研究過Stable Diffusion接口文檔的小夥伴們肯定知道，文檔中并沒有提供模型參數，那麼如何實作api切換模型呢？
我們先來看原先的sd-webui的代碼，找到模型接收請求參數的中心代碼，然後自己修改源碼，将這些請求參數傳遞到這段中心函數中去。
StableDiffusionProcessingTxt2Img
首要咱們來看最重要的txt2img的代碼，中心的類便是modules.processing中的StableDiffusionProcessingTxt2Img類，它的init函數接納以下的參數：
def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs) 代碼中的縮寫hr代表的便是webui中的Hires.fix，相關的參數對應的是webui中的這些選項：
接下來，能夠看到還有很多其他的參數沒有看到，其實這些參數都是在StableDiffusionProcessingTxt2Img的父類：StableDiffusionProcessing類的init中指定的：
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
self.outpath_samples: str = outpath_samples # 生成的圖檔的儲存路徑，和下面的do_not_save_samples配合運用
self.outpath_grids: str = outpath_grids
self.prompt: str = prompt # 正向提示詞
self.prompt_for_display: str = None
self.negative_prompt: str = (negative_prompt or "") # 反向提示詞
self.styles: list = styles or []
self.seed: int = seed # 種子，-1表明運用随機種子
self.sampler_name: str = sampler_name # 采樣方法，比方"DPM++ SDE Karras"
self.batch_size: int = batch_size # 每批生成的數量？
self.n_iter: int = n_iter
self.steps: int = steps # UI中的sampling steps
self.cfg_scale: float = cfg_scale # UI中的CFG Scale，提示詞相關性
self.width: int = width # 生成圖像的寬度
self.height: int = height # 生成圖像的高度
self.restore_faces: bool = restore_faces # 是否運用面部修正
self.tiling: bool = tiling # 是否運用可平鋪（tilling）
self.do_not_save_samples: bool = do_not_save_samples api接口中模型是如何加載的
我們來看modules/api/api.py中text2imgapi代碼：
def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
......
with self.queue_lock:
p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
......
return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) 從代碼中可以看出加載的模型是從shared.sd_model擷取的，但是這樣加載的模型不是使用者次元而是全局的，當我們api傳過來的模型與目前模型不一樣的時候，我們就需要重新加載模型，那麼就需要直接調用modules/sd_models.py中的reload_model_weights(sd_model=None, info=None)函數，咱們隻需傳入info參數就行，用info參數來指定咱們想要加載的模型，而在這個函數中，會自動判斷咱們想要加載的模型和目前模型是否相同，相同的話就不加載。
從函數簽名很難看出來info字段是一個什麼樣的參數，經過我對代碼的研究，我發現info其實便是下面這個類：
class CheckpointInfo:
def __init__(self, filename):
self.filename = filename
abspath = os.path.abspath(filename)
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
elif abspath.startswith(model_path):
name = abspath.replace(model_path, '')
else:
name = os.path.basename(filename)
if name.startswith("\\") or name.startswith("/"):
name = name[1:]
self.name = name
self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
self.hash = model_hash(filename)
self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name)
self.shorthash = self.sha256[0:10] if self.sha256 else None
self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else []) init裡的一大串其實都不用管，咱們隻需要指定filename就行了。是以用如下的示例代碼就能夠手動加載一個指定的模型：
from modules import sd_models
checkpoint_info = sd_models.CheckpointInfo("模型的全路徑名稱")
sd_models.reload_model_weights(info=checkpoint_info) 看完這裡，我們就可以直接修改源碼了：
1.修改 modules/api/models.py中的StableDiffusionTxt2ImgProcessingAPI增加模型名稱
StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
"StableDiffusionProcessingTxt2Img",
StableDiffusionProcessingTxt2Img,
[
{"key": "sampler_index", "type": str, "default": "Euler"},
{"key": "script_name", "type": str, "default": None},
{"key": "script_args", "type": list, "default": []},
{"key": "send_images", "type": bool, "default": True},
{"key": "save_images", "type": bool, "default": False},
{"key": "alwayson_scripts", "type": dict, "default": {}},
{"key": "model_name", "type": str, "default": None},
]
).generate_model() 2.修改modules/processing.py中的StableDiffusionProcessingTxt2Img，增加模型名稱接收：
def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '',model_name: str=None, **kwargs): 3.修改modules/api/api.py中text2imgapi代碼：
def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
......
model_name=txt2imgreq.model_name
if model_name is None:
raise HTTPException(status_code=404, detail="model_name not found")
......
with self.queue_lock:
checkpoint_info = sd_models.CheckpointInfo(os.path.join(models_path,'Stable-diffusion',model_name))
sd_models.reload_model_weights(info=checkpoint_info)
p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
...... 到此，我們就完成了文生圖api接口切換模型了,同理，我們也可對圖生圖api增加模型切換。下篇我們将會介紹如何增加任務id及通過任務id查詢任務進度。另外，我們也做了一個繪畫聊天的小程式，可以掃碼體驗：
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