stable-diffusion VS dalle-flow

What are the differences between the stable diffusion projects by Stability-AI and RunwayML? In addition, a similar project Diffusers by HuggingFace also exists, is that different as well?

Ok. I refreshed my memory (here). RunwayML did the training. Stability AI donated the compute. At least. 1 member of the german research team has gone to work for RML and basically continued his work there. I'm not clear at all on the relationship between SAI and RML.

Pretty sure. There's an in painting model that is available! I heard it's pretty good! https://github.com/runwayml/stable-diffusion

so use it with the fork it was designed to work on: https://github.com/runwayml/stable-diffusion

source: https://github.com/runwayml/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md

If this gets taken down because of the cucks at Stability, you can also download the model weights at https://github.com/runwayml/stable-diffusion

Jina AI is really into generative AI. It started out with DALL·E Flow, swiftly followed by DiscoArt. And then…🦗🦗*🦗🦗. At least for a while…

you can host your own https://github.com/jina-ai/dalle-flow

For example for all the multimodal stuff like clipseg and upscalers, I'm using isolated executors through jina flow: https://github.com/jina-ai/dalle-flow/tree/main/executors

clipseg is an image segmentation method used to find a mask for an image from a prompt. I implemented it as an executor for dalle-flow and added it to my bot yasd-discord-bot.

Merged into [dalle-flow](https://github.com/jina-ai/dalle-flow/pull/112) this morning and works on my Discord bot [yasd-discord-bot](https://github.com/AmericanPresidentJimmyCarter/yasd-discord-bot).

https://github.com/jina-ai/dalle-flow with ports of some code from https://github.com/lstein/stable-diffusion plus some stuff specific to my uses (mostly more exposed settings and meta data on the outputs).

dalle flow - a more advanced version of dall-e mini, running dall-e mega and a diffusion model (free colab), free

Another option is https://github.com/jina-ai/dalle-flow combines DALL-E Mini with some other image processing models, and they have a pre-built Docker image that you could run locally. However, because it loads additional image processing models, you'll need about 21 GB of GPU RAM which is more than a 2080 TI has. You could always try to edit their Dockerfile and re-build it to remove the other models.

For the second half of this article, we’ll use meadowdata/meadowrun-dallemini-demo which contains a notebook for running multiple models as sequential batch jobs to generate images using Meadowrun. The combination of models is inspired by jina-ai/dalle-flow.