Switch some more prints to logging.
This commit is contained in:
comfyanonymous
parent
0ed72befe1
commit
2a813c3b09
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@ -4,6 +4,7 @@ import torch.nn.functional as F
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from torch import nn, einsum
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from einops import rearrange, repeat
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from typing import Optional, Any
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import logging
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from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding
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from .sub_quadratic_attention import efficient_dot_product_attention
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@ -20,7 +21,7 @@ ops = comfy.ops.disable_weight_init
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# CrossAttn precision handling
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if args.dont_upcast_attention:
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print("disabling upcasting of attention")
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logging.info("disabling upcasting of attention")
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_ATTN_PRECISION = "fp16"
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else:
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_ATTN_PRECISION = "fp32"
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@ -274,12 +275,12 @@ def attention_split(q, k, v, heads, mask=None):
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model_management.soft_empty_cache(True)
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if cleared_cache == False:
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cleared_cache = True
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print("out of memory error, emptying cache and trying again")
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logging.warning("out of memory error, emptying cache and trying again")
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continue
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steps *= 2
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if steps > 64:
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raise e
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print("out of memory error, increasing steps and trying again", steps)
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logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
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else:
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raise e
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@ -351,17 +352,17 @@ def attention_pytorch(q, k, v, heads, mask=None):
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optimized_attention = attention_basic
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if model_management.xformers_enabled():
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print("Using xformers cross attention")
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logging.info("Using xformers cross attention")
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optimized_attention = attention_xformers
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elif model_management.pytorch_attention_enabled():
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print("Using pytorch cross attention")
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logging.info("Using pytorch cross attention")
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optimized_attention = attention_pytorch
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else:
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if args.use_split_cross_attention:
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print("Using split optimization for cross attention")
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logging.info("Using split optimization for cross attention")
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optimized_attention = attention_split
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else:
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print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
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logging.info("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
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optimized_attention = attention_sub_quad
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optimized_attention_masked = optimized_attention
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@ -5,6 +5,7 @@ import torch.nn as nn
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import numpy as np
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from einops import rearrange
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from typing import Optional, Any
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import logging
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from comfy import model_management
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import comfy.ops
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@ -190,7 +191,7 @@ def slice_attention(q, k, v):
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steps *= 2
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if steps > 128:
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raise e
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print("out of memory error, increasing steps and trying again", steps)
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logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
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return r1
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@ -235,7 +236,7 @@ def pytorch_attention(q, k, v):
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out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
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out = out.transpose(2, 3).reshape(B, C, H, W)
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except model_management.OOM_EXCEPTION as e:
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print("scaled_dot_product_attention OOMed: switched to slice attention")
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logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
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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)
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return out
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@ -268,13 +269,13 @@ class AttnBlock(nn.Module):
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padding=0)
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if model_management.xformers_enabled_vae():
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print("Using xformers attention in VAE")
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logging.info("Using xformers attention in VAE")
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self.optimized_attention = xformers_attention
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elif model_management.pytorch_attention_enabled():
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print("Using pytorch attention in VAE")
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logging.info("Using pytorch attention in VAE")
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self.optimized_attention = pytorch_attention
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else:
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print("Using split attention in VAE")
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logging.info("Using split attention in VAE")
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self.optimized_attention = normal_attention
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def forward(self, x):
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@ -562,7 +563,7 @@ class Decoder(nn.Module):
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block_in = ch*ch_mult[self.num_resolutions-1]
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curr_res = resolution // 2**(self.num_resolutions-1)
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self.z_shape = (1,z_channels,curr_res,curr_res)
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print("Working with z of shape {} = {} dimensions.".format(
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logging.debug("Working with z of shape {} = {} dimensions.".format(
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self.z_shape, np.prod(self.z_shape)))
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# z to block_in
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@ -4,6 +4,7 @@ import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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import logging
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from .util import (
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checkpoint,
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@ -359,7 +360,7 @@ def apply_control(h, control, name):
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try:
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h += ctrl
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except:
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print("warning control could not be applied", h.shape, ctrl.shape)
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logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape))
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return h
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class UNetModel(nn.Module):
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@ -496,7 +497,7 @@ class UNetModel(nn.Module):
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if isinstance(self.num_classes, int):
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self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device)
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elif self.num_classes == "continuous":
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print("setting up linear c_adm embedding layer")
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logging.debug("setting up linear c_adm embedding layer")
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self.label_emb = nn.Linear(1, time_embed_dim)
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elif self.num_classes == "sequential":
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assert adm_in_channels is not None
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@ -14,6 +14,7 @@ import torch
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from torch import Tensor
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from torch.utils.checkpoint import checkpoint
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import math
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import logging
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try:
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from typing import Optional, NamedTuple, List, Protocol
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@ -170,7 +171,7 @@ def _get_attention_scores_no_kv_chunking(
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attn_probs = attn_scores.softmax(dim=-1)
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del attn_scores
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except model_management.OOM_EXCEPTION:
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print("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
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logging.warning("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
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attn_scores -= attn_scores.max(dim=-1, keepdim=True).values
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torch.exp(attn_scores, out=attn_scores)
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summed = torch.sum(attn_scores, dim=-1, keepdim=True)
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@ -4,6 +4,7 @@ import torch
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import collections
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from comfy import model_management
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import math
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import logging
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def get_area_and_mult(conds, x_in, timestep_in):
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area = (x_in.shape[2], x_in.shape[3], 0, 0)
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@ -625,7 +626,7 @@ def calculate_sigmas_scheduler(model, scheduler_name, steps):
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elif scheduler_name == "sgm_uniform":
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sigmas = normal_scheduler(model, steps, sgm=True)
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else:
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print("error invalid scheduler", scheduler_name)
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logging.error("error invalid scheduler {}".format(scheduler_name))
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return sigmas
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def sampler_object(name):
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@ -1,7 +1,7 @@
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#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)
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import torch
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import logging
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def Fourier_filter(x, threshold, scale):
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# FFT
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@ -49,7 +49,7 @@ class FreeU:
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try:
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hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
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except:
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print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.")
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logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
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on_cpu_devices[hsp.device] = True
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
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else:
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@ -95,7 +95,7 @@ class FreeU_V2:
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try:
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hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
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except:
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print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.")
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logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
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on_cpu_devices[hsp.device] = True
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
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else:
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@ -1,6 +1,7 @@
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import comfy.utils
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import folder_paths
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import torch
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import logging
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def load_hypernetwork_patch(path, strength):
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sd = comfy.utils.load_torch_file(path, safe_load=True)
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@ -23,7 +24,7 @@ def load_hypernetwork_patch(path, strength):
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}
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if activation_func not in valid_activation:
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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)
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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))
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return None
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out = {}
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18
main.py
18
main.py
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@ -54,15 +54,15 @@ import threading
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import gc
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from comfy.cli_args import args
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import logging
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if os.name == "nt":
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import logging
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logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
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if __name__ == "__main__":
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if args.cuda_device is not None:
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os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
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print("Set cuda device to:", args.cuda_device)
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logging.info("Set cuda device to: {}".format(args.cuda_device))
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if args.deterministic:
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if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ:
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@ -88,7 +88,7 @@ def cuda_malloc_warning():
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if b in device_name:
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cuda_malloc_warning = True
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if cuda_malloc_warning:
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print("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
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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")
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def prompt_worker(q, server):
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e = execution.PromptExecutor(server)
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@ -121,7 +121,7 @@ def prompt_worker(q, server):
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current_time = time.perf_counter()
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execution_time = current_time - execution_start_time
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print("Prompt executed in {:.2f} seconds".format(execution_time))
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logging.info("Prompt executed in {:.2f} seconds".format(execution_time))
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flags = q.get_flags()
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free_memory = flags.get("free_memory", False)
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@ -182,14 +182,14 @@ def load_extra_path_config(yaml_path):
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full_path = y
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if base_path is not None:
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full_path = os.path.join(base_path, full_path)
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print("Adding extra search path", x, full_path)
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logging.info("Adding extra search path {} {}".format(x, full_path))
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folder_paths.add_model_folder_path(x, full_path)
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if __name__ == "__main__":
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if args.temp_directory:
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temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
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print(f"Setting temp directory to: {temp_dir}")
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logging.info(f"Setting temp directory to: {temp_dir}")
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folder_paths.set_temp_directory(temp_dir)
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cleanup_temp()
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@ -224,7 +224,7 @@ if __name__ == "__main__":
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if args.output_directory:
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output_dir = os.path.abspath(args.output_directory)
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print(f"Setting output directory to: {output_dir}")
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logging.info(f"Setting output directory to: {output_dir}")
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folder_paths.set_output_directory(output_dir)
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#These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes
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@ -234,7 +234,7 @@ if __name__ == "__main__":
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if args.input_directory:
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input_dir = os.path.abspath(args.input_directory)
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print(f"Setting input directory to: {input_dir}")
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logging.info(f"Setting input directory to: {input_dir}")
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folder_paths.set_input_directory(input_dir)
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if args.quick_test_for_ci:
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@ -252,6 +252,6 @@ if __name__ == "__main__":
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try:
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loop.run_until_complete(run(server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start))
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except KeyboardInterrupt:
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print("\nStopped server")
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logging.info("\nStopped server")
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cleanup_temp()
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2
nodes.py
2
nodes.py
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@ -1904,7 +1904,7 @@ def load_custom_node(module_path, ignore=set()):
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return False
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except Exception as e:
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logging.warning(traceback.format_exc())
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logging.warning(f"Cannot import {module_path} module for custom nodes:", e)
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logging.warning(f"Cannot import {module_path} module for custom nodes: {e}")
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return False
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def load_custom_nodes():
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@ -413,8 +413,8 @@ class PromptServer():
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try:
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out[x] = node_info(x)
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except Exception as e:
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print(f"[ERROR] An error occurred while retrieving information for the '{x}' node.", file=sys.stderr)
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traceback.print_exc()
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logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
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logging.error(traceback.format_exc())
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return web.json_response(out)
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@routes.get("/object_info/{node_class}")
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@ -641,6 +641,6 @@ class PromptServer():
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json_data = handler(json_data)
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except Exception as e:
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logging.warning(f"[ERROR] An error occurred during the on_prompt_handler processing")
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traceback.print_exc()
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logging.warning(traceback.format_exc())
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return json_data
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