[CodeCamp2023-645]Add dreambooth new cfg (#2042)

* new config of dreambooth

* add dreambooth mmagic new_config

* fix import name bug

---------

Co-authored-by: YanxingLiu <liuyanxing98@foxmail.com>
Co-authored-by: rangoliu <liuwenran@users.noreply.github.com>

mmagic/configs/dreambooth/dreambooth-finetune_text_encoder.py

@@ -0,0 +1,95 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.gen_default_runtime import *
+
+from mmengine.dataset.sampler import InfiniteSampler
+from torch.optim import AdamW
+
+from mmagic.datasets.dreambooth_dataset import DreamBoothDataset
+from mmagic.datasets.transforms.aug_shape import Resize
+from mmagic.datasets.transforms.formatting import PackInputs
+from mmagic.datasets.transforms.loading import LoadImageFromFile
+from mmagic.engine import VisualizationHook
+from mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor
+from mmagic.models.editors.disco_diffusion.clip_wrapper import ClipWrapper
+from mmagic.models.editors.dreambooth import DreamBooth
+
+# config for model
+stable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'
+
+val_prompts = [
+    'a sks dog in basket', 'a sks dog on the mountain',
+    'a sks dog beside a swimming pool', 'a sks dog on the desk',
+    'a sleeping sks dog', 'a screaming sks dog', 'a man in the garden'
+]
+
+model = dict(
+    type=DreamBooth,
+    vae=dict(
+        type='AutoencoderKL',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='vae'),
+    unet=dict(
+        type='UNet2DConditionModel',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='unet',
+    ),
+    text_encoder=dict(
+        type=ClipWrapper,
+        clip_type='huggingface',
+        pretrained_model_name_or_path=stable_diffusion_v15_url,
+        subfolder='text_encoder'),
+    tokenizer=stable_diffusion_v15_url,
+    finetune_text_encoder=True,
+    scheduler=dict(
+        type='DDPMScheduler',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='scheduler'),
+    test_scheduler=dict(
+        type='DDIMScheduler',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='scheduler'),
+    data_preprocessor=dict(type=DataPreprocessor),
+    val_prompts=val_prompts)
+
+train_cfg = dict(max_iters=1000)
+
+optim_wrapper.update(
+    modules='.*unet',
+    optimizer=dict(type=AdamW, lr=5e-6),
+    accumulative_counts=4  # batch size = 4 * 1 = 4
+)
+
+pipeline = [
+    dict(type=LoadImageFromFile, key='img', channel_order='rgb'),
+    dict(type=Resize, scale=(512, 512)),
+    dict(type=PackInputs)
+]
+
+dataset = dict(
+    type=DreamBoothDataset,
+    data_root='./data/dreambooth',
+    concept_dir='imgs',
+    prompt='a photo of sks dog',
+    pipeline=pipeline)
+train_dataloader = dict(
+    dataset=dataset,
+    num_workers=16,
+    sampler=dict(type=InfiniteSampler, shuffle=True),
+    persistent_workers=True,
+    batch_size=1)
+val_cfg = val_evaluator = val_dataloader = None
+test_cfg = test_evaluator = test_dataloader = None
+
+# hooks
+default_hooks.update(dict(logger=dict(interval=10)))
+custom_hooks = [
+    dict(
+        type=VisualizationHook,
+        interval=50,
+        fixed_input=True,
+        vis_kwargs_list=dict(type='Data', name='fake_img'),
+        n_samples=1)
+]

mmagic/configs/dreambooth/dreambooth-prior_pre.py

@@ -0,0 +1,8 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .dreambooth import *
+
+# config for model
+model.update(dict(prior_loss_weight=1, class_prior_prompt='a dog'))

mmagic/configs/dreambooth/dreambooth.py

@@ -0,0 +1,93 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.gen_default_runtime import *
+
+from mmengine.dataset.sampler import InfiniteSampler
+from torch.optim import AdamW
+
+from mmagic.datasets.dreambooth_dataset import DreamBoothDataset
+from mmagic.datasets.transforms.aug_shape import Resize
+from mmagic.datasets.transforms.formatting import PackInputs
+from mmagic.datasets.transforms.loading import LoadImageFromFile
+from mmagic.engine import VisualizationHook
+from mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor
+from mmagic.models.editors.disco_diffusion.clip_wrapper import ClipWrapper
+from mmagic.models.editors.dreambooth import DreamBooth
+
+stable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'
+
+val_prompts = [
+    'a sks dog in basket', 'a sks dog on the mountain',
+    'a sks dog beside a swimming pool', 'a sks dog on the desk',
+    'a sleeping sks dog', 'a screaming sks dog', 'a man in the garden'
+]
+
+model = dict(
+    type=DreamBooth,
+    vae=dict(
+        type='AutoencoderKL',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='vae'),
+    unet=dict(
+        type='UNet2DConditionModel',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='unet',
+    ),
+    text_encoder=dict(
+        type=ClipWrapper,
+        clip_type='huggingface',
+        pretrained_model_name_or_path=stable_diffusion_v15_url,
+        subfolder='text_encoder'),
+    tokenizer=stable_diffusion_v15_url,
+    scheduler=dict(
+        type='DDPMScheduler',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='scheduler'),
+    test_scheduler=dict(
+        type='DDIMScheduler',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='scheduler'),
+    data_preprocessor=dict(type=DataPreprocessor),
+    val_prompts=val_prompts)
+
+train_cfg = dict(max_iters=1000)
+
+optim_wrapper.update(
+    modules='.*unet',
+    optimizer=dict(type=AdamW, lr=5e-6),
+    accumulative_counts=4  # batch size = 4 * 1 = 4
+)
+
+pipeline = [
+    dict(type=LoadImageFromFile, key='img', channel_order='rgb'),
+    dict(type=Resize, scale=(512, 512)),
+    dict(type=PackInputs)
+]
+
+dataset = dict(
+    type=DreamBoothDataset,
+    data_root='./data/dreambooth',
+    concept_dir='imgs',
+    prompt='a photo of sks dog',
+    pipeline=pipeline)
+train_dataloader = dict(
+    dataset=dataset,
+    num_workers=16,
+    sampler=dict(type=InfiniteSampler, shuffle=True),
+    persistent_workers=True,
+    batch_size=1)
+val_cfg = val_evaluator = val_dataloader = None
+test_cfg = test_evaluator = test_dataloader = None
+
+# hooks
+default_hooks.update(dict(logger=dict(interval=10)))
+custom_hooks = [
+    dict(
+        type=VisualizationHook,
+        interval=50,
+        fixed_input=True,
+        vis_kwargs_list=dict(type='Data', name='fake_img'),
+        n_samples=1)
+]

mmagic/configs/dreambooth/dreambooth_lora-prior_pre.py

@@ -0,0 +1,7 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .dreambooth_lora import *
+
+model.update(dict(prior_loss_weight=1, class_prior_prompt='a dog'))

mmagic/configs/dreambooth/dreambooth_lora.py

@@ -0,0 +1,97 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.gen_default_runtime import *
+
+from mmengine.dataset.sampler import InfiniteSampler
+from torch.optim import AdamW
+
+from mmagic.datasets.dreambooth_dataset import DreamBoothDataset
+from mmagic.datasets.transforms.aug_shape import Resize
+from mmagic.datasets.transforms.formatting import PackInputs
+from mmagic.datasets.transforms.loading import LoadImageFromFile
+from mmagic.engine import VisualizationHook
+from mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor
+from mmagic.models.editors.disco_diffusion.clip_wrapper import ClipWrapper
+from mmagic.models.editors.dreambooth import DreamBooth
+
+stable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'
+
+val_prompts = [
+    'a sks dog in basket', 'a sks dog on the mountain',
+    'a sks dog beside a swimming pool', 'a sks dog on the desk',
+    'a sleeping sks dog', 'a screaming sks dog', 'a man in the garden'
+]
+lora_config = dict(target_modules=['to_q', 'to_k', 'to_v'])
+
+model = dict(
+    type=DreamBooth,
+    vae=dict(
+        type='AutoencoderKL',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='vae'),
+    unet=dict(
+        type='UNet2DConditionModel',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='unet',
+    ),
+    text_encoder=dict(
+        type=ClipWrapper,
+        clip_type='huggingface',
+        pretrained_model_name_or_path=stable_diffusion_v15_url,
+        subfolder='text_encoder'),
+    tokenizer=stable_diffusion_v15_url,
+    scheduler=dict(
+        type='DDPMScheduler',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='scheduler'),
+    test_scheduler=dict(
+        type='DDIMScheduler',
+        from_pretrained=stable_diffusion_v15_url,
+        subfolder='scheduler'),
+    data_preprocessor=dict(type=DataPreprocessor),
+    prior_loss_weight=0,
+    val_prompts=val_prompts,
+    lora_config=lora_config)
+
+train_cfg = dict(max_iters=1000)
+
+optim_wrapper = dict(
+    # Only optimize LoRA mappings
+    modules='.*.lora_mapping',
+    # NOTE: lr should be larger than dreambooth finetuning
+    optimizer=dict(type=AdamW, lr=5e-4),
+    accumulative_counts=1)
+
+pipeline = [
+    dict(type=LoadImageFromFile, key='img', channel_order='rgb'),
+    dict(type=Resize, scale=(512, 512)),
+    dict(type=PackInputs)
+]
+dataset = dict(
+    type=DreamBoothDataset,
+    data_root='./data/dreambooth',
+    # TODO: rename to instance
+    concept_dir='imgs',
+    prompt='a photo of sks dog',
+    pipeline=pipeline)
+train_dataloader = dict(
+    dataset=dataset,
+    num_workers=16,
+    sampler=dict(type=InfiniteSampler, shuffle=True),
+    persistent_workers=True,
+    batch_size=1)
+val_cfg = val_evaluator = val_dataloader = None
+test_cfg = test_evaluator = test_dataloader = None
+
+# hooks
+default_hooks.update(dict(logger=dict(interval=10)))
+custom_hooks = [
+    dict(
+        type=VisualizationHook,
+        interval=50,
+        fixed_input=True,
+        vis_kwargs_list=dict(type='Data', name='fake_img'),
+        n_samples=1)
+]