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ComfyDeploy: How ComfyUI_Lam works in ComfyUI?
What is ComfyUI_Lam?
This extension has some useful nodes, loops, wechat public number +AI chat drawing, distributed cluster
How to install it in ComfyDeploy?
Head over to the machine page
- Click on the "Create a new machine" button
- Select the
Editbuild steps - Add a new step -> Custom Node
- Search for
ComfyUI_Lamand select it - Close the build step dialig and then click on the "Save" button to rebuild the machine
ComfyUI_Lam
介绍
基于comfyUI开发的插件
使用说明
下载放到comfyUI的插件目录，如下:
特别说明该版本执行最新版“ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z” 里面带cu118的版本地址如下：
https://github.com/comfyanonymous/ComfyUI/releases/download/latest/ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z
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根据图片解压到指定插件目录及名称， 将insightface.rar解压到..\ComfyUI_windows_portable\python_embeded\Lib\site-packages目录 将venv.rar解压到..\ComfyUI_windows_portable\python_embeded\Lib\site-packages目录 然后运行install.bat文件，未报错后就可以了，
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模型地址，及存放路径： lama模型： https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetLama.pth ..\ComfyUI\models\lama\ControlNetLama.pth SadTalker模型： https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/mapping_00109-model.pth.tar ..\ComfyUI\models\SadTalker\mapping_00109-model.pth.tar https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/mapping_00229-model.pth.tar ..\ComfyUI\models\SadTalker\mapping_00229-model.pth.tar https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/SadTalker_V0.0.2_256.safetensors ..\ComfyUI\models\SadTalker\SadTalker_V0.0.2_256.safetensors https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/SadTalker_V0.0.2_512.safetensors ..\ComfyUI\models\SadTalker\SadTalker_V0.0.2_512.safetensors
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gfpgan是根据启动目录来的，根据提示对应 https://github.com/xinntao/facexlib/releases/download/v0.1.0/alignment_WFLW_4HG.pth ..\ComfyUI_windows_portable\gfpgan\weights\alignment_WFLW_4HG.pth https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth ..\ComfyUI_windows_portable\gfpgan\weights\detection_Resnet50_Final.pth https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth ..\ComfyUI_windows_portable\gfpgan\weights\GFPGANv1.4.pth https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth ..\ComfyUI_windows_portable\gfpgan\weights\parsing_parsenet.pth
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image-face-fusion模型换脸模型 链接：https://pan.baidu.com/s/19DOgJQ_RHNAjfNrzSr2uTQ?pwd=gf0p 提取码：gf0p 解压到 ..\ComfyUI\models\image-face-fusion 目录
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roop-face-swap模型换脸模型 链接：https://pan.baidu.com/s/1cJeRtqgdeNW21Hljwuv-tA?pwd=b4yy 提取码：b4yy 解压到 ..\ComfyUI\models\roop-face-swap 目录
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修改comfyUI根目录下的execution.py文件，修改内容 搜索 “def recursive_execute” 位置参考图如下：
新增内容：
#循环添加代码----------开始----------
def get_del_keys(key, prompt,uniqueIds):
keys=[]
for k,v in prompt.items():
if k in uniqueIds :
continue
for k1,v1 in v['inputs'].items():
if type(v1)==list and v1[0]==key:
keys.append(k)
keys=keys+get_del_keys(k,prompt,uniqueIds)
return keys
#循环添加代码-----------结束---------
#循环添加代码----------开始----------
startNum=None
startData={}
delKeys=[]
backhaul={}
clTypes=['ForInnerEnd','IfInnerExecute','DoWhileEnd']
oldPrompt=None
if class_type in clTypes:
oldPrompt=copy.deepcopy(prompt)
if class_type=='ForInnerEnd':
startNum=prompt[unique_id]['inputs']['total'][0]
inputNum=prompt[unique_id]['inputs']['obj'][0]
maxKeyStr=sorted(list(prompt.keys()), key=lambda x: int(x.split(':')[0]))[-1]
maxKey = int(maxKeyStr.split(':')[0])
delKeys=list(set(get_del_keys(startNum,prompt,[startNum,unique_id])))
delKeys.append(startNum)
delKeys = list(filter(lambda x: x != inputNum, delKeys))
for key in delKeys:
outputs.pop(key, None)
startInput=prompt[startNum]['inputs']
if isinstance(startInput['total'],list) or isinstance(startInput['stop'],list) or isinstance(startInput['i'],list):
result = recursive_execute(server, prompt, outputs, startNum, extra_data, executed, prompt_id, outputs_ui, object_storage)
if result[0] is not True:
return result
for x in startInput:
input_data = startInput[x]
if isinstance(input_data, list):
startData[x]=outputs[input_data[0]][input_data[1]][0]
else:
startData[x]=input_data
for i in range(startData['i']+1,startData['total'],startData['stop']):
prompt[str(maxKey+i)]=prompt[inputNum]
prompt[unique_id]['inputs']['obj'+str(i)]=[str(maxKey+i),prompt[unique_id]['inputs']['obj'][-1]]
if i==startInput['i']+1:
backhaul['obj'+str(i)]=prompt[unique_id]['inputs']['obj']
else:
backhaul['obj'+str(i)]=[str(maxKey+i-startInput['stop']),prompt[unique_id]['inputs']['obj'][-1]]
elif class_type=='DoWhileEnd':
startNum=prompt[unique_id]['inputs']['start'][0]
inputNum=prompt[unique_id]['inputs']['ANY'][0]
delKeys=list(set(get_del_keys(startNum,prompt,[startNum,unique_id])))
delKeys.append(startNum)
delKeys.append(inputNum)
#循环添加代码-----------结束---------
#循环添加代码----------开始----------
if class_type=='ForInnerEnd' and x !='obj' and x.startswith('obj'):
if startNum!=None:
if isinstance(prompt[startNum]['inputs']['i'],list):
prompt[startNum]['inputs']['i']=startData['i']+startData['stop']
else:
prompt[startNum]['inputs']['i']=prompt[startNum]['inputs']['i']+startData['stop']
prompt[startNum]['inputs']['obj']=backhaul[x]
for key in delKeys:
outputs.pop(key, None)
#循环添加代码-----------结束---------
#判断选择添加代码-----------开始---------
if class_type=='DoWhileEnd' and x == 'ANY':
any=outputs[inputNum][prompt[unique_id]['inputs']['ANY'][1]][0]
i=0
while any:
for key in delKeys:
outputs.pop(key, None)
i=i+1
outputs['i']=[[i]]
prompt[startNum]['inputs']['i']=['i',0]
result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui, object_storage)
if result[0] is not True:
return result
any=outputs[inputNum][prompt[unique_id]['inputs']['ANY'][1]][0]
if class_type=='IfInnerExecute' and x=='ANY':
if outputs[input_unique_id][output_index][0]:
inputs['IF_FALSE']=oldPrompt[unique_id]['inputs']['IF_TRUE']
else:
inputs['IF_TRUE']=oldPrompt[unique_id]['inputs']['IF_FALSE']
#判断选择添加代码-----------结束---------
#循环添加代码----------开始----------
if class_type=='ForInnerEnd' or class_type=='DoWhileEnd':
prompt[startNum]['inputs']=oldPrompt[startNum]['inputs']
prompt[unique_id]['inputs']=oldPrompt[unique_id]['inputs']
outputs.pop(startNum, None)
result = recursive_execute(server, prompt, outputs, startNum, extra_data, executed, prompt_id, outputs_ui, object_storage)
if result[0] is not True:
return result
if class_type=='IfInnerExecute':
prompt[unique_id]['inputs']=oldPrompt[unique_id]['inputs']
#循环添加代码-----------结束---------
搜索 “def validate_prompt
'''优化输出开始'''
inputKeys=[]
for k,v in prompt.items():
for k1,v1 in v['inputs'].items():
if type(v1)==list and len(v1)==2:
inputKeys.append(v1[0])
for x in prompt:
class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True and x not in inputKeys:
outputs.add(x)
'''优化输出结束'''