Switched to new bucket system that matched sdxl trained buckets. Fixed requirements. Updated embeddings to work with sdxl. Added method to train lora with an embedding at the trigger. Still testing but works amazingly well from what I can see

toolkit/stable_diffusion_model.py

@@ -442,21 +442,25 @@ class StableDiffusion:
         return noise
 
     def get_time_ids_from_latents(self, latents: torch.Tensor):
-        bs, ch, h, w = list(latents.shape)
-
-        height = h * VAE_SCALE_FACTOR
-        width = w * VAE_SCALE_FACTOR
-
-        dtype = latents.dtype
-
         if self.is_xl:
-            prompt_ids = train_tools.get_add_time_ids(
-                height,
-                width,
-                dynamic_crops=False,  # look into this
-                dtype=dtype,
-            ).to(self.device_torch, dtype=dtype)
-            return prompt_ids
+            bs, ch, h, w = list(latents.shape)
+
+            height = h * VAE_SCALE_FACTOR
+            width = w * VAE_SCALE_FACTOR
+
+            dtype = latents.dtype
+            # just do it without any cropping nonsense
+            target_size = (height, width)
+            original_size = (height, width)
+            crops_coords_top_left = (0, 0)
+            add_time_ids = list(original_size + crops_coords_top_left + target_size)
+            add_time_ids = torch.tensor([add_time_ids])
+            add_time_ids = add_time_ids.to(latents.device, dtype=dtype)
+
+            batch_time_ids = torch.cat(
+                [add_time_ids for _ in range(bs)]
+            )
+            return batch_time_ids
         else:
             return None
 
@@ -682,7 +686,7 @@ class StableDiffusion:
         if self.vae.device == 'cpu':
             self.vae.to(self.device)
         latents = latents.to(device, dtype=dtype)
-        latents = latents / 0.18215
+        latents = latents / self.vae.config['scaling_factor']
         images = self.vae.decode(latents).sample
         images = images.to(device, dtype=dtype)