Spaces:
Runtime error
Runtime error
File size: 2,854 Bytes
412c852 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
# model settings
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=0)
# adapted from stable-diffusion/configs/stable-diffusion/v1-inference.yaml
stable_diffusion_cfg = dict(
base_learning_rate=0.0001,
target='ldm.models.diffusion.ddpm.LatentDiffusion',
checkpoint='https://download.openmmlab.com/mmsegmentation/v0.5/'
'vpd/stable_diffusion_v1-5_pretrain_third_party.pth',
params=dict(
linear_start=0.00085,
linear_end=0.012,
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,
conditioning_key='crossattn',
monitor='val/loss_simple_ema',
scale_factor=0.18215,
use_ema=False,
scheduler_config=dict(
target='ldm.lr_scheduler.LambdaLinearScheduler',
params=dict(
warm_up_steps=[10000],
cycle_lengths=[10000000000000],
f_start=[1e-06],
f_max=[1.0],
f_min=[1.0])),
unet_config=dict(
target='ldm.modules.diffusionmodules.openaimodel.UNetModel',
params=dict(
image_size=32,
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=dict(
target='ldm.models.autoencoder.AutoencoderKL',
params=dict(
embed_dim=4,
monitor='val/rec_loss',
ddconfig=dict(
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=dict(target='torch.nn.Identity'))),
cond_stage_config=dict(
target='ldm.modules.encoders.modules.AbstractEncoder')))
model = dict(
type='DepthEstimator',
data_preprocessor=data_preprocessor,
backbone=dict(
type='VPD',
diffusion_cfg=stable_diffusion_cfg,
),
)
# some of the parameters in stable-diffusion model will not be updated
# during training
find_unused_parameters = True
|