Merge branch 'main' into build/no-actions-on-draft

ldm/invoke/CLI.py

@@ -786,8 +786,8 @@ def _get_model_name(existing_names,completer,default_name:str='')->str:
         model_name = input(f'Short name for this model [{default_name}]: ').strip()
         if len(model_name)==0:
             model_name = default_name
-        if not re.match('^[\w._+-]+$',model_name):
-            print('** model name must contain only words, digits and the characters "._+-" **')
+        if not re.match('^[\w._+:/-]+$',model_name):
+            print('** model name must contain only words, digits and the characters "._+:/-" **')
         elif model_name != default_name and model_name in existing_names:
             print(f'** the name {model_name} is already in use. Pick another.')
         else:

ldm/invoke/generator/diffusers_pipeline.py

@@ -544,6 +544,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
 
         init_image = init_image.to(device=device, dtype=latents_dtype)
+        mask = mask.to(device=device, dtype=latents_dtype)
 
         if init_image.dim() == 3:
             init_image = init_image.unsqueeze(0)
@@ -562,17 +563,22 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         if mask.dim() == 3:
             mask = mask.unsqueeze(0)
-        mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR) \
+        latent_mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR) \
             .to(device=device, dtype=latents_dtype)
 
         guidance: List[Callable] = []
 
         if is_inpainting_model(self.unet):
+            # You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
+            # (that's why there's a mask!) but it seems to really want that blanked out.
+            masked_init_image = init_image * torch.where(mask < 0.5, 1, 0)
+            masked_latents = self.non_noised_latents_from_image(masked_init_image, device=device, dtype=latents_dtype)
+
             # TODO: we should probably pass this in so we don't have to try/finally around setting it.
             self.invokeai_diffuser.model_forward_callback = \
-                AddsMaskLatents(self._unet_forward, mask, init_image_latents)
+                AddsMaskLatents(self._unet_forward, latent_mask, masked_latents)
         else:
-            guidance.append(AddsMaskGuidance(mask, init_image_latents, self.scheduler, noise))
+            guidance.append(AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise))
 
         try:
             result_latents, result_attention_maps = self.latents_from_embeddings(

ldm/invoke/generator/txt2img2img.py

@@ -3,10 +3,10 @@ ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
 '''
 
 import math
-from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
 from typing import Callable, Optional
 
 import torch
+from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
 
 from ldm.invoke.generator.base import Generator
 from ldm.invoke.generator.diffusers_pipeline import trim_to_multiple_of, StableDiffusionGeneratorPipeline, \
@@ -128,18 +128,13 @@ class Txt2Img2Img(Generator):
             scaled_width = width
             scaled_height = height
 
-        device      = self.model.device
+        device = self.model.device
+        channels = self.latent_channels
+        if channels == 9:
+            channels = 4  # we don't really want noise for all the mask channels
+        shape = (1, channels,
+                 scaled_height // self.downsampling_factor, scaled_width // self.downsampling_factor)
         if self.use_mps_noise or device.type == 'mps':
-            return torch.randn([1,
-                                self.latent_channels,
-                                scaled_height // self.downsampling_factor,
-                                scaled_width  // self.downsampling_factor],
-                               dtype=self.torch_dtype(),
-                               device='cpu').to(device)
+            return torch.randn(shape, dtype=self.torch_dtype(), device='cpu').to(device)
         else:
-            return torch.randn([1,
-                                self.latent_channels,
-                                scaled_height // self.downsampling_factor,
-                                scaled_width  // self.downsampling_factor],
-                               dtype=self.torch_dtype(),
-                               device=device)
+            return torch.randn(shape, dtype=self.torch_dtype(), device=device)

ldm/invoke/model_manager.py

@@ -125,7 +125,7 @@ class ModelManager(object):
         Set the default model. The change will not take
         effect until you call model_manager.commit()
         '''
-        assert model_name in self.models,f"unknown model '{model_name}'"
+        assert model_name in self.model_names(), f"unknown model '{model_name}'"
 
         config = self.config
         for model in config: