Fooocus框架代码分析

本文主要是介绍Fooocus框架代码分析，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

由于Fooocus的优秀推理能力，后续考虑从webui切换到Fooocus上，因此对其中的代码要进行深入分析，Fooocus的sdxl版本在11g的显存上跑起来压力不大，但是webui的sdxl版本起码12g。尤其要对比其和webui的优化点，但是在代码层面，并不是和webui同一档次的框架，webui采用了支持hook式的插件系统，但是fooocus因为其midjourney的指向，所以并不是走三方插件的路子。

在autodl上：python launch.py --listen --port 6006

entry_with_update.py

# 启动
python entry_with_update.py --listenlaunch.py ->
prepare_environment()->
ini_comfy_args()->
- args_manager.py -> args = comfy_cli.args ->backend/headless/comfy/cli_args.py
download_models()->webui.py ->
- run_button.click().then(fn=generate_clicked)->
-- modules/async_worker.py->workers()->threading.Thread(target=worker).start()
-- handler()-> # 传入参数并配置
-- prompt_processing/vary/upscale/inpaint/controlnet/
-- imgs = pipeline.process_diffusin(...)-> 

modules/default_pipeline.py -> process_diffusion() 主pipeline，webui中是StableDiffusionProcessingTxt2Img和StableDiffusionProcessingImg2Img两个核心接口。

if latent is None:empty_latent = core.generate_empty_latent(width,height,1)
else:empty_latent = latentsampled_latent = core.ksampler(final_unet,final_refiner,positive_cond,negative_cond,empty_latent,steps,denoise,callback,cfg_scale,sampler_name,scheduler_name,switch)    
decoded_latent = core.decode_vae(vae,sampled_latent,...)
images = core.pytorch_to_numpy(decoded_latent)  

默认方法：refresh_everything()

refresh_everything()->refresh_refiner_model(refiner_model_name)
refresh_base_model(base_model_name)
refresh_loras(loras)prepare_text_encoder(True)

core.py -> generate_empty_latent()

opEmptyLatentImage.generate(width,height,batch_size)[0]- backend/headless/nodes.py -> EmptyLatentImage.generate()
- latent = torch.zeros([bs,4,height//8,width//8]) -> {'samples':latent}

core.py->ksampler()->backend/headless/comfy/sample.py

core->ksampler()latent_image = latent['sampler']
noise = comfy.sample.prepare_noise(latent_image,seed,...)samples = comfy.sample.sample(model,noise,steps,cfg,sampler_name,scheduler,positive,negative,latent_image,...)
- backend/headless/comfy/sample.py->sample()
- real_model,positive_copy,negative_copy,noise_mask,models=prepare_sampling(model,noise.shape,positive...)
- sampler = comfy.samplers.KSampler(...)
- sampler = sampler.sample(noise,positive_copy,negative_copy,cfg,latent_image...)
-- sampler = sampler_class(self.sampler)
-- sample(self.model,noise,positive,...)

sampler_class()->backend/headless/comfy/samplers.py

sampler_class(name)->
sampler = ksampler(name)->class KSAMPLER(Sampler)

sample->modules/sample_hijack.py (backend/headless/comfy/samplers.py) 劫持了comfy中的sample

sample_hijack(model,noise,positive,negative,cfg,device,sampler,sigmas,model_options,latent_image,denoise_mask,callback,...) ->positive = positive[:]
negative = negative[:]model_wrap = model_wrap(model)
- model_denoise = CFGNoisePredictor(model)
- model_wrap = k_diffusion_external.CompVisDenoiser(model_denoise)calculate_start_end_timesteps(model_wrap,negative)
calculate_start_end_timesteps(model_wrap,positive)
for c in positive/negative:create_cond_with_same_area_if_none(negative/positive,c)
pre_run_control(model_wrap,negative+positive) # cfg相关latent_image = model.process_latent_in(latent_image)samples = samplers.sample(model_wrap,sigmas,extra_args,...)
model.process_latent_out(samples)

backend/headless/comfy/samplers.py

class KSAMPLER->sample(model_wrap,sigmas,extra_args,callback,...)
model_k = KSamplerOInpaint(model_wrap)if sampler_name == "dpm_fast":samples = k_diffusion_sampling.sample_dpm_fast(model_k,noise,...)
elif sampler_name == "dpm_adaptive":samples = k_diffusion_sampling.sample_dpm_adaptive(model_k,noise,...)
else:samples = getattr(k_diffusion_sampling,"sample_{}".format(sampler_name))(model_k,noise,...)

backend/headless/comfy/k_diffusion/sampling.py

sampler_dpmpp_2m_sde_gpu(model,x,sigmas,extra_args,callback,...)
noise_sampler = BrownianTreeNoiseSampler(x,sigma_min,..) if noise_sampler is None else noise_sampler
sample_dpmpp_2m_sde(model,x,...)

sample_dpmpp_2m_sde

for i in trange(len(sigmas)-1):denoised = model(x,sigmas[i]*s_in,**extra_args)if callback is not None:callback({'x':x,'i':i,'sigma':sigma[i],'sigma_hat':sigmas[i],'denoised':denoised})if sigmas[i+1] == 0:x = denoisedelse:# DPM-Solver++(2M) SDEt, s = -sigmas[i].log(), -sigmas[i + 1].log()h = s - teta_h = eta * hx = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoisedif old_denoised is not None:r = h_last / hif solver_type == 'heun':x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)elif solver_type == 'midpoint':x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)if eta:x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise

backend/headless/nodes.py 节点就是类

NODE_CLASS_MAPPINGS = {"KSampler": KSampler,"CheckpointLoaderSimple": CheckpointLoaderSimple,"CLIPTextEncode": CLIPTextEncode,"CLIPSetLastLayer": CLIPSetLastLayer,"VAEDecode": VAEDecode,"VAEEncode": VAEEncode,"VAEEncodeForInpaint": VAEEncodeForInpaint,"VAELoader": VAELoader,"EmptyLatentImage": EmptyLatentImage,"LatentUpscale": LatentUpscale,"LatentUpscaleBy": LatentUpscaleBy,"LatentFromBatch": LatentFromBatch,"RepeatLatentBatch": RepeatLatentBatch,"SaveImage": SaveImage,"PreviewImage": PreviewImage,"LoadImage": LoadImage,"LoadImageMask": LoadImageMask,"ImageScale": ImageScale,"ImageScaleBy": ImageScaleBy,"ImageInvert": ImageInvert,"ImageBatch": ImageBatch,"ImagePadForOutpaint": ImagePadForOutpaint,"EmptyImage": EmptyImage,"ConditioningAverage": ConditioningAverage,"ConditioningCombine": ConditioningCombine,"ConditioningConcat": ConditioningConcat,"ConditioningSetArea": ConditioningSetArea,"ConditioningSetAreaPercentage": ConditioningSetAreaPercentage,"ConditioningSetMask": ConditioningSetMask,"KSamplerAdvanced": KSamplerAdvanced,"SetLatentNoiseMask": SetLatentNoiseMask,"LatentComposite": LatentComposite,"LatentBlend": LatentBlend,"LatentRotate": LatentRotate,"LatentFlip": LatentFlip,"LatentCrop": LatentCrop,"LoraLoader": LoraLoader,"CLIPLoader": CLIPLoader,"UNETLoader": UNETLoader,"DualCLIPLoader": DualCLIPLoader,"CLIPVisionEncode": CLIPVisionEncode,"StyleModelApply": StyleModelApply,"unCLIPConditioning": unCLIPConditioning,"ControlNetApply": ControlNetApply,"ControlNetApplyAdvanced": ControlNetApplyAdvanced,"ControlNetLoader": ControlNetLoader,"DiffControlNetLoader": DiffControlNetLoader,"StyleModelLoader": StyleModelLoader,"CLIPVisionLoader": CLIPVisionLoader,"VAEDecodeTiled": VAEDecodeTiled,"VAEEncodeTiled": VAEEncodeTiled,"unCLIPCheckpointLoader": unCLIPCheckpointLoader,"GLIGENLoader": GLIGENLoader,"GLIGENTextBoxApply": GLIGENTextBoxApply,"CheckpointLoader": CheckpointLoader,"DiffusersLoader": DiffusersLoader,"LoadLatent": LoadLatent,"SaveLatent": SaveLatent,"ConditioningZeroOut": ConditioningZeroOut,"ConditioningSetTimestepRange": ConditioningSetTimestepRange,
}

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