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models，comfy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_txt2img=ScriptRunner()/scripts_img2img=ScriptRunner()/scripts_postpro=scripts_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_id="txt2img_column_size", scale=4): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width") height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height") .... 调用接口入口：
txt2img_args = dict( fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']), _js="submit", 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_encoditioning=self.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.CFGDenoisersd_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()->
