Merge upstream

comfy/cli_args.py

@@ -209,7 +209,7 @@ def parse_args(parser: Optional[argparse.ArgumentParser] = None) -> Configuratio
     if args.disable_auto_launch:
         args.auto_launch = False
 
-    logging_level = logging.WARNING
+    logging_level = logging.INFO
     if args.verbose:
         logging_level = logging.DEBUG
 

comfy/cmd/extra_model_paths.py

@@ -1,6 +1,6 @@
 import os
 import yaml
-
+import logging
 
 def load_extra_path_config(yaml_path):
     from . import folder_paths
@@ -21,5 +21,5 @@ def load_extra_path_config(yaml_path):
                 full_path = y
                 if base_path is not None:
                     full_path = os.path.join(base_path, full_path)
-                print("Adding extra search path", x, full_path)
+                logging.info(f"Adding extra search path {x} ({full_path})")
                 folder_paths.add_model_folder_path(x, full_path)

comfy/cmd/folder_paths.py

@@ -1,6 +1,7 @@
 import os
 import sys
 import time
+import logging
 from pkg_resources import resource_filename
 
 from ..cli_args import args
@@ -16,7 +17,7 @@ elif args.cwd is not None:
         try:
             os.makedirs(args.cwd, exist_ok=True)
         except:
-            print("Failed to create custom working directory")
+            logging.error("Failed to create custom working directory")
     # wrap the path to prevent slashedness from glitching out common path checks
     base_path = os.path.realpath(args.cwd)
 else:
@@ -52,7 +53,7 @@ if not os.path.exists(input_directory):
     try:
         os.makedirs(input_directory)
     except:
-        print("Failed to create input directory")
+        logging.error("Failed to create input directory")
 
 def set_output_directory(output_dir):
     global output_directory
@@ -154,7 +155,7 @@ def recursive_search(directory, excluded_dir_names=None):
     try:
         dirs[directory] = os.path.getmtime(directory)
     except FileNotFoundError:
-        print(f"Warning: Unable to access {directory}. Skipping this path.")
+        logging.warning(f"Warning: Unable to access {directory}. Skipping this path.")
 
     for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
         subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
@@ -167,7 +168,7 @@ def recursive_search(directory, excluded_dir_names=None):
             try:
                 dirs[path] = os.path.getmtime(path)
             except FileNotFoundError:
-                print(f"Warning: Unable to access {path}. Skipping this path.")
+                logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
                 continue
     return result, dirs
 
@@ -257,7 +258,7 @@ def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height
               "\n full_output_folder: " + os.path.abspath(full_output_folder) + \
               "\n         output_dir: " + output_dir + \
               "\n         commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder)))
-        print(err)
+        logging.error(err)
         raise Exception(err)
 
     try:

comfy/cmd/latent_preview.py

@@ -6,6 +6,7 @@ from ..cli_args_types import LatentPreviewMethod
 from ..taesd.taesd import TAESD
 from ..cmd import folder_paths
 from .. import utils
+import logging
 
 MAX_PREVIEW_RESOLUTION = 512
 
@@ -70,7 +71,7 @@ def get_previewer(device, latent_format):
                 taesd = TAESD(None, taesd_decoder_path).to(device)
                 previewer = TAESDPreviewerImpl(taesd)
             else:
-                print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
+                logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
 
         if previewer is None:
             if latent_format.latent_rgb_factors is not None:

comfy/cmd/main.py

@@ -5,6 +5,7 @@ import warnings
 warnings.filterwarnings("ignore", message="torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.")
 options.enable_args_parsing()
 
+import logging
 import os
 import importlib.util
 
@@ -26,7 +27,7 @@ def execute_prestartup_script():
             spec.loader.exec_module(module)
             return True
         except Exception as e:
-            print(f"Failed to execute startup-script: {script_path} / {e}")
+            logging.error(f"Failed to execute startup-script: {script_path} / {e}")
         return False
 
     node_paths = folder_paths.get_folder_paths("custom_nodes")
@@ -45,14 +46,13 @@ def execute_prestartup_script():
                 success = execute_script(script_path)
                 node_prestartup_times.append((time.perf_counter() - time_before, module_path, success))
     if len(node_prestartup_times) > 0:
-        print("\nPrestartup times for custom nodes:")
+        logging.info("\nPrestartup times for custom nodes:")
         for n in sorted(node_prestartup_times):
             if n[2]:
                 import_message = ""
             else:
                 import_message = " (PRESTARTUP FAILED)"
-            print("{:6.1f} seconds{}:".format(n[0], import_message), n[1])
+            logging.info("{:6.1f} seconds{}:".format(n[0], import_message), n[1])
-        print()
 
 
 execute_prestartup_script()
@@ -74,7 +74,7 @@ if os.name == "nt":
 
 if args.cuda_device is not None:
     os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
-    print("Set cuda device to:", args.cuda_device)
+    logging.info("Set cuda device to:", args.cuda_device)
 
 if args.deterministic:
     if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ:
@@ -124,7 +124,7 @@ def prompt_worker(q: AbstractPromptQueue, _server: server_module.PromptServer):
 
             current_time = time.perf_counter()
             execution_time = current_time - execution_start_time
-            print("Prompt executed in {:.2f} seconds".format(execution_time))
+            logging.info("Prompt executed in {:.2f} seconds".format(execution_time))
 
         flags = q.get_flags()
         free_memory = flags.get("free_memory", False)
@@ -183,14 +183,14 @@ def cuda_malloc_warning():
             if b in device_name:
                 cuda_malloc_warning = True
         if cuda_malloc_warning:
-            print(
+            logging.warning(
                 "\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
 
 
 async def main():
     if args.temp_directory:
         temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
-        print(f"Setting temp directory to: {temp_dir}")
+        logging.debug(f"Setting temp directory to: {temp_dir}")
         folder_paths.set_temp_directory(temp_dir)
     cleanup_temp()
 
@@ -263,7 +263,7 @@ async def main():
 
     if args.output_directory:
         output_dir = os.path.abspath(args.output_directory)
-        print(f"Setting output directory to: {output_dir}")
+        logging.debug(f"Setting output directory to: {output_dir}")
         folder_paths.set_output_directory(output_dir)
 
     # These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes
@@ -273,7 +273,7 @@ async def main():
 
     if args.input_directory:
         input_dir = os.path.abspath(args.input_directory)
-        print(f"Setting input directory to: {input_dir}")
+        logging.debug(f"Setting input directory to: {input_dir}")
         folder_paths.set_input_directory(input_dir)
 
     if args.quick_test_for_ci:
@@ -297,7 +297,7 @@ async def main():
     except asyncio.CancelledError:
         if distributed:
             await q.close()
-        print("\nStopped server")
+        logging.debug("\nStopped server")
 
     cleanup_temp()
 

comfy/cmd/server.py

@@ -12,6 +12,7 @@ from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 from io import BytesIO
 
+import logging
 import json
 import os
 import uuid
@@ -33,7 +34,6 @@ from .. import utils
 from .. import model_management
 from ..component_model.executor_types import ExecutorToClientProgress
 from ..component_model.file_output_path import file_output_path
-from ..nodes.package import import_all_nodes_in_workspace
 from ..nodes.package_typing import ExportedNodes
 from ..vendor.appdirs import user_data_dir
 from ..app.user_manager import UserManager
@@ -43,7 +43,7 @@ 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
@@ -136,7 +136,7 @@ class PromptServer(ExecutorToClientProgress):
 
                 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
@@ -426,9 +426,8 @@ class PromptServer(ExecutorToClientProgress):
                 try:
                     out[x] = node_info(x)
                 except Exception as e:
-                    print(f"[ERROR] An error occurred while retrieving information for the '{x}' node.",
+                    logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
-                          file=sys.stderr)
+                    logging.error(traceback.format_exc())
-                    traceback.print_exc()
             return web.json_response(out)
 
         @routes.get("/object_info/{node_class}")
@@ -461,7 +460,7 @@ class PromptServer(ExecutorToClientProgress):
 
         @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()
@@ -495,7 +494,7 @@ class PromptServer(ExecutorToClientProgress):
                     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)
@@ -805,8 +804,8 @@ class PromptServer(ExecutorToClientProgress):
         await site.start()
 
         if verbose:
-            print("Starting server\n")
+            logging.info("Starting server\n")
-            print("To see the GUI go to: http://{}:{}".format("localhost" if address == "0.0.0.0" else address, port))
+            logging.info("To see the GUI go to: http://{}:{}".format("localhost" if address == "0.0.0.0" else address, port))
         if call_on_start is not None:
             call_on_start(address, port)
 
@@ -818,8 +817,8 @@ class PromptServer(ExecutorToClientProgress):
             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()
+                logging.warning(traceback.format_exc())
 
         return json_data
 

comfy/cmd/worker.py

@@ -1,6 +1,7 @@
 import asyncio
 import itertools
 import os
+import logging
 
 from .extra_model_paths import load_extra_path_config
 from .. import options
@@ -19,7 +20,7 @@ async def main():
 
     if args.cuda_device is not None:
         os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
-        print("Set cuda device to:", args.cuda_device)
+        logging.info(f"Set cuda device to: {args.cuda_device}")
 
     if args.deterministic:
         if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ:
@@ -28,21 +29,21 @@ async def main():
     # configure paths
     if args.output_directory:
         output_dir = os.path.abspath(args.output_directory)
-        print(f"Setting output directory to: {output_dir}")
+        logging.info(f"Setting output directory to: {output_dir}")
         from ..cmd import folder_paths
         
         folder_paths.set_output_directory(output_dir)
     
     if args.input_directory:
         input_dir = os.path.abspath(args.input_directory)
-        print(f"Setting input directory to: {input_dir}")
+        logging.info(f"Setting input directory to: {input_dir}")
         from ..cmd import folder_paths
     
         folder_paths.set_input_directory(input_dir)
 
     if args.temp_directory:
         temp_dir = os.path.abspath(args.temp_directory)
-        print(f"Setting temp directory to: {temp_dir}")
+        logging.info(f"Setting temp directory to: {temp_dir}")
         from ..cmd import folder_paths
 
         folder_paths.set_temp_directory(temp_dir)

comfy/controlnet.py

@@ -433,7 +433,7 @@ 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]
@@ -546,6 +546,6 @@ 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/ldm/modules/attention.py

@@ -4,6 +4,7 @@ import torch.nn.functional as F
 from torch import nn, einsum
 from einops import rearrange, repeat
 from typing import Optional, Any
+import logging
 
 from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding
 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -19,7 +20,7 @@ ops = ops.disable_weight_init
 
 # CrossAttn precision handling
 if args.dont_upcast_attention:
-    print("disabling upcasting of attention")
+    logging.info("disabling upcasting of attention")
     _ATTN_PRECISION = "fp16"
 else:
     _ATTN_PRECISION = "fp32"
@@ -273,12 +274,12 @@ def attention_split(q, k, v, heads, mask=None):
                 model_management.soft_empty_cache(True)
                 if cleared_cache == False:
                     cleared_cache = True
-                    print("out of memory error, emptying cache and trying again")
+                    logging.warning("out of memory error, emptying cache and trying again")
                     continue
                 steps *= 2
                 if steps > 64:
                     raise e
-                print("out of memory error, increasing steps and trying again", steps)
+                logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
             else:
                 raise e
 
@@ -350,17 +351,17 @@ def attention_pytorch(q, k, v, heads, mask=None):
 optimized_attention = attention_basic
 
 if model_management.xformers_enabled():
-    print("Using xformers cross attention")
+    logging.info("Using xformers cross attention")
     optimized_attention = attention_xformers
 elif model_management.pytorch_attention_enabled():
-    print("Using pytorch cross attention")
+    logging.info("Using pytorch cross attention")
     optimized_attention = attention_pytorch
 else:
     if args.use_split_cross_attention:
-        print("Using split optimization for cross attention")
+        logging.info("Using split optimization for cross attention")
         optimized_attention = attention_split
     else:
-        print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
+        logging.info("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
         optimized_attention = attention_sub_quad
 
 optimized_attention_masked = optimized_attention

comfy/ldm/modules/diffusionmodules/model.py

@@ -5,6 +5,7 @@ import torch.nn as nn
 import numpy as np
 from einops import rearrange
 from typing import Optional, Any
+import logging
 
 from .... import model_management
 from .... import ops
@@ -190,7 +191,7 @@ def slice_attention(q, k, v):
             steps *= 2
             if steps > 128:
                 raise e
-            print("out of memory error, increasing steps and trying again", steps)
+            logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
 
     return r1
 
@@ -235,7 +236,7 @@ def pytorch_attention(q, k, v):
         out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
         out = out.transpose(2, 3).reshape(B, C, H, W)
     except model_management.OOM_EXCEPTION as e:
-        print("scaled_dot_product_attention OOMed: switched to slice attention")
+        logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
         out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
     return out
 
@@ -268,13 +269,13 @@ class AttnBlock(nn.Module):
                                         padding=0)
 
         if model_management.xformers_enabled_vae():
-            print("Using xformers attention in VAE")
+            logging.info("Using xformers attention in VAE")
             self.optimized_attention = xformers_attention
         elif model_management.pytorch_attention_enabled():
-            print("Using pytorch attention in VAE")
+            logging.info("Using pytorch attention in VAE")
             self.optimized_attention = pytorch_attention
         else:
-            print("Using split attention in VAE")
+            logging.info("Using split attention in VAE")
             self.optimized_attention = normal_attention
 
     def forward(self, x):
@@ -562,7 +563,7 @@ class Decoder(nn.Module):
         block_in = ch*ch_mult[self.num_resolutions-1]
         curr_res = resolution // 2**(self.num_resolutions-1)
         self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
+        logging.debug("Working with z of shape {} = {} dimensions.".format(
             self.z_shape, np.prod(self.z_shape)))
 
         # z to block_in

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -4,6 +4,7 @@ import torch as th
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
+import logging
 
 from .util import (
     checkpoint,
@@ -359,7 +360,7 @@ def apply_control(h, control, name):
             try:
                 h += ctrl
             except:
-                print("warning control could not be applied", h.shape, ctrl.shape)
+                logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape))
     return h
 
 class UNetModel(nn.Module):
@@ -496,7 +497,7 @@ class UNetModel(nn.Module):
             if isinstance(self.num_classes, int):
                 self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device)
             elif self.num_classes == "continuous":
-                print("setting up linear c_adm embedding layer")
+                logging.debug("setting up linear c_adm embedding layer")
                 self.label_emb = nn.Linear(1, time_embed_dim)
             elif self.num_classes == "sequential":
                 assert adm_in_channels is not None

comfy/ldm/modules/sub_quadratic_attention.py

@@ -14,6 +14,7 @@ import torch
 from torch import Tensor
 from torch.utils.checkpoint import checkpoint
 import math
+import logging
 
 try:
 	from typing import Optional, NamedTuple, List, Protocol
@@ -170,7 +171,7 @@ def _get_attention_scores_no_kv_chunking(
         attn_probs = attn_scores.softmax(dim=-1)
         del attn_scores
     except model_management.OOM_EXCEPTION:
-        print("ran out of memory while running softmax in  _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
+        logging.warning("ran out of memory while running softmax in  _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
         attn_scores -= attn_scores.max(dim=-1, keepdim=True).values
         torch.exp(attn_scores, out=attn_scores)
         summed = torch.sum(attn_scores, dim=-1, keepdim=True)

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("model_type {}".format(model_type.name))
-        logging.info("adm {}".format(self.adm_channels))
+        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

@@ -34,7 +34,7 @@ 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
@@ -46,7 +46,7 @@ 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.
 
@@ -122,7 +122,7 @@ 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")
@@ -148,7 +148,7 @@ 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")
@@ -216,11 +216,11 @@ 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 or cpu_state == CPUState.MPS:
-    logging.warning("Forcing FP16.")
+    logging.info("Forcing FP16.")
     FORCE_FP16 = True
 
 if lowvram_available:
@@ -234,12 +234,12 @@ 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'):
@@ -257,11 +257,11 @@ 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 = []
 
@@ -304,7 +304,7 @@ 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"):
@@ -352,7 +352,7 @@ 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=[]):
@@ -396,7 +396,7 @@ 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:
@@ -406,7 +406,7 @@ 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/nodes/base_nodes.py

@@ -5,6 +5,7 @@ import json
 import hashlib
 import math
 import random
+import logging
 
 from PIL import Image, ImageOps, ImageSequence
 from PIL.PngImagePlugin import PngInfo
@@ -68,7 +69,7 @@ class ConditioningAverage :
         out = []
 
         if len(conditioning_from) > 1:
-            print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+            logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
 
         cond_from = conditioning_from[0][0]
         pooled_output_from = conditioning_from[0][1].get("pooled_output", None)
@@ -107,7 +108,7 @@ class ConditioningConcat:
         out = []
 
         if len(conditioning_from) > 1:
-            print("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+            logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
 
         cond_from = conditioning_from[0][0]
 

comfy/samplers.py

@@ -4,6 +4,7 @@ import torch
 import collections
 from . import model_management
 import math
+import logging
 
 from .sampler_names import SCHEDULER_NAMES, SAMPLER_NAMES
 
@@ -626,7 +627,7 @@ def calculate_sigmas_scheduler(model, scheduler_name, steps):
     elif scheduler_name == "sgm_uniform":
         sigmas = normal_scheduler(model, steps, sgm=True)
     else:
-        print("error invalid scheduler", scheduler_name)
+        logging.error("error invalid scheduler {}".format(scheduler_name))
     return sigmas
 
 def sampler_object(name):

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 = 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 model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
 def load_unet(unet_path):

comfy/utils.py

@@ -26,7 +26,7 @@ def load_torch_file(ckpt, safe_load=False, device=None):
         else:
             pl_sd = torch.load(ckpt, map_location=device, pickle_module=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:

comfy_extras/nodes/nodes_freelunch.py

@@ -1,7 +1,7 @@
 #code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)
 
 import torch
-
+import logging
 
 def Fourier_filter(x, threshold, scale):
     # FFT
@@ -49,7 +49,7 @@ class FreeU:
                     try:
                         hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
                     except:
-                        print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.")
+                        logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
                         on_cpu_devices[hsp.device] = True
                         hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
                 else:
@@ -95,7 +95,7 @@ class FreeU_V2:
                     try:
                         hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
                     except:
-                        print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.")
+                        logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
                         on_cpu_devices[hsp.device] = True
                         hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
                 else:

comfy_extras/nodes/nodes_hypernetwork.py

@@ -1,6 +1,7 @@
 from comfy import utils
 from comfy.cmd import folder_paths
 import torch
+import logging
 
 def load_hypernetwork_patch(path, strength):
     sd = utils.load_torch_file(path, safe_load=True)
@@ -23,7 +24,7 @@ def load_hypernetwork_patch(path, strength):
     }
 
     if activation_func not in valid_activation:
-        print("Unsupported Hypernetwork format, if you report it I might implement it.", path, " ", activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout)
+        logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {}   {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout))
         return None
 
     out = {}