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first (in)stable steps

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rromb 2022-05-27 11:46:04 +02:00
parent f7a6152022
commit 9a419a1b14
4 changed files with 310 additions and 66 deletions
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130
configs/stable-diffusion/txt2img-ldm-vae-f8.yaml Normal file
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@ -0,0 +1,130 @@
model:
base_learning_rate: 1.0e-04 # TODO: run with scale_lr False
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 32
channels: 4
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 10000 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 128 # 320 # TODO increase
attention_resolutions: [ 4, 2, 1 ] # is equal to fixed spatial resolution: 32 , 16 , 8
num_res_blocks: 2
channel_mult: [ 1,2,4,4 ]
#num_head_channels: 32
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 1280
use_checkpoint: True
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ckpt_path: "/home/robin/projects/latent-diffusion/models/first_stage_models/kl-f8/model.ckpt"
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.BERTEmbedder
params:
n_embed: 1280
n_layer: 3 #32 # TODO: increase
data:
target: ldm.data.laion.WebDataModuleFromConfig
params:
tar_base: "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/"
batch_size: 60
num_workers: 4
n_nodes: 2 # TODO: runs with two gpus
train:
shards: '{000000..000010}.tar -' # TODO: wild guess, change
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.RandomCrop
params:
size: 512
shuffle: 5000
n_examples: 16519100 # TODO: find out
validation:
shards: '{000011..000012}.tar -' # TODO: wild guess, change
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.CenterCrop
params:
size: 512
shuffle: 0
n_examples: 60000 # TODO: find out
val_num_workers: 2
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000 # 5000
max_images: 8
increase_log_steps: False
log_first_step: True
trainer:
replace_sampler_ddp: False
benchmark: True
val_check_interval: 20000 # every 20k training steps
num_sanity_val_steps: 0

173
ldm/data/laion.py
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@ -2,7 +2,178 @@ import webdataset as wds
from PIL import Image
import io
import os
import torchvision
from PIL import Image
import glob
import random
import numpy as np
import pytorch_lightning as pl
from tqdm import tqdm
from omegaconf import OmegaConf
from einops import rearrange
import torch
from ldm.util import instantiate_from_config
def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
"""Take a list of samples (as dictionary) and create a batch, preserving the keys.
If `tensors` is True, `ndarray` objects are combined into
tensor batches.
:param dict samples: list of samples
:param bool tensors: whether to turn lists of ndarrays into a single ndarray
:returns: single sample consisting of a batch
:rtype: dict
"""
batched = {key: [] for key in samples[0]}
# assert isinstance(samples[0][first_key], (list, tuple)), type(samples[first_key])
for s in samples:
[batched[key].append(s[key]) for key in batched]
result = {}
for key in batched:
if isinstance(batched[key][0], (int, float)):
if combine_scalars:
result[key] = np.array(list(batched[key]))
elif isinstance(batched[key][0], torch.Tensor):
if combine_tensors:
# import torch
result[key] = torch.stack(list(batched[key]))
elif isinstance(batched[key][0], np.ndarray):
if combine_tensors:
result[key] = np.array(list(batched[key]))
else:
result[key] = list(batched[key])
# result.append(b)
return result
class WebDataModuleFromConfig(pl.LightningDataModule):
def __init__(self, tar_base, batch_size, train=None, validation=None,
test=None, num_workers=4, load_ddp=True, n_nodes=1,
**kwargs):
super().__init__(self)
print(f'Setting tar base to {tar_base}')
self.tar_base = tar_base
self.batch_size = batch_size
self.num_workers = num_workers
self.train = train
self.validation = validation
self.test = test
self.load_ddp = load_ddp
self.multinode = n_nodes > 1
self.n_nodes = n_nodes # n gpu ??
def make_loader(self, dataset_config, train=True):
if 'image_transforms' in dataset_config:
image_transforms = [instantiate_from_config(tt) for tt in dataset_config.image_transforms]
else:
image_transforms = []
image_transforms.extend([torchvision.transforms.ToTensor(),
torchvision.transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
image_transforms = torchvision.transforms.Compose(image_transforms)
if 'transforms' in dataset_config:
transforms_config = OmegaConf.to_container(dataset_config.transforms)
else:
transforms_config = dict()
transform_dict = {dkey: load_partial_from_config(transforms_config[dkey]) if transforms_config[
dkey] != 'identity' else identity
for dkey in transforms_config}
img_key = dataset_config.get('image_key', 'jpeg')
transform_dict.update({img_key: image_transforms})
shuffle = dataset_config.get('shuffle', 0)
# TODO fid strategy when n exmples not known beforehand
n_examples = dataset_config.get('n_examples', 1e6) // self.n_nodes
shards_to_load = dataset_config.shards
dset_name = 'unknown'
if isinstance(shards_to_load, str):
print(f'Loading tars based on the string {shards_to_load}')
tars = os.path.join(self.tar_base, shards_to_load)
start_shard_id, end_shard_id = dataset_config.shards.split('{')[-1].split('}')[0].split('..')
n_shards = int(end_shard_id) - int(start_shard_id) + 1
dset_name = dataset_config.shards.split('-')[0]
elif isinstance(shards_to_load, int):
print(f'Creating tar list, max shard is {shards_to_load}')
try:
tars = [tf for tf in natsorted(glob(os.path.join(self.tar_base, '*.tar'))) if
int(tf.split('/')[-1].split('.')[0]) < shards_to_load]
n_shards = len(tars)
random.shuffle(tars)
except ValueError as e:
print('tarfile names should follow the pattern <zero_padded_number>.tar . Check names of the files')
raise e
else:
raise ValueError(
'shards should be either a string containing consecutive shards or an int defining the max shard number')
print(f'Got {n_shards} shard files in datafolder for {"training" if train else "validation"}')
# if self.num_workers > 0:
# assert n_shards % self.num_workers == 0 , f'Number of workers which is {self.num_workers} does not evenly divide number of shards which is {n_shards}'
print(f'Loading webdataset based dataloader based on {n_shards} of {dset_name} dataset.')
# start creating the dataset
nodesplitter = wds.shardlists.split_by_node if self.multinode else wds.shardlists.single_node_only
epoch_length = n_examples // (self.batch_size)
dset = wds.WebDataset(tars, nodesplitter=nodesplitter).shuffle(shuffle)
with_epoch_args = {'nsamples': n_examples, 'nbatches': epoch_length}
if 'filters' in dataset_config:
for stage in tqdm(dataset_config.filters,
desc=f'Applying the following filters: {[f for f in dataset_config.filters]}'):
f = getattr(dset, stage)
dset = f(dset, *dataset_config.filters[stage].args,
**dataset_config.filters[stage].get('kwargs', dict()))
print(f'Dataset holding {len(dset.pipeline[0].urls)} shards')
dset = (dset
.decode('pil')
# .to_tuple("jpg;png;jpeg pickle cls hls")
# .map_tuple(image_transforms,load_partial_from_config(nns_transform) if 'target' in nns_transform else identity,identity,identity)
.map_dict(**transform_dict)
.repeat()
.batched(self.batch_size, partial=False,
collation_fn=dict_collation_fn)
.with_length(n_examples)
.with_epoch(**with_epoch_args)
)
loader = wds.WebLoader(dset, batch_size=None, shuffle=False,
num_workers=self.num_workers)
return loader, n_examples
def train_dataloader(self):
assert self.train is not None
loader, dset_size = self.make_loader(self.train)
# if self.load_ddp:
# loader = loader.ddp_equalize(dset_size // self.batch_size)
return loader
def val_dataloader(self):
assert self.train is not None
loader, _ = self.make_loader(self.validation, train=False)
return loader
def test_dataloader(self):
assert self.train is not None
loader, _ = self.make_loader(self.test, train=False)
return loader
if __name__ == "__main__":
url = "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/000000.tar -"
@ -18,7 +189,7 @@ if __name__ == "__main__":
image = Image.open(io.BytesIO(example["jpg"]))
outdir = "tmp"
os.makedirs(outdir, exist_ok=True)
image.save(os.path.join(outdir, example["__key__"]+".png"))
image.save(os.path.join(outdir, example["__key__"] + ".png"))
def load_example(example):

66
ldm/models/diffusion/ddpm.py
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@ -664,7 +664,7 @@ class LatentDiffusion(DDPM):
if cond_key is None:
cond_key = self.cond_stage_key
if cond_key != self.first_stage_key:
if cond_key in ['caption', 'coordinates_bbox']:
if cond_key in ['caption', 'coordinates_bbox', "txt"]:
xc = batch[cond_key]
elif cond_key == 'class_label':
xc = batch
@ -762,66 +762,6 @@ class LatentDiffusion(DDPM):
else:
return self.first_stage_model.decode(z)
# same as above but without decorator
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
@ -1268,8 +1208,8 @@ class LatentDiffusion(DDPM):
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption"]:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
elif self.cond_stage_key in ["caption", "txt"]:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key])
log["conditioning"] = xc
elif self.cond_stage_key == 'class_label':
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])

3
main.py
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@ -667,8 +667,11 @@ if __name__ == "__main__":
data.prepare_data()
data.setup()
print("#### Data #####")
try:
for k in data.datasets:
print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
except:
print("datasets not yet initialized.")
# configure learning rate
bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate