Spaces:
Runtime error
Runtime error
# from __future__ import annotations | |
# | |
# import torch | |
# | |
# import sgm.models.diffusion | |
# import sgm.modules.diffusionmodules.denoiser_scaling | |
# import sgm.modules.diffusionmodules.discretizer | |
# from modules import devices, shared, prompt_parser | |
# from modules import torch_utils | |
# | |
# from backend import memory_management | |
# | |
# | |
# def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]): | |
# | |
# for embedder in self.conditioner.embedders: | |
# embedder.ucg_rate = 0.0 | |
# | |
# width = getattr(batch, 'width', 1024) or 1024 | |
# height = getattr(batch, 'height', 1024) or 1024 | |
# is_negative_prompt = getattr(batch, 'is_negative_prompt', False) | |
# aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score | |
# | |
# devices_args = dict(device=self.forge_objects.clip.patcher.current_device, dtype=memory_management.text_encoder_dtype()) | |
# | |
# sdxl_conds = { | |
# "txt": batch, | |
# "original_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1), | |
# "crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left], **devices_args).repeat(len(batch), 1), | |
# "target_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1), | |
# "aesthetic_score": torch.tensor([aesthetic_score], **devices_args).repeat(len(batch), 1), | |
# } | |
# | |
# force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in batch) | |
# c = self.conditioner(sdxl_conds, force_zero_embeddings=['txt'] if force_zero_negative_prompt else []) | |
# | |
# return c | |
# | |
# | |
# def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond, *args, **kwargs): | |
# if self.model.diffusion_model.in_channels == 9: | |
# x = torch.cat([x] + cond['c_concat'], dim=1) | |
# | |
# return self.model(x, t, cond, *args, **kwargs) | |
# | |
# | |
# def get_first_stage_encoding(self, x): # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility | |
# return x | |
# | |
# | |
# sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning | |
# sgm.models.diffusion.DiffusionEngine.apply_model = apply_model | |
# sgm.models.diffusion.DiffusionEngine.get_first_stage_encoding = get_first_stage_encoding | |
# | |
# | |
# def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text, nvpt): | |
# res = [] | |
# | |
# for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'encode_embedding_init_text')]: | |
# encoded = embedder.encode_embedding_init_text(init_text, nvpt) | |
# res.append(encoded) | |
# | |
# return torch.cat(res, dim=1) | |
# | |
# | |
# def tokenize(self: sgm.modules.GeneralConditioner, texts): | |
# for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]: | |
# return embedder.tokenize(texts) | |
# | |
# raise AssertionError('no tokenizer available') | |
# | |
# | |
# | |
# def process_texts(self, texts): | |
# for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]: | |
# return embedder.process_texts(texts) | |
# | |
# | |
# def get_target_prompt_token_count(self, token_count): | |
# for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'get_target_prompt_token_count')]: | |
# return embedder.get_target_prompt_token_count(token_count) | |
# | |
# | |
# # those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist | |
# sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text | |
# sgm.modules.GeneralConditioner.tokenize = tokenize | |
# sgm.modules.GeneralConditioner.process_texts = process_texts | |
# sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count | |
# | |
# | |
# def extend_sdxl(model): | |
# """this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase.""" | |
# | |
# dtype = torch_utils.get_param(model.model.diffusion_model).dtype | |
# model.model.diffusion_model.dtype = dtype | |
# model.model.conditioning_key = 'crossattn' | |
# model.cond_stage_key = 'txt' | |
# # model.cond_stage_model will be set in sd_hijack | |
# | |
# model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps" | |
# | |
# discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization() | |
# model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=torch.float32) | |
# | |
# model.conditioner.wrapped = torch.nn.Module() | |
# | |
# | |
# sgm.modules.attention.print = shared.ldm_print | |
# sgm.modules.diffusionmodules.model.print = shared.ldm_print | |
# sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print | |
# sgm.modules.encoders.modules.print = shared.ldm_print | |
# | |
# # this gets the code to load the vanilla attention that we override | |
# sgm.modules.attention.SDP_IS_AVAILABLE = True | |
# sgm.modules.attention.XFORMERS_IS_AVAILABLE = False | |