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dc9d63c | 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 | # Copyright 2026 inclusionAI and The HuggingFace Team. All rights reserved.
#
# Licensed under the MIT License. See the LICENSE file in this repository.
import torch
from diffusers import AutoModel
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
# VAE spatial downsampling (8) times the transformer patch size (2)
SPATIAL_COMPRESSION = 16
LATENT_CHANNELS = 16
class MingImageTextInputStep(ModularPipelineBlocks):
model_name = "ming-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", AutoModel)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("num_images_per_prompt"),
InputParam(
"prompt_embeds",
required=True,
type_hint=list[torch.Tensor],
description="Query-token conditioning, one tensor per prompt. Can be generated in the text_encoder step.",
),
InputParam(
"prompt_embeds_2",
required=True,
type_hint=list[torch.Tensor],
description="Text-token conditioning, one tensor per prompt. Can be generated in the text_encoder step.",
),
InputParam(
"negative_prompt_embeds",
type_hint=list[torch.Tensor],
description="Unconditional query-token conditioning. Can be generated in the text_encoder step.",
),
InputParam(
"negative_prompt_embeds_2",
type_hint=list[torch.Tensor],
description="Unconditional text-token conditioning. 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="Query-token conditioning repeated per image.",
),
OutputParam(
"prompt_embeds_2",
type_hint=list[torch.Tensor],
kwargs_type="denoiser_input_fields",
description="Text-token conditioning repeated per image.",
),
OutputParam(
"negative_prompt_embeds",
type_hint=list[torch.Tensor],
kwargs_type="denoiser_input_fields",
description="Unconditional query-token conditioning repeated per image.",
),
OutputParam(
"negative_prompt_embeds_2",
type_hint=list[torch.Tensor],
kwargs_type="denoiser_input_fields",
description="Unconditional text-token conditioning repeated per image.",
),
]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
if len(block_state.prompt_embeds) != len(block_state.prompt_embeds_2):
raise ValueError(
f"`prompt_embeds` ({len(block_state.prompt_embeds)}) and `prompt_embeds_2` "
f"({len(block_state.prompt_embeds_2)}) 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.prompt_embeds_2 = repeat(block_state.prompt_embeds_2)
block_state.negative_prompt_embeds = repeat(block_state.negative_prompt_embeds)
block_state.negative_prompt_embeds_2 = repeat(block_state.negative_prompt_embeds_2)
self.set_block_state(state, block_state)
return components, state
class MingImagePrepareLatentsStep(ModularPipelineBlocks):
model_name = "ming-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=2048),
InputParam.template("width", default=2048),
InputParam(
"latents",
type_hint=torch.Tensor,
description="Initial noise of shape `(batch_size * num_images_per_prompt, 16, height // 8, width // 8)`.",
),
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, 16, height // 8, width // 8)`.",
)
]
@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 // 8,
block_state.width // 8,
)
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 MingImageSetTimestepsStep(ModularPipelineBlocks):
model_name = "ming-image"
@property
def description(self) -> str:
return (
"Step that sets the scheduler timesteps: `num_inference_steps` sigmas from 1 down to 0, shifted by the "
"scheduler's static `shift`. The schedule does not depend on the resolution. Sets the scheduler's "
"`sigma_min` to 0."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("num_inference_steps", default=12),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("timesteps", type_hint=torch.Tensor, description="The timesteps of the denoising loop."),
]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
# The original pipeline runs the schedule down to sigma 0 by setting `sigma_min` on the scheduler and letting
# it build the sigmas; doing the same reproduces its float64 schedule exactly (custom `sigmas` would be cast to
# float32 before shifting).
components.scheduler.sigma_min = 0.0
components.scheduler.set_timesteps(block_state.num_inference_steps, device=components._execution_device)
block_state.timesteps = components.scheduler.timesteps
self.set_block_state(state, block_state)
return components, state
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