diff --git a/aiserver.py b/aiserver.py
index 5529d725..78b30259 100644
--- a/aiserver.py
+++ b/aiserver.py
@@ -1477,7 +1477,7 @@ def general_startup(override_args=None):
     parser.add_argument("--cacheonly", action='store_true', help="Does not save the model to the models folder when it has been downloaded in the cache")
     parser.add_argument("--customsettings", help="Preloads arguements from json file. You only need to provide the location of the json file. Use customsettings.json template file. It can be renamed if you wish so that you can store multiple configurations. Leave any settings you want as default as null. Any values you wish to set need to be in double quotation marks")
     parser.add_argument("--no_ui", action='store_true', default=False, help="Disables the GUI and Socket.IO server while leaving the API server running.")
-    parser.add_argument("--summarizer_model", action='store', default="philschmid/bart-large-cnn-samsum", help="Huggingface model to use for summarization. Defaults to sshleifer/distilbart-cnn-12-6")
+    parser.add_argument("--summarizer_model", action='store', default="pszemraj/led-large-book-summary", help="Huggingface model to use for summarization. Defaults to pszemraj/led-large-book-summary")
     parser.add_argument("--max_summary_length", action='store', default=75, help="Maximum size for summary to send to image generation")
     parser.add_argument("--multi_story", action='store_true', default=False, help="Allow multi-story mode (experimental)")
     parser.add_argument("--peft", type=str, help="Specify the path or HuggingFace ID of a Peft to load it. Not supported on TPU. (Experimental)") 
@@ -7586,8 +7586,11 @@ def text2img_api(prompt, art_guide="") -> Image.Image:
 @socketio.on("clear_generated_image")
 @logger.catch
 def UI2_clear_generated_image(data):
-    koboldai_vars.picture = ""
-    koboldai_vars.picture_prompt = ""
+    if 'action_id' in data and data['action_id'] is not None:
+        koboldai_vars.actions.clear_picture(data['action_id'])
+    else:
+        koboldai_vars.picture = ""
+        koboldai_vars.picture_prompt = ""
 
 #==================================================================#
 # Retrieve previous images
@@ -7600,7 +7603,9 @@ def UI_2_get_story_image(data):
     print(filename)
     if filename is not None:
         with open(filename, "rb") as image_file:
-            return base64.b64encode(image_file.read()).decode("utf-8") 
+            return {'img': base64.b64encode(image_file.read()).decode("utf-8"), 'action_id': action_id}
+    else:
+        return {'img': None, 'action_id': action_id}
 
 #@logger.catch
 def get_items_locations_from_text(text):
@@ -7651,16 +7656,19 @@ def get_items_locations_from_text(text):
 #==================================================================#
 def summarize(text, max_length=100, min_length=30, unload=True):
     from transformers import pipeline as summary_pipeline
+    from transformers import AutoConfig
     start_time = time.time()
     if koboldai_vars.summarizer is None:
         if os.path.exists("functional_models/{}".format(args.summarizer_model.replace('/', '_'))):
             koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
             koboldai_vars.summarizer = AutoModelForSeq2SeqLM.from_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
+            koboldai_vars.summary_model_config = AutoConfig.from_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
         else:
             koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained(args.summarizer_model, cache_dir="cache")
             koboldai_vars.summarizer = AutoModelForSeq2SeqLM.from_pretrained(args.summarizer_model, cache_dir="cache")
             koboldai_vars.summary_tokenizer.save_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), max_shard_size="500MiB")
             koboldai_vars.summarizer.save_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), max_shard_size="500MiB")
+            koboldai_vars.summary_model_config = AutoConfig.from_pretrained(args.summarizer_model, cache_dir="cache")
 
     #Try GPU accel
     if koboldai_vars.hascuda and torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_reserved(0) >= 1645778560:
@@ -7674,9 +7682,27 @@ def summarize(text, max_length=100, min_length=30, unload=True):
     #Actual sumarization
     start_time = time.time()
     #make sure text is less than 1024 tokens, otherwise we'll crash
-    if len(koboldai_vars.summary_tokenizer.encode(text)) > 1000:
-        text = koboldai_vars.summary_tokenizer.decode(koboldai_vars.summary_tokenizer.encode(text)[:1000])
-    output = tpool.execute(summarizer, text, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text']
+    max_tokens = koboldai_vars.summary_model_config.max_encoder_position_embeddings if hasattr(koboldai_vars.summary_model_config, 'max_encoder_position_embeddings') else 1024
+    logger.info("Using max summary tokens of {}".format(max_tokens))
+    if len(koboldai_vars.summary_tokenizer.encode(text)) > max_tokens:
+        text_list = koboldai_vars.actions.sentence_re.findall(text)
+        i=0
+        while i <= len(text_list)-2:
+            if len(koboldai_vars.summary_tokenizer.encode(text_list[i] + text_list[i+1])) < max_tokens:
+                text_list[i] = text_list[i] + text_list[i+1]
+                del text_list[i+1]
+            else:
+                i+=1
+                    
+        
+    else:
+        text_list = [text]
+    
+    output = []
+    logger.info("Summarizing with {} chunks of length {}".format(len(text_list), [len(koboldai_vars.summary_tokenizer.encode(x)) for x in text_list]))
+    for text in text_list:
+        output.append(tpool.execute(summarizer, text, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text'])
+    output = " ".join(output)
     logger.debug("Time to summarize: {}".format(time.time()-start_time))
     #move model back to CPU to save precious vram
     torch.cuda.empty_cache()
@@ -7696,40 +7722,33 @@ def summarize(text, max_length=100, min_length=30, unload=True):
 @socketio.on("refresh_auto_memory")
 @logger.catch
 def UI_2_refresh_auto_memory(data):
+    max_output_length=500
+    from transformers import AutoConfig
     koboldai_vars.auto_memory = "Generating..."
     if koboldai_vars.summary_tokenizer is None:
-        if os.path.exists("models/{}".format(args.summarizer_model.replace('/', '_'))):
-            koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
+        if os.path.exists("functional_models/{}".format(args.summarizer_model.replace('/', '_'))):
+            koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
+            koboldai_vars.summary_model_config = AutoConfig.from_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
         else:
             koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained(args.summarizer_model, cache_dir="cache")
-    #first, let's get all of our game text and split it into sentences
-    sentences = [x[0] for x in koboldai_vars.actions.to_sentences()]
-    sentences_lengths = [len(koboldai_vars.summary_tokenizer.encode(x)) for x in sentences]
+            koboldai_vars.summary_model_config = AutoConfig.from_pretrained(args.summarizer_model, cache_dir="cache")
+    max_tokens = koboldai_vars.summary_model_config.max_encoder_position_embeddings if hasattr(koboldai_vars.summary_model_config, 'max_encoder_position_embeddings') else 1024
+
+    #first, let's get all of our game text
+    sentences = "".join([x[0] for x in koboldai_vars.actions.to_sentences()])
     
     pass_number = 1
-    while len(koboldai_vars.summary_tokenizer.encode("".join(sentences))) > 1000:
-        #Now let's split them into 1000 token chunks
-        summary_chunks = [""]
-        summary_chunk_lengths = [0]
-        for i in range(len(sentences)):
-            if summary_chunk_lengths[-1] + sentences_lengths[i] <= 1000:
-                summary_chunks[-1] += sentences[i]
-                summary_chunk_lengths[-1] += sentences_lengths[i]
-            else:
-                summary_chunks.append(sentences[i])
-                summary_chunk_lengths.append(sentences_lengths[i])
-        new_sentences = []
-        i=0
-        for summary_chunk in summary_chunks:
-            logger.debug("summarizing chunk {}".format(i))
-            new_sentences.extend(re.split("(?<=[.!?])\s+", summarize(summary_chunk, unload=False)))
-            i+=1
+    while len(koboldai_vars.summary_tokenizer.encode(sentences)) > max_tokens:
+        new_sentences = summarize(sentences, unload=False, max_length=max_output_length)
         logger.debug("Pass {}:\nSummarized to {} sentencees from {}".format(pass_number, len(new_sentences), len(sentences)))
         sentences = new_sentences
-        koboldai_vars.auto_memory += "Pass {}:\n{}\n\n".format(pass_number, "\n".join(sentences))
+        koboldai_vars.auto_memory += "Pass {}:\n{}\n\n".format(pass_number, sentences)
         pass_number+=1
     logger.debug("OK, doing final summarization")
-    output = summarize(" ".join(sentences))
+    if len(koboldai_vars.summary_tokenizer.encode(sentences)) > max_output_length:
+        output = summarize(sentences, max_length=max_output_length)
+    else:
+        output = sentences
     koboldai_vars.auto_memory += "\n\n Final Result:\n" + output
 
 
diff --git a/koboldai_settings.py b/koboldai_settings.py
index 1c02d359..aaf128da 100644
--- a/koboldai_settings.py
+++ b/koboldai_settings.py
@@ -687,8 +687,9 @@ class settings(object):
 
 class model_settings(settings):
     local_only_variables = ['apikey', 'default_preset']
-    no_save_variables = ['modelconfig', 'custmodpth', 'generated_tkns', 
-                         'loaded_layers', 'total_layers', 'total_download_chunks', 'downloaded_chunks', 'presets', 'default_preset', 
+    no_save_variables = ['modelconfig', 'custmodpth', 'generated_tkns',
+                         'loaded_layers', 'total_layers', 'loaded_checkpoints', 'total_checkpoints',
+                         'total_download_chunks', 'downloaded_chunks', 'presets', 'default_preset',
                          'welcome', 'welcome_default', 'simple_randomness', 'simple_creativity', 'simple_repitition',
                          'badwordsids', 'uid_presets', 'model', 'model_type', 'lazy_load', 'fp32_model', 'modeldim', 'horde_wait_time', 'horde_queue_position', 'horde_queue_size', 'newlinemode', 'tqdm_progress', 'tqdm_rem_time', '_tqdm']
     settings_name = "model"
@@ -705,6 +706,8 @@ class model_settings(settings):
         self.generated_tkns = 0    # If using a backend that supports Lua generation modifiers, how many tokens have already been generated, otherwise 0
         self.loaded_layers = 0     # Used in UI 2 to show model loading progress
         self.total_layers = 0      # Same as above
+        self.loaded_checkpoints = 0
+        self.total_checkpoints = 1
         self.total_download_chunks = 0 # tracks how much of the model has downloaded for the UI 2
         self.downloaded_chunks = 0 #as above
         self._tqdm        = tqdm.tqdm(total=self.genamt, file=self.ignore_tqdm())    # tqdm agent for generating tokens. This will allow us to calculate the remaining time
@@ -829,13 +832,22 @@ class model_settings(settings):
         #Setup TQDP for model loading
         elif name == "loaded_layers" and '_tqdm' in self.__dict__:
             if value == 0:
-                self._tqdm.reset(total=self.total_layers)
+                self._tqdm.reset(total=self.total_layers if self.total_checkpoints == 1 else 1000)
                 self.tqdm_progress = 0
             else:
-                self._tqdm.update(1)
-                self.tqdm_progress = int(float(self.loaded_layers)/float(self.total_layers)*100)
+                if self.total_checkpoints == 1:
+                    self._tqdm.update(1)
+                elif self.total_layers != 0 and self.total_checkpoints != 0:
+                    proper_progress = (self.loaded_checkpoints + value/self.total_layers)/self.total_checkpoints*1000
+                    self._tqdm.update(proper_progress - self._tqdm.n)
+                    
+                self.tqdm_progress = int(float(self._tqdm.n)/float(self._tqdm.total)*100)
+                
                 if self._tqdm.format_dict['rate'] is not None:
-                    self.tqdm_rem_time = str(datetime.timedelta(seconds=int(float(self.total_layers-self.loaded_layers)/self._tqdm.format_dict['rate'])))  
+                    elapsed = self._tqdm.format_dict["elapsed"]
+                    rate = self._tqdm.format_dict["rate"]
+                    remaining = (self._tqdm.total - self._tqdm.n) / rate if rate and self._tqdm.total else 0
+                    self.tqdm_rem_time = str(datetime.timedelta(seconds=remaining))
         #Setup TQDP for model downloading
         elif name == "total_download_chunks" and '_tqdm' in self.__dict__:
             self._tqdm.reset(total=value)
@@ -1247,11 +1259,11 @@ class undefined_settings(settings):
 class system_settings(settings):
     local_only_variables = ['lua_state', 'lua_logname', 'lua_koboldbridge', 'lua_kobold', 
                             'lua_koboldcore', 'regex_sl', 'acregex_ai', 'acregex_ui', 'comregex_ai', 'comregex_ui',
-                            'sp', '_horde_pid', 'inference_config', 'image_pipeline', 
+                            'sp', '_horde_pid', 'inference_config', 'image_pipeline', 'summary_model_config',
                             'summarizer', 'summary_tokenizer', 'tts_model', 'rng_states', 'comregex_ai', 'comregex_ui', 'colab_arg']
-    no_save_variables = ['lua_state', 'lua_logname', 'lua_koboldbridge', 'lua_kobold', 
+    no_save_variables = ['lua_state', 'lua_logname', 'lua_koboldbridge', 'lua_kobold', 'summary_model_config',
                          'lua_koboldcore', 'sp', 'sp_length', '_horde_pid', 'horde_share', 'aibusy', 
-                         'serverstarted', 'inference_config', 'image_pipeline', 'summarizer', 'on_colab'
+                         'serverstarted', 'inference_config', 'image_pipeline', 'summarizer', 'on_colab', 'quiet',
                          'summary_tokenizer', 'use_colab_tpu', 'noai', 'disable_set_aibusy', 'cloudflare_link', 'tts_model',
                          'generating_image', 'bit_8_available', 'host', 'hascuda', 'usegpu', 'rng_states', 'comregex_ai', 'comregex_ui', 'git_repository', 'git_branch', 'colab_arg',
                          'disable_model_load']
@@ -1335,6 +1347,7 @@ class system_settings(settings):
         self.image_pipeline = None
         self.summarizer = None
         self.summary_tokenizer = None
+        self.summary_model_config = {}
         self.keep_img_gen_in_memory = False
         self.cookies = {} #cookies for colab since colab's URL changes, cookies are lost
         self.experimental_features = False
@@ -2233,6 +2246,12 @@ class KoboldStoryRegister(object):
             self.actions[action_id]['picture_filename'] = filename
             self.actions[action_id]['picture_prompt'] = prompt
     
+    def clear_picture(self, action_id):
+        action_id = int(action_id)
+        if action_id in self.actions:
+            del self.actions[action_id]['picture_filename']
+            del self.actions[action_id]['picture_prompt']
+    
     def get_picture(self, action_id):
         if action_id == -1:
             if self.story_settings.prompt_picture_filename == "":
diff --git a/modeling/inference_models/exllama/class.py b/modeling/inference_models/exllama/class.py
index f688d611..b52c2d65 100644
--- a/modeling/inference_models/exllama/class.py
+++ b/modeling/inference_models/exllama/class.py
@@ -340,7 +340,8 @@ class model_backend(InferenceModel):
 
             self._post_token_gen(self.generator.sequence)
 
-            utils.koboldai_vars.generated_tkns += 1
+            #This is taken care of in the core stopper class that's called below. If you're not using core stoppers then it should remain here
+            #utils.koboldai_vars.generated_tkns += 1
 
             # Apply stoppers
             do_stop = False
@@ -374,6 +375,12 @@ class model_backend(InferenceModel):
         return tokenizer
 
     def get_requested_parameters(self, model_name, model_path, menu_path, parameters = {}):
+        saved_data = {'layers': [], 'max_ctx': 2048, 'compress_emb': 1, 'ntk_alpha': 1}
+        if os.path.exists("settings/{}.exllama.model_backend.settings".format(model_name.replace("/", "_"))) and 'base_url' not in vars(self):
+            with open("settings/{}.exllama.model_backend.settings".format(model_name.replace("/", "_")), "r") as f:
+                temp = json.load(f)
+                for key in temp:
+                    saved_data[key] = temp[key]
         requested_parameters = []
         gpu_count = torch.cuda.device_count()
         layer_count = self.model_config["n_layer"] if isinstance(self.model_config, dict) else self.model_config.num_layers if hasattr(self.model_config, "num_layers") else self.model_config.n_layer if hasattr(self.model_config, "n_layer") else self.model_config.num_hidden_layers if hasattr(self.model_config, 'num_hidden_layers') else None
@@ -400,7 +407,7 @@ class model_backend(InferenceModel):
                                             "step": 1,
                                             "check": {"sum": ["{}_Layers".format(i) for i in range(gpu_count)], "value": layer_count, 'check': "="},
                                             "check_message": "The sum of assigned layers must equal {}".format(layer_count),
-                                            "default": [layer_count if i == 0 else 0],
+                                            "default": saved_data['layers'][i] if len(saved_data['layers']) > i else layer_count if i==0 else 0,
                                             "tooltip": "The number of layers to put on {}.".format(torch.cuda.get_device_name(i)),
                                             "menu_path": "Layers",
                                             "extra_classes": "",
@@ -415,7 +422,7 @@ class model_backend(InferenceModel):
             "min": 2048,
             "max": 16384,
             "step": 512,
-            "default": 2048,
+            "default": saved_data['max_ctx'],
             "tooltip": "The maximum context size the model supports",
             "menu_path": "Configuration",
             "extra_classes": "",
@@ -430,7 +437,7 @@ class model_backend(InferenceModel):
             "min": 1,
             "max": 8,
             "step": 0.25,
-            "default": 1,
+            "default": saved_data['compress_emb'],
             "tooltip": "If the model requires compressed embeddings, set them here",
             "menu_path": "Configuration",
             "extra_classes": "",
@@ -445,7 +452,7 @@ class model_backend(InferenceModel):
             "min": 1,
             "max": 32,
             "step": 0.25,
-            "default": 1,
+            "default": saved_data['ntk_alpha'],
             "tooltip": "NTK alpha value",
             "menu_path": "Configuration",
             "extra_classes": "",
@@ -490,3 +497,21 @@ class model_backend(InferenceModel):
 
         self.model_name = parameters['custom_model_name'] if 'custom_model_name' in parameters else parameters['id']
         self.path = parameters['path'] if 'path' in parameters else None
+
+    def _save_settings(self):
+        with open(
+            "settings/{}.exllama.model_backend.settings".format(
+                self.model_name.replace("/", "_")
+            ),
+            "w",
+        ) as f:
+            json.dump(
+                {
+                    "layers": self.layers if "layers" in vars(self) else [],
+                    "max_ctx": self.model_config.max_seq_len,
+                    "compress_emb": self.model_config.compress_pos_emb,
+                    "ntk_alpha": self.model_config.alpha_value
+                },
+                f,
+                indent="",
+            )
\ No newline at end of file
diff --git a/modeling/inference_models/exllamav2/class.py b/modeling/inference_models/exllamav2/class.py
index f398faf7..a3c66cc9 100644
--- a/modeling/inference_models/exllamav2/class.py
+++ b/modeling/inference_models/exllamav2/class.py
@@ -314,7 +314,8 @@ class model_backend(InferenceModel):
 
                 self._post_token_gen(self.generator.sequence_ids)
 
-                utils.koboldai_vars.generated_tkns += 1
+                #utils.koboldai_vars.generated_tkns += 1
+
 
                 # Apply stoppers
                 do_stop = False
@@ -349,6 +350,12 @@ class model_backend(InferenceModel):
         return tokenizer
 
     def get_requested_parameters(self, model_name, model_path, menu_path, parameters = {}):
+        saved_data = {'max_ctx': 2048, 'compress_emb': 1, 'ntk_alpha': 1}
+        if os.path.exists("settings/{}.exllama.model_backend.settings".format(model_name.replace("/", "_"))) and 'base_url' not in vars(self):
+            with open("settings/{}.exllama.model_backend.settings".format(model_name.replace("/", "_")), "r") as f:
+                temp = json.load(f)
+                for key in temp:
+                    saved_data[key] = temp[key]
         requested_parameters = []
         gpu_count = torch.cuda.device_count()
         layer_count = self.model_config["n_layer"] if isinstance(self.model_config, dict) else self.model_config.num_layers if hasattr(self.model_config, "num_layers") else self.model_config.n_layer if hasattr(self.model_config, "n_layer") else self.model_config.num_hidden_layers if hasattr(self.model_config, 'num_hidden_layers') else None
@@ -361,7 +368,7 @@ class model_backend(InferenceModel):
             "min": 2048,
             "max": 16384,
             "step": 512,
-            "default": 2048,
+            "default": saved_data['max_ctx'],
             "tooltip": "The maximum context size the model supports",
             "menu_path": "Configuration",
             "extra_classes": "",
@@ -376,7 +383,7 @@ class model_backend(InferenceModel):
             "min": 1,
             "max": 8,
             "step": 0.25,
-            "default": 1,
+            "default": saved_data['compress_emb'],
             "tooltip": "If the model requires compressed embeddings, set them here",
             "menu_path": "Configuration",
             "extra_classes": "",
@@ -391,7 +398,7 @@ class model_backend(InferenceModel):
             "min": 1,
             "max": 32,
             "step": 0.25,
-            "default": 1,
+            "default": saved_data['ntk_alpha'],
             "tooltip": "NTK alpha value",
             "menu_path": "Configuration",
             "extra_classes": "",
@@ -418,4 +425,21 @@ class model_backend(InferenceModel):
             self.model_config.sdp_thd = 0
 
         self.model_name = parameters['custom_model_name'] if 'custom_model_name' in parameters else parameters['id']
-        self.path = parameters['path'] if 'path' in parameters else None
\ No newline at end of file
+        self.path = parameters['path'] if 'path' in parameters else None
+        
+    def _save_settings(self):
+        with open(
+            "settings/{}.exllamav2.model_backend.settings".format(
+                self.model_name.replace("/", "_")
+            ),
+            "w",
+        ) as f:
+            json.dump(
+                {
+                    "max_ctx": self.model_config.max_seq_len,
+                    "compress_emb": self.model_config.compress_pos_emb,
+                    "ntk_alpha": self.model_config.alpha_value
+                },
+                f,
+                indent="",
+            )
\ No newline at end of file
diff --git a/modeling/lazy_loader.py b/modeling/lazy_loader.py
index eece7d2f..f4ce6fb7 100644
--- a/modeling/lazy_loader.py
+++ b/modeling/lazy_loader.py
@@ -52,7 +52,8 @@ import zipfile
 import pickle
 import torch
 import os
-from typing import Any, Callable, Dict, Optional, Tuple, Type
+import json
+from typing import Any, Callable, Dict, List, Optional, Tuple, Type
 
 from torch.nn import Module
 from torch.storage import UntypedStorage
@@ -398,6 +399,18 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, miss
                 if input_name not in self._modules and input_name not in local_state:
                     unexpected_keys.append(key)
 
+def get_sharded_torch_checkpoints(dir: str) -> List[str]:
+    try:
+        with open(os.path.join(dir, "pytorch_model.bin.index.json")) as file:
+            j = json.load(file)
+    except FileNotFoundError:
+        return []
+
+    try:
+        return list(set(j["weight_map"].values()))
+    except KeyError:
+        return []
+
 @contextlib.contextmanager
 def use_lazy_load(
     enable=True,
@@ -410,6 +423,8 @@ def use_lazy_load(
         return
 
     begin_time = time.time()
+    utils.koboldai_vars.total_checkpoints = 0
+    utils.koboldai_vars.loaded_checkpoints = 0
 
     try:
         LazyloadPatches.__enter__()
@@ -421,6 +436,14 @@ def use_lazy_load(
         old_torch_load = torch.load
 
         def torch_load(f, map_location=None, pickle_module=pickle, **pickle_load_args):
+            if not utils.koboldai_vars.total_checkpoints:
+                checkpoints = get_sharded_torch_checkpoints(os.path.dirname(f))
+                # `checkpoints` may be empty if there is an error--return 1 in
+                # this case. Either there was no checkpoint index file (most
+                # common case), or there was a compatibility issue while reading
+                # it.
+                utils.koboldai_vars.total_checkpoints = len(checkpoints) or 1
+
             model_dict = old_torch_load(
                 f=f,
                 map_location=map_location,
diff --git a/modeling/patches.py b/modeling/patches.py
index 5664ec07..35bc9eec 100644
--- a/modeling/patches.py
+++ b/modeling/patches.py
@@ -220,6 +220,7 @@ class LazyloadPatches:
 # BEGIN PATCH
         utils.bar = tqdm(total=len(state_dict), desc="Loading model tensors", file=utils.UIProgressBarFile(), position=1)
         utils.koboldai_vars.total_layers = len(state_dict)
+        utils.koboldai_vars.loaded_layers = 0
 
         for param_name, param in sorted(
             state_dict.items(),
@@ -325,6 +326,7 @@ class LazyloadPatches:
                         fp16_statistics=fp16_statistics,
                     )
 
+        utils.koboldai_vars.loaded_checkpoints += 1
         return error_msgs, offload_index, state_dict_index
 
 
diff --git a/static/koboldai.js b/static/koboldai.js
index 3762be6c..b15796ab 100644
--- a/static/koboldai.js
+++ b/static/koboldai.js
@@ -2008,11 +2008,12 @@ function selected_model_info(sent_data) {
 				slider_number.onchange = function() { document.getElementById(this.id.replace("_text", "")).value = this.value;};
 
 				var slider = new_setting.querySelector('#blank_model_settings_slider');
-				slider.value = item['default'];
 				slider.min = item['min'];
 				slider.max = item['max'];
+				slider.step = item["step"];
 				slider.setAttribute("data_type", item['unit']);
 				slider.id = loader + "|" + item['id'] + "_value";
+				slider.value = item['default'];
 				if ('check' in item) {
 					slider.check_data = item['check'];
 					slider_number.check_data = item['check'];
@@ -3602,6 +3603,7 @@ function stream_tokens(tokens) {
 
 			streamBuffer.textContent += streaming.buffer[0];
 			streaming.buffer = streaming.buffer.slice(1);
+			streamBuffer.scrollIntoView({ block: "end" });
 		}
 
 		streaming.typeyTimeout = setTimeout(_char, 10);
@@ -3621,6 +3623,7 @@ function stream_tokens(tokens) {
 		streaming.buffer += tokens[0];
 	} else {
 		streamBuffer.textContent += tokens[0];
+		streamBuffer.scrollIntoView({ block: "end" });
 	}
 }
 
@@ -3874,12 +3877,13 @@ function change_image(data) {
 
 	$el("#image-loading").classList.add("hidden");
 
-	if (data != undefined) {
+	if (data.img != undefined) {
 		var image = new Image();
-		image.src = 'data:image/png;base64,'+data;
+		image.src = 'data:image/png;base64,'+data.img;
 		image.classList.add("action_image");
 		image.setAttribute("context-menu", "generated-image");
 		image.addEventListener("click", imgGenView);
+		image.setAttribute('action_id', data.action_id);
 		image_area.appendChild(image);
 	}
 }
@@ -7395,11 +7399,12 @@ function imgGenDownload() {
 function imgGenClear() {
 	const image = $el(".action_image");
 	if (!image) return;
+	action_id = image.getAttribute('action_id');
 	image.remove();
 
 	const container = $el("#action\\ image");
 	container.removeAttribute("tooltip");
-	socket.emit("clear_generated_image", {});
+	socket.emit("clear_generated_image", {'action_id': action_id});
 }
 
 function imgGenRetry() {