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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

67 lines
2.0 KiB
Python

# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
from diffusers.pipelines.flux2.image_processor import Flux2ImageProcessor
from sglang.multimodal_gen.runtime.pipelines_core import LoRAPipeline, Req
from sglang.multimodal_gen.runtime.pipelines_core.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines_core.stages.progressive_resolution.flux_2 import (
Flux2ProgressiveDenoisingStage,
)
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
logger = init_logger(__name__)
def compute_empirical_mu(batch: Req, server_args: ServerArgs):
num_steps = batch.num_inference_steps
image_seq_len = batch.raw_latent_shape[1]
a1, b1 = 8.73809524e-05, 1.89833333
a2, b2 = 0.00016927, 0.45666666
if image_seq_len > 4300:
mu = a2 * image_seq_len + b2
return "mu", float(mu)
m_200 = a2 * image_seq_len + b2
m_10 = a1 * image_seq_len + b1
a = (m_200 - m_10) / 190.0
b = m_200 - 200.0 * a
mu = a * num_steps + b
return "mu", float(mu)
class Flux2Pipeline(LoRAPipeline, ComposedPipelineBase):
pipeline_name = "Flux2Pipeline"
_required_config_modules = [
"text_encoder",
"tokenizer",
"vae",
"transformer",
"scheduler",
]
def create_pipeline_stages(self, server_args: ServerArgs):
vae_image_processor = Flux2ImageProcessor(
vae_scale_factor=server_args.pipeline_config.vae_config.arch_config.vae_scale_factor
* 2
)
self.add_standard_ti2i_stages(
include_input_validation=True,
vae_image_processor=vae_image_processor,
prompt_encoding="text",
image_vae_stage_kwargs={"vae_image_processor": vae_image_processor},
prepare_extra_timestep_kwargs=[compute_empirical_mu],
progressive_denoising_stage_cls=Flux2ProgressiveDenoisingStage,
)
EntryClass = Flux2Pipeline