diff --git a/caption_cog.py b/caption_cog.py
index c3867a5..b5feb9b 100644
--- a/caption_cog.py
+++ b/caption_cog.py
@@ -23,6 +23,7 @@ from typing import Generator
 import torch
 
 from PIL import Image
+import PIL.ImageOps as ImageOps
 from pynvml import *
 
 from transformers import AutoModelForCausalLM, LlamaTokenizer
@@ -53,19 +54,29 @@ def main(args):
         load_in_4bit=not args.disable_4bit,
     )
 
-    do_sample = args.num_beams > 1
+    do_sample = args.top_k is not None or args.top_p is not None or args.temp is not None
+    if do_sample:
+        args.top_k = args.top_k or 50
+        args.top_p = args.top_p or 1.0
+        args.temp = args.temp or 1.0
+
+    args.append = args.append or ""
+    if len(args.append) > 0 and not args.append.startswith(" "):
+        args.append = " " + args.append
 
     gen_kwargs = {
-        "max_length": args.max_length, 
-        "do_sample": do_sample, 
-        "num_beams": args.num_beams, 
-        "temperature": args.temp, 
-        "top_k": args.top_k, 
-        "top_p": args.top_p, 
-        "repetition_penalty": args.repetition_penalty, 
-        "no_repeat_ngram_size": args.no_repeat_ngram_size, 
+        "max_length": args.max_length,
+        "do_sample": do_sample,
+        "length_penalty": args.length_penalty,
+        "num_beams": args.num_beams,
+        "temperature": args.temp,
+        "top_k": args.top_k,
+        "top_p": args.top_p,
+        "repetition_penalty": args.repetition_penalty,
+        "no_repeat_ngram_size": args.no_repeat_ngram_size,
         "min_new_tokens": args.min_new_tokens,
         "max_new_tokens": args.max_new_tokens,
+        "length_penalty": args.length_penalty,
     }
 
     if args.max_new_tokens is not None:
@@ -73,9 +84,12 @@ def main(args):
         del gen_kwargs["max_length"]
 
     if not do_sample:
-        print(f"** num_beams set to 1, sampling is disabled")
+        print(f"** Using greedy sampling")
         del gen_kwargs["top_k"]
         del gen_kwargs["top_p"]
+        del gen_kwargs["temperature"]
+    else:
+        print(f"** Sampling enabled")
 
     force_words_ids = None
     if args.force_words is not None:
@@ -103,6 +117,14 @@ def main(args):
 
         start_time = time.time()
         image = Image.open(image_path)
+
+        try:
+            image = image.convert('RGB')
+            image = ImageOps.exif_transpose(image)
+        except Exception as e:
+            print(f"Non-fatal error processing {image_path}: {e}")
+            continue
+
         inputs = model.build_conversation_input_ids(tokenizer, query=args.prompt, history=[], images=[image])  # chat mode
         inputs = {
             'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
@@ -115,6 +137,7 @@ def main(args):
             outputs = model.generate(**inputs, **gen_kwargs, force_words_ids=force_words_ids, bad_words_ids=bad_words_ids)
             outputs_without_prompt = outputs[:, inputs['input_ids'].shape[1]:]
             caption = tokenizer.decode(outputs_without_prompt[0], skip_special_tokens=True)
+            caption += args.append
 
             with open(candidate_caption_path, "w") as f:
                 f.write(caption)
@@ -137,40 +160,64 @@ EXAMPLES = """ex.
 Basic example:
   python caption_cog.py --image_dir /mnt/mydata/kyrie/ --prompt 'Describe this image in detail, including the subject matter and medium of the artwork.'
 
+Use probabilistic sampling by using any of top_k, top_p, or temp:
+  python caption_cog.py --image_dir \"c:/users/chadley/my documents/pictures\" --prompt \"What is this?\" --top_p 0.9 
+  
 Use beam search and probabilistic sampling:
-  python caption_cog.py --image_dir \"c:/users/chadley/my documents/pictures\" --prompt 'Write a description.' --max_new_tokens 75 --num_beams 4 --temp 0.9 --top_k 3 --top_p 0.9 --repetition_penalty 1.0 --no_repeat_ngram_size 0 --min_new_tokens 5\n
+  python caption_cog.py --image_dir \"c:/users/chadley/my documents/pictures\" --prompt \"Write a description.\" --max_new_tokens 75 --num_beams 4 --temp 0.9 --top_k 3 --top_p 0.9 --repetition_penalty 1.0 --no_repeat_ngram_size 0 --min_new_tokens 5
 
 Force "cat" and "dog" and disallow the word "depicts":
   python caption_cog.py --image_dir /mnt/lcl/nvme/mldata/test --num_beams 3 --force_words "cat,dog" --bad_words "depicts"
 
+Use a lot of beams and try to control the length with length_penalty:
+  python caption_cog.py --image_dir /mnt/lcl/nvme/mldata/test --num_beams 8 --length_penalty 0.8 --prompt "Write a single sentence description."
+
 Notes:
-  numbeams > 1 enables probabilistic sampling, which is required for the temperature, top_k, top_p parameters to function. More beams is more opinions on the next token, but slower and more VRAM intensive as it is done in batch mode.
-    Increasing num_beams has a substantial impact on VRAM and speed.  Ex beams =1 ~13.3gb, beams = 4 ~ 23.7GB
-    Speed is linearly proportional to num_beams, so 4 beams is 4x slower than 1 beam.
-  Max_length and max_new_tokens are mutually exclusive.  If max_new_tokens is set, max_length is ignored.
+  1. Setting top_k, top_p, or temp enables probabilistic sampling (aka "do_sample"), otherwise greedy sampling is used.
+    a. num_beams 1 and do_sample false uses "greedy decoding"
+    b. num_beams 1 and do_sample true uses "multinomial sampling"
+    c. num_beams > 1 and do_sample true uses "beam-search multinomial sampling"
+    d. num_beams > 1 and do_sample false uses "beam-search decoding"
+  2. Max_length and max_new_tokens are mutually exclusive.  If max_new_tokens is set, max_length is ignored.  Default is max_length 2048 if nothing set.
+    Using Max may abruptly end caption, consider modifying prompt or use length_penalty instead.
+
+Find more info on the Huggingface Transformers documentation: https://huggingface.co/docs/transformers/main_classes/text_generation
+Parameters definitions and use map directly to their API.
 """
 
-DESCRIPTION = f"** {Fore.LIGHTBLUE_EX}CogVLM captioning script{Style.RESET_ALL} **\n"
+DESCRIPTION = f"** {Fore.LIGHTBLUE_EX}CogVLM captioning script{Style.RESET_ALL} **\n Use --help for usage."
 
 if __name__ == "__main__":
-    argparser = argparse.ArgumentParser(description=DESCRIPTION, epilog=EXAMPLES)
+    argparser = argparse.ArgumentParser()
     argparser.add_argument("--disable_4bit", action="store_true", help="Disables 4bit inference for compatibility or experimentation. Bad for VRAM, fallback is bf16.")
-    argparser.add_argument("--temp", type=float, default=1.0, help="Temperature for sampling")
-    argparser.add_argument("--num_beams", type=int, default=2, help="Number of beams for sampling, see notes.")
-    argparser.add_argument("--top_k", type=int, default=0, help="Top-k, filter k highest probability tokens before sampling")
-    argparser.add_argument("--top_p", type=float, default=1.0, help="Top-p, selects from top tokens with cumulative probability >= p")
+    argparser.add_argument("--temp", type=float, default=None, help="Temperature for sampling")
+    argparser.add_argument("--num_beams", type=int, default=2, help="Number of beams for beam search, default 1 (off)")
+    argparser.add_argument("--top_k", type=int, default=None, help="Top-k, filter k highest probability tokens before sampling")
+    argparser.add_argument("--top_p", type=float, default=None, help="Top-p, for sampling, selects from top tokens with cumulative probability >= p")
     argparser.add_argument("--repetition_penalty", type=float, default=1.0, help="Repetition penalty")
     argparser.add_argument("--no_repeat_ngram_size", type=int, default=0, help="No repetition n-gram size")
     argparser.add_argument("--min_new_tokens", type=int, default=5, help="Minimum number of tokens in returned caption.")
     argparser.add_argument("--max_new_tokens", type=int, default=None, help="Maximum number of tokens in returned caption.")
     argparser.add_argument("--max_length", type=int, default=2048, help="Alternate to max_new_tokens, limits context.")
-    argparser.add_argument("--prompt", type=str, default="Describe this image.", help="Prompt that will guide captioning")
+    argparser.add_argument("--length_penalty", type=float, default=1.0, help="Length penalty, lower values encourage shorter captions.")
+    argparser.add_argument("--prompt", type=str, default="Write a description.", help="Prompt that will guide captioning")
     argparser.add_argument("--image_dir", type=str, default=None, help="Path to folder of images to caption")
     argparser.add_argument("--no_overwrite", action="store_true", help="Skips captioning images that already have a caption file.")
     argparser.add_argument("--force_words", type=str, default=None, help="Forces the model to include these words in the caption, use CSV format.")
     argparser.add_argument("--bad_words", type=str, default=None, help="Words that will not be allowed, use CSV format.")
+    argparser.add_argument("--append", type=str, default=None, help="Extra string to append to all captions. ex. 'painted by John Doe'")
     args = argparser.parse_args()
 
+    print(DESCRIPTION)
+    print(EXAMPLES)
+
+    if args.top_k is not None or args.top_p is not None or args.temp is not None:
+        print(f"** Sampling enabled.")
+        args.sampling = True
+        args.top_k = args.top_k or 50
+        args.top_p = args.top_p or 1.0
+        args.temp = args.temp or 1.0
+
     print(DESCRIPTION)
     print(EXAMPLES)
     if args.image_dir is None: