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Stable Diffusion XL LoRA Training — diffengine 1.0.0 documentation

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

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

Following is the example config fixed from the stable_diffusion_xl_lora_pokemon_blip config file in configs/stable_diffusion_xl_lora/stable_diffusion_xl_lora_pokemon_blip.py:

from mmengine.config import read_base

from diffengine.engine.hooks import PeftSaveHook, VisualizationHook

with read_base():
    from .._base_.datasets.pokemon_blip_xl import *
    from .._base_.default_runtime import *
    from .._base_.models.stable_diffusion_xl_lora import *
    from .._base_.schedules.stable_diffusion_50e import *


custom_hooks = [
    dict(
        type=VisualizationHook,
        prompt=["yoda pokemon"] * 4,
        height=1024,
        width=1024),
    dict(type=PeftSaveHook),  # Need to change from SDCheckpointHook
]

The script also allows you to finetune the text_encoder along with the unet, LoRA parameters.

from mmengine.config import read_base

from diffengine.engine.hooks import PeftSaveHook, VisualizationHook

with read_base():
    from .._base_.datasets.pokemon_blip_xl import *
    from .._base_.default_runtime import *
    from .._base_.models.stable_diffusion_xl_lora import *
    from .._base_.schedules.stable_diffusion_50e import *

model.update(
    text_encoder_lora_config=dict(  # set LoRA and rank parameter
                type="LoRA", r=4,
                target_modules=["q_proj", "k_proj", "v_proj", "out_proj"]),
    finetune_text_encoder=True  # fine tune text encoder
)

custom_hooks = [
    dict(
        type=VisualizationHook,
        prompt=["yoda pokemon"] * 4,
        height=1024,
        width=1024),
    dict(type=PeftSaveHook),  # Need to change from SDCheckpointHook
]

Run LoRA training:

# single gpu
$ diffengine train ${CONFIG_FILE}
# Example
$ diffengine train stable_diffusion_xl_lora_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.

from pathlib import Path

import torch
from diffusers import DiffusionPipeline, AutoencoderKL
from peft import PeftModel

checkpoint = Path('work_dirs/stable_diffusion_xl_lora_pokemon_blip/step20850')
prompt = 'yoda pokemon'

vae = AutoencoderKL.from_pretrained(
    'madebyollin/sdxl-vae-fp16-fix',
    torch_dtype=torch.float16,
)
pipe = DiffusionPipeline.from_pretrained(
    'stabilityai/stable-diffusion-xl-base-1.0', vae=vae, torch_dtype=torch.float16)
pipe.to('cuda')
pipe.unet = PeftModel.from_pretrained(pipe.unet, checkpoint / "unet", adapter_name="default")
if (checkpoint / "text_encoder_one").exists():
    pipe.text_encoder_one = PeftModel.from_pretrained(
        pipe.text_encoder_one, checkpoint / "text_encoder_one", adapter_name="default"
    )
if (checkpoint / "text_encoder_two").exists():
    pipe.text_encoder_one = PeftModel.from_pretrained(
        pipe.text_encoder_two, checkpoint / "text_encoder_two", adapter_name="default"
    )

image = pipe(
    prompt,
    num_inference_steps=50,
    height=1024,
    width=1024,
).images[0]
image.save('demo.png')

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

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