116 lines
4.9 KiB
Python
116 lines
4.9 KiB
Python
from __future__ import annotations
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import torch
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import sgm.models.diffusion
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import sgm.modules.diffusionmodules.denoiser_scaling
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import sgm.modules.diffusionmodules.discretizer
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from modules import devices, shared, prompt_parser
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from modules import torch_utils
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def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
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for embedder in self.conditioner.embedders:
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embedder.ucg_rate = 0.0
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width = getattr(batch, 'width', 1024) or 1024
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height = getattr(batch, 'height', 1024) or 1024
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is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
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aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
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devices_args = dict(device=devices.device, dtype=devices.dtype)
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sdxl_conds = {
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"txt": batch,
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"original_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
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"crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left], **devices_args).repeat(len(batch), 1),
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"target_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
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"aesthetic_score": torch.tensor([aesthetic_score], **devices_args).repeat(len(batch), 1),
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}
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force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in batch)
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c = self.conditioner(sdxl_conds, force_zero_embeddings=['txt'] if force_zero_negative_prompt else [])
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return c
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def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
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sd = self.model.state_dict()
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diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
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if diffusion_model_input is not None:
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if diffusion_model_input.shape[1] == 9:
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x = torch.cat([x] + cond['c_concat'], dim=1)
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return self.model(x, t, cond)
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def get_first_stage_encoding(self, x): # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility
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return x
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sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
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sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
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sgm.models.diffusion.DiffusionEngine.get_first_stage_encoding = get_first_stage_encoding
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def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text, nvpt):
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res = []
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for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'encode_embedding_init_text')]:
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encoded = embedder.encode_embedding_init_text(init_text, nvpt)
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res.append(encoded)
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return torch.cat(res, dim=1)
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def tokenize(self: sgm.modules.GeneralConditioner, texts):
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for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]:
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return embedder.tokenize(texts)
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raise AssertionError('no tokenizer available')
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def process_texts(self, texts):
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for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:
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return embedder.process_texts(texts)
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def get_target_prompt_token_count(self, token_count):
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for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'get_target_prompt_token_count')]:
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return embedder.get_target_prompt_token_count(token_count)
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# those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist
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sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text
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sgm.modules.GeneralConditioner.tokenize = tokenize
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sgm.modules.GeneralConditioner.process_texts = process_texts
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sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count
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def extend_sdxl(model):
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"""this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
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dtype = torch_utils.get_param(model.model.diffusion_model).dtype
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model.model.diffusion_model.dtype = dtype
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model.model.conditioning_key = 'crossattn'
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model.cond_stage_key = 'txt'
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# model.cond_stage_model will be set in sd_hijack
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model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
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discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
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model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=torch.float32)
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model.conditioner.wrapped = torch.nn.Module()
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sgm.modules.attention.print = shared.ldm_print
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sgm.modules.diffusionmodules.model.print = shared.ldm_print
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sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print
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sgm.modules.encoders.modules.print = shared.ldm_print
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# this gets the code to load the vanilla attention that we override
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sgm.modules.attention.SDP_IS_AVAILABLE = True
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sgm.modules.attention.XFORMERS_IS_AVAILABLE = False
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