cleanup

ldm/invoke/generator/img2img.py

@@ -51,9 +51,8 @@ class Img2Img(Generator):
                 img_callback = step_callback,
                 unconditional_guidance_scale=cfg_scale,
                 unconditional_conditioning=uc,
-                init_latent = self.init_latent,
+                init_latent = self.init_latent, # changes how noising is performed in ksampler
                 extra_conditioning_info = extra_conditioning_info
-                # changes how noising is performed in ksampler
             )
 
             return self.sample_to_image(samples)

ldm/invoke/txt2mask.py

@@ -29,9 +29,9 @@ work fine.
 
 import torch
 import numpy as  np
-from models.clipseg import CLIPDensePredT
+from clipseg_models.clipseg import CLIPDensePredT
 from einops import rearrange, repeat
-from PIL import Image
+from PIL import Image, ImageOps
 from torchvision import transforms
 
 CLIP_VERSION = 'ViT-B/16'
@@ -50,9 +50,14 @@ class SegmentedGrayscale(object):
         discrete_heatmap = self.heatmap.lt(threshold).int()
         return self._rescale(Image.fromarray(np.uint8(discrete_heatmap*255),mode='L'))
 
-    def to_transparent(self)->Image:
+    def to_transparent(self,invert:bool=False)->Image:
         transparent_image = self.image.copy()
-        transparent_image.putalpha(self.to_grayscale())
+        gs = self.to_grayscale()
+        # The following line looks like a bug, but isn't.
+        # For img2img, we want the selected regions to be transparent,
+        # but to_grayscale() returns the opposite.
+        gs = ImageOps.invert(gs) if not invert else gs
+        transparent_image.putalpha(gs)
         return transparent_image
 
     # unscales and uncrops the 352x352 heatmap so that it matches the image again
@@ -79,7 +84,7 @@ class Txt2Mask(object):
         self.model.load_state_dict(torch.load(CLIPSEG_WEIGHTS, map_location=torch.device('cpu')), strict=False)
 
     @torch.no_grad()
-    def segment(self, image:Image, prompt:str) -> SegmentedGrayscale:
+    def segment(self, image, prompt:str) -> SegmentedGrayscale:
         '''
         Given a prompt string such as "a bagel", tries to identify the object in the
         provided image and returns a SegmentedGrayscale object in which the brighter
@@ -94,6 +99,10 @@ class Txt2Mask(object):
             transforms.Resize((CLIPSEG_SIZE, CLIPSEG_SIZE)), # must be multiple of 64...
         ])
 
+        if type(image) is str:
+            image = Image.open(image).convert('RGB')
+
+        image = ImageOps.exif_transpose(image)
         img = self._scale_and_crop(image)
         img = transform(img).unsqueeze(0)
 

ldm/models/diffusion/ddim.py

@@ -1,5 +1,4 @@
 """SAMPLING ONLY."""
-from typing import Union
 
 import torch
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
@@ -29,7 +28,7 @@ class DDIMSampler(Sampler):
     def p_sample(
             self,
             x,
-            c: Union[torch.Tensor, list],
+            c,
             t,
             index,
             repeat_noise=False,

ldm/modules/diffusionmodules/model.py

@@ -8,7 +8,7 @@ import numpy as np
 from einops import rearrange
 
 from ldm.util import instantiate_from_config
-#from ldm.modules.attention import LinearAttention
+from ldm.modules.attention import LinearAttention
 
 import psutil
 
@@ -151,10 +151,10 @@ class ResnetBlock(nn.Module):
 
         return x + h
 
-#class LinAttnBlock(LinearAttention):
-#    """to match AttnBlock usage"""
-#    def __init__(self, in_channels):
-#        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
+class LinAttnBlock(LinearAttention):
+    """to match AttnBlock usage"""
+    def __init__(self, in_channels):
+        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
 
 
 class AttnBlock(nn.Module):