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Commit 65397ce6 authored by comfyanonymous's avatar comfyanonymous
Browse files

Replace prints with logging and add --verbose argument.

parent 4656273e
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Showing with 90 additions and 65 deletions
comfy/cli_args.py
View file @ 65397ce6
......@@ -114,6 +114,9 @@ parser.add_argument("--disable-metadata", action="store_true", help="Disable sav
parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
if comfy.options.args_parsing:
args = parser.parse_args()
else:
......@@ -124,3 +127,10 @@ if args.windows_standalone_build:
if args.disable_auto_launch:
args.auto_launch = False
import logging
logging_level = logging.WARNING
if args.verbose:
logging_level = logging.DEBUG
logging.basicConfig(format="%(message)s", level=logging_level)
comfy/clip_vision.py
View file @ 65397ce6
......@@ -2,6 +2,7 @@ from .utils import load_torch_file, transformers_convert, state_dict_prefix_repl
import os
import torch
import json
import logging
import comfy.ops
import comfy.model_patcher
......@@ -99,7 +100,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
clip = ClipVisionModel(json_config)
m, u = clip.load_sd(sd)
if len(m) > 0:
print("missing clip vision:", m)
logging.warning("missing clip vision: {}".format(m))
u = set(u)
keys = list(sd.keys())
for k in keys:
......
comfy/controlnet.py
View file @ 65397ce6
import torch
import math
import os
import logging
import comfy.utils
import comfy.model_management
import comfy.model_detection
......@@ -367,7 +368,7 @@ def load_controlnet(ckpt_path, model=None):
leftover_keys = controlnet_data.keys()
if len(leftover_keys) > 0:
print("leftover keys:", leftover_keys)
logging.warning("leftover keys: {}".format(leftover_keys))
controlnet_data = new_sd
pth_key = 'control_model.zero_convs.0.0.weight'
......@@ -382,7 +383,7 @@ def load_controlnet(ckpt_path, model=None):
else:
net = load_t2i_adapter(controlnet_data)
if net is None:
print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path)
logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
return net
if controlnet_config is None:
......@@ -417,7 +418,7 @@ def load_controlnet(ckpt_path, model=None):
cd = controlnet_data[x]
cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
else:
print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
class WeightsLoader(torch.nn.Module):
pass
......@@ -426,7 +427,12 @@ def load_controlnet(ckpt_path, model=None):
missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
else:
missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
print(missing, unexpected)
if len(missing) > 0:
logging.warning("missing controlnet keys: {}".format(missing))
if len(unexpected) > 0:
logging.info("unexpected controlnet keys: {}".format(unexpected))
global_average_pooling = False
filename = os.path.splitext(ckpt_path)[0]
......@@ -536,9 +542,9 @@ def load_t2i_adapter(t2i_data):
missing, unexpected = model_ad.load_state_dict(t2i_data)
if len(missing) > 0:
print("t2i missing", missing)
logging.warning("t2i missing {}".format(missing))
if len(unexpected) > 0:
print("t2i unexpected", unexpected)
logging.info("t2i unexpected {}".format(unexpected))
return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm)
comfy/diffusers_convert.py
View file @ 65397ce6
import re
import torch
import logging
# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
......@@ -177,7 +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:
print(f"Reshaping {k} for SD format")
logging.info(f"Reshaping {k} for SD format")
new_state_dict[k] = reshape_weight_for_sd(v)
return new_state_dict
......
comfy/lora.py
View file @ 65397ce6
import comfy.utils
import logging
LORA_CLIP_MAP = {
"mlp.fc1": "mlp_fc1",
......@@ -156,7 +157,7 @@ def load_lora(lora, to_load):
for x in lora.keys():
if x not in loaded_keys:
print("lora key not loaded", x)
logging.warning("lora key not loaded: {}".format(x))
return patch_dict
def model_lora_keys_clip(model, key_map={}):
......
comfy/model_base.py
View file @ 65397ce6
import torch
import logging
from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
from comfy.ldm.cascade.stage_c import StageC
from comfy.ldm.cascade.stage_b import StageB
......@@ -66,8 +67,8 @@ class BaseModel(torch.nn.Module):
if self.adm_channels is None:
self.adm_channels = 0
self.inpaint_model = False
print("model_type", model_type.name)
print("adm", self.adm_channels)
logging.warning("model_type {}".format(model_type.name))
logging.info("adm {}".format(self.adm_channels))
def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
sigma = t
......@@ -183,10 +184,10 @@ class BaseModel(torch.nn.Module):
to_load = self.model_config.process_unet_state_dict(to_load)
m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
if len(m) > 0:
print("unet missing:", m)
logging.warning("unet missing: {}".format(m))
if len(u) > 0:
print("unet unexpected:", u)
logging.warning("unet unexpected: {}".format(u))
del to_load
return self
......
comfy/model_detection.py
View file @ 65397ce6
import comfy.supported_models
import comfy.supported_models_base
import logging
def count_blocks(state_dict_keys, prefix_string):
count = 0
......@@ -186,7 +187,7 @@ def model_config_from_unet_config(unet_config):
if model_config.matches(unet_config):
return model_config(unet_config)
print("no match", unet_config)
logging.error("no match {}".format(unet_config))
return None
def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False):
......
comfy/model_management.py
View file @ 65397ce6
import psutil
import logging
from enum import Enum
from comfy.cli_args import args
import comfy.utils
......@@ -29,7 +30,7 @@ lowvram_available = True
xpu_available = False
if args.deterministic:
print("Using deterministic algorithms for pytorch")
logging.warning("Using deterministic algorithms for pytorch")
torch.use_deterministic_algorithms(True, warn_only=True)
directml_enabled = False
......@@ -41,7 +42,7 @@ if args.directml is not None:
directml_device = torch_directml.device()
else:
directml_device = torch_directml.device(device_index)
print("Using directml with device:", torch_directml.device_name(device_index))
logging.warning("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.
......@@ -117,10 +118,10 @@ 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)
print("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
logging.warning("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:
print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
logging.warning("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
set_vram_to = VRAMState.LOW_VRAM
try:
......@@ -143,12 +144,10 @@ else:
pass
try:
XFORMERS_VERSION = xformers.version.__version__
print("xformers version:", XFORMERS_VERSION)
logging.warning("xformers version: {}".format(XFORMERS_VERSION))
if XFORMERS_VERSION.startswith("0.0.18"):
print()
print("WARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
print("Please downgrade or upgrade xformers to a different version.")
print()
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")
XFORMERS_ENABLED_VAE = False
except:
pass
......@@ -213,11 +212,11 @@ elif args.highvram or args.gpu_only:
FORCE_FP32 = False
FORCE_FP16 = False
if args.force_fp32:
print("Forcing FP32, if this improves things please report it.")
logging.warning("Forcing FP32, if this improves things please report it.")
FORCE_FP32 = True
if args.force_fp16:
print("Forcing FP16.")
logging.warning("Forcing FP16.")
FORCE_FP16 = True
if lowvram_available:
......@@ -231,12 +230,12 @@ if cpu_state != CPUState.GPU:
if cpu_state == CPUState.MPS:
vram_state = VRAMState.SHARED
print(f"Set vram state to: {vram_state.name}")
logging.warning(f"Set vram state to: {vram_state.name}")
DISABLE_SMART_MEMORY = args.disable_smart_memory
if DISABLE_SMART_MEMORY:
print("Disabling smart memory management")
logging.warning("Disabling smart memory management")
def get_torch_device_name(device):
if hasattr(device, 'type'):
......@@ -254,11 +253,11 @@ def get_torch_device_name(device):
return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
try:
print("Device:", get_torch_device_name(get_torch_device()))
logging.warning("Device: {}".format(get_torch_device_name(get_torch_device())))
except:
print("Could not pick default device.")
logging.warning("Could not pick default device.")
print("VAE dtype:", VAE_DTYPE)
logging.warning("VAE dtype: {}".format(VAE_DTYPE))
current_loaded_models = []
......@@ -301,7 +300,7 @@ class LoadedModel:
raise e
if lowvram_model_memory > 0:
print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024))
logging.warning("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"):
......@@ -314,7 +313,7 @@ class LoadedModel:
elif hasattr(m, "weight"): #only modules with comfy_cast_weights can be set to lowvram mode
m.to(self.device)
mem_counter += module_size(m)
print("lowvram: loaded module regularly", m)
logging.warning("lowvram: loaded module regularly {}".format(m))
self.model_accelerated = True
......@@ -348,7 +347,7 @@ def unload_model_clones(model):
to_unload = [i] + to_unload
for i in to_unload:
print("unload clone", i)
logging.warning("unload clone {}".format(i))
current_loaded_models.pop(i).model_unload()
def free_memory(memory_required, device, keep_loaded=[]):
......@@ -390,7 +389,7 @@ def load_models_gpu(models, memory_required=0):
models_already_loaded.append(loaded_model)
else:
if hasattr(x, "model"):
print(f"Requested to load {x.model.__class__.__name__}")
logging.warning(f"Requested to load {x.model.__class__.__name__}")
models_to_load.append(loaded_model)
if len(models_to_load) == 0:
......@@ -400,7 +399,7 @@ def load_models_gpu(models, memory_required=0):
free_memory(extra_mem, d, models_already_loaded)
return
print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
logging.warning(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/model_patcher.py
View file @ 65397ce6
import torch
import copy
import inspect
import logging
import comfy.utils
import comfy.model_management
......@@ -187,7 +188,7 @@ class ModelPatcher:
model_sd = self.model_state_dict()
for key in self.patches:
if key not in model_sd:
print("could not patch. key doesn't exist in model:", key)
logging.warning("could not patch. key doesn't exist in model: {}".format(key))
continue
weight = model_sd[key]
......@@ -236,7 +237,7 @@ class ModelPatcher:
w1 = v[0]
if alpha != 0.0:
if w1.shape != weight.shape:
print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
else:
weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
elif patch_type == "lora": #lora/locon
......@@ -252,7 +253,7 @@ class ModelPatcher:
try:
weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
except Exception as e:
print("ERROR", key, e)
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "lokr":
w1 = v[0]
w2 = v[1]
......@@ -291,7 +292,7 @@ class ModelPatcher:
try:
weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
except Exception as e:
print("ERROR", key, e)
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "loha":
w1a = v[0]
w1b = v[1]
......@@ -320,7 +321,7 @@ class ModelPatcher:
try:
weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
except Exception as e:
print("ERROR", key, e)
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "glora":
if v[4] is not None:
alpha *= v[4] / v[0].shape[0]
......@@ -330,9 +331,12 @@ class ModelPatcher:
b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
try:
weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
else:
print("patch type not recognized", patch_type, key)
logging.warning("patch type not recognized {} {}".format(patch_type, key))
return weight
......
comfy/sd.py
View file @ 65397ce6
import torch
from enum import Enum
import logging
from comfy import model_management
from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
......@@ -37,7 +38,7 @@ def load_model_weights(model, sd):
w = sd.pop(x)
del w
if len(m) > 0:
print("missing", m)
logging.warning("missing {}".format(m))
return model
def load_clip_weights(model, sd):
......@@ -81,7 +82,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
k1 = set(k1)
for x in loaded:
if (x not in k) and (x not in k1):
print("NOT LOADED", x)
logging.warning("NOT LOADED {}".format(x))
return (new_modelpatcher, new_clip)
......@@ -225,10 +226,10 @@ class VAE:
m, u = self.first_stage_model.load_state_dict(sd, strict=False)
if len(m) > 0:
print("Missing VAE keys", m)
logging.warning("Missing VAE keys {}".format(m))
if len(u) > 0:
print("Leftover VAE keys", u)
logging.info("Leftover VAE keys {}".format(u))
if device is None:
device = model_management.vae_device()
......@@ -291,7 +292,7 @@ class VAE:
samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
except model_management.OOM_EXCEPTION as e:
print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
pixel_samples = self.decode_tiled_(samples_in)
pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
......@@ -317,7 +318,7 @@ class VAE:
samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
except model_management.OOM_EXCEPTION as e:
print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
samples = self.encode_tiled_(pixel_samples)
return samples
......@@ -393,10 +394,10 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
for c in clip_data:
m, u = clip.load_sd(c)
if len(m) > 0:
print("clip missing:", m)
logging.warning("clip missing: {}".format(m))
if len(u) > 0:
print("clip unexpected:", u)
logging.info("clip unexpected: {}".format(u))
return clip
def load_gligen(ckpt_path):
......@@ -534,21 +535,21 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
clip = CLIP(clip_target, embedding_directory=embedding_directory)
m, u = clip.load_sd(clip_sd, full_model=True)
if len(m) > 0:
print("clip missing:", m)
logging.warning("clip missing: {}".format(m))
if len(u) > 0:
print("clip unexpected:", u)
logging.info("clip unexpected {}:".format(u))
else:
print("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
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:
print("left over keys:", left_over)
logging.info("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"):
print("loaded straight to GPU")
logging.warning("loaded straight to GPU")
model_management.load_model_gpu(model_patcher)
return (model_patcher, clip, vae, clipvision)
......@@ -577,7 +578,7 @@ def load_unet_state_dict(sd): #load unet in diffusers format
if k in sd:
new_sd[diffusers_keys[k]] = sd.pop(k)
else:
print(diffusers_keys[k], k)
logging.warning("{} {}".format(diffusers_keys[k], k))
offload_device = model_management.unet_offload_device()
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
......@@ -588,14 +589,14 @@ 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:
print("left over keys in unet:", left_over)
logging.warning("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):
sd = comfy.utils.load_torch_file(unet_path)
model = load_unet_state_dict(sd)
if model is None:
print("ERROR UNSUPPORTED UNET", unet_path)
logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
return model
......
comfy/sd1_clip.py
View file @ 65397ce6
......@@ -8,6 +8,7 @@ import zipfile
from . import model_management
import comfy.clip_model
import json
import logging
def gen_empty_tokens(special_tokens, length):
start_token = special_tokens.get("start", None)
......@@ -137,7 +138,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
tokens_temp += [next_new_token]
next_new_token += 1
else:
print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored", y.shape[0], current_embeds.weight.shape[1])
logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1]))
while len(tokens_temp) < len(x):
tokens_temp += [self.special_tokens["pad"]]
out_tokens += [tokens_temp]
......@@ -329,9 +330,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
else:
embed = torch.load(embed_path, map_location="cpu")
except Exception as e:
print(traceback.format_exc())
print()
print("error loading embedding, skipping loading:", embedding_name)
logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
return None
if embed_out is None:
......@@ -422,7 +421,7 @@ class SDTokenizer:
embedding_name = word[len(self.embedding_identifier):].strip('\n')
embed, leftover = self._try_get_embedding(embedding_name)
if embed is None:
print(f"warning, embedding:{embedding_name} does not exist, ignoring")
logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
else:
if len(embed.shape) == 1:
tokens.append([(embed, weight)])
......
comfy/utils.py
View file @ 65397ce6
......@@ -5,6 +5,7 @@ import comfy.checkpoint_pickle
import safetensors.torch
import numpy as np
from PIL import Image
import logging
def load_torch_file(ckpt, safe_load=False, device=None):
if device is None:
......@@ -14,14 +15,14 @@ def load_torch_file(ckpt, safe_load=False, device=None):
else:
if safe_load:
if not 'weights_only' in torch.load.__code__.co_varnames:
print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
safe_load = False
if safe_load:
pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
else:
pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
logging.info(f"Global Step: {pl_sd['global_step']}")
if "state_dict" in pl_sd:
sd = pl_sd["state_dict"]
else:
......
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