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 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) height = getattr(batch, 'height', 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=devices.device, dtype=devices.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): sd = self.model.state_dict() diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None) if diffusion_model_input is not None: if diffusion_model_input.shape[1] == 9: x = torch.cat([x] + cond['c_concat'], dim=1) return self.model(x, t, cond) 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