Le 22 mars 2008 à Chelles !

Freedownload : http://soundcloud.com/dakrewlegroupe/augmente-remix-by-opulsif<br />Da Krew on Facebook : https://www.facebook.com/pages/DA-KREW/24279839447<br />Da Krew on Twitter : https://twitter.com/#!/dakrewlegroupe

Setup Instructions (Python 3.10.11, 4090, working on Windows): https://pastebin.com/YpqNSHFy

If you are still running into cuda issues after trying p13.txt take a read at this: https://pastebin.com/eSiVGGzA

Requirements:

Probably can run it if you have at least 6Gb of VRAM

Anaconda

Git

Generative-Models github

SVD or SVD_XT

Download Links:

Anaconda: https://www.anaconda.com/download

Git: https://git-scm.com/downloads

Generative-Models Github: https://github.com/Stability-AI/generative-models

SVD: https://huggingface.co/stabili....tyai/stable-video-di

SVD_XT: https://huggingface.co/stabili....tyai/stable-video-di

Info:

Convert your images to videos with Stable Video Diffusion!

https://stability.ai/news/stab....le-video-diffusion-o

Bloomberg's Rachel Metz and Mark Bergen discuss their reporting on risks facing the London-based startup Stability AI after its breakout success with Stable Diffusion. They speak with Ed Ludlow on "Bloomberg Technology."
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⁣Tecknat Barn Svenska:Aladdin (1992) DVDRIPPEN (Svenska) Trailer (3D)

Luis Gonzalez Jr. doing Tricking & BBoying. <br />Summer 2012

<B>Download: http://tinyurl.com/gphpvx3<P>Miss Miss Na Kita - Hambog ng sagpr krew ft.Xykimac ng Zamurai

Freedownload : http://soundcloud.com/dakrewlegroupe/sets/remix-or-die/<br />Da Krew on Facebook : https://www.facebook.com/pages/DA-KREW/24279839447<br />Da Krew on Twitter : https://twitter.com/#!/dakrewlegroupe

Sudah lebih 24 jam pesawat MH370 Malaysia Airlines (MAS) dari Kuala Lumpur ke Beijing dilaporkan hilang, namun masih belum ada sebarang petunjuk mengenai kedudukan pesawat jenis boeing 777-200 itu.<br /><br />Justeru Malaysia Airlines merayu seluruh rakyat Malaysia dan masyarakat di serata dunia untuk mendoakan keselamatan pesawat miliknya yang hilang dalam penerbangan dari Kuala Lumpur ke Beijing sejak awal pagi Sabtu.<br /><br />Sila layari http://www.astroawani.com untuk berita selanjutnya.

I have this routing Combi wired between the Mastering Output and Hardware I/O of my default song template so I don't have to bother doing the routing every time I want to do internal sampling in Reason.

Download the Combi2 patch here: https://forum.reasontalk.com/viewtopic.php?f=69&t=7526275

(All lesson resources are available at http://course.fast.ai.) This is the first lesson of part 2 of Practical Deep Learning for Coders. It starts with a tutorial on how to use pipelines in the Diffusers library to generate images. Diffusers is (in our opinion!) the best library available at the moment for image generation. It has many features and is very flexible. We explain how to use its many features, and discuss options for accessing the GPU resources needed to use the library.

We talk about some of the nifty tweaks available when using Stable Diffusion in Diffusers, and show how to use them: guidance scale (for varying the amount the prompt is used), negative prompts (for removing concepts from an image), image initialisation (for starting with an existing image), textual inversion (for adding your own concepts to generated images), Dreambooth (an alternative approach to textual inversion).

The second half of the lesson covers the key concepts involved in Stable Diffusion:
- CLIP embeddings
- The VAE (variational autoencoder)
- Predicting noise with the unet
- Removing noise with schedulers.

You can discuss this lesson, and access links to all notebooks and resources from it, at this forum topic: https://forums.fast.ai/t/lesso....n-9-part-2-preview/1

0:00 - Introduction
6:38 - This course vs DALL-E 2
10:38 - How to take full advantage of this course
12:14 - Cloud computing options
14:58 - Getting started (Github, notebooks to play with, resources)
20:48 - Diffusion notebook from Hugging Face
26:59 - How stable diffusion works
30:06 - Diffusion notebook (guidance scale, negative prompts, init image, textual inversion, Dreambooth)
45:00 - Stable diffusion explained
53:04 - Math notation correction
1:14:37 - Creating a neural network to predict noise in an image
1:27:46 - Working with images and compressing the data with autoencoders
1:40:12 - Explaining latents that will be input into the unet
1:43:54 - Adding text as one hot encoded input to the noise and drawing (aka guidance)
1:47:06 - How to represent numbers vs text embeddings in our model with CLIP encoders
1:53:13 - CLIP encoder loss function
2:00:55 - Caveat regarding "time steps"
2:07:04 Why don’t we do this all in one step?

Thanks to fmussari for the transcript, and to Raymond-Wu (on forums.fast.ai) for the timestamps.