diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4733ce97a..4d7babf93 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,6 +3,7 @@
 ## Update for 2023-08-31
 
 - diffusers:
+  - ability to interrupt (stop/skip) model generate  
   - add `diffusers_force_zeros` setting  
     create zero-tensor for prompt if prompt is empty (positive or negative)  
   - add `diffusers_aesthetics_score` setting  
diff --git a/README.md b/README.md
index 4a7e8f5c7..3b4ac2972 100644
--- a/README.md
+++ b/README.md
@@ -48,7 +48,6 @@ All Individual features are not listed here, instead check [ChangeLog](CHANGELOG
   This is the default backend and it is fully compatible with all existing functionality and extensions  
 - **Diffusers**: Based on new [Huggingface Diffusers](https://huggingface.co/docs/diffusers/index) implementation  
   It is also the only backend that supports **Stable Diffusion XL** model  
-  Support for legacy workflows and extensions is limited, but it's being expanded  
   See [wiki article](https://github.com/vladmandic/automatic/wiki/Diffusers) for more information  
 
 ## Model support
@@ -57,9 +56,10 @@ Additional models will be added as they become available and there is public int
 
 - Stable Diffusion 1.x and 2.x including all variants
 - Stable Diffusion XL
-- Kandinsky 2.1
+- Kandinsky 2.1 and 2.2
 - DeepFloyd IF
 - UniDiffusion
+- SD-Distilled (all variants)
 
 ## Platform support
 
diff --git a/modules/lora_diffusers.py b/modules/lora_diffusers.py
index b2fe8fc00..59bf57b24 100644
--- a/modules/lora_diffusers.py
+++ b/modules/lora_diffusers.py
@@ -45,7 +45,6 @@ def load_diffusers_lora(name, lora, strength = 1.0):
         lora_state['multiplier'].append(strength)
         if shared.opts.diffusers_lora_loader == "diffusers default":
             pipe.load_lora_weights(lora.filename, cache_dir=shared.opts.diffusers_dir, local_files_only=True, lora_scale=strength)
-            shared.log.info(f"LoRA loaded: {name} {lora_state['multiplier']}")
         else:
             from safetensors.torch import load_file
             lora_sd = load_file(lora.filename)
@@ -61,9 +60,9 @@ def load_diffusers_lora(name, lora, strength = 1.0):
                 lora_network.to(shared.device, dtype=pipe.unet.dtype)
                 lora_network.apply_to(multiplier=strength)
             lora_state['all_loras'].append(lora_network)
-            shared.log.info(f"LoRA loaded: {name}:{strength} loader={shared.opts.diffusers_lora_loader}")
+        shared.log.info(f"LoRA loaded: {name} strength={strength} loader={shared.opts.diffusers_lora_loader}")
     except Exception as e:
-        shared.log.error(f"Diffusers LoRA loading failed: {name} {e}")
+        shared.log.error(f"LoRA loading failed: {name} {e}")
 
 
 # Diffusersで動くLoRA。このファイル単独で完結する。
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index d021dfff1..87c142cb5 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -52,6 +52,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
         if p.is_hr_pass:
             shared.state.sampling_steps += p.hr_second_pass_steps
         shared.state.current_latent = latents
+        if shared.state.interrupted or shared.state.skipped:
+            raise AssertionError('Interrupted...')
 
     def full_vae_decode(latents, model):
         shared.log.debug(f'VAE decode: name={sd_vae.loaded_vae_file if sd_vae.loaded_vae_file is not None else "baked"} dtype={model.vae.dtype} upcast={model.vae.config.get("force_upcast", None)} images={latents.shape[0]}')
@@ -162,8 +164,6 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
             args['callback_steps'] = 1
         if 'callback' in possible:
             args['callback'] = diffusers_callback
-        if 'cross_attention_kwargs' in possible and lora_state['active'] and shared.opts.diffusers_lora_loader == "diffusers default":
-            args['cross_attention_kwargs'] = { 'scale': lora_state['multiplier'][0]}
         for arg in kwargs:
             if arg in possible:
                 args[arg] = kwargs[arg]
@@ -284,7 +284,10 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
     )
     p.extra_generation_params['CFG rescale'] = p.diffusers_guidance_rescale
     p.extra_generation_params["Eta DDIM"] = shared.opts.eta_ddim if shared.opts.eta_ddim is not None and shared.opts.eta_ddim > 0 else None
-    output = shared.sd_model(**base_args) # pylint: disable=not-callable
+    try:
+        output = shared.sd_model(**base_args) # pylint: disable=not-callable
+    except AssertionError as e:
+        shared.log.info(e)
 
     if lora_state['active']:
         p.extra_generation_params['LoRA method'] = shared.opts.diffusers_lora_loader
@@ -320,7 +323,10 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
                 strength=p.denoising_strength,
                 desc='Hires',
             )
-            output = shared.sd_model(**hires_args) # pylint: disable=not-callable
+            try:
+                output = shared.sd_model(**hires_args) # pylint: disable=not-callable
+            except AssertionError as e:
+                shared.log.info(e)
 
     # optional refiner pass or decode
     if is_refiner_enabled:
@@ -361,7 +367,11 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
                 clip_skip=p.clip_skip,
                 desc='Refiner',
             )
-            refiner_output = shared.sd_refiner(**refiner_args) # pylint: disable=not-callable
+            try:
+                refiner_output = shared.sd_refiner(**refiner_args) # pylint: disable=not-callable
+            except AssertionError as e:
+                shared.log.info(e)
+
             p.extra_generation_params['Image CFG scale'] = p.image_cfg_scale if p.image_cfg_scale is not None else None
             p.extra_generation_params['Refiner steps'] = p.refiner_steps
             p.extra_generation_params['Refiner start'] = p.refiner_start