start f16-higher res config

This commit is contained in:
rromb 2022-06-16 17:12:54 +02:00
parent bbbeebf9a8
commit c790c34e21
2 changed files with 161 additions and 6 deletions

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@ -0,0 +1,129 @@
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: 48
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: # TODO: add
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: 48 # not really needed
in_channels: 16
out_channels: 16
model_channels: 320 # TODO: scale model here
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
ckpt_path: "models/first_stage_models/kl-f16/model.ckpt"
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/laion5b/laion2B-data/"
batch_size: 10
num_workers: 4
multinode: True
min_size: 384 # TODO: experiment. Note: for 2B, images are stored at max 384 resolution
train:
shards: '{000000..231317}.tar -'
shuffle: 10000
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 768
interpolation: 3
- target: torchvision.transforms.RandomCrop
params:
size: 768
# NOTE use enough shards to avoid empty validation loops in workers
validation:
shards: '{231318..231349}.tar -'
shuffle: 0
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 768
interpolation: 3
- target: torchvision.transforms.CenterCrop
params:
size: 768
lightning:
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
num_sanity_val_steps: 0
accumulate_grad_batches: 2

View file

@ -7,6 +7,7 @@ from tqdm import tqdm, trange
from itertools import islice from itertools import islice
from einops import rearrange from einops import rearrange
from torchvision.utils import make_grid from torchvision.utils import make_grid
import time
from ldm.util import instantiate_from_config from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.ddim import DDIMSampler
@ -63,6 +64,12 @@ if __name__ == "__main__":
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples", help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
) )
parser.add_argument(
"--skip_save",
action='store_true',
help="do not save indiviual samples. For speed measurements.",
)
parser.add_argument( parser.add_argument(
"--ddim_steps", "--ddim_steps",
type=int, type=int,
@ -103,6 +110,19 @@ if __name__ == "__main__":
help="image width, in pixel space", help="image width, in pixel space",
) )
parser.add_argument(
"--C",
type=int,
default=4,
help="latent channels",
)
parser.add_argument(
"--f",
type=int,
default=8,
help="downsampling factor, most often 8 or 16",
)
parser.add_argument( parser.add_argument(
"--n_samples", "--n_samples",
type=int, type=int,
@ -184,6 +204,7 @@ if __name__ == "__main__":
with torch.no_grad(): with torch.no_grad():
with model.ema_scope(): with model.ema_scope():
tic = time.time()
for n in trange(opt.n_iter, desc="Sampling"): for n in trange(opt.n_iter, desc="Sampling"):
all_samples = list() all_samples = list()
for prompts in tqdm(data, desc="data"): for prompts in tqdm(data, desc="data"):
@ -193,7 +214,7 @@ if __name__ == "__main__":
if isinstance(prompts, tuple): if isinstance(prompts, tuple):
prompts = list(prompts) prompts = list(prompts)
c = model.get_learned_conditioning(prompts) c = model.get_learned_conditioning(prompts)
shape = [4, opt.H//8, opt.W//8] shape = [opt.C, opt.H//opt.f, opt.W//opt.f]
samples_ddim, _ = sampler.sample(S=opt.ddim_steps, samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
conditioning=c, conditioning=c,
batch_size=opt.n_samples, batch_size=opt.n_samples,
@ -207,6 +228,7 @@ if __name__ == "__main__":
x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)
if not opt.skip_save:
for x_sample in x_samples_ddim: for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:05}.png")) Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:05}.png"))
@ -224,4 +246,8 @@ if __name__ == "__main__":
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png')) Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1 grid_count += 1
print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.") toc = time.time()
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.")