T2I Adapter Training¶
You can also check configs/t2i_adapter/README.md
file.
Configs¶
All configuration files are placed under the configs/t2i_adapter
folder.
Following is the example config fixed from the stable_diffusion_xl_t2i_adapter_fill50k config file in configs/t2i_adapter/stable_diffusion_xl_t2i_adapter_fill50k.py
:
from mmengine.config import read_base
with read_base():
from .._base_.datasets.fill50k_t2i_adapter_xl import *
from .._base_.default_runtime import *
from .._base_.models.stable_diffusion_xl_t2i_adapter import *
from .._base_.schedules.stable_diffusion_3e import *
optim_wrapper.update(
optimizer=dict(lr=1e-5),
accumulative_counts=2,
)
Run training¶
Run train
# single gpu
$ diffengine train ${CONFIG_FILE}
# multi gpus
$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
# Example
$ diffengine train stable_diffusion_xl_t2i_adapter_fill50k
Inference with diffusers¶
Once you have trained a model, specify the path to the saved model and utilize it for inference using the diffusers.pipeline
module.
import torch
from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, AutoencoderKL
from diffusers.utils import load_image
checkpoint = 'work_dirs/stable_diffusion_xl_t2i_adapter_fill50k/step75000'
prompt = 'cyan circle with brown floral background'
condition_image = load_image(
'https://github.com/okotaku/diffengine/assets/24734142/1af9dbb0-b056-435c-bc4b-62a823889191'
).resize((1024, 1024))
adapter = T2IAdapter.from_pretrained(
checkpoint, subfolder='adapter', torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained(
'madebyollin/sdxl-vae-fp16-fix',
torch_dtype=torch.float16,
)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-base-1.0', adapter=adapter, vae=vae, torch_dtype=torch.float16)
pipe.to('cuda')
image = pipe(
prompt,
image=condition_image,
num_inference_steps=50,
).images[0]
image.save('demo.png')
Results Example¶
stable_diffusion_xl_t2i_adapter_fill50k¶
You can check configs/t2i_adapter/README.md
for more details.