diff --git a/CHANGELOG.md b/CHANGELOG.md
index db4ffa67f..537c24247 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,7 +11,9 @@ We're back with another update with over 50 commits!
 - A lot of [outpainting](https://vladmandic.github.io/sdnext-docs/Outpaint/) goodies  
 - Support for new models: [AlphaVLLM Lumina 2](https://github.com/Alpha-VLLM/Lumina-Image-2.0) and [Ostris Flex.1-Alpha](https://huggingface.co/ostris/Flex.1-alpha)  
 - And new **Mixture-of-Diffusers** regional tiling pipeline  
-- Following last weeks **interrogate/captioning** rewrite, added **JoyTag** and **JoyCaption** to list of supported models  
+- Follow-up to last weeks **interrogate/captioning** rewrite  
+  now with redesigned captioning UI and  
+  added **JoyTag**, **JoyCaption**, **Google PaliGemma**, **ToriiGate 0.4** to list of supported models  
 - Some changes to **prompt parsing** to allow more control as well as more flexibility when mouting SDNext server to custom URL  
 - Of course, cumulative fixes...
 
@@ -28,13 +30,13 @@ We're back with another update with over 50 commits!
       *english, croatian, spanish, french, italian, portuguese, chinese, japanese, korean, russian*  
     - set in *settings -> user interface -> language*  
     - [localization](https://vladmandic.github.io/sdnext-docs/Locale/) documentation  
-  - **UI**:  
+  - **UI**  
     - force browser cache-invalidate on page load  
-- **Docs**
+- **Docs**  
   - New [Outpaint](https://vladmandic.github.io/sdnext-docs/Outpaint/) step-by-step guide  
   - Updated [Docker](https://github.com/vladmandic/sdnext/wiki/Docker) guide  
     includes build and publish and both local and cloud examples  
-- **Models**
+- **Models**  
   - [AlphaVLLM Lumina 2](https://github.com/Alpha-VLLM/Lumina-Image-2.0)  
     new foundation model for image generation based o Gemma-2-2B text encoder and a flow-based diffusion transformer  
     fully supports offloading and on-the-fly quantization  
@@ -44,18 +46,23 @@ We're back with another update with over 50 commits!
     result is model smaller than *Flux.1-Dev*, but with similar capabilities  
     fully supports offloading and on-the-fly quantization  
     simply select from *networks -> models -> reference*  
-- **Pipelines**
+- **Pipelines**  
   - [Mixture-of-Diffusers](https://huggingface.co/posts/elismasilva/251775641926329)  
     Regional tiling type of a solution for SDXL models  
     select from *scripts -> mixture of diffusers*  
   - [Automatic Color Inpaint]  
     Automatically creates mask based on selected color and triggers inpaint  
     simply select in *scripts -> automatic color inpaint* when in img2img mode  
-- **Interrogate/Captioning**
+- **Interrogate/Captioning**  
+  - Redesigned captioning UI  
+    split from Process tab into separate tab  
+    split `clip` vs `vlm` models processing  
+    direct *send-to* buttons on all tabs  
   - [JoyTag](https://huggingface.co/fancyfeast/joytag)
-  - [JoyCaption](https://huggingface.co/fancyfeast/llama-joycaption-alpha-two-hf-llava)  
-    *note*: this is a very large model based on LLama 3.1
-- **Docker** 
+  - [JoyCaption 2](https://huggingface.co/fancyfeast/llama-joycaption-alpha-two-hf-llava)  
+  - [Google PaliGemma 2](https://huggingface.co/google/paligemma2-3b-pt-224)
+  - [ToriiGate 0.4 7B](https://huggingface.co/Minthy/ToriiGate-v0.4-7B),
+- **Docker**  
   - updated **CUDA** receipe to `torch==2.6.0` with `cuda==12.6` and add prebuilt image  
   - added **ROCm** receipe and prebuilt image  
   - added **IPEX** receipe and add prebuilt image  
@@ -80,7 +87,7 @@ We're back with another update with over 50 commits!
   - **Styles**  
     ability to save and/or restore prompts before or after parsing of wildcards  
     set in *settings -> networks -> styles*  
-  - **Access tokens**
+  - **Access tokens**  
     persist *models -> hugginface -> token*  
     persist *models -> civitai -> token*  
   - global switch to lancosz method for all interal resize ops and bicubic for interpolation ops  
diff --git a/javascript/ui.js b/javascript/ui.js
index 7193f9470..9df1a26e4 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -155,6 +155,11 @@ function switch_to_control(...args) {
   return Array.from(arguments);
 }
 
+function switch_to_caption(...args) {
+  switchToTab('Caption');
+  return Array.from(arguments);
+}
+
 function get_tab_index(tabId) {
   let res = 0;
   gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button')
diff --git a/modules/api/endpoints.py b/modules/api/endpoints.py
index 52c4b5301..7bdc31c6a 100644
--- a/modules/api/endpoints.py
+++ b/modules/api/endpoints.py
@@ -113,7 +113,7 @@ def post_vqa(req: models.ReqVQA):
     image = helpers.decode_base64_to_image(req.image)
     image = image.convert('RGB')
     from modules.interrogate import vqa
-    answer = vqa.interrogate(req.question, image, req.model)
+    answer = vqa.interrogate(req.question, '', image, req.model)
     return models.ResVQA(answer=answer)
 
 def post_unload_checkpoint():
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index fe65efe19..613c09ff2 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -101,6 +101,8 @@ def create_buttons(tabs_list):
             name = 'Process'
         elif name == 'control':
             name = 'Control'
+        elif name == 'caption':
+            name = 'Caption'
         buttons[tab] = gr.Button(f"➠ {name}", elem_id=f"{tab}_tab")
     return buttons
 
diff --git a/modules/infotext.py b/modules/infotext.py
index 017691681..e8788c2f5 100644
--- a/modules/infotext.py
+++ b/modules/infotext.py
@@ -90,6 +90,9 @@ def parse(infotext):
     # debug(f'Negative: {negative}')
 
     params = dict(re_param.findall(remaining))
+    if len(list(params)) == 0:
+        params['Prompt'] = infotext
+        return params
     params['Prompt'] = prompt
     params['Negative prompt'] = negative
     for key, val in params.copy().items():
diff --git a/modules/interrogate/openclip.py b/modules/interrogate/openclip.py
index 9cd658db2..fd10d09ab 100644
--- a/modules/interrogate/openclip.py
+++ b/modules/interrogate/openclip.py
@@ -6,6 +6,7 @@ import threading
 import re
 import torch
 import torch.hub # pylint: disable=ungrouped-imports
+import gradio as gr
 from PIL import Image
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
@@ -423,7 +424,13 @@ def analyze_image(image, clip_model, blip_model):
     movement_ranks = dict(zip(top_movements, ci.similarities(image_features, top_movements)))
     trending_ranks = dict(zip(top_trendings, ci.similarities(image_features, top_trendings)))
     flavor_ranks = dict(zip(top_flavors, ci.similarities(image_features, top_flavors)))
-    return medium_ranks, artist_ranks, movement_ranks, trending_ranks, flavor_ranks
+    return [
+        gr.update(value=medium_ranks, visible=True),
+        gr.update(value=artist_ranks, visible=True),
+        gr.update(value=movement_ranks, visible=True),
+        gr.update(value=trending_ranks, visible=True),
+        gr.update(value=flavor_ranks, visible=True),
+    ]
 
 
 interrogator = InterrogateModels()
diff --git a/modules/interrogate/vqa.py b/modules/interrogate/vqa.py
index f08169d9b..8ce6e5e77 100644
--- a/modules/interrogate/vqa.py
+++ b/modules/interrogate/vqa.py
@@ -31,7 +31,9 @@ vlm_models = {
     "Microsoft GIT VQA Base": "microsoft/git-base-vqav2", # 0.7GB
     "Microsoft GIT VQA Large": "microsoft/git-large-vqav2", # 1.6GB
     "ToriiGate 0.4 2B": "Minthy/ToriiGate-v0.4-2B",
+    "ToriiGate 0.4 7B": "Minthy/ToriiGate-v0.4-7B",
     "ViLT Base": "dandelin/vilt-b32-finetuned-vqa", # 0.5GB
+    "Google PaliGemma 2 3B": "google/paligemma2-3b-pt-224",
     "JoyCaption": "fancyfeast/llama-joycaption-alpha-two-hf-llava", # 0.7GB
     "JoyTag": "fancyfeast/joytag", # 17.4GB
     # "DeepSeek VL2 Tiny": "deepseek-ai/deepseek-vl2-tiny", # broken
@@ -86,7 +88,7 @@ def qwen(question: str, image: Image.Image, repo: str = None):
         model = transformers.Qwen2VLForConditionalGeneration.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
         processor = transformers.AutoProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
         loaded = repo
-    model.to(devices.device, devices.dtype)
+    model = model.to(devices.device, devices.dtype)
     if len(question) < 2:
         question = "Describe the image."
     question = question.replace('<', '').replace('>', '')
@@ -121,6 +123,30 @@ def qwen(question: str, image: Image.Image, repo: str = None):
     return response
 
 
+def paligemma(question: str, image: Image.Image, repo: str = None):
+    global processor, model, loaded # pylint: disable=global-statement
+    if model is None or loaded != repo:
+        shared.log.debug(f'Interrogate load: vlm="{repo}"')
+        processor = transformers.PaliGemmaProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
+        model = transformers.PaliGemmaForConditionalGeneration.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir, torch_dtype=devices.dtype)
+        loaded = repo
+    model = model.to(devices.device, devices.dtype)
+    if len(question) < 2:
+        question = "Describe the image."
+    question = question.replace('<', '').replace('>', '')
+    model_inputs = processor(text=question, images=image, return_tensors="pt").to(devices.device, devices.dtype)
+    input_len = model_inputs["input_ids"].shape[-1]
+    with devices.inference_context():
+        generation = model.generate(
+            **model_inputs,
+            max_new_tokens=shared.opts.interrogate_vlm_max_length,
+            do_sample=shared.opts.interrogate_vlm_do_sample,
+        )
+    generation = generation[0][input_len:]
+    response = processor.decode(generation, skip_special_tokens=True)
+    return response
+
+
 def smol(question: str, image: Image.Image, repo: str = None):
     global processor, model, loaded # pylint: disable=global-statement
     if model is None or loaded != repo:
@@ -295,14 +321,14 @@ def florence(question: str, image: Image.Image, repo: str = None, revision: str
             pixel_values=pixel_values,
             max_new_tokens=shared.opts.interrogate_vlm_max_length,
             num_beams=shared.opts.interrogate_vlm_num_beams,
-            do_sample=False
+            do_sample=shared.opts.interrogate_vlm_do_sample,
         )
         generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
         response = processor.post_process_generation(generated_text, task="task", image_size=(image.width, image.height))
     return response
 
 
-def interrogate(question, image, model_name):
+def interrogate(question, prompt, image, model_name):
     t0 = time.time()
     if isinstance(image, list):
         image = image[0] if len(image) > 0 else None
@@ -314,6 +340,8 @@ def interrogate(question, image, model_name):
         image.thumbnail((768, 768), Image.Resampling.HAMMING)
     if image.mode != 'RGB':
         image = image.convert('RGB')
+    if prompt is not None and len(prompt) > 0:
+        question = prompt
     from modules import modelloader
     modelloader.hf_login()
     try:
@@ -352,6 +380,8 @@ def interrogate(question, image, model_name):
         elif 'deepseek' in vqa_model.lower():
             from modules.interrogate import deepseek
             answer = deepseek.predict(question, image, vqa_model)
+        elif 'paligemma' in vqa_model.lower():
+            answer = paligemma(question, image, vqa_model)
         else:
             answer = 'unknown model'
     except Exception as e:
diff --git a/modules/shared.py b/modules/shared.py
index d4d6d06c4..b8f5e3fd7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -926,8 +926,10 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
     "interrogate_vlm_sep": OptionInfo("<h2>VLM</h2>", "", gr.HTML),
     "interrogate_vlm_model": OptionInfo(list(vlm_models)[0], "VLM: default model", gr.Dropdown, {"choices": list(vlm_models)}),
     "interrogate_vlm_prompt": OptionInfo(vlm_prompts[2], "VLM: default prompt", DropdownEditable, {"choices": vlm_prompts }),
-    "interrogate_vlm_num_beams": OptionInfo(3, "VLM: num beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
-    "interrogate_vlm_max_length": OptionInfo(512, "VLM: max length", gr.Slider, {"minimum": 1, "maximum": 4096, "step": 1}),
+    "interrogate_vlm_num_beams": OptionInfo(3, "VLM: num beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1, "visible": False}),
+    "interrogate_vlm_max_length": OptionInfo(512, "VLM: max length", gr.Slider, {"minimum": 1, "maximum": 4096, "step": 1, "visible": False}),
+    "interrogate_vlm_do_sample": OptionInfo(False, "VLM: use sample method"),
+    "interrogate_vlm_temperature": OptionInfo(0.6, "VLM: num beams", gr.Slider, {"minimum": 0.1, "maximum": 1.0, "step": 0.11, "visible": False}),
 
     "deepbooru_sep": OptionInfo("<h2>DeepBooru</h2>", "", gr.HTML),
     "deepbooru_score_threshold": OptionInfo(0.65, "DeepBooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
diff --git a/modules/ui.py b/modules/ui.py
index 04e813cd8..15718dbb9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -151,6 +151,11 @@ def create_ui(startup_timer = None):
         ui_postprocessing.create_ui()
         timer.startup.record("ui-extras")
 
+    with gr.Blocks(analytics_enabled=False) as caption_interface:
+        from modules import ui_caption
+        ui_caption.create_ui()
+        timer.startup.record("ui-caption")
+
     with gr.Blocks(analytics_enabled=False) as models_interface:
         from modules import ui_models
         ui_models.create_ui()
@@ -389,6 +394,7 @@ def create_ui(startup_timer = None):
     interfaces += [(img2img_interface, "Image", "img2img")]
     interfaces += [(control_interface, "Control", "control")] if control_interface is not None else []
     interfaces += [(extras_interface, "Process", "process")]
+    interfaces += [(caption_interface, "Caption", "caption")]
     interfaces += [(gallery_interface, "Gallery", "gallery")]
     interfaces += [(models_interface, "Models", "models")]
     interfaces += script_callbacks.ui_tabs_callback()
diff --git a/modules/ui_caption.py b/modules/ui_caption.py
new file mode 100644
index 000000000..a306122e0
--- /dev/null
+++ b/modules/ui_caption.py
@@ -0,0 +1,98 @@
+import gradio as gr
+from modules import shared, ui_common, generation_parameters_copypaste
+from modules.interrogate import openclip
+
+
+def update_vlm_params(*args):
+    vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample = args
+    shared.opts.interrogate_vlm_max_length = vlm_max_tokens
+    shared.opts.interrogate_vlm_num_beams = vlm_num_beams
+    shared.opts.interrogate_vlm_temperature = vlm_temperature
+    shared.opts.interrogate_vlm_do_sample = vlm_do_sample
+
+
+def create_ui():
+    with gr.Row(equal_height=False, variant='compact', elem_classes="caption"):
+        with gr.Column(variant='compact'):
+            with gr.Row():
+                image = gr.Image(type='pil', label="Image")
+            with gr.Tabs(elem_id="mode_caption"):
+                with gr.Tab("CLiP Interrogate"):
+                    with gr.Row():
+                        clip_model = gr.Dropdown([], value=shared.opts.interrogate_clip_model, label='CLiP model')
+                        ui_common.create_refresh_button(clip_model, openclip.refresh_clip_models, lambda: {"choices": openclip.refresh_clip_models()}, 'refresh_interrogate_models')
+                        blip_model = gr.Dropdown(list(openclip.caption_models), value=shared.opts.interrogate_blip_model, label='Caption model')
+                        mode = gr.Dropdown(openclip.caption_types, label='Mode', value='fast')
+                    with gr.Accordion(label='Advanced', open=False, visible=True):
+                        with gr.Row():
+                            caption_max_length = gr.Slider(label='Max length', value=shared.opts.interrogate_clip_max_length, minimum=16, maximum=512, min_width=300)
+                            chunk_size = gr.Slider(label='Chunk size', value=1024, minimum=256, maximum=4096, min_width=300)
+                        with gr.Row():
+                            min_flavors = gr.Slider(label='Min flavors', value=2, minimum=1, maximum=16, min_width=300)
+                            max_flavors = gr.Slider(label='Max flavors', value=8, minimum=1, maximum=64, min_width=300)
+                            flavor_intermediate_count = gr.Slider(label='Intermediates', value=1024, minimum=256, maximum=4096)
+                        caption_max_length.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
+                        chunk_size.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
+                        min_flavors.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
+                        max_flavors.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
+                        flavor_intermediate_count.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
+                    with gr.Accordion(label='Batch', open=False, visible=True):
+                        with gr.Row():
+                            batch_files = gr.File(label="Files", show_label=True, file_count='multiple', file_types=['image'], type='file', interactive=True, height=100)
+                        with gr.Row():
+                            batch_folder = gr.File(label="Folder", show_label=True, file_count='directory', file_types=['image'], type='file', interactive=True, height=100)
+                        with gr.Row():
+                            batch_str = gr.Text(label="Folder", value="", interactive=True)
+                        with gr.Row():
+                            batch = gr.Text(label="Prompts", lines=10)
+                        with gr.Row():
+                            clip_model = gr.Dropdown([], value='ViT-L-14/openai', label='CLiP Batch Model')
+                            ui_common.create_refresh_button(clip_model, openclip.refresh_clip_models, lambda: {"choices": openclip.refresh_clip_models()}, 'refresh_interrogate_models')
+                        with gr.Row(elem_id='interrogate_buttons_batch'):
+                            btn_interrogate_batch = gr.Button("Batch interrogate", elem_id="interrogate_btn_interrogate", variant='primary')
+                        with gr.Row():
+                            save_output = gr.Checkbox(label='Save output', value=True, elem_id="extras_save_output")
+                    with gr.Row(elem_id='interrogate_buttons_image'):
+                        btn_interrogate_img = gr.Button("Interrogate", elem_id="interrogate_btn_interrogate", variant='primary')
+                        btn_analyze_img = gr.Button("Analyze", elem_id="interrogate_btn_analyze", variant='primary')
+                with gr.Tab("VLM Caption"):
+                    from modules.interrogate import vqa
+                    with gr.Row():
+                        vqa_question = gr.Dropdown(label="Predefined question", allow_custom_value=False, choices=vqa.vlm_prompts, value=vqa.vlm_prompts[2])
+                    with gr.Row():
+                        vqa_prompt = gr.Textbox(label="Prompt", placeholder="optionally enter custom prompt", lines=2)
+                    with gr.Row(elem_id='interrogate_buttons_query'):
+                        vqa_model = gr.Dropdown(list(vqa.vlm_models), value=list(vqa.vlm_models)[0], label='VLM Model')
+                    with gr.Accordion(label='Advanced', open=False, visible=True):
+                        with gr.Row():
+                            vlm_max_tokens = gr.Slider(label='Max tokens', value=shared.opts.interrogate_vlm_max_length, minimum=16, maximum=4096, step=1)
+                            vlm_num_beams = gr.Slider(label='Num beams', value=shared.opts.interrogate_vlm_num_beams, minimum=1, maximum=16, step=1)
+                            vlm_temperature = gr.Slider(label='Temperature', value=shared.opts.interrogate_vlm_temperature, minimum=0.1, maximum=1.0, step=0.01)
+                        with gr.Row():
+                            vlm_do_sample = gr.Checkbox(label='Use sample', value=shared.opts.interrogate_vlm_do_sample)
+                        vlm_max_tokens.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample], outputs=[])
+                        vlm_num_beams.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample], outputs=[])
+                        vlm_temperature.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample], outputs=[])
+                        vlm_do_sample.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample], outputs=[])
+                    with gr.Row(elem_id='interrogate_buttons_query'):
+                        vqa_submit = gr.Button("Caption", elem_id="interrogate_btn_interrogate", variant='primary')
+        with gr.Column(variant='compact'):
+            with gr.Row():
+                prompt = gr.Textbox(label="Answer", lines=8, placeholder="ai generated image description")
+            with gr.Row(elem_id="interrogate_labels"):
+                medium = gr.Label(elem_id="interrogate_label_medium", label="Medium", num_top_classes=5, visible=False)
+                artist = gr.Label(elem_id="interrogate_label_artist", label="Artist", num_top_classes=5, visible=False)
+                movement = gr.Label(elem_id="interrogate_label_movement", label="Movement", num_top_classes=5, visible=False)
+                trending = gr.Label(elem_id="interrogate_label_trending", label="Trending", num_top_classes=5, visible=False)
+                flavor = gr.Label(elem_id="interrogate_label_flavor", label="Flavor", num_top_classes=5, visible=False)
+            with gr.Row(elem_id='copy_buttons_interrogate'):
+                copy_interrogate_buttons = generation_parameters_copypaste.create_buttons(["txt2img", "img2img", "control", "extras"])
+
+    btn_interrogate_img.click(openclip.interrogate_image, inputs=[image, clip_model, blip_model, mode], outputs=[prompt])
+    btn_analyze_img.click(openclip.analyze_image, inputs=[image, clip_model, blip_model], outputs=[medium, artist, movement, trending, flavor])
+    btn_interrogate_batch.click(fn=openclip.interrogate_batch, inputs=[batch_files, batch_folder, batch_str, clip_model, blip_model, mode, save_output], outputs=[batch])
+    vqa_submit.click(vqa.interrogate, inputs=[vqa_question, vqa_prompt, image, vqa_model], outputs=[prompt])
+
+    for tabname, button in copy_interrogate_buttons.items():
+        generation_parameters_copypaste.register_paste_params_button(generation_parameters_copypaste.ParamBinding(paste_button=button, tabname=tabname, source_text_component=prompt, source_image_component=image,))
+    generation_parameters_copypaste.add_paste_fields("caption", image, None)
diff --git a/modules/ui_common.py b/modules/ui_common.py
index efc40e1e0..d408890bb 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -275,7 +275,7 @@ def create_output_panel(tabname, preview=True, prompt=None, height=None):
                 if not shared.native:
                     buttons = generation_parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
                 else:
-                    buttons = generation_parameters_copypaste.create_buttons(["txt2img", "img2img", "control", "extras"])
+                    buttons = generation_parameters_copypaste.create_buttons(["txt2img", "img2img", "control", "extras", "caption"])
 
             download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
             with gr.Group():
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py
index 0614e7569..27806e35c 100644
--- a/modules/ui_postprocessing.py
+++ b/modules/ui_postprocessing.py
@@ -1,8 +1,6 @@
 import json
 import gradio as gr
 from modules import scripts, shared, ui_common, postprocessing, call_queue, generation_parameters_copypaste
-from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call # pylint: disable=unused-import
-from modules.interrogate import openclip
 
 
 def submit_info(image):
@@ -25,81 +23,14 @@ def create_ui():
                 with gr.Tab('Process Image', id="single_image", elem_id="extras_single_tab") as tab_single:
                     with gr.Row():
                         extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
-                    with gr.Row(elem_id='copy_buttons_process'):
-                        copy_process_buttons = generation_parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "control"])
                 with gr.Tab('Process Batch', id="batch_process", elem_id="extras_batch_process_tab") as tab_batch:
                     image_batch = gr.Files(label="Batch process", interactive=True, elem_id="extras_image_batch")
                 with gr.Tab('Process Folder', id="batch_from_directory", elem_id="extras_batch_directory_tab") as tab_batch_dir:
                     extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
                     extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
                     show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
-
-                with gr.Tab("Interrogate Image"):
-                    with gr.Row():
-                        image = gr.Image(type='pil', label="Image")
-                    with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", lines=3)
-                    with gr.Row(elem_id="interrogate_labels"):
-                        medium = gr.Label(elem_id="interrogate_label_medium", label="Medium", num_top_classes=5)
-                        artist = gr.Label(elem_id="interrogate_label_artist", label="Artist", num_top_classes=5)
-                        movement = gr.Label(elem_id="interrogate_label_movement", label="Movement", num_top_classes=5)
-                        trending = gr.Label(elem_id="interrogate_label_trending", label="Trending", num_top_classes=5)
-                        flavor = gr.Label(elem_id="interrogate_label_flavor", label="Flavor", num_top_classes=5)
-                    with gr.Row():
-                        clip_model = gr.Dropdown([], value=shared.opts.interrogate_clip_model, label='CLiP model')
-                        ui_common.create_refresh_button(clip_model, openclip.refresh_clip_models, lambda: {"choices": openclip.refresh_clip_models()}, 'refresh_interrogate_models')
-                        blip_model = gr.Dropdown(list(openclip.caption_models), value=shared.opts.interrogate_blip_model, label='Caption model')
-                        mode = gr.Dropdown(openclip.caption_types, label='Mode', value='fast')
-                    with gr.Accordion(label='Advanced', open=False, visible=True):
-                        with gr.Row():
-                            caption_max_length = gr.Number(label='Max length', value=shared.opts.interrogate_clip_max_length, minimum=16, maximum=512, min_width=300)
-                            chunk_size = gr.Number(label='Chunk size', value=1024, minimum=256, maximum=4096, min_width=300)
-                            min_flavors = gr.Number(label='Min flavors', value=2, minimum=1, maximum=16, min_width=300)
-                            max_flavors = gr.Number(label='Max flavors', value=8, minimum=1, maximum=64, min_width=300)
-                            flavor_intermediate_count = gr.Number(label='Intermediates', value=1024, minimum=256, maximum=4096)
-                            caption_max_length.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
-                            chunk_size.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
-                            min_flavors.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
-                            max_flavors.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
-                            flavor_intermediate_count.change(fn=openclip.update_interrogate_params, inputs=[caption_max_length, chunk_size, min_flavors, max_flavors, flavor_intermediate_count], outputs=[])
-                    with gr.Row(elem_id='interrogate_buttons_image'):
-                        btn_interrogate_img = gr.Button("Interrogate", elem_id="interrogate_btn_interrogate", variant='primary')
-                        btn_analyze_img = gr.Button("Analyze", elem_id="interrogate_btn_analyze", variant='primary')
-                        btn_unload = gr.Button("Unload", elem_id="interrogate_btn_unload")
-                    with gr.Row(elem_id='copy_buttons_interrogate'):
-                        copy_interrogate_buttons = generation_parameters_copypaste.create_buttons(["txt2img", "img2img", "extras", "control"])
-                    btn_interrogate_img.click(openclip.interrogate_image, inputs=[image, clip_model, blip_model, mode], outputs=prompt)
-                    btn_analyze_img.click(openclip.analyze_image, inputs=[image, clip_model, blip_model], outputs=[medium, artist, movement, trending, flavor])
-                    btn_unload.click(openclip.unload_clip_model)
-                with gr.Tab("Interrogate Batch"):
-                    with gr.Row():
-                        batch_files = gr.File(label="Files", show_label=True, file_count='multiple', file_types=['image'], type='file', interactive=True, height=100)
-                    with gr.Row():
-                        batch_folder = gr.File(label="Folder", show_label=True, file_count='directory', file_types=['image'], type='file', interactive=True, height=100)
-                    with gr.Row():
-                        batch_str = gr.Text(label="Folder", value="", interactive=True)
-                    with gr.Row():
-                        batch = gr.Text(label="Prompts", lines=10)
-                    with gr.Row():
-                        clip_model = gr.Dropdown([], value='ViT-L-14/openai', label='CLiP Batch Model')
-                        ui_common.create_refresh_button(clip_model, openclip.refresh_clip_models, lambda: {"choices": openclip.refresh_clip_models()}, 'refresh_interrogate_models')
-                    with gr.Row(elem_id='interrogate_buttons_batch'):
-                        btn_interrogate_batch = gr.Button("Interrogate", elem_id="interrogate_btn_interrogate", variant='primary')
-                with gr.Tab("Visual Query"):
-                    from modules.interrogate import vqa
-                    with gr.Row():
-                        vqa_image = gr.Image(type='pil', label="Image")
-                    with gr.Row():
-                        vqa_question = gr.Dropdown(label="Question", allow_custom_value=True, choices=vqa.vlm_prompts, value=vqa.vlm_prompts[2])
-                    with gr.Row():
-                        vqa_answer = gr.Textbox(label="Answer", lines=5)
-                    with gr.Row(elem_id='interrogate_buttons_query'):
-                        vqa_model = gr.Dropdown(list(vqa.vlm_models), value=list(vqa.vlm_models)[0], label='VLM Model')
-                        vqa_submit = gr.Button("Interrogate", elem_id="interrogate_btn_interrogate", variant='primary')
-                    vqa_submit.click(vqa.interrogate, inputs=[vqa_question, vqa_image, vqa_model], outputs=[vqa_answer])
-
-                with gr.Row():
-                    save_output = gr.Checkbox(label='Save output', value=True, elem_id="extras_save_output")
+            with gr.Row():
+                save_output = gr.Checkbox(label='Save output', value=True, elem_id="extras_save_output")
 
             script_inputs = scripts.scripts_postproc.setup_ui()
         with gr.Column():
@@ -114,20 +45,18 @@ def create_ui():
             gr.HTML('File metadata')
             exif_info = gr.HTML(elem_id="pnginfo_html_info")
             gen_info = gr.Text(elem_id="pnginfo_gen_info", visible=False)
+            with gr.Row(elem_id='copy_buttons_process'):
+                copy_process_buttons = generation_parameters_copypaste.create_buttons(["txt2img", "img2img", "control", "caption"])
+
         for tabname, button in copy_process_buttons.items():
             generation_parameters_copypaste.register_paste_params_button(generation_parameters_copypaste.ParamBinding(paste_button=button, tabname=tabname, source_text_component=gen_info, source_image_component=extras_image))
-        for tabname, button in copy_interrogate_buttons.items():
-            generation_parameters_copypaste.register_paste_params_button(generation_parameters_copypaste.ParamBinding(paste_button=button, tabname=tabname, source_text_component=prompt, source_image_component=image,))
-
+        generation_parameters_copypaste.add_paste_fields("extras", extras_image, None)
 
     tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
     tab_batch.select(fn=lambda: 1, inputs=[], outputs=[tab_index])
     tab_batch_dir.select(fn=lambda: 2, inputs=[], outputs=[tab_index])
-    extras_image.change(
-        fn=wrap_gradio_call(submit_info),
-        inputs=[extras_image],
-        outputs=[html_info_formatted, exif_info, gen_info],
-    )
+    extras_image.change(fn=submit_info, inputs=[extras_image], outputs=[html_info_formatted, exif_info, gen_info])
+    extras_image.change(fn=scripts.scripts_postproc.image_changed, inputs=[], outputs=[])
     submit.click(
         _js="submit_postprocessing",
         fn=call_queue.wrap_gradio_gpu_call(submit_process, extra_outputs=[None, ''], name='Postprocess'),
@@ -148,15 +77,3 @@ def create_ui():
             html_log,
         ]
     )
-    btn_interrogate_batch.click(
-        fn=openclip.interrogate_batch,
-        inputs=[batch_files, batch_folder, batch_str, clip_model, blip_model, mode, save_output],
-        outputs=[batch],
-    )
-
-    generation_parameters_copypaste.add_paste_fields("extras", extras_image, None)
-
-    extras_image.change(
-        fn=scripts.scripts_postproc.image_changed,
-        inputs=[], outputs=[]
-    )