php网站开发文章管理系统,网站制作建设是做什么,做网站 大文件,网站建设实验的总结采样器这块基本都是用的k-diffusion#xff0c;模型用的是stability的原生项目generative-models中的sgm#xff0c;这点和fooocus不同#xff0c;fooocus底层依赖comfyui中的models#xff0c;comfy是用load_state_dict的方式解析的#xff0c;用的load_checkpoint_guess…采样器这块基本都是用的k-diffusion模型用的是stability的原生项目generative-models中的sgm这点和fooocus不同fooocus底层依赖comfyui中的modelscomfy是用load_state_dict的方式解析的用的load_checkpoint_guess_config函数这个函数webui中也有。
webui在paths中导入了generative-models在sd_model_config中导入了config.sdxl和config.sdxl_refiner两个config模型使用sgm下的models/diffusion/DiffusionEngine初始化refiner和base的模型几乎是一致的。
python webui.py --port 6006 --no-half-vae
webui()-initialize()-
initialize_rest()-
- sd_samplers.py - set_samplers()-sd_samplers_kdiffusion.py-
- extensions.py - list_extensions()
- initialize_util.py - restore_config_state_file()
- sd_models.py - list_models()
- localization.py - list_localizations()
- scripts.load_scripts() - scripts.py
-- scripts_txt2imgScriptRunner()/scripts_img2imgScriptRunner()/scripts_postproscripts_postprocessing.ScriptPostprocessingRunner()(scripts_postprocessing.py)
- modelloader.py - load_upscaler()
- sd_vae.py - refresh_vae_list()
- textual_inversion/textual_inversion.py - list_textual_inversion_templates()
- script_callbacks.py - on_list_optimizers(sd_hijack_optimizations.list_optimizers)
- sd_hijack.py - list_optimizers()
- sd_unet.py - list_unets()
- load_model - shared.py
- shared_items.py - reload_hypernetworks() # 这种方式现在几乎不用了
- ui_extra_networks.py - initialize()/register_default_pages()
- extra_networks.py - initialize()/register_default_extra_networks()ui.py - ui.create_ui()ui.py
一些基础参数的初始化也在这里关于ui设计在webui中代码还是挺多的
ui_components.py 一些设计的ui组件
shared_items.py 重复的一些item
下面就是一个FormRow
elif category dimensions:with FormRow():with gr.Column(elem_idtxt2img_column_size, scale4):width gr.Slider(minimum64, maximum2048, step8, labelWidth, value512, elem_idtxt2img_width)height gr.Slider(minimum64, maximum2048, step8, labelHeight, value512, elem_idtxt2img_height)....调用接口入口
txt2img_args dict(fnwrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs[None, , ]),_jssubmit,inputs[dummy_component,toprow.prompt,toprow.negative_prompt,toprow.ui_styles.dropdown,steps,sampler_name,batch_count,batch_size,cfg_scale,height,width,enable_hr,denoising_strength,hr_scale,hr_upscaler,hr_second_pass_steps,hr_resize_x,hr_resize_y,hr_checkpoint_name,hr_sampler_name,hr_prompt,hr_negative_prompt,override_settings,] custom_inputs,
txt2img.py
p processing.StableDiffusionProcessingTxt2Img(sd_model,,prompt,negative_prompt,sampler_name,...)-
processed processing.process_images(p)processing.py
res process_image_inner(p)
- sample_ddim p.sample(conditioning,unconditional_conditioning,seeds,subseeds,subseed_strength,prompts)- StableDiffusionProcessingTxt2Img.sample()
-- self.sampler sd_sampler.create_sampler(self.sampler_name,self.sd_model)
-- samples self.samplers.sample(c,uc,image_encoditioningself.txt2img_image_conditioning(x))sd_samplers_kdiffusion.py
sample()-
samples self.launch_sampling(steps,lambda:self.func(self.model_wrap_cfg,x,self.sampler_extra_args,...))
model_rap_cfg:CFGDenoiseKDiffusion-sd_samplers_cfg_denoiser.CFGDenoiser
sd_samplers_common.py
func() sample_dpmpp_2m -repositories/k-diffusion/k_diffusion/sampling.py
sample_dpmpp_2m()-
- denoised model(x,sigmas[i]*s_in,**extra_args)-
...
# 此处就是去噪产生图片的过程
modules/sd_samplers_cfg_denoiser.py model
CFGDenoiser()-
forward(x:2x4x128x128,sigma:[14.6146,14.6146],uncond:SchedulePromptConditionings,cond:MulticondLearnedConditioning,cond_scale:7,s_min_uncond:0,image_cond:2x5x1x1)-
denoised:2x4x128x128
# unet预测都被封装在这里
modules/sd_models.py 这块主要是
reload_model_weights()-
sd_model reuse_model_from_already_loaded(sd_model,checkpoint_info,...)
load_model()
....
modules/sd_samplers_common.py
sd_models.reload_model_weights(refiner_checkpoint_info)
cfg_denoiser.update_inner_model()-modules/sd_samplers_cfg_denoiser.py
forward()-
