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fix:some models were not successfully written to easyCache,resulting in slow secondary diffusion

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yolain
2024-05-16 01:01:03 +08:00
parent 01f17ff02b
commit fee7e7bf73
4 changed files with 62 additions and 46 deletions
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README.md
+1
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@@ -41,6 +41,7 @@
**v1.1.7**
- 修复 一些模型(如controlnet模型等)未成功写入缓存，导致修改前置节点束参数（如提示词）需要二次载入模型的问题
- 增加 `easy prompt` - 主体和光影预置项，后期可能会调整
- 增加 `easy icLightApply` - 重绘光影, 从[ComfyUI-IC-Light](https://github.com/huchenlei/ComfyUI-IC-Light)优化
- 增加 `easy imageSplitGrid` - 图像网格拆分
py/easyNodes.py
+6 -20
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@@ -1942,7 +1942,7 @@ class loraStackLoader:
loras.append((lora_name, model_strength, clip_strength))
return (loras,)
class controlnetNameStack:
class controlnetStack:
def get_file_list(filenames):
return [file for file in filenames if file != "put_models_here.txt" and "lllite" not in file]
@@ -1959,7 +1959,7 @@ class controlnetNameStack:
"start_percent_1": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"switch_2": (["Off", "On"],),
"controlnet_2": (s.controlnets,),
"`controlnet`_2": (s.controlnets,),
"controlnet_strength_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"start_percent_2": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
@@ -2003,7 +2003,7 @@ class controlnetSimple:
def controlnetApply(self, pipe, image, control_net_name, control_net=None, strength=1, scale_soft_weights=1):
positive, negative = easyControlnet().apply(control_net_name, image, pipe["positive"], pipe["negative"], strength, 0, 1, control_net, scale_soft_weights)
positive, negative = easyControlnet().apply(control_net_name, image, pipe["positive"], pipe["negative"], strength, 0, 1, control_net, scale_soft_weights, None, easyCache)
new_pipe = {
"model": pipe['model'],
@@ -2055,7 +2055,7 @@ class controlnetAdvanced:
def controlnetApply(self, pipe, image, control_net_name, control_net=None, strength=1, start_percent=0, end_percent=1, scale_soft_weights=1):
positive, negative = easyControlnet().apply(control_net_name, image, pipe["positive"], pipe["negative"],
strength, start_percent, end_percent, control_net, scale_soft_weights)
strength, start_percent, end_percent, control_net, scale_soft_weights, None, easyCache)
new_pipe = {
"model": pipe['model'],
@@ -2972,7 +2972,8 @@ class instantID:
# Apply InstantID
if "ApplyInstantID" in ALL_NODE_CLASS_MAPPINGS:
instantid_apply = ALL_NODE_CLASS_MAPPINGS['ApplyInstantID']
control_net = easyControlnet().load_controlnet(control_net_name, control_net, cn_soft_weights)
if control_net is None:
control_net = easyCache.load_controlnet(control_net_name, cn_soft_weights)
model, positive, negative = instantid_apply().apply_instantid(instantid_model, insightface_model, control_net, image, model, positive, negative, start_at, end_at, weight=weight, ip_weight=None, cn_strength=cn_strength, noise=noise, image_kps=image_kps, mask=mask)
else:
self.error()
@@ -4191,9 +4192,6 @@ class samplerFull(LayerDiffuse):
def run(self, pipe, steps, cfg, sampler_name, scheduler, denoise, image_output, link_id, save_prefix, seed=None, model=None, positive=None, negative=None, latent=None, vae=None, clip=None, xyPlot=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False, downscale_options=None):
# Clean loaded_objects
easyCache.update_loaded_objects(prompt)
samp_model = model.clone() if model is not None else pipe["model"].clone()
samp_positive = positive if positive is not None else pipe["positive"]
samp_negative = negative if negative is not None else pipe["negative"]
@@ -4314,10 +4312,6 @@ class samplerFull(LayerDiffuse):
spent_time = 'Diffusion:' + str((end_time-start_time)/1000)+'″, VAEDecode:' + str((end_decode_time-end_time)/1000)+'″ '
results = easySave(new_images, save_prefix, image_output, prompt, extra_pnginfo)
sampler.update_value_by_id("results", my_unique_id, results)
# Clean loaded_objects
easyCache.update_loaded_objects(prompt)
new_pipe = {
**pipe,
@@ -4339,8 +4333,6 @@ class samplerFull(LayerDiffuse):
}
}
sampler.update_value_by_id("pipe_line", my_unique_id, new_pipe)
del pipe
if image_output == 'Preview&Choose':
@@ -4468,10 +4460,6 @@ class samplerFull(LayerDiffuse):
output_images, samp_model)
results = easySave(images, save_prefix, image_output, prompt, extra_pnginfo)
sampler.update_value_by_id("results", my_unique_id, results)
# Clean loaded_objects
easyCache.update_loaded_objects(prompt)
new_pipe = {
**pipe,
@@ -4490,8 +4478,6 @@ class samplerFull(LayerDiffuse):
"loader_settings": pipe["loader_settings"],
}
sampler.update_value_by_id("pipe_line", my_unique_id, new_pipe)
del pipe
if hasattr(ModelPatcher, "original_calculate_weight"):
py/libs/controlnet.py
+3 -17
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@@ -7,26 +7,12 @@ class easyControlnet:
def __init__(self):
pass
def load_controlnet(self, control_net_name, control_net, scale_soft_weights):
if control_net is None:
if scale_soft_weights < 1:
if "ScaledSoftControlNetWeights" in NODE_CLASS_MAPPINGS:
soft_weight_cls = NODE_CLASS_MAPPINGS['ScaledSoftControlNetWeights']
(weights, timestep_keyframe) = soft_weight_cls().load_weights(scale_soft_weights, False)
cn_adv_cls = NODE_CLASS_MAPPINGS['ControlNetLoaderAdvanced']
control_net, = cn_adv_cls().load_controlnet(control_net_name, timestep_keyframe)
else:
raise Exception(f"[Advanced-ControlNet Not Found] you need to install 'COMFYUI-Advanced-ControlNet'")
else:
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
control_net = comfy.controlnet.load_controlnet(controlnet_path)
return control_net
def apply(self, control_net_name, image, positive, negative, strength, start_percent=0, end_percent=1, control_net=None, scale_soft_weights=1, mask=None):
def apply(self, control_net_name, image, positive, negative, strength, start_percent=0, end_percent=1, control_net=None, scale_soft_weights=1, mask=None, easyCache=None):
if strength == 0:
return (positive, negative)
control_net = self.load_controlnet(control_net_name, control_net, scale_soft_weights)
if control_net is None:
control_net = easyCache.load_controlnet(control_net_name, scale_soft_weights)
if mask is not None:
mask = mask.to(self.device)
py/libs/loader.py
+52 -9
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@@ -1,16 +1,19 @@
import time, os, psutil
import comfy.utils
import comfy.sd
import comfy.controlnet
import folder_paths
from nodes import NODE_CLASS_MAPPINGS
from collections import defaultdict
from ..log import log_node_info, log_node_error
stable_diffusion_loaders = ["easy a1111Loader", "easy comfyLoader", "easy zero123Loader", "easy svdLoader"]
stable_diffusion_loaders = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader", "easy zero123Loader", "easy svdLoader"]
stable_cascade_loaders = ["easy cascadeLoader"]
controlnet_loaders = ["easy controlnetLoader", "easy controlnetLoaderADV"]
instant_loaders = ["easy instantIDApply", "easy instantIDApplyADV"]
cascade_vae_node = ["easy preSamplingCascade", "easy fullCascadeKSampler"]
model_merge_node = ["easy XYInputs: ModelMergeBlocks"]
lora_widget = ["easy a1111Loader", "easy comfyLoader"]
lora_widget = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader"]
class easyLoader:
def __init__(self):
@@ -22,8 +25,9 @@ class easyLoader:
"bvae": defaultdict(tuple),
"vae": defaultdict(object),
"lora": defaultdict(dict), # {lora_name: {UID: (model_lora, clip_lora)}}
"controlnet": defaultdict(dict),
}
self.memory_threshold = self.determine_memory_threshold(0.7)
self.memory_threshold = self.determine_memory_threshold(0.9)
self.lora_name_cache = []
def clean_values(self, values: str):
@@ -50,9 +54,17 @@ class easyLoader:
for key in keys - desired_names:
del self.loaded_objects[object_type][key]
def get_input_value(self, entry, key):
def get_input_value(self, entry, key, prompt=None):
val = entry["inputs"][key]
return val if isinstance(val, str) else val[0]
if isinstance(val, str):
return val
elif isinstance(val, list):
if prompt is not None and val[0]:
return prompt[val[0]]['inputs'][key]
else:
return val[0]
else:
return str(val)
def process_pipe_loader(self, entry, desired_ckpt_names, desired_vae_names, desired_lora_names, desired_lora_settings, num_loras=3, suffix=""):
for idx in range(1, num_loras + 1):
@@ -71,10 +83,10 @@ class easyLoader:
desired_vae_names = set()
desired_lora_names = set()
desired_lora_settings = set()
desired_controlnet_names = set()
for entry in prompt.values():
class_type = entry["class_type"]
if class_type in lora_widget:
lora_name = self.get_input_value(entry, "lora_name")
desired_lora_names.add(lora_name)
@@ -82,7 +94,7 @@ class easyLoader:
desired_lora_settings.add(setting)
if class_type in stable_diffusion_loaders:
desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name"))
desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name", prompt))
desired_vae_names.add(self.get_input_value(entry, "vae_name"))
elif class_type in stable_cascade_loaders:
@@ -99,6 +111,16 @@ class easyLoader:
if decode_vae_name and decode_vae_name != 'None':
desired_vae_names.add(decode_vae_name)
elif class_type in controlnet_loaders:
control_net_name = self.get_input_value(entry, "control_net_name", prompt)
scale_soft_weights = self.get_input_value(entry, "scale_soft_weights")
desired_controlnet_names.add(f'{control_net_name};{scale_soft_weights}')
elif class_type in instant_loaders:
control_net_name = self.get_input_value(entry, "control_net_name", prompt)
scale_soft_weights = self.get_input_value(entry, "cn_soft_weights")
desired_controlnet_names.add(f'{control_net_name};{scale_soft_weights}')
elif class_type in model_merge_node:
desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name_1"))
desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name_2"))
@@ -106,7 +128,7 @@ class easyLoader:
if vae_use != 'Use Model 1' and vae_use != 'Use Model 2':
desired_vae_names.add(vae_use)
object_types = ["ckpt", "unet", "clip", "bvae", "vae", "lora"]
object_types = ["ckpt", "unet", "clip", "bvae", "vae", "lora", "controlnet"]
for object_type in object_types:
if object_type == 'unet':
desired_names = desired_unet_names
@@ -117,6 +139,8 @@ class easyLoader:
desired_names = desired_ckpt_names
elif object_type == "vae":
desired_names = desired_vae_names
elif object_type == "controlnet":
desired_names = desired_controlnet_names
else:
desired_names = desired_lora_names
self.clear_unused_objects(desired_names, object_type)
@@ -155,7 +179,7 @@ class easyLoader:
current_memory = self.get_memory_usage()
if current_memory < self.memory_threshold:
return
eviction_order = ["vae", "lora", "bvae", "clip", "ckpt"]
eviction_order = ["vae", "lora", "bvae", "clip", "ckpt", "controlnet"]
for obj_type in eviction_order:
if current_memory < self.memory_threshold:
break
@@ -225,6 +249,25 @@ class easyLoader:
return model
def load_controlnet(self, control_net_name, scale_soft_weights=1):
unique_id = f'{control_net_name};{str(scale_soft_weights)}'
if unique_id in self.loaded_objects["controlnet"]:
return self.loaded_objects["controlnet"][unique_id][0]
if scale_soft_weights < 1:
if "ScaledSoftControlNetWeights" in NODE_CLASS_MAPPINGS:
soft_weight_cls = NODE_CLASS_MAPPINGS['ScaledSoftControlNetWeights']
(weights, timestep_keyframe) = soft_weight_cls().load_weights(scale_soft_weights, False)
cn_adv_cls = NODE_CLASS_MAPPINGS['ControlNetLoaderAdvanced']
control_net, = cn_adv_cls().load_controlnet(control_net_name, timestep_keyframe)
else:
raise Exception(
f"[Advanced-ControlNet Not Found] you need to install 'COMFYUI-Advanced-ControlNet'")
else:
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
control_net = comfy.controlnet.load_controlnet(controlnet_path)
self.add_to_cache("controlnet", unique_id, control_net)
self.eviction_based_on_memory()
return control_net
def load_clip(self, clip_name, type='stable_diffusion'):
if type == 'stable_diffusion':
clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION