Merge branch 'main' of github.com:pesser/stable-diffusion into main
This commit is contained in:
rromb
commit
5c3f6795fa
|
|
@ -2,7 +2,6 @@ model:
|
|||
base_learning_rate: 1.0e-04
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
ckpt_path: "/home/mchorse/stable-diffusion-ckpts/256pretrain-2022-06-09.ckpt"
|
||||
linear_start: 0.00085
|
||||
linear_end: 0.0120
|
||||
num_timesteps_cond: 1
|
||||
|
|
@ -20,7 +19,7 @@ model:
|
|||
scheduler_config: # 10000 warmup steps
|
||||
target: ldm.lr_scheduler.LambdaLinearScheduler
|
||||
params:
|
||||
warm_up_steps: [ 10000 ]
|
||||
warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
|
||||
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
|
||||
f_start: [ 1.e-6 ]
|
||||
f_max: [ 1. ]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,135 @@
|
|||
model:
|
||||
base_learning_rate: 1.0e-04
|
||||
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: 64
|
||||
channels: 4
|
||||
cond_stage_trainable: false # Note: different from the one we trained before
|
||||
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: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
|
||||
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 # unused
|
||||
in_channels: 4
|
||||
out_channels: 4
|
||||
model_channels: 320
|
||||
attention_resolutions: [ 4, 2, 1 ]
|
||||
num_res_blocks: 2
|
||||
channel_mult: [ 1, 2, 4, 4 ]
|
||||
num_heads: 8
|
||||
use_spatial_transformer: True
|
||||
transformer_depth: 1
|
||||
context_dim: 768
|
||||
use_checkpoint: True
|
||||
legacy: False
|
||||
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
embed_dim: 4
|
||||
monitor: val/rec_loss
|
||||
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.FrozenCLIPEmbedder
|
||||
|
||||
|
||||
data:
|
||||
target: ldm.data.laion.WebDataModuleFromConfig
|
||||
params:
|
||||
tar_base: "__improvedaesthetic__"
|
||||
batch_size: 4
|
||||
num_workers: 4
|
||||
multinode: True
|
||||
train:
|
||||
shards: '{00000..17279}.tar -'
|
||||
shuffle: 10000
|
||||
image_key: jpg
|
||||
image_transforms:
|
||||
- target: torchvision.transforms.Resize
|
||||
params:
|
||||
size: 512
|
||||
interpolation: 3
|
||||
- target: torchvision.transforms.RandomCrop
|
||||
params:
|
||||
size: 512
|
||||
|
||||
# NOTE use enough shards to avoid empty validation loops in workers
|
||||
validation:
|
||||
shards: '{17280..17535}.tar -'
|
||||
shuffle: 0
|
||||
image_key: jpg
|
||||
image_transforms:
|
||||
- target: torchvision.transforms.Resize
|
||||
params:
|
||||
size: 512
|
||||
interpolation: 3
|
||||
- target: torchvision.transforms.CenterCrop
|
||||
params:
|
||||
size: 512
|
||||
|
||||
|
||||
lightning:
|
||||
find_unused_parameters: False
|
||||
|
||||
modelcheckpoint:
|
||||
params:
|
||||
every_n_train_steps: 5000
|
||||
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 5000
|
||||
max_images: 4
|
||||
increase_log_steps: False
|
||||
log_first_step: False
|
||||
log_images_kwargs:
|
||||
use_ema_scope: False
|
||||
inpaint: False
|
||||
plot_progressive_rows: False
|
||||
plot_diffusion_rows: False
|
||||
N: 4
|
||||
unconditional_guidance_scale: 3.0
|
||||
unconditional_guidance_label: [""]
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
||||
val_check_interval: 5000000 # really sorry
|
||||
num_sanity_val_steps: 0
|
||||
accumulate_grad_batches: 2
|
||||
|
|
@ -0,0 +1,132 @@
|
|||
model:
|
||||
base_learning_rate: 1.0e-04
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.001
|
||||
linear_end: 0.015
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
first_stage_key: "jpg"
|
||||
cond_stage_key: "txt"
|
||||
image_size: 64
|
||||
channels: 16
|
||||
cond_stage_trainable: false # Note: different from the one we trained before
|
||||
conditioning_key: crossattn
|
||||
monitor: val/loss_simple_ema
|
||||
scale_factor: 0.22765929 # magic number
|
||||
|
||||
# NOTE disabled for resuming
|
||||
#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: 64 # not really needed
|
||||
in_channels: 16
|
||||
out_channels: 16
|
||||
model_channels: 320
|
||||
attention_resolutions: [ 4, 2, 1 ]
|
||||
num_res_blocks: 2
|
||||
channel_mult: [ 1, 2, 4, 4 ]
|
||||
num_heads: 8
|
||||
use_spatial_transformer: True
|
||||
transformer_depth: 1
|
||||
context_dim: 768
|
||||
use_checkpoint: True
|
||||
legacy: False
|
||||
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
embed_dim: 16
|
||||
monitor: val/rec_loss
|
||||
ddconfig:
|
||||
double_z: True
|
||||
z_channels: 16
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult: [ 1,1,2,2,4 ] # num_down = len(ch_mult)-1
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: [ 16 ]
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
|
||||
cond_stage_config:
|
||||
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
|
||||
|
||||
|
||||
data:
|
||||
target: ldm.data.laion.WebDataModuleFromConfig
|
||||
params:
|
||||
tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
|
||||
batch_size: 3
|
||||
num_workers: 4
|
||||
multinode: True
|
||||
train:
|
||||
shards: '{00000..17279}.tar -'
|
||||
shuffle: 10000
|
||||
image_key: jpg
|
||||
image_transforms:
|
||||
- target: torchvision.transforms.Resize
|
||||
params:
|
||||
size: 1024
|
||||
interpolation: 3
|
||||
- target: torchvision.transforms.RandomCrop
|
||||
params:
|
||||
size: 1024
|
||||
|
||||
# NOTE use enough shards to avoid empty validation loops in workers
|
||||
validation:
|
||||
shards: '{17280..17535}.tar -'
|
||||
shuffle: 0
|
||||
image_key: jpg
|
||||
image_transforms:
|
||||
- target: torchvision.transforms.Resize
|
||||
params:
|
||||
size: 1024
|
||||
interpolation: 3
|
||||
- target: torchvision.transforms.CenterCrop
|
||||
params:
|
||||
size: 1024
|
||||
|
||||
|
||||
lightning:
|
||||
find_unused_parameters: False
|
||||
|
||||
modelcheckpoint:
|
||||
params:
|
||||
every_n_train_steps: 2000
|
||||
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 2000
|
||||
max_images: 2
|
||||
increase_log_steps: False
|
||||
log_first_step: False
|
||||
log_images_kwargs:
|
||||
use_ema_scope: False
|
||||
inpaint: False
|
||||
plot_progressive_rows: False
|
||||
plot_diffusion_rows: False
|
||||
N: 2
|
||||
unconditional_guidance_scale: 5.0
|
||||
unconditional_guidance_label: [""]
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
||||
val_check_interval: 5000000
|
||||
num_sanity_val_steps: 0
|
||||
accumulate_grad_batches: 4
|
||||
|
|
@ -0,0 +1,137 @@
|
|||
model:
|
||||
base_learning_rate: 8.e-05
|
||||
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: false # Note: different from the one we trained before
|
||||
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 # unused
|
||||
in_channels: 4
|
||||
out_channels: 4
|
||||
model_channels: 416
|
||||
attention_resolutions: [ 4, 2, 1 ]
|
||||
num_res_blocks: [ 2, 2, 2, 2 ]
|
||||
channel_mult: [ 1, 2, 4, 4 ]
|
||||
disable_self_attentions: [ False, False, False, False ] # converts the self-attention to a cross-attention layer if true
|
||||
num_heads: 8
|
||||
use_spatial_transformer: True
|
||||
transformer_depth: 1
|
||||
context_dim: 768
|
||||
use_checkpoint: True
|
||||
legacy: False
|
||||
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
embed_dim: 4
|
||||
monitor: val/rec_loss
|
||||
ckpt_path: "/fsx/stable-diffusion/stable-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.FrozenCLIPEmbedder
|
||||
|
||||
|
||||
data:
|
||||
target: ldm.data.laion.WebDataModuleFromConfig
|
||||
params:
|
||||
tar_base: "__improvedaesthetic__"
|
||||
batch_size: 8
|
||||
num_workers: 4
|
||||
multinode: True
|
||||
train:
|
||||
shards: '{00000..17279}.tar -'
|
||||
shuffle: 10000
|
||||
image_key: jpg
|
||||
image_transforms:
|
||||
- target: torchvision.transforms.Resize
|
||||
params:
|
||||
size: 256
|
||||
interpolation: 3
|
||||
- target: torchvision.transforms.RandomCrop
|
||||
params:
|
||||
size: 256
|
||||
|
||||
# # NOTE use enough shards to avoid empty validation loops in workers
|
||||
validation:
|
||||
shards: '{17280..17535}.tar -'
|
||||
shuffle: 0
|
||||
image_key: jpg
|
||||
image_transforms:
|
||||
- target: torchvision.transforms.Resize
|
||||
params:
|
||||
size: 256
|
||||
interpolation: 3
|
||||
- target: torchvision.transforms.CenterCrop
|
||||
params:
|
||||
size: 256
|
||||
|
||||
|
||||
lightning:
|
||||
find_unused_parameters: false
|
||||
modelcheckpoint:
|
||||
params:
|
||||
every_n_train_steps: 5000
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
disabled: True
|
||||
batch_frequency: 2500
|
||||
max_images: 4
|
||||
increase_log_steps: False
|
||||
log_first_step: False
|
||||
log_images_kwargs:
|
||||
use_ema_scope: False
|
||||
inpaint: False
|
||||
plot_progressive_rows: False
|
||||
plot_diffusion_rows: False
|
||||
N: 4
|
||||
unconditional_guidance_scale: 3.0
|
||||
unconditional_guidance_label: [""]
|
||||
|
||||
trainer:
|
||||
#replace_sampler_ddp: False
|
||||
benchmark: True
|
||||
val_check_interval: 5000000 # really sorry
|
||||
num_sanity_val_steps: 0
|
||||
accumulate_grad_batches: 1
|
||||
|
|
@ -151,12 +151,7 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
|
|||
if self.tar_base == "__improvedaesthetic__":
|
||||
print("## Warning, loading the same improved aesthetic dataset "
|
||||
"for all splits and ignoring shards parameter.")
|
||||
urls = []
|
||||
for i in range(1, 65):
|
||||
for j in range(512):
|
||||
for k in range(5):
|
||||
urls.append(f's3://s-laion/improved-aesthetics-laion-2B-en-subsets/aesthetics/{i:02d}/{j:03d}/{k:05d}.tar')
|
||||
tars = [f'pipe:aws s3 cp {url} -' for url in urls]
|
||||
tars = "pipe:aws s3 cp s3://s-laion/improved-aesthetics-laion-2B-en-subsets/aesthetics_tars/{000000..060207}.tar -"
|
||||
else:
|
||||
tars = os.path.join(self.tar_base, dataset_config.shards)
|
||||
|
||||
|
|
@ -314,7 +309,8 @@ if __name__ == "__main__":
|
|||
from pytorch_lightning.trainer.supporters import CombinedLoader, CycleIterator
|
||||
|
||||
#config = OmegaConf.load("configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml")
|
||||
config = OmegaConf.load("configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml")
|
||||
#config = OmegaConf.load("configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml")
|
||||
config = OmegaConf.load("configs/stable-diffusion/txt2img-v2-clip-encoder-improved_aesthetics-256.yaml")
|
||||
datamod = WebDataModuleFromConfig(**config["data"]["params"])
|
||||
dataloader = datamod.train_dataloader()
|
||||
|
||||
|
|
|
|||
32
main.py
32
main.py
|
|
@ -21,6 +21,9 @@ from ldm.data.base import Txt2ImgIterableBaseDataset
|
|||
from ldm.util import instantiate_from_config
|
||||
|
||||
|
||||
MULTINODE_HACKS = True
|
||||
|
||||
|
||||
def get_parser(**parser_kwargs):
|
||||
def str2bool(v):
|
||||
if isinstance(v, bool):
|
||||
|
|
@ -268,6 +271,9 @@ class SetupCallback(Callback):
|
|||
os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
|
||||
print("Project config")
|
||||
print(OmegaConf.to_yaml(self.config))
|
||||
if MULTINODE_HACKS:
|
||||
import time
|
||||
time.sleep(5)
|
||||
OmegaConf.save(self.config,
|
||||
os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
|
||||
|
||||
|
|
@ -278,7 +284,7 @@ class SetupCallback(Callback):
|
|||
|
||||
else:
|
||||
# ModelCheckpoint callback created log directory --- remove it
|
||||
if not self.resume and os.path.exists(self.logdir):
|
||||
if not MULTINODE_HACKS and not self.resume and os.path.exists(self.logdir):
|
||||
dst, name = os.path.split(self.logdir)
|
||||
dst = os.path.join(dst, "child_runs", name)
|
||||
os.makedirs(os.path.split(dst)[0], exist_ok=True)
|
||||
|
|
@ -759,9 +765,19 @@ if __name__ == "__main__":
|
|||
del callbacks_cfg['ignore_keys_callback']
|
||||
|
||||
trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
|
||||
if not "plugins" in trainer_kwargs:
|
||||
trainer_kwargs["plugins"] = list()
|
||||
if not lightning_config.get("find_unused_parameters", True):
|
||||
from pytorch_lightning.plugins import DDPPlugin
|
||||
trainer_kwargs["plugins"] = DDPPlugin(find_unused_parameters=False)
|
||||
trainer_kwargs["plugins"].append(DDPPlugin(find_unused_parameters=False))
|
||||
if MULTINODE_HACKS:
|
||||
# disable resume from hpc ckpts
|
||||
# NOTE below only works in later versions
|
||||
# from pytorch_lightning.plugins.environments import SLURMEnvironment
|
||||
# trainer_kwargs["plugins"].append(SLURMEnvironment(auto_requeue=False))
|
||||
# hence we monkey patch things
|
||||
from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
|
||||
setattr(CheckpointConnector, "hpc_resume_path", None)
|
||||
|
||||
trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
|
||||
trainer.logdir = logdir ###
|
||||
|
|
@ -833,6 +849,18 @@ if __name__ == "__main__":
|
|||
raise
|
||||
if not opt.no_test and not trainer.interrupted:
|
||||
trainer.test(model, data)
|
||||
except RuntimeError as err:
|
||||
if MULTINODE_HACKS:
|
||||
import requests
|
||||
import datetime
|
||||
import os
|
||||
import socket
|
||||
device = os.environ.get("CUDA_VISIBLE_DEVICES", "?")
|
||||
hostname = socket.gethostname()
|
||||
ts = datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')
|
||||
resp = requests.get('http://169.254.169.254/latest/meta-data/instance-id')
|
||||
print(f'ERROR at {ts} on {hostname}/{resp.text} (CUDA_VISIBLE_DEVICES={device}): {type(err).__name__}: {err}', flush=True)
|
||||
raise err
|
||||
except Exception:
|
||||
if opt.debug and trainer.global_rank == 0:
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,218 @@
|
|||
import os
|
||||
import glob
|
||||
import subprocess
|
||||
import time
|
||||
import fire
|
||||
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from pytorch_lightning import seed_everything
|
||||
from omegaconf import OmegaConf
|
||||
from ldm.util import instantiate_from_config
|
||||
from ldm.models.diffusion.plms import PLMSSampler
|
||||
from einops import rearrange
|
||||
from torchvision.utils import make_grid
|
||||
from PIL import Image
|
||||
import contextlib
|
||||
|
||||
|
||||
def load_model_from_config(config, ckpt, verbose=False):
|
||||
pl_sd = torch.load(ckpt, map_location="cpu")
|
||||
gs = pl_sd["global_step"]
|
||||
sd = pl_sd["state_dict"]
|
||||
model = instantiate_from_config(config.model)
|
||||
m, u = model.load_state_dict(sd, strict=True)
|
||||
model.cuda()
|
||||
model.eval()
|
||||
return model, gs
|
||||
|
||||
|
||||
def read_prompts(path):
|
||||
with open(path, "r") as f:
|
||||
prompts = f.read().splitlines()
|
||||
return prompts
|
||||
|
||||
|
||||
def split_in_batches(iterator, n):
|
||||
out = []
|
||||
for elem in iterator:
|
||||
out.append(elem)
|
||||
if len(out) == n:
|
||||
yield out
|
||||
out = []
|
||||
if len(out) > 0:
|
||||
yield out
|
||||
|
||||
|
||||
class Sampler(object):
|
||||
def __init__(self, out_dir, ckpt_path, cfg_path, prompts_path, shape, seed=42):
|
||||
self.out_dir = out_dir
|
||||
self.ckpt_path = ckpt_path
|
||||
self.cfg_path = cfg_path
|
||||
self.prompts_path = prompts_path
|
||||
self.seed = seed
|
||||
|
||||
self.batch_size = 1
|
||||
self.scale = 10
|
||||
self.shape = shape
|
||||
self.n_steps = 100
|
||||
self.nrow = 8
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def sample(self, model, prompts, ema=True):
|
||||
seed = self.seed
|
||||
batch_size = self.batch_size
|
||||
scale = self.scale
|
||||
n_steps = self.n_steps
|
||||
|
||||
shape = self.shape
|
||||
|
||||
print("Sampling model.")
|
||||
print("ckpt_path", self.ckpt_path)
|
||||
print("cfg_path", self.cfg_path)
|
||||
print("prompts_path", self.prompts_path)
|
||||
print("out_dir", self.out_dir)
|
||||
print("seed", self.seed)
|
||||
print("batch_size", batch_size)
|
||||
print("scale", scale)
|
||||
print("n_steps", n_steps)
|
||||
print("shape", shape)
|
||||
|
||||
prompts = list(split_in_batches(prompts, batch_size))
|
||||
|
||||
sampler = PLMSSampler(model)
|
||||
all_samples = list()
|
||||
|
||||
ctxt = model.ema_scope if ema else contextlib.nullcontext
|
||||
|
||||
with ctxt():
|
||||
for prompts_batch in tqdm(prompts, desc="prompts"):
|
||||
uc = None
|
||||
if scale != 1.0:
|
||||
uc = model.get_learned_conditioning(batch_size * [""])
|
||||
c = model.get_learned_conditioning(prompts_batch)
|
||||
|
||||
seed_everything(seed)
|
||||
|
||||
samples_latent, _ = sampler.sample(
|
||||
S=n_steps,
|
||||
conditioning=c,
|
||||
batch_size=batch_size,
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=0.0,
|
||||
dynamic_threshold=None,
|
||||
)
|
||||
|
||||
samples = model.decode_first_stage(samples_latent)
|
||||
samples = torch.clamp((samples+1.0)/2.0, min=0.0, max=1.0)
|
||||
|
||||
all_samples.append(samples)
|
||||
|
||||
all_samples = torch.cat(all_samples, 0)
|
||||
return all_samples
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def __call__(self):
|
||||
config = OmegaConf.load(self.cfg_path)
|
||||
model, global_step = load_model_from_config(config, self.ckpt_path)
|
||||
print(f"Restored model at global step {global_step}.")
|
||||
|
||||
prompts = read_prompts(self.prompts_path)
|
||||
|
||||
all_samples = self.sample(model, prompts, ema=True)
|
||||
self.save_as_grid("grid_with_wings", all_samples, global_step)
|
||||
all_samples = self.sample(model, prompts, ema=False)
|
||||
self.save_as_grid("grid_without_wings", all_samples, global_step)
|
||||
|
||||
|
||||
def save_as_grid(self, name, grid, global_step):
|
||||
grid = make_grid(grid, nrow=self.nrow)
|
||||
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
|
||||
|
||||
os.makedirs(self.out_dir, exist_ok=True)
|
||||
filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
|
||||
name,
|
||||
global_step,
|
||||
0,
|
||||
0,
|
||||
)
|
||||
grid_path = os.path.join(self.out_dir, filename)
|
||||
Image.fromarray(grid.astype(np.uint8)).save(grid_path)
|
||||
print(f"---> {grid_path}")
|
||||
|
||||
|
||||
class Checker(object):
|
||||
def __init__(self, ckpt_path, callback, wait_for_file=5, interval=60):
|
||||
self._cached_stamp = 0
|
||||
self.filename = ckpt_path
|
||||
self.callback = callback
|
||||
self.interval = interval
|
||||
self.wait_for_file = wait_for_file
|
||||
|
||||
def check(self):
|
||||
while True:
|
||||
stamp = os.stat(self.filename).st_mtime
|
||||
if stamp != self._cached_stamp:
|
||||
while True:
|
||||
# try to wait until checkpoint is fully written
|
||||
previous_stamp = stamp
|
||||
time.sleep(self.wait_for_file)
|
||||
stamp = os.stat(self.filename).st_mtime
|
||||
if stamp != previous_stamp:
|
||||
print(f"File is still changing. Waiting {self.wait_for_file} seconds.")
|
||||
else:
|
||||
break
|
||||
|
||||
self._cached_stamp = stamp
|
||||
# file has changed, so do something...
|
||||
print(f"{self.__class__.__name__}: Detected a new file at "
|
||||
f"{self.filename}, calling back.")
|
||||
self.callback()
|
||||
|
||||
else:
|
||||
time.sleep(self.interval)
|
||||
|
||||
|
||||
def run(prompts_path="scripts/prompts/prompts-with-wings.txt",
|
||||
watch_log_dir=None, out_dir=None, ckpt_path=None, cfg_path=None,
|
||||
H=256,
|
||||
W=None,
|
||||
C=4,
|
||||
F=8,
|
||||
wait_for_file=5,
|
||||
interval=60):
|
||||
|
||||
if out_dir is None:
|
||||
assert watch_log_dir is not None
|
||||
out_dir = os.path.join(watch_log_dir, "images/checker")
|
||||
|
||||
if ckpt_path is None:
|
||||
assert watch_log_dir is not None
|
||||
ckpt_path = os.path.join(watch_log_dir, "checkpoints/last.ckpt")
|
||||
|
||||
if cfg_path is None:
|
||||
assert watch_log_dir is not None
|
||||
configs = glob.glob(os.path.join(watch_log_dir, "configs/*-project.yaml"))
|
||||
cfg_path = sorted(configs)[-1]
|
||||
|
||||
if W is None:
|
||||
assert H is not None
|
||||
W = H
|
||||
if H is None:
|
||||
assert W is not None
|
||||
H = W
|
||||
shape = [C, H//F, W//F]
|
||||
sampler = Sampler(out_dir, ckpt_path, cfg_path, prompts_path, shape=shape)
|
||||
|
||||
checker = Checker(ckpt_path, sampler, wait_for_file=wait_for_file, interval=interval)
|
||||
checker.check()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run)
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
#!/bin/bash
|
||||
|
||||
# mpi version for node rank
|
||||
H=`hostname`
|
||||
THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
|
||||
export NODE_RANK=${THEID}
|
||||
echo THEID=$THEID
|
||||
|
||||
echo "##########################################"
|
||||
echo MASTER_ADDR=${MASTER_ADDR}
|
||||
echo MASTER_PORT=${MASTER_PORT}
|
||||
echo NODE_RANK=${NODE_RANK}
|
||||
echo WORLD_SIZE=${WORLD_SIZE}
|
||||
echo "##########################################"
|
||||
# debug environment worked great so we stick with it
|
||||
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
|
||||
# env with pip dependencies from stable diffusion's requirements.txt
|
||||
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
|
||||
conda activate stable
|
||||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_improvedaesthetics.yaml"
|
||||
|
||||
# resume and set new seed to reshuffle data
|
||||
EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-11T20-16-11_txt2img-1p4B-multinode-clip-encoder-high-res-512_improvedaesthetic/checkpoints/last.ckpt"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${EXTRA} --logdir rlogs"
|
||||
|
||||
# debugging
|
||||
#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
|
||||
|
||||
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
#!/bin/bash
|
||||
#SBATCH --partition=compute-od-gpu
|
||||
#SBATCH --job-name=stable-diffusion-v1-improvedaesthetics
|
||||
#SBATCH --nodes 20
|
||||
#SBATCH --ntasks-per-node 1
|
||||
#SBATCH --cpus-per-gpu=4
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH --exclusive
|
||||
#SBATCH --output=%x_%j.out
|
||||
#SBATCH --comment "Key=Monitoring,Value=ON"
|
||||
|
||||
module load intelmpi
|
||||
source /opt/intel/mpi/latest/env/vars.sh
|
||||
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
|
||||
all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
|
||||
export NCCL_PROTO=simple
|
||||
export PATH=/opt/amazon/efa/bin:$PATH
|
||||
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
|
||||
export FI_EFA_FORK_SAFE=1
|
||||
export FI_LOG_LEVEL=1
|
||||
export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
|
||||
export NCCL_DEBUG=info
|
||||
export PYTHONFAULTHANDLER=1
|
||||
export CUDA_LAUNCH_BLOCKING=0
|
||||
export OMPI_MCA_mtl_base_verbose=1
|
||||
export FI_EFA_ENABLE_SHM_TRANSFER=0
|
||||
export FI_PROVIDER=efa
|
||||
export FI_EFA_TX_MIN_CREDITS=64
|
||||
export NCCL_TREE_THRESHOLD=0
|
||||
|
||||
# sent to sub script
|
||||
export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
|
||||
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
|
||||
export MASTER_PORT=12802
|
||||
export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
|
||||
export WORLD_SIZE=$COUNT_NODE
|
||||
|
||||
echo go $COUNT_NODE
|
||||
echo $HOSTNAMES
|
||||
echo $WORLD_SIZE
|
||||
|
||||
mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v1_improvedaesthetics/launcher.sh
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
#!/bin/bash
|
||||
|
||||
# mpi version for node rank
|
||||
H=`hostname`
|
||||
THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
|
||||
export NODE_RANK=${THEID}
|
||||
echo THEID=$THEID
|
||||
|
||||
echo "##########################################"
|
||||
echo MASTER_ADDR=${MASTER_ADDR}
|
||||
echo MASTER_PORT=${MASTER_PORT}
|
||||
echo NODE_RANK=${NODE_RANK}
|
||||
echo WORLD_SIZE=${WORLD_SIZE}
|
||||
echo "##########################################"
|
||||
# debug environment worked great so we stick with it
|
||||
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
|
||||
# env with pip dependencies from stable diffusion's requirements.txt
|
||||
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
|
||||
conda activate stable
|
||||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v2_laionhr1024.yaml"
|
||||
|
||||
# resume and set new seed to reshuffle data
|
||||
EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-12T00-50-44_txt2img-multinode-clip-encoder-f16-1024-laion-hr/checkpoints/last.ckpt"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${EXTRA} --logdir rlogs"
|
||||
|
||||
# debugging
|
||||
#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
|
||||
|
||||
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
#!/bin/bash
|
||||
#SBATCH --partition=compute-od-gpu
|
||||
#SBATCH --job-name=stable-diffusion-v2-laionhr1024
|
||||
#SBATCH --nodes 20
|
||||
#SBATCH --ntasks-per-node 1
|
||||
#SBATCH --cpus-per-gpu=4
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH --exclusive
|
||||
#SBATCH --output=%x_%j.out
|
||||
#SBATCH --comment "Key=Monitoring,Value=ON"
|
||||
|
||||
module load intelmpi
|
||||
source /opt/intel/mpi/latest/env/vars.sh
|
||||
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
|
||||
all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
|
||||
export NCCL_PROTO=simple
|
||||
export PATH=/opt/amazon/efa/bin:$PATH
|
||||
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
|
||||
export FI_EFA_FORK_SAFE=1
|
||||
export FI_LOG_LEVEL=1
|
||||
export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
|
||||
export NCCL_DEBUG=info
|
||||
export PYTHONFAULTHANDLER=1
|
||||
export CUDA_LAUNCH_BLOCKING=0
|
||||
export OMPI_MCA_mtl_base_verbose=1
|
||||
export FI_EFA_ENABLE_SHM_TRANSFER=0
|
||||
export FI_PROVIDER=efa
|
||||
export FI_EFA_TX_MIN_CREDITS=64
|
||||
export NCCL_TREE_THRESHOLD=0
|
||||
|
||||
# sent to sub script
|
||||
export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
|
||||
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
|
||||
export MASTER_PORT=12802
|
||||
export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
|
||||
export WORLD_SIZE=$COUNT_NODE
|
||||
|
||||
echo go $COUNT_NODE
|
||||
echo $HOSTNAMES
|
||||
echo $WORLD_SIZE
|
||||
|
||||
mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v2_laionhr1024/launcher.sh
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
#!/bin/bash
|
||||
|
||||
# mpi version for node rank
|
||||
H=`hostname`
|
||||
THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
|
||||
export NODE_RANK=${THEID}
|
||||
echo THEID=$THEID
|
||||
|
||||
echo "##########################################"
|
||||
echo MASTER_ADDR=${MASTER_ADDR}
|
||||
echo MASTER_PORT=${MASTER_PORT}
|
||||
echo NODE_RANK=${NODE_RANK}
|
||||
echo WORLD_SIZE=${WORLD_SIZE}
|
||||
echo "##########################################"
|
||||
# debug environment worked great so we stick with it
|
||||
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
|
||||
# env with pip dependencies from stable diffusion's requirements.txt
|
||||
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
|
||||
conda activate stable
|
||||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG=configs/stable-diffusion/v3_pretraining.yaml
|
||||
|
||||
# resume and set new seed to reshuffle data
|
||||
EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/rlogs/2022-07-11T22-57-10_txt2img-v2-clip-encoder-improved_aesthetics-256/checkpoints/last.ckpt"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${EXTRA} --logdir rlogs"
|
||||
|
||||
# debugging
|
||||
#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
|
||||
|
||||
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
#!/bin/bash
|
||||
#SBATCH --partition=compute-od-gpu
|
||||
#SBATCH --job-name=stable-diffusion-v3-pretraining
|
||||
#SBATCH --nodes 44
|
||||
#SBATCH --ntasks-per-node 1
|
||||
#SBATCH --cpus-per-gpu=4
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH --exclusive
|
||||
#SBATCH --output=%x_%j.out
|
||||
#SBATCH --comment "Key=Monitoring,Value=ON"
|
||||
|
||||
module load intelmpi
|
||||
source /opt/intel/mpi/latest/env/vars.sh
|
||||
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
|
||||
all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
|
||||
export NCCL_PROTO=simple
|
||||
export PATH=/opt/amazon/efa/bin:$PATH
|
||||
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
|
||||
export FI_EFA_FORK_SAFE=1
|
||||
export FI_LOG_LEVEL=1
|
||||
export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
|
||||
export NCCL_DEBUG=info
|
||||
export PYTHONFAULTHANDLER=1
|
||||
export CUDA_LAUNCH_BLOCKING=0
|
||||
export OMPI_MCA_mtl_base_verbose=1
|
||||
export FI_EFA_ENABLE_SHM_TRANSFER=0
|
||||
export FI_PROVIDER=efa
|
||||
export FI_EFA_TX_MIN_CREDITS=64
|
||||
export NCCL_TREE_THRESHOLD=0
|
||||
|
||||
# sent to sub script
|
||||
export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
|
||||
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
|
||||
export MASTER_PORT=12802
|
||||
export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
|
||||
export WORLD_SIZE=$COUNT_NODE
|
||||
|
||||
echo go $COUNT_NODE
|
||||
echo $HOSTNAMES
|
||||
echo $WORLD_SIZE
|
||||
|
||||
mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v3_pretraining/launcher.sh
|
||||
|
|
@ -40,7 +40,7 @@ def load_model_from_config(config, ckpt, verbose=False):
|
|||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
parser.add_argument(
|
||||
|
|
@ -258,3 +258,7 @@ if __name__ == "__main__":
|
|||
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
|
||||
f"Sampling took {toc-tic}s, i.e. produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec."
|
||||
f" \nEnjoy.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
|
|||
Loading…
Reference in New Issue