InstructPix2Pix Training¶
You can also check configs/instruct_pix2pix/README.md
file.
Configs¶
All configuration files are placed under the configs/instruct_pix2pix
folder.
Following is the example config fixed from the stable_diffusion_xl_instruct_pix2pix config file in configs/instruct_pix2pix/stable_diffusion_xl_instruct_pix2pix.py
:
from mmengine.config import read_base
with read_base():
from .._base_.datasets.instructpix2pix_xl import *
from .._base_.default_runtime import *
from .._base_.models.stable_diffusion_xl_instruct_pix2pix import *
from .._base_.schedules.stable_diffusion_3e import *
optim_wrapper.update(
optimizer=dict(
type="Adafactor",
lr=3e-5,
weight_decay=1e-2,
scale_parameter=False,
relative_step=False),
accumulative_counts=4)
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_instruct_pix2pix
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.
Before inferencing, we should convert weights for diffusers format,
$ diffengine convert ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
# Example
$ diffengine convert stable_diffusion_xl_instruct_pix2pix work_dirs/stable_diffusion_xl_instruct_pix2pix/epoch_3.pth work_dirs/stable_diffusion_xl_instruct_pix2pix --save-keys unet
Then we can run inference.
import torch
from diffusers import StableDiffusionXLInstructPix2PixPipeline, UNet2DConditionModel, AutoencoderKL
from diffusers.utils import load_image
checkpoint = 'work_dirs/stable_diffusion_xl_instruct_pix2pix'
prompt = 'make the mountains snowy'
condition_image = load_image(
'https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png'
).resize((1024, 1024))
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 = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-base-1.0', unet=unet, 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_instruct_pix2pix¶
You can check configs/instruct_pix2pix/README.md
for more details.