From 598da5cd4928618b166886d3485ce30ce3a43490 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sun, 19 Nov 2023 15:50:06 +0800 Subject: Use options instead of cmd_args --- modules/sd_models.py | 61 +++++++++++++++++++++++++++------------------------- 1 file changed, 32 insertions(+), 29 deletions(-) (limited to 'modules/sd_models.py') diff --git a/modules/sd_models.py b/modules/sd_models.py index a6c8b2fa..eb491434 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -339,10 +339,28 @@ class SkipWritingToConfig: SkipWritingToConfig.skip = self.previous +def check_fp8(model): + if model is None: + return None + if devices.get_optimal_device_name() == "mps": + enable_fp8 = False + elif shared.opts.fp8_storage == "Enable": + enable_fp8 = True + elif getattr(model, "is_sdxl", False) and shared.opts.fp8_storage == "Enable for SDXL": + enable_fp8 = True + else: + enable_fp8 = False + return enable_fp8 + + def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer): sd_model_hash = checkpoint_info.calculate_shorthash() timer.record("calculate hash") + if not check_fp8(model) and devices.fp8: + # prevent model to load state dict in fp8 + model.half() + if not SkipWritingToConfig.skip: shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title @@ -395,34 +413,16 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer devices.dtype_unet = torch.float16 timer.record("apply half()") - if devices.get_optimal_device_name() == "mps": - enable_fp8 = False - elif shared.cmd_opts.opt_unet_fp8_storage: - enable_fp8 = True - elif model.is_sdxl and shared.cmd_opts.opt_unet_fp8_storage_xl: - enable_fp8 = True - else: - enable_fp8 = False - - if enable_fp8: + if check_fp8(model): devices.fp8 = True - if model.is_sdxl: - cond_stage = model.conditioner - else: - cond_stage = model.cond_stage_model - - for module in cond_stage.modules(): - if isinstance(module, torch.nn.Linear): + first_stage = model.first_stage_model + model.first_stage_model = None + for module in model.modules(): + if isinstance(module, torch.nn.Conv2d): module.to(torch.float8_e4m3fn) - - if devices.device == devices.cpu: - for module in model.model.diffusion_model.modules(): - if isinstance(module, torch.nn.Conv2d): - module.to(torch.float8_e4m3fn) - elif isinstance(module, torch.nn.Linear): - module.to(torch.float8_e4m3fn) - else: - model.model.diffusion_model = model.model.diffusion_model.to(torch.float8_e4m3fn) + elif isinstance(module, torch.nn.Linear): + module.to(torch.float8_e4m3fn) + model.first_stage_model = first_stage timer.record("apply fp8") else: devices.fp8 = False @@ -769,7 +769,7 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer): return None -def reload_model_weights(sd_model=None, info=None): +def reload_model_weights(sd_model=None, info=None, forced_reload=False): checkpoint_info = info or select_checkpoint() timer = Timer() @@ -781,11 +781,14 @@ def reload_model_weights(sd_model=None, info=None): current_checkpoint_info = None else: current_checkpoint_info = sd_model.sd_checkpoint_info - if sd_model.sd_model_checkpoint == checkpoint_info.filename: + if check_fp8(sd_model) != devices.fp8: + # load from state dict again to prevent extra numerical errors + forced_reload = True + elif sd_model.sd_model_checkpoint == checkpoint_info.filename: return sd_model sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer) - if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename: + if not forced_reload and sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename: return sd_model if sd_model is not None: -- cgit v1.2.3