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revise memory formulation for special amd like #343

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lllyasviel 2024-02-24 09:36:52 -08:00
parent 79bdb78619
commit 0f09d98814
1 changed files with 11 additions and 9 deletions
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20
ldm_patched/modules/model_base.py
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@ -6,6 +6,7 @@ import torch
from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
from ldm_patched.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
from ldm_patched.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
import ldm_patched.ldm.modules.attention
import ldm_patched.modules.model_management
import ldm_patched.modules.conds
import ldm_patched.modules.ops
@ -209,17 +210,18 @@ class BaseModel(torch.nn.Module):
self.inpaint_model = True
def memory_required(self, input_shape):
area = input_shape[0] * input_shape[2] * input_shape[3]
dtype = self.manual_cast_dtype if self.manual_cast_dtype is not None else self.get_dtype()
dtype_size = ldm_patched.modules.model_management.dtype_size(dtype)
if ldm_patched.modules.model_management.xformers_enabled() or ldm_patched.modules.model_management.pytorch_attention_flash_attention():
dtype = self.get_dtype()
if self.manual_cast_dtype is not None:
dtype = self.manual_cast_dtype
#TODO: this needs to be tweaked
area = input_shape[0] * input_shape[2] * input_shape[3]
return (area * ldm_patched.modules.model_management.dtype_size(dtype) / 50) * (1024 * 1024)
scaler = 1.25
else:
#TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
area = input_shape[0] * input_shape[2] * input_shape[3]
return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
scaler = 1.75
if ldm_patched.ldm.modules.attention._ATTN_PRECISION == "fp32":
dtype_size = 4
return scaler * area * dtype_size * 16384
def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):