From ee71eee1818f6f6eba9895c93ba25e0cad27e069 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 29 Apr 2023 12:36:50 +0300 Subject: stuff related to torch version change --- modules/safe.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) (limited to 'modules/safe.py') diff --git a/modules/safe.py b/modules/safe.py index 82d44be3..dadf319c 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -1,6 +1,5 @@ # this code is adapted from the script contributed by anon from /h/ -import io import pickle import collections import sys @@ -12,11 +11,9 @@ import _codecs import zipfile import re - # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage - def encode(*args): out = _codecs.encode(*args) return out @@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler): def persistent_load(self, saved_id): assert saved_id[0] == 'storage' - return TypedStorage() + return TypedStorage(_internal=True) def find_class(self, module, name): if self.extra_handler is not None: -- cgit v1.2.3 From 5ab7f213bec2f816f9c5644becb32eb72c8ffb89 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 2 May 2023 09:20:35 +0300 Subject: fix an error that prevents running webui on torch<2.0 without --disable-safe-unpickle --- CHANGELOG.md | 4 ++++ modules/safe.py | 6 +++++- 2 files changed, 9 insertions(+), 1 deletion(-) (limited to 'modules/safe.py') diff --git a/CHANGELOG.md b/CHANGELOG.md index 8b8a3611..8d2f96e5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,7 @@ +## 1.1.1 +### Bug Fixes: + * fix an error that prevents running webui on torch<2.0 without --disable-safe-unpickle + ## 1.1.0 ### Features: * switch to torch 2.0.0 (except for AMD GPUs) diff --git a/modules/safe.py b/modules/safe.py index dadf319c..e6c2f2c0 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -24,7 +24,11 @@ class RestrictedUnpickler(pickle.Unpickler): def persistent_load(self, saved_id): assert saved_id[0] == 'storage' - return TypedStorage(_internal=True) + + try: + return TypedStorage(_internal=True) + except TypeError: + return TypedStorage() # PyTorch before 2.0 does not have the _internal argument def find_class(self, module, name): if self.extra_handler is not None: -- cgit v1.2.3 From 550256db1ce18778a9d56ff343d844c61b9f9b83 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 10 May 2023 11:19:16 +0300 Subject: ruff manual fixes --- extensions-builtin/LDSR/sd_hijack_autoencoder.py | 10 +++++----- extensions-builtin/LDSR/sd_hijack_ddpm_v1.py | 14 +++++++------- extensions-builtin/SwinIR/swinir_model_arch.py | 6 +++++- extensions-builtin/SwinIR/swinir_model_arch_v2.py | 11 +++++++++-- modules/api/api.py | 18 ++++++++++++------ modules/codeformer/codeformer_arch.py | 7 +++++-- modules/codeformer/vqgan_arch.py | 4 ++-- modules/generation_parameters_copypaste.py | 4 ++-- modules/models/diffusion/ddpm_edit.py | 14 ++++++++------ modules/models/diffusion/uni_pc/uni_pc.py | 7 +++++-- modules/safe.py | 2 +- modules/sd_samplers_compvis.py | 2 +- modules/textual_inversion/image_embedding.py | 2 +- modules/textual_inversion/learn_schedule.py | 4 ++-- pyproject.toml | 5 ++++- 15 files changed, 69 insertions(+), 41 deletions(-) (limited to 'modules/safe.py') diff --git a/extensions-builtin/LDSR/sd_hijack_autoencoder.py b/extensions-builtin/LDSR/sd_hijack_autoencoder.py index f457ca93..8cc82d54 100644 --- a/extensions-builtin/LDSR/sd_hijack_autoencoder.py +++ b/extensions-builtin/LDSR/sd_hijack_autoencoder.py @@ -24,7 +24,7 @@ class VQModel(pl.LightningModule): n_embed, embed_dim, ckpt_path=None, - ignore_keys=[], + ignore_keys=None, image_key="image", colorize_nlabels=None, monitor=None, @@ -62,7 +62,7 @@ class VQModel(pl.LightningModule): print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or []) self.scheduler_config = scheduler_config self.lr_g_factor = lr_g_factor @@ -81,11 +81,11 @@ class VQModel(pl.LightningModule): if context is not None: print(f"{context}: Restored training weights") - def init_from_ckpt(self, path, ignore_keys=list()): + def init_from_ckpt(self, path, ignore_keys=None): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: - for ik in ignore_keys: + for ik in ignore_keys or []: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] @@ -270,7 +270,7 @@ class VQModel(pl.LightningModule): class VQModelInterface(VQModel): def __init__(self, embed_dim, *args, **kwargs): - super().__init__(embed_dim=embed_dim, *args, **kwargs) + super().__init__(*args, embed_dim=embed_dim, **kwargs) self.embed_dim = embed_dim def encode(self, x): diff --git a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py index d8fc30e3..f16d6504 100644 --- a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py +++ b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py @@ -48,7 +48,7 @@ class DDPMV1(pl.LightningModule): beta_schedule="linear", loss_type="l2", ckpt_path=None, - ignore_keys=[], + ignore_keys=None, load_only_unet=False, monitor="val/loss", use_ema=True, @@ -100,7 +100,7 @@ class DDPMV1(pl.LightningModule): if monitor is not None: self.monitor = monitor if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet) self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) @@ -182,13 +182,13 @@ class DDPMV1(pl.LightningModule): if context is not None: print(f"{context}: Restored training weights") - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + def init_from_ckpt(self, path, ignore_keys=None, only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: - for ik in ignore_keys: + for ik in ignore_keys or []: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] @@ -444,7 +444,7 @@ class LatentDiffusionV1(DDPMV1): conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) - super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + super().__init__(*args, conditioning_key=conditioning_key, **kwargs) self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key @@ -1418,10 +1418,10 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1): # TODO: move all layout-specific hacks to this class def __init__(self, cond_stage_key, *args, **kwargs): assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' - super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) + super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs) def log_images(self, batch, N=8, *args, **kwargs): - logs = super().log_images(batch=batch, N=N, *args, **kwargs) + logs = super().log_images(*args, batch=batch, N=N, **kwargs) key = 'train' if self.training else 'validation' dset = self.trainer.datamodule.datasets[key] diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py index 863f42db..75f7bedc 100644 --- a/extensions-builtin/SwinIR/swinir_model_arch.py +++ b/extensions-builtin/SwinIR/swinir_model_arch.py @@ -644,13 +644,17 @@ class SwinIR(nn.Module): """ def __init__(self, img_size=64, patch_size=1, in_chans=3, - embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6], + embed_dim=96, depths=None, num_heads=None, window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', **kwargs): super(SwinIR, self).__init__() + + depths = depths or [6, 6, 6, 6] + num_heads = num_heads or [6, 6, 6, 6] + num_in_ch = in_chans num_out_ch = in_chans num_feat = 64 diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py index 0e28ae6e..d4c0b0da 100644 --- a/extensions-builtin/SwinIR/swinir_model_arch_v2.py +++ b/extensions-builtin/SwinIR/swinir_model_arch_v2.py @@ -74,9 +74,12 @@ class WindowAttention(nn.Module): """ def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0., - pretrained_window_size=[0, 0]): + pretrained_window_size=None): super().__init__() + + pretrained_window_size = pretrained_window_size or [0, 0] + self.dim = dim self.window_size = window_size # Wh, Ww self.pretrained_window_size = pretrained_window_size @@ -698,13 +701,17 @@ class Swin2SR(nn.Module): """ def __init__(self, img_size=64, patch_size=1, in_chans=3, - embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6], + embed_dim=96, depths=None, num_heads=None, window_size=7, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', **kwargs): super(Swin2SR, self).__init__() + + depths = depths or [6, 6, 6, 6] + num_heads = num_heads or [6, 6, 6, 6] + num_in_ch = in_chans num_out_ch = in_chans num_feat = 64 diff --git a/modules/api/api.py b/modules/api/api.py index f52d371b..9efb558e 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -34,14 +34,16 @@ import piexif.helper def upscaler_to_index(name: str): try: return [x.name.lower() for x in shared.sd_upscalers].index(name.lower()) - except Exception: - raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") + except Exception as e: + raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e + def script_name_to_index(name, scripts): try: return [script.title().lower() for script in scripts].index(name.lower()) - except Exception: - raise HTTPException(status_code=422, detail=f"Script '{name}' not found") + except Exception as e: + raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e + def validate_sampler_name(name): config = sd_samplers.all_samplers_map.get(name, None) @@ -50,20 +52,23 @@ def validate_sampler_name(name): return name + def setUpscalers(req: dict): reqDict = vars(req) reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None) reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None) return reqDict + def decode_base64_to_image(encoding): if encoding.startswith("data:image/"): encoding = encoding.split(";")[1].split(",")[1] try: image = Image.open(BytesIO(base64.b64decode(encoding))) return image - except Exception: - raise HTTPException(status_code=500, detail="Invalid encoded image") + except Exception as e: + raise HTTPException(status_code=500, detail="Invalid encoded image") from e + def encode_pil_to_base64(image): with io.BytesIO() as output_bytes: @@ -94,6 +99,7 @@ def encode_pil_to_base64(image): return base64.b64encode(bytes_data) + def api_middleware(app: FastAPI): rich_available = True try: diff --git a/modules/codeformer/codeformer_arch.py b/modules/codeformer/codeformer_arch.py index 00c407de..ff1c0b4b 100644 --- a/modules/codeformer/codeformer_arch.py +++ b/modules/codeformer/codeformer_arch.py @@ -161,10 +161,13 @@ class Fuse_sft_block(nn.Module): class CodeFormer(VQAutoEncoder): def __init__(self, dim_embd=512, n_head=8, n_layers=9, codebook_size=1024, latent_size=256, - connect_list=['32', '64', '128', '256'], - fix_modules=['quantize','generator']): + connect_list=None, + fix_modules=None): super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size) + connect_list = connect_list or ['32', '64', '128', '256'] + fix_modules = fix_modules or ['quantize', 'generator'] + if fix_modules is not None: for module in fix_modules: for param in getattr(self, module).parameters(): diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py index 820e6b12..b24a0394 100644 --- a/modules/codeformer/vqgan_arch.py +++ b/modules/codeformer/vqgan_arch.py @@ -326,7 +326,7 @@ class Generator(nn.Module): @ARCH_REGISTRY.register() class VQAutoEncoder(nn.Module): - def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256, + def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256, beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): super().__init__() logger = get_root_logger() @@ -337,7 +337,7 @@ class VQAutoEncoder(nn.Module): self.embed_dim = emb_dim self.ch_mult = ch_mult self.resolution = img_size - self.attn_resolutions = attn_resolutions + self.attn_resolutions = attn_resolutions or [16] self.quantizer_type = quantizer self.encoder = Encoder( self.in_channels, diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index f1c59c46..7fbbe707 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -19,14 +19,14 @@ registered_param_bindings = [] class ParamBinding: - def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=[]): + def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None): self.paste_button = paste_button self.tabname = tabname self.source_text_component = source_text_component self.source_image_component = source_image_component self.source_tabname = source_tabname self.override_settings_component = override_settings_component - self.paste_field_names = paste_field_names + self.paste_field_names = paste_field_names or [] def reset(): diff --git a/modules/models/diffusion/ddpm_edit.py b/modules/models/diffusion/ddpm_edit.py index 09432117..af4dea15 100644 --- a/modules/models/diffusion/ddpm_edit.py +++ b/modules/models/diffusion/ddpm_edit.py @@ -52,7 +52,7 @@ class DDPM(pl.LightningModule): beta_schedule="linear", loss_type="l2", ckpt_path=None, - ignore_keys=[], + ignore_keys=None, load_only_unet=False, monitor="val/loss", use_ema=True, @@ -107,7 +107,7 @@ class DDPM(pl.LightningModule): print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet) # If initialing from EMA-only checkpoint, create EMA model after loading. if self.use_ema and not load_ema: @@ -194,7 +194,9 @@ class DDPM(pl.LightningModule): if context is not None: print(f"{context}: Restored training weights") - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + def init_from_ckpt(self, path, ignore_keys=None, only_model=False): + ignore_keys = ignore_keys or [] + sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] @@ -473,7 +475,7 @@ class LatentDiffusion(DDPM): conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) - super().__init__(conditioning_key=conditioning_key, *args, load_ema=load_ema, **kwargs) + super().__init__(*args, conditioning_key=conditioning_key, load_ema=load_ema, **kwargs) self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key @@ -1433,10 +1435,10 @@ class Layout2ImgDiffusion(LatentDiffusion): # TODO: move all layout-specific hacks to this class def __init__(self, cond_stage_key, *args, **kwargs): assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' - super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) + super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs) def log_images(self, batch, N=8, *args, **kwargs): - logs = super().log_images(batch=batch, N=N, *args, **kwargs) + logs = super().log_images(*args, batch=batch, N=N, **kwargs) key = 'train' if self.training else 'validation' dset = self.trainer.datamodule.datasets[key] diff --git a/modules/models/diffusion/uni_pc/uni_pc.py b/modules/models/diffusion/uni_pc/uni_pc.py index a4c4ef4e..6f8ad631 100644 --- a/modules/models/diffusion/uni_pc/uni_pc.py +++ b/modules/models/diffusion/uni_pc/uni_pc.py @@ -178,13 +178,13 @@ def model_wrapper( model, noise_schedule, model_type="noise", - model_kwargs={}, + model_kwargs=None, guidance_type="uncond", #condition=None, #unconditional_condition=None, guidance_scale=1., classifier_fn=None, - classifier_kwargs={}, + classifier_kwargs=None, ): """Create a wrapper function for the noise prediction model. @@ -275,6 +275,9 @@ def model_wrapper( A noise prediction model that accepts the noised data and the continuous time as the inputs. """ + model_kwargs = model_kwargs or [] + classifier_kwargs = classifier_kwargs or [] + def get_model_input_time(t_continuous): """ Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. diff --git a/modules/safe.py b/modules/safe.py index e6c2f2c0..2d5b972f 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -104,7 +104,7 @@ def check_pt(filename, extra_handler): def load(filename, *args, **kwargs): - return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs) + return load_with_extra(filename, *args, extra_handler=global_extra_handler, **kwargs) def load_with_extra(filename, extra_handler=None, *args, **kwargs): diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index 7427648f..b1ee3be7 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -55,7 +55,7 @@ class VanillaStableDiffusionSampler: def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs): x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning) - res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs) + res = self.orig_p_sample_ddim(x_dec, cond, ts, *args, unconditional_conditioning=unconditional_conditioning, **kwargs) x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res) diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index ee0e850a..d85a4888 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -17,7 +17,7 @@ class EmbeddingEncoder(json.JSONEncoder): class EmbeddingDecoder(json.JSONDecoder): def __init__(self, *args, **kwargs): - json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs) + json.JSONDecoder.__init__(self, *args, object_hook=self.object_hook, **kwargs) def object_hook(self, d): if 'TORCHTENSOR' in d: diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py index f63fc72f..fda58898 100644 --- a/modules/textual_inversion/learn_schedule.py +++ b/modules/textual_inversion/learn_schedule.py @@ -32,8 +32,8 @@ class LearnScheduleIterator: self.maxit += 1 return assert self.rates - except (ValueError, AssertionError): - raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') + except (ValueError, AssertionError) as e: + raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') from e def __iter__(self): diff --git a/pyproject.toml b/pyproject.toml index 2f65fd6c..346a0cde 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,6 +24,9 @@ ignore = [ ] - [tool.ruff.per-file-ignores] "webui.py" = ["E402"] # Module level import not at top of file + +[tool.ruff.flake8-bugbear] +# Allow default arguments like, e.g., `data: List[str] = fastapi.Query(None)`. +extend-immutable-calls = ["fastapi.Depends", "fastapi.security.HTTPBasic"] \ No newline at end of file -- cgit v1.2.3 From a5121e7a0623db328a9462d340d389ed6737374a Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 10 May 2023 11:37:18 +0300 Subject: fixes for B007 --- extensions-builtin/LDSR/ldsr_model_arch.py | 2 +- extensions-builtin/Lora/lora.py | 2 +- extensions-builtin/ScuNET/scripts/scunet_model.py | 2 +- extensions-builtin/SwinIR/swinir_model_arch.py | 2 +- extensions-builtin/SwinIR/swinir_model_arch_v2.py | 2 +- modules/codeformer_model.py | 2 +- modules/esrgan_model.py | 8 ++------ modules/extra_networks.py | 2 +- modules/generation_parameters_copypaste.py | 2 +- modules/hypernetworks/hypernetwork.py | 12 ++++++------ modules/images.py | 2 +- modules/interrogate.py | 4 ++-- modules/prompt_parser.py | 14 +++++++------- modules/safe.py | 4 ++-- modules/scripts.py | 10 +++++----- modules/scripts_postprocessing.py | 8 ++++---- modules/sd_hijack_clip.py | 2 +- modules/shared.py | 6 +++--- modules/textual_inversion/learn_schedule.py | 2 +- modules/textual_inversion/textual_inversion.py | 10 +++++----- modules/ui.py | 6 +++--- modules/ui_extra_networks.py | 2 +- modules/ui_tempdir.py | 2 +- modules/upscaler.py | 2 +- pyproject.toml | 1 - scripts/prompts_from_file.py | 2 +- scripts/sd_upscale.py | 4 ++-- scripts/xyz_grid.py | 2 +- 28 files changed, 57 insertions(+), 62 deletions(-) (limited to 'modules/safe.py') diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py index a5fb8907..27e38549 100644 --- a/extensions-builtin/LDSR/ldsr_model_arch.py +++ b/extensions-builtin/LDSR/ldsr_model_arch.py @@ -88,7 +88,7 @@ class LDSR: x_t = None logs = None - for n in range(n_runs): + for _ in range(n_runs): if custom_shape is not None: x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device) x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0]) diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 9795540f..7b56136f 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -418,7 +418,7 @@ def infotext_pasted(infotext, params): added = [] - for k, v in params.items(): + for k in params: if not k.startswith("AddNet Model "): continue diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index aa2fdb3a..1f5ea0d3 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -132,7 +132,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler): model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) model.load_state_dict(torch.load(filename), strict=True) model.eval() - for k, v in model.named_parameters(): + for _, v in model.named_parameters(): v.requires_grad = False model = model.to(device) diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py index 75f7bedc..de195d9b 100644 --- a/extensions-builtin/SwinIR/swinir_model_arch.py +++ b/extensions-builtin/SwinIR/swinir_model_arch.py @@ -848,7 +848,7 @@ class SwinIR(nn.Module): H, W = self.patches_resolution flops += H * W * 3 * self.embed_dim * 9 flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): + for layer in self.layers: flops += layer.flops() flops += H * W * 3 * self.embed_dim * self.embed_dim flops += self.upsample.flops() diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py index d4c0b0da..15777af9 100644 --- a/extensions-builtin/SwinIR/swinir_model_arch_v2.py +++ b/extensions-builtin/SwinIR/swinir_model_arch_v2.py @@ -1001,7 +1001,7 @@ class Swin2SR(nn.Module): H, W = self.patches_resolution flops += H * W * 3 * self.embed_dim * 9 flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): + for layer in self.layers: flops += layer.flops() flops += H * W * 3 * self.embed_dim * self.embed_dim flops += self.upsample.flops() diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index 8e56cb89..ececdbae 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -94,7 +94,7 @@ def setup_model(dirname): self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) self.face_helper.align_warp_face() - for idx, cropped_face in enumerate(self.face_helper.cropped_faces): + for cropped_face in self.face_helper.cropped_faces: cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer) diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 85aa6934..a009eb42 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -16,9 +16,7 @@ def mod2normal(state_dict): # this code is copied from https://github.com/victorca25/iNNfer if 'conv_first.weight' in state_dict: crt_net = {} - items = [] - for k, v in state_dict.items(): - items.append(k) + items = list(state_dict) crt_net['model.0.weight'] = state_dict['conv_first.weight'] crt_net['model.0.bias'] = state_dict['conv_first.bias'] @@ -52,9 +50,7 @@ def resrgan2normal(state_dict, nb=23): if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict: re8x = 0 crt_net = {} - items = [] - for k, v in state_dict.items(): - items.append(k) + items = list(state_dict) crt_net['model.0.weight'] = state_dict['conv_first.weight'] crt_net['model.0.bias'] = state_dict['conv_first.bias'] diff --git a/modules/extra_networks.py b/modules/extra_networks.py index 1978673d..f9db41bc 100644 --- a/modules/extra_networks.py +++ b/modules/extra_networks.py @@ -91,7 +91,7 @@ def deactivate(p, extra_network_data): """call deactivate for extra networks in extra_network_data in specified order, then call deactivate for all remaining registered networks""" - for extra_network_name, extra_network_args in extra_network_data.items(): + for extra_network_name in extra_network_data: extra_network = extra_network_registry.get(extra_network_name, None) if extra_network is None: continue diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 7fbbe707..b0e945a1 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -247,7 +247,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model lines.append(lastline) lastline = '' - for i, line in enumerate(lines): + for line in lines: line = line.strip() if line.startswith("Negative prompt:"): done_with_prompt = True diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6ef0bfdf..38ef074f 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -177,34 +177,34 @@ class Hypernetwork: def weights(self): res = [] - for k, layers in self.layers.items(): + for layers in self.layers.values(): for layer in layers: res += layer.parameters() return res def train(self, mode=True): - for k, layers in self.layers.items(): + for layers in self.layers.values(): for layer in layers: layer.train(mode=mode) for param in layer.parameters(): param.requires_grad = mode def to(self, device): - for k, layers in self.layers.items(): + for layers in self.layers.values(): for layer in layers: layer.to(device) return self def set_multiplier(self, multiplier): - for k, layers in self.layers.items(): + for layers in self.layers.values(): for layer in layers: layer.multiplier = multiplier return self def eval(self): - for k, layers in self.layers.items(): + for layers in self.layers.values(): for layer in layers: layer.eval() for param in layer.parameters(): @@ -619,7 +619,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi try: sd_hijack_checkpoint.add() - for i in range((steps-initial_step) * gradient_step): + for _ in range((steps-initial_step) * gradient_step): if scheduler.finished: break if shared.state.interrupted: diff --git a/modules/images.py b/modules/images.py index 7392cb8b..c4e98c75 100644 --- a/modules/images.py +++ b/modules/images.py @@ -149,7 +149,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): return ImageFont.truetype(Roboto, fontsize) def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize): - for i, line in enumerate(lines): + for line in lines: fnt = initial_fnt fontsize = initial_fontsize while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0: diff --git a/modules/interrogate.py b/modules/interrogate.py index a1c8e537..111b1322 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -207,8 +207,8 @@ class InterrogateModels: image_features /= image_features.norm(dim=-1, keepdim=True) - for name, topn, items in self.categories(): - matches = self.rank(image_features, items, top_count=topn) + for cat in self.categories(): + matches = self.rank(image_features, cat.items, top_count=cat.topn) for match, score in matches: if shared.opts.interrogate_return_ranks: res += f", ({match}:{score/100:.3f})" diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 3a720721..b4aff704 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -143,7 +143,7 @@ def get_learned_conditioning(model, prompts, steps): conds = model.get_learned_conditioning(texts) cond_schedule = [] - for i, (end_at_step, text) in enumerate(prompt_schedule): + for i, (end_at_step, _) in enumerate(prompt_schedule): cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i])) cache[prompt] = cond_schedule @@ -219,8 +219,8 @@ def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_s res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for i, cond_schedule in enumerate(c): target_index = 0 - for current, (end_at, cond) in enumerate(cond_schedule): - if current_step <= end_at: + for current, entry in enumerate(cond_schedule): + if current_step <= entry.end_at_step: target_index = current break res[i] = cond_schedule[target_index].cond @@ -234,13 +234,13 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step): tensors = [] conds_list = [] - for batch_no, composable_prompts in enumerate(c.batch): + for composable_prompts in c.batch: conds_for_batch = [] - for cond_index, composable_prompt in enumerate(composable_prompts): + for composable_prompt in composable_prompts: target_index = 0 - for current, (end_at, cond) in enumerate(composable_prompt.schedules): - if current_step <= end_at: + for current, entry in enumerate(composable_prompt.schedules): + if current_step <= entry.end_at_step: target_index = current break diff --git a/modules/safe.py b/modules/safe.py index 2d5b972f..1e791c5b 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -95,11 +95,11 @@ def check_pt(filename, extra_handler): except zipfile.BadZipfile: - # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle + # if it's not a zip file, it's an old pytorch format, with five objects written to pickle with open(filename, "rb") as file: unpickler = RestrictedUnpickler(file) unpickler.extra_handler = extra_handler - for i in range(5): + for _ in range(5): unpickler.load() diff --git a/modules/scripts.py b/modules/scripts.py index d945b89f..0c12ebd5 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -231,7 +231,7 @@ def load_scripts(): syspath = sys.path def register_scripts_from_module(module): - for key, script_class in module.__dict__.items(): + for script_class in module.__dict__.values(): if type(script_class) != type: continue @@ -295,9 +295,9 @@ class ScriptRunner: auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data() - for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data: - script = script_class() - script.filename = path + for script_data in auto_processing_scripts + scripts_data: + script = script_data.script_class() + script.filename = script_data.path script.is_txt2img = not is_img2img script.is_img2img = is_img2img @@ -492,7 +492,7 @@ class ScriptRunner: module = script_loading.load_module(script.filename) cache[filename] = module - for key, script_class in module.__dict__.items(): + for script_class in module.__dict__.values(): if type(script_class) == type and issubclass(script_class, Script): self.scripts[si] = script_class() self.scripts[si].filename = filename diff --git a/modules/scripts_postprocessing.py b/modules/scripts_postprocessing.py index b11568c0..6751406c 100644 --- a/modules/scripts_postprocessing.py +++ b/modules/scripts_postprocessing.py @@ -66,9 +66,9 @@ class ScriptPostprocessingRunner: def initialize_scripts(self, scripts_data): self.scripts = [] - for script_class, path, basedir, script_module in scripts_data: - script: ScriptPostprocessing = script_class() - script.filename = path + for script_data in scripts_data: + script: ScriptPostprocessing = script_data.script_class() + script.filename = script_data.path if script.name == "Simple Upscale": continue @@ -124,7 +124,7 @@ class ScriptPostprocessingRunner: script_args = args[script.args_from:script.args_to] process_args = {} - for (name, component), value in zip(script.controls.items(), script_args): + for (name, component), value in zip(script.controls.items(), script_args): # noqa B007 process_args[name] = value script.process(pp, **process_args) diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py index 9fa5c5c5..c0c350f6 100644 --- a/modules/sd_hijack_clip.py +++ b/modules/sd_hijack_clip.py @@ -223,7 +223,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): self.hijack.fixes = [x.fixes for x in batch_chunk] for fixes in self.hijack.fixes: - for position, embedding in fixes: + for position, embedding in fixes: # noqa: B007 used_embeddings[embedding.name] = embedding z = self.process_tokens(tokens, multipliers) diff --git a/modules/shared.py b/modules/shared.py index e2691585..913c9e63 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -211,7 +211,7 @@ class OptionInfo: def options_section(section_identifier, options_dict): - for k, v in options_dict.items(): + for v in options_dict.values(): v.section = section_identifier return options_dict @@ -579,7 +579,7 @@ class Options: section_ids = {} settings_items = self.data_labels.items() - for k, item in settings_items: + for _, item in settings_items: if item.section not in section_ids: section_ids[item.section] = len(section_ids) @@ -740,7 +740,7 @@ def walk_files(path, allowed_extensions=None): if allowed_extensions is not None: allowed_extensions = set(allowed_extensions) - for root, dirs, files in os.walk(path): + for root, _, files in os.walk(path): for filename in files: if allowed_extensions is not None: _, ext = os.path.splitext(filename) diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py index fda58898..c56bea45 100644 --- a/modules/textual_inversion/learn_schedule.py +++ b/modules/textual_inversion/learn_schedule.py @@ -12,7 +12,7 @@ class LearnScheduleIterator: self.it = 0 self.maxit = 0 try: - for i, pair in enumerate(pairs): + for pair in pairs: if not pair.strip(): continue tmp = pair.split(':') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index c37bb2ad..47035332 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -29,7 +29,7 @@ textual_inversion_templates = {} def list_textual_inversion_templates(): textual_inversion_templates.clear() - for root, dirs, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir): + for root, _, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir): for fn in fns: path = os.path.join(root, fn) @@ -198,7 +198,7 @@ class EmbeddingDatabase: if not os.path.isdir(embdir.path): return - for root, dirs, fns in os.walk(embdir.path, followlinks=True): + for root, _, fns in os.walk(embdir.path, followlinks=True): for fn in fns: try: fullfn = os.path.join(root, fn) @@ -215,7 +215,7 @@ class EmbeddingDatabase: def load_textual_inversion_embeddings(self, force_reload=False): if not force_reload: need_reload = False - for path, embdir in self.embedding_dirs.items(): + for embdir in self.embedding_dirs.values(): if embdir.has_changed(): need_reload = True break @@ -228,7 +228,7 @@ class EmbeddingDatabase: self.skipped_embeddings.clear() self.expected_shape = self.get_expected_shape() - for path, embdir in self.embedding_dirs.items(): + for embdir in self.embedding_dirs.values(): self.load_from_dir(embdir) embdir.update() @@ -469,7 +469,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st try: sd_hijack_checkpoint.add() - for i in range((steps-initial_step) * gradient_step): + for _ in range((steps-initial_step) * gradient_step): if scheduler.finished: break if shared.state.interrupted: diff --git a/modules/ui.py b/modules/ui.py index 84d661b2..83bfb7d8 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -416,7 +416,7 @@ def create_sampler_and_steps_selection(choices, tabname): def ordered_ui_categories(): user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))} - for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)): + for _, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)): yield category @@ -1646,7 +1646,7 @@ def create_ui(): with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo: with gr.Row(elem_id="quicksettings", variant="compact"): - for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])): + for _i, k, _item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])): component = create_setting_component(k, is_quicksettings=True) component_dict[k] = component @@ -1673,7 +1673,7 @@ def create_ui(): outputs=[text_settings, result], ) - for i, k, item in quicksettings_list: + for _i, k, _item in quicksettings_list: component = component_dict[k] info = opts.data_labels[k] diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index ab585917..2fd82e8e 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -90,7 +90,7 @@ class ExtraNetworksPage: subdirs = {} for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]: - for root, dirs, files in os.walk(parentdir): + for root, dirs, _ in os.walk(parentdir): for dirname in dirs: x = os.path.join(root, dirname) diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py index cac73c51..f05049e1 100644 --- a/modules/ui_tempdir.py +++ b/modules/ui_tempdir.py @@ -72,7 +72,7 @@ def cleanup_tmpdr(): if temp_dir == "" or not os.path.isdir(temp_dir): return - for root, dirs, files in os.walk(temp_dir, topdown=False): + for root, _, files in os.walk(temp_dir, topdown=False): for name in files: _, extension = os.path.splitext(name) if extension != ".png": diff --git a/modules/upscaler.py b/modules/upscaler.py index e145be30..8acb6e96 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -55,7 +55,7 @@ class Upscaler: dest_w = int(img.width * scale) dest_h = int(img.height * scale) - for i in range(3): + for _ in range(3): shape = (img.width, img.height) img = self.do_upscale(img, selected_model) diff --git a/pyproject.toml b/pyproject.toml index 346a0cde..c88907be 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,6 @@ ignore = [ "I001", # Import block is un-sorted or un-formatted "C901", # Function is too complex "C408", # Rewrite as a literal - "B007", # Loop control variable not used within loop body ] diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py index 149bc85f..27af5ff6 100644 --- a/scripts/prompts_from_file.py +++ b/scripts/prompts_from_file.py @@ -156,7 +156,7 @@ class Script(scripts.Script): images = [] all_prompts = [] infotexts = [] - for n, args in enumerate(jobs): + for args in jobs: state.job = f"{state.job_no + 1} out of {state.job_count}" copy_p = copy.copy(p) diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py index d873a09c..0b1d3096 100644 --- a/scripts/sd_upscale.py +++ b/scripts/sd_upscale.py @@ -56,7 +56,7 @@ class Script(scripts.Script): work = [] - for y, h, row in grid.tiles: + for _y, _h, row in grid.tiles: for tiledata in row: work.append(tiledata[2]) @@ -85,7 +85,7 @@ class Script(scripts.Script): work_results += processed.images image_index = 0 - for y, h, row in grid.tiles: + for _y, _h, row in grid.tiles: for tiledata in row: tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height)) image_index += 1 diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index 332e0ecd..38a20381 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -704,7 +704,7 @@ class Script(scripts.Script): if not include_sub_grids: # Done with sub-grids, drop all related information: - for sg in range(z_count): + for _ in range(z_count): del processed.images[1] del processed.all_prompts[1] del processed.all_seeds[1] -- cgit v1.2.3 From cb5f61281a95be72fc812b7d350b6ec23e2f9bdd Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Sat, 13 May 2023 11:04:26 -0400 Subject: Allow bf16 in safe unpickler --- modules/safe.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/safe.py') diff --git a/modules/safe.py b/modules/safe.py index 1e791c5b..e8f50774 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -40,7 +40,7 @@ class RestrictedUnpickler(pickle.Unpickler): return getattr(collections, name) if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']: return getattr(torch._utils, name) - if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32']: + if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32', 'BFloat16Storage']: return getattr(torch, name) if module == 'torch.nn.modules.container' and name in ['ParameterDict']: return getattr(torch.nn.modules.container, name) -- cgit v1.2.3