lint: new ruff linter

Makefile

@@ -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.

imaginairy/cmds.py

@@ -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"):

imaginairy/enhancers/clip_masking.py

@@ -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
 

imaginairy/enhancers/describe_image_blip.py

@@ -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(
             [

imaginairy/enhancers/describe_image_clip.py

@@ -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)

imaginairy/enhancers/face_restoration_codeformer.py

@@ -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

imaginairy/enhancers/prompt_expansion.py

@@ -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}"

imaginairy/enhancers/upscale_realesrgan.py

@@ -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

imaginairy/img_utils.py

@@ -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

imaginairy/model_manager.py

@@ -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`

imaginairy/modules/attention.py

@@ -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__(

imaginairy/modules/autoencoder.py

@@ -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=""):

imaginairy/modules/clip_embedders.py

@@ -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,

imaginairy/modules/diffusion/ddpm.py

@@ -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__(

imaginairy/modules/diffusion/model.py

@@ -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)

imaginairy/modules/diffusion/openaimodel.py

@@ -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}, "

imaginairy/modules/diffusion/util.py

@@ -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:

imaginairy/modules/encoders.py

@@ -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 = [

imaginairy/modules/midas/midas/midas_net.py

@@ -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

imaginairy/modules/midas/midas/midas_net_custom.py

@@ -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

imaginairy/modules/midas/midas/vit.py

@@ -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"]

imaginairy/modules/midas/utils.py

@@ -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)
 

imaginairy/safety.py

@@ -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)
 

imaginairy/samplers/ddim.py

@@ -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
     """

imaginairy/samplers/kdiff.py

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

imaginairy/samplers/plms.py

@@ -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

imaginairy/schema.py

@@ -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

imaginairy/utils.py

@@ -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:

imaginairy/vendored/blip/blip.py

@@ -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__()
 

imaginairy/vendored/blip/blip_itm.py

@@ -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__()
 

imaginairy/vendored/blip/blip_nlvr.py

@@ -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__()
 

imaginairy/vendored/blip/blip_pretrain.py

@@ -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():

imaginairy/vendored/blip/blip_retrieval.py

@@ -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__()
 

imaginairy/vendored/blip/blip_vqa.py

@@ -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__()
 

imaginairy/vendored/blip/med.py

@@ -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

imaginairy/vendored/blip/nlvr_encoder.py

@@ -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)

imaginairy/vendored/blip/vit.py

@@ -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):

imaginairy/vendored/clip/clip.py

@@ -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
     ----------

imaginairy/vendored/clip/model.py

@@ -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(

imaginairy/vendored/clip/simple_tokenizer.py

@@ -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()}

imaginairy/vendored/clipseg/__init__.py

@@ -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)

imaginairy/vendored/codeformer/codeformer_arch.py

@@ -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:

imaginairy/vendored/codeformer/vqgan_arch.py

@@ -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)

imaginairy/vendored/k_diffusion/__init__.py

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

imaginairy/vendored/k_diffusion/models/__init__.py

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

imaginairy/vendored/k_diffusion/models/image_v1.py

@@ -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,

imaginairy/vendored/k_diffusion/sampling.py

@@ -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):

requirements-dev.in

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

requirements-dev.txt

@@ -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:

scripts/prep_vocab_lists.py

@@ -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__":

setup.py

@@ -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(

tests/test_api.py

@@ -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),

tox.ini

@@ -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