diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 252968efa..f77db5eb4 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -261,13 +261,13 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
     if shared.opts.diffusers_move_base and not shared.sd_model.has_accelerate:
         shared.sd_model.to(devices.device)
 
-    is_img2img = (sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING)
-    use_refiner_start = (is_refiner_enabled and not p.is_hr_pass and not is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
-    use_denoise_start = (is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
+    is_img2img = bool(sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING)
+    use_refiner_start = bool(is_refiner_enabled and not p.is_hr_pass and not is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
+    use_denoise_start = bool(is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
 
     def calculate_base_steps():
         if use_refiner_start:
-            return int(p.steps // p.refiner_start + 1)
+            return int(p.steps // p.refiner_start + 1) if shared.sd_model_type == 'sdxl' else p.steps
         elif use_denoise_start and shared.sd_model_type == 'sdxl':
             return int(p.steps // (1 - p.refiner_start))
         elif is_img2img:
@@ -358,13 +358,14 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
 
         if shared.opts.diffusers_move_refiner and not shared.sd_refiner.has_accelerate:
             shared.sd_refiner.to(devices.device)
+        refiner_is_sdxl = bool("StableDiffusionXL" in shared.sd_refiner.__class__.__name__)
         p.ops.append('refine')
         for i in range(len(output.images)):
             refiner_args = set_pipeline_args(
                 model=shared.sd_refiner,
                 prompts=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts[i],
                 negative_prompts=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts[i],
-                num_inference_steps=int(p.refiner_steps // (1 - p.refiner_start)) if p.refiner_start > 0 and p.refiner_start < 1 else int(p.refiner_steps // p.denoising_strength + 1),
+                num_inference_steps=int(p.refiner_steps // (1 - p.refiner_start)) if p.refiner_start > 0 and p.refiner_start < 1 and refiner_is_sdxl else int(p.refiner_steps // p.denoising_strength + 1) if refiner_is_sdxl else p.refiner_steps,
                 eta=shared.opts.eta_ddim,
                 strength=p.denoising_strength,
                 guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,