ROCm-docker
diffusers
ROCm-docker | diffusers | |
---|---|---|
3 | 266 | |
392 | 22,646 | |
1.0% | 2.8% | |
5.1 | 9.9 | |
24 days ago | 3 days ago | |
Shell | Python | |
MIT License | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ROCm-docker
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
https://rocm.docs.amd.com/projects/install-on-linux/en/lates... links to ROCm/ROCm-docker: https://github.com/ROCm/ROCm-docker which is the source of docker.io/rocm/rocm-terminal: https://hub.docker.com/r/rocm/rocm-terminal :
docker run -it --device=/dev/kfd --device=/dev/dri --group-add video rocm/rocm-terminal
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Stable Diffusion PR optimizes VRAM, generate 576x1280 images with 6 GB VRAM
Not sure about the 6600, but there is a guide for Linux at least:
https://m.youtube.com/watch?v=d_CgaHyA_n4&feature=emb_logo
And this is somehow relevant (possibly), as I kept the link open.
https://github.com/RadeonOpenCompute/ROCm-docker/issues/38
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It's working perfectly under Linux
As for the Docker image, I suppose you could compile the image (https://hub.docker.com/r/rocm/pytorch) by yourself using the sources (https://github.com/RadeonOpenCompute/ROCm-docker#building-images), which seems to be quite a bit of work. Better, you could just use an older tag of the upstream image, eg. rocm4.1.1_ubuntu18.04_py3.6_pytorch instead of rocm4.2_ubuntu18.04_py3.6_caffe2 or latest . Just make sure your container version matches your host ROCm version.
diffusers
- StableDiffusionSafetyChecker
- 🧨 diffusers 0.24.0 is out with Kandinsky 3.0, IP Adapters, and others
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What am I missing here? wheres the RND coming from?
I'm missing something about the random factor, from the sample code from https://github.com/huggingface/diffusers/blob/main/README.md
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T2IAdapter+ControlNet at the same time
Hey people, I noticed that combining these two methods in a single forward pass increases the controllability of the generation quite a bit. I was kind of puzzled that sometimes ControlNet yielded better results than T2IAdapter for some cases, and sometimes it was the other way around, so I decided to test both at the same time, and results were quite nice. Some visuals and more motivation here: https://github.com/huggingface/diffusers/issues/5847 And it was already merged here: https://github.com/huggingface/diffusers/pull/5869
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Won't you benchmark me?
Open Parti Prompts: The better way to evaluate diffusion models (repo)
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kohya_ss error. How do I solve this?
You have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
- Making a ControlNet inpaint for sdxl
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Stable Diffusion Gets a Major Boost with RTX Acceleration
For developers, TensorRT support also exists for the diffusers library via community pipelines. [1] It's limited, but if you're only supporting a subset of features, it can help.
In general, these insane speed boosts comes at the cost of bleeding edge features.
[1] https://github.com/huggingface/diffusers/blob/28e8d1f6ec82a6...
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Mysterious weights when training UNET
I was training sdxl UNET base model, with the diffusers library, which was going great until around step 210k when the weights suddenly turned back to their original values and stayed that way. I also tried with the ema version, which didn't change at all. I also looked at the tensor's weight values directly which confirmed my suspicions.
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I Made Stable Diffusion XL Smarter by Finetuning It on Bad AI-Generated Images
Merging LoRAs is essentially taking a weighted average of the LoRA adapter weights. It's more common in other UIs.
diffusers is working on a PR for it: https://github.com/huggingface/diffusers/pull/4473
What are some alternatives?
awesome-kubernetes - A curated list for awesome kubernetes sources :ship::tada:
stable-diffusion-webui - Stable Diffusion web UI
AiDungeon2-Docker-ROCm - Runs an AIDungeon2 fork in Docker on AMD ROCm hardware.
stable-diffusion - A latent text-to-image diffusion model
ZLUDA - CUDA on AMD GPUs
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
stable-diffusion - Go to lstein/stable-diffusion for all the best stuff and a stable release. This repository is my testing ground and it's very likely that I've done something that will break it.
invisible-watermark - python library for invisible image watermark (blind image watermark)
docker-elk - The Elastic stack (ELK) powered by Docker and Compose.
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
Dokku - A docker-powered PaaS that helps you build and manage the lifecycle of applications
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.