diff --git a/CHANGELOG.md b/CHANGELOG.md
index 98fc35937..8d195bbc9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2023-01-02
+## Update for 2023-01-03
 
 Following-up on a major release, some more functionality in new Control module  
 And it also includes fixes for all reported issues so far  
@@ -18,8 +18,12 @@ And it also includes fixes for all reported issues so far
   - auto-refresh available models on tab activate  
   - reduce usage of temp files  
   - add context menu to action buttons  
+  - resize by now applies to input image or frame individually  
+    allows for processing where input images are of different sizes  
   - fix input image size  
+  - fix video color mode  
   - fix correct image mode  
+  - fix batch/folder/video modes  
 - **Improvements**  
   - allow deployment without git clone  
     for example, you can now deploy a zip of the sdnext folder  
@@ -29,6 +33,8 @@ And it also includes fixes for all reported issues so far
   - cli: sdapi.py allow manual api invoke  
     example: `python cli/sdapi.py /sdapi/v1/sd-models`  
   - memory: add ram usage monitoring in addition to gpu memory usage monitoring  
+  - vae: enable taesd batch decode  
+    enable/disable with settings -> diffusers > vae slicing  
   - updated core requirements  
 - **Compile**
   - new option: **fused projections**  
@@ -55,6 +61,7 @@ And it also includes fixes for all reported issues so far
   - sampler: guard against invalid sampler index  
   - config: reset default cfg scale to 6.0  
   - processing: correct display metadata  
+  - live preview: fix when using `bfloat16`
   - upscale: fix ldsr
 
 ## Update for 2023-12-29
diff --git a/extensions-builtin/sd-webui-controlnet b/extensions-builtin/sd-webui-controlnet
index 4cf15d1c9..8bd8057cc 160000
--- a/extensions-builtin/sd-webui-controlnet
+++ b/extensions-builtin/sd-webui-controlnet
@@ -1 +1 @@
-Subproject commit 4cf15d1c9c565b8d0c5f782a89c5a6286dc6e6ff
+Subproject commit 8bd8057cca51fb0d951a9b8be42bc9873b6087da
diff --git a/javascript/control.js b/javascript/control.js
index 95d288eae..495e3a771 100644
--- a/javascript/control.js
+++ b/javascript/control.js
@@ -1,7 +1,12 @@
 function controlInputMode(inputMode, ...args) {
-  if (!gradioApp().getElementById('control_input_select').classList.contains('hidden')) inputMode = 'Select';
-  else if (!gradioApp().getElementById('control_input_resize').classList.contains('hidden')) inputMode = 'Outpaint';
-  else if (!gradioApp().getElementById('control_input_inpaint').classList.contains('hidden')) inputMode = 'Inpaint';
+  const tab = gradioApp().querySelector('#control-tab-input button.selected');
+  if (!tab) return ['Select', ...args];
+  inputMode = tab.innerText;
+  if (inputMode === 'Image') {
+    if (!gradioApp().getElementById('control_input_select').classList.contains('hidden')) inputMode = 'Select';
+    else if (!gradioApp().getElementById('control_input_resize').classList.contains('hidden')) inputMode = 'Outpaint';
+    else if (!gradioApp().getElementById('control_input_inpaint').classList.contains('hidden')) inputMode = 'Inpaint';
+  }
   return [inputMode, ...args];
 }
 
diff --git a/javascript/progressBar.js b/javascript/progressBar.js
index 85c339f26..238a2988e 100644
--- a/javascript/progressBar.js
+++ b/javascript/progressBar.js
@@ -85,7 +85,7 @@ function requestProgress(id_task, progressEl, galleryEl, atEnd = null, onProgres
   let livePreview;
   let img;
 
-  const init = () => {
+  const initLivePreview = () => {
     img = new Image();
     if (parentGallery) {
       livePreview = document.createElement('div');
@@ -123,7 +123,7 @@ function requestProgress(id_task, progressEl, galleryEl, atEnd = null, onProgres
         return;
       }
       setProgress(res);
-      if (res.live_preview && !livePreview) init();
+      if (res.live_preview && !livePreview) initLivePreview();
       if (res.live_preview && galleryEl) img.src = res.live_preview;
       if (onProgress) onProgress(res);
       setTimeout(() => start(id_task, id_live_preview), opts.live_preview_refresh_period || 500);
diff --git a/modules/control/run.py b/modules/control/run.py
index ee04231f8..147363722 100644
--- a/modules/control/run.py
+++ b/modules/control/run.py
@@ -130,9 +130,6 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
     if resize_mode != 0 or inputs is None or inputs == [None]:
         p.width = width # pylint: disable=attribute-defined-outside-init
         p.height = height # pylint: disable=attribute-defined-outside-init
-        if selected_scale_tab == 1:
-            width = int(width * scale_by)
-            height = int(height * scale_by)
     else:
         del p.width
         del p.height
@@ -293,6 +290,8 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
                     w, h = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
                     codec = util.decode_fourcc(video.get(cv2.CAP_PROP_FOURCC))
                     status, frame = video.read()
+                    if status:
+                        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                     shared.log.debug(f'Control: input video: path={inputs} frames={frames} fps={fps} size={w}x{h} codec={codec}')
                 except Exception as e:
                     msg = f'Control: video open failed: path={inputs} {e}'
@@ -344,6 +343,9 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
                     # resize
                     if p.resize_mode != 0 and input_image is not None:
                         p.extra_generation_params["Control resize"] = f'{resize_time}: {resize_name}'
+                        if selected_scale_tab == 1:
+                            width = int(input_image.width * scale_by)
+                            height = int(input_image.height * scale_by)
                     if p.resize_mode != 0 and input_image is not None and resize_time == 'Before':
                         debug(f'Control resize: image={input_image} width={width} height={height} mode={p.resize_mode} name={resize_name} sequence={resize_time}')
                         input_image = images.resize_image(p.resize_mode, input_image, width, height, resize_name)
@@ -502,6 +504,8 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
 
                 if video is not None and frame is not None:
                     status, frame = video.read()
+                    if status:
+                        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                     debug(f'Control: video frame={index} frames={frames} status={status} skip={index % (video_skip_frames + 1)} progress={index/frames:.2f}')
                 else:
                     status = False
diff --git a/modules/devices.py b/modules/devices.py
index cc5c8eda2..956d8fb93 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -181,7 +181,7 @@ def test_fp16():
     if shared.cmd_opts.experimental:
         return True
     try:
-        x = torch.tensor([[1.5,.0,.0,.0]]).to(device).half()
+        x = torch.tensor([[1.5,.0,.0,.0]]).to(device=device, dtype=torch.float16)
         layerNorm = torch.nn.LayerNorm(4, eps=0.00001, elementwise_affine=True, dtype=torch.float16, device=device)
         _y = layerNorm(x)
         return True
@@ -228,20 +228,20 @@ def set_cuda_params():
         dtype = torch.float32
         dtype_vae = torch.float32
         dtype_unet = torch.float32
-    if shared.opts.cuda_dtype == 'BF16' or dtype == torch.bfloat16:
+        fp16_ok = None
+        bf16_ok = None
+    elif shared.opts.cuda_dtype == 'BF16' or dtype == torch.bfloat16:
+        fp16_ok = test_fp16()
         bf16_ok = test_bf16()
         dtype = torch.bfloat16 if bf16_ok else torch.float16
         dtype_vae = torch.bfloat16 if bf16_ok else torch.float16
         dtype_unet = torch.bfloat16 if bf16_ok else torch.float16
-    else:
-        bf16_ok = False
-    if shared.opts.cuda_dtype == 'FP16' or dtype == torch.float16:
+    elif shared.opts.cuda_dtype == 'FP16' or dtype == torch.float16:
         fp16_ok = test_fp16()
+        bf16_ok = None
         dtype = torch.float16 if fp16_ok else torch.float32
         dtype_vae = torch.float16 if fp16_ok else torch.float32
         dtype_unet = torch.float16 if fp16_ok else torch.float32
-    else:
-        fp16_ok = False
     if shared.opts.no_half:
         log.info('Torch override dtype: no-half set')
         dtype = torch.float32
diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index bd9425de7..7e98f44ad 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -80,12 +80,15 @@ def full_vae_encode(image, model):
 
 
 def taesd_vae_decode(latents):
-    debug(f'VAE decode: name=TAESD images={len(latents)} latents={latents.shape}')
+    debug(f'VAE decode: name=TAESD images={len(latents)} latents={latents.shape} slicing={shared.opts.diffusers_vae_slicing}')
     if len(latents) == 0:
         return []
-    decoded = torch.zeros((len(latents), 3, latents.shape[2] * 8, latents.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
-    for i in range(latents.shape[0]):
-        decoded[i] = sd_vae_taesd.decode(latents[i])
+    if shared.opts.diffusers_vae_slicing:
+        decoded = torch.zeros((len(latents), 3, latents.shape[2] * 8, latents.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
+        for i in range(latents.shape[0]):
+            decoded[i] = sd_vae_taesd.decode(latents[i])
+    else:
+        decoded = sd_vae_taesd.decode(latents)
     return decoded
 
 
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 88f511a5d..e749aa378 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -31,13 +31,15 @@ def setup_img2img_steps(p, steps=None):
 
 
 def single_sample_to_image(sample, approximation=None):
-    # sample should be [4,64,64]
     if approximation is None:
         approximation = approximation_indexes.get(shared.opts.show_progress_type, None)
         if approximation is None:
             warn_once('Unknown decode type, please reset preview method')
             approximation = 0
 
+    # normal sample is [4,64,64]
+    if sample.dtype == torch.bfloat16:
+        sample = sample.to(torch.float16)
     if len(sample.shape) > 4: # likely unknown video latent (e.g. svd)
         return Image.new(mode="RGB", size=(512, 512))
     if len(sample.shape) == 4 and sample.shape[0]: # likely animatediff latent
@@ -47,7 +49,7 @@ def single_sample_to_image(sample, approximation=None):
     elif approximation == 1: # Approximate
         x_sample = sd_vae_approx.nn_approximation(sample) * 0.5 + 0.5
         if shared.sd_model_type == "sdxl":
-            x_sample = x_sample[[2,1,0],:,:] # BGR to RGB
+            x_sample = x_sample[[2,1,0], :, :] # BGR to RGB
     elif approximation == 2: # TAESD
         x_sample = sd_vae_taesd.decode(sample)
         x_sample = (1.0 + x_sample) / 2.0 # preview requires smaller range
@@ -58,10 +60,12 @@ def single_sample_to_image(sample, approximation=None):
         return Image.new(mode="RGB", size=(512, 512))
 
     try:
+        if x_sample.dtype == torch.bfloat16:
+           x_sample.to(torch.float16)
         transform = T.ToPILImage()
         image = transform(x_sample)
     except Exception as e:
-        warn_once(f'Transform tensor to image: {e}')
+        warn_once(f'Live preview: {e}')
         image = Image.new(mode="RGB", size=(512, 512))
     return image
 
diff --git a/modules/sd_vae_approx.py b/modules/sd_vae_approx.py
index 2a7b0c9ef..14a67fca1 100644
--- a/modules/sd_vae_approx.py
+++ b/modules/sd_vae_approx.py
@@ -42,10 +42,10 @@ def nn_approximation(sample): # Approximate NN
         approx_weights = torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None)
         sd_vae_approx_model.load_state_dict(approx_weights)
         sd_vae_approx_model.eval()
-        sd_vae_approx_model.to(devices.device, devices.dtype)
+        sd_vae_approx_model.to(devices.device, sample.dtype)
         shared.log.debug(f'Load VAE decode approximate: model="{model_path}"')
     try:
-        in_sample = sample.to(devices.device, devices.dtype).unsqueeze(0)
+        in_sample = sample.to(devices.device).unsqueeze(0)
         x_sample = sd_vae_approx_model(in_sample)
         x_sample = x_sample[0]
         return x_sample
@@ -71,7 +71,9 @@ def cheap_approximation(sample): # Approximate simple
         ]).reshape(3, 4, 1, 1)
         simple_bias = None
     try:
-        x_sample = nn.functional.conv2d(sample, simple_weights.to(sample.device, sample.dtype), simple_bias.to(sample.device, sample.dtype) if simple_bias is not None else None) # pylint: disable=not-callable
+        weights = simple_weights.to(sample.device, sample.dtype)
+        bias = simple_bias.to(sample.device, sample.dtype) if simple_bias is not None else None
+        x_sample = nn.functional.conv2d(sample, weights, bias) # pylint: disable=not-callable
         return x_sample
     except Exception as e:
         shared.log.error(f'Decode simple: {e}')
diff --git a/modules/shared.py b/modules/shared.py
index c3d5b3120..6008d0af5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -367,7 +367,7 @@ options_templates.update(options_section(('advanced', "Inference Settings"), {
     "inference_other_sep": OptionInfo("<h2>Other</h2>", "", gr.HTML),
     "batch_frame_mode": OptionInfo(False, "Process multiple images in batch in parallel"),
     "inference_mode": OptionInfo("no-grad", "Torch inference mode", gr.Radio, {"choices": ["no-grad", "inference-mode", "none"]}),
-    "sd_vae_sliced_encode": OptionInfo(False, "VAE Slicing (original)"),
+    "sd_vae_sliced_encode": OptionInfo(False, "VAE sliced encode"),
 }))
 
 options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
diff --git a/modules/taesd/sd_vae_taesd.py b/modules/taesd/sd_vae_taesd.py
index 82817695e..5b38ed259 100644
--- a/modules/taesd/sd_vae_taesd.py
+++ b/modules/taesd/sd_vae_taesd.py
@@ -58,10 +58,19 @@ def decode(latents):
             taesd_models[f'{model_class}-decoder'] = TAESD(decoder_path=model_path, encoder_path=None)
             vae = taesd_models[f'{model_class}-decoder']
             vae.to(devices.device, devices.dtype_vae)
-    enc = latents.unsqueeze(0).to(devices.device, devices.dtype_vae)
-    image = vae.decoder(enc).clamp(0, 1).detach()
-    image = 2.0 * image - 1.0 # typical normalized range except for preview which runs denormalization
-    return image[0]
+    latents.to(devices.device, devices.dtype_vae)
+    if len(latents.shape) == 3:
+        latents = latents.unsqueeze(0)
+        image = vae.decoder(latents).clamp(0, 1).detach()
+        image = 2.0 * image - 1.0 # typical normalized range except for preview which runs denormalization
+        return image[0]
+    elif len(latents.shape) == 4:
+        image = vae.decoder(latents).clamp(0, 1).detach()
+        image = 2.0 * image - 1.0 # typical normalized range except for preview which runs denormalization
+        return image
+    else:
+        shared.log.error(f'TAESD decode unsupported latent type: {latents.shape}')
+        return latents
 
 
 def encode(image):
diff --git a/modules/ui_control.py b/modules/ui_control.py
index 9c06965ca..ea9de4336 100644
--- a/modules/ui_control.py
+++ b/modules/ui_control.py
@@ -155,7 +155,7 @@ def expand_mask(image: Image.Image, blur: int = 0, erode: int = 3, dilate: int =
     return image_mask
 
 
-def select_input(input_mode, input_image, selected_init, init_type, input_resize, input_inpaint, mask_blur, mask_overlap):
+def select_input(input_mode, input_image, selected_init, init_type, input_resize, input_inpaint, input_video, input_batch, input_folder, mask_blur, mask_overlap):
     global busy, input_source, input_init, input_mask # pylint: disable=global-statement
     busy = True
     if input_mode == 'Select':
@@ -164,6 +164,12 @@ def select_input(input_mode, input_image, selected_init, init_type, input_resize
         selected_input = input_resize
     elif input_mode == 'Inpaint':
         selected_input = input_inpaint
+    elif input_mode == 'Video':
+        selected_input = input_video
+    elif input_mode == 'Batch':
+        selected_input = input_batch
+    elif input_mode == 'Folder':
+        selected_input = input_folder
     else:
         selected_input = None
     if selected_input is None:
@@ -318,7 +324,7 @@ def create_ui(_blocks: gr.Blocks=None):
                     with gr.Row():
                         input_type = gr.Radio(label="Input type", choices=['Control only', 'Init image same as control', 'Separate init image'], value='Control only', type='index', elem_id='control_input_type')
                     with gr.Row():
-                        denoising_strength = gr.Slider(minimum=0.01, maximum=0.99, step=0.01, label='Denoising strength', value=0.50, elem_id="control_denoising_strength")
+                        denoising_strength = gr.Slider(minimum=0.01, maximum=1.0, step=0.01, label='Denoising strength', value=0.50, elem_id="control_denoising_strength")
                     with gr.Row():
                         mask_blur = gr.Slider(minimum=0, maximum=100, step=1, label='Blur', value=8, elem_id="control_mask_blur")
                         mask_overlap = gr.Slider(minimum=0, maximum=100, step=1, label='Overlap', value=8, elem_id="control_mask_overlap")
@@ -338,7 +344,7 @@ def create_ui(_blocks: gr.Blocks=None):
                         video_skip_frames = gr.Slider(minimum=0, maximum=100, step=1, label='Skip input frames', value=0, elem_id="control_video_skip_frames")
                     with gr.Row():
                         video_type = gr.Dropdown(label='Video file', choices=['None', 'GIF', 'PNG', 'MP4'], value='None')
-                        video_duration = gr.Slider(label='Duration', minimum=0.25, maximum=10, step=0.25, value=2, visible=False)
+                        video_duration = gr.Slider(label='Duration', minimum=0.25, maximum=300, step=0.25, value=2, visible=False)
                     with gr.Row():
                         video_loop = gr.Checkbox(label='Loop', value=True, visible=False)
                         video_pad = gr.Slider(label='Pad frames', minimum=0, maximum=24, step=1, value=1, visible=False)
@@ -677,7 +683,7 @@ def create_ui(_blocks: gr.Blocks=None):
                 btn_prompt_counter.click(fn=call_queue.wrap_queued_call(ui.update_token_counter), inputs=[prompt, steps], outputs=[prompt_counter])
                 btn_negative_counter.click(fn=call_queue.wrap_queued_call(ui.update_token_counter), inputs=[negative, steps], outputs=[negative_counter])
 
-                select_fields = [input_mode, input_image, init_image, input_type, input_resize, input_inpaint, mask_blur, mask_overlap]
+                select_fields = [input_mode, input_image, init_image, input_type, input_resize, input_inpaint, input_video, input_batch, input_folder, mask_blur, mask_overlap]
                 select_output = [output_tabs, result_txt]
                 select_dict = dict(
                     fn=select_input,
diff --git a/wiki b/wiki
index 4e9d25efd..ff8c39689 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 4e9d25efd5d5e0f65d04cbca3506ed725437cb0b
+Subproject commit ff8c3968918bcdaeffc7345cca684423c5e619a8