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Latent Consistency Models Training — diffengine 1.0.0 documentation

You can also check configs/lcm/README.md file.

All configuration files are placed under the configs/lcm folder.

Following is the example config fixed from the lcm_xl_pokemon_blip config file in configs/lcm/lcm_xl_pokemon_blip.py:

from mmengine.config import read_base

from diffengine.engine.hooks import (
    LCMEMAUpdateHook,
    SDCheckpointHook,
    VisualizationHook,
)

with read_base():
    from .._base_.datasets.pokemon_blip_xl_pre_compute import *
    from .._base_.default_runtime import *
    from .._base_.models.lcm_xl import *
    from .._base_.schedules.lcm_xl_50e import *


train_dataloader.update(batch_size=2)

optim_wrapper.update(accumulative_counts=2)  # update every four times

custom_hooks = [
    dict(
        type=VisualizationHook,
        prompt=["yoda pokemon"] * 4,
        height=1024,
        width=1024),
    dict(type=SDCheckpointHook),
    dict(type=LCMEMAUpdateHook),
]

Run train

# single gpu
$ diffengine train ${CONFIG_FILE}
# Example
$ diffengine train lcm_xl_pokemon_blip

# multi gpus
$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}

Once you have trained a model, specify the path to the saved model and utilize it for inference using the diffusers.pipeline module.

Before inferencing, we should convert weights for diffusers format,

$ diffengine convert ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
# Example
$ diffengine convert lcm_xl_pokemon_blip work_dirs/lcm_xl_pokemon_blip/epoch_50.pth work_dirs/lcm_xl_pokemon_blip --save-keys unet

Then we can run inference.

import torch
from diffusers import DiffusionPipeline, AutoencoderKL, LCMScheduler, UNet2DConditionModel

checkpoint = 'work_dirs/lcm_xl_pokemon_blip'
prompt = 'yoda pokemon'

unet = UNet2DConditionModel.from_pretrained(
    checkpoint, subfolder='unet', torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained(
    'madebyollin/sdxl-vae-fp16-fix',
    torch_dtype=torch.float16,
)
pipe = DiffusionPipeline.from_pretrained(
    'stabilityai/stable-diffusion-xl-base-1.0',
    unet=unet,
    scheduler=LCMScheduler.from_pretrained(
      "stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler"),
    vae=vae, torch_dtype=torch.float16)
pipe.to('cuda')

image = pipe(
    prompt,
    num_inference_steps=4,
    height=1024,
    width=1024,
    guidance_scale=1.0,
).images[0]
image.save('demo.png')

You can check configs/lcm/README.md for more details.

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