Change log levels.

Logging level now defaults to info. --verbose sets it to debug.

comfy/cli_args.py

@@ -129,7 +129,7 @@ if args.disable_auto_launch:
     args.auto_launch = False
 
 import logging
-logging_level = logging.WARNING
+logging_level = logging.INFO
 if args.verbose:
     logging_level = logging.DEBUG
 

comfy/controlnet.py

@@ -432,7 +432,7 @@ def load_controlnet(ckpt_path, model=None):
         logging.warning("missing controlnet keys: {}".format(missing))
 
     if len(unexpected) > 0:
-        logging.info("unexpected controlnet keys: {}".format(unexpected))
+        logging.debug("unexpected controlnet keys: {}".format(unexpected))
 
     global_average_pooling = False
     filename = os.path.splitext(ckpt_path)[0]
@@ -545,6 +545,6 @@ def load_t2i_adapter(t2i_data):
         logging.warning("t2i missing {}".format(missing))
 
     if len(unexpected) > 0:
-        logging.info("t2i unexpected {}".format(unexpected))
+        logging.debug("t2i unexpected {}".format(unexpected))
 
     return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm)

comfy/diffusers_convert.py

@@ -178,7 +178,7 @@ def convert_vae_state_dict(vae_state_dict):
     for k, v in new_state_dict.items():
         for weight_name in weights_to_convert:
             if f"mid.attn_1.{weight_name}.weight" in k:
-                logging.info(f"Reshaping {k} for SD format")
+                logging.debug(f"Reshaping {k} for SD format")
                 new_state_dict[k] = reshape_weight_for_sd(v)
     return new_state_dict
 

comfy/model_base.py

@@ -67,8 +67,8 @@ class BaseModel(torch.nn.Module):
         if self.adm_channels is None:
             self.adm_channels = 0
         self.inpaint_model = False
-        logging.warning("model_type {}".format(model_type.name))
-        logging.info("adm {}".format(self.adm_channels))
+        logging.info("model_type {}".format(model_type.name))
+        logging.debug("adm {}".format(self.adm_channels))
 
     def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
         sigma = t

comfy/model_management.py

@@ -30,7 +30,7 @@ lowvram_available = True
 xpu_available = False
 
 if args.deterministic:
-    logging.warning("Using deterministic algorithms for pytorch")
+    logging.info("Using deterministic algorithms for pytorch")
     torch.use_deterministic_algorithms(True, warn_only=True)
 
 directml_enabled = False
@@ -42,7 +42,7 @@ if args.directml is not None:
         directml_device = torch_directml.device()
     else:
         directml_device = torch_directml.device(device_index)
-    logging.warning("Using directml with device: {}".format(torch_directml.device_name(device_index)))
+    logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index)))
     # torch_directml.disable_tiled_resources(True)
     lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
 
@@ -118,7 +118,7 @@ def get_total_memory(dev=None, torch_total_too=False):
 
 total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
 total_ram = psutil.virtual_memory().total / (1024 * 1024)
-logging.warning("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
+logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
 if not args.normalvram and not args.cpu:
     if lowvram_available and total_vram <= 4096:
         logging.warning("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
@@ -144,7 +144,7 @@ else:
             pass
         try:
             XFORMERS_VERSION = xformers.version.__version__
-            logging.warning("xformers version: {}".format(XFORMERS_VERSION))
+            logging.info("xformers version: {}".format(XFORMERS_VERSION))
             if XFORMERS_VERSION.startswith("0.0.18"):
                 logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
                 logging.warning("Please downgrade or upgrade xformers to a different version.\n")
@@ -212,11 +212,11 @@ elif args.highvram or args.gpu_only:
 FORCE_FP32 = False
 FORCE_FP16 = False
 if args.force_fp32:
-    logging.warning("Forcing FP32, if this improves things please report it.")
+    logging.info("Forcing FP32, if this improves things please report it.")
     FORCE_FP32 = True
 
 if args.force_fp16:
-    logging.warning("Forcing FP16.")
+    logging.info("Forcing FP16.")
     FORCE_FP16 = True
 
 if lowvram_available:
@@ -230,12 +230,12 @@ if cpu_state != CPUState.GPU:
 if cpu_state == CPUState.MPS:
     vram_state = VRAMState.SHARED
 
-logging.warning(f"Set vram state to: {vram_state.name}")
+logging.info(f"Set vram state to: {vram_state.name}")
 
 DISABLE_SMART_MEMORY = args.disable_smart_memory
 
 if DISABLE_SMART_MEMORY:
-    logging.warning("Disabling smart memory management")
+    logging.info("Disabling smart memory management")
 
 def get_torch_device_name(device):
     if hasattr(device, 'type'):
@@ -253,11 +253,11 @@ def get_torch_device_name(device):
         return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
 
 try:
-    logging.warning("Device: {}".format(get_torch_device_name(get_torch_device())))
+    logging.info("Device: {}".format(get_torch_device_name(get_torch_device())))
 except:
     logging.warning("Could not pick default device.")
 
-logging.warning("VAE dtype: {}".format(VAE_DTYPE))
+logging.info("VAE dtype: {}".format(VAE_DTYPE))
 
 current_loaded_models = []
 
@@ -300,7 +300,7 @@ class LoadedModel:
             raise e
 
         if lowvram_model_memory > 0:
-            logging.warning("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024)))
+            logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024)))
             mem_counter = 0
             for m in self.real_model.modules():
                 if hasattr(m, "comfy_cast_weights"):
@@ -347,7 +347,7 @@ def unload_model_clones(model):
             to_unload = [i] + to_unload
 
     for i in to_unload:
-        logging.warning("unload clone {}".format(i))
+        logging.debug("unload clone {}".format(i))
         current_loaded_models.pop(i).model_unload()
 
 def free_memory(memory_required, device, keep_loaded=[]):
@@ -389,7 +389,7 @@ def load_models_gpu(models, memory_required=0):
             models_already_loaded.append(loaded_model)
         else:
             if hasattr(x, "model"):
-                logging.warning(f"Requested to load {x.model.__class__.__name__}")
+                logging.info(f"Requested to load {x.model.__class__.__name__}")
             models_to_load.append(loaded_model)
 
     if len(models_to_load) == 0:
@@ -399,7 +399,7 @@ def load_models_gpu(models, memory_required=0):
                 free_memory(extra_mem, d, models_already_loaded)
         return
 
-    logging.warning(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
+    logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
 
     total_memory_required = {}
     for loaded_model in models_to_load:

comfy/sd.py

@@ -229,7 +229,7 @@ class VAE:
             logging.warning("Missing VAE keys {}".format(m))
 
         if len(u) > 0:
-            logging.info("Leftover VAE keys {}".format(u))
+            logging.debug("Leftover VAE keys {}".format(u))
 
         if device is None:
             device = model_management.vae_device()
@@ -397,7 +397,7 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             logging.warning("clip missing: {}".format(m))
 
         if len(u) > 0:
-            logging.info("clip unexpected: {}".format(u))
+            logging.debug("clip unexpected: {}".format(u))
     return clip
 
 def load_gligen(ckpt_path):
@@ -538,18 +538,18 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
                     logging.warning("clip missing: {}".format(m))
 
                 if len(u) > 0:
-                    logging.info("clip unexpected {}:".format(u))
+                    logging.debug("clip unexpected {}:".format(u))
             else:
                 logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
 
     left_over = sd.keys()
     if len(left_over) > 0:
-        logging.info("left over keys: {}".format(left_over))
+        logging.debug("left over keys: {}".format(left_over))
 
     if output_model:
         model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device)
         if inital_load_device != torch.device("cpu"):
-            logging.warning("loaded straight to GPU")
+            logging.info("loaded straight to GPU")
             model_management.load_model_gpu(model_patcher)
 
     return (model_patcher, clip, vae, clipvision)
@@ -589,7 +589,7 @@ def load_unet_state_dict(sd): #load unet in diffusers format
     model.load_model_weights(new_sd, "")
     left_over = sd.keys()
     if len(left_over) > 0:
-        logging.warning("left over keys in unet: {}".format(left_over))
+        logging.info("left over keys in unet: {}".format(left_over))
     return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
 def load_unet(unet_path):

comfy/utils.py

@@ -22,7 +22,7 @@ def load_torch_file(ckpt, safe_load=False, device=None):
         else:
             pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
         if "global_step" in pl_sd:
-            logging.info(f"Global Step: {pl_sd['global_step']}")
+            logging.debug(f"Global Step: {pl_sd['global_step']}")
         if "state_dict" in pl_sd:
             sd = pl_sd["state_dict"]
         else:

nodes.py

@@ -1925,14 +1925,14 @@ def load_custom_nodes():
             node_import_times.append((time.perf_counter() - time_before, module_path, success))
 
     if len(node_import_times) > 0:
-        logging.warning("\nImport times for custom nodes:")
+        logging.info("\nImport times for custom nodes:")
         for n in sorted(node_import_times):
             if n[2]:
                 import_message = ""
             else:
                 import_message = " (IMPORT FAILED)"
-            logging.warning("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
-        logging.warning("")
+            logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
+        logging.info("")
 
 def init_custom_nodes():
     extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras")

server.py

@@ -17,6 +17,7 @@ from io import BytesIO
 
 import aiohttp
 from aiohttp import web
+import logging
 
 import mimetypes
 from comfy.cli_args import args
@@ -33,7 +34,7 @@ async def send_socket_catch_exception(function, message):
     try:
         await function(message)
     except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
-        print("send error:", err)
+        logging.warning("send error: {}".format(err))
 
 @web.middleware
 async def cache_control(request: web.Request, handler):
@@ -111,7 +112,7 @@ class PromptServer():
                     
                 async for msg in ws:
                     if msg.type == aiohttp.WSMsgType.ERROR:
-                        print('ws connection closed with exception %s' % ws.exception())
+                        logging.warning('ws connection closed with exception %s' % ws.exception())
             finally:
                 self.sockets.pop(sid, None)
             return ws
@@ -446,7 +447,7 @@ class PromptServer():
 
         @routes.post("/prompt")
         async def post_prompt(request):
-            print("got prompt")
+            logging.info("got prompt")
             resp_code = 200
             out_string = ""
             json_data =  await request.json()
@@ -478,7 +479,7 @@ class PromptServer():
                     response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
                     return web.json_response(response)
                 else:
-                    print("invalid prompt:", valid[1])
+                    logging.warning("invalid prompt: {}".format(valid[1]))
                     return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
             else:
                 return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
@@ -626,8 +627,8 @@ class PromptServer():
         await site.start()
 
         if verbose:
-            print("Starting server\n")
-            print("To see the GUI go to: http://{}:{}".format(address, port))
+            logging.info("Starting server\n")
+            logging.info("To see the GUI go to: http://{}:{}".format(address, port))
         if call_on_start is not None:
             call_on_start(address, port)
 
@@ -639,7 +640,7 @@ class PromptServer():
             try:
                 json_data = handler(json_data)
             except Exception as e:
-                print(f"[ERROR] An error occurred during the on_prompt_handler processing")
+                logging.warning(f"[ERROR] An error occurred during the on_prompt_handler processing")
                 traceback.print_exc()
 
         return json_data