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refactor: removes unused code and configurations (#405)

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Co-authored-by: jaydrennan
pull/417/head
jaydrennan 5 months ago committed by GitHub
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8 changed files with 2 additions and 368 deletions
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2
README.md
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@ -366,7 +366,7 @@ imagine --tile-x -w 1024 -h 512 "360 degree equirectangular panorama photograph
Use depth maps for amazing "translations" of existing images.
```bash
>> imagine --model SD-2.0-depth --init-image girl_with_a_pearl_earring_large.jpg --init-image-strength 0.05 "professional headshot photo of a woman with a pearl earring" -r 4 -w 1024 -h 1024 --steps 50
>> imagine --init-image girl_with_a_pearl_earring_large.jpg --init-image-strength 0.05 "professional headshot photo of a woman with a pearl earring" -r 4 -w 1024 -h 1024 --steps 50
```
<p float="left">
<img src="tests/data/girl_with_a_pearl_earring.jpg" width="256"> ➡️

2
imaginairy/api/generate.py
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@ -286,7 +286,7 @@ def _generate_single_image_compvis(
if control_inputs:
control_modes = [c.mode for c in prompt.control_inputs]
if inpaint_method == "auto":
if prompt.model_weights in {"SD-1.5", "SD-2.0"}:
if prompt.model_weights in {"SD-1.5"}:
inpaint_method = "finetune"
else:
inpaint_method = "controlnet"

68
imaginairy/configs/stable-diffusion-v2-inference-v.yaml
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@ -1,68 +0,0 @@
model:
base_learning_rate: 1.0e-4
target: imaginairy.modules.diffusion.ddpm.LatentDiffusion
params:
parameterization: "v"
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: 64
channels: 4
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False # we set this to false because this is an inference only config
unet_config:
target: imaginairy.modules.diffusion.openaimodel.UNetModel
params:
use_checkpoint: True
use_fp16: False
image_size: 32 # unused
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_head_channels: 64 # need to fix for flash-attn
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
first_stage_config:
target: imaginairy.modules.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
#attn_type: "vanilla-xformers"
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: imaginairy.modules.encoders.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"

67
imaginairy/configs/stable-diffusion-v2-inference.yaml
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@ -1,67 +0,0 @@
model:
base_learning_rate: 1.0e-4
target: imaginairy.modules.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: 64
channels: 4
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False # we set this to false because this is an inference only config
unet_config:
target: imaginairy.modules.diffusion.openaimodel.UNetModel
params:
use_checkpoint: True
use_fp16: False
image_size: 32 # unused
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_head_channels: 64 # need to fix for flash-attn
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
first_stage_config:
target: imaginairy.modules.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
#attn_type: "vanilla-xformers"
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: imaginairy.modules.encoders.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"

158
imaginairy/configs/stable-diffusion-v2-inpainting-inference.yaml
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@ -1,158 +0,0 @@
model:
base_learning_rate: 5.0e-05
target: imaginairy.modules.diffusion.ddpm.LatentInpaintDiffusion
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: 64
channels: 4
cond_stage_trainable: false
conditioning_key: hybrid
scale_factor: 0.18215
monitor: val/loss_simple_ema
finetune_keys: null
use_ema: False
unet_config:
target: imaginairy.modules.diffusion.openaimodel.UNetModel
params:
use_checkpoint: True
image_size: 32 # unused
in_channels: 9
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_head_channels: 64 # need to fix for flash-attn
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
first_stage_config:
target: imaginairy.modules.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
#attn_type: "vanilla-xformers"
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: imaginairy.modules.encoders.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"
data:
target: ldm.data.laion.WebDataModuleFromConfig
params:
tar_base: null # for concat as in LAION-A
p_unsafe_threshold: 0.1
filter_word_list: "data/filters.yaml"
max_pwatermark: 0.45
batch_size: 8
num_workers: 6
multinode: True
min_size: 512
train:
shards:
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -"
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -"
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -"
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -"
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar"
shuffle: 10000
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.RandomCrop
params:
size: 512
postprocess:
target: ldm.data.laion.AddMask
params:
mode: "512train-large"
p_drop: 0.25
# NOTE use enough shards to avoid empty validation loops in workers
validation:
shards:
- "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - "
shuffle: 0
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.CenterCrop
params:
size: 512
postprocess:
target: ldm.data.laion.AddMask
params:
mode: "512train-large"
p_drop: 0.25
lightning:
find_unused_parameters: True
modelcheckpoint:
params:
every_n_train_steps: 5000
callbacks:
metrics_over_trainsteps_checkpoint:
params:
every_n_train_steps: 10000
image_logger:
target: main.ImageLogger
params:
enable_autocast: False
disabled: False
batch_frequency: 1000
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: 5.0
unconditional_guidance_label: [""]
ddim_steps: 50 # todo check these out for depth2img,
ddim_eta: 0.0 # todo check these out for depth2img,
trainer:
benchmark: True
val_check_interval: 5000000
num_sanity_val_steps: 0
accumulate_grad_batches: 1

65
imaginairy/modules/diffusion/ddpm.py
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@ -1981,71 +1981,6 @@ class LatentInpaintDiffusion(LatentDiffusion):
return z, all_conds
class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
"""
condition on monocular depth estimation.
"""
def __init__(self, depth_stage_config, concat_keys=("midas_in",), **kwargs):
super().__init__(concat_keys=concat_keys, **kwargs)
self.depth_model = instantiate_from_config(depth_stage_config)
self.depth_stage_key = concat_keys[0]
@torch.no_grad()
def get_input(
self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False
):
# note: restricted to non-trainable encoders currently
assert (
not self.cond_stage_trainable
), "trainable cond stages not yet supported for depth2img"
z, c, x, xrec, xc = super().get_input(
batch,
self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=bs,
)
assert self.concat_keys is not None
assert len(self.concat_keys) == 1
c_cat = []
for ck in self.concat_keys:
cc = batch[ck]
if bs is not None:
cc = cc[:bs]
cc = cc.to(self.device)
cc = self.depth_model(cc)
cc = torch.nn.functional.interpolate(
cc,
size=z.shape[2:],
mode="bicubic",
align_corners=False,
)
depth_min, depth_max = torch.amin(
cc, dim=[1, 2, 3], keepdim=True
), torch.amax(cc, dim=[1, 2, 3], keepdim=True)
cc = 2.0 * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.0
c_cat.append(cc)
c_cat = torch.cat(c_cat, dim=1)
all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
if return_first_stage_outputs:
return z, all_conds, x, xrec, xc
return z, all_conds
@torch.no_grad()
def log_images(self, *args, **kwargs):
log = super().log_images(*args, **kwargs)
depth = self.depth_model(args[0][self.depth_stage_key])
depth_min, depth_max = torch.amin(
depth, dim=[1, 2, 3], keepdim=True
), torch.amax(depth, dim=[1, 2, 3], keepdim=True)
log["depth"] = 2.0 * (depth - depth_min) / (depth_max - depth_min) - 1.0
return log
class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
"""
condition on low-res image (and optionally on some spatial noise augmentation).

3
imaginairy/schema.py
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@ -541,9 +541,6 @@ class ImaginePrompt(BaseModel, protected_namespaces=()):
v = v.lower()
if info.data.get("model") == "SD-2.0-v" and v == SolverName.PLMS:
raise ValueError("PLMS solvers is not supported for SD-2.0-v model.")
if info.data.get("model") == "edit" and v in (
SolverName.PLMS,
SolverName.DDIM,

5
tests/test_utils/test_model_cache.py
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@ -27,12 +27,7 @@ def create_model_of_n_bytes(n):
@pytest.mark.parametrize(
"model_version",
[
# "SD-1.4",
"SD-1.5",
# "SD-2.0",
# "SD-2.0-v",
# "SD-2.1",
# "SD-2.1-v",
"openjourney-v1",
"openjourney-v2",
"openjourney-v4",

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