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add configs for training unconditional/class-conditional ldms

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100
README.md
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@ -55,18 +55,7 @@ bash scripts/download_first_stages.sh
```
The first stage models can then be found in `models/first_stage_models/<model_spec>`
### Training autoencoder models
Configs for training a KL-regularized autoencoder on ImageNet are provided at `configs/autoencoder`.
Training can be started by running
```
CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/autoencoder/<config_spec> -t --gpus 0,
```
where `config_spec` is one of {`autoencoder_kl_8x8x64.yaml`(f=32, d=64), `autoencoder_kl_16x16x16.yaml`(f=16, d=16),
`autoencoder_kl_32x32x4`(f=8, d=4), `autoencoder_kl_64x64x3`(f=4, d=3)}.
For training VQ-regularized models, see the [taming-transformers](https://github.com/CompVis/taming-transformers)
repository.
## Pretrained LDMs
@ -78,9 +67,10 @@ repository.
| LSUN-Bedrooms | Unconditional Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=1)| 2.95 (3.0) | 2.22 (2.23)| 0.66 | 0.48 | https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip | |
| ImageNet | Class-conditional Image Synthesis | LDM-VQ-8 (200 DDIM steps, eta=1) | 7.77(7.76)* /15.82** | 201.56(209.52)* /78.82** | 0.84* / 0.65** | 0.35* / 0.63** | https://ommer-lab.com/files/latent-diffusion/cin.zip | *: w/ guiding, classifier_scale 10 **: w/o guiding, scores in bracket calculated with script provided by [ADM](https://github.com/openai/guided-diffusion) |
| Conceptual Captions | Text-conditional Image Synthesis | LDM-VQ-f4 (100 DDIM steps, eta=0) | 16.79 | 13.89 | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/text2img.zip | finetuned from LAION |
| OpenImages | Super-resolution | N/A | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip | BSR image degradation |
| OpenImages | Super-resolution | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip | BSR image degradation |
| OpenImages | Layout-to-Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=0) | 32.02 | 15.92 | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip | |
| Landscapes (finetuned 512) | Semantic Image Synthesis | LDM-VQ-4 (100 DDIM steps, eta=1) | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip | |
| Landscapes | Semantic Image Synthesis | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip | |
| Landscapes | Semantic Image Synthesis | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip | finetuned on resolution 512x512 |
### Get the models
@ -116,10 +106,90 @@ python scripts/inpaint.py --indir data/inpainting_examples/ --outdir outputs/inp
`indir` should contain images `*.png` and masks `<image_fname>_mask.png` like
the examples provided in `data/inpainting_examples`.
# Train your own LDMs
## Data preparation
### Faces
For downloading the CelebA-HQ and FFHQ datasets, proceed as described in the [taming-transformers](https://github.com/CompVis/taming-transformers#celeba-hq)
repository.
### LSUN
The LSUN datasets can be conveniently downloaded via the script available [here](https://github.com/fyu/lsun).
We performed a custom split into training and validation images, and provide the corresponding filenames
at [https://ommer-lab.com/files/lsun.zip](https://ommer-lab.com/files/lsun.zip).
After downloading, extract them to `./data/lsun`. The beds/cats/churches subsets should
also be placed/symlinked at `./data/lsun/bedrooms`/`./data/lsun/cats`/`./data/lsun/churches`, respectively.
### ImageNet
The code will try to download (through [Academic
Torrents](http://academictorrents.com/)) and prepare ImageNet the first time it
is used. However, since ImageNet is quite large, this requires a lot of disk
space and time. If you already have ImageNet on your disk, you can speed things
up by putting the data into
`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` (which defaults to
`~/.cache/autoencoders/data/ILSVRC2012_{split}/data/`), where `{split}` is one
of `train`/`validation`. It should have the following structure:
```
${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/
├── n01440764
│ ├── n01440764_10026.JPEG
│ ├── n01440764_10027.JPEG
│ ├── ...
├── n01443537
│ ├── n01443537_10007.JPEG
│ ├── n01443537_10014.JPEG
│ ├── ...
├── ...
```
If you haven't extracted the data, you can also place
`ILSVRC2012_img_train.tar`/`ILSVRC2012_img_val.tar` (or symlinks to them) into
`${XDG_CACHE}/autoencoders/data/ILSVRC2012_train/` /
`${XDG_CACHE}/autoencoders/data/ILSVRC2012_validation/`, which will then be
extracted into above structure without downloading it again. Note that this
will only happen if neither a folder
`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` nor a file
`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/.ready` exist. Remove them
if you want to force running the dataset preparation again.
## Model Training
Logs and checkpoints for trained models are saved to `logs/<START_DATE_AND_TIME>_<config_spec>`.
### Training autoencoder models
Configs for training a KL-regularized autoencoder on ImageNet are provided at `configs/autoencoder`.
Training can be started by running
```
CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/autoencoder/<config_spec>.yaml -t --gpus 0,
```
where `config_spec` is one of {`autoencoder_kl_8x8x64`(f=32, d=64), `autoencoder_kl_16x16x16`(f=16, d=16),
`autoencoder_kl_32x32x4`(f=8, d=4), `autoencoder_kl_64x64x3`(f=4, d=3)}.
For training VQ-regularized models, see the [taming-transformers](https://github.com/CompVis/taming-transformers)
repository.
### Training LDMs
In ``configs/latent-diffusion/`` we provide configs for training LDMs on the LSUN-, CelebA-HQ, FFHQ and ImageNet datasets.
Training can be started by running
```shell script
CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0,
```
where ``<config_spec>`` is one of {`celebahq-ldm-vq-4`(f=4, VQ-reg. autoencoder, spatial size 64x64x3),`ffhq-ldm-vq-4`(f=4, VQ-reg. autoencoder, spatial size 64x64x3),
`lsun_bedrooms-ldm-vq-4`(f=4, VQ-reg. autoencoder, spatial size 64x64x3),
`lsun_churches-ldm-vq-4`(f=8, KL-reg. autoencoder, spatial size 32x32x4),`cin-ldm-vq-8`(f=8, VQ-reg. autoencoder, spatial size 32x32x4)}.
## Coming Soon...
* Code for training LDMs and the corresponding compression models.
* Inference scripts for conditional LDMs for various conditioning modalities.
* More inference scripts for conditional LDMs.
* In the meantime, you can play with our colab notebook https://colab.research.google.com/drive/1xqzUi2iXQXDqXBHQGP9Mqt2YrYW6cx-J?usp=sharing
* We will also release some further pretrained models.

86
configs/latent-diffusion/celebahq-ldm-vq-4.yaml Normal file
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@ -0,0 +1,86 @@
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
image_size: 64
channels: 3
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
# note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 64 for f4
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ckpt_path: models/first_stage_models/vq-f4/model.ckpt
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: taming.data.faceshq.CelebAHQTrain
params:
size: 256
validation:
target: taming.data.faceshq.CelebAHQValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True

98
configs/latent-diffusion/cin-ldm-vq-f8.yaml Normal file
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@ -0,0 +1,98 @@
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: class_label
image_size: 32
channels: 4
cond_stage_trainable: true
conditioning_key: crossattn
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 4
out_channels: 4
model_channels: 256
attention_resolutions:
#note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 32 for f8
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 4
num_head_channels: 32
use_spatial_transformer: true
transformer_depth: 1
context_dim: 512
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 4
n_embed: 16384
ckpt_path: configs/first_stage_models/vq-f8/model.yaml
ddconfig:
double_z: false
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 2
- 4
num_res_blocks: 2
attn_resolutions:
- 32
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.ClassEmbedder
params:
embed_dim: 512
key: class_label
data:
target: main.DataModuleFromConfig
params:
batch_size: 64
num_workers: 12
wrap: false
train:
target: ldm.data.imagenet.ImageNetTrain
params:
config:
size: 256
validation:
target: ldm.data.imagenet.ImageNetValidation
params:
config:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True

85
configs/latent-diffusion/ffhq-ldm-vq-4.yaml Normal file
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@ -0,0 +1,85 @@
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
image_size: 64
channels: 3
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
# note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 64 for f4
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ckpt_path: configs/first_stage_models/vq-f4/model.yaml
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 42
num_workers: 5
wrap: false
train:
target: taming.data.faceshq.FFHQTrain
params:
size: 256
validation:
target: taming.data.faceshq.FFHQValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True

85
configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml Normal file
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@ -0,0 +1,85 @@
model:
base_learning_rate: 2.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
image_size: 64
channels: 3
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 3
out_channels: 3
model_channels: 224
attention_resolutions:
# note: this isn\t actually the resolution but
# the downsampling factor, i.e. this corresnponds to
# attention on spatial resolution 8,16,32, as the
# spatial reolution of the latents is 64 for f4
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_head_channels: 32
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
ckpt_path: configs/first_stage_models/vq-f4/model.yaml
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config: __is_unconditional__
data:
target: main.DataModuleFromConfig
params:
batch_size: 48
num_workers: 5
wrap: false
train:
target: ldm.data.lsun.LSUNBedroomsTrain
params:
size: 256
validation:
target: ldm.data.lsun.LSUNBedroomsValidation
params:
size: 256
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 8
increase_log_steps: False
trainer:
benchmark: True

10
configs/latent-diffusion/lsun_churches_f8-autoencoder-ldm.yaml → configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml
View File

@ -45,7 +45,7 @@ model:
params:
embed_dim: 4
monitor: "val/rec_loss"
ckpt_path: "/export/compvis-nfs/user/ablattma/logs/braket/2021-11-26T11-25-56_lsun_churches-convae-f8-ft_from_oi/checkpoints/step=000180071-fidfrechet_inception_distance=2.335.ckpt"
ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
ddconfig:
double_z: True
z_channels: 4
@ -65,7 +65,7 @@ model:
data:
target: main.DataModuleFromConfig
params:
batch_size: 24 # TODO: was 96 in our experiments
batch_size: 96
num_workers: 5
wrap: False
train:
@ -82,14 +82,10 @@ lightning:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000 # TODO 5000
batch_frequency: 5000
max_images: 8
increase_log_steps: False
metrics_over_trainsteps_checkpoint:
target: pytorch_lightning.callbacks.ModelCheckpoint
params:
every_n_train_steps: 20000
trainer:
benchmark: True

9
ldm/models/diffusion/ddim.py
View File

@ -5,8 +5,7 @@ import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.models.diffusion.ddpm import noise_like
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
class DDIMSampler(object):
@ -27,8 +26,7 @@ class DDIMSampler(object):
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
alphas_cumprod = self.model.alphas_cumprod
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32, device=self.model.device)
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
@ -73,7 +71,8 @@ class DDIMSampler(object):
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100
log_every_t=100,
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):

57
ldm/models/diffusion/ddpm.py
View File

@ -16,14 +16,14 @@ from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from PIL import Image
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
__conditioning_keys__ = {'concat': 'c_concat',
@ -37,12 +37,6 @@ def disabled_train(self, mode=True):
return self
def noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
@ -119,6 +113,7 @@ class DDPM(pl.LightningModule):
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
@ -1188,7 +1183,6 @@ class LatentDiffusion(DDPM):
if start_T is not None:
timesteps = min(timesteps, start_T)
print(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
@ -1222,7 +1216,7 @@ class LatentDiffusion(DDPM):
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None):
mask=None, x0=None, shape=None,**kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
@ -1238,10 +1232,28 @@ class LatentDiffusion(DDPM):
mask=mask, x0=x0)
@torch.no_grad()
def log_images(self, batch, N=8, n_row=4, sample=True, sample_ddim=False, return_keys=None,
def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
shape,cond,verbose=False,**kwargs)
else:
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
return_intermediates=True,**kwargs)
return samples, intermediates
@torch.no_grad()
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
plot_diffusion_rows=True, **kwargs):
# TODO: maybe add option for ddim sampling via DDIMSampler class
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True,
@ -1288,7 +1300,9 @@ class LatentDiffusion(DDPM):
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
@ -1299,8 +1313,11 @@ class LatentDiffusion(DDPM):
self.first_stage_model, IdentityFirstStage):
# also display when quantizing x0 while sampling
with self.ema_scope("Plotting Quantized Denoised"):
samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta,
quantize_denoised=True)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
# quantize_denoised=True)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_x0_quantized"] = x_samples
@ -1312,19 +1329,17 @@ class LatentDiffusion(DDPM):
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
mask = mask[:, None, ...]
with self.ema_scope("Plotting Inpaint"):
samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
quantize_denoised=False, x0=z[:N], mask=mask)
samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_inpainting"] = x_samples
log["mask"] = mask
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row_inpainting"] = denoise_grid
# outpaint
with self.ema_scope("Plotting Outpaint"):
samples = self.sample(cond=c, batch_size=N, return_intermediates=False,
quantize_denoised=False, x0=z[:N], mask=1. - mask)
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_outpainting"] = x_samples

6
ldm/modules/diffusionmodules/util.py
View File

@ -259,3 +259,9 @@ class HybridConditioner(nn.Module):
c_concat = self.concat_conditioner(c_concat)
c_crossattn = self.crossattn_conditioner(c_crossattn)
return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
def noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()

5
main.py
View File

@ -676,7 +676,10 @@ if __name__ == "__main__":
ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))
else:
ngpu = 1
accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches or 1
if 'accumulate_grad_batches' in lightning_config.trainer:
accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
else:
accumulate_grad_batches = 1
print(f"accumulate_grad_batches = {accumulate_grad_batches}")
lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
if opt.scale_lr:

59
models/ldm/semantic_synthesis256/config.yaml Normal file
View File

@ -0,0 +1,59 @@
model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: segmentation
image_size: 64
channels: 3
concat_mode: true
cond_stage_trainable: true
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 6
out_channels: 3
model_channels: 128
attention_resolutions:
- 32
- 16
- 8
num_res_blocks: 2
channel_mult:
- 1
- 4
- 8
num_heads: 8
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
ddconfig:
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.SpatialRescaler
params:
n_stages: 2
in_channels: 182
out_channels: 3

4
scripts/download_first_stages.sh
View File

@ -4,10 +4,10 @@ wget -O models/first_stage_models/kl-f8/model.zip https://ommer-lab.com/files/la
wget -O models/first_stage_models/kl-f16/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f16.zip
wget -O models/first_stage_models/kl-f32/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f32.zip
wget -O models/first_stage_models/vq-f4/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4.zip
wget -O models/first_stage_models/vq-f4-noattn/model.zip https://heibox.uni-heidelberg.de/f/9c6681f64bb94338a069/?dl=1
wget -O models/first_stage_models/vq-f4-noattn/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4-noattn.zip
wget -O models/first_stage_models/vq-f8/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8.zip
wget -O models/first_stage_models/vq-f8-n256/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip
wget -O models/first_stage_models/vq-f16/model.zip https://heibox.uni-heidelberg.de/f/0e42b04e2e904890a9b6/?dl=1
wget -O models/first_stage_models/vq-f16/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f16.zip

9
scripts/download_models.sh
View File

@ -6,9 +6,10 @@ wget -O models/ldm/lsun_beds256/lsun_beds-256.zip https://ommer-lab.com/files/la
wget -O models/ldm/text2img256/model.zip https://ommer-lab.com/files/latent-diffusion/text2img.zip
wget -O models/ldm/cin256/model.zip https://ommer-lab.com/files/latent-diffusion/cin.zip
wget -O models/ldm/semantic_synthesis512/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip
wget -O models/ldm/semantic_synthesis256/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip
wget -O models/ldm/bsr_sr/model.zip https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip
wget -O models/ldm/layout2img-openimages256/model.zip https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip
wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1
wget -O models/ldm/inpainting_big/model.zip https://ommer-lab.com/files/latent-diffusion/inpainting_big.zip
@ -33,10 +34,16 @@ unzip -o model.zip
cd ../semantic_synthesis512
unzip -o model.zip
cd ../semantic_synthesis256
unzip -o model.zip
cd ../bsr_sr
unzip -o model.zip
cd ../layout2img-openimages256
unzip -o model.zip
cd ../inpainting_big
unzip -o model.zip
cd ../..