error handling on incompatible batch-sizes

modules/processing_diffusers.py

@@ -298,8 +298,10 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
         clean['generator'] = generator_device
         clean['parser'] = parser
         for k, v in clean.items():
-            if isinstance(v, torch.Tensor) or isinstance(v, np.ndarray) or (isinstance(v, list) and len(v) > 0 and (isinstance(v[0], torch.Tensor) or isinstance(v[0], np.ndarray))):
+            if isinstance(v, torch.Tensor) or isinstance(v, np.ndarray):
                 clean[k] = v.shape
+            if isinstance(v, list) and len(v) > 0 and (isinstance(v[0], torch.Tensor) or isinstance(v[0], np.ndarray)):
+                clean[k] = [x.shape for x in v]
         shared.log.debug(f'Diffuser pipeline: {model.__class__.__name__} task={sd_models.get_diffusers_task(model)} set={clean}')
         if p.hdr_clamp or p.hdr_maximize or p.hdr_brightness != 0 or p.hdr_color != 0 or p.hdr_sharpen != 0:
             txt = 'HDR:'

scripts/differential_diffusion.py

@@ -1970,6 +1970,9 @@ class Script(scripts.Script):
             p.task_args['map'] = image_map
             if shared.sd_model_type == 'sdxl':
                 p.task_args['original_image'] = image_init
+            if p.batch_size > 1:
+                shared.log.warning(f'Differential-diffusion: batch-size={p.batch_size} parallel processing not supported')
+                p.batch_size = 1
             shared.log.debug(f'Differential-diffusion: pipeline={pipe.__class__.__name__} strength={strength} model={model} auto={image is None}')
             shared.sd_model = pipe
             sd_models.move_model(pipe.vae, devices.device, force=True)