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  1. octaflop revised this gist Sep 7, 2022. 1 changed file with 8 additions and 7 deletions.
    15 changes: 8 additions & 7 deletions sept_sd_talk_notes.md
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    # Stable Diffusion: Machine Learning for the Masses
    # Machine Learning for the Masses: A Group Discussion and Demonstration of Python-Powered AI Challenging our Collective Understanding of Beauty and Art

    ## Faris Chebib
    ## v0.0.1
    @@ -7,17 +7,18 @@
    - Machine Learning
    - Art
    - Previous Examples
    -
    - [Dall-E](https://openai.com/dall-e-2/)
    - ![bf94ed23c0d790b9d044d98ed943b5f0.png](:/3107d330290341e08757535a9fe1accb)
    - ![image](https://user-images.githubusercontent.com/6115/188996631-6888d82a-f50a-4a1a-9cb9-d95e97bdac2d.png)
    - [Imagen](https://imagen.research.google/)
    - ![50e5dd80b435b050fa85df9b9095188b.png](:/3f03e10c3f3c4c64a7e5067fa55bc8ee)
    - ![image](https://user-images.githubusercontent.com/6115/188996647-f6a2324f-c133-4b6b-af9e-41214eb65c2e.png)
    - [Stable Diffusion](https://stability.ai/)
    - ![37e3662e1bc1b5a8fab46e6628ff822f.png](:/8fbca226807e424dbcdf13c1de57817b)
    - ![image](https://user-images.githubusercontent.com/6115/188996659-f02fe043-748f-4f70-b58d-33bce1250034.png)
    - Introduction
    1. Good results
    2. free
    3. open source
    4. low comp requirement
    2. Free
    3. Open Source
    4. Low compute requirement
    5. Extremely accessible
    - Paper
    - How to read a research paper
  2. octaflop created this gist Sep 7, 2022.
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    # Stable Diffusion: Machine Learning for the Masses

    ## Faris Chebib
    ## v0.0.1
    ### Presented at SLCPython Sept 2022 Meetup

    - Machine Learning
    - Art
    - Previous Examples
    - [Dall-E](https://openai.com/dall-e-2/)
    - ![bf94ed23c0d790b9d044d98ed943b5f0.png](:/3107d330290341e08757535a9fe1accb)
    - [Imagen](https://imagen.research.google/)
    - ![50e5dd80b435b050fa85df9b9095188b.png](:/3f03e10c3f3c4c64a7e5067fa55bc8ee)
    - [Stable Diffusion](https://stability.ai/)
    - ![37e3662e1bc1b5a8fab46e6628ff822f.png](:/8fbca226807e424dbcdf13c1de57817b)
    - Introduction
    1. Good results
    2. free
    3. open source
    4. low comp requirement
    5. Extremely accessible
    - Paper
    - How to read a research paper
    - Compared to Others
    - Examples
    - **Python Code, running locally**
    - [Running Diffusion AI Locally](:/cf362d3753274f0fb2915f0fa84884c7)
    - Ethics
    - Questions
    - References
    - [The SD Repo](https://github.com/CompVis/stable-diffusion)
    - [Replicate](https://replicate.com/)
    - [Lambda GPU](https://lambdalabs.com/service/gpu-cloud)
    - [NightCafe Blog](https://medium.com/nightcafe-creator/stable-diffusion-tutorial-how-to-use-stable-diffusion-157785632eb3)
    - [LMU Paper](https://ommer-lab.com/research/latent-diffusion-models/)
    - [Peacasso](https://github.com/victordibia/peacasso)
    - [Collab Notebook](https://github.com/pinilpypinilpy/sd-webui-colab-simplified)
    - [better collab notebook](https://colab.research.google.com/drive/1KeNq05lji7p-WDS2BL-86Z8Y9SluGng4?usp=sharing)
    - [Two Minute Papers](https://www.youtube.com/watch?v=nVhmFski3vg&t=142s)
    - [Edan Meyer](https://www.youtube.com/watch?v=ltLNYA3lWAQ)
    - [His Collab](https://colab.research.google.com/drive/1_kbRZPTjnFgViPrmGcUsaszEdYa8XTpq?usp=sharing)
    - [Public release notes](https://stability.ai/blog/stable-diffusion-public-release)
    - [Public(?) Demo](https://huggingface.co/spaces/stabilityai/stable-diffusion)