Furry Stable Diffusion AI: Complete Setup Guide & Model Downloadss
Disclaimer: Guide presented here are from Furry Diffusion Discord and not my own work. Sign up now and join 14 thousand stable diffusion enthusiasts to learn more on furry diffusion ai, updates, diagnosis and latest news.
What is Stable Diffusion?
Stable diffusion refers to stochastic processes with heavy tails and long-range dependence. It is a type of Levy process, which is a stochastic process with stationary and independent increments.
A stable diffusion process draws increments from a stable distribution. With this distribution, the variance increases as the measurement interval is extended.
Stable Diffusion 2.1 Model Explained
Furry Diffusion AI: Setup Guide & Downloads
1 . Local Installation
Complete steps for local installation can be found here at Rentry but to give you an overview, here’s the summary of the steps:
Step 1: Install Git (Page)
– During the installation process, ensure that the option ‘Git Bash -> Windows Explorer integration’ is selected.
Step 2: Continue by cloning the WebUI repository to the location you desire:
Right-click any location and choose “Git Bash here”
Step 3: Download the model(s) of your choice:
Official Stable Diffusion 2.1: Huggingface (Must Sign Up)
Trinart2: (Anime concept art/’Pixiv style’ trained SD 1.4) Download HERE
Additional models can be found on this page (Most in beta)
Step 4: Rename your.ckpt file “model.ckpt” and save it in the /models/Stable-diffusion folder.
Step 6: Run webui-user.bat as a regular user, not as administrator. You are done.
It is highly advisable to visit the full guide presented above before carrying on your project.
You can find a direct github link to AUTOMATIC-1111’s WebUI here.
This download simply includes the UI tool. To use it with a custom model, download one of the models listed in the “Model Downloads” section, rename it “model.ckpt,” and save it in the /models/Stable-diffusion folder.
If you need a more complete guide on the local installation which includes the requirements, the process, installing dependencies, getting and converting the Stable Diffusion Mode and more, check this guide from travelneil. Part 1 & Part 2 of the Guide.
Every file supposedly will update weekly so don’t worry about outdated content.
2. Model Downloads: Stable Diffusion Furry
The “.ckpt” file extension is commonly used for checkpoint files in the TensorFlow framework, which is a popular platform for building and training machine learning models. The “e18” in the file name could indicate that the checkpoint was saved after the model had been trained for 18 epochs. An epoch in machine learning refers to one full pass through the training data during the training process.e621 was used to train a general-purpose model.
Please do note that during training, the word explicit was misspelt as explict. Some of the models will be larger in file size.
“Zack3D - Kinky Furry CV1”
Pony-diffusion is a latent text-to-image diffusion model that has been fine-tuned to become conditioned on excellent pony SFW-like images.
WARNING: This model’s v2 is considerably more capable of producing NSFW content than v1 was, thus it’s advised to use the’safe’ tag in conjunction with a negative prompt for any image characteristics you might want to turn off. Negative prompts like “3d” or “sfm” should be used to better match v1 results because the v2 model also has a minor 3d bias.
An early finetuned checkpoint of waifu-diffusion on top of Stable Diffusion V1-4, a latent image diffusion model trained on LAION2B-en, was the model first utilised for fine-tuning.
This specific checkpoint has been improved using a learning rate of 5.0e-6 for 4 epochs on roughly 450k pony and furry text-image combinations (using tags from derpibooru and e621) that have all received scores of at least 250.
Links to Pony Diffusion V1 information and downloads
Complete Code here.
3. Online Tools: Furry Diffusion AI
Operating & Running on Google Colab
Google Colab (short for Colaboratory) is a cloud-based platform that provides a free Jupyter notebook environment for developing and running machine learning and deep learning models. It is a web-based tool that allows users to write and run Python code, and it provides easy access to popular libraries for data analysis and machine learning, such as TensorFlow and PyTorch. Google Colab also offers features like hardware acceleration with GPUs, real-time collaboration, and the ability to save and share notebooks on Google Drive.
Here is a well-liked collaboration for the fuzzy models’ user-friendliness: https://colab.research.google.com/drive/128k7amGCLNO1JGaZhKl0Pju8X7SCcf8V
How to use Google Colab:
- Go to the Google Colab website and sign in to your Google account.
- Continue by clicking on “New Notebook” to create a new notebook.
- In the notebook, you can write and run code cells, add text cells to provide explanations, and insert images or other media.
- Use the “Runtime” menu to run cells, restart the notebook, and change the runtime type or hardware accelerator.
- Save your notebook to your Google Drive account or download it as a Jupyter notebook file (.ipynb).
Special Note : You can choose the model you want for this colab by using the dropdown menu on the right side of one of the codeblocks. Go straight to step 4 if the model you want is listed.
Download the file, rename it to model.ckpt, and then upload it to your Google Drive if the model isn’t shown (drive.google.com).
You’ll receive a link to the Gradio app after the final block of code completes. Click it to start, then enjoy yourself!
4. Troubleshooting of any Possible Problems or Error
If the Colab crashed
It has crashed if, after clicking “generate,” nothing happens. Simply reload the current tab in your browser. Crashing can occur if the resolution is cranked up too high, if the batch parameters are tweaked too far, or for no apparent reason at all.
If the Colab Timed Out
You might want to visit the colab browser tab every 15 minutes while using Gradio and just do something to avoid timing out the session. Open menus, scroll, etc.
If the model failed to download
You most likely hit a bandwidth limit due to the volume of traffic. If this occurs, you must pick a Custom model and give the model yourself. Download the model, rename it model.ckpt, and save it to your Google Drive (drive.google.com).
If you ran into a usage limit at Google Colab?
For free users, you receive a few hours every day. It depends on traffic and your long-term resource consumption.
5. Important Directories: Directory of Commercial Services and Discord Bots
Novelai.net: Originally a service for AI word production, they’ve expanded into image generation as well, and they provide specialty models, one of which is furry. NSFW-friendly. Sometimes it’s referred to as NAIGen. https://novelai.net/
Dreamstudio.ai: As the first to market, some of Stability’s new products can be discovered here first. It doesn’t specialise in furry, although it can occasionally pull off some lovely SFW characters. New users are given a number of free generations to try out.
The Gooey Pack: Uses Zack3D’s goo/latex model described above
PurpleSmart.ai: Runs the MLP model described above
Alternate Guide, NovelAI Setup, Setup for Running under 4GB, Linux Installation, and Updates can be found here at Rentry.
6. What is the apparent difference between furry_epoch4 and yiffy-e18?
The biggest distinction is that whereas y-e18 recognises furry artist names, f-e4 does not. Someone created a conversion table for y-e18 to f-e4 prompts:
- change any ‘uploaded to e621, explicit content’ e.g to just ‘e621 nsfw’
- remove any furry artist names, it doesn’t respond to them
- add pretty art styles from base SD
- slightly change tag weighting (angles and poses seem to need less weighting, species and type (feral/anthro) seem to need more)
- mentioning texture/detail is MUCH MORE EFFECTIVE (addding ‘fluffy fur texture’ changed style dramatically
- -negative prompts seem similarly effective, no need to change much
7. How do I start getting better images?
Spend 90% of your time crafting good prompts and 10% on adjusting anything else.
When it comes to the quality of a work of art, the name of the style and the artist’s reputation is extremely important.
Check out the various writing prompts that have been posted in this sub for some ideas. One common way to improve the effectiveness of prompts is to add the authors’ names (such as “by Michael & Inessa Garmash, Ruan Jia, Pino Daeni”); chunin’s style is also rather simple to imitate.
The default settings of a good prompt should reliably provide good photos.
Stable Diffusion Furry Finetune Models
Furry Diffusion AI: Stable Diffusion Furry Prompt
Best 100 Stable Diffusion Furry Prompts can be found here.
Sample Prompt “ beautiful furry digital art portrait commission of an androgynous furry anthro wolf fursona wearing punk clothes in the streets of a cyberpunk city. neon signs. character design by charlie bowater, ross tran, artgerm, and makoto shinkai, detailed, inked “
More detailed prompts from all over the world can be found here.
Sample of Stable Diffusion Images: Furry arts
We have written a complete guide on Stable Diffusion and its comparison with Dall E 2 & Midjourney. You can read our Midjourney vs Dall-E2 vs Stable Diffusion content.
Furry Diffusion AI: Sample of Stable Diffusion Images on furry arts
Prompt: furry guy
Prompt: Furry Girl
Prompt: Furry Monsters
Prompt: furry flowers furry art
Prompt: Furry Family
Prompt: Furry art on Furry House
Stable Diffusion Furry: Get the Hash of Stable Diffusion Model
A stable diffusion model, in the context of hash functions, is a family of hash functions that uses the diffusion process to get a hash value from the input data.
Locally Install Stable Diffusion in 3 minutes!
Complete video Guide to help you in the installation of Stable Diffusion.
Possible Errors in Using Stable Diffusions
Stable diffusions are stochastic processes used to model random fluctuations in various natural and social phenomena. However, while using stable diffusions, one may encounter errors that can undermine the accuracy and reliability of the models.
One possible error is the choice of an inappropriate stable parameter, which can result in an unrealistic fit of the model to the data. The stable parameter determines the degree of stability of the process and influences its long-term behavior, such as its variance and skewness.
If the parameter is too high or too low, the model may either overestimate or underestimate the variability of the data, leading to biased predictions and incorrect inferences. Therefore, it is crucial to select the stable parameter carefully based on the characteristics of the data and the theoretical assumptions of the model.
Another error in using stable diffusions is the violation of the assumption of stationarity, which requires that the statistical properties of the process remain constant over time. In practice, many phenomena exhibit non-stationary behavior, such as trends, seasonality, and abrupt changes, which can invalidate the stability assumption and distort the estimation of the model parameters.
Ignoring the non-stationarity can lead to errors in the estimation of the drift and diffusion coefficients, which determine the mean and variance of the process, respectively. Furthermore, non-stationarity can affect the estimation of the stable parameter and introduce spurious correlations between the process and other variables.
Therefore, it is essential to diagnose and correct non-stationarity before fitting a stable diffusion model to the data, for instance, by detrending, deseasonalizing, or incorporating regime-switching dynamics.
Quality Assurance in Using Furry Stable Diffusions
The evaluation of the data quality is a crucial component of QA when employing stable diffusions. The performance and validity of the model are impacted by the data’s quality, which can also add biases and errors into estimation.
Therefore, it is essential to examine for completeness, consistency, accuracy, and relevance when determining the data quality. Data cleaning, preprocessing, and exploratory analysis can be used to find outliers, missing values, and data modifications in order to do this.
Additionally, since doing so can help with replication and result evaluation, it is crucial to document the data quality and the procedures taken to ensure its integrity. Overall, using stable diffusions with quality assurance can assist ensure the accuracy and dependability of the model and its output.
We have presented a complete setup guide on Furry Stable Diffusion AI with download links from every possible resource. This is by far the most comprehensive guide on the internet on Furry Diffusion AI.
Let us know what you think of this in the comment section below as well as any questions that you might have.
Meanwhile, it is common to encounter errors while using stable diffusions and while downloading its models. So, if you encountered any error, please leave your question in the comment section so we can help you sort it out.
Happy using Stable Diffusion.
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Danesh is a scientist and a content writer with more than 2 years of experience. He is also a published author of a science fiction children’s book titled Imaginary Tales.
AI has always been in his mind and soul ever since the cult classic movie 2001: A Space Odyssey inspired him to become a writer. Seeing a lot of stigma and misconceptions on AI, he has decided to found Ava Machina as an Hub for people from different backgrounds to gather and learn about AI through expert insights as well as redirecting them to the right source.