lint(ldm.invoke.generator): 🚮 remove unused imports

ldm/invoke/generator/base.py

@@ -2,17 +2,19 @@
 Base class for ldm.invoke.generator.*
 including img2img, txt2img, and inpaint
 '''
-import torch
-import numpy as  np
-import random
 import os
 import os.path as osp
+import random
 import traceback
-from tqdm import tqdm, trange
+
+import numpy as np
+import torch
 from PIL import Image, ImageFilter, ImageChops
 import cv2 as cv
-from einops import rearrange, repeat
+from einops import rearrange
 from pytorch_lightning import seed_everything
+from tqdm import trange
+
 from ldm.invoke.devices import choose_autocast
 from ldm.util import rand_perlin_2d
 

ldm/invoke/generator/embiggen.py

@@ -3,15 +3,17 @@ ldm.invoke.generator.embiggen descends from ldm.invoke.generator
 and generates with ldm.invoke.generator.img2img
 '''
 
-import torch
 import numpy as np
-from tqdm import trange
+import torch
 from PIL import Image
+from tqdm import trange
+
+from ldm.invoke.devices import choose_autocast
 from ldm.invoke.generator.base import Generator
 from ldm.invoke.generator.img2img import Img2Img
-from ldm.invoke.devices import choose_autocast
 from ldm.models.diffusion.ddim import DDIMSampler
 
+
 class Embiggen(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
@@ -493,7 +495,7 @@ class Embiggen(Generator):
                     # Layer tile onto final image
                     outputsuperimage.alpha_composite(intileimage, (left, top))
             else:
-                print(f'Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation.')
+                print('Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation.')
 
             # after internal loops and patching up return Embiggen image
             return outputsuperimage

ldm/invoke/generator/img2img.py

@@ -2,15 +2,15 @@
 ldm.invoke.generator.img2img descends from ldm.invoke.generator
 '''
 
-import torch
-import numpy as  np
 import PIL
-from torch import Tensor
+import numpy as np
+import torch
 from PIL import Image
+from torch import Tensor
+
 from ldm.invoke.devices import choose_autocast
 from ldm.invoke.generator.base import Generator
-from ldm.models.diffusion.ddim import DDIMSampler
+
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 
 class Img2Img(Generator):
     def __init__(self, model, precision):

ldm/invoke/generator/inpaint.py

@@ -3,21 +3,21 @@ ldm.invoke.generator.inpaint descends from ldm.invoke.generator
 '''
 
 import math
-import torch
+
-import torchvision.transforms as T
-import numpy as  np
-import cv2 as cv
 import PIL
+import cv2 as cv
+import numpy as np
+import torch
 from PIL import Image, ImageFilter, ImageOps, ImageChops
-from skimage.exposure.histogram_matching import match_histograms
+from einops import repeat
-from einops import rearrange, repeat
+
 from ldm.invoke.devices import choose_autocast
+from ldm.invoke.generator.base import downsampling
 from ldm.invoke.generator.img2img import Img2Img
+from ldm.invoke.globals import Globals
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.ksampler import KSampler
-from ldm.invoke.generator.base import downsampling
 from ldm.util import debug_image
-from ldm.invoke.globals import Globals
 
 infill_methods: list[str] = list()
 
@@ -251,7 +251,7 @@ class Inpaint(Img2Img):
         # klms samplers not supported yet, so ignore previous sampler
         if isinstance(sampler,KSampler):
             print(
-                f">> Using recommended DDIM sampler for inpainting."
+                ">> Using recommended DDIM sampler for inpainting."
             )
             sampler = DDIMSampler(self.model, device=self.model.device)
 

ldm/invoke/generator/omnibus.py

@@ -1,14 +1,14 @@
 """omnibus module to be used with the runwayml 9-channel custom inpainting model"""
 
 import torch
-import numpy as  np
+from PIL import Image, ImageOps
 from einops import repeat
-from PIL import Image, ImageOps, ImageChops
+
 from ldm.invoke.devices import choose_autocast
-from ldm.invoke.generator.base import downsampling
 from ldm.invoke.generator.img2img import Img2Img
 from ldm.invoke.generator.txt2img import Txt2Img
 
+
 class Omnibus(Img2Img,Txt2Img):
     def __init__(self, model, precision):
         super().__init__(model, precision)
@@ -58,8 +58,6 @@ class Omnibus(Img2Img,Txt2Img):
 
         self.mask_blur_radius = mask_blur_radius
 
-        t_enc = steps
-
         if init_image is not None and mask_image is not None: # inpainting
             masked_image = init_image * (1 - mask_image)  # masked image is the image masked by mask - masked regions zero
 

ldm/invoke/generator/txt2img.py

@@ -3,9 +3,8 @@ ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
 '''
 
 import torch
-import numpy as  np
+
 from ldm.invoke.generator.base import Generator
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 
 
 class Txt2Img(Generator):

ldm/invoke/generator/txt2img2img.py

@@ -2,15 +2,16 @@
 ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
 '''
 
-import torch
-import numpy as  np
 import math
-from ldm.invoke.generator.base import Generator
+
-from ldm.models.diffusion.ddim import DDIMSampler
+import torch
-from ldm.invoke.generator.omnibus import Omnibus
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from PIL import Image
 
+from ldm.invoke.generator.base import Generator
+from ldm.invoke.generator.omnibus import Omnibus
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
 class Txt2Img2Img(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)