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import torch.nn
from ldm_patched.ldm.util import instantiate_from_config
from ldm_patched.modules.model_management import unet_dtype, text_encoder_dtype, get_torch_device
from lightning_fabric.utilities.device_dtype_mixin import _DeviceDtypeModuleMixin
from omegaconf import OmegaConf
from modules.shared import opts
def _alpha():
from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule
import numpy as np
betas = make_beta_schedule("linear", 1000, linear_start=0.00085, linear_end=0.0120)
return np.cumprod(1.0 - betas, axis=0)
class DiffusionEngine(_DeviceDtypeModuleMixin, torch.nn.Module):
def __init__(
self,
network_config: OmegaConf,
denoiser_config: OmegaConf,
first_stage_config: OmegaConf,
conditioner_config: OmegaConf,
scale_factor: float = 1.0,
disable_first_stage_autocast=False,
input_key: str = "jpg",
*args,
**kwargs,
):
super().__init__()
self.input_key = input_key
self.model = DiffusionWrapper(network_config)
self.denoiser = instantiate_from_config(denoiser_config)
self.sampler = None
self.conditioner = instantiate_from_config(conditioner_config)
self.instantiate_first_stage(first_stage_config)
self.disable_first_stage_autocast = disable_first_stage_autocast
self.scale_factor = scale_factor
# === extend_sdxl ===
self.model.diffusion_model.dtype = unet_dtype()
self.model.conditioning_key = "crossattn"
self.cond_stage_key = "txt"
self.parameterization = self.denoiser.scaling.parameterization
self.alphas_cumprod = torch.asarray(_alpha(), device=get_torch_device(), dtype=torch.float32)
self.conditioner.wrapped = torch.nn.Module()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
def decode_first_stage(self, *args, **kwargs):
raise NotImplementedError
def encode_first_stage(self, *args, **kwargs):
raise NotImplementedError
def forward(self, *args, **kwargs):
raise NotImplementedError
def sample(self, *args, **kwargs):
raise NotImplementedError
# ========== sd_models_xl.py ========== #
def get_learned_conditioning(self, batch: list[str]):
width = getattr(batch, "width", 1024)
height = getattr(batch, "height", 1024)
is_negative_prompt = getattr(batch, "is_negative_prompt", False)
force_zero_negative_prompt = is_negative_prompt and all(x == "" for x in batch)
aesthetic_score = opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else opts.sdxl_refiner_high_aesthetic_score
devices_args = dict(device=self.forge_objects.clip.patcher.current_device, dtype=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([opts.sdxl_crop_top, 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),
}
return self.conditioner(sdxl_conds, force_zero_embeddings=["txt"] if force_zero_negative_prompt else [])
def apply_model(self, 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): # for compatibility
return x
class DiffusionWrapper(_DeviceDtypeModuleMixin, torch.nn.Module):
def __init__(self, model_config):
super().__init__()
self.diffusion_model = instantiate_from_config(model_config)
def forward(self, *args, **kwargs):
return None
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