diff --git a/CHANGELOG.md b/CHANGELOG.md
index a79f2d3a5..6a2da8074 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -28,6 +28,14 @@
   - download using networks -> reference
   - see [wiki](https://github.com/vladmandic/automatic/wiki/Stable-Cascade) for details
   - currently requires 10GB VRAM, lighter version is in development
+- [LEdit++](https://leditsplusplus-project.static.hf.space/index.html)
+  - context aware img2img method with image analysis and positive/negative prompt handling  
+  - enable via img2img -> scripts -> ledit
+  - uses following params from standard img2img: cfg scale (recommended ~3), steps (recommended ~50), denoise strength (recommended ~0.7)
+  - can use postive and/or negative prompt to guide editing process
+    - positive prompt: what to enhance, strength and threshold for auto-masking
+    - negative prompt: what to remove, strength and threshold for auto-masking  
+  - *note*: not compatible with model offloading
 - **Visual Query** visual query & answer in process tab  
   - go to process -> visual query  
   - ask your questions, e.g. "describe the image", "what is behind the subject", "what are predominant colors of the image?"
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 0c785cbb1..8638bd83e 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -89,7 +89,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
                     ip_adapter_scales[i] *= float(step <= pipe.num_timesteps * ip_adapter_ends[i])
                     debug(f"Callback: IP Adapter scales={ip_adapter_scales}")
                 pipe.set_ip_adapter_scale(ip_adapter_scales)
-        if step != pipe.num_timesteps:
+        if step != getattr(pipe, 'num_timesteps', 0):
             kwargs = processing_correction.correction_callback(p, timestep, kwargs)
         if p.scheduled_prompt and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
             try:
@@ -99,7 +99,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
                 kwargs["negative_prompt_embeds"] = p.negative_embeds[j][0:1].expand(kwargs["negative_prompt_embeds"].shape)
             except Exception as e:
                 shared.log.debug(f"Callback: {e}")
-        if step == int(pipe.num_timesteps * p.cfg_end) and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
+        if step == int(getattr(pipe, 'num_timesteps', 100) * p.cfg_end) and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
             pipe._guidance_scale = 0.0 # pylint: disable=protected-access
             for key in {"prompt_embeds", "negative_prompt_embeds", "add_text_embeds", "add_time_ids"} & set(kwargs):
                 kwargs[key] = kwargs[key].chunk(2)[-1]
@@ -316,8 +316,6 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
 
     def update_sampler(sd_model, second_pass=False):
         sampler_selection = p.hr_sampler_name if second_pass else p.sampler_name
-        if sd_model.__class__.__name__ in ['AmusedPipeline']:
-            return # models with their own schedulers
         if hasattr(sd_model, 'scheduler') and sampler_selection != 'Default':
             sampler = sd_samplers.all_samplers_map.get(sampler_selection, None)
             if sampler is None:
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 381ec37bf..91fc06168 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1136,9 +1136,9 @@ class DiffusersTaskType(Enum):
 
 
 def get_diffusers_task(pipe: diffusers.DiffusionPipeline) -> DiffusersTaskType:
-    if pipe.__class__.__name__ == "StableVideoDiffusionPipeline":
+    if pipe.__class__.__name__ in ["StableVideoDiffusionPipeline", "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL"]:
         return DiffusersTaskType.IMAGE_2_IMAGE
-    if pipe.__class__.__name__ == "StableDiffusionXLInstructPix2PixPipeline":
+    elif pipe.__class__.__name__ == "StableDiffusionXLInstructPix2PixPipeline":
         return DiffusersTaskType.INSTRUCT
     elif pipe.__class__ in diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING.values():
         return DiffusersTaskType.IMAGE_2_IMAGE
diff --git a/scripts/ledits.py b/scripts/ledits.py
new file mode 100644
index 000000000..b87bb0fc1
--- /dev/null
+++ b/scripts/ledits.py
@@ -0,0 +1,101 @@
+import diffusers
+import gradio as gr
+from modules import scripts, processing, shared, devices, sd_models
+
+
+class Script(scripts.Script):
+    def title(self):
+        return 'LEdits++'
+
+    def show(self, is_img2img):
+        return is_img2img if shared.backend == shared.Backend.DIFFUSERS else False
+
+    # return signature is array of gradio components
+    def ui(self, _is_img2img):
+        with gr.Row():
+            gr.HTML('<a href="https://leditsplusplus-project.static.hf.space/index.html">&nbsp LEdits++</a><br>')
+        with gr.Row():
+            edit_start = gr.Slider(label='Edit start', minimum=0.0, maximum=1.0, step=0.01, value=0.1)
+            edit_stop = gr.Slider(label='Edit stop', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
+            intersect_mask = gr.Checkbox(label='Smooth mask', value=True)
+        with gr.Row():
+            prompt1 = gr.Textbox(show_label=False, placeholder='Positive prompt')
+            scale1 = gr.Slider(label='Scale', minimum=0.0, maximum=1.0, step=0.01, value=0.5)
+            threshold1 = gr.Slider(label='Threshold', minimum=0.0, maximum=1.0, step=0.01, value=0.9)
+        with gr.Row():
+            prompt2 = gr.Textbox(show_label=False, placeholder='Negative prompt')
+            scale2 = gr.Slider(label='Scale', minimum=0.0, maximum=1.0, step=0.01, value=0.5)
+            threshold2 = gr.Slider(label='Threshold', minimum=0.0, maximum=1.0, step=0.01, value=0.9)
+        return [edit_start, edit_stop, intersect_mask, prompt1, scale1, threshold1, prompt2, scale2, threshold2]
+
+    def run(self, p: processing.StableDiffusionProcessing, edit_start, edit_stop, intersect_mask, prompt1, scale1, threshold1, prompt2, scale2, threshold2): # pylint: disable=arguments-differ, unused-argument
+        image = getattr(p, 'init_images', None)
+        if len(prompt1) == 0 and len(prompt2) == 0:
+            shared.log.error('LEdits: no prompts')
+            return None
+        if image is None or len(image) == 0:
+            shared.log.error('LEdits: no init_images')
+            return None
+        else:
+            image = image[0]
+        if shared.sd_model_type != 'sd' and shared.sd_model_type != 'sdxl':
+            shared.log.error(f'LEdits: invalid model type: {shared.sd_model_type}')
+            return None
+
+        orig_pipeline = shared.sd_model
+        orig_offload = shared.opts.diffusers_model_cpu_offload
+        orig_prompt_attention = shared.opts.prompt_attention
+        shared.opts.data['diffusers_model_cpu_offload'] = False
+        shared.opts.data['prompt_attention'] = 'Fixed attention'
+        # shared.sd_model.maybe_free_model_hooks() # ledits is not compatible with offloading
+        # shared.sd_model.has_accelerate = False
+        sd_models.move_model(shared.sd_model, devices.device, force=True)
+        if shared.sd_model_type == 'sd':
+            shared.sd_model = sd_models.switch_pipe(diffusers.LEditsPPPipelineStableDiffusion, shared.sd_model)
+        elif shared.sd_model_type == 'sdxl':
+            shared.sd_model = sd_models.switch_pipe(diffusers.LEditsPPPipelineStableDiffusionXL, shared.sd_model)
+        if str(devices.dtype) == 'torch.float16':
+            shared.sd_model.vae.config.force_upcast = False # not compatible
+
+        shared.sd_model.scheduler = diffusers.DPMSolverMultistepScheduler.from_config(shared.sd_model.scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2) # ledits is very picky
+        p.sampler_name = 'Default'
+        invert_args = {
+            'image': image,
+            'source_prompt': p.prompt,
+            'source_guidance_scale': p.cfg_scale,
+            'num_inversion_steps': p.steps,
+            'skip': 1.0 - p.denoising_strength, # invert start
+            'generator': None, # not supported
+        }
+        shared.log.info(f'LEdits invert: {invert_args}')
+        _output = shared.sd_model.invert(**invert_args)
+        p.task_args = {
+            'editing_prompt': [],
+            'reverse_editing_direction': [],
+            'edit_guidance_scale': [],
+            'edit_threshold': [],
+            'edit_warmup_steps': int(edit_start * p.steps),
+            'edit_cooldown_steps': int((1.0 - edit_stop) * p.steps) if edit_stop < 1.0 else None,
+            'use_intersect_mask': intersect_mask, # smoothing?
+            'generator': None,
+            'guidance_rescale': 0.0, # bug in pipeline if guidance rescale is enabled
+        }
+        if len(prompt1) > 0:
+            p.task_args['editing_prompt'].append(prompt1)
+            p.task_args['reverse_editing_direction'].append(False)
+            p.task_args['edit_guidance_scale'].append(10.0 * scale1)
+            p.task_args['edit_threshold'].append(threshold1)
+        if len(prompt2) > 0:
+            p.task_args['editing_prompt'].append(prompt2)
+            p.task_args['reverse_editing_direction'].append(True)
+            p.task_args['edit_guidance_scale'].append(10.0 * scale2)
+            p.task_args['edit_threshold'].append(threshold2)
+
+        shared.log.info(f'LEdits: {p.task_args}')
+        processed = processing.process_images(p)
+
+        # restore pipeline
+        shared.sd_model = orig_pipeline
+        shared.opts.data['prompt_attention'] = orig_prompt_attention
+        shared.opts.data['diffusers_model_cpu_offload'] = orig_offload
+        return processed