diff --git a/modules/dml/hijack/tomesd.py b/modules/dml/hijack/tomesd.py
index ff7de1a38..79de721df 100644
--- a/modules/dml/hijack/tomesd.py
+++ b/modules/dml/hijack/tomesd.py
@@ -1,8 +1,8 @@
 from typing import Type
 import torch
-import tomesd
 from modules.dml.hijack.utils import catch_nan
 
+
 def make_tome_block(block_class: Type[torch.nn.Module]) -> Type[torch.nn.Module]:
     class ToMeBlock(block_class):
         # Save for unpatching later
@@ -19,4 +19,9 @@ def make_tome_block(block_class: Type[torch.nn.Module]) -> Type[torch.nn.Module]
             return x
 
     return ToMeBlock
-tomesd.patch.make_tome_block = make_tome_block
+
+try:
+    import tomesd
+    tomesd.patch.make_tome_block = make_tome_block
+except Exception:
+    pass
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 2445e24ff..93b5d3dd1 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -247,8 +247,8 @@ def parse_generation_parameters(infotext, no_prompt=False):
         else:
             params[k] = v
     if not no_prompt:
-        params["Prompt"] = prompt.replace('Prompt:', '').strip(' ,')
-        params["Negative prompt"] = negative.replace('Negative prompt:', '').strip(' ,')
+        params["Prompt"] = prompt.replace('Prompt:', '').strip(' ,\n')
+        params["Negative prompt"] = negative.replace('Negative prompt:', '').strip(' ,\n')
     debug(f"Parse: {params}")
     return params
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 1dd6a6abb..e70ff4a47 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1620,6 +1620,8 @@ def apply_token_merging(sd_model):
             shared.log.warning('Token merging not supported with HyperTile for UNet')
             return
         try:
+            import installer
+            installer.install('tomesd', 'tomesd', ignore=False)
             import tomesd
             tomesd.apply_patch(
                 sd_model,
diff --git a/requirements.txt b/requirements.txt
index 200f43a19..7fdd72093 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -57,8 +57,7 @@ pandas
 protobuf==3.20.3
 pytorch_lightning==1.9.4
 tokenizers==0.19.1
-transformers==4.40.1
-tomesd==0.1.3
+transformers==4.40.2
 urllib3==1.26.18
 Pillow==10.3.0
 timm==0.9.16
diff --git a/scripts/mulan.py b/scripts/mulan.py
new file mode 100644
index 000000000..5c658f450
--- /dev/null
+++ b/scripts/mulan.py
@@ -0,0 +1,138 @@
+# https://github.com/mulanai/MuLan
+# https://huggingface.co/mulanai/mulan-lang-adapter
+# https://huggingface.co/OpenGVLab/InternVL-14B-224px
+
+"""
+- [MuLan](https://github.com/mulanai/MuLan) Multi-langunage prompts - wirte your prompts in ~110 auto-detected languages!  
+  Compatible with SD15 and SDXL  
+  Enable in scripts -> MuLan and set encoder to `InternVL-14B-224px` encoder  
+  (that is currently only supported encoder, but others will be added)
+  Note: Model will be auto-downloaded on first use: note its huge size of 27GB  
+  Even executing it in FP16 context will require ~16GB of VRAM for text encoder alone  
+  *Note*: Uses fixed prompt parser, so no prompt attention will be used  
+
+Examples:
+- English: photo of a beautiful woman wearing a white bikini on a beach with a city skyline in the background
+- Croatian: fotografija lijepe žene u bijelom bikiniju na plaži s gradskim obzorom u pozadini
+- Italian: Foto di una bella donna che indossa un bikini bianco su una spiaggia con lo skyline di una città sullo sfondo
+- Spanish: Foto de una hermosa mujer con un bikini blanco en una playa con un horizonte de la ciudad en el fondo
+- German: Foto einer schönen Frau in einem weißen Bikini an einem Strand mit einer Skyline der Stadt im Hintergrund
+- Arabic: صورة لامرأة جميلة ترتدي بيكيني أبيض على شاطئ مع أفق المدينة في الخلفية
+- Japanese: 街のスカイラインを背景にビーチで白いビキニを着た美しい女性の写真
+- Chinese: 一个美丽的女人在海滩上穿着白色比基尼的照片，背景是城市天际线
+- Korean: 도시의 스카이라인을 배경으로 해변에서 흰색 비키니를 입은 아름 다운 여성의 사진
+"""
+
+import gradio as gr
+from modules import shared, scripts, processing, devices
+
+
+ENCODERS =[
+    # 'None',
+    'OpenGVLab/InternVL-14B-224px',
+    # 'OpenGVLab/InternViT-6B-224px',
+    # 'OpenGVLab/InternViT-6B-448px-V1-0',
+    # 'OpenGVLab/InternViT-6B-448px-V1-2',
+    # 'OpenGVLab/InternViT-6B-448px-V1-5',
+]
+GITPATH = 'git+https://github.com/mulanai/MuLan'
+
+pipe_type = None
+adapter = None
+text_encoder = None
+tokenizer = None
+text_encoder_path = None
+
+
+class Script(scripts.Script):
+    def title(self):
+        return 'MuLan'
+
+    def show(self, is_img2img):
+        if shared.cmd_opts.experimental:
+            return True if shared.backend == shared.Backend.DIFFUSERS else False
+        else:
+            return False
+
+    def ui(self, _is_img2img):
+        with gr.Row():
+            gr.HTML('<a href="https://github.com/mulanai/MuLan">&nbsp MuLan</a><br>')
+        with gr.Row():
+            selected_encoder = gr.Dropdown(label='Encoder', choices=ENCODERS, value=ENCODERS[0])
+        return [selected_encoder]
+
+    def run(self, p: processing.StableDiffusionProcessing, selected_encoder): # pylint: disable=arguments-differ
+        global pipe_type, adapter, text_encoder, tokenizer, text_encoder_path # pylint: disable=global-statement
+        if not selected_encoder or selected_encoder == 'None':
+            return
+        # create pipeline
+        if shared.sd_model_type != 'sd' and shared.sd_model_type != 'sdxl':
+            shared.log.error(f'MuLan: incorrect base model: {shared.sd_model.__class__.__name__}')
+            return
+
+        adapter_path = None
+        if shared.sd_model_type == 'sd':
+            adapter_path = 'mulanai/mulan-lang-adapter::sd15_aesthetic.pth'
+        if shared.sd_model_type == 'sdxl':
+            adapter_path = 'mulanai/mulan-lang-adapter::sdxl_aesthetic.pth'
+        if adapter_path is None:
+            return
+
+        # install-on-demand
+        import installer
+        installer.install(GITPATH, 'mulankit')
+        import mulankit
+
+        # backup pipeline and params
+        orig_pipeline = shared.sd_model
+        orig_prompt_attention = shared.opts.prompt_attention
+
+        # mulan only works with single image, single prompt and in fixed attention
+        p.batch_size = 1
+        p.n_iter = 1
+        shared.opts.prompt_attention = 'Fixed attention'
+        if isinstance(p.prompt, list):
+            p.prompt = p.prompt[0]
+        p.task_args['prompt'] = p.prompt
+        if isinstance(p.negative_prompt, list):
+            p.prompt = p.negative_prompt[0]
+        p.task_args['negative_prompt'] = p.negative_prompt
+
+        if pipe_type != ('sd15' if shared.sd_model_type == 'sd' else 'sdxl'):
+            pipe_type = 'sd15' if shared.sd_model_type == 'sd' else 'sdxl'
+            adapter = None
+        if text_encoder is None or tokenizer is None or text_encoder_path != selected_encoder:
+            text_encoder_path = selected_encoder
+            shared.log.debug(f'MuLan loading: encoder="{text_encoder_path}"')
+            text_encoder = None
+            tokenizer = None
+            devices.torch_gc(force=True)
+            text_encoder, tokenizer = mulankit.api.load_internvl(text_encoder_path, text_encoder, tokenizer, torch_dtype=shared.sd_model.text_encoder.dtype)
+            devices.torch_gc(force=True)
+        if adapter is None:
+            shared.log.debug(f'MuLan loading: adapter="{adapter_path}"')
+            adapter = None
+            devices.torch_gc(force=True)
+            adapter = mulankit.api.load_adapter(adapter_path, type=pipe_type)
+            devices.torch_gc(force=True)
+
+        if not getattr(shared.sd_model, 'mulan', False):
+            shared.log.info(f'MuLan apply: adapter="{adapter_path}" encoder="{text_encoder_path}"')
+            # mulankit.setup(force_sdxl_zero_empty_prompt=False, force_sdxl_zero_pool_prompt=False)
+            shared.sd_model = mulankit.transform(shared.sd_model,
+                adapter=adapter,
+                adapter_path=adapter_path,
+                text_encoder=text_encoder,
+                text_encoder_path=text_encoder_path,
+                pipe_type=pipe_type,
+                replace=False)
+            shared.sd_model.mulan = True
+            devices.torch_gc(force=True)
+
+        processing.fix_seed(p)
+        processed: processing.Processed = processing.process_images(p) # runs processing using main loop
+
+        # restore pipeline and params
+        shared.opts.data['prompt_attention'] = orig_prompt_attention
+        shared.sd_model = orig_pipeline
+        return processed