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lint: new ruff linter

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Bryce 2023-01-01 20:14:22 -08:00 committed by Bryce Drennan
parent a1871e9d3a
commit 1381c7fed4
53 changed files with 256 additions and 266 deletions
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3
Makefile
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@ -27,7 +27,8 @@ init: require_pyenv ## Setup a dev environment for local development.
af: autoformat ## Alias for `autoformat`
autoformat: ## Run the autoformatter.
@pycln . --all --quiet --extend-exclude __init__\.py
@isort --atomic --profile black .
@# ERA,T201
@-ruff --extend-ignore ANN,ARG001,C90,DTZ,D100,D101,D102,D103,D202,D203,D212,D415,E501,RET504,S101,UP006,UP007 --extend-select C,D400,I,UP,W --unfixable T,ERA --fix-only .
@black .
test: ## Run the tests.

4
imaginairy/cmds.py
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@ -223,7 +223,7 @@ def imagine_cmd(
model_weights_path,
prompt_library_path,
):
"""Have the AI generate images. alias:imagine"""
"""Have the AI generate images. alias:imagine."""
if ctx.invoked_subcommand is not None:
return
@ -303,7 +303,7 @@ def aimg():
@click.argument("image_filepaths", nargs=-1)
@aimg.command()
def describe(image_filepaths):
"""Generate text descriptions of images"""
"""Generate text descriptions of images."""
imgs = []
for p in image_filepaths:
if p.startswith("http"):

2
imaginairy/enhancers/clip_masking.py
View File

@ -14,7 +14,7 @@ from imaginairy.vendored.clipseg import CLIPDensePredT
weights_url = "https://github.com/timojl/clipseg/raw/master/weights/rd64-uni.pth"
@lru_cache()
@lru_cache
def clip_mask_model():
from imaginairy.paths import PKG_ROOT # noqa

4
imaginairy/enhancers/describe_image_blip.py
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@ -17,7 +17,7 @@ if "mps" in device:
BLIP_EVAL_SIZE = 384
@lru_cache()
@lru_cache
def blip_model():
from imaginairy.paths import PKG_ROOT # noqa
@ -35,7 +35,7 @@ def blip_model():
def generate_caption(image, min_length=30):
"""Given an image, return a caption"""
"""Given an image, return a caption."""
gpu_image = (
transforms.Compose(
[

4
imaginairy/enhancers/describe_image_clip.py
View File

@ -10,7 +10,7 @@ from imaginairy.vendored import clip
device = "cuda" if torch.cuda.is_available() else "cpu"
@lru_cache()
@lru_cache
def get_model():
model_name = "ViT-L/14"
model, preprocess = clip.load(model_name, device=device)
@ -18,7 +18,7 @@ def get_model():
def find_img_text_similarity(image: Image.Image, phrases: Sequence):
"""Find the likelihood of a list of textual concepts existing in the image"""
"""Find the likelihood of a list of textual concepts existing in the image."""
model, preprocess = get_model()
image = preprocess(image).unsqueeze(0).to(device)

6
imaginairy/enhancers/face_restoration_codeformer.py
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@ -14,7 +14,7 @@ from imaginairy.vendored.codeformer.codeformer_arch import CodeFormer
logger = logging.getLogger(__name__)
@lru_cache()
@lru_cache
def codeformer_model():
model = CodeFormer(
dim_embd=512,
@ -31,10 +31,10 @@ def codeformer_model():
return model
@lru_cache()
@lru_cache
def face_restore_helper():
"""
Provide a singleton of FaceRestoreHelper
Provide a singleton of FaceRestoreHelper.
FaceRestoreHelper loads a model internally so we need to cache it
or we end up with a memory leak

12
imaginairy/enhancers/prompt_expansion.py
View File

@ -15,9 +15,9 @@ formatter = Formatter()
PROMPT_EXPANSION_PATTERN = re.compile(r"[|a-z0-9_ -]+")
@lru_cache()
@lru_cache
def prompt_library_filepaths(prompt_library_paths=None):
"""Return all available category/filepath pairs"""
"""Return all available category/filepath pairs."""
prompt_library_paths = [] if not prompt_library_paths else prompt_library_paths
combined_prompt_library_filepaths = {}
for prompt_path in DEFAULT_PROMPT_LIBRARY_PATHS + list(prompt_library_paths):
@ -27,15 +27,15 @@ def prompt_library_filepaths(prompt_library_paths=None):
return combined_prompt_library_filepaths
@lru_cache()
@lru_cache
def category_list(prompt_library_paths=None):
"""Return the names of available phrase-lists"""
"""Return the names of available phrase-lists."""
categories = list(prompt_library_filepaths(prompt_library_paths).keys())
categories.sort()
return categories
@lru_cache()
@lru_cache
def prompt_library_filepath(library_path):
lookup = {}
@ -70,7 +70,7 @@ def get_phrases(category_name, prompt_library_paths=None):
def expand_prompts(prompt_text, n=1, prompt_library_paths=None):
"""
Replaces {vars} with random samples of corresponding phraselists
Replaces {vars} with random samples of corresponding phraselists.
Example:
p = "a happy {animal}"

2
imaginairy/enhancers/upscale_realesrgan.py
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@ -10,7 +10,7 @@ from imaginairy.model_manager import get_cached_url_path
from imaginairy.utils import get_device
@lru_cache()
@lru_cache
def realesrgan_upsampler():
model = RRDBNet(
num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4

5
imaginairy/img_utils.py
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@ -21,7 +21,10 @@ def pillow_fit_image_within(image: PIL.Image.Image, max_height=512, max_width=51
if resize_ratio != 1:
w, h = int(w * resize_ratio), int(h * resize_ratio)
w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
# resize to integer multiple of 64
w -= w % 64
h -= h % 64
if (w, h) != image.size:
image = image.resize((w, h), resample=Image.Resampling.LANCZOS)
return image

8
imaginairy/model_manager.py
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@ -29,7 +29,7 @@ class HuggingFaceAuthorizationError(RuntimeError):
class MemoryAwareModel:
"""Wraps a model to allow dynamic loading/unloading as needed"""
"""Wraps a model to allow dynamic loading/unloading as needed."""
def __init__(self, config_path, weights_path, half_mode=None):
self._config_path = config_path
@ -122,7 +122,7 @@ def get_diffusion_model(
for_inpainting=False,
):
"""
Load a diffusion model
Load a diffusion model.
Weights location may also be shortcut name, e.g. "SD-1.5"
"""
@ -148,7 +148,7 @@ def _get_diffusion_model(
for_inpainting=False,
):
"""
Load a diffusion model
Load a diffusion model.
Weights location may also be shortcut name, e.g. "SD-1.5"
"""
@ -217,7 +217,7 @@ def get_cache_dir():
def get_cached_url_path(url):
"""
Gets the contents of a url, but caches the response indefinitely
Gets the contents of a url, but caches the response indefinitely.
While we attempt to use the cached_path from huggingface transformers, we fall back
to our own implementation if the url does not provide an etag header, which `cached_path`

16
imaginairy/modules/attention.py
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@ -184,7 +184,7 @@ class CrossAttention(nn.Module):
k = self.to_k(context) * self.scale
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
q, k, v = (rearrange(t, "b n (h d) -> (b h) n d", h=h) for t in (q, k, v))
# force cast to fp32 to avoid overflowing
if ATTENTION_PRECISION_OVERRIDE == "fp32":
@ -219,8 +219,8 @@ class CrossAttention(nn.Module):
v_in = self.to_v(context)
del context, x
q, k, v = map(
lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q_in, k_in, v_in)
q, k, v = (
rearrange(t, "b n (h d) -> (b h) n d", h=h) for t in (q_in, k_in, v_in)
)
del q_in, k_in, v_in
@ -300,13 +300,13 @@ class MemoryEfficientCrossAttention(nn.Module):
v = self.to_v(context)
b, _, _ = q.shape
q, k, v = map(
lambda t: t.unsqueeze(3)
q, k, v = (
t.unsqueeze(3)
.reshape(b, t.shape[1], self.heads, self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b * self.heads, t.shape[1], self.dim_head)
.contiguous(),
(q, k, v),
.contiguous()
for t in (q, k, v)
)
# actually compute the attention, what we cannot get enough of
@ -392,7 +392,7 @@ class SpatialTransformer(nn.Module):
and reshape to b, t, d.
Then apply standard transformer action.
Finally, reshape to image
NEW: use_linear for more efficiency instead of the 1x1 convs
NEW: use_linear for more efficiency instead of the 1x1 convs.
"""
def __init__(

4
imaginairy/modules/autoencoder.py
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@ -168,7 +168,9 @@ class AutoencoderKL(pl.LightningModule):
def validation_step(self, batch, batch_idx):
log_dict = self._validation_step(batch, batch_idx)
with self.ema_scope():
log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
log_dict_ema = self._validation_step( # noqa
batch, batch_idx, postfix="_ema"
)
return log_dict
def _validation_step(self, batch, batch_idx, postfix=""):

2
imaginairy/modules/clip_embedders.py
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@ -9,7 +9,7 @@ from imaginairy.vendored import clip
class FrozenCLIPEmbedder(nn.Module):
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
"""Uses the CLIP transformer encoder for text (from Hugging Face)."""
def __init__(
self,

69
imaginairy/modules/diffusion/ddpm.py
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@ -3,7 +3,7 @@ wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
-- merci.
"""
import itertools
import logging
@ -66,7 +66,7 @@ class DDPM(pl.LightningModule):
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=tuple(),
ignore_keys=(),
load_only_unet=False,
monitor="val/loss",
use_ema=True,
@ -286,7 +286,7 @@ class DDPM(pl.LightningModule):
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=tuple(), only_model=False):
def init_from_ckpt(self, path, ignore_keys=(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
@ -664,7 +664,7 @@ def _TileModeConv2DConvForward(
class LatentDiffusion(DDPM):
"""main class"""
"""main class."""
def __init__(
self,
@ -728,7 +728,7 @@ class LatentDiffusion(DDPM):
)
def tile_mode(self, tile_mode):
"""For creating seamless tiles"""
"""For creating seamless tiles."""
tile_mode = tile_mode or ""
tile_x = "x" in tile_mode
tile_y = "y" in tile_mode
@ -904,9 +904,12 @@ class LatentDiffusion(DDPM):
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
fold_params = {
"kernel_size": kernel_size,
"dilation": 1,
"padding": 0,
"stride": stride,
}
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
@ -918,17 +921,20 @@ class LatentDiffusion(DDPM):
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
fold_params = {
"kernel_size": kernel_size,
"dilation": 1,
"padding": 0,
"stride": stride,
}
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(
kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1,
padding=0,
stride=(stride[0] * uf, stride[1] * uf),
)
fold_params2 = {
"kernel_size": (kernel_size[0] * uf, kernel_size[0] * uf),
"dilation": 1,
"padding": 0,
"stride": (stride[0] * uf, stride[1] * uf),
}
fold = torch.nn.Fold(
output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2
)
@ -944,17 +950,20 @@ class LatentDiffusion(DDPM):
)
elif df > 1 and uf == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
fold_params = {
"kernel_size": kernel_size,
"dilation": 1,
"padding": 0,
"stride": stride,
}
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(
kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1,
padding=0,
stride=(stride[0] // df, stride[1] // df),
)
fold_params2 = {
"kernel_size": (kernel_size[0] // df, kernel_size[0] // df),
"dilation": 1,
"padding": 0,
"stride": (stride[0] // df, stride[1] // df),
}
fold = torch.nn.Fold(
output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2
)
@ -1370,7 +1379,7 @@ class DiffusionWrapper(pl.LightningModule):
class LatentFinetuneDiffusion(LatentDiffusion):
"""
Basis for different finetunas, such as inpainting or depth2image
To disable finetuning mode, set finetune_keys to None
To disable finetuning mode, set finetune_keys to None.
"""
def __init__(
@ -1399,7 +1408,7 @@ class LatentFinetuneDiffusion(LatentDiffusion):
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
def init_from_ckpt(self, path, ignore_keys=tuple(), only_model=False):
def init_from_ckpt(self, path, ignore_keys=(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
@ -1606,7 +1615,7 @@ class LatentInpaintDiffusion(LatentDiffusion):
class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
"""
condition on monocular depth estimation
condition on monocular depth estimation.
"""
def __init__(self, depth_stage_config, concat_keys=("midas_in",), **kwargs):
@ -1671,7 +1680,7 @@ class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
"""
condition on low-res image (and optionally on some spatial noise augmentation)
condition on low-res image (and optionally on some spatial noise augmentation).
"""
def __init__(

17
imaginairy/modules/diffusion/model.py
View File

@ -288,7 +288,7 @@ class MemoryEfficientAttnBlock(nn.Module):
"""
Uses xformers efficient implementation,
see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
Note: this is a single-head self-attention operation
Note: this is a single-head self-attention operation.
"""
#
@ -320,16 +320,16 @@ class MemoryEfficientAttnBlock(nn.Module):
# compute attention
B, C, H, W = q.shape
q, k, v = map(lambda x: rearrange(x, "b c h w -> b (h w) c"), (q, k, v))
q, k, v = map(
lambda t: t.unsqueeze(3)
q, k, v = (rearrange(x, "b c h w -> b (h w) c") for x in (q, k, v))
q, k, v = (
t.unsqueeze(3)
.reshape(B, t.shape[1], 1, C)
.permute(0, 2, 1, 3)
.reshape(B * 1, t.shape[1], C)
.contiguous(),
(q, k, v),
.contiguous()
for t in (q, k, v)
)
out = xformers.ops.memory_efficient_attention(
q, k, v, attn_bias=None, op=self.attention_op
)
@ -704,8 +704,7 @@ class Decoder(nn.Module):
self.give_pre_end = give_pre_end
self.tanh_out = tanh_out
# compute in_ch_mult, block_in and curr_res at lowest res
in_ch_mult = (1,) + tuple(ch_mult)
# compute block_in and curr_res at lowest res
block_in = ch * ch_mult[self.num_resolutions - 1]
curr_res = resolution // 2 ** (self.num_resolutions - 1)
self.z_shape = (1, z_channels, curr_res, curr_res)

15
imaginairy/modules/diffusion/openaimodel.py
View File

@ -30,7 +30,7 @@ def convert_module_to_f32(_):
class AttentionPool2d(nn.Module):
"""
Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py.
"""
def __init__(
@ -123,7 +123,7 @@ class Upsample(nn.Module):
class TransposedUpsample(nn.Module):
"""Learned 2x upsampling without padding"""
"""Learned 2x upsampling without padding."""
def __init__(self, channels, out_channels=None, ks=5):
super().__init__()
@ -346,7 +346,7 @@ def count_flops_attn(model, _x, y):
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
)
).
"""
b, c, *spatial = y[0].shape
num_spatial = int(np.prod(spatial))
@ -359,7 +359,7 @@ def count_flops_attn(model, _x, y):
class QKVAttentionLegacy(nn.Module):
"""
A module which performs QKV attention. Matches legacy QKVAttention + input/output heads shaping
A module which performs QKV attention. Matches legacy QKVAttention + input/output heads shaping.
"""
def __init__(self, n_heads):
@ -530,11 +530,10 @@ class UNetModel(nn.Module):
if num_attention_blocks is not None:
assert len(num_attention_blocks) == len(self.num_res_blocks)
assert all(
map(
lambda i: self.num_res_blocks[i] >= num_attention_blocks[i],
range(len(num_attention_blocks)),
)
self.num_res_blocks[i] >= num_attention_blocks[i]
for i in range(len(num_attention_blocks))
)
print(
f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
f"This option has LESS priority than attention_resolutions {attention_resolutions}, "

2
imaginairy/modules/diffusion/util.py
View File

@ -57,7 +57,7 @@ def make_beta_schedule(
def frange(start, stop, step):
"""Range but handles floats"""
"""Range but handles floats."""
x = start
while True:
if x >= stop:

6
imaginairy/modules/encoders.py
View File

@ -54,7 +54,7 @@ def disabled_train(self, mode=True): # noqa
class FrozenT5Embedder(AbstractEncoder):
"""Uses the T5 transformer encoder for text"""
"""Uses the T5 transformer encoder for text."""
def __init__(
self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True
@ -94,7 +94,7 @@ class FrozenT5Embedder(AbstractEncoder):
class FrozenCLIPEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
"""Uses the CLIP transformer encoder for text (from huggingface)."""
LAYERS = ["last", "pooled", "hidden"]
@ -155,7 +155,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
class FrozenOpenCLIPEmbedder(AbstractEncoder):
"""
Uses the OpenCLIP transformer encoder for text
Uses the OpenCLIP transformer encoder for text.
"""
LAYERS = [

2
imaginairy/modules/midas/midas/midas_net.py
View File

@ -1,7 +1,7 @@
"""
MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py.
"""
import torch
from torch import nn

2
imaginairy/modules/midas/midas/midas_net_custom.py
View File

@ -1,7 +1,7 @@
"""
MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py.
"""
import torch
from torch import nn

2
imaginairy/modules/midas/midas/vit.py
View File

@ -57,7 +57,7 @@ class Transpose(nn.Module):
def forward_vit(pretrained, x):
b, c, h, w = x.shape
glob = pretrained.model.forward_flex(x)
pretrained.model.forward_flex(x)
layer_1 = pretrained.activations["1"]
layer_2 = pretrained.activations["2"]

6
imaginairy/modules/midas/utils.py
View File

@ -105,15 +105,15 @@ def write_pfm(path, image, scale=1):
else:
raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
file.write("PF\n" if color else "Pf\n".encode())
file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
file.write("PF\n" if color else b"Pf\n")
file.write(b"%d %d\n" % (image.shape[1], image.shape[0]))
endian = image.dtype.byteorder
if endian == "<" or endian == "=" and sys.byteorder == "little":
scale = -scale
file.write("%f\n".encode() % scale)
file.write(b"%f\n" % scale)
image.tofile(file)

6
imaginairy/safety.py
View File

@ -115,7 +115,7 @@ class EnhancedStableDiffusionSafetyChecker(
return safety_results
@lru_cache()
@lru_cache
def safety_models():
safety_model_id = "CompVis/stable-diffusion-safety-checker"
monkeypatch_safety_cosine_distance()
@ -126,14 +126,14 @@ def safety_models():
return safety_feature_extractor, safety_checker
@lru_cache()
@lru_cache
def monkeypatch_safety_cosine_distance():
orig_cosine_distance = safety_checker_mod.cosine_distance
def cosine_distance_float32(image_embeds, text_embeds):
"""
In some environments we need to distance to be in float32
but it was coming as BFloat16
but it was coming as BFloat16.
"""
return orig_cosine_distance(image_embeds, text_embeds).to(torch.float32)

2
imaginairy/samplers/ddim.py
View File

@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
class DDIMSampler(ImageSampler):
"""
Denoising Diffusion Implicit Models
Denoising Diffusion Implicit Models.
https://arxiv.org/abs/2010.02502
"""

2
imaginairy/samplers/kdiff.py
View File

@ -208,7 +208,7 @@ class LMSSampler(KDiffusionSampler):
class CFGDenoiser(nn.Module):
"""
Conditional forward guidance wrapper
Conditional forward guidance wrapper.
"""
def __init__(self, model):

2
imaginairy/samplers/plms.py
View File

@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
class PLMSSampler(ImageSampler):
"""
probabilistic least-mean-squares
probabilistic least-mean-squares.
Provenance:
https://github.com/CompVis/latent-diffusion/commit/f0c4e092c156986e125f48c61a0edd38ba8ad059

2
imaginairy/schema.py
View File

@ -216,7 +216,7 @@ class ImaginePrompt:
class ExifCodes:
"""https://www.awaresystems.be/imaging/tiff/tifftags/baseline.html"""
"""https://www.awaresystems.be/imaging/tiff/tifftags/baseline.html."""
ImageDescription = 0x010E
Software = 0x0131

22
imaginairy/utils.py
View File

@ -13,9 +13,9 @@ from torch.overrides import handle_torch_function, has_torch_function_variadic
logger = logging.getLogger(__name__)
@lru_cache()
@lru_cache
def get_device() -> str:
"""Return the best torch backend available"""
"""Return the best torch backend available."""
if torch.cuda.is_available():
return "cuda"
@ -25,9 +25,9 @@ def get_device() -> str:
return "cpu"
@lru_cache()
@lru_cache
def get_hardware_description(device_type: str) -> str:
"""Description of the hardware being used"""
"""Description of the hardware being used."""
desc = platform.platform()
if device_type == "cuda":
desc += "-" + torch.cuda.get_device_name(0)
@ -37,7 +37,7 @@ def get_hardware_description(device_type: str) -> str:
def get_obj_from_str(import_path: str, reload=False) -> Any:
"""
Gets a python object from a string reference if it's location
Gets a python object from a string reference if it's location.
Example: "functools.lru_cache"
"""
@ -50,7 +50,7 @@ def get_obj_from_str(import_path: str, reload=False) -> Any:
def instantiate_from_config(config: Union[dict, str]) -> Any:
"""Instantiate an object from a config dict"""
"""Instantiate an object from a config dict."""
if "target" not in config:
if config == "__is_first_stage__":
return None
@ -65,7 +65,7 @@ def instantiate_from_config(config: Union[dict, str]) -> Any:
@contextmanager
def platform_appropriate_autocast(precision="autocast"):
"""
Allow calculations to run in mixed precision, which can be faster
Allow calculations to run in mixed precision, which can be faster.
"""
precision_scope = nullcontext
# autocast not supported on CPU
@ -111,7 +111,7 @@ def _fixed_layer_norm(
@contextmanager
def fix_torch_nn_layer_norm():
"""https://github.com/CompVis/stable-diffusion/issues/25#issuecomment-1221416526"""
"""https://github.com/CompVis/stable-diffusion/issues/25#issuecomment-1221416526."""
orig_function = functional.layer_norm
functional.layer_norm = _fixed_layer_norm
try:
@ -123,7 +123,7 @@ def fix_torch_nn_layer_norm():
@contextmanager
def fix_torch_group_norm():
"""
Patch group_norm to cast the weights to the same type as the inputs
Patch group_norm to cast the weights to the same type as the inputs.
From what I can understand all the other repos just switch to full precision instead
of addressing this. I think this would make things slower but I'm not sure.
@ -158,7 +158,7 @@ def fix_torch_group_norm():
def randn_seeded(seed: int, size: List[int]) -> Tensor:
"""Generate a random tensor with a given seed"""
"""Generate a random tensor with a given seed."""
g_cpu = torch.Generator()
g_cpu.manual_seed(seed)
noise = torch.randn(
@ -170,7 +170,7 @@ def randn_seeded(seed: int, size: List[int]) -> Tensor:
def check_torch_working():
"""Check that torch is working"""
"""Check that torch is working."""
try:
torch.randn(1, device=get_device())
except RuntimeError as e:

6
imaginairy/vendored/blip/blip.py
View File

@ -3,7 +3,7 @@
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
* By Junnan Li.
"""
import warnings
@ -34,7 +34,7 @@ class BLIP_Base(nn.Module):
Args:
med_config (str): path for the mixture of encoder-decoder model's configuration file
image_size (int): input image size
vit (str): model size of vision transformer
vit (str): model size of vision transformer.
"""
super().__init__()
@ -102,7 +102,7 @@ class BLIP_Decoder(nn.Module):
Args:
med_config (str): path for the mixture of encoder-decoder model's configuration file
image_size (int): input image size
vit (str): model size of vision transformer
vit (str): model size of vision transformer.
"""
super().__init__()

2
imaginairy/vendored/blip/blip_itm.py
View File

@ -19,7 +19,7 @@ class BLIP_ITM(nn.Module):
Args:
med_config (str): path for the mixture of encoder-decoder model's configuration file
image_size (int): input image size
vit (str): model size of vision transformer
vit (str): model size of vision transformer.
"""
super().__init__()

2
imaginairy/vendored/blip/blip_nlvr.py
View File

@ -21,7 +21,7 @@ class BLIP_NLVR(nn.Module):
Args:
med_config (str): path for the mixture of encoder-decoder model's configuration file
image_size (int): input image size
vit (str): model size of vision transformer
vit (str): model size of vision transformer.
"""
super().__init__()

15
imaginairy/vendored/blip/blip_pretrain.py
View File

@ -3,8 +3,10 @@
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
* By Junnan Li.
"""
from typing import List
import transformers
from models.med import BertConfig, BertLMHeadModel, BertModel
@ -32,7 +34,7 @@ class BLIP_Pretrain(nn.Module):
Args:
med_config (str): path for the mixture of encoder-decoder model's configuration file
image_size (int): input image size
vit (str): model size of vision transformer
vit (str): model size of vision transformer.
"""
super().__init__()
@ -329,9 +331,6 @@ def concat_all_gather(tensor):
return output
from typing import List
def tie_encoder_decoder_weights(
encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key: str
):
@ -368,9 +367,9 @@ def tie_encoder_decoder_weights(
len(encoder_modules) > 0
), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
all_encoder_weights = set(
[module_name + "/" + sub_name for sub_name in encoder_modules.keys()]
)
all_encoder_weights = {
module_name + "/" + sub_name for sub_name in encoder_modules.keys()
}
encoder_layer_pos = 0
for name, module in decoder_modules.items():
if name.isdigit():

2
imaginairy/vendored/blip/blip_retrieval.py
View File

@ -22,7 +22,7 @@ class BLIP_Retrieval(nn.Module):
Args:
med_config (str): path for the mixture of encoder-decoder model's configuration file
image_size (int): input image size
vit (str): model size of vision transformer
vit (str): model size of vision transformer.
"""
super().__init__()

2
imaginairy/vendored/blip/blip_vqa.py
View File

@ -19,7 +19,7 @@ class BLIP_VQA(nn.Module):
Args:
med_config (str): path for the mixture of encoder-decoder model's configuration file
image_size (int): input image size
vit (str): model size of vision transformer
vit (str): model size of vision transformer.
"""
super().__init__()

8
imaginairy/vendored/blip/med.py
View File

@ -5,7 +5,7 @@
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
* Based on huggingface code base
* https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
* https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert.
"""
import math
@ -611,7 +611,7 @@ class BertPreTrainedModel(PreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids"]
def _init_weights(self, module):
"""Initialize the weights"""
"""Initialize the weights."""
if isinstance(module, (nn.Linear, nn.Embedding)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
@ -654,7 +654,7 @@ class BertModel(BertPreTrainedModel):
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
class PreTrainedModel.
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@ -977,7 +977,7 @@ class BertLMHeadModel(BertPreTrainedModel):
>>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
>>> prediction_logits = outputs.logits.
"""
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict

4
imaginairy/vendored/blip/nlvr_encoder.py
View File

@ -656,7 +656,7 @@ class BertPreTrainedModel(PreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids"]
def _init_weights(self, module):
"""Initialize the weights"""
"""Initialize the weights."""
if isinstance(module, (nn.Linear, nn.Embedding)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
@ -699,7 +699,7 @@ class BertModel(BertPreTrainedModel):
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
class PreTrainedModel.
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)

10
imaginairy/vendored/blip/vit.py
View File

@ -5,7 +5,7 @@
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
* Based on timm code base
* https://github.com/rwightman/pytorch-image-models/tree/master/timm
* https://github.com/rwightman/pytorch-image-models/tree/master/timm.
"""
from functools import partial
@ -19,7 +19,7 @@ from timm.models.vision_transformer import PatchEmbed
class Mlp(nn.Module):
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
"""MLP as used in Vision Transformer, MLP-Mixer and related networks."""
def __init__(
self,
@ -156,7 +156,7 @@ class Block(nn.Module):
class VisionTransformer(nn.Module):
"""Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
https://arxiv.org/abs/2010.11929
https://arxiv.org/abs/2010.11929.
"""
def __init__(
@ -195,7 +195,7 @@ class VisionTransformer(nn.Module):
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
norm_layer: (nn.Module): normalization layer
norm_layer: (nn.Module): normalization layer.
"""
super().__init__()
self.num_features = (
@ -282,7 +282,7 @@ class VisionTransformer(nn.Module):
@torch.no_grad()
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ""):
"""Load weights from .npz checkpoints for official Google Brain Flax implementation"""
"""Load weights from .npz checkpoints for official Google Brain Flax implementation."""
import numpy as np
def _n2p(w, t=True):

6
imaginairy/vendored/clip/clip.py
View File

@ -109,7 +109,7 @@ def _transform(n_px):
def available_models() -> List[str]:
"""Returns the names of available CLIP models"""
"""Returns the names of available CLIP models."""
return list(_MODELS.keys())
@ -119,7 +119,7 @@ def load(
jit: bool = False,
download_root: str = None,
):
"""Load a CLIP model
"""Load a CLIP model.
Parameters
----------
@ -246,7 +246,7 @@ def tokenize(
texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False
) -> Union[torch.IntTensor, torch.LongTensor]:
"""
Returns the tokenized representation of given input string(s)
Returns the tokenized representation of given input string(s).
Parameters
----------

12
imaginairy/vendored/clip/model.py
View File

@ -117,7 +117,7 @@ class ModifiedResNet(nn.Module):
A ResNet class that is similar to torchvision's but contains the following changes:
- There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
- Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
- The final pooling layer is a QKV attention instead of an average pool
- The final pooling layer is a QKV attention instead of an average pool.
"""
def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
@ -447,7 +447,7 @@ class CLIP(nn.Module):
def convert_weights(model: nn.Module):
"""Convert applicable model parameters to fp16"""
"""Convert applicable model parameters to fp16."""
def _convert_weights_to_fp16(l):
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
@ -495,11 +495,11 @@ def build_model(state_dict: dict):
else:
counts: list = [
len(
set(
{
k.split(".")[2]
for k in state_dict
if k.startswith(f"visual.layer{b}")
)
}
)
for b in [1, 2, 3, 4]
]
@ -521,9 +521,7 @@ def build_model(state_dict: dict):
transformer_width = state_dict["ln_final.weight"].shape[0]
transformer_heads = transformer_width // 64
transformer_layers = len(
set(
k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")
)
{k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")}
)
model = CLIP(

6
imaginairy/vendored/clip/simple_tokenizer.py
View File

@ -7,14 +7,14 @@ import ftfy
import regex as re
@lru_cache()
@lru_cache
def default_bpe():
return os.path.join(
os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz"
)
@lru_cache()
@lru_cache
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
@ -65,7 +65,7 @@ def whitespace_clean(text):
return text
class SimpleTokenizer(object):
class SimpleTokenizer:
def __init__(self, bpe_path: str = default_bpe()):
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}

12
imaginairy/vendored/clipseg/__init__.py
View File

@ -4,7 +4,6 @@ from os.path import basename, dirname, isfile, join
import torch
from torch import nn
from torch.nn import functional as nnf
from torch.nn.modules.activation import ReLU
def precompute_clip_vectors():
@ -182,7 +181,7 @@ class CLIPDenseBase(nn.Module):
k: torch.from_numpy(v) for k, v in precomp.items()
}
else:
self.precomputed_prompts = dict()
self.precomputed_prompts = {}
def rescaled_pos_emb(self, new_size):
assert len(new_size) == 2
@ -383,11 +382,7 @@ def clip_load_untrained(version):
transformer_width = state_dict["ln_final.weight"].shape[0]
transformer_heads = transformer_width // 64
transformer_layers = len(
set(
k.split(".")[2]
for k in state_dict
if k.startswith(f"transformer.resblocks")
)
{k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")}
)
return CLIP(
@ -717,12 +712,11 @@ class CLIPSegMultiLabel(nn.Module):
def __init__(self, model) -> None:
super().__init__()
from third_party.JoEm.data_loader import VOC, get_seen_idx, get_unseen_idx
from third_party.JoEm.data_loader import VOC
self.pascal_classes = VOC
from general_utils import load_model
from models.clipseg import CLIPDensePredT
# self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False)
self.clipseg = load_model(model, strict=False)

6
imaginairy/vendored/codeformer/codeformer_arch.py
View File

@ -95,7 +95,7 @@ class PositionEmbeddingSine(nn.Module):
def _get_activation_fn(activation):
"""Return an activation function given a string"""
"""Return an activation function given a string."""
if activation == "relu":
return F.relu
if activation == "gelu":
@ -186,9 +186,7 @@ class CodeFormer(VQAutoEncoder):
connect_list=["32", "64", "128", "256"],
fix_modules=["quantize", "generator"],
):
super(CodeFormer, self).__init__(
512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size
)
super().__init__(512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size)
if fix_modules is not None:
for module in fix_modules:

6
imaginairy/vendored/codeformer/vqgan_arch.py
View File

@ -1,6 +1,6 @@
"""
VQGAN code, adapted from the original created by the Unleashing Transformers authors:
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py.
"""
@ -25,7 +25,7 @@ def swish(x):
# Define VQVAE classes
class VectorQuantizer(nn.Module):
def __init__(self, codebook_size, emb_dim, beta):
super(VectorQuantizer, self).__init__()
super().__init__()
self.codebook_size = codebook_size # number of embeddings
self.emb_dim = emb_dim # dimension of embedding
self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
@ -173,7 +173,7 @@ class Upsample(nn.Module):
class ResBlock(nn.Module):
def __init__(self, in_channels, out_channels=None):
super(ResBlock, self).__init__()
super().__init__()
self.in_channels = in_channels
self.out_channels = in_channels if out_channels is None else out_channels
self.norm1 = normalize(in_channels)

12
imaginairy/vendored/k_diffusion/__init__.py
View File

@ -1,12 +0,0 @@
from . import (
augmentation,
config,
evaluation,
external,
gns,
layers,
models,
sampling,
utils,
)
from .layers import Denoiser

1
imaginairy/vendored/k_diffusion/models/__init__.py
View File

@ -1 +0,0 @@
from .image_v1 import ImageDenoiserModelV1

28
imaginairy/vendored/k_diffusion/models/image_v1.py
View File

@ -56,18 +56,20 @@ class DBlock(layers.ConditionedSequential):
)
)
if self_attn:
norm = lambda c_in: layers.AdaGN(
feats_in, c_in, max(1, my_c_out // group_size)
)
def norm(c_in):
return layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size))
modules.append(
layers.SelfAttention2d(
my_c_out, max(1, my_c_out // head_size), norm, dropout_rate
)
)
if cross_attn:
norm = lambda c_in: layers.AdaGN(
feats_in, c_in, max(1, my_c_out // group_size)
)
def norm(c_in):
return layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size))
modules.append(
layers.CrossAttention2d(
my_c_out,
@ -111,18 +113,20 @@ class UBlock(layers.ConditionedSequential):
)
)
if self_attn:
norm = lambda c_in: layers.AdaGN(
feats_in, c_in, max(1, my_c_out // group_size)
)
def norm(c_in):
return layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size))
modules.append(
layers.SelfAttention2d(
my_c_out, max(1, my_c_out // head_size), norm, dropout_rate
)
)
if cross_attn:
norm = lambda c_in: layers.AdaGN(
feats_in, c_in, max(1, my_c_out // group_size)
)
def norm(c_in):
return layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size))
modules.append(
layers.CrossAttention2d(
my_c_out,

25
imaginairy/vendored/k_diffusion/sampling.py
View File

@ -798,8 +798,12 @@ def sample_dpmpp_2s_ancestral(
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
sigma_fn = lambda t: t.neg().exp()
t_fn = lambda sigma: sigma.to("cpu").log().neg().to(x.device)
def sigma_fn(t):
return t.neg().exp()
def t_fn(sigma):
return sigma.to("cpu").log().neg().to(x.device)
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
@ -856,8 +860,12 @@ def sample_dpmpp_sde(
)
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
sigma_fn = lambda t: t.neg().exp()
t_fn = lambda sigma: sigma.to("cpu").log().neg().to(x.device)
def sigma_fn(t):
return t.neg().exp()
def t_fn(sigma):
return sigma.to("cpu").log().neg().to(x.device)
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
@ -906,8 +914,13 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
"""DPM-Solver++(2M)."""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
sigma_fn = lambda t: t.neg().exp()
t_fn = lambda sigma: sigma.to("cpu").log().neg().to(x.device)
def sigma_fn(t):
return t.neg().exp()
def t_fn(sigma):
return sigma.to("cpu").log().neg().to(x.device)
old_denoised = None
for i in trange(len(sigmas) - 1, disable=disable):

2
requirements-dev.in
View File

@ -1,7 +1,7 @@
black
coverage
isort
pycln
ruff
pydocstyle
pylama
pylint

90
requirements-dev.txt
View File

@ -20,7 +20,7 @@ astroid==2.12.13
# via pylint
async-timeout==4.0.2
# via aiohttp
attrs==22.1.0
attrs==22.2.0
# via
# aiohttp
# pytest
@ -28,11 +28,11 @@ basicsr==1.4.2
# via
# gfpgan
# realesrgan
black==22.10.0
black==22.12.0
# via -r requirements-dev.in
cachetools==5.2.0
# via google-auth
certifi==2022.9.24
certifi==2022.12.7
# via requests
charset-normalizer==2.1.1
# via
@ -43,30 +43,29 @@ click==8.1.3
# black
# click-shell
# imaginAIry (setup.py)
# typer
click-shell==2.1
# via imaginAIry (setup.py)
contourpy==1.0.6
# via matplotlib
coverage==6.5.0
coverage==7.0.1
# via -r requirements-dev.in
cycler==0.11.0
# via matplotlib
diffusers==0.8.1
diffusers==0.11.1
# via imaginAIry (setup.py)
dill==0.3.6
# via pylint
einops==0.3.0
# via imaginAIry (setup.py)
exceptiongroup==1.0.4
exceptiongroup==1.1.0
# via pytest
facexlib==0.2.5
# via
# gfpgan
# realesrgan
fairscale==0.4.12
fairscale==0.4.13
# via imaginAIry (setup.py)
filelock==3.8.0
filelock==3.9.0
# via
# diffusers
# huggingface-hub
@ -93,7 +92,7 @@ gfpgan==1.3.8
# via
# imaginAIry (setup.py)
# realesrgan
google-auth==2.14.1
google-auth==2.15.0
# via
# google-auth-oauthlib
# tb-nightly
@ -102,11 +101,11 @@ google-auth-oauthlib==0.4.6
# via
# tb-nightly
# tensorboard
grpcio==1.50.0
grpcio==1.51.1
# via
# tb-nightly
# tensorboard
huggingface-hub==0.11.0
huggingface-hub==0.11.1
# via
# diffusers
# open-clip-torch
@ -120,11 +119,11 @@ imageio==2.9.0
# via
# imaginAIry (setup.py)
# scikit-image
importlib-metadata==5.1.0
importlib-metadata==6.0.0
# via diffusers
iniconfig==1.1.1
# via pytest
isort==5.10.1
isort==5.11.4
# via
# -r requirements-dev.in
# pylint
@ -134,11 +133,9 @@ kornia==0.6
# via imaginAIry (setup.py)
lazy-object-proxy==1.8.0
# via astroid
libcst==0.4.9
# via pycln
llvmlite==0.39.1
# via numba
lmdb==1.3.0
lmdb==1.4.0
# via
# basicsr
# gfpgan
@ -154,14 +151,12 @@ mccabe==0.7.0
# via
# pylama
# pylint
multidict==6.0.2
multidict==6.0.4
# via
# aiohttp
# yarl
mypy-extensions==0.4.3
# via
# black
# typing-inspect
# via black
networkx==2.8.8
# via scikit-image
numba==0.56.4
@ -195,15 +190,15 @@ oauthlib==3.2.2
# via requests-oauthlib
omegaconf==2.1.1
# via imaginAIry (setup.py)
open-clip-torch==2.7.0
open-clip-torch==2.9.1
# via imaginAIry (setup.py)
opencv-python==4.6.0.66
opencv-python==4.7.0.68
# via
# basicsr
# facexlib
# gfpgan
# realesrgan
packaging==21.3
packaging==22.0
# via
# huggingface-hub
# kornia
@ -214,11 +209,9 @@ packaging==21.3
# scikit-image
# torchmetrics
# transformers
pathspec==0.9.0
# via
# black
# pycln
pillow==9.3.0
pathspec==0.10.3
# via black
pillow==9.4.0
# via
# basicsr
# diffusers
@ -229,7 +222,7 @@ pillow==9.3.0
# realesrgan
# scikit-image
# torchvision
platformdirs==2.5.4
platformdirs==2.6.2
# via
# black
# pylint
@ -238,6 +231,7 @@ pluggy==1.0.0
protobuf==3.20.3
# via
# imaginAIry (setup.py)
# open-clip-torch
# tb-nightly
# tensorboard
psutil==5.9.4
@ -248,8 +242,6 @@ pyasn1==0.4.8
# rsa
pyasn1-modules==0.2.8
# via google-auth
pycln==2.1.2
# via -r requirements-dev.in
pycodestyle==2.10.0
# via pylama
pydeprecate==0.3.1
@ -262,12 +254,10 @@ pyflakes==3.0.1
# via pylama
pylama==8.4.1
# via -r requirements-dev.in
pylint==2.15.6
pylint==2.15.9
# via -r requirements-dev.in
pyparsing==3.0.9
# via
# matplotlib
# packaging
# via matplotlib
pytest==7.2.0
# via
# -r requirements-dev.in
@ -288,9 +278,7 @@ pyyaml==6.0
# basicsr
# gfpgan
# huggingface-hub
# libcst
# omegaconf
# pycln
# pytorch-lightning
# timm
# transformers
@ -307,6 +295,7 @@ requests==2.28.1
# diffusers
# fsspec
# huggingface-hub
# imaginAIry (setup.py)
# requests-oauthlib
# responses
# tb-nightly
@ -319,6 +308,8 @@ responses==0.22.0
# via -r requirements-dev.in
rsa==4.9
# via google-auth
ruff==0.0.206
# via -r requirements-dev.in
scikit-image==0.19.3
# via basicsr
scipy==1.9.3
@ -329,14 +320,15 @@ scipy==1.9.3
# gfpgan
# scikit-image
# torchdiffeq
sentencepiece==0.1.97
# via open-clip-torch
six==1.16.0
# via
# google-auth
# grpcio
# python-dateutil
snowballstemmer==2.2.0
# via pydocstyle
tb-nightly==2.12.0a20221125
tb-nightly==2.12.0a20230101
# via
# basicsr
# gfpgan
@ -366,10 +358,8 @@ tomli==2.0.1
# pylint
# pytest
tomlkit==0.11.6
# via
# pycln
# pylint
torch==1.13.0
# via pylint
torch==1.13.1
# via
# basicsr
# facexlib
@ -390,7 +380,7 @@ torchmetrics==0.6.0
# via
# imaginAIry (setup.py)
# pytorch-lightning
torchvision==0.14.0
torchvision==0.14.1
# via
# basicsr
# facexlib
@ -412,20 +402,14 @@ tqdm==4.64.1
# transformers
transformers==4.19.2
# via imaginAIry (setup.py)
typer==0.7.0
# via pycln
types-toml==0.10.8.1
# via responses
typing-extensions==4.4.0
# via
# huggingface-hub
# libcst
# pytorch-lightning
# torch
# torchvision
# typing-inspect
typing-inspect==0.8.0
# via libcst
urllib3==1.26.13
# via
# requests
@ -446,9 +430,9 @@ yapf==0.32.0
# via
# basicsr
# gfpgan
yarl==1.8.1
yarl==1.8.2
# via aiohttp
zipp==3.10.0
zipp==3.11.0
# via importlib-metadata
# The following packages are considered to be unsafe in a requirements file:

4
scripts/prep_vocab_lists.py
View File

@ -51,7 +51,7 @@ def timed(description):
def make_txts():
src_json = f"{CURDIR}/../downloads/noodle-soup-prompts/nsp_pantry.json"
dst_folder = f"{CURDIR}/../imaginairy/vendored/noodle_soup_prompts"
with open(src_json, "r", encoding="utf-8") as f:
with open(src_json, encoding="utf-8") as f:
prompts = json.load(f)
categories = []
for c in prompts.keys():
@ -65,7 +65,7 @@ def make_txts():
renamed_c = category_renames.get(c, c)
with gzip.open(f"{dst_folder}/{renamed_c}.txt.gz", "wb") as f:
for p in filtered_phrases:
f.write(f"{p}\n".encode("utf-8"))
f.write(f"{p}\n".encode())
if __name__ == "__main__":

2
setup.py
View File

@ -1,6 +1,6 @@
from setuptools import find_packages, setup
with open("README.md", "r", encoding="utf-8") as f:
with open("README.md", encoding="utf-8") as f:
readme = f.read()
setup(

28
tests/test_api.py
View File

@ -43,7 +43,7 @@ compare_prompts = [
"model_version", ["SD-1.4", "SD-1.5", "SD-2.0", "SD-2.0-v", "SD-2.1", "SD-2.1-v"]
)
def test_model_versions(filename_base_for_orig_outputs, model_version):
"""Test that we can switch between model versions"""
"""Test that we can switch between model versions."""
prompts = []
for prompt_text in compare_prompts:
prompts.append(
@ -172,19 +172,19 @@ def test_img_to_img_fruit_2_gold_repeat():
img = LazyLoadingImage(filepath=f"{TESTS_FOLDER}/data/bowl_of_fruit.jpg")
run_count = 1
kwargs = dict(
prompt="a white bowl filled with gold coins. sharp focus",
prompt_strength=12,
init_image=img,
init_image_strength=0.2,
mask_prompt="(fruit OR stem{*5} OR fruit stem)",
mask_mode="replace",
steps=20,
seed=946188797,
sampler_type="plms",
fix_faces=True,
upscale=True,
)
kwargs = {
"prompt": "a white bowl filled with gold coins. sharp focus",
"prompt_strength": 12,
"init_image": img,
"init_image_strength": 0.2,
"mask_prompt": "(fruit OR stem{*5} OR fruit stem)",
"mask_mode": "replace",
"steps": 20,
"seed": 946188797,
"sampler_type": "plms",
"fix_faces": True,
"upscale": True,
}
prompts = [
ImaginePrompt(**kwargs),
ImaginePrompt(**kwargs),

2
tox.ini
View File

@ -13,7 +13,7 @@ linters = pylint,pycodestyle,pydocstyle,pyflakes,mypy
ignore =
Z999,C0103,C0301,C0302,C0114,C0115,C0116,
Z999,D100,D101,D102,D103,D105,D106,D107,D200,D202,D203,D205,D212,D400,D401,D406,D407,D413,D415,D417,
Z999,E203,E501,E1101,
Z999,E203,E501,E1101,E1131,
Z999,R0901,R0902,R0903,R0904,R0193,R0912,R0913,R0914,R0915,R1702,
Z999,W0221,W0511,W0612,W0613,W1203