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# Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
# https://arxiv.org/abs/2210.00939
# https://github.com/SusungHong/Self-Attention-Guidance

# (1) Install (see also https://github.com/finegrain-ai/refiners#install)
git clone https://github.com/finegrain-ai/refiners.git
cd refiners
poetry install --all-extras
poetry run pip install --upgrade torch torchvision

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# T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
# https://github.com/TencentARC/T2I-Adapter
# https://huggingface.co/blog/t2i-sdxl-adapters

# (1) Install (see also https://github.com/finegrain-ai/refiners#install)
git clone https://github.com/finegrain-ai/refiners.git
cd refiners
poetry install --all-extras
poetry run pip install --upgrade torch torchvision

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# IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
# https://github.com/tencent-ailab/IP-Adapter

# (1) Install (see also https://github.com/finegrain-ai/refiners#install)
git clone https://github.com/finegrain-ai/refiners.git
cd refiners
poetry install --all-extras
poetry run pip install --upgrade torch torchvision

# (2) Convert weights into Refiners format

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See: Masterminds of Programming: Conversations with the Creators of Major Programming Languages, Lua chapter.

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# Example: to run `torchtest.topK` do
luajit -ltorch -e "torch.test('topK')"

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# e.g. with lsqlite3
#
# see https://github.com/keplerproject/luarocks/blob/7a7c124/src/luarocks/deps.lua#L522-L529
luarocks SQLITE_INCDIR=/usr/local/opt/sqlite/include SQLITE_LIBDIR=/usr/local/opt/sqlite/lib install lsqlite3

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#include <stdio.h>
#include <curl/curl.h>

int
main(void)
{
  curl_version_info_data *info = curl_version_info(CURLVERSION_NOW);
  printf("async DNS: %d\n", !!(info->features & CURL_VERSION_ASYNCHDNS));
  return 0;
}

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require 'torch'

local argcheck = require 'argcheck'
local env      = require 'argcheck.env'

function env.istype(obj, typename)
  if typename == 'torch.Tensor' then
    -- could also check the storage type!
    return torch.isTensor(obj)
  else

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require 'torch'
-- pre-req: luarocks install csv
local csv = require "csv"

local inputs, labels = {}, {}

local f, i = csv.open("data.csv"), 0
for fields in f:lines() do
  if i > 0 then -- skip header
    inputs[i] = {unpack(fields, 1, 18)}

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# http://stedolan.github.io/jq/

json='{"foo": {"genre":"deep house"}, "bar": {"genre": "progressive house"}, "baz": {"genre": "dubstep"}}'
echo "$json" | jq '[to_entries | .[] | select(.value.genre | contains("house"))] | from_entries'
# {
#   "bar": {
#     "genre": "progressive house"
#   },
#   "foo": {
#     "genre": "deep house"