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ComfyUI
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Change log levels. 

Logging level now defaults to info. --verbose sets it to debug.
This commit is contained in:
comfyanonymous 2024-03-11 13:54:56 -04:00
parent dc6d4151a2
commit 0ed72befe1
9 changed files with 38 additions and 37 deletions
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2
comfy/cli_args.py
View File

@ -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

4
comfy/controlnet.py
View File

@ -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)

2
comfy/diffusers_convert.py
View File

@ -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

4
comfy/model_base.py
View File

@ -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

28
comfy/model_management.py
View File

@ -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:

12
comfy/sd.py
View File

@ -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):

2
comfy/utils.py
View File

@ -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:

6
nodes.py
View File

@ -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")

15
server.py
View File

@ -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