Merge branch 'main' of github.com:pesser/stable-diffusion into main
This commit is contained in:
Robin Rombach
commit
55a0485475
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@ -0,0 +1,133 @@
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|||
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
|
||||
|
||||
#ckpt_path: "/home/mchorse/stable-diffusion-ckpts/768f16-2022-06-23-pruned.ckpt"
|
||||
|
||||
#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
|
||||
|
|
@ -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: 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
|
||||
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: 1
|
||||
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: 2500
|
||||
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: 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: 2
|
||||
|
|
@ -0,0 +1,132 @@
|
|||
model:
|
||||
base_learning_rate: 7.5e-05
|
||||
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: 2
|
||||
|
|
@ -366,6 +366,7 @@ def example03():
|
|||
dataset = (dataset
|
||||
.select(filter_keys)
|
||||
.decode('pil', handler=wds.warn_and_continue))
|
||||
n_save = 20
|
||||
n_total = 0
|
||||
n_large = 0
|
||||
n_large_nowm = 0
|
||||
|
|
@ -375,6 +376,9 @@ def example03():
|
|||
n_large += 1
|
||||
if filter_watermark(example):
|
||||
n_large_nowm += 1
|
||||
if n_large_nowm < n_save+1:
|
||||
image = example["jpg"]
|
||||
image.save(os.path.join("tmp", f"{n_large_nowm-1:06}.png"))
|
||||
|
||||
if i%500 == 0:
|
||||
print(i)
|
||||
|
|
|
|||
1
main.py
1
main.py
|
|
@ -350,6 +350,7 @@ class ImageLogger(Callback):
|
|||
if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
|
||||
hasattr(pl_module, "log_images") and
|
||||
callable(pl_module.log_images) and
|
||||
batch_idx > 5 and
|
||||
self.max_images > 0):
|
||||
logger = type(pl_module.logger)
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,16 @@
|
|||
A portrait of Abraham Lincoln
|
||||
A portrait of Barack Obama
|
||||
A portrait of a nekomimi girl smiling
|
||||
a portrait of isaac newton the alchemist
|
||||
A portrait of Friedrich Nietzsche wearing an open double breasted suit with a bowtie
|
||||
Portrait of a cyberpunk cyborg man wearing alternate reality goggles
|
||||
Portrait of a woman screaming
|
||||
A portrait of a man in a flight jacket leaning against a biplane
|
||||
a cold landscape by Albert Bierstadt
|
||||
the monument of the ancients by van gogh
|
||||
the universal library
|
||||
a vision of paradise. unreal engine
|
||||
matte painting of cozy underground bunker wholefoods aisle, trending on artstation
|
||||
illustration of wooly mammoths reclaiming the arctic, trending on artstation
|
||||
a mountain range in the desert, Provia, Velvia
|
||||
the gateway between dreams, trending on ArtStation
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
a cityscape at night
|
||||
starry night by cyberpunk
|
||||
A fantasy painting of a city in a deep valley by Ivan Aivazovsky
|
||||
An oil painting of The New York City Skyline by Natalia Goncharova
|
||||
a rainy city street in the style of cyberpunk noir, trending on ArtStation
|
||||
an astral city in the style of cyberpunk noir art deco
|
||||
The Golden Gate Bridge in the style of art deco
|
||||
a city on a 70s science fiction novel cover
|
||||
An oil painting of A Vase Of Flowers
|
||||
still life oil painting of a smooth silver steel tungsten square cube box by Albrecht Dürer
|
||||
An oil painting of a bookshelf crammed with books, trending on artstation
|
||||
An N95 respirator mask in the style of art deco
|
||||
a surreal and organic stone monument to a plutonium atom
|
||||
oil painting of a candy dish of glass candies, mints, and other assorted sweets
|
||||
illustration of a ford model-t in pristine condition, trending on artstation
|
||||
illustration of DEC minicomputer console monitor retrocomputing teletype interdata PDP-11 univac, trending on artstation
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
The Rise Of Consciousness
|
||||
The Human Utility Function
|
||||
Revolution of the Souls
|
||||
a good amphetamine spirit
|
||||
Control The Soul
|
||||
The Lunatic, The Lover, and The Poet
|
||||
A Planet Ruled By Angels
|
||||
the Tower of Babel by J.M.W. Turner
|
||||
sketch of a 3D printer by Leonardo da Vinci
|
||||
In The Style Of M.C. Escher
|
||||
A cup of coffee by Picasso
|
||||
The US Capitol Building in the style of Kandinsky
|
||||
A Mysterious Orb by Andy Warhol
|
||||
The everlasting zero, a glimpse of a million, by Salvador Dali
|
||||
a painting of a haunted house with Halloween decorations by Giovanni Paolo Panini
|
||||
a painting of drops of Venus by Vincent van Gogh
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
ascii art of a man riding a bicycle
|
||||
cyberpunk noir art deco detective in space
|
||||
a cyborg angel in the style of ukiyo-e
|
||||
Hell in the style of pointillism
|
||||
Moloch in the style of socialist realism
|
||||
Metaphysics in the style of WPAP
|
||||
advertisement for a psychedelic virtual reality headset, 16 bit sprite pixel art
|
||||
a watercolor painting of a Christmas tree
|
||||
control room monitors televisions screens computers hacker lab, concept art, matte painting, trending on artstation
|
||||
a group of surgeons wait to cryonically suspend a patient
|
||||
technological singularity cult by James Gurney
|
||||
an autogyro flying car, trending on artstation
|
||||
illustration of airship zepplins in the skies, trending on artstation
|
||||
watercolor illustration of a martian colony geodesic dome aquaponics farming on the surface, trending on artstation
|
||||
humanity is killed by AI, by James Gurney
|
||||
the Vitruvian Man as a propaganda poster for transhumanism
|
||||
|
|
@ -2,6 +2,7 @@
|
|||
#SBATCH --partition=compute-od-gpu
|
||||
#SBATCH --job-name=stable-diffusion-512cont-improvedaesthetics
|
||||
#SBATCH --nodes=20
|
||||
#SBATCH --exclusive
|
||||
#SBATCH --gpus-per-node=8
|
||||
#SBATCH --cpus-per-gpu=4
|
||||
#SBATCH --ntasks-per-node=1
|
||||
|
|
@ -28,6 +29,7 @@ export NCCL_TREE_THRESHOLD=0
|
|||
|
||||
# pytorch multinode vars
|
||||
# node rank should be set in launcher script
|
||||
export HOSTNAMES=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
|
||||
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
|
||||
export MASTER_PORT=11338
|
||||
export WORLD_SIZE=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l)
|
||||
|
|
@ -36,4 +38,4 @@ echo MASTER_ADDR=${MASTER_ADDR}
|
|||
echo MASTER_PORT=${MASTER_PORT}
|
||||
echo WORLD_SIZE=${WORLD_SIZE}
|
||||
|
||||
srun --output=%x_%j.%n.out bash /fsx/stable-diffusion/stable-diffusion/scripts/slurm/resume_512/launcher.sh
|
||||
mpirun -n $WORLD_SIZE -perhost 1 bash /fsx/stable-diffusion/stable-diffusion/scripts/slurm/resume_512/launcher.sh
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ conda activate stable
|
|||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG=configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512-improvedaesthetic.yaml
|
||||
EXTRA="model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-07T16-15-18_txt2img-1p4B-multinode-clip-encoder-high-res-512/checkpoints/last.ckpt"
|
||||
EXTRA="model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-09T11-06-38_txt2img-1p4B-multinode-clip-encoder-high-res-512_improvedaesthetic/checkpoints/last.ckpt"
|
||||
DEBUG="-d True lightning.callbacks.image_logger.params.batch_frequency=5"
|
||||
|
||||
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA #$DEBUG
|
||||
|
|
|
|||
|
|
@ -28,12 +28,15 @@ export NCCL_TREE_THRESHOLD=0
|
|||
|
||||
# pytorch multinode vars
|
||||
# node rank should be set in launcher script
|
||||
export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
|
||||
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
|
||||
export MASTER_PORT=11338
|
||||
export WORLD_SIZE=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l)
|
||||
export MASTER_PORT=12802
|
||||
export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
|
||||
export WORLD_SIZE=$COUNT_NODE
|
||||
|
||||
echo MASTER_ADDR=${MASTER_ADDR}
|
||||
echo MASTER_PORT=${MASTER_PORT}
|
||||
echo WORLD_SIZE=${WORLD_SIZE}
|
||||
|
||||
srun --output=%x_%j.%n.out bash /fsx/stable-diffusion/stable-diffusion/scripts/slurm/resume_512_improvedaesthetic/launcher.sh
|
||||
#srun --output=%x_%j.%n.out bash /fsx/stable-diffusion/stable-diffusion/scripts/slurm/resume_512_improvedaesthetic/launcher.sh
|
||||
mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/resume_512_improvedaesthetic/launcher2.sh
|
||||
|
|
|
|||
|
|
@ -14,7 +14,8 @@ conda activate stable
|
|||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG=configs/stable-diffusion/txt2img-multinode-clip-encoder-f16-768-laion-hr.yaml
|
||||
EXTRA="model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints/f16-33k+12k-hr_pruned.ckpt"
|
||||
# EXTRA="model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints/f16-33k+12k-hr_pruned.ckpt"
|
||||
EXTRA="model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-09T20-06-38_txt2img-multinode-clip-encoder-f16-768-laion-hr/checkpoints/last.ckpt"
|
||||
DEBUG="-d True lightning.callbacks.image_logger.params.batch_frequency=5"
|
||||
|
||||
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA #$DEBUG
|
||||
|
|
|
|||
|
|
@ -24,7 +24,8 @@ CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_impro
|
|||
|
||||
# resume and set new seed to reshuffle data
|
||||
#EXTRA="--seed 718 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints2/v1pp/v1pp-flatline.ckpt"
|
||||
EXTRA="--seed 718 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-22T07-45-07_v1_improvedaesthetics/checkpoints/last.ckpt"
|
||||
#EXTRA="--seed 718 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-22T07-45-07_v1_improvedaesthetics/checkpoints/last.ckpt"
|
||||
EXTRA="--seed 719 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-22T12-32-32_v1_improvedaestheticsv1_iahr_torch111/checkpoints/last.ckpt"
|
||||
|
||||
# only images >= 512 and pwatermark <= 0.4999
|
||||
EXTRA="${EXTRA} data.params.min_size=512 data.params.max_pwatermark=0.4999"
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@
|
|||
#SBATCH --exclusive
|
||||
#SBATCH --output=%x_%j.out
|
||||
#SBATCH --comment "Key=Monitoring,Value=ON"
|
||||
#SBATCH --no-requeue
|
||||
|
||||
module load intelmpi
|
||||
source /opt/intel/mpi/latest/env/vars.sh
|
||||
|
|
|
|||
|
|
@ -0,0 +1,36 @@
|
|||
#!/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 "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
|
||||
conda activate stable
|
||||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v2_laionhr1024_2.yaml"
|
||||
|
||||
# resume and set new seed to reshuffle data
|
||||
#EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-12T00-50-44_txt2img-multinode-clip-encoder-f16-1024-laion-hr/checkpoints/last.ckpt"
|
||||
#EXTRA="--seed 715 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-15T16-49-34_v2_laionhr1024/checkpoints/last.ckpt"
|
||||
EXTRA="--seed 716 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-18T17-40-24_v2_laionhr1024/checkpoints/last.ckpt"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${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 $EXTRA
|
||||
~
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
#!/bin/bash
|
||||
#SBATCH --partition=compute-od-gpu
|
||||
#SBATCH --job-name=stable-diffusion-v2-laionhr1024
|
||||
#SBATCH --nodes 32
|
||||
#SBATCH --ntasks-per-node 1
|
||||
#SBATCH --cpus-per-gpu=4
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH --exclusive
|
||||
#SBATCH --output=%x_%j.out
|
||||
#SBATCH --comment "Key=Monitoring,Value=ON"
|
||||
|
||||
module load intelmpi
|
||||
source /opt/intel/mpi/latest/env/vars.sh
|
||||
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
|
||||
all/lib:/opt/aws-ofi-nccl/lib:$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 $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v2_laionhr1024_2/launcher.sh
|
||||
|
||||
|
|
@ -25,7 +25,8 @@ CONFIG=configs/stable-diffusion/v3_pretraining.yaml
|
|||
|
||||
# resume and set new seed to reshuffle data
|
||||
#EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/rlogs/2022-07-11T22-57-10_txt2img-v2-clip-encoder-improved_aesthetics-256/checkpoints/last.ckpt"
|
||||
EXTRA="--seed 715 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-14T21-03-49_txt2img-v2-clip-encoder-improved_aesthetics-256/checkpoints/last.ckpt"
|
||||
#EXTRA="--seed 715 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-14T21-03-49_txt2img-v2-clip-encoder-improved_aesthetics-256/checkpoints/last.ckpt"
|
||||
EXTRA="--seed 716 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-22T09-25-26_v3_pretraining/checkpoints/last.ckpt"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${EXTRA} --logdir rlogs"
|
||||
|
|
|
|||
Loading…
Reference in New Issue