Apply requested changes

Co-Authored-By: psychedelicious <4822129+psychedelicious@users.noreply.github.com>

invokeai/app/invocations/compel.py

@@ -157,7 +157,15 @@ class CompelInvocation(BaseInvocation):
 
 
 class SDXLPromptInvocationBase:
-    def run_clip_compel(self, context, clip_field, prompt, get_pooled, lora_prefix, zero_on_empty):
+    def run_clip_compel(
+        self,
+        context: InvocationContext,
+        clip_field: ClipField,
+        prompt: str,
+        get_pooled: bool,
+        lora_prefix: str,
+        zero_on_empty: bool,
+    ):
         tokenizer_info = context.services.model_manager.get_model(
             **clip_field.tokenizer.dict(),
             context=context,

invokeai/app/invocations/image.py

@@ -705,12 +705,16 @@ class MaskEdgeInvocation(BaseInvocation, PILInvocationConfig):
 
 
 class ColorCorrectInvocation(BaseInvocation, PILInvocationConfig):
+    """
+    Shifts the colors of a target image to match the reference image, optionally 
+    using a mask to only color-correct certain regions of the target image.
+    """
 
     type: Literal["color_correct"] = "color_correct"
 
-    init: Optional[ImageField] = Field(default=None, description="Initial image")
+    image: Optional[ImageField] = Field(default=None, description="The image to color-correct")
-    result: Optional[ImageField] = Field(default=None, description="Resulted image")
+    reference: Optional[ImageField] = Field(default=None, description="Reference image for color-correction")
-    mask: Optional[ImageField] = Field(default=None, description="Mask image")
+    mask: Optional[ImageField] = Field(default=None, description="Mask to use when applying color-correction")
     mask_blur_radius: float = Field(default=8, description="Mask blur radius")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -721,11 +725,11 @@ class ColorCorrectInvocation(BaseInvocation, PILInvocationConfig):
             ).convert("L")
 
         init_image = context.services.images.get_pil_image(
-            self.init.image_name
+            self.reference.image_name
         )
 
         result = context.services.images.get_pil_image(
-            self.result.image_name
+            self.image.image_name
         ).convert("RGBA")
 
 

invokeai/app/invocations/latent.py

@@ -336,7 +336,9 @@ class TextToLatentsInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         with SilenceWarnings():
             noise = context.services.latents.get(self.noise.latents_name)
-            seed = self.noise.seed or 0
+            seed = self.noise.seed
+            if seed is None:
+                seed = 0
 
             # Get the source node id (we are invoking the prepared node)
             graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
@@ -420,6 +422,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
     # Inputs
     noise: Optional[LatentsField] = Field(description="The noise to use (test override for future optional)")
 
+    # denoising_start = 1 - strength
     denoising_start: float = Field(default=0.0, ge=0, le=1, description="")
     #denoising_end: float = Field(default=1.0, ge=0, le=1, description="")
 
@@ -462,16 +465,23 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         with SilenceWarnings():  # this quenches NSFW nag from diffusers
-            latent = context.services.latents.get(self.latents.latents_name)
+            seed = None
-            seed = self.latents.seed or 0
-
             noise = None
             if self.noise is not None:
                 noise = context.services.latents.get(self.noise.latents_name)
-                if self.noise.seed is not None:
+                seed = self.noise.seed
-                    seed = self.noise.seed
 
-            mask = self.prep_mask_tensor(self.mask, context, latent)
+            if self.latents is not None:
+                latents = context.services.latents.get(self.latents.latents_name)
+                if seed is None:
+                    seed = self.latents.seed
+            else:
+                latents = torch.zeros_like(noise)
+
+            if seed is None:
+                seed = 0
+
+            mask = self.prep_mask_tensor(self.mask, context, latents)
 
             # Get the source node id (we are invoking the prepared node)
             graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
@@ -497,7 +507,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
                 unet_info.context.model, _lora_loader()
             ), unet_info as unet:
-                latent = latent.to(device=unet.device, dtype=unet.dtype)
+                latents = latents.to(device=unet.device, dtype=unet.dtype)
                 if noise is not None:
                     noise = noise.to(device=unet.device, dtype=unet.dtype)
                 if mask is not None:
@@ -516,7 +526,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
                     model=pipeline,
                     context=context,
                     control_input=self.control,
-                    latents_shape=latent.shape,
+                    latents_shape=latents.shape,
                     # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
                     do_classifier_free_guidance=True,
                     exit_stack=exit_stack,
@@ -531,7 +541,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
                 )
 
                 result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
-                    latents=latent,
+                    latents=latents,
                     timesteps=timesteps,
                     noise=noise,
                     seed=seed,

invokeai/app/util/step_callback.py

@@ -58,6 +58,8 @@ def stable_diffusion_step_callback(
     # TODO: only output a preview image when requested
 
     if base_model in [BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner]:
+        # fast latents preview matrix for sdxl
+        # generated by @StAlKeR7779
         sdxl_latent_rgb_factors = torch.tensor(
             [
                 #   R        G        B
@@ -72,9 +74,6 @@ def stable_diffusion_step_callback(
 
         sdxl_smooth_matrix = torch.tensor(
             [
-                # [ 0.0478,  0.1285,  0.0478],
-                # [ 0.1285,  0.2948,  0.1285],
-                # [ 0.0478,  0.1285,  0.0478],
                 [0.0358, 0.0964, 0.0358],
                 [0.0964, 0.4711, 0.0964],
                 [0.0358, 0.0964, 0.0358],