lycoris, strong linting, model keyword, circular imports

.markdownlint.json

@@ -0,0 +1,7 @@
+{
+  "MD012": false,
+  "MD013": false,
+  "MD033": false,
+  "MD036": false,
+  "MD041": false
+}

.pylintrc

@@ -133,6 +133,7 @@ disable=raw-checker-failed,
         missing-class-docstring,
         logging-fstring-interpolation,
         import-outside-toplevel,
+        consider-iterating-dictionary,
 enable=c-extension-no-member
 
 [METHOD_ARGS]

README.md

@@ -75,6 +75,7 @@ Fork adds extra functionality:
 - [Dynamic Thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding)
 - [Steps Animation](https://github.com/vladmandic/sd-extension-steps-animation)
 - [Seed Travel](https://github.com/yownas/seed_travel)
+- [Model Keyword](https://github.com/mix1009/model-keyword)
 
 <br>
 

modules/call_queue.py

@@ -1,7 +1,9 @@
 import html
 import threading
 import time
-import cProfile, pstats, io
+import cProfile
+import pstats
+import io
 
 from modules import shared, progress, errors
 
@@ -103,4 +105,3 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
         return tuple(res)
 
     return f
-

modules/cmd_args.py

@@ -1,6 +1,6 @@
 import argparse
 import os
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
+from modules.paths_internal import data_path, sd_default_config, sd_model_file
 
 parser = argparse.ArgumentParser(description="Stable Diffusion", formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=55,indent_increment=2,width=200))
 

modules/codeformer_model.py

@@ -5,7 +5,6 @@ import cv2
 import torch
 
 import modules.face_restoration
-import modules.shared
 from modules import shared, devices, modelloader, errors
 from modules.paths import models_path
 
@@ -32,7 +31,6 @@ def setup_model(dirname):
     try:
         from torchvision.transforms.functional import normalize
         from modules.codeformer.codeformer_arch import CodeFormer
-        from basicsr.utils.download_util import load_file_from_url
         from basicsr.utils import imwrite, img2tensor, tensor2img
         from facelib.utils.face_restoration_helper import FaceRestoreHelper
         from facelib.detection.retinaface import retinaface

modules/deepbooru.py

@@ -2,7 +2,6 @@ import os
 import re
 
 import torch
-from PIL import Image
 import numpy as np
 
 from modules import modelloader, paths, deepbooru_model, devices, images, shared

modules/deepbooru_model.py

@@ -675,4 +675,3 @@ class DeepDanbooruModel(nn.Module):
         self.tags = state_dict.get('tags', [])
 
         super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})
-

modules/errors.py

@@ -44,7 +44,7 @@ def run(code, task):
     try:
         code()
     except Exception as e:
-        display(task, e)
+        display(e, task)
 
 
 def exception():

modules/esrgan_model.py

@@ -6,7 +6,7 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgan_model_arch as arch
-from modules import shared, modelloader, images, devices
+from modules import modelloader, images, devices
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
@@ -118,7 +118,7 @@ def infer_params(state_dict):
 
     nf = state_dict["model.0.weight"].shape[0]
     in_nc = state_dict["model.0.weight"].shape[1]
-    out_nc = out_nc
+    # out_nc = out_nc
     scale = 2 ** scale2x
 
     return in_nc, out_nc, nf, nb, plus, scale

modules/esrgan_model_arch.py

@@ -1,8 +1,6 @@
 # this file is adapted from https://github.com/victorca25/iNNfer
 
-from collections import OrderedDict
 import math
-import functools
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

modules/extensions.py

@@ -1,6 +1,4 @@
 import os
-import sys
-
 import time
 import git
 

modules/extra_networks.py

@@ -74,7 +74,7 @@ def activate(p, extra_network_data):
         try:
             extra_network.activate(p, extra_network_args)
         except Exception as e:
-            errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}")
+            errors.display(e, f"Error activating extra network {extra_network_name} with arguments {extra_network_args}")
 
     for extra_network_name, extra_network in extra_network_registry.items():
         args = extra_network_data.get(extra_network_name, None)
@@ -84,14 +84,14 @@ def activate(p, extra_network_data):
         try:
             extra_network.activate(p, [])
         except Exception as e:
-            errors.display(e, f"activating extra network {extra_network_name}")
+            errors.display(e, f"Error activating extra network {extra_network_name}")
 
 
 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, _extra_network_args in extra_network_data.items():
         extra_network = extra_network_registry.get(extra_network_name, None)
         if extra_network is None:
             continue
@@ -99,7 +99,7 @@ def deactivate(p, extra_network_data):
         try:
             extra_network.deactivate(p)
         except Exception as e:
-            errors.display(e, f"deactivating extra network {extra_network_name}")
+            errors.display(e, f"Error deactivating extra network {extra_network_name}")
 
     for extra_network_name, extra_network in extra_network_registry.items():
         args = extra_network_data.get(extra_network_name, None)
@@ -109,7 +109,7 @@ def deactivate(p, extra_network_data):
         try:
             extra_network.deactivate(p)
         except Exception as e:
-            errors.display(e, f"deactivating unmentioned extra network {extra_network_name}")
+            errors.display(e, f"Error deactivating unmentioned extra network {extra_network_name}")
 
 
 re_extra_net = re.compile(r"<(\w+):([^>]+)>")
@@ -144,4 +144,3 @@ def parse_prompts(prompts):
         res.append(updated_prompt)
 
     return res, extra_data
-

modules/extra_networks_hypernet.py

@@ -1,4 +1,4 @@
-from modules import extra_networks, shared, extra_networks
+from modules import extra_networks, shared
 from modules.hypernetworks import hypernetwork
 
 

modules/generation_parameters_copypaste.py

@@ -1,15 +1,11 @@
 import base64
-import html
 import io
-import math
 import os
 import re
-from pathlib import Path
 
 import gradio as gr
 from modules.paths import data_path
 from modules import shared, ui_tempdir, script_callbacks
-import tempfile
 from PIL import Image
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'

modules/gfpgan_model.py

@@ -1,5 +1,4 @@
 import os
-import sys
 
 import modules.face_restoration
 from modules import paths, shared, devices, modelloader, errors

modules/hashes.py

@@ -84,8 +84,3 @@ def sha256(filename, title):
     dump_cache()
 
     return sha256_value
-
-
-
-
-

modules/images.py

@@ -1,5 +1,4 @@
 import datetime
-import sys
 
 import pytz
 import io

modules/img2img.py

@@ -1,6 +1,4 @@
-import math
 import os
-import sys
 
 import numpy as np
 from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops

modules/interrogate.py

@@ -10,7 +10,6 @@ import torch.hub
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
-import modules.shared as shared
 from modules import devices, paths, shared, lowvram, modelloader, errors
 
 blip_image_eval_size = 384

modules/localization.py

@@ -1,7 +1,6 @@
 import json
 import os
 import sys
-import modules.shared as shared
 import modules.errors as errors
 
 
@@ -11,7 +10,7 @@ localizations = {}
 def list_localizations(dirname):
     localizations.clear()
     return localizations
-
+    """
     for file in os.listdir(dirname):
         fn, ext = os.path.splitext(file)
         if ext.lower() != ".json":
@@ -23,7 +22,7 @@ def list_localizations(dirname):
     for file in scripts.list_scripts("localizations", ".json"):
         fn, ext = os.path.splitext(file.filename)
         localizations[fn] = file.path
-
+    """
 
 def localization_js(current_localization_name):
     fn = localizations.get(current_localization_name, None)

modules/mac_specific.py

@@ -1,6 +1,5 @@
 import torch
 import platform
-from modules import paths
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
 

modules/paths.py

@@ -1,9 +1,16 @@
 import os
 import sys
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir
-
-import modules.safe
+import modules.paths_internal
 
+data_path = modules.paths_internal.data_path
+script_path = modules.paths_internal.script_path
+models_path = modules.paths_internal.models_path
+sd_configs_path = modules.paths_internal.sd_configs_path
+sd_default_config = modules.paths_internal.sd_default_config
+sd_model_file = modules.paths_internal.sd_model_file
+default_sd_model_file = modules.paths_internal.default_sd_model_file
+extensions_dir = modules.paths_internal.extensions_dir
+extensions_builtin_dir = modules.paths_internal.extensions_builtin_dir
 
 # data_path = cmd_opts_pre.data
 sys.path.insert(0, script_path)

modules/paths_internal.py

@@ -4,7 +4,6 @@ import argparse
 import os
 
 script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
-
 sd_configs_path = os.path.join(script_path, "configs")
 sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -14,7 +13,6 @@ default_sd_model_file = sd_model_file
 parser_pre = argparse.ArgumentParser(add_help=False)
 parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
 cmd_opts_pre = parser_pre.parse_known_args()[0]
-
 data_path = cmd_opts_pre.data_dir
 
 models_path = os.path.join(data_path, "models")

modules/processing.py

@@ -2,7 +2,6 @@ import json
 import math
 import os
 import sys
-import warnings
 
 import torch
 import numpy as np
@@ -10,7 +9,7 @@ from PIL import Image, ImageFilter, ImageOps
 import random
 import cv2
 from skimage import exposure
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts

modules/progress.py

@@ -2,7 +2,6 @@ import base64
 import io
 import time
 
-import gradio as gr
 from pydantic import BaseModel, Field
 
 from modules.shared import opts

modules/realesrgan_model.py

@@ -7,7 +7,6 @@ from basicsr.utils.download_util import load_file_from_url
 
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import cmd_opts, opts
-import modules.shared as shared
 import modules.errors as errors
 
 

modules/safe.py

@@ -1,15 +1,13 @@
 # this code is adapted from the script contributed by anon from /h/
 
-import io
 import pickle
 import collections
-import sys
+import zipfile
+import re
 
 import torch
 import numpy
 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

modules/script_callbacks.py

@@ -1,12 +1,9 @@
-import sys
-from collections import namedtuple
 import inspect
-import modules.shared as shared
-import modules.errors as errors
+from collections import namedtuple
 from typing import Optional, Dict, Any
-
 from fastapi import FastAPI
 from gradio import Blocks
+import modules.errors as errors
 
 
 def report_exception(e, c, job):
@@ -32,22 +29,22 @@ class CFGDenoiserParams:
     def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
         self.x = x
         """Latent image representation in the process of being denoised"""
-        
+
         self.image_cond = image_cond
         """Conditioning image"""
-        
+
         self.sigma = sigma
         """Current sigma noise step value"""
-        
+
         self.sampling_step = sampling_step
         """Current Sampling step number"""
-        
+
         self.total_sampling_steps = total_sampling_steps
         """Total number of sampling steps planned"""
-        
+
         self.text_cond = text_cond
         """ Encoder hidden states of text conditioning from prompt"""
-        
+
         self.text_uncond = text_uncond
         """ Encoder hidden states of text conditioning from negative prompt"""
 
@@ -231,7 +228,7 @@ def add_callback(callbacks, fun):
 
     callbacks.append(ScriptCallback(filename, fun))
 
-    
+
 def remove_current_script_callbacks():
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'

modules/script_loading.py

@@ -1,9 +1,6 @@
 import os
-import sys
-import modules.shared as shared
-import modules.errors as errors
 import importlib.util
-from types import ModuleType
+import modules.errors as errors
 
 
 def load_module(path):

modules/sd_hijack.py

@@ -1,15 +1,7 @@
+from types import MethodType
+from rich import print
 import torch
 from torch.nn.functional import silu
-from types import MethodType
-
-import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
-from modules.hypernetworks import hypernetwork
-from modules.shared import cmd_opts, opts
-from modules.shared_items import list_crossattention
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
-from rich import print
-
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.modules.diffusionmodules.openaimodel
@@ -17,6 +9,12 @@ import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 import ldm.modules.encoders.modules
 
+import modules.textual_inversion.textual_inversion
+from modules import devices, sd_hijack_optimizations, shared
+from modules.hypernetworks import hypernetwork
+from modules.shared import opts
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
+
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
@@ -36,7 +34,7 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
-    
+
     optimization_method = None
 
     can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention"))
@@ -97,12 +95,12 @@ def fix_checkpoint():
 def weighted_loss(sd_model, pred, target, mean=True):
     #Calculate the weight normally, but ignore the mean
     loss = sd_model._old_get_loss(pred, target, mean=False)
-    
+
     #Check if we have weights available
     weight = getattr(sd_model, '_custom_loss_weight', None)
     if weight is not None:
         loss *= weight
-    
+
     #Return the loss, as mean if specified
     return loss.mean() if mean else loss
 
@@ -110,7 +108,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
     try:
         #Temporarily append weights to a place accessible during loss calc
         sd_model._custom_loss_weight = w
-        
+
         #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
         #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
         if not hasattr(sd_model, '_old_get_loss'):
@@ -123,9 +121,9 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
         try:
             #Delete temporary weights if appended
             del sd_model._custom_loss_weight
-        except AttributeError as e:
+        except AttributeError:
             pass
-            
+
         #If we have an old loss function, reset the loss function to the original one
         if hasattr(sd_model, '_old_get_loss'):
             sd_model.get_loss = sd_model._old_get_loss
@@ -138,7 +136,7 @@ def apply_weighted_forward(sd_model):
 def undo_weighted_forward(sd_model):
     try:
         del sd_model.weighted_forward
-    except AttributeError as e:
+    except AttributeError:
         pass
 
 
@@ -200,7 +198,7 @@ class StableDiffusionModelHijack:
 
     def undo_hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
-            m.cond_stage_model = m.cond_stage_model.wrapped 
+            m.cond_stage_model = m.cond_stage_model.wrapped
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped

modules/sd_hijack_inpainting.py

@@ -1,9 +1,5 @@
-import os
 import torch
 
-from einops import repeat
-from omegaconf import ListConfig
-
 import ldm.models.diffusion.ddpm
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms

modules/sd_hijack_ip2p.py

@@ -1,8 +1,4 @@
-import collections
 import os.path
-import sys
-import gc
-import time
 
 def should_hijack_ip2p(checkpoint_info):
     from modules import sd_models_config

modules/sd_hijack_open_clip.py

@@ -2,7 +2,6 @@ import open_clip.tokenizer
 import torch
 
 from modules import sd_hijack_clip, devices
-from modules.shared import opts
 
 tokenizer = open_clip.tokenizer._tokenizer
 

modules/sd_hijack_optimizations.py

@@ -1,5 +1,4 @@
 import math
-import sys
 import psutil
 
 import torch
@@ -391,63 +390,63 @@ def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None
         return scaled_dot_product_attention_forward(self, x, context, mask)
 
 def cross_attention_attnblock_forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q1 = self.q(h_)
-        k1 = self.k(h_)
-        v = self.v(h_)
+    h_ = x
+    h_ = self.norm(h_)
+    q1 = self.q(h_)
+    k1 = self.k(h_)
+    v = self.v(h_)
 
-        # compute attention
-        b, c, h, w = q1.shape
+    # compute attention
+    b, c, h, w = q1.shape
 
-        q2 = q1.reshape(b, c, h*w)
-        del q1
+    q2 = q1.reshape(b, c, h*w)
+    del q1
 
-        q = q2.permute(0, 2, 1)   # b,hw,c
-        del q2
+    q = q2.permute(0, 2, 1)   # b,hw,c
+    del q2
 
-        k = k1.reshape(b, c, h*w) # b,c,hw
-        del k1
+    k = k1.reshape(b, c, h*w) # b,c,hw
+    del k1
 
-        h_ = torch.zeros_like(k, device=q.device)
+    h_ = torch.zeros_like(k, device=q.device)
 
-        mem_free_total = get_available_vram()
+    mem_free_total = get_available_vram()
 
-        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
-        mem_required = tensor_size * 2.5
-        steps = 1
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+    mem_required = tensor_size * 2.5
+    steps = 1
 
-        if mem_required > mem_free_total:
-            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+    if mem_required > mem_free_total:
+        steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
 
-        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-        for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
+    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+    for i in range(0, q.shape[1], slice_size):
+        end = i + slice_size
 
-            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-            w2 = w1 * (int(c)**(-0.5))
-            del w1
-            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
-            del w2
+        w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w2 = w1 * (int(c)**(-0.5))
+        del w1
+        w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
+        del w2
 
-            # attend to values
-            v1 = v.reshape(b, c, h*w)
-            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
-            del w3
+        # attend to values
+        v1 = v.reshape(b, c, h*w)
+        w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
+        del w3
 
-            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-            del v1, w4
+        h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        del v1, w4
 
-        h2 = h_.reshape(b, c, h, w)
-        del h_
+    h2 = h_.reshape(b, c, h, w)
+    del h_
 
-        h3 = self.proj_out(h2)
-        del h2
+    h3 = self.proj_out(h2)
+    del h2
 
-        h3 += x
+    h3 += x
+
+    return h3
 
-        return h3
-    
 def xformers_attnblock_forward(self, x):
     try:
         h_ = x

modules/sd_hijack_xlmr.py

@@ -1,8 +1,6 @@
-import open_clip.tokenizer
 import torch
 
 from modules import sd_hijack_clip, devices
-from modules.shared import opts
 
 
 class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):

modules/sd_models.py

@@ -2,23 +2,21 @@ import collections
 import os.path
 import sys
 import gc
-import torch
 import re
-import safetensors.torch
-from omegaconf import OmegaConf
+import io
 from os import mkdir
 from urllib import request
+from rich import print, progress # pylint: disable=W0622
+import torch
+import safetensors.torch
+from omegaconf import OmegaConf
 import ldm.modules.midas as midas
-import io
-
 from ldm.util import instantiate_from_config
 
 from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
-from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack
 from modules.timer import Timer
 
-from rich import print, progress
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -57,8 +55,8 @@ class CheckpointInfo:
 
     def register(self):
         checkpoints_list[self.title] = self
-        for id in self.ids:
-            checkpoint_alisases[id] = self
+        for i in self.ids:
+            checkpoint_alisases[i] = self
 
     def calculate_shorthash(self):
         self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
@@ -79,9 +77,7 @@ class CheckpointInfo:
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
-
-    from transformers import logging, CLIPModel
-
+    from transformers import logging
     logging.set_verbosity_error()
 except Exception:
     pass
@@ -160,7 +156,7 @@ def model_hash(filename):
 
 def select_checkpoint():
     model_checkpoint = shared.opts.sd_model_checkpoint
-        
+
     checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
     if checkpoint_info is not None:
         return checkpoint_info
@@ -232,7 +228,7 @@ def read_metadata_from_safetensors(filename):
             if isinstance(v, str) and v[0:1] == '{':
                 try:
                     res[k] = json.loads(v)
-                except Exception as e:
+                except Exception:
                     pass
 
         return res
@@ -264,7 +260,7 @@ def read_state_dict(checkpoint_file):
 def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
     if checkpoint_info in checkpoints_loaded:
         # use checkpoint cache
-        print(f"Loading weights from cache")
+        print("Loading weights from cache")
         return checkpoints_loaded[checkpoint_info]
 
     res = read_state_dict(checkpoint_info.filename)
@@ -368,7 +364,7 @@ def enable_midas_autodownload():
         if not os.path.exists(path):
             if not os.path.exists(midas_path):
                 mkdir(midas_path)
-    
+
             print(f"Downloading midas model weights for {model_type} to {path}")
             request.urlretrieve(midas_urls[model_type], path)
             print(f"{model_type} downloaded")
@@ -444,7 +440,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     try:
         with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
             sd_model = instantiate_from_config(sd_config.model)
-    except Exception as e:
+    except Exception:
         sd_model = instantiate_from_config(sd_config.model)
 
     sd_model.used_config = checkpoint_config
@@ -481,7 +477,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
 
 
 def reload_model_weights(sd_model=None, info=None):
-    from modules import lowvram, devices, sd_hijack
+    from modules import lowvram, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
     if not sd_model:
@@ -517,7 +513,7 @@ def reload_model_weights(sd_model=None, info=None):
 
     try:
         load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-    except Exception as e:
+    except Exception:
         print("Failed to load checkpoint, restoring previous")
         load_model_weights(sd_model, current_checkpoint_info, None, timer)
         raise
@@ -534,8 +530,8 @@ def reload_model_weights(sd_model=None, info=None):
 
     print(f"Weights loaded in {timer.summary()}")
 
-def unload_model_weights(sd_model=None, info=None):
-    from modules import lowvram, devices, sd_hijack
+def unload_model_weights(sd_model=None, _info=None):
+    from modules import sd_hijack
     timer = Timer()
 
     if shared.sd_model:

modules/sd_models_config.py

@@ -1,24 +1,20 @@
-import re
 import os
 
 import torch
 
-from modules import shared, paths, sd_disable_initialization
+from modules import paths, sd_disable_initialization
 
-sd_configs_path = shared.sd_configs_path
 sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
-
-
-config_default = shared.sd_default_config
+config_default = paths.sd_default_config
 config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
 config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
 config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
 config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
 config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
 config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
-config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
-config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
-config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
+config_inpainting = os.path.join(paths.sd_configs_path, "v1-inpainting-inference.yaml")
+config_instruct_pix2pix = os.path.join(paths.sd_configs_path, "instruct-pix2pix.yaml")
+config_alt_diffusion = os.path.join(paths.sd_configs_path, "alt-diffusion-inference.yaml")
 
 
 def is_using_v_parameterization_for_sd2(state_dict):
@@ -64,7 +60,7 @@ def is_using_v_parameterization_for_sd2(state_dict):
     return out < -1
 
 
-def guess_model_config_from_state_dict(sd, filename):
+def guess_model_config_from_state_dict(sd, _filename):
     if sd is None:
         return None
     sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
@@ -118,4 +114,3 @@ def find_checkpoint_config_near_filename(info):
         return config
 
     return None
-

modules/sd_samplers_kdiffusion.py

@@ -1,7 +1,6 @@
 from collections import deque
-import torch
 import inspect
-import einops
+import torch
 import k_diffusion.sampling
 from modules import prompt_parser, devices, sd_samplers_common
 
@@ -94,10 +93,10 @@ class CFGDenoiser(torch.nn.Module):
 
         if shared.sd_model.model.conditioning_key == "crossattn-adm":
             image_uncond = torch.zeros_like(image_cond)
-            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} 
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
         else:
             image_uncond = image_cond
-            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} 
+            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
 
         if not is_edit_model:
             x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
@@ -295,7 +294,7 @@ class KDiffusionSampler:
 
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
-        
+
         extra_params_kwargs = self.initialize(p)
         parameters = inspect.signature(self.func).parameters
 
@@ -359,4 +358,3 @@ class KDiffusionSampler:
         }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
-

modules/sd_vae.py

@@ -1,12 +1,9 @@
-import torch
-import safetensors.torch
 import os
 import collections
-from collections import namedtuple
-from modules import paths, shared, devices, script_callbacks, sd_models
 import glob
 from copy import deepcopy
 from rich import print
+from modules import paths, shared, devices, script_callbacks, sd_models
 
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 vae_dict = {}

modules/shared.py

@@ -1,4 +1,3 @@
-import argparse
 import datetime
 import json
 import os
@@ -12,18 +11,19 @@ import modules.interrogate
 import modules.memmon
 import modules.styles
 import modules.devices as devices
-from modules import script_loading, errors, ui_components, shared_items, cmd_args, errors
-from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir
+from modules import script_loading, errors, ui_components, shared_items, cmd_args
+from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # pylint: disable=W0611
+import modules.paths_internal as paths
 
+from setup import log as setup_log # pylint: disable=E0611
 errors.install()
 demo = None
-from setup import log as setup_log # pylint: disable=E0611
 log = setup_log
 
 parser = cmd_args.parser
 
-script_loading.preload_extensions(extensions_dir, parser)
-script_loading.preload_extensions(extensions_builtin_dir, parser)
+script_loading.preload_extensions(paths.extensions_dir, parser)
+script_loading.preload_extensions(paths.extensions_builtin_dir, parser)
 
 if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
     cmd_opts = parser.parse_args()
@@ -68,7 +68,7 @@ clip_model = None
 
 def reload_hypernetworks():
     from modules.hypernetworks import hypernetwork
-    global hypernetworks
+    global hypernetworks # pylint: disable=W0603
     hypernetworks = hypernetwork.list_hypernetworks(opts.hypernetwork_dir)
 
 
@@ -154,8 +154,8 @@ class State:
         if self.current_latent is None:
             return
 
-        import modules.sd_samplers
         if opts.show_progress_grid:
+            import modules.sd_samplers # pylint: disable=W0621
             self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
         else:
             self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
@@ -188,24 +188,24 @@ class OptionInfo:
 
 
 def options_section(section_identifier, options_dict):
-    for k, v in options_dict.items():
+    for _k, v in options_dict.items():
         v.section = section_identifier
 
     return options_dict
 
 
 def list_checkpoint_tiles():
-    import modules.sd_models
+    import modules.sd_models # pylint: disable=W0621
     return modules.sd_models.checkpoint_tiles()
 
 
 def refresh_checkpoints():
-    import modules.sd_models
+    import modules.sd_models # pylint: disable=W0621
     return modules.sd_models.list_models()
 
 
 def list_samplers():
-    import modules.sd_samplers
+    import modules.sd_samplers # pylint: disable=W0621
     return modules.sd_samplers.all_samplers
 
 
@@ -245,18 +245,18 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
 }))
 
 options_templates.update(options_section(('system-paths', "System Paths"), {
-    "ckpt_dir": OptionInfo(os.path.join(models_path, 'Stable-diffusion'), "Path to directory with stable diffusion checkpoints"),
-    "vae_dir": OptionInfo(os.path.join(models_path, 'VAE'), "Path to directory with VAE files"),
-    "embeddings_dir": OptionInfo(os.path.join(models_path, 'embeddings'), "Embeddings directory for textual inversion"),
-    "embeddings_templates_dir": OptionInfo(os.path.join(script_path, 'train/templates'), "Embeddings train templates directory"),
-    "hypernetwork_dir": OptionInfo(os.path.join(models_path, 'hypernetworks'), "Hypernetwork directory"),
-    "codeformer_models_path": OptionInfo(os.path.join(models_path, 'Codeformer'), "Path to directory with codeformer model file(s)."),
-    "gfpgan_models_path": OptionInfo(os.path.join(models_path, 'GFPGAN'), "Path to directory with GFPGAN model file(s)"),
-    "esrgan_models_path": OptionInfo(os.path.join(models_path, 'ESRGAN'), "Path to directory with ESRGAN model file(s)"),
-    "bsrgan_models_path": OptionInfo(os.path.join(models_path, 'BSRGAN'), "Path to directory with BSRGAN model file(s)"),
-    "realesrgan_models_path": OptionInfo(os.path.join(models_path, 'RealESRGAN'), "Path to directory with RealESRGAN model file(s)"),
-    "clip_models_path": OptionInfo(os.path.join(models_path, 'CLIP'), "Path to directory with CLIP model file(s)"),
-    "lora_dir": OptionInfo(os.path.join(models_path, 'Lora'), "Path to directory with Lora network(s)"),
+    "ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Path to directory with stable diffusion checkpoints"),
+    "vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Path to directory with VAE files"),
+    "embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Embeddings directory for textual inversion"),
+    "embeddings_templates_dir": OptionInfo(os.path.join(paths.script_path, 'train/templates'), "Embeddings train templates directory"),
+    "hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Hypernetwork directory"),
+    "codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Path to directory with codeformer model file(s)."),
+    "gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Path to directory with GFPGAN model file(s)"),
+    "esrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'ESRGAN'), "Path to directory with ESRGAN model file(s)"),
+    "bsrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'BSRGAN'), "Path to directory with BSRGAN model file(s)"),
+    "realesrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'RealESRGAN'), "Path to directory with RealESRGAN model file(s)"),
+    "clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Path to directory with CLIP model file(s)"),
+    "lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Path to directory with Lora network(s)"),
     # "gfpgan_model": OptionInfo("", "GFPGAN model file name"),
 }))
 
@@ -647,7 +647,7 @@ def listfiles(dirname):
 
 
 def html_path(filename):
-    return os.path.join(script_path, "html", filename)
+    return os.path.join(paths.script_path, "html", filename)
 
 
 def html(filename):

modules/sub_quadratic_attention.py

@@ -11,11 +11,11 @@
 #   https://arxiv.org/abs/2112.05682v2
 
 from functools import partial
+import math
+from typing import Optional, NamedTuple, List
 import torch
 from torch import Tensor
 from torch.utils.checkpoint import checkpoint
-import math
-from typing import Optional, NamedTuple, List
 
 
 def narrow_trunc(
@@ -179,7 +179,7 @@ def efficient_dot_product_attention(
             chunk_idx,
             min(query_chunk_size, q_tokens)
         )
-    
+
     summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale)
     summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
     compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
@@ -201,7 +201,7 @@ def efficient_dot_product_attention(
             key=key,
             value=value,
         )
-    
+
     # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
     # and pass slices to be mutated, instead of torch.cat()ing the returned slices
     res = torch.cat([

modules/txt2img.py

@@ -1,11 +1,9 @@
 import modules.scripts
 from modules import sd_samplers
 from modules.generation_parameters_copypaste import create_override_settings_dict
-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
-    StableDiffusionProcessingImg2Img, process_images
-from modules.shared import opts, cmd_opts
+from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.shared import opts
 import modules.shared as shared
-import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 

modules/ui.py

@@ -1,29 +1,21 @@
-import html
 import json
-import math
 import mimetypes
 import os
-import platform
-import random
 import sys
-import tempfile
-import time
-from functools import partial, reduce
+from functools import reduce
 import warnings
 
 import gradio as gr
 import gradio.routes
 import gradio.utils
 import numpy as np
-from PIL import Image, PngImagePlugin
+from PIL import Image
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing
 from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML
 from modules.paths import script_path, data_path
-
-from modules.shared import opts, cmd_opts, restricted_opts
-
+from modules.shared import opts, cmd_opts
 import modules.codeformer_model
 import modules.generation_parameters_copypaste as parameters_copypaste
 import modules.gfpgan_model
@@ -34,11 +26,9 @@ import modules.errors as errors
 import modules.styles
 import modules.textual_inversion.ui
 from modules import prompt_parser
-from modules.images import save_image
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.textual_inversion import textual_inversion
-import modules.hypernetworks.ui
 from modules.generation_parameters_copypaste import image_from_url_text
 import modules.extras
 
@@ -1069,7 +1059,7 @@ def create_ui():
                         process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
                         process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
                         process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
-                    
+
                     with gr.Column(visible=False) as process_multicrop_col:
                         gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
                         with gr.Row():
@@ -1081,7 +1071,7 @@ def create_ui():
                         with gr.Row():
                             process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
                             process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
-   
+
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
@@ -1124,7 +1114,7 @@ def create_ui():
                     with FormRow():
                         embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
                         hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
-                    
+
                     with FormRow():
                         clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
                         clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
@@ -1454,7 +1444,6 @@ def create_ui():
                 gr.HTML(shared.html("licenses.html"), elem_id="licenses")
 
             gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
-            
 
         def unload_sd_weights():
             modules.sd_models.unload_model_weights()
@@ -1631,7 +1620,7 @@ def create_ui():
             key = path + "/" + field
 
             if getattr(obj, 'custom_script_source', None) is not None:
-              key = 'customscript/' + obj.custom_script_source + '/' + key
+                key = 'customscript/' + obj.custom_script_source + '/' + key
 
             if getattr(obj, 'do_not_save_to_config', False):
                 return

modules/ui_common.py

@@ -3,9 +3,9 @@ import html
 import os
 import platform
 import sys
+import subprocess as sp
 
 import gradio as gr
-import subprocess as sp
 
 from modules import call_queue, shared
 from modules.generation_parameters_copypaste import image_from_url_text
@@ -101,7 +101,6 @@ def initial_image():
     return [img]
 
 def create_output_panel(tabname, outdir):
-    from modules import shared
     import modules.generation_parameters_copypaste as parameters_copypaste
 
     def open_folder(f):

modules/ui_extensions.py

@@ -1,15 +1,14 @@
 import json
 import os.path
-import sys
 import time
-
-import git
-
-import gradio as gr
-import html
 import shutil
 import errno
+import html
 
+import git
+import gradio as gr
+
+from rich import print
 from modules import extensions, shared, paths, errors
 from modules.call_queue import wrap_gradio_gpu_call
 
@@ -48,7 +47,7 @@ def apply_and_restart(disable_list, update_list, disable_all):
     shared.state.need_restart = True
 
 
-def check_updates(id_task, disable_list):
+def check_updates(_id_task, disable_list):
     check_access()
 
     disabled = json.loads(disable_list)
@@ -134,10 +133,10 @@ def install_extension_from_url(dirname, url):
     if dirname is None or dirname == "":
         *parts, last_part = url.split('/')
         last_part = normalize_git_url(last_part)
-
         dirname = last_part
 
     target_dir = os.path.join(extensions.extensions_dir, dirname)
+    print(f'Installing extension: {url} into {target_dir}')
     assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
 
     normalized_url = normalize_git_url(url)
@@ -155,18 +154,14 @@ def install_extension_from_url(dirname, url):
             os.rename(tmpdir, target_dir)
         except OSError as err:
             if err.errno == errno.EXDEV:
-                # Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
-                # Since we can't use a rename, do the slower but more versitile shutil.move()
                 shutil.move(tmpdir, target_dir)
             else:
-                # Something else, not enough free space, permissions, etc.  rethrow it so that it gets handled.
                 raise err
 
         from launch import run_extension_installer
         run_extension_installer(target_dir)
-
         extensions.list_extensions()
-        return [extension_table(), html.escape(f"Installed into {target_dir}. Use Installed tab to restart.")]
+        return [extension_table(), html.escape(f"Installed into {target_dir}")]
     finally:
         shutil.rmtree(tmpdir, True)
 
@@ -290,7 +285,7 @@ def create_ui():
     import modules.ui
 
     with gr.Blocks(analytics_enabled=False) as ui:
-        with gr.Tabs(elem_id="tabs_extensions") as tabs:
+        with gr.Tabs(elem_id="tabs_extensions"):
             with gr.TabItem("Installed"):
 
                 with gr.Row(elem_id="extensions_installed_top"):
@@ -300,14 +295,14 @@ def create_ui():
                     extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
                     extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
 
-                html = ""
+                txt = ""
                 if shared.opts.disable_all_extensions != "none":
-                    html = """
+                    txt = """
 <span style="color: var(--primary-400);">
     "Disable all extensions" was set, change it to "none" to load all extensions again
 </span>
                     """
-                info = gr.HTML(html)
+                info = gr.HTML(txt)
                 extensions_table = gr.HTML(lambda: extension_table())
 
                 apply.click(
@@ -335,9 +330,9 @@ def create_ui():
                     hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
                     sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")
 
-                with gr.Row(): 
+                with gr.Row():
                     search_extensions_text = gr.Text(label="Search").style(container=False)
-                   
+
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()
 

modules/ui_extra_networks.py

@@ -1,15 +1,14 @@
+import json
+import html
 import glob
 import os.path
 import urllib.parse
 from pathlib import Path
 from PIL import PngImagePlugin
+import gradio as gr
 
 from modules import shared
 from modules.images import read_info_from_image
-import gradio as gr
-import json
-import html
-
 from modules.generation_parameters_copypaste import image_from_url_text
 
 extra_pages = []
@@ -317,4 +316,3 @@ def setup_ui(ui, gallery):
         inputs=[ui.preview_target_filename, gallery, ui.preview_target_filename],
         outputs=[*ui.pages]
     )
-

modules/ui_extra_networks_checkpoints.py

@@ -28,4 +28,3 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
 
     def allowed_directories_for_previews(self):
         return [v for v in [shared.opts.ckpt_dir, sd_models.model_path] if v is not None]
-

modules/ui_extra_networks_hypernets.py

@@ -27,4 +27,3 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
 
     def allowed_directories_for_previews(self):
         return [shared.opts.hypernetwork_dir]
-

modules/upscaler.py

@@ -2,11 +2,8 @@ import os
 from abc import abstractmethod
 
 import PIL
-import numpy as np
-import torch
 from PIL import Image
 
-import modules.shared
 from modules import modelloader, shared
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
@@ -27,13 +24,13 @@ class Upscaler:
 
     def __init__(self, create_dirs=False):
         self.mod_pad_h = None
-        self.tile_size = modules.shared.opts.ESRGAN_tile
-        self.tile_pad = modules.shared.opts.ESRGAN_tile_overlap
-        self.device = modules.shared.device
+        self.tile_size = shared.opts.ESRGAN_tile
+        self.tile_pad = shared.opts.ESRGAN_tile_overlap
+        self.device = shared.device
         self.img = None
         self.output = None
         self.scale = 1
-        self.half = not modules.shared.cmd_opts.no_half
+        self.half = not shared.cmd_opts.no_half
         self.pre_pad = 0
         self.mod_scale = None
 

setup.py

@@ -319,19 +319,22 @@ def check_version():
     except ImportError:
         return
     logging.getLogger("urllib3").setLevel(logging.WARNING)
-    commits = requests.get('https://api.github.com/repos/vladmandic/automatic/branches/master', timeout=10).json()
-    if commits['commit']['sha'] != commit:
-        if args.upgrade:
-            update('.')
-            ver = git('log -1 --pretty=format:"%h %ad"')
-            log.info(f'Updated to version: {ver}')
-        else:
-            log.info(f'Latest available version: {commits["commit"]["commit"]["author"]["date"]}')
-    if not args.noupdate:
-        log.info('Updating Wiki')
-        update(os.path.join(os.path.dirname(__file__), "wiki"))
-        update(os.path.join(os.path.dirname(__file__), "wiki", "origin-wiki"))
-
+    commits = None
+    try:
+        commits = requests.get('https://api.github.com/repos/vladmandic/automatic/branches/master', timeout=10).json()
+        if commits['commit']['sha'] != commit:
+            if args.upgrade:
+                update('.')
+                ver = git('log -1 --pretty=format:"%h %ad"')
+                log.info(f'Updated to version: {ver}')
+            else:
+                log.info(f'Latest available version: {commits["commit"]["commit"]["author"]["date"]}')
+        if not args.noupdate:
+            log.info('Updating Wiki')
+            update(os.path.join(os.path.dirname(__file__), "wiki"))
+            update(os.path.join(os.path.dirname(__file__), "wiki", "origin-wiki"))
+    except Exception as e:
+        log.error(f'Failed to check version: {e} {commits}')
 
 # check if we can run setup in quick mode
 def check_timestamp():

ui-config.json

@@ -1013,5 +1013,201 @@
     "train/Drop out tags when creating prompts./maximum": 1,
     "train/Drop out tags when creating prompts./step": 0.1,
     "train/Choose latent sampling method/visible": true,
-    "train/Choose latent sampling method/value": "once"
+    "train/Choose latent sampling method/value": "once",
+    "customscript/model_keyword.py/txt2img/multiplier/visible": true,
+    "customscript/model_keyword.py/txt2img/multiplier/value": 0.7,
+    "customscript/model_keyword.py/txt2img/multiplier/minimum": 0,
+    "customscript/model_keyword.py/txt2img/multiplier/maximum": 2,
+    "customscript/model_keyword.py/txt2img/multiplier/step": 0.01,
+    "txt2img/Keyword(trigger word)/visible": true,
+    "txt2img/Keyword(trigger word)/value": "",
+    "txt2img/result/visible": true,
+    "txt2img/result/value": "",
+    "customscript/model_keyword.py/img2img/multiplier/visible": true,
+    "customscript/model_keyword.py/img2img/multiplier/value": 0.7,
+    "customscript/model_keyword.py/img2img/multiplier/minimum": 0,
+    "customscript/model_keyword.py/img2img/multiplier/maximum": 2,
+    "customscript/model_keyword.py/img2img/multiplier/step": 0.01,
+    "img2img/Keyword(trigger word)/visible": true,
+    "img2img/Keyword(trigger word)/value": "",
+    "img2img/result/visible": true,
+    "img2img/result/value": "",
+    "customscript/additional_networks.py/txt2img/Enable/visible": true,
+    "customscript/additional_networks.py/txt2img/Enable/value": false,
+    "customscript/additional_networks.py/txt2img/Separate UNet/Text Encoder weights/visible": true,
+    "customscript/additional_networks.py/txt2img/Separate UNet/Text Encoder weights/value": false,
+    "customscript/additional_networks.py/txt2img/Network module 1/visible": true,
+    "customscript/additional_networks.py/txt2img/Network module 1/value": "LoRA",
+    "customscript/additional_networks.py/txt2img/Model 1/visible": true,
+    "customscript/additional_networks.py/txt2img/Model 1/value": "None",
+    "txt2img/Weight 1/visible": true,
+    "txt2img/Weight 1/value": 1.0,
+    "txt2img/Weight 1/minimum": -1.0,
+    "txt2img/Weight 1/maximum": 2.0,
+    "txt2img/Weight 1/step": 0.05,
+    "customscript/additional_networks.py/txt2img/UNet Weight 1/value": 1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 1/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 1/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 1/step": 0.05,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 1/value": 1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 1/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 1/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 1/step": 0.05,
+    "customscript/additional_networks.py/txt2img/Network module 2/visible": true,
+    "customscript/additional_networks.py/txt2img/Network module 2/value": "LoRA",
+    "customscript/additional_networks.py/txt2img/Model 2/visible": true,
+    "customscript/additional_networks.py/txt2img/Model 2/value": "None",
+    "txt2img/Weight 2/visible": true,
+    "txt2img/Weight 2/value": 1.0,
+    "txt2img/Weight 2/minimum": -1.0,
+    "txt2img/Weight 2/maximum": 2.0,
+    "txt2img/Weight 2/step": 0.05,
+    "customscript/additional_networks.py/txt2img/UNet Weight 2/value": 1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 2/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 2/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 2/step": 0.05,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 2/value": 1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 2/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 2/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 2/step": 0.05,
+    "customscript/additional_networks.py/txt2img/Network module 3/visible": true,
+    "customscript/additional_networks.py/txt2img/Network module 3/value": "LoRA",
+    "customscript/additional_networks.py/txt2img/Model 3/visible": true,
+    "customscript/additional_networks.py/txt2img/Model 3/value": "None",
+    "txt2img/Weight 3/visible": true,
+    "txt2img/Weight 3/value": 1.0,
+    "txt2img/Weight 3/minimum": -1.0,
+    "txt2img/Weight 3/maximum": 2.0,
+    "txt2img/Weight 3/step": 0.05,
+    "customscript/additional_networks.py/txt2img/UNet Weight 3/value": 1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 3/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 3/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 3/step": 0.05,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 3/value": 1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 3/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 3/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 3/step": 0.05,
+    "customscript/additional_networks.py/txt2img/Network module 4/visible": true,
+    "customscript/additional_networks.py/txt2img/Network module 4/value": "LoRA",
+    "customscript/additional_networks.py/txt2img/Model 4/visible": true,
+    "customscript/additional_networks.py/txt2img/Model 4/value": "None",
+    "txt2img/Weight 4/visible": true,
+    "txt2img/Weight 4/value": 1.0,
+    "txt2img/Weight 4/minimum": -1.0,
+    "txt2img/Weight 4/maximum": 2.0,
+    "txt2img/Weight 4/step": 0.05,
+    "customscript/additional_networks.py/txt2img/UNet Weight 4/value": 1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 4/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 4/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 4/step": 0.05,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 4/value": 1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 4/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 4/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 4/step": 0.05,
+    "customscript/additional_networks.py/txt2img/Network module 5/visible": true,
+    "customscript/additional_networks.py/txt2img/Network module 5/value": "LoRA",
+    "customscript/additional_networks.py/txt2img/Model 5/visible": true,
+    "customscript/additional_networks.py/txt2img/Model 5/value": "None",
+    "txt2img/Weight 5/visible": true,
+    "txt2img/Weight 5/value": 1.0,
+    "txt2img/Weight 5/minimum": -1.0,
+    "txt2img/Weight 5/maximum": 2.0,
+    "txt2img/Weight 5/step": 0.05,
+    "customscript/additional_networks.py/txt2img/UNet Weight 5/value": 1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 5/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 5/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/UNet Weight 5/step": 0.05,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 5/value": 1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 5/minimum": -1.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 5/maximum": 2.0,
+    "customscript/additional_networks.py/txt2img/TEnc Weight 5/step": 0.05,
+    "customscript/additional_networks.py/img2img/Enable/visible": true,
+    "customscript/additional_networks.py/img2img/Enable/value": false,
+    "customscript/additional_networks.py/img2img/Separate UNet/Text Encoder weights/visible": true,
+    "customscript/additional_networks.py/img2img/Separate UNet/Text Encoder weights/value": false,
+    "customscript/additional_networks.py/img2img/Network module 1/visible": true,
+    "customscript/additional_networks.py/img2img/Network module 1/value": "LoRA",
+    "customscript/additional_networks.py/img2img/Model 1/visible": true,
+    "customscript/additional_networks.py/img2img/Model 1/value": "None",
+    "img2img/Weight 1/visible": true,
+    "img2img/Weight 1/value": 1.0,
+    "img2img/Weight 1/minimum": -1.0,
+    "img2img/Weight 1/maximum": 2.0,
+    "img2img/Weight 1/step": 0.05,
+    "customscript/additional_networks.py/img2img/UNet Weight 1/value": 1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 1/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 1/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 1/step": 0.05,
+    "customscript/additional_networks.py/img2img/TEnc Weight 1/value": 1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 1/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 1/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 1/step": 0.05,
+    "customscript/additional_networks.py/img2img/Network module 2/visible": true,
+    "customscript/additional_networks.py/img2img/Network module 2/value": "LoRA",
+    "customscript/additional_networks.py/img2img/Model 2/visible": true,
+    "customscript/additional_networks.py/img2img/Model 2/value": "None",
+    "img2img/Weight 2/visible": true,
+    "img2img/Weight 2/value": 1.0,
+    "img2img/Weight 2/minimum": -1.0,
+    "img2img/Weight 2/maximum": 2.0,
+    "img2img/Weight 2/step": 0.05,
+    "customscript/additional_networks.py/img2img/UNet Weight 2/value": 1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 2/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 2/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 2/step": 0.05,
+    "customscript/additional_networks.py/img2img/TEnc Weight 2/value": 1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 2/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 2/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 2/step": 0.05,
+    "customscript/additional_networks.py/img2img/Network module 3/visible": true,
+    "customscript/additional_networks.py/img2img/Network module 3/value": "LoRA",
+    "customscript/additional_networks.py/img2img/Model 3/visible": true,
+    "customscript/additional_networks.py/img2img/Model 3/value": "None",
+    "img2img/Weight 3/visible": true,
+    "img2img/Weight 3/value": 1.0,
+    "img2img/Weight 3/minimum": -1.0,
+    "img2img/Weight 3/maximum": 2.0,
+    "img2img/Weight 3/step": 0.05,
+    "customscript/additional_networks.py/img2img/UNet Weight 3/value": 1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 3/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 3/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 3/step": 0.05,
+    "customscript/additional_networks.py/img2img/TEnc Weight 3/value": 1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 3/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 3/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 3/step": 0.05,
+    "customscript/additional_networks.py/img2img/Network module 4/visible": true,
+    "customscript/additional_networks.py/img2img/Network module 4/value": "LoRA",
+    "customscript/additional_networks.py/img2img/Model 4/visible": true,
+    "customscript/additional_networks.py/img2img/Model 4/value": "None",
+    "img2img/Weight 4/visible": true,
+    "img2img/Weight 4/value": 1.0,
+    "img2img/Weight 4/minimum": -1.0,
+    "img2img/Weight 4/maximum": 2.0,
+    "img2img/Weight 4/step": 0.05,
+    "customscript/additional_networks.py/img2img/UNet Weight 4/value": 1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 4/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 4/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 4/step": 0.05,
+    "customscript/additional_networks.py/img2img/TEnc Weight 4/value": 1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 4/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 4/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 4/step": 0.05,
+    "customscript/additional_networks.py/img2img/Network module 5/visible": true,
+    "customscript/additional_networks.py/img2img/Network module 5/value": "LoRA",
+    "customscript/additional_networks.py/img2img/Model 5/visible": true,
+    "customscript/additional_networks.py/img2img/Model 5/value": "None",
+    "img2img/Weight 5/visible": true,
+    "img2img/Weight 5/value": 1.0,
+    "img2img/Weight 5/minimum": -1.0,
+    "img2img/Weight 5/maximum": 2.0,
+    "img2img/Weight 5/step": 0.05,
+    "customscript/additional_networks.py/img2img/UNet Weight 5/value": 1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 5/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 5/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/UNet Weight 5/step": 0.05,
+    "customscript/additional_networks.py/img2img/TEnc Weight 5/value": 1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 5/minimum": -1.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 5/maximum": 2.0,
+    "customscript/additional_networks.py/img2img/TEnc Weight 5/step": 0.05
 }

webui.py

@@ -4,12 +4,10 @@ import signal
 import re
 import logging
 import warnings
-
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 from setup import log
-
-from modules import paths, timer, errors
+from modules import timer, errors
 
 errors.install()
 startup_timer = timer.Timer()
@@ -35,7 +33,10 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__:
     torch.__long_version__ = torch.__version__
     torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
 
-from modules import shared, devices, sd_samplers, upscaler, extensions, ui_tempdir, ui_extra_networks
+from modules import shared, extensions, ui_tempdir, ui_extra_networks
+import modules.devices
+import modules.sd_samplers
+import modules.upscaler
 import modules.codeformer_model as codeformer
 import modules.face_restoration
 import modules.gfpgan_model as gfpgan