diff --git a/CHANGELOG.md b/CHANGELOG.md
index 537c24247..d4110af98 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,12 @@
 
 ## Update for 2025-02-14
 
+### TODO
+
+- VLM ModernUI support
+- CLiP Move settings
+- CLiP Batch progress bar
+
 ### Highlight for 2025-02-14
 
 We're back with another update with over 50 commits!  
@@ -58,10 +64,18 @@ We're back with another update with over 50 commits!
     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 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),
+  - Add VLM advanced params: max-tokens, num-beams, temperature, top-k, top-p, do-sample  
+    params are saved in `config.json` and used when using quick interrogate  
+    params that are set to 0 mean use model defaults  
+  - Add VLM batch processing  
+    for example, can be used to caption your training dataset in one go  
+    add option to append to captions file, can be used to run multiple captioning models in sequence  
+    add progress bar  
+  - Add additional VLM models:  
+    [JoyTag](https://huggingface.co/fancyfeast/joytag)  
+    [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  
diff --git a/modules/interrogate/openclip.py b/modules/interrogate/openclip.py
index fd10d09ab..3449dba28 100644
--- a/modules/interrogate/openclip.py
+++ b/modules/interrogate/openclip.py
@@ -228,13 +228,15 @@ class InterrogateModels:
 # --------- interrrogate ui
 
 class BatchWriter:
-    def __init__(self, folder):
+    def __init__(self, folder, mode='w'):
         self.folder = folder
-        self.csv, self.file = None, None
+        self.csv = None
+        self.file = None
+        self.mode = mode
 
     def add(self, file, prompt):
         txt_file = os.path.splitext(file)[0] + ".txt"
-        with open(os.path.join(self.folder, txt_file), 'w', encoding='utf-8') as f:
+        with open(os.path.join(self.folder, txt_file), self.mode, encoding='utf-8') as f:
             f.write(prompt)
 
     def close(self):
@@ -354,7 +356,7 @@ def interrogate_image(image, clip_model, blip_model, mode):
     return prompt
 
 
-def interrogate_batch(batch_files, batch_folder, batch_str, clip_model, blip_model, mode, write):
+def interrogate_batch(batch_files, batch_folder, batch_str, clip_model, blip_model, mode, write, append):
     files = []
     if batch_files is not None:
         files += [f.name for f in batch_files]
@@ -388,7 +390,8 @@ def interrogate_batch(batch_files, batch_folder, batch_str, clip_model, blip_mod
                 captions.append(caption)
         # second pass: interrogate
         if write:
-            writer = BatchWriter(os.path.dirname(files[0]))
+            mode = 'w' if not append else 'a'
+            writer = BatchWriter(os.path.dirname(files[0]), mode=mode)
         for idx, file in enumerate(files):
             try:
                 if shared.state.interrupted:
diff --git a/modules/interrogate/vqa.py b/modules/interrogate/vqa.py
index 8ce6e5e77..f98a566b4 100644
--- a/modules/interrogate/vqa.py
+++ b/modules/interrogate/vqa.py
@@ -1,4 +1,5 @@
 import io
+import os
 import time
 import json
 import base64
@@ -77,10 +78,30 @@ def clean(response, question):
     if question in response:
         response = response.split(question, 1)[1]
     response = response.replace('\n', '').replace('\r', '').replace('\t', '').strip()
+    if response.startswith('"'):
+        response = response[1:]
+    if response.endswith('"'):
+        response = response[:-1]
     response = response.replace('Assistant:', '').strip()
     return response
 
 
+def get_kwargs():
+    kwargs = {
+        'max_new_tokens': shared.opts.interrogate_vlm_max_length,
+        'do_sample': shared.opts.interrogate_vlm_do_sample,
+    }
+    if shared.opts.interrogate_vlm_num_beams > 0:
+        kwargs['num_beams'] = shared.opts.interrogate_vlm_num_beams
+    if shared.opts.interrogate_vlm_temperature > 0:
+        kwargs['temperature'] = shared.opts.interrogate_vlm_temperature
+    if shared.opts.interrogate_vlm_top_k > 0:
+        kwargs['top_k'] = shared.opts.interrogate_vlm_top_k
+    if shared.opts.interrogate_vlm_top_p > 0:
+        kwargs['top_p'] = shared.opts.interrogate_vlm_top_p
+    return kwargs
+
+
 def qwen(question: str, image: Image.Image, repo: str = None):
     global processor, model, loaded # pylint: disable=global-statement
     if model is None or loaded != repo:
@@ -113,7 +134,7 @@ def qwen(question: str, image: Image.Image, repo: str = None):
     inputs = inputs.to(devices.device, devices.dtype)
     output_ids = model.generate(
         **inputs,
-        max_new_tokens=shared.opts.interrogate_vlm_max_length,
+        **get_kwargs(),
     )
     generated_ids = [
         output_ids[len(input_ids) :]
@@ -139,8 +160,7 @@ def paligemma(question: str, image: Image.Image, repo: str = None):
     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,
+            **get_kwargs(),
         )
     generation = generation[0][input_len:]
     response = processor.decode(generation, skip_special_tokens=True)
@@ -184,7 +204,7 @@ def smol(question: str, image: Image.Image, repo: str = None):
     inputs = inputs.to(devices.device, devices.dtype)
     output_ids = model.generate(
         **inputs,
-        max_new_tokens=shared.opts.interrogate_vlm_max_length,
+        **get_kwargs(),
     )
     response = processor.batch_decode(output_ids,skip_special_tokens=True)
     return response
@@ -297,7 +317,7 @@ def florence(question: str, image: Image.Image, repo: str = None, revision: str
         return R
     revision = None
     if '@' in repo:
-        repo, revision = model.split('@')
+        repo, revision = repo.split('@')
     if model is None or loaded != repo:
         shared.log.debug(f'Interrogate load: vlm="{repo}" path="{shared.opts.hfcache_dir}"')
         transformers.dynamic_module_utils.get_imports = get_imports
@@ -319,16 +339,16 @@ def florence(question: str, image: Image.Image, repo: str = None, revision: str
         generated_ids = model.generate(
             input_ids=input_ids,
             pixel_values=pixel_values,
-            max_new_tokens=shared.opts.interrogate_vlm_max_length,
-            num_beams=shared.opts.interrogate_vlm_num_beams,
-            do_sample=shared.opts.interrogate_vlm_do_sample,
+            **get_kwargs()
         )
         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, prompt, image, model_name):
+def interrogate(question, prompt, image, model_name, quiet:bool=False):
+    if not quiet:
+        shared.state.begin('Caption')
     t0 = time.time()
     if isinstance(image, list):
         image = image[0] if len(image) > 0 else None
@@ -337,7 +357,7 @@ def interrogate(question, prompt, image, model_name):
     if image is None:
         return ''
     if image.width > 768 or image.height > 768:
-        image.thumbnail((768, 768), Image.Resampling.HAMMING)
+        image.thumbnail((768, 768), Image.Resampling.LANCZOS)
     if image.mode != 'RGB':
         image = image.convert('RGB')
     if prompt is not None and len(prompt) > 0:
@@ -392,5 +412,64 @@ def interrogate(question, prompt, image, model_name):
     devices.torch_gc()
     answer = clean(answer, question)
     t1 = time.time()
-    shared.log.debug(f'Interrogate: type=vlm model="{model_name}" repo="{vqa_model}" time={t1-t0:.2f}')
+    if not quiet:
+        shared.log.debug(f'Interrogate: type=vlm model="{model_name}" repo="{vqa_model}" args={get_kwargs()} time={t1-t0:.2f}')
+        shared.state.end()
     return answer
+
+
+def batch(model_name, batch_files, batch_folder, batch_str, question, prompt, write, append):
+    class BatchWriter:
+        def __init__(self, folder, mode='w'):
+            self.folder = folder
+            self.csv = None
+            self.file = None
+            self.mode = mode
+
+        def add(self, file, prompt):
+            txt_file = os.path.splitext(file)[0] + ".txt"
+            with open(os.path.join(self.folder, txt_file), self.mode, encoding='utf-8') as f:
+                f.write(prompt)
+
+        def close(self):
+            if self.file is not None:
+                self.file.close()
+
+    files = []
+    if batch_files is not None:
+        files += [f.name for f in batch_files]
+    if batch_folder is not None:
+        files += [f.name for f in batch_folder]
+    if batch_str is not None and len(batch_str) > 0 and os.path.exists(batch_str) and os.path.isdir(batch_str):
+        files += [os.path.join(batch_str, f) for f in os.listdir(batch_str) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]
+    if len(files) == 0:
+        shared.log.error('Interrogate batch no images')
+        return ''
+    shared.state.begin('Caption batch')
+    prompts = []
+    if write:
+        mode = 'w' if not append else 'a'
+        writer = BatchWriter(os.path.dirname(files[0]), mode=mode)
+    import rich.progress as rp
+    orig_offload = shared.opts.interrogate_offload
+    shared.opts.interrogate_offload = False
+    pbar = rp.Progress(rp.TextColumn('[cyan]Caption:'), rp.BarColumn(), rp.TaskProgressColumn(), rp.TimeRemainingColumn(), rp.TimeElapsedColumn(), rp.TextColumn('[cyan]{task.description}'), console=shared.console)
+    with pbar:
+        task = pbar.add_task(total=len(files), description='starting...')
+        for file in files:
+            pbar.update(task, advance=1, description=file)
+            try:
+                if shared.state.interrupted:
+                    break
+                image = Image.open(file)
+                prompt = interrogate(question, prompt, image, model_name, quiet=True)
+                prompts.append(prompt)
+                if write:
+                    writer.add(file, prompt)
+            except Exception as e:
+                shared.log.error(f'Interrogate batch: {e}')
+    if write:
+        writer.close()
+    shared.opts.interrogate_offload = orig_offload
+    shared.state.end()
+    return '\n\n'.join(prompts)
diff --git a/modules/shared.py b/modules/shared.py
index b8f5e3fd7..9d8633eeb 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -909,7 +909,7 @@ options_templates.update(options_section(('control', "Control Options"), {
 
 options_templates.update(options_section(('interrogate', "Interrogate"), {
     "interrogate_default_type": OptionInfo("OpenCLiP", "Default type", gr.Radio, {"choices": ["OpenCLiP", "VLM", "DeepBooru"]}),
-    "interrogate_offload": OptionInfo(True, "Interrogate: offload models "),
+    "interrogate_offload": OptionInfo(True, "Offload models "),
     "interrogate_score": OptionInfo(False, "Include scores in results when available"),
 
     "interrogate_clip_sep": OptionInfo("<h2>OpenCLiP</h2>", "", gr.HTML),
@@ -929,7 +929,9 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
     "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}),
+    "interrogate_vlm_temperature": OptionInfo(0, "VLM: num beams", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.01, "visible": False}),
+    "interrogate_vlm_top_k": OptionInfo(0, "VLM: top-k", gr.Slider, {"minimum": 0, "maximum": 99, "step": 1, "visible": False}),
+    "interrogate_vlm_top_p": OptionInfo(0, "VLM: top-p", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.01, "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_caption.py b/modules/ui_caption.py
index a306122e0..dbf00a916 100644
--- a/modules/ui_caption.py
+++ b/modules/ui_caption.py
@@ -4,11 +4,14 @@ from modules.interrogate import openclip
 
 
 def update_vlm_params(*args):
-    vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample = args
+    vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p = 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
+    shared.opts.interrogate_vlm_top_k = vlm_top_k
+    shared.opts.interrogate_vlm_top_p = vlm_top_p
+    shared.opts.save(shared.config_filename)
 
 
 def create_ui():
@@ -19,67 +22,80 @@ def create_ui():
             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')
+                        clip_model = gr.Dropdown([], value=shared.opts.interrogate_clip_model, label='CLiP model', elem_id='clip_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')
+                        blip_model = gr.Dropdown(list(openclip.caption_models), value=shared.opts.interrogate_blip_model, label='Caption model', elem_id='clip_blip_model')
+                        clip_mode = gr.Dropdown(openclip.caption_types, label='Mode', value='fast', elem_id='clip_clip_mode')
                     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)
+                            clip_caption_max_length = gr.Slider(label='Max length', value=shared.opts.interrogate_clip_max_length, minimum=16, maximum=512, elem_id='clip_caption_max_length')
+                            clip_chunk_size = gr.Slider(label='Chunk size', value=1024, minimum=256, maximum=4096, elem_id='clip_chunk_size')
                         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=[])
+                            clip_min_flavors = gr.Slider(label='Min flavors', value=2, minimum=1, maximum=16, elem_id='clip_min_flavors')
+                            clip_max_flavors = gr.Slider(label='Max flavors', value=8, minimum=1, maximum=64, elem_id='clip_max_flavors')
+                            clip_flavor_intermediate_count = gr.Slider(label='Intermediates', value=1024, minimum=256, maximum=4096, elem_id='clip_flavor_intermediate_count')
+                        clip_caption_max_length.change(fn=openclip.update_interrogate_params, inputs=[clip_caption_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_intermediate_count], outputs=[])
+                        clip_chunk_size.change(fn=openclip.update_interrogate_params, inputs=[clip_caption_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_intermediate_count], outputs=[])
+                        clip_min_flavors.change(fn=openclip.update_interrogate_params, inputs=[clip_caption_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_intermediate_count], outputs=[])
+                        clip_max_flavors.change(fn=openclip.update_interrogate_params, inputs=[clip_caption_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_intermediate_count], outputs=[])
+                        clip_flavor_intermediate_count.change(fn=openclip.update_interrogate_params, inputs=[clip_caption_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_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)
+                            clip_batch_files = gr.File(label="Files", show_label=True, file_count='multiple', file_types=['image'], type='file', interactive=True, height=100, elem_id='clip_batch_files')
                         with gr.Row():
-                            batch_folder = gr.File(label="Folder", show_label=True, file_count='directory', file_types=['image'], type='file', interactive=True, height=100)
+                            clip_batch_folder = gr.File(label="Folder", show_label=True, file_count='directory', file_types=['image'], type='file', interactive=True, height=100, elem_id='clip_batch_folder')
                         with gr.Row():
-                            batch_str = gr.Text(label="Folder", value="", interactive=True)
+                            clip_batch_str = gr.Text(label="Folder", value="", interactive=True, elem_id='clip_batch_str')
                         with gr.Row():
-                            batch = gr.Text(label="Prompts", lines=10)
+                            clip_save_output = gr.Checkbox(label='Save caption files', value=True, elem_id="clip_save_output")
+                            clip_save_append = gr.Checkbox(label='Append caption files', value=False, elem_id="clip_save_append")
                         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')
+                            btn_clip_interrogate_batch = gr.Button("Batch interrogate", variant='primary', elem_id="btn_clip_interrogate_batch")
+                    with gr.Row():
+                        btn_clip_interrogate_img = gr.Button("Interrogate", variant='primary', elem_id="btn_clip_interrogate_img")
+                        btn_clip_analyze_img = gr.Button("Analyze", variant='primary', elem_id="btn_clip_analyze_img")
                 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])
+                        vlm_question = gr.Dropdown(label="Predefined question", allow_custom_value=False, choices=vqa.vlm_prompts, value=vqa.vlm_prompts[2], elem_id='vlm_question')
                     with gr.Row():
-                        vqa_prompt = gr.Textbox(label="Prompt", placeholder="optionally enter custom prompt", lines=2)
+                        vlm_prompt = gr.Textbox(label="Prompt", placeholder="optionally enter custom prompt", lines=2, elem_id='vlm_prompt')
                     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')
+                        vlm_model = gr.Dropdown(list(vqa.vlm_models), value=list(vqa.vlm_models)[0], label='VLM Model', elem_id='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)
+                            vlm_max_tokens = gr.Slider(label='Max tokens', value=shared.opts.interrogate_vlm_max_length, minimum=16, maximum=4096, step=1, elem_id='vlm_max_tokens')
+                            vlm_num_beams = gr.Slider(label='Num beams', value=shared.opts.interrogate_vlm_num_beams, minimum=1, maximum=16, step=1, elem_id='vlm_num_beams')
+                            vlm_temperature = gr.Slider(label='Temperature', value=shared.opts.interrogate_vlm_temperature, minimum=0.1, maximum=1.0, step=0.01, elem_id='vlm_temperature')
                         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')
+                            vlm_top_k = gr.Slider(label='Top-K', value=shared.opts.interrogate_vlm_top_k, minimum=0, maximum=99, step=1, elem_id='vlm_top_k')
+                            vlm_top_p = gr.Slider(label='Top-P', value=shared.opts.interrogate_vlm_top_p, minimum=0.0, maximum=1.0, step=0.01, elem_id='vlm_top_p')
+                        with gr.Row():
+                            vlm_do_sample = gr.Checkbox(label='Use sample', value=shared.opts.interrogate_vlm_do_sample, elem_id='vlm_do_sample')
+                        vlm_max_tokens.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p], outputs=[])
+                        vlm_num_beams.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p], outputs=[])
+                        vlm_temperature.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p], outputs=[])
+                        vlm_do_sample.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p], outputs=[])
+                        vlm_top_k.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p], outputs=[])
+                        vlm_top_p.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p], outputs=[])
+                    with gr.Accordion(label='Batch', open=False, visible=True):
+                        with gr.Row():
+                            vlm_batch_files = gr.File(label="Files", show_label=True, file_count='multiple', file_types=['image'], type='file', interactive=True, height=100, elem_id='vlm_batch_files')
+                        with gr.Row():
+                            vlm_batch_folder = gr.File(label="Folder", show_label=True, file_count='directory', file_types=['image'], type='file', interactive=True, height=100, elem_id='vlm_batch_folder')
+                        with gr.Row():
+                            vlm_batch_str = gr.Text(label="Folder", value="", interactive=True, elem_id='vlm_batch_str')
+                        with gr.Row():
+                            vlm_save_output = gr.Checkbox(label='Save caption files', value=True, elem_id="vlm_save_output")
+                            vlm_save_append = gr.Checkbox(label='Append caption files', value=False, elem_id="vlm_save_append")
+                        with gr.Row(elem_id='interrogate_buttons_batch'):
+                            btn_vlm_caption_batch = gr.Button("Batch caption", variant='primary', elem_id="btn_vlm_caption_batch")
+                    with gr.Row():
+                        btn_vlm_caption = gr.Button("Caption", variant='primary', elem_id="btn_vlm_caption")
         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"):
+            with gr.Row():
                 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)
@@ -88,10 +104,11 @@ def create_ui():
             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])
+    btn_clip_interrogate_img.click(openclip.interrogate_image, inputs=[image, clip_model, blip_model, clip_mode], outputs=[prompt])
+    btn_clip_analyze_img.click(openclip.analyze_image, inputs=[image, clip_model, blip_model], outputs=[medium, artist, movement, trending, flavor])
+    btn_clip_interrogate_batch.click(fn=openclip.interrogate_batch, inputs=[clip_batch_files, clip_batch_folder, clip_batch_str, clip_model, blip_model, clip_mode, clip_save_output, clip_save_append], outputs=[prompt])
+    btn_vlm_caption.click(fn=vqa.interrogate, inputs=[vlm_question, vlm_prompt, image, vlm_model], outputs=[prompt])
+    btn_vlm_caption_batch.click(fn=vqa.batch, inputs=[vlm_model, vlm_batch_files, vlm_batch_folder, vlm_batch_str, vlm_question, vlm_prompt, vlm_save_output, vlm_save_append], 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,))
diff --git a/requirements.txt b/requirements.txt
index e15d141a2..f61890cbe 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -40,7 +40,7 @@ compel==2.0.3
 torchsde==0.2.6
 antlr4-python3-runtime==4.9.3
 requests==2.32.3
-tqdm==4.66.5
+tqdm==4.67.1
 accelerate==1.3.0
 opencv-contrib-python-headless==4.9.0.80
 einops==0.4.1