Spaces:
Running on Zero
Running on Zero
File size: 4,530 Bytes
9a03d09 | 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 | """The two halves of a **split** MiniMax-H3 deployment.
MiniMax-H3 is modular-only, and `MiniMaxH3Blocks` is a `SequentialPipelineBlocks` of eight steps:
setup -> text_encoder -> vae_encoder -> prepare_layout -> prepare_latents -> set_timesteps -> denoise -> decode
The conditioner (a 62.14 GiB Qwen3-VL) and the denoiser (a 61.73 GiB transformer plus ~20.5 GiB of float32 VAEs) do
not fit on one 95 GiB card unquantized, so this module cuts the sequence in two at the `text_encoder` step:
* `MiniMaxH3ConditionerBlocks` = `[setup, text_encoder]` — loads `text_encoder` / `tokenizer` / `processor` only,
and emits `prompt_embeds` + `text_token_tags`, which is the whole wire format between the two halves.
* `MiniMaxH3GeneratorBlocks` = everything else — loads `transformer` / `vae` / `audio_vae` / the two
schedulers only, and takes `prompt_embeds` + `text_token_tags` as *inputs*.
`setup` runs on both sides on purpose. It owns no component (it is PIL and arithmetic), it resolves the canvas, the
`17 * n + 5` frame count and the latent geometry, and it puts the keyframes onto that canvas — which the conditioner
needs to build its vision blocks and the generator needs to encode with the video VAE. Running it twice over the same
inputs is deterministic; the conditioner half returns its resolved `height` / `width` / `num_frames` anyway, so the
caller can pin them explicitly on the generator half.
"""
from diffusers.modular_pipelines.minimax_h3.before_denoise import (
MiniMaxH3PrepareLatentsStep,
MiniMaxH3PrepareLayoutStep,
MiniMaxH3SetTimestepsStep,
)
from diffusers.modular_pipelines.minimax_h3.before_encoder import MiniMaxH3SetupStep
from diffusers.modular_pipelines.minimax_h3.denoise import MiniMaxH3DenoiseStep
from diffusers.modular_pipelines.minimax_h3.encoders import MiniMaxH3TextEncoderStep
from diffusers.modular_pipelines.minimax_h3.modular_blocks_minimax_h3 import (
MiniMaxH3AutoKeyframeVaeEncoderStep,
MiniMaxH3DecodeStep,
_generation_outputs,
)
from diffusers.modular_pipelines.modular_pipeline import SequentialPipelineBlocks
from diffusers.modular_pipelines.modular_pipeline_utils import OutputParam
class MiniMaxH3ConditionerBlocks(SequentialPipelineBlocks):
"""The conditioner half of a split MiniMax-H3: the request plan plus the Qwen3-VL read at its 50th layer."""
model_name = "minimax-h3"
block_classes = [MiniMaxH3SetupStep, MiniMaxH3TextEncoderStep]
block_names = ["setup", "text_encoder"]
@property
def description(self):
return (
"The conditioner half of a split MiniMax-H3 deployment: resolves the request plan (canvas, frame count, "
"latent geometry, keyframes on the canvas) and encodes MiniMax-H3's presentation of it into the "
"`prompt_embeds` / `text_token_tags` pair the denoising half consumes."
)
@property
def outputs(self):
return [
OutputParam.template("prompt_embeds"),
OutputParam("text_token_tags", description="The per-row modality tag of every row of `prompt_embeds`."),
OutputParam("height", type_hint=int, description="Resolved height of the generated video in pixels."),
OutputParam("width", type_hint=int, description="Resolved width of the generated video in pixels."),
OutputParam("num_frames", type_hint=int, description="Resolved number of frames, of the form 17 * n + 5."),
]
class MiniMaxH3GeneratorBlocks(SequentialPipelineBlocks):
"""The denoising half of a split MiniMax-H3: `MiniMaxH3Blocks` with its `text_encoder` step removed."""
model_name = "minimax-h3"
block_classes = [
MiniMaxH3SetupStep,
MiniMaxH3AutoKeyframeVaeEncoderStep,
MiniMaxH3PrepareLayoutStep,
MiniMaxH3PrepareLatentsStep,
MiniMaxH3SetTimestepsStep,
MiniMaxH3DenoiseStep,
MiniMaxH3DecodeStep,
]
block_names = [
"setup",
"vae_encoder",
"prepare_layout",
"prepare_latents",
"set_timesteps",
"denoise",
"decode",
]
@property
def description(self):
return (
"The denoising half of a split MiniMax-H3 deployment: `MiniMaxH3Blocks` without its text-encoder step, so "
"`prompt_embeds` and `text_token_tags` come in as inputs and the 62.14 GiB Qwen3-VL conditioner is never "
"loaded here."
)
@property
def outputs(self):
return _generation_outputs()
|