Merge upstream

comfy/analytics/analytics.py

@@ -39,7 +39,7 @@ def initialize_event_tracking(loop: Optional[asyncio.AbstractEventLoop] = None):
     # patch nodes
     from ..nodes.base_nodes import SaveImage, CLIPTextEncode, LoraLoader, CheckpointLoaderSimple
     from ..cmd.execution import PromptQueue
-    from comfy.component_model.queue_types import QueueItem
+    from ..component_model.queue_types import QueueItem
 
     prompt_queue_put = PromptQueue.put
 

comfy/cli_args.py

@@ -176,6 +176,7 @@ def create_parser() -> argparse.ArgumentParser:
                         help="This name will be used by the frontends and workers to exchange prompt requests and replies. Progress updates will be prefixed by the queue name, followed by a '.', then the user ID")
     parser.add_argument("--external-address", required=False,
                         help="Specifies a base URL for external addresses reported by the API, such as for image paths.")
+    parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
 
     # now give plugins a chance to add configuration
     for entry_point in entry_points().select(group='comfyui.custom_config'):
@@ -208,6 +209,12 @@ def parse_args(parser: Optional[argparse.ArgumentParser] = None) -> Configuratio
     if args.disable_auto_launch:
         args.auto_launch = False
 
+    logging_level = logging.WARNING
+    if args.verbose:
+        logging_level = logging.DEBUG
+
+    logging.basicConfig(format="%(message)s", level=logging_level)
+
     return Configuration(**vars(args))
 
 

comfy/client/sdxl_with_refiner_workflow.py

@@ -1,7 +1,7 @@
 import copy
 from typing import TypeAlias, Union
 
-from comfy.api.components.schema.prompt import PromptDict, Prompt
+from ..api.components.schema.prompt import PromptDict, Prompt
 
 JSON: TypeAlias = Union[dict[str, "JSON"], list["JSON"], str, int, float, bool, None]
 _BASE_PROMPT: JSON = {

comfy/clip_model.py

@@ -1,5 +1,5 @@
 import torch
-from comfy.ldm.modules.attention import optimized_attention_for_device
+from .ldm.modules.attention import optimized_attention_for_device
 
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):

comfy/clip_vision.py

@@ -2,6 +2,7 @@ from .utils import load_torch_file, transformers_convert, state_dict_prefix_repl
 import os
 import torch
 import json
+import logging
 
 from . import ops
 from . import 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/cmd/execution.py

@@ -53,8 +53,7 @@ def get_input_data(inputs, class_def, unique_id, outputs=None, prompt=None, extr
             if h[x] == "PROMPT":
                 input_data_all[x] = [prompt]
             if h[x] == "EXTRA_PNGINFO":
-                if "extra_pnginfo" in extra_data:
+                input_data_all[x] = [extra_data.get('extra_pnginfo', None)]
-                    input_data_all[x] = [extra_data['extra_pnginfo']]
             if h[x] == "UNIQUE_ID":
                 input_data_all[x] = [unique_id]
     return input_data_all

comfy/component_model/executor_types.py

@@ -3,7 +3,7 @@ from __future__ import annotations  # for Python 3.7-3.9
 from typing_extensions import NotRequired, TypedDict
 from typing import Optional, Literal, Protocol, TypeAlias, Union
 
-from comfy.component_model.queue_types import BinaryEventTypes
+from .queue_types import BinaryEventTypes
 
 
 class ExecInfo(TypedDict):

comfy/controlnet.py

@@ -1,6 +1,7 @@
 import torch
 import math
 import os
+import logging
 
 from . import utils
 from . import model_management
@@ -368,7 +369,7 @@ 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'
@@ -383,7 +384,7 @@ 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:
@@ -418,7 +419,7 @@ 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
@@ -427,7 +428,12 @@ 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]
@@ -537,9 +543,9 @@ 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

@@ -1,5 +1,6 @@
 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/distributed/distributed_prompt_client.py

@@ -6,7 +6,7 @@ from typing import Optional
 from aio_pika import connect_robust
 from aio_pika.patterns import RPC
 
-from comfy.distributed.distributed_types import RpcRequest, RpcReply
+from .distributed_types import RpcRequest, RpcReply
 
 
 class DistributedPromptClient:

comfy/distributed/history.py

@@ -5,7 +5,7 @@ from typing import Optional, OrderedDict, List, Dict
 import collections
 from itertools import islice
 
-from comfy.component_model.queue_types import HistoryEntry, QueueItem, ExecutionStatus, MAXIMUM_HISTORY_SIZE
+from ..component_model.queue_types import HistoryEntry, QueueItem, ExecutionStatus, MAXIMUM_HISTORY_SIZE
 
 
 class History:

comfy/gligen.py

@@ -4,7 +4,7 @@ import torch
 from torch import nn
 from .ldm.modules.attention import CrossAttention
 from inspect import isfunction
-from comfy.ops import manual_cast
+from .ops import manual_cast
 ops = manual_cast
 
 def exists(val):

comfy/lora.py

@@ -1,3 +1,4 @@
+import logging
 from . import utils
 
 LORA_CLIP_MAP = {
@@ -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

@@ -1,4 +1,5 @@
 import torch
+import logging
 from .ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
 from .ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from .ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
@@ -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)
+        logging.warning("model_type {}".format(model_type.name))
-        print("adm", self.adm_channels)
+        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
@@ -168,7 +169,7 @@ class BaseModel(torch.nn.Module):
 
         c_concat = kwargs.get("noise_concat", None)
         if c_concat is not None:
-            out['c_concat'] = comfy.conds.CONDNoiseShape(data)
+            out['c_concat'] = conds.CONDNoiseShape(data)
 
         return out
 
@@ -182,10 +183,10 @@ 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

@@ -1,5 +1,6 @@
 from . import supported_models
 from . import 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

@@ -1,4 +1,5 @@
 import psutil
+import logging
 from enum import Enum
 from .cli_args import args
 from . import utils
@@ -33,7 +34,7 @@ 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
@@ -45,7 +46,7 @@ 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.
 
@@ -121,10 +122,10 @@ 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:
@@ -147,12 +148,10 @@ 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()
+                logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
-                print("WARNING: 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")
-                print("Please downgrade or upgrade xformers to a different version.")
-                print()
                 XFORMERS_ENABLED_VAE = False
         except:
             pass
@@ -217,11 +216,11 @@ 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 or cpu_state == CPUState.MPS:
-    print("Forcing FP16.")
+    logging.warning("Forcing FP16.")
     FORCE_FP16 = True
 
 if lowvram_available:
@@ -235,12 +234,12 @@ 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'):
@@ -258,11 +257,11 @@ 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 = []
 
@@ -305,7 +304,7 @@ 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"):
@@ -318,7 +317,7 @@ 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
 
@@ -353,7 +352,7 @@ 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=[]):
@@ -397,7 +396,7 @@ 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:
@@ -407,7 +406,7 @@ 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

@@ -1,6 +1,7 @@
 import torch
 import copy
 import inspect
+import logging
 
 from . import utils
 from . import 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 * 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 = model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
                 b2 = model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
 
-                weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
+                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/model_sampling.py

@@ -1,5 +1,5 @@
 import torch
-from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
+from .ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
 
 class EPS:

comfy/sd.py

@@ -1,5 +1,6 @@
 import torch
 from enum import Enum
+import logging
 
 from . 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 = 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 model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
 def load_unet(unet_path):
     sd = 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

@@ -9,6 +9,7 @@ from . import model_management
 from pkg_resources import resource_filename
 from . import clip_model
 import json
+import logging
 
 def gen_empty_tokens(special_tokens, length):
     start_token = special_tokens.get("start", None)
@@ -140,7 +141,7 @@ 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]
@@ -332,9 +333,7 @@ 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())
+        logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
-        print()
-        print("error loading embedding, skipping loading:", embedding_name)
         return None
 
     if embed_out is None:
@@ -429,7 +428,7 @@ 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

@@ -9,7 +9,7 @@ import numpy as np
 from PIL import Image
 from tqdm import tqdm
 from contextlib import contextmanager
-
+import logging
 
 def load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
@@ -19,14 +19,14 @@ 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=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:

comfy_extras/nodes/nodes_custom_sampler.py

@@ -230,6 +230,23 @@ class SamplerDPMPP_SDE:
         sampler = samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
         return (sampler, )
 
+class SamplerEulerAncestral:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                      }
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta, s_noise):
+        sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
+        return (sampler, )
+
 class SamplerCustom:
     @classmethod
     def INPUT_TYPES(s):
@@ -290,6 +307,7 @@ NODE_CLASS_MAPPINGS = {
     "VPScheduler": VPScheduler,
     "SDTurboScheduler": SDTurboScheduler,
     "KSamplerSelect": KSamplerSelect,
+    "SamplerEulerAncestral": SamplerEulerAncestral,
     "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
     "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
     "SplitSigmas": SplitSigmas,

comfy_extras/nodes/nodes_model_merging.py

@@ -86,6 +86,50 @@ class CLIPMergeSimple:
             m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
         return (m, )
 
+
+class CLIPSubtract:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip1": ("CLIP",),
+                              "clip2": ("CLIP",),
+                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "merge"
+
+    CATEGORY = "advanced/model_merging"
+
+    def merge(self, clip1, clip2, multiplier):
+        m = clip1.clone()
+        kp = clip2.get_key_patches()
+        for k in kp:
+            if k.endswith(".position_ids") or k.endswith(".logit_scale"):
+                continue
+            m.add_patches({k: kp[k]}, - multiplier, multiplier)
+        return (m, )
+
+
+class CLIPAdd:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip1": ("CLIP",),
+                              "clip2": ("CLIP",),
+                              }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "merge"
+
+    CATEGORY = "advanced/model_merging"
+
+    def merge(self, clip1, clip2):
+        m = clip1.clone()
+        kp = clip2.get_key_patches()
+        for k in kp:
+            if k.endswith(".position_ids") or k.endswith(".logit_scale"):
+                continue
+            m.add_patches({k: kp[k]}, 1.0, 1.0)
+        return (m, )
+
+
 class ModelMergeBlocks:
     @classmethod
     def INPUT_TYPES(s):
@@ -278,6 +322,8 @@ NODE_CLASS_MAPPINGS = {
     "ModelMergeAdd": ModelAdd,
     "CheckpointSave": CheckpointSave,
     "CLIPMergeSimple": CLIPMergeSimple,
+    "CLIPMergeSubtract": CLIPSubtract,
+    "CLIPMergeAdd": CLIPAdd,
     "CLIPSave": CLIPSave,
     "VAESave": VAESave,
 }