removed now unused TargetImage method defs

Author: dmarx

src/pytti/LossAug/LatentLossClass.py

@@ -31,31 +31,6 @@ def set_comp(self, pil_image, device=DEVICE):
         self.has_latent = False
         self.direct_loss.set_comp(pil_image.resize(self.image_shape, Image.LANCZOS))
 
-    @classmethod
-    @vram_usage_mode("Latent Image Loss")
-    @torch.no_grad()
-    def TargetImage(
-        cls, prompt_string, image_shape, pil_image=None, is_path=False, device=DEVICE
-    ):
-        text, weight, stop = parse(
-            prompt_string, r"(?<!^http)(?<!s):|:(?!/)", ["", "1", "-inf"]
-        )
-        weight, mask = parse(weight, r"_", ["1", ""])
-        text = text.strip()
-        mask = mask.strip()
-        if pil_image is None and text != "" and is_path:
-            pil_image = Image.open(fetch(text)).convert("RGB")
-        comp = (
-            MSELoss.make_comp(pil_image)
-            if pil_image is not None
-            else torch.zeros(1, 1, 1, 1, device=device)
-        )
-        out = cls(comp, weight, stop, text + " (latent)", image_shape)
-        if pil_image is not None:
-            out.set_comp(pil_image)
-        out.set_mask(mask)
-        return out
-
     def set_mask(self, mask, inverted=False):
         self.direct_loss.set_mask(mask, inverted)
         super().set_mask(mask, inverted)

src/pytti/LossAug/MSELossClass.py

@@ -30,30 +30,6 @@ def __init__(
         self.register_buffer("mask", torch.ones(1, 1, 1, 1, device=self.device))
         self.use_mask = False
 
-    @classmethod
-    @vram_usage_mode("Loss Augs")
-    @torch.no_grad()
-    def TargetImage(
-        cls, prompt_string, image_shape, pil_image=None, is_path=False, device=None
-    ):
-        if device is None:
-            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        text, weight, stop, mask, pil_image = parse_subprompt(
-            prompt_string, is_path=is_path, pil_image=pil_image
-        )
-
-        if pil_image:
-            im = pil_image.resize(image_shape, Image.LANCZOS)
-            comp = cls.make_comp(im)
-        else:
-            comp = torch.zeros(1, 1, 1, 1, device=device)
-
-        if image_shape is None:
-            image_shape = pil_image.size
-        out = cls(comp, weight, stop, text + " (direct)", image_shape, device=device)
-        out.set_mask(mask)
-        return out
-
     @torch.no_grad()
     def set_mask(self, mask, inverted=False, device=None):
         if device is None:

src/pytti/LossAug/__init__.py

@@ -8,10 +8,3 @@
 from pytti.LossAug.EdgeLossClass import EdgeLoss
 from pytti.LossAug.LatentLossClass import LatentLoss
 from pytti.LossAug.HSVLossClass import HSVLoss
-
-# from loguru import logger
-# logger.debug(dir(HSVLoss))
-# logger.debug(help(HSVLoss.TargetImage))
-
-# yeesh the ordering fragility in here...
-# TO DO: let's make this a class attribute on something