add some codes for robust

modules/processing.py

@@ -108,6 +108,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
     else:
         sd = sd_model.model.state_dict()
         diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
+        if diffusion_model_input is not None:
             if diffusion_model_input.shape[1] == 9:
                 # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
                 image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
@@ -378,6 +379,7 @@ class StableDiffusionProcessing:
 
         sd = self.sampler.model_wrap.inner_model.model.state_dict()
         diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
+        if diffusion_model_input is not None:
             if diffusion_model_input.shape[1] == 9:
                 return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
 

modules/sd_models_xl.py

@@ -36,6 +36,7 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
 def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
     sd = self.model.state_dict()
     diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
+    if diffusion_model_input is not None:
         if diffusion_model_input.shape[1] == 9:
             x = torch.cat([x] + cond['c_concat'], dim=1)