lora-dir, model-dir and login auth

Added lora-dir, model-dir, and a login auth arguments that points to a file contains usernames and passwords in the format of "u:pw,u:pw,..."

modules/LoRA.py

@@ -30,7 +30,7 @@ def add_lora_to_model(lora_name):
             elif shared.args.load_in_8bit:
                 params['device_map'] = {'': 0}
             
-        shared.model = PeftModel.from_pretrained(shared.model, Path(f"loras/{lora_name}"), **params)
+        shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_name}"), **params)
         if not shared.args.load_in_8bit and not shared.args.cpu:
             shared.model.half()
             if not hasattr(shared.model, "hf_device_map"):

modules/models.py

@@ -46,9 +46,9 @@ def load_model(model_name):
     # Default settings
     if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
         if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
-            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
+            model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), device_map='auto', load_in_8bit=True)
         else:
-            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
+            model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
             if torch.has_mps:
                 device = torch.device('mps')
                 model = model.to(device)
@@ -76,11 +76,11 @@ def load_model(model_name):
                             num_bits=4, group_size=64,
                             group_dim=2, symmetric=False))
 
-        model = OptLM(f"facebook/{shared.model_name}", env, "models", policy)
+        model = OptLM(f"facebook/{shared.model_name}", env, shared.model_name, policy)
 
     # DeepSpeed ZeRO-3
     elif shared.args.deepspeed:
-        model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
+        model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
         model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
         model.module.eval() # Inference
         print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
@@ -89,8 +89,8 @@ def load_model(model_name):
     elif shared.is_RWKV:
         from modules.RWKV import RWKVModel, RWKVTokenizer
 
-        model = RWKVModel.from_pretrained(Path(f'models/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
-        tokenizer = RWKVTokenizer.from_pretrained(Path('models'))
+        model = RWKVModel.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
+        tokenizer = RWKVTokenizer.from_pretrained(Path(shared.model_name))
 
         return model, tokenizer
 
@@ -142,7 +142,7 @@ def load_model(model_name):
             if shared.args.disk:
                 params["offload_folder"] = shared.args.disk_cache_dir
 
-        checkpoint = Path(f'models/{shared.model_name}')
+        checkpoint = Path(f'{shared.args.model_dir}/{shared.model_name}')
 
         if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto':
             config = AutoConfig.from_pretrained(checkpoint)
@@ -159,10 +159,10 @@ def load_model(model_name):
         model = AutoModelForCausalLM.from_pretrained(checkpoint, **params)
 
     # Loading the tokenizer
-    if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path("models/gpt-j-6B/").exists():
-        tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
+    if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
+        tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
     else:
-        tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{shared.model_name}/"))
+        tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}/"))
     tokenizer.truncation_side = 'left'
 
     print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")

modules/shared.py

@@ -106,6 +106,9 @@ parser.add_argument('--listen-port', type=int, help='The listening port that the
 parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
 parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
 parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
+parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" with format like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--model-dir", type=str, default='models/', help="Path to directory with all the models")
+parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras")
 args = parser.parse_args()
 
 # Provisional, this will be deleted later

server.py

@@ -31,9 +31,9 @@ if settings_file is not None:
 
 def get_available_models():
     if shared.args.flexgen:
-        return sorted([re.sub('-np$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('-np')], key=str.lower)
+        return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.model_name}/').glob('*')) if item.name.endswith('-np')], key=str.lower)
     else:
-        return sorted([re.sub('.pth$', '', item.name) for item in list(Path('models/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
+        return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.model_name}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
 
 def get_available_presets():
     return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower)
@@ -48,7 +48,7 @@ def get_available_softprompts():
     return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('softprompts').glob('*.zip'))), key=str.lower)
 
 def get_available_loras():
-    return ['None'] + sorted([item.name for item in list(Path('loras/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
+    return ['None'] + sorted([item.name for item in list(Path('shared.args.lora_dir').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
 
 def load_model_wrapper(selected_model):
     if selected_model != shared.model_name:
@@ -448,11 +448,15 @@ def create_interface():
             extensions_module.create_extensions_block()
 
     # Launch the interface
+    gradio_auth_creds = []
+    with open(shared.args.gradio_auth_path, 'r', encoding="utf8") as file:
+        for line in file.readlines():
+            gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()]
     shared.gradio['interface'].queue()
     if shared.args.listen:
-        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
+        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=[tuple(cred.split(':')) for cred in gradio_auth_creds] if gradio_auth_creds else None)
     else:
-        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
+        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=[tuple(cred.split(':')) for cred in gradio_auth_creds] if gradio_auth_creds else None)
 
 create_interface()