diff --git a/scepter/methods/studio/chatbot/models/ace_0.6b_1024.yaml b/scepter/methods/studio/chatbot/models/ace_0.6b_1024.yaml
index 668dac2..c988e5f 100644
--- a/scepter/methods/studio/chatbot/models/ace_0.6b_1024.yaml
+++ b/scepter/methods/studio/chatbot/models/ace_0.6b_1024.yaml
@@ -1,6 +1,6 @@
 NAME: ACE_0.6B_1024
 IS_DEFAULT: False
-USE_DYNAMIC_MODEL: False
+USE_DYNAMIC_MODEL: True
 DEFAULT_PARAS:
   PARAS:
   #
diff --git a/scepter/methods/studio/chatbot/models/ace_0.6b_1024_refiner.yaml b/scepter/methods/studio/chatbot/models/ace_0.6b_1024_refiner.yaml
index 5647bc1..94f04a4 100644
--- a/scepter/methods/studio/chatbot/models/ace_0.6b_1024_refiner.yaml
+++ b/scepter/methods/studio/chatbot/models/ace_0.6b_1024_refiner.yaml
@@ -1,6 +1,6 @@
 NAME: ACE_0.6B_1024_REFINER
 IS_DEFAULT: False
-USE_DYNAMIC_MODEL: False
+USE_DYNAMIC_MODEL: True
 DEFAULT_PARAS:
   PARAS:
   #
diff --git a/scepter/methods/studio/chatbot/models/ace_0.6b_512.yaml b/scepter/methods/studio/chatbot/models/ace_0.6b_512.yaml
index 0872224..c803d67 100644
--- a/scepter/methods/studio/chatbot/models/ace_0.6b_512.yaml
+++ b/scepter/methods/studio/chatbot/models/ace_0.6b_512.yaml
@@ -1,6 +1,6 @@
 NAME: ACE_0.6B_512
 IS_DEFAULT: True
-USE_DYNAMIC_MODEL: False
+USE_DYNAMIC_MODEL: True
 DEFAULT_PARAS:
   PARAS:
   #
diff --git a/scepter/modules/inference/ace_inference.py b/scepter/modules/inference/ace_inference.py
index fa8b37e..fd1769d 100644
--- a/scepter/modules/inference/ace_inference.py
+++ b/scepter/modules/inference/ace_inference.py
@@ -156,17 +156,17 @@ class RefinerInference(DiffusionInference):
             noise.append(noise_)
         noise, x_shapes = pack_imagelist_into_tensor(noise)
         if reverse_scale > 0:
-            if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
             x_samples = [x.unsqueeze(0) for x in x_samples]
             x_start = self.encode_first_stage(x_samples, **kwargs)
-            if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
+            self.dynamic_unload(self.first_stage_model,
                                 'first_stage_model',
-                                skip_loaded=True)
+                                skip_loaded=not self.use_dynamic_model)
             x_start, _ = pack_imagelist_into_tensor(x_start)
         else:
             x_start = None
         # cond stage
-        if self.use_dynamic_model: self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
+        self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
         function_name, dtype = self.get_function_info(self.cond_stage_model)
         with torch.autocast('cuda',
                             enabled=dtype == 'float16',
@@ -174,12 +174,12 @@ class RefinerInference(DiffusionInference):
             ctx = getattr(get_model(self.cond_stage_model),
                           function_name)(prompt)
             ctx["x_shapes"] = x_shapes
-        if self.use_dynamic_model: self.dynamic_unload(self.cond_stage_model,
+        self.dynamic_unload(self.cond_stage_model,
                             'cond_stage_model',
-                            skip_loaded=True)
+                            skip_loaded=not self.use_dynamic_model)
 
 
-        if self.use_dynamic_model: self.dynamic_load(self.diffusion_model, 'diffusion_model')
+        self.dynamic_load(self.diffusion_model, 'diffusion_model')
         # UNet use input n_prompt
         function_name, dtype = self.get_function_info(
             self.diffusion_model)
@@ -207,14 +207,14 @@ class RefinerInference(DiffusionInference):
                 x=x_start,
                 **kwargs).float()
         latent = unpack_tensor_into_imagelist(latent, x_shapes)
-        if self.use_dynamic_model: self.dynamic_unload(self.diffusion_model,
+        self.dynamic_unload(self.diffusion_model,
                             'diffusion_model',
-                            skip_loaded=True)
-        if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
+                            skip_loaded=not self.use_dynamic_model)
+        self.dynamic_load(self.first_stage_model, 'first_stage_model')
         x_samples = self.decode_first_stage(latent)
-        if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
+        self.dynamic_unload(self.first_stage_model,
                             'first_stage_model',
-                            skip_loaded=True)
+                            skip_loaded=not self.use_dynamic_model)
         return x_samples
 
 
@@ -398,11 +398,11 @@ class ACEInference(DiffusionInference):
         if use_ace and (not is_txt_image or refiner_scale <= 0):
             ctx, null_ctx = {}, {}
             # Get Noise Shape
-            if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
             x = self.encode_first_stage(image)
-            if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
+            self.dynamic_unload(self.first_stage_model,
                                 'first_stage_model',
-                                skip_loaded=True)
+                                skip_loaded=not self.use_dynamic_model)
             noise = [
                 torch.empty(*i.shape, device=we.device_id).normal_(generator=g)
                 for i in x
@@ -416,7 +416,7 @@ class ACEInference(DiffusionInference):
             ctx['x_mask'] = null_ctx['x_mask'] = cond_mask
 
             # Encode Prompt
-            if self.use_dynamic_model: self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
+            self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
             function_name, dtype = self.get_function_info(self.cond_stage_model)
             cont, cont_mask = getattr(get_model(self.cond_stage_model),
                                       function_name)(prompt)
@@ -426,14 +426,14 @@ class ACEInference(DiffusionInference):
                                                 function_name)(n_prompt)
             null_cont, null_cont_mask = self.cond_stage_embeddings(
                 prompt, edit_image, null_cont, null_cont_mask)
-            if self.use_dynamic_model: self.dynamic_unload(self.cond_stage_model,
+            self.dynamic_unload(self.cond_stage_model,
                                 'cond_stage_model',
-                                skip_loaded=False)
+                                skip_loaded=not self.use_dynamic_model)
             ctx['crossattn'] = cont
             null_ctx['crossattn'] = null_cont
 
             # Encode Edit Images
-            if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
             edit_image = [to_device(i, strict=False) for i in edit_image]
             edit_image_mask = [to_device(i, strict=False) for i in edit_image_mask]
             e_img, e_mask = [], []
@@ -444,14 +444,14 @@ class ACEInference(DiffusionInference):
                     m = [None] * len(u)
                 e_img.append(self.encode_first_stage(u, **kwargs))
                 e_mask.append([self.interpolate_func(i) for i in m])
-            if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
+            self.dynamic_unload(self.first_stage_model,
                                 'first_stage_model',
-                                skip_loaded=True)
+                                skip_loaded=not self.use_dynamic_model)
             null_ctx['edit'] = ctx['edit'] = e_img
             null_ctx['edit_mask'] = ctx['edit_mask'] = e_mask
 
             # Diffusion Process
-            if self.use_dynamic_model: self.dynamic_load(self.diffusion_model, 'diffusion_model')
+            self.dynamic_load(self.diffusion_model, 'diffusion_model')
             function_name, dtype = self.get_function_info(self.diffusion_model)
             with torch.autocast('cuda',
                                 enabled=dtype in ('float16', 'bfloat16'),
@@ -492,17 +492,17 @@ class ACEInference(DiffusionInference):
                     guide_rescale=guide_rescale,
                     return_intermediate=None,
                     **kwargs)
-            if self.use_dynamic_model: self.dynamic_unload(self.diffusion_model,
+            self.dynamic_unload(self.diffusion_model,
                                 'diffusion_model',
-                                skip_loaded=False)
+                                skip_loaded=not self.use_dynamic_model)
 
             # Decode to Pixel Space
-            if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
             samples = unpack_tensor_into_imagelist(latent, x_shapes)
             x_samples = self.decode_first_stage(samples)
-            if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
+            self.dynamic_unload(self.first_stage_model,
                                 'first_stage_model',
-                                skip_loaded=False)
+                                skip_loaded=not self.use_dynamic_model)
             x_samples = [x.squeeze(0) for x in x_samples]
         else:
             x_samples = image
diff --git a/scepter/modules/solver/diffusion_solver.py b/scepter/modules/solver/diffusion_solver.py
index 3f5c14a..2d90d7b 100644
--- a/scepter/modules/solver/diffusion_solver.py
+++ b/scepter/modules/solver/diffusion_solver.py
@@ -197,7 +197,7 @@ class LatentDiffusionSolver(BaseSolver):
         else:
             self.logger.info('Use default backend.')
         self.use_scaler = cfg.get('USE_SCALER', True)
-        self.enable_gradscaler = cfg.get('ENABLE_GRADSCALER', True)
+        self.enable_gradscaler = cfg.get('ENABLE_GRADSCALER', False)
         self.use_orig_params = cfg.get('USE_ORIG_PARAMS', False)
         self.model_shard = cfg.get('SHARDING_STRATEGY', 'full_shard')
         self.sharding_size = cfg.get('SHARDING_SIZE', None)
@@ -422,8 +422,10 @@ class LatentDiffusionSolver(BaseSolver):
                                                     process_group=None)
                 else:
                     self.scaler = amp.GradScaler(enabled=self.enable_gradscaler)
-            else:
+            elif self.cfg.DTYPE in ['float16']:
                 self.scaler = amp.GradScaler()
+            else:
+                self.scaler = None
         else:
             self.scaler = None
 
diff --git a/scepter/modules/utils/visualization.py b/scepter/modules/utils/visualization.py
index 8f2810d..82ba90f 100644
--- a/scepter/modules/utils/visualization.py
+++ b/scepter/modules/utils/visualization.py
@@ -1,6 +1,10 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
+import copy
 from enum import Enum
+import os
+
+from scepter.modules.utils.file_system import FS
 
 
 class Media(Enum):
@@ -13,15 +17,17 @@ class Media(Enum):
 
 
 class HtmlVisualization(object):
-    def __init__(self,
-                 allow_annotation=False,
-                 slice_size=1000,
-                 align='center',
-                 width_scale='60%',
-                 title='Visualization',
-                 height=600,
-                 width=None,
-                 text_cols=40):
+    def __init__(
+            self,
+            allow_annotation=False,
+            slice_size=1000,
+            align='center',
+            width_scale='60%',
+            title='Visualization',
+            height=600,
+            width=None,
+            text_cols=40
+    ):
         self.content_list = []
         self.rows_meta = []
         self.allow_annotation = allow_annotation
@@ -31,9 +37,9 @@ class HtmlVisualization(object):
         self.title = title
         self.html_start = '<html>'
         self.html_head = f'<head><meta charset="utf-8"><title>{title}</title></head>'
-        self.height = height if height is not None else '600'
-        self.width = width if width is not None else 'auto'
-        self.text_cols = text_cols if text_cols is not None else 'auto'
+        self.height = height if height is not None else "600"
+        self.width = width if width is not None else "auto"
+        self.text_cols = text_cols if text_cols is not None else "auto"
         self.html_style = ('''
             <style> \n
                 .container {
@@ -82,10 +88,12 @@ class HtmlVisualization(object):
                     resize: none; \n
                     border: 1px solid #ccc; \n
                 } \n
+                .large-checkbox {transform: scale(2.5); margin-left: 20px; margin-bottom: 20px; vertical-align: middle;} \n
             </style> \n
             \n
-        '''.replace('{width_scale}', self.width_scale).replace(
-            '{align}', self.align).replace('{pair_height}', f'{self.height}'))
+        '''.replace('{width_scale}',
+                    self.width_scale).replace('{align}', self.align)
+                           .replace('{pair_height}', f'{self.height}'))
 
         self.html_body_script = '''
                 <script>\n
@@ -99,6 +107,7 @@ class HtmlVisualization(object):
                             isDragging = true;\n
                         });\n
 
+
                         container.addEventListener('mouseup', () => {\n
                             isDragging = true;\n
                         });\n
@@ -111,6 +120,9 @@ class HtmlVisualization(object):
 
                             let percentage = (clientX - left) / width * 100;\n
 
+
+                            // 限制百分比在0到100之间\n
+
                             percentage = Math.max(0, Math.min(100, percentage));\n
 
                             media2.style.clipPath = `inset(0 ${100 - percentage}% 0 0)`;\n
@@ -120,6 +132,7 @@ class HtmlVisualization(object):
                             console.info(slider.style.left);\n
 
                         });\n
+                        // 初始化滑块位置\n
                         slider.style.left = '50%';\n
                     });\n
                 </script>\n
@@ -152,16 +165,17 @@ class HtmlVisualization(object):
 
                 '''
         self.label_button = (
-            '<table><tr><td>' +
-            "<button style='height: 50px;' type=\"button\" onclick=\"saveSamples()\">Save Samples</button>"
-            + '</td></tr></table>')
+                '<table><tr><td>' +
+                "<button style='height: 50px;' type=\"button\" onclick=\"saveSamples()\">Save Samples</button>"
+                + '</td></tr></table>')
 
     def format_col(self,
                    content='',
                    label='',
                    type=Media.TEXT,
                    show_label=True,
-                   cols_span=1):
+                   cols_span=1
+                   ):
         if type == Media.TEXT:
             ret_str = '<textarea'  # noqa: E501
             if self.height is not None:
@@ -171,7 +185,7 @@ class HtmlVisualization(object):
                 cols = f"cols={self.text_cols * cols_span}"
                 ret_str += f" {cols}"
             ret_str += f'>"{content}"</textarea>'
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         elif type == Media.IMAGE:
             ret_str = f'<img src="{content}"'
             if self.height is not None:
@@ -181,7 +195,7 @@ class HtmlVisualization(object):
                 width = f'width="{self.width}"'
                 ret_str += f" {width}"
             ret_str += '  >'
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         elif type == Media.VIDEO:
             ret_str = '<video'  # noqa
             if self.height is not None:
@@ -192,70 +206,85 @@ class HtmlVisualization(object):
                 ret_str += f" {width}"
             ret_str += ' preload="none" autoplay muted loop>'
             ret_str += f'<source src="{content}" type="video/mp4"></video>'
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         elif type == Media.AUDIO:
             ret_str = f'<audio src="{content}" controls>'
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         elif type == Media.IMAGE_PAIR:
             assert isinstance(content, (list, tuple)) and len(content) == 2
-            ret_str = f'\n'  # noqa
-            ret_str += f'       <div class="container"'  # noqa
-            ret_str += (
-                f'> \n'
-                f'      <div class="image" id="media1">'  # noqa
-                f'          <img src="{content[1]}" alt="before">\n'  # noqa
-                f'      </div>\n'  # noqa
-                f'      <div class="image" id="media2" style="clip-path: inset(0 50% 0 0);">\n'  # noqa
-                f'            <img src="{content[0]}" alt="after">\n'  # noqa
-                f'      </div>\n'  # noqa
-                f'      <div class="slider" id="slider"></div>\n'  # noqa
-                f'')
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            ret_str = f'\n'
+            ret_str += f'       <div class="container"'
+            ret_str += (f'> \n'
+                        f'      <div class="image" id="media1">'
+                        f'          <img src="{content[1]}" alt="before">\n'
+                        f'      </div>\n'
+                        f'      <div class="image" id="media2" style="clip-path: inset(0 50% 0 0);">\n'
+                        f'            <img src="{content[0]}" alt="after">\n'
+                        f'      </div>\n'
+                        f'      <div class="slider" id="slider"></div>\n'
+                        f'')
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         elif type == Media.VIDEO_PAIR:
             assert isinstance(content, (list, tuple)) and len(content) == 2
-            ret_str = f'\n'  # noqa
-            ret_str += f'       <div class="container"'  # noqa
-            ret_str += (
-                f'> \n'
-                f'      <video autoplay muted loop class="video" id="media1"><source src="{content[1]}" type="video/mp4"></video>\n'  # noqa
-                f'      <video autoplay muted loop class="video" id="media2" style="clip-path: inset(0 50% 0 0);"><source src="{content[0]}" type="video/mp4"></video>\n'  # noqa
-                f'      <div class="slider" id="slider"></div>\n'  # noqa
-                f'</div>')
-            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ''
+            ret_str = f'\n'
+            ret_str += f'       <div class="container"'
+            ret_str += (f'> \n'
+                        f'      <video autoplay muted loop class="video" id="media1"><source src="{content[1]}" type="video/mp4"></video>\n'
+                        f'      <video autoplay muted loop class="video" id="media2" style="clip-path: inset(0 50% 0 0);"><source src="{content[0]}" type="video/mp4"></video>\n'
+                        f'      <div class="slider" id="slider"></div>\n'
+                        f'</div>')
+            sec_ret_str = f'<font size="3"><strong>{label}<strong></font>' if show_label else ""
         else:
             raise NotImplementedError
+
+        if self.allow_annotation:
+            ret_str = f'<label for="sample#sample_id#">{ret_str}</label>'
+
         if cols_span > 1:
             ret_str = f'<th colspan="{cols_span}">{ret_str}</th>\n'
-            sec_ret_str = f'<th colspan="{cols_span}">{sec_ret_str}</th>\n' if not sec_ret_str == '' else sec_ret_str
+            sec_ret_str = f'<th colspan="{cols_span}">{sec_ret_str}</th>\n' if not sec_ret_str == "" else sec_ret_str
         else:
             ret_str = f'<td>{ret_str}</td>\n'
-            sec_ret_str = f'<td align="center">{sec_ret_str}</td>\n' if not sec_ret_str == '' else sec_ret_str
+            sec_ret_str = f'<td align="center">{sec_ret_str}</td>\n' if not sec_ret_str == "" else sec_ret_str
         return [ret_str, sec_ret_str]
 
     def format_row(self):
-        sample_id = 0
-        all_sample_html = []
-        for one_content, one_row_meta in zip(self.content_list,
-                                             self.rows_meta):
-            one_row_str = '<tr>'
-            one_row_str += '\n'.join([v[0] for v in one_content])
-            if self.allow_annotation:
-                row_meta = '#;#'.join(one_row_meta)
-                one_row_str += (
-                    f'<td><input type="checkbox" class="large-checkbox" '
-                    f'id="sample{sample_id}" name="sample[]" value="{row_meta}"></td>\n'
-                )
-            one_row_str += '</tr><tr>'
-            one_row_str += '\n'.join([v[1] for v in one_content])  # noqa
-            if self.allow_annotation:  # noqa
-                one_row_str += f'<td></td>\n'  # noqa
-            one_row_str += '</tr>'
-            if self.allow_annotation:
-                one_row_str = f'<label for="sample{sample_id}">{one_row_str}</label>'
-            all_sample_html.append(one_row_str)
-            sample_id += 1
 
-        return '<table>' + '\n'.join(all_sample_html) + '</table>'
+        all_sample_html = []
+        current_content_list = copy.deepcopy(self.content_list)
+        current_rows_meta = copy.deepcopy(self.rows_meta)
+
+        while len(current_content_list) > 0:
+            sample_id = 0
+            batch_content_list = current_content_list[:self.slice_size]
+            current_content_list = current_content_list[self.slice_size:]
+            batch_rows_meta = current_rows_meta[:self.slice_size]
+            current_rows_meta = current_rows_meta[self.slice_size:]
+            current_sample_html = []
+            for one_content, one_row_meta in zip(batch_content_list,
+                                                 batch_rows_meta):
+                one_row_str = '<tr>'
+                if not self.allow_annotation:
+                    one_row_str += '\n'.join([v[0] for v in one_content])
+                else:
+                    one_row_str += '\n'.join([v[0].replace('#sample_id#', f'{sample_id}')  for v in one_content])
+                    row_meta = '#;#'.join(one_row_meta)
+                    one_row_str += (
+                        f'<td><input type="checkbox" class="large-checkbox" '
+                        f'id="sample{sample_id}" name="sample[]" value="{row_meta}"></td>\n'
+                    )
+                one_row_str += '</tr><tr>'
+                one_row_str += '\n'.join([v[1] for v in one_content])  # noqa
+                if self.allow_annotation:  # noqa
+                    one_row_str += f'<td></td>\n'  # noqa
+                one_row_str += '</tr>'
+                # if self.allow_annotation:
+                #     one_row_str = f'<label for="sample{sample_id}">{one_row_str}</label>'
+                current_sample_html.append(one_row_str)
+                sample_id += 1
+            all_sample_html.append("<table>" + '\n'.join(current_sample_html) + "</table>")
+
+        return all_sample_html
 
     def add_record(self,
                    content,
@@ -275,11 +304,8 @@ class HtmlVisualization(object):
         if col_id > len(self.content_list[row_id]):
             raise RuntimeError(
                 'col_id should be next number of the last col_id.')
-        format_col = self.format_col(content,
-                                     f"{row_id}-{col_id}: {label}",
-                                     type,
-                                     show_label=show_label,
-                                     cols_span=cols_span)
+        format_col = self.format_col(content, f"{row_id}-{col_id}: {label}",
+                                     type, show_label=show_label, cols_span=cols_span)
 
         annotation_meta = annotation_meta if annotation_meta else ''
         if col_id == len(self.content_list[row_id]):
@@ -291,14 +317,30 @@ class HtmlVisualization(object):
 
     def save_html(self, path):
         html_body = self.format_row()
-        ret_html_list = [
-            self.html_start, self.html_head, self.html_style,
-            self.html_body.replace('{BODY}', html_body)
-        ]
-        if self.allow_annotation:
-            ret_html_list.append(self.label_button)
-            ret_html_list.append(self.html_script)
-        ret_html_list.append(self.html_end)
-        ret_html = '\n'.join(ret_html_list)
-        with open(path, 'w') as f:
-            f.write(ret_html)
+        if isinstance(html_body, list) and len(html_body) > 1:
+            try:
+                os.makedirs(path, exist_ok=True)
+            except:
+                print("Create folder path failed.")
+            for html_id, one_html in enumerate(html_body):
+                ret_html_list = [
+                    self.html_start, self.html_head, self.html_style,
+                    self.html_body.replace('{BODY}', one_html)
+                ]
+                if self.allow_annotation:
+                    ret_html_list.append(self.label_button)
+                    ret_html_list.append(self.html_script)
+                ret_html_list.append(self.html_end)
+                ret_html = '\n'.join(ret_html_list)
+                FS.put_object(ret_html.encode(), os.path.join(path, f"{html_id}.html"))
+        else:
+            ret_html_list = [
+                self.html_start, self.html_head, self.html_style,
+                self.html_body.replace('{BODY}', html_body[0])
+            ]
+            if self.allow_annotation:
+                ret_html_list.append(self.label_button)
+                ret_html_list.append(self.html_script)
+            ret_html_list.append(self.html_end)
+            ret_html = '\n'.join(ret_html_list)
+            FS.put_object(ret_html.encode(), path)