Spaces:
Sleeping
Sleeping
File size: 1,518 Bytes
8483373 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
import torch
import torchvision
import os
import gc
import tqdm
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from transformers import CLIPTextModel
from peft import PeftModel, LoraConfig
from lora_w2w import LoRAw2w
from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel, LMSDiscreteScheduler
from peft.utils.save_and_load import load_peft_weights, set_peft_model_state_dict
from transformers import AutoTokenizer, PretrainedConfig
from PIL import Image
import warnings
warnings.filterwarnings("ignore")
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
PNDMScheduler,
StableDiffusionPipeline
)
######## Sampling utilities
def sample_weights(unet, proj, mean, std, v, device, factor = 1.0):
# get mean and standard deviation for each principal component
m = torch.mean(proj, 0)
standev = torch.std(proj, 0)
del proj
torch.cuda.empty_cache()
# sample
sample = torch.zeros([1, 1000]).to(device)
for i in range(1000):
sample[0, i] = torch.normal(m[i], factor*standev[i], (1,1))
# load weights into network
network = LoRAw2w( sample, mean, std, v,
unet,
rank=1,
multiplier=1.0,
alpha=27.0,
train_method="xattn-strict"
).to(device, torch.bfloat16)
return network
|