Clay Mullis afiaka87

## dalle_blog_semantic_search.ipynb

      
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## zeta_quantize.ipynb

      
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                zeta_quantize.ipynb
              
          
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## finetune_clip_wds.py
from torchvision.transforms.transforms import GaussianBlur
import webdataset as wds
import io
from PIL import Image
from clip.loader import TextImageDataset
from clip.clip import load, tokenize
from torchvision import transforms as T
from torch.utils.data import DataLoader
from torch.optim import Adam
import torch.nn.functional as F

## image_to_image_and_score.py
import inspect
from typing import List, Optional, Union, Tuple

import numpy as np
import torch

from PIL import Image
from diffusers import (
    AutoencoderKL,
    DDIMScheduler,

## predict.py
import os
from typing import List

import numpy as np
import torch
from cog import BasePredictor, Input, Path
from diffusers import (
    AutoencoderKL,
    LMSDiscreteScheduler,
    UNet2DConditionModel,

## finetune_glide_v2.py
# https://wandb.ai/afiaka87/glide_finetune/runs/3fj69lfc?workspace=user-afiaka87
from lzma import MODE_NORMAL
from PIL import Image
import os
import wandb
from IPython.display import display
import torch as th

from glide_text2im import xf
from glide_text2im.download import load_checkpoint

## dalle_mini_prompts.json
[
    "t-shirt, size M",
    "flower dress, size M",
    "a t-shirt of an avocado",
    "a rainbow hat",
    "white snow covered mountain under blue sky during daytime",
    "aerial view of the beach during daytime",
    "aerial view of the beach at night",
    "double rainbow over a lake",
    "a beautiful sunset at a beach with a shell on the shore",

## codegen_discord.py
import discord
from datetime import datetime

import replicate

import asyncio
import logging

logging.basicConfig(level=logging.INFO)
import os

## blipify.py
import pathlib
from csv import writer
import torch
import tqdm
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from data import create_loader

## detic_image_folder.py
#%%
# cd Detic/
# %%
import detectron2
from detectron2.utils.logger import setup_logger
from pathlib import Path
from random import randint, choice
import time
import PIL
from PIL import Image
	from torchvision.transforms.transforms import GaussianBlur
	import webdataset as wds
	import io
	from PIL import Image
	from clip.loader import TextImageDataset
	from clip.clip import load, tokenize
	from torchvision import transforms as T
	from torch.utils.data import DataLoader
	from torch.optim import Adam
	import torch.nn.functional as F
	import inspect
	from typing import List, Optional, Union, Tuple

	import numpy as np
	import torch

	from PIL import Image
	from diffusers import (
	AutoencoderKL,
	DDIMScheduler,
	import os
	from typing import List

	import numpy as np
	import torch
	from cog import BasePredictor, Input, Path
	from diffusers import (
	AutoencoderKL,
	LMSDiscreteScheduler,
	UNet2DConditionModel,
	# https://wandb.ai/afiaka87/glide_finetune/runs/3fj69lfc?workspace=user-afiaka87
	from lzma import MODE_NORMAL
	from PIL import Image
	import os
	import wandb
	from IPython.display import display
	import torch as th

	from glide_text2im import xf
	from glide_text2im.download import load_checkpoint
	[
	"t-shirt, size M",
	"flower dress, size M",
	"a t-shirt of an avocado",
	"a rainbow hat",
	"white snow covered mountain under blue sky during daytime",
	"aerial view of the beach during daytime",
	"aerial view of the beach at night",
	"double rainbow over a lake",
	"a beautiful sunset at a beach with a shell on the shore",
	import discord
	from datetime import datetime

	import replicate

	import asyncio
	import logging

	logging.basicConfig(level=logging.INFO)
	import os
	import pathlib
	from csv import writer
	import torch
	import tqdm
	from PIL import Image
	from torch.utils.data import Dataset
	from torchvision import transforms
	from torchvision.transforms.functional import InterpolationMode

	from data import create_loader
	#%%
	# cd Detic/
	# %%
	import detectron2
	from detectron2.utils.logger import setup_logger
	from pathlib import Path
	from random import randint, choice
	import time
	import PIL
	from PIL import Image