Replace print with logging (#6138)

* Replace print with logging

* nit

* nit

* nit

* nit

* nit

* nit

.ci/update_windows/update.py

@@ -28,7 +28,7 @@ def pull(repo, remote_name='origin', branch='master'):
 
                 if repo.index.conflicts is not None:
                     for conflict in repo.index.conflicts:
-                        print('Conflicts found in:', conflict[0].path)
+                        print('Conflicts found in:', conflict[0].path)  # noqa: T201
                     raise AssertionError('Conflicts, ahhhhh!!')
 
                 user = repo.default_signature
@@ -49,18 +49,18 @@ repo_path = str(sys.argv[1])
 repo = pygit2.Repository(repo_path)
 ident = pygit2.Signature('comfyui', 'comfy@ui')
 try:
-    print("stashing current changes")
+    print("stashing current changes")  # noqa: T201
     repo.stash(ident)
 except KeyError:
-    print("nothing to stash")
+    print("nothing to stash")  # noqa: T201
 backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
-print("creating backup branch: {}".format(backup_branch_name))
+print("creating backup branch: {}".format(backup_branch_name))  # noqa: T201
 try:
     repo.branches.local.create(backup_branch_name, repo.head.peel())
 except:
     pass
 
-print("checking out master branch")
+print("checking out master branch")  # noqa: T201
 branch = repo.lookup_branch('master')
 if branch is None:
     ref = repo.lookup_reference('refs/remotes/origin/master')
@@ -72,7 +72,7 @@ else:
     ref = repo.lookup_reference(branch.name)
     repo.checkout(ref)
 
-print("pulling latest changes")
+print("pulling latest changes")  # noqa: T201
 pull(repo)
 
 if "--stable" in sys.argv:
@@ -94,7 +94,7 @@ if "--stable" in sys.argv:
     if latest_tag is not None:
         repo.checkout(latest_tag)
 
-print("Done!")
+print("Done!")  # noqa: T201
 
 self_update = True
 if len(sys.argv) > 2:

app/user_manager.py

@@ -38,8 +38,8 @@ class UserManager():
         if not os.path.exists(user_directory):
             os.makedirs(user_directory, exist_ok=True)
             if not args.multi_user:
-                print("****** User settings have been changed to be stored on the server instead of browser storage. ******")
+                logging.warning("****** User settings have been changed to be stored on the server instead of browser storage. ******")
-                print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
+                logging.warning("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
 
         if args.multi_user:
             if os.path.isfile(self.get_users_file()):

comfy/cldm/cldm.py

@@ -160,7 +160,6 @@ class ControlNet(nn.Module):
             if isinstance(self.num_classes, int):
                 self.label_emb = nn.Embedding(num_classes, time_embed_dim)
             elif self.num_classes == "continuous":
-                print("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/extra_samplers/uni_pc.py

@@ -2,6 +2,7 @@
 
 import torch
 import math
+import logging
 
 from tqdm.auto import trange
 
@@ -474,7 +475,7 @@ class UniPC:
             return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
 
     def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
-        print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
+        logging.info(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
         ns = self.noise_schedule
         assert order <= len(model_prev_list)
 
@@ -518,7 +519,6 @@ class UniPC:
             A_p = C_inv_p
 
         if use_corrector:
-            print('using corrector')
             C_inv = torch.linalg.inv(C)
             A_c = C_inv
 

comfy/hooks.py

@@ -5,6 +5,7 @@ import math
 import torch
 import numpy as np
 import itertools
+import logging
 
 if TYPE_CHECKING:
     from comfy.model_patcher import ModelPatcher, PatcherInjection
@@ -575,7 +576,7 @@ def load_hook_lora_for_models(model: 'ModelPatcher', clip: 'CLIP', lora: dict[st
     k1 = set(k1)
     for x in loaded:
         if (x not in k) and (x not in k1):
-            print(f"NOT LOADED {x}")
+            logging.warning(f"NOT LOADED {x}")
     return (new_modelpatcher, new_clip, hook_group)
 
 def _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dict[str, HookGroup], cache: dict[tuple[HookGroup, HookGroup], HookGroup]):

comfy/ldm/aura/mmdit.py

@@ -381,7 +381,6 @@ class MMDiT(nn.Module):
         pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
         self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
         self.h_max, self.w_max = target_dim
-        print("PE extended to", target_dim)
 
     def pe_selection_index_based_on_dim(self, h, w):
         h_p, w_p = h // self.patch_size, w // self.patch_size

comfy/ldm/modules/diffusionmodules/util.py

@@ -9,6 +9,7 @@
 
 
 import math
+import logging
 import torch
 import torch.nn as nn
 import numpy as np
@@ -130,7 +131,7 @@ def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timestep
     # add one to get the final alpha values right (the ones from first scale to data during sampling)
     steps_out = ddim_timesteps + 1
     if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
+        logging.info(f'Selected timesteps for ddim sampler: {steps_out}')
     return steps_out
 
 
@@ -142,8 +143,8 @@ def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
     # according the the formula provided in https://arxiv.org/abs/2010.02502
     sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
     if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+        logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
+        logging.info(f'For the chosen value of eta, which is {eta}, '
               f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
     return sigmas, alphas, alphas_prev
 

comfy/ldm/util.py

@@ -1,4 +1,5 @@
 import importlib
+import logging
 
 import torch
 from torch import optim
@@ -23,7 +24,7 @@ def log_txt_as_img(wh, xc, size=10):
         try:
             draw.text((0, 0), lines, fill="black", font=font)
         except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
+            logging.warning("Cant encode string for logging. Skipping.")
 
         txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
         txts.append(txt)
@@ -65,7 +66,7 @@ def mean_flat(tensor):
 def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+        logging.info(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
     return total_params
 
 

comfy/model_base.py

@@ -770,7 +770,6 @@ class Flux(BaseModel):
             mask = torch.ones_like(noise)[:, :1]
 
         mask = torch.mean(mask, dim=1, keepdim=True)
-        print(mask.shape)
         mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center")
         mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8)
         mask = utils.resize_to_batch_size(mask, noise.shape[0])

comfy/model_management.py

@@ -1084,7 +1084,7 @@ def unload_all_models():
 
 
 def resolve_lowvram_weight(weight, model, key): #TODO: remove
-    print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
+    logging.warning("The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
     return weight
 
 #TODO: might be cleaner to put this somewhere else

comfy/model_patcher.py

@@ -773,7 +773,7 @@ class ModelPatcher:
         return self.model.device
 
     def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):
-        print("WARNING the ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead")
+        logging.warning("The ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead")
         return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)
 
     def cleanup(self):
@@ -1029,7 +1029,7 @@ class ModelPatcher:
                 if cached_weights is not None:
                     for key in cached_weights:
                         if key not in model_sd_keys:
-                            print(f"WARNING cached hook could not patch. key does not exist in model: {key}")
+                            logging.warning(f"Cached hook could not patch. Key does not exist in model: {key}")
                             continue
                         self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter)
                 else:
@@ -1039,7 +1039,7 @@ class ModelPatcher:
                         original_weights = self.get_key_patches()
                     for key in relevant_patches:
                         if key not in model_sd_keys:
-                            print(f"WARNING cached hook would not patch. key does not exist in model: {key}")
+                            logging.warning(f"Cached hook would not patch. Key does not exist in model: {key}")
                             continue
                         self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights,
                                                             memory_counter=memory_counter)

comfy/sd.py

@@ -940,11 +940,11 @@ def load_diffusion_model(unet_path, model_options={}):
     return model
 
 def load_unet(unet_path, dtype=None):
-    print("WARNING: the load_unet function has been deprecated and will be removed please switch to: load_diffusion_model")
+    logging.warning("The load_unet function has been deprecated and will be removed please switch to: load_diffusion_model")
     return load_diffusion_model(unet_path, model_options={"dtype": dtype})
 
 def load_unet_state_dict(sd, dtype=None):
-    print("WARNING: the load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict")
+    logging.warning("The load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict")
     return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype})
 
 def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}):

comfy/sd1_clip.py

@@ -41,8 +41,7 @@ class ClipTokenWeightEncoder:
                 to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len))
             else:
                 to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
-            print(to_encode)
+
-                
         o = self.encode(to_encode)
         out, pooled = o[:2]
 

comfy_extras/chainner_models/model_loading.py

@@ -1,5 +1,6 @@
+import logging
 from spandrel import ModelLoader
 
 def load_state_dict(state_dict):
-    print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
+    logging.warning("comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
     return ModelLoader().load_from_state_dict(state_dict).eval()

comfy_extras/nodes_hooks.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 from typing import TYPE_CHECKING, Union
+import logging
 import torch
 from collections.abc import Iterable
 
@@ -539,7 +540,7 @@ class CreateHookKeyframesInterpolated:
                 is_first = False
             prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps))
             if print_keyframes:
-                print(f"Hook Keyframe - start_percent:{percent} = {strength}")
+                logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}")
         return (prev_hook_kf,)
 
 class CreateHookKeyframesFromFloats:
@@ -588,7 +589,7 @@ class CreateHookKeyframesFromFloats:
                 is_first = False
             prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps))
             if print_keyframes:
-                print(f"Hook Keyframe - start_percent:{percent} = {strength}")
+                logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}")
         return (prev_hook_kf,)
 #------------------------------------------
 ###########################################

main.py

@@ -63,7 +63,7 @@ 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
 
     if args.disable_all_custom_nodes:
@@ -85,14 +85,14 @@ 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()
+        logging.info("")
 
 apply_custom_paths()
 execute_prestartup_script()

new_updater.py

@@ -32,4 +32,4 @@ def update_windows_updater():
     except:
         pass
     shutil.copy(bat_path, dest_bat_path)
-    print("Updated the windows standalone package updater.")
+    print("Updated the windows standalone package updater.")  # noqa: T201

ruff.toml

@@ -4,7 +4,10 @@ lint.ignore = ["ALL"]
 # Enable specific rules
 lint.select = [
     "S307",  # suspicious-eval-usage
+    "T201",  # print-usage
     # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
     # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
     "F",
-]
+]
+
+exclude = ["*.ipynb"]

tests-unit/server/routes/internal_routes_test.py

@@ -89,9 +89,9 @@ async def test_routes_added_to_app(aiohttp_client_factory, internal_routes):
     client = await aiohttp_client_factory()
     try:
         resp = await client.get('/files')
-        print(f"Response received: status {resp.status}")
+        print(f"Response received: status {resp.status}")  # noqa: T201
     except Exception as e:
-        print(f"Exception occurred during GET request: {e}")
+        print(f"Exception occurred during GET request: {e}")  # noqa: T201
         raise
 
     assert resp.status != 404, "Route /files does not exist"

tests/conftest.py

@@ -28,7 +28,7 @@ def pytest_collection_modifyitems(items):
     last_items = []
     for test_name in LAST_TESTS:
         for item in items.copy():
-            print(item.module.__name__, item)
+            print(item.module.__name__, item)  # noqa: T201
             if item.module.__name__  == test_name:
                 last_items.append(item)
                 items.remove(item)

tests/inference/test_execution.py

@@ -134,7 +134,7 @@ class TestExecution:
         use_lru, lru_size = request.param
         if use_lru:
             pargs += ['--cache-lru', str(lru_size)]
-        print("Running server with args:", pargs)
+        print("Running server with args:", pargs)  # noqa: T201
         p = subprocess.Popen(pargs)
         yield
         p.kill()
@@ -150,8 +150,8 @@ class TestExecution:
             try:
                 comfy_client.connect(listen=listen, port=port)
             except ConnectionRefusedError as e:
-                print(e)
+                print(e)  # noqa: T201
-                print(f"({i+1}/{n_tries}) Retrying...")
+                print(f"({i+1}/{n_tries}) Retrying...")  # noqa: T201
             else:
                 break
         return comfy_client

tests/inference/test_inference.py

@@ -171,8 +171,8 @@ class TestInference:
             try:
                 comfy_client.connect(listen=listen, port=port)
             except ConnectionRefusedError as e:
-                print(e)
+                print(e)  # noqa: T201
-                print(f"({i+1}/{n_tries}) Retrying...")
+                print(f"({i+1}/{n_tries}) Retrying...")  # noqa: T201
             else:
                 break
         return comfy_client