Birch-san

Mahouko prompts used for latent walk (WD 1.5 beta 1)

kimono + bridge + river

anime

positive prompt:

beautiful, 1girl, a smiling and winking girl, wearing a dark kimono, taking a selfie on a bridge over a river. detailed hair, portrait, floating hair, anime, carnelian, best aesthetic, best quality, ribbon, outdoors, good posture, watercolor (medium), traditional media, ponytail'

Install latest Nvidia driver -- 525 is CUDA 12-era, but is backwards-compatible with 11.8:

apt install nvidia-driver-525 nvidia-dkms-525

Install CUDA 11.8 (use the runfile install, tell it not to replace your driver if you have a newer driver, switch to gcc 11):
https://developer.nvidia.com/cuda-11-8-0-download-archive

Install cuDNN 8 (requires registration):

I started with a basic Ubuntu 22.10 installation, where I chose in the installer to use ZFS as my volume manager.
I wanted to encrypt my home folder.

I followed the article (and comments, including Christoph Hagemann's) from:
https://talldanestale.dk/2020/04/06/zfs-and-homedir-encryption/

To achieve:

Home directory (a ZFS rpool mount) is encrypted
You are only prompted for password if you are trying to login to that user

	from torch import FloatTensor

	def softmax(x: FloatTensor, dim=-1) -> FloatTensor:
	maxes = x.max(dim, keepdim=True).values
	diffs = x-maxes
	x_exp = diffs.exp()
	x_exp_sum = x_exp.sum(dim, keepdim=True)
	quotient = x_exp/x_exp_sum
	return quotient

	No operator found for `memory_efficient_attention_forward` with inputs:
	query : shape=(32, 4096, 1, 40) (torch.float16)
	key : shape=(32, 77, 1, 40) (torch.float16)
	value : shape=(32, 77, 1, 40) (torch.float16)
	attn_bias : <class 'torch.Tensor'>
	p : 0.0
	`cutlassF` is not supported because:
	attn_bias.shape[-1] % 8 != 0
	`flshattF` is not supported because:
	attn_bias type is <class 'torch.Tensor'>

	#!/usr/bin/env bash

	# makes a video like:
	# https://twitter.com/Birchlabs/status/1632185133435437057
	# expects folders with filenames such as:
	# /home/birch/git/diffusers-play/out_dynthreshoff/00000.679566949.cfg07.50.png

	i=0
	CFG_COUNTER=''
	FONT='fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf'

	from torch import FloatTensor, LongTensor, Tensor, Size, lerp, zeros_like
	from torch.linalg import norm

	# adapted to PyTorch from:
	# https://gist.github.com/dvschultz/3af50c40df002da3b751efab1daddf2c
	# most of the extra complexity is to support:
	# - many-dimensional vectors
	# - v0 or v1 with last dim all zeroes, or v0 ~colinear with v1
	# - falls back to lerp()
	# - conditional logic implemented with parallelism rather than Python loops

	# suppose we want to matmul some matrix against some column vector..
	# the usual way is this:
	mat = np.array([[1.3e2, 8.e2], [1.6e1, 5.e-1]])
	vec = np.array([2.1e2, 3.6e-4])
	mat @ vec
	array([27300.288 , 3360.00018])

	# but what if we wanted to exploit some efficiencies..
	# - addition can use less cycles / energy / silicon / time than multiplication
	# - for machine learning training: we want to represent a wide range of exponents, but don't need such range on the mantissa

	Screenshots in comments below
	https://canary.discord.com/channels/930499730843250783/950914873800396841/1026450454068084798

	from torch import Tensor, FloatTensor
	from typing import Protocol, Optional
	from k_diffusion.external import CompVisDenoiser
	from k_diffusion.sampling import sample_heun

	class DiffusionModel(Protocol):
	def __call__(self, x: Tensor, sigma: Tensor, **kwargs) -> Tensor: ...

	class DiffusionModelMixin(DiffusionModel):
	inner_model: DiffusionModel