comfyui的服务器端是用aiohtttp写的，webui是fastapi直接构建的，但是其实comfyui的这种设计思路是很好的，也许我们不需要在后端起一个复杂的前台，但是可以借助json结构化pipeline，然后利用node节点流把整个流程重新映射出来。有些串联的pipeline是比较复杂的，但是串联起来可以实现一些比较好的功能，而这些功能其实可以被放在一个框架中训练，这是很值得思考和算法化的地方。
1.启动
pip install -r requirements.txt2.代码分析
main.py->
comfy->cli_args.py：server = server.PromptServer(loop)->-
q = execution.PromptQueue(server)->init_custom_nodes()->nodes.py->load_custom_node()->load_custom_nodes()threading.Thread(target=prompt_worker,daemon=True,args=(q,server,)).start()- prompt_worker()->- queue_item = q.get() -> self.queue- item,item_id = queue_item prompt_id = item[1]- e.execute(item[2],prompt_id,item[3],item[4]) ->-- with torch.inference_mode() ->-- for x in prompt: recursive_output_delete_if_changed(prompt,self.old_prompt,self.outputs,x)->--- inputs = prompt[unique_id]['inputs'] class_type = prompt[unique_id]['class_type'] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] ->--- input_data_all = get_input_data(inputs,class_def,unique_id,outputs) ->--- is_changed = map_node_over_list(class_def,input_data_all,"IS_CHANGED") ->--- results.append(getattr(obj,func)(**slice_dict(input_data_all,i)))loop.run_until_complete(run(server,address,port,...))comfyui中主要实现node节点的就是getattr(obj，func)方法，实现之后再存入节点中，下次取。
nodes.py 中存了大量的节点，是提前定义的，comfy_extras中也存了很多后来加入的节点，都放在NODE_CLASS_MAPPINGS中。
comfy中实现了具体的方法，当安装外部插件时，新增的后端代码放在custom_nodes中，前端代码放在web中，comfyui中的前端代码都在web/extension/core中，还算是一个前后分开的项目。
具体的节点调用方法，我这里有个简单的工作流，尝试着走完全流程来看下结果：
{"last_node_id": 9,"last_link_id": 9,"nodes": [{"id": 7,"type": "CLIPTextEncode","pos": [413,389],"size": {"0": 425.27801513671875,"1": 180.6060791015625},"flags": {},"order": 3,"mode": 0,"inputs": [{"name": "clip","type": "CLIP","link": 5}],"outputs": [{"name": "CONDITIONING","type": "CONDITIONING","links": [6],"slot_index": 0}],"properties": {"Node name for S&R": "CLIPTextEncode"},"widgets_values": ["text, watermark"]},{"id": 5,"type": "EmptyLatentImage","pos": [473,609],"size": {"0": 315,"1": 106},"flags": {},"order": 0,"mode": 0,"outputs": [{"name": "LATENT","type": "LATENT","links": [2],"slot_index": 0}],"properties": {"Node name for S&R": "EmptyLatentImage"},"widgets_values": [512,512,2]},{"id": 8,"type": "VAEDecode","pos": [1209,188],"size": {"0": 210,"1": 46},"flags": {},"order": 5,"mode": 0,"inputs": [{"name": "samples","type": "LATENT","link": 7},{"name": "vae","type": "VAE","link": 8}],"outputs": [{"name": "IMAGE","type": "IMAGE","links": [9],"slot_index": 0}],"properties": {"Node name for S&R": "VAEDecode"}},{"id": 3,"type": "KSampler","pos": [863,186],"size": {"0": 315,"1": 262},"flags": {},"order": 4,"mode": 0,"inputs": [{"name": "model","type": "MODEL","link": 1},{"name": "positive","type": "CONDITIONING","link": 4},{"name": "negative","type": "CONDITIONING","link": 6},{"name": "latent_image","type": "LATENT","link": 2}],"outputs": [{"name": "LATENT","type": "LATENT","links": [7],"slot_index": 0}],"properties": {"Node name for S&R": "KSampler"},"widgets_values": [710912628627374,"randomize",20,8,"dpmpp_3m_sde_gpu","normal",1]},{"id": 6,"type": "CLIPTextEncode","pos": [415,186],"size": {"0": 422.84503173828125,"1": 164.31304931640625},"flags": {},"order": 2,"mode": 0,"inputs": [{"name": "clip","type": "CLIP","link": 3}],"outputs": [{"name": "CONDITIONING","type": "CONDITIONING","links": [4],"slot_index": 0}],"properties": {"Node name for S&R": "CLIPTextEncode"},"widgets_values": ["beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"]},{"id": 9,"type": "SaveImage","pos": [1451,189],"size": [210,270],"flags": {},"order": 6,"mode": 0,"inputs": [{"name": "images","type": "IMAGE","link": 9}],"properties": {},"widgets_values": ["ComfyUI"]},{"id": 4,"type": "CheckpointLoaderSimple","pos": [26,474],"size": {"0": 315,"1": 98},"flags": {},"order": 1,"mode": 0,"outputs": [{"name": "MODEL","type": "MODEL","links": [1],"slot_index": 0},{"name": "CLIP","type": "CLIP","links": [3,5],"slot_index": 1},{"name": "VAE","type": "VAE","links": [8],"slot_index": 2}],"properties": {"Node name for S&R": "CheckpointLoaderSimple"},"widgets_values": ["revAnimated_v122.safetensors"]}],"links": [[1,4,0,3,0,"MODEL"],[2,5,0,3,3,"LATENT"],[3,4,1,6,0,"CLIP"],[4,6,0,3,1,"CONDITIONING"],[5,4,1,7,0,"CLIP"],[6,7,0,3,2,"CONDITIONING"],[7,3,0,8,0,"LATENT"],[8,4,2,8,1,"VAE"],[9,8,0,9,0,"IMAGE"]],"groups": [],"config": {},"extra": {},"version": 0.4
}
Load Checkpoint->CheckpointLoaderSimple
input_data_all:{'ckpt_name': ['revAnimated_v122.safetensors']}
obj:<nodes.CheckpointLoaderSimple object at 0x7f3f9b3af640>
func:load_checkpoint
nodes.py->CheckpointLoaderSimple.load_checkpoint()
- RETURN_TYPES=("MODEL","CLIP","VAE")=右边的节点；FUNCTION="load_checkpoint"节点中的方法
- INPUT_TYPES=要输入的节点
- out = comfy.sd.load_checkpoint_guess_config(ckpt_path,...)->
-- sd = comfy.utils.load_torch_file(ckpt_path)
-- model = model_config.get_model(sd,"model.diffusion_model.")
-- model.load_model_weights()
-- vae = VAE(sd=vae_sd)
-- clip = CLIP(clip_target, embedding_directory=embedding_directory)
-- m, u = clip.load_sd(clip_sd, full_model=True)
-- model_patcher = comfy.model_patcher.ModelPatcher()
[(<comfy.model_patcher.ModelPatcher object at 0x7f35fc07dab0>, <comfy.sd.CLIP object at 0x7f35fc1937f0>, <comfy.sd.VAE object at 0x7f35ffd36320>)]CLIP Text Encode(Prompt)->CLIPTextEncode
input_data_all:{'text': ['beautiful scenery nature glass bottle landscape, , purple galaxy bottle,'], 'clip': [<comfy.sd.CLIP object at 0x7f35fc1937f0>]}
obj:<nodes.CLIPTextEncode object at 0x7f35fc193760>
func:"encode"
nodes.py->CLIPTextEncode.encode()
- RETURN_TYPES=("CONDITIONING") FUNCTION="encode" INPUT_TYPES {"required":{"text":("STRING",{"multiline":True}),"clip":("CLIP",)}}
- tokens = clip.tokenize(text)
-- comfyui.comfy.sd.CLIP.tokenize->
-- self.tokenizer.tokenize_with_weights(text,return_word_ids)
--- comfyui.comfy.sd1_clip.SD1Tokenizer.tokenize_with_weights(text,..)
- cond,pooled = clip.encode_from_tokens(tokens,return_pooled=True)
cond:1x77x768 pooled:1x768Empty Latent Image->EmptyLatentImage
input_data_all:{'width': [512], 'height': [512], 'batch_size': [2]}
obj:<nodes.EmptyLatentImage object at 0x7f36006d7640>
func:"generat"
nodes.py->EmptyLatentImage.generate
- latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
({"samples":latent})KSampler->Ksampler
input_data_all:'seed': [50385774161222], 'steps': [20], 'cfg': [8.0], 'sampler_name': ['dpmpp_3m_sde_gpu'], 'scheduler': ['normal'], 'denoise': [1.0], 'model': [<comfy.model_patcher.ModelPatcher object at 0x7f35fc07dab0>], 'positive':....
obj:<nodes.KSampler object at 0x7f35fc193b20>
func:sample
nodes.py->Ksampler.sample
- common_ksampler(...)->
-- latent_image = latent['sample']
-- noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
-- samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, ....)
--- real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask)
--- sampler = comfy.samplers.KSampler(real_model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
--- samples = sampler.sample(noise, ....)....
