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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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60 lines
2.4 KiB
Python
60 lines
2.4 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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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(self, 'target_width', 1024)
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height = getattr(self, 'target_height', 1024)
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sdxl_conds = {
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"txt": batch,
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"original_size_as_tuple": torch.tensor([height, width]).repeat(len(batch), 1).to(devices.device, devices.dtype),
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"crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left]).repeat(len(batch), 1).to(devices.device, devices.dtype),
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"target_size_as_tuple": torch.tensor([height, width]).repeat(len(batch), 1).to(devices.device, devices.dtype),
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}
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c = self.conditioner(sdxl_conds)
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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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return self.model(x, t, cond)
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def extend_sdxl(model):
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dtype = next(model.model.diffusion_model.parameters()).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_model = [x for x in model.conditioner.embedders if 'CLIPEmbedder' in type(x).__name__][0]
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model.cond_stage_key = model.cond_stage_model.input_key
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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=dtype)
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model.is_xl = True
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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.modules.attention.print = lambda *args: None
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sgm.modules.diffusionmodules.model.print = lambda *args: None
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sgm.modules.diffusionmodules.openaimodel.print = lambda *args: None
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sgm.modules.encoders.modules.print = lambda *args: None
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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 |