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SDXL support

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AUTOMATIC1111
2023-07-12 23:52:43 +03:00
parent af081211ee
commit da464a3fb3
16 changed files with 241 additions and 44 deletions
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27
modules/sd_models_xl.py
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@@ -1,18 +1,30 @@
from __future__ import annotations
import sys
import torch
import sgm.models.diffusion
import sgm.modules.diffusionmodules.denoiser_scaling
import sgm.modules.diffusionmodules.discretizer
from modules import devices
from modules import devices, shared, prompt_parser
def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: list[str]):
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
c = self.conditioner({'txt': batch})
width = getattr(self, 'target_width', 1024)
height = getattr(self, 'target_height', 1024)
sdxl_conds = {
"txt": batch,
"original_size_as_tuple": torch.tensor([height, width]).repeat(len(batch), 1).to(devices.device, devices.dtype),
"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),
"target_size_as_tuple": torch.tensor([height, width]).repeat(len(batch), 1).to(devices.device, devices.dtype),
}
c = self.conditioner(sdxl_conds)
return c
@@ -26,7 +38,7 @@ def extend_sdxl(model):
model.model.diffusion_model.dtype = dtype
model.model.conditioning_key = 'crossattn'
model.cond_stage_model = [x for x in model.conditioner.embedders if type(x).__name__ == 'FrozenOpenCLIPEmbedder'][0]
model.cond_stage_model = [x for x in model.conditioner.embedders if 'CLIPEmbedder' in type(x).__name__][0]
model.cond_stage_key = model.cond_stage_model.input_key
model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
@@ -34,7 +46,14 @@ def extend_sdxl(model):
discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
model.is_xl = True
sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
sgm.modules.attention.print = lambda *args: None
sgm.modules.diffusionmodules.model.print = lambda *args: None
sgm.modules.diffusionmodules.openaimodel.print = lambda *args: None
sgm.modules.encoders.modules.print = lambda *args: None