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import torch
from ldm_patched.modules import ops, model_management
from ldm_patched.modules.model_patcher import ModelPatcher
from transformers import modeling_utils
class DiffusersModelPatcher:
def __init__(self, pipeline_class, dtype=torch.float16, *args, **kwargs):
load_device = model_management.get_torch_device()
offload_device = torch.device("cpu")
if not model_management.should_use_fp16(device=load_device):
dtype = torch.float32
self.dtype = dtype
with ops.use_patched_ops(ops.manual_cast):
with modeling_utils.no_init_weights():
self.pipeline = pipeline_class.from_pretrained(*args, **kwargs)
if hasattr(self.pipeline, "unet"):
if hasattr(self.pipeline.unet, "set_attn_processor"):
try:
from diffusers.models.attention_processor import AttnProcessor2_0
self.pipeline.unet.set_attn_processor(AttnProcessor2_0())
print("Attention optimization applied to DiffusersModelPatcher")
except ImportError:
print("Failed to import diffusers; please install it manually")
self.pipeline = self.pipeline.to(device=offload_device)
if self.dtype is torch.float16:
self.pipeline = self.pipeline.half()
self.pipeline.eval()
self.patcher = ModelPatcher(
model=self.pipeline,
load_device=load_device,
offload_device=offload_device,
)
def prepare_memory_before_sampling(self, batchsize, latent_width, latent_height):
area = 2 * batchsize * latent_width * latent_height
inference_memory = (((area * 0.6) / 0.9) + 1024) * (2**20)
model_management.load_models_gpu(models=[self.patcher], memory_required=inference_memory)
def move_tensor_to_current_device(self, x):
return x.to(device=self.patcher.current_device, dtype=self.dtype)
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