
return [e for e in embeds for _ in range(block_state.num_images_per_prompt)]
block_state.prompt_embeds = repeat(block_state.prompt_embeds)
block_state.negative_prompt_embeds = repeat(negative_prompt_embeds)
self.set_block_state(state, block_state)
return components, state
class MOSSImagePrepareLatentsStep(ModularPipelineBlocks):
model_name = "moss-image"
@property
def description(self) -> str:
return "Prepare latents step that creates the initial float32 noise for text-to-image generation."
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("height", default=1024),
InputParam.template("width", default=1024),
InputParam(
"latents",
type_hint=torch.Tensor,
description="Initial noise of shape `(batch_size * num_images_per_prompt, 128, height // 16, "
"width // 16)`.",
),
InputParam.template("num_images_per_prompt"),
InputParam.template("generator"),
InputParam(
"batch_size",
required=True,
type_hint=int,
description="Number of prompts. Can be generated in the input step.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"latents",
type_hint=torch.Tensor,
description="The initial noisy latents, float32, of shape `(batch, 128, height // 16, width // 16)`.",
)
]
@staticmethod
def check_inputs(height, width):
if height % SPATIAL_COMPRESSION != 0 or width % SPATIAL_COMPRESSION != 0:
raise ValueError(
f"`height` and `width` have to be divisible by {SPATIAL_COMPRESSION} but are {height} and {width}."
)
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
self.check_inputs(block_state.height, block_state.width)
shape = (
block_state.batch_size * block_state.num_images_per_prompt,
LATENT_CHANNELS,
block_state.height // SPATIAL_COMPRESSION,
block_state.width // SPATIAL_COMPRESSION,
)
# Sample in float32: noise cast from bfloat16 would have lost its precision
if block_state.latents is None:
block_state.latents = randn_tensor(
shape, generator=block_state.generator, device=components._execution_device, dtype=torch.float32
)
elif block_state.latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {tuple(block_state.latents.shape)}, expected {shape}")
else:
block_state.latents = block_state.latents.to(components._execution_device, torch.float32)
self.set_block_state(state, block_state)
return components, state
class MOSSImageSetTimestepsStep(ModularPipelineBlocks):
model_name = "moss-image"
@property
def description(self) -> str:
return (
"Step that sets the scheduler timesteps. By default the sigmas run linearly from 1 down to "
"`shift / (num_train_timesteps + shift - 1)` and the scheduler applies its static `shift`, so the last "
"step lands at `shift**2 / (num_train_timesteps - 1 + shift**2)` before the final step to 0."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec(
"scheduler",
FlowMatchEulerDiscreteScheduler,
config=FrozenDict({"shift": 5.0}),
default_creation_method="from_config",
)
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("num_inference_steps", default=28),
InputParam.template("sigmas"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("timesteps", type_hint=torch.Tensor, description="The timesteps of the denoising loop."),
OutputParam("num_inference_steps", type_hint=int, description="The number of denoising steps."),
]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
scheduler = components.scheduler
sigmas = block_state.sigmas
if sigmas is None:
shift = scheduler.config.shift
lowest = shift / (scheduler.config.num_train_timesteps + shift - 1)
sigmas = np.linspace(1.0, lowest, block_state.num_inference_steps)
scheduler.set_timesteps(sigmas=sigmas, device=components._execution_device)
scheduler.set_begin_index(0)
block_state.timesteps = scheduler.timesteps
block_state.num_inference_steps = len(block_state.timesteps)
self.set_block_state(state, block_state)
return components, state
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