Bug Fixes

extensions_built_in/sd_trainer/SDTrainer.py

@@ -1086,6 +1086,7 @@ class SDTrainer(BaseSDTrainProcess):
                         is_training=True,
                         has_been_preprocessed=True,
                         quad_count=quad_count,
+                        batch_size=noisy_latents.shape[0]
                     )
 
                 with self.timer('encode_prompt'):

toolkit/custom_adapter.py

@@ -705,10 +705,11 @@ class CustomAdapter(torch.nn.Module):
             is_training=False,
             has_been_preprocessed=False,
             quad_count=4,
+            batch_size=1,
     ) -> PromptEmbeds:
         if self.adapter_type == 'ilora' or self.adapter_type == 'vision_direct' or self.adapter_type == 'te_augmenter':
             if tensors_0_1 is None:
-                tensors_0_1 = self.get_empty_clip_image(1)
+                tensors_0_1 = self.get_empty_clip_image(batch_size)
                 has_been_preprocessed = True
 
             with torch.no_grad():

toolkit/models/ilora.py

@@ -78,7 +78,13 @@ class InstantLoRAMidModule(torch.nn.Module):
             raise e
         # apply tanh to limit values to -1 to 1
         # scaler = torch.tanh(scaler)
-        return x * scaler
+        try:
+            return x * scaler
+        except Exception as e:
+            print(e)
+            print(x.shape)
+            print(scaler.shape)
+            raise e
 
 
 class InstantLoRAModule(torch.nn.Module):

toolkit/stable_diffusion_model.py

@@ -39,7 +39,7 @@ from toolkit.pipelines import CustomStableDiffusionXLPipeline, CustomStableDiffu
     StableDiffusionKDiffusionXLPipeline, StableDiffusionXLRefinerPipeline
 from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, T2IAdapter, DDPMScheduler, \
     StableDiffusionXLAdapterPipeline, StableDiffusionAdapterPipeline, DiffusionPipeline, \
-    StableDiffusionXLImg2ImgPipeline, LCMScheduler, Transformer2DModel, AutoencoderTiny
+    StableDiffusionXLImg2ImgPipeline, LCMScheduler, Transformer2DModel, AutoencoderTiny, ControlNetModel
 import diffusers
 from diffusers import \
     AutoencoderKL, \
@@ -142,7 +142,7 @@ class StableDiffusion:
 
         # to hold network if there is one
         self.network = None
-        self.adapter: Union['T2IAdapter', 'IPAdapter', 'ReferenceAdapter', None] = None
+        self.adapter: Union['ControlNetModel', 'T2IAdapter', 'IPAdapter', 'ReferenceAdapter', None] = None
         self.is_xl = model_config.is_xl
         self.is_v2 = model_config.is_v2
         self.is_ssd = model_config.is_ssd