diff --git a/CHANGELOG.md b/CHANGELOG.md
index 16c64afb2..59573f410 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -14,6 +14,8 @@
   - implement img2img and inpainting (experimental)  
     actual support and qualiy depends on model  
     it works as expected for sd 1.5, but not so much for sd-xl for now  
+  - implement limited stop/interrupt for diffusers
+    works between stages, not within steps  
   - add option to save image before refiner pass  
   - option to set vae upcast in settings  
   - enable fp16 vae decode when using optimized vae  
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 4fcf36ac9..726917808 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -112,10 +112,21 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
     )
     output = shared.sd_model(**pipe_args) # pylint: disable=not-callable
 
+    if shared.state.interrupted or shared.state.skipped:
+        return results
+
     if shared.sd_refiner is None or not p.enable_hr:
         output.images = vae_decode(output.images, shared.sd_model)
 
     if shared.sd_refiner is not None and p.enable_hr:
+        for i in range(len(output.images)):
+            if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_refiner and hasattr(shared.sd_model, 'vae'):
+                from modules.processing import create_infotext
+                info=create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, [], iteration=p.iteration, position_in_batch=i)
+                decoded = vae_decode(output.images, shared.sd_model, output_type='pil')
+                for i in range(len(decoded)):
+                    images.save_image(decoded[i], path=p.outpath_samples, basename="", seed=seeds[i], prompt=prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-refiner")
+
         if shared.opts.diffusers_move_base:
             shared.log.debug('Moving base model to CPU')
             shared.sd_model.to('cpu')
@@ -126,18 +137,13 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
                 sampler = sd_samplers.all_samplers_map.get("UniPC")
             sd_samplers.create_sampler(sampler.name, shared.sd_refiner) # TODO(Patrick): For wrapped pipelines this is currently a no-op
 
+        if shared.state.interrupted or shared.state.skipped:
+            return results
+
         shared.sd_refiner.to(devices.device)
         devices.torch_gc()
 
         for i in range(len(output.images)):
-
-            if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_refiner and hasattr(shared.sd_model, 'vae'):
-                from modules.processing import create_infotext
-                info=create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, [], iteration=p.iteration, position_in_batch=i)
-                decoded = vae_decode(output.images, shared.sd_model, output_type='pil')
-                for i in range(len(decoded)):
-                    images.save_image(decoded[i], path=p.outpath_samples, basename="", seed=seeds[i], prompt=prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-refiner")
-
             pipe_args = set_pipeline_args(
                 model=shared.sd_refiner,
                 prompt=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
@@ -154,6 +160,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
                 output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
             )
             output = shared.sd_refiner(**pipe_args) # pylint: disable=not-callable
+            if shared.state.interrupted or shared.state.skipped:
+                return results
+
             output.images = vae_decode(output.images, shared.sd_model)
             results.append(output.images[0])
 
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py
index ca5b55ac6..88b7ec8a5 100644
--- a/scripts/xyz_grid.py
+++ b/scripts/xyz_grid.py
@@ -263,7 +263,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
         shared.state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"
         processed: Processed = cell(x, y, z, ix, iy, iz)
         if processed_result is None:
-            # Use our first processed result object as a template container to hold our full results
             processed_result = copy(processed)
             processed_result.images = [None] * list_size
             processed_result.all_prompts = [None] * list_size
@@ -272,7 +271,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
             processed_result.index_of_first_image = 1
         idx = index(ix, iy, iz)
         if processed.images:
-            # Non-empty list indicates some degree of success.
             processed_result.images[idx] = processed.images[0]
             processed_result.all_prompts[idx] = processed.prompt
             processed_result.all_seeds[idx] = processed.seed
@@ -282,7 +280,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
             cell_size = (processed_result.width, processed_result.height)
             if processed_result.images[0] is not None:
                 cell_mode = processed_result.images[0].mode
-                #This corrects size in case of batches:
                 cell_size = processed_result.images[0].size
             processed_result.images[idx] = Image.new(cell_mode, cell_size)
 
@@ -318,7 +315,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
                         process_cell(x, y, z, ix, iy, iz)
 
     if not processed_result:
-        # Should never happen, I've only seen it on one of four open tabs and it needed to refresh.
         shared.log.error("XYZ grid: Processing could not begin, you may need to refresh the tab or restart the service")
         return Processed(p, [])
     elif not any(processed_result.images):