From 2a813c3b09292c9aeab622ddf65d77e5d8171d0d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 11 Mar 2024 16:24:47 -0400 Subject: [PATCH] Switch some more prints to logging. --- comfy/ldm/modules/attention.py | 15 ++++++++------- comfy/ldm/modules/diffusionmodules/model.py | 13 +++++++------ .../modules/diffusionmodules/openaimodel.py | 5 +++-- comfy/ldm/modules/sub_quadratic_attention.py | 3 ++- comfy/samplers.py | 3 ++- comfy_extras/nodes_freelunch.py | 6 +++--- comfy_extras/nodes_hypernetwork.py | 3 ++- main.py | 18 +++++++++--------- nodes.py | 2 +- server.py | 6 +++--- 10 files changed, 40 insertions(+), 34 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 48399bc0..f116efee 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -4,6 +4,7 @@ import torch.nn.functional as F 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 @@ ops = comfy.ops.disable_weight_init # 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 diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py index cc81c1f2..fabc5c5e 100644 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -5,6 +5,7 @@ import torch.nn as nn 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 @@ class AttnBlock(nn.Module): 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 @@ class Decoder(nn.Module): 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 diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py index cf89ae01..d782eff3 100644 --- a/comfy/ldm/modules/diffusionmodules/openaimodel.py +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -4,6 +4,7 @@ import torch as th 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 @@ class UNetModel(nn.Module): 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 diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py index cb0896b0..1bc4138c 100644 --- a/comfy/ldm/modules/sub_quadratic_attention.py +++ b/comfy/ldm/modules/sub_quadratic_attention.py @@ -14,6 +14,7 @@ import torch 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) diff --git a/comfy/samplers.py b/comfy/samplers.py index 6863be4e..16b4514e 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -4,6 +4,7 @@ import torch 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): diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py index 7764aa0b..6f1d87bf 100644 --- a/comfy_extras/nodes_freelunch.py +++ b/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 @@ class FreeU: 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 @@ class FreeU_V2: 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: diff --git a/comfy_extras/nodes_hypernetwork.py b/comfy_extras/nodes_hypernetwork.py index f692945a..cafafa6a 100644 --- a/comfy_extras/nodes_hypernetwork.py +++ b/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 = {} diff --git a/main.py b/main.py index 5d07ce2d..3dee72e3 100644 --- a/main.py +++ b/main.py @@ -54,15 +54,15 @@ import threading 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 @@ if __name__ == "__main__": 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 @@ if __name__ == "__main__": 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 @@ if __name__ == "__main__": 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() diff --git a/nodes.py b/nodes.py index e2f35dc2..453f6e60 100644 --- a/nodes.py +++ b/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(): diff --git a/server.py b/server.py index d748a47d..5642bd5e 100644 --- a/server.py +++ b/server.py @@ -413,8 +413,8 @@ class PromptServer(): 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 @@ class PromptServer(): 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