r
/
stable-diffusion-finetune
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

add configs for training unconditional/class-conditional ldms

This commit is contained in:
ablattmann 2021-12-22 15:57:23 +01:00
parent f8b4a07105
commit 171cf29fb5
13 changed files with 562 additions and 53 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

100
README.md
View File

@ -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
View File

@ -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
View File

@ -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
View File

@ -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
View File

@ -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):

59
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,
quantize_denoised=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 ../..