Switch some more prints to logging.

comfy/ldm/modules/attention.py

@@ -4,6 +4,7 @@
 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
@@ -20,7 +21,7 @@
 
 # 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"
@@ -274,12 +275,12 @@ 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
 
@@ -351,17 +352,17 @@ 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 numpy as np
 from einops import rearrange
 from typing import Optional, Any
+import logging
 
 from comfy import model_management
 import comfy.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 @@ def __init__(self, in_channels):
                                         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 @@ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
         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.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 @@ def __init__(
             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 @@
 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/samplers.py

@@ -4,6 +4,7 @@
 import collections
 from comfy import model_management
 import math
+import logging
 
 def get_area_and_mult(conds, x_in, timestep_in):
     area = (x_in.shape[2], x_in.shape[3], 0, 0)
@@ -625,7 +626,7 @@ 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_extras/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 @@ def output_block_patch(h, hsp, transformer_options):
                     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 @@ def output_block_patch(h, hsp, transformer_options):
                     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_hypernetwork.py

@@ -1,6 +1,7 @@
 import comfy.utils
 import folder_paths
 import torch
+import logging
 
 def load_hypernetwork_patch(path, strength):
     sd = comfy.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 = {}

main.py

@@ -54,15 +54,15 @@ def execute_script(script_path):
 import gc
 
 from comfy.cli_args import args
+import logging
 
 if os.name == "nt":
-    import logging
     logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
 
 if __name__ == "__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("Set cuda device to: {}".format(args.cuda_device))
 
     if args.deterministic:
         if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ:
@@ -88,7 +88,7 @@ def cuda_malloc_warning():
             if b in device_name:
                 cuda_malloc_warning = True
         if cuda_malloc_warning:
-            print("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
+            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")
 
 def prompt_worker(q, server):
     e = execution.PromptExecutor(server)
@@ -121,7 +121,7 @@ def prompt_worker(q, server):
 
             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)
@@ -182,14 +182,14 @@ 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("Adding extra search path {} {}".format(x, full_path))
                 folder_paths.add_model_folder_path(x, full_path)
 
 
 if __name__ == "__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.info(f"Setting temp directory to: {temp_dir}")
         folder_paths.set_temp_directory(temp_dir)
     cleanup_temp()
 
@@ -224,7 +224,7 @@ def load_extra_path_config(yaml_path):
 
     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}")
         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
@@ -234,7 +234,7 @@ def load_extra_path_config(yaml_path):
 
     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}")
         folder_paths.set_input_directory(input_dir)
 
     if args.quick_test_for_ci:
@@ -252,6 +252,6 @@ def startup_server(address, port):
     try:
         loop.run_until_complete(run(server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start))
     except KeyboardInterrupt:
-        print("\nStopped server")
+        logging.info("\nStopped server")
 
     cleanup_temp()

nodes.py

@@ -1904,7 +1904,7 @@ def load_custom_node(module_path, ignore=set()):
             return False
     except Exception as e:
         logging.warning(traceback.format_exc())
-        logging.warning(f"Cannot import {module_path} module for custom nodes:", e)
+        logging.warning(f"Cannot import {module_path} module for custom nodes: {e}")
         return False
 
 def load_custom_nodes():

server.py

@@ -413,8 +413,8 @@ async def get_object_info(request):
                 try:
                     out[x] = node_info(x)
                 except Exception as e:
-                    print(f"[ERROR] An error occurred while retrieving information for the '{x}' node.", file=sys.stderr)
-                    traceback.print_exc()
+                    logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
+                    logging.error(traceback.format_exc())
             return web.json_response(out)
 
         @routes.get("/object_info/{node_class}")
@@ -641,6 +641,6 @@ def trigger_on_prompt(self, json_data):
                 json_data = handler(json_data)
             except Exception as e:
                 logging.warning(f"[ERROR] An error occurred during the on_prompt_handler processing")
-                traceback.print_exc()
+                logging.warning(traceback.format_exc())
 
         return json_data