stable-diffusion
diffusers
stable-diffusion | diffusers | |
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17 | 268 | |
1,412 | 23,519 | |
- | 4.1% | |
2.9 | 9.9 | |
5 months ago | 3 days ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
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stable-diffusion
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Is it possible to merge VAEs?
Download this training project: git clone https://github.com/justinpinkney/stable-diffusion.git
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how can i install this Image mixer onto automatic1111's webui.?
looks like it's using the https://github.com/justinpinkney/stable-diffusion/blob/4ac995b6f663b74dfe65400285e193d4167d259c/scripts/gradio_image_mixer.py to do the bulk of the work meaning the core functionality is built into stable diffusion, seems the UI just isn't built to support it. Their ckpt is here too https://huggingface.co/lambdalabs/image-mixer/tree/main.
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Image Mixer CUDA Out of Memory
Any idea how to make Image Mixer work in this build? On RTX 3060 with 12Gb of memory I get the message:
- Ideas fo new feature for AI generation techniques
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AI Image Editing from Text! Imagic Explained
References: â–ºRead the full article: https://www.louisbouchard.ai/imagic/ â–ºKawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I. and Irani, M., 2022. Imagic: Text-Based Real Image Editing with Diffusion Models. arXiv preprint arXiv:2210.09276. â–º Use it with Stable Diffusion: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb â–ºMy Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/
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Imagic ( Google's Text-Based Image Editing ) implemented in Stable Diffusion
The notebook: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb
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[D] DreamBooth Stable Diffusion training in 10 GB VRAM, using xformers, 8bit adam, gradient checkpointing and caching latents.
There's a script for the SD --> Diffusers here: https://github.com/justinpinkney/stable-diffusion/blob/main/scripts/convert_sd_to_diffusers.py
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[P] How to fine tune stable diffusion: how we made the text-to-pokemon model at Lambda
You can start with the github which contains the code: https://github.com/justinpinkney/stable-diffusion
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Pokemon Stable Diffusion : A fine tuned model of Stable Diffusion to only create Pokemon
Hmmm, I just double checked the hashes of my local file, what's on huggingface, and what you showed above and they all match. I'm not familiar with that repo, so maybe something weird is going on. I tested it using the original txt2img script in the stable diffusion repo:
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List of Stable Diffusion systems - Part 2
*PICK\* (Added Sep. 12, 2022) Web app Stable Diffusion Image Variations by lambdalabs. GitHub repo. Generates variations of an input image without use of a text prompt. Censored.
diffusers
- Diffusion Models
- 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.
What are some alternatives?
cog-stable-diffusion - Diffusers Stable Diffusion as a Cog model
stable-diffusion-webui - Stable Diffusion web UI
material_stable_diffusion - Tileable Stable Diffusion - Cog model
stable-diffusion - A latent text-to-image diffusion model
stability-sdk - SDK for interacting with stability.ai APIs (e.g. stable diffusion inference)
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
merge-models - Merges two latent diffusion models at a user-defined ratio
invisible-watermark - python library for invisible image watermark (blind image watermark)
CrossAttentionControl - Unofficial implementation of "Prompt-to-Prompt Image Editing with Cross Attention Control" with Stable Diffusion
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
make-a-video-pytorch - Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch
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.