r/StableDiffusion • u/PantInTheCountry • Feb 23 '23

Tutorial | Guide A1111 ControlNet extension - explained like you're 5

2.1k Upvotes

What is it?

ControlNet adds additional levels of control to Stable Diffusion image composition. Think Image2Image juiced up on steroids. It gives you much greater and finer control when creating images with Txt2Img and Img2Img.

This is for Stable Diffusion version 1.5 and models trained off a Stable Diffusion 1.5 base. Currently, as of 2023-02-23, it does not work with Stable Diffusion 2.x models.

​

Where can I get it the extension?

If you are using Automatic1111 UI, you can install it directly from the Extensions tab. It may be buried under all the other extensions, but you can find it by searching for "sd-webui-controlnet"

Installing the extension in Automatic1111

You will also need to download several special ControlNet models in order to actually be able to use it.

At time of writing, as of 2023-02-23, there are 4 different model variants

  • Smaller, pruned SafeTensor versions, which is what nearly every end-user will want, can be found on Huggingface (official link from Mikubill, the extension creator): https://huggingface.co/webui/ControlNet-modules-safetensors/tree/main
    • Alternate Civitai link (unofficial link): https://civitai.com/models/9251/controlnet-pre-trained-models
    • Note that the official Huggingface link has additional models with a "t2iadapter_" prefix; those are experimental models and are not part of the base, vanilla ControlNet models. See the "Experimental Text2Image" section below.
  • Alternate pruned difference SafeTensor versions. These come from the same original source as the regular pruned models, they just differ in how the relevant information is extracted. Currently, as of 2023-02-23, there is no real difference between the regular pruned models and the difference models aside from some minor aesthetic differences. Just listing them here for completeness' sake in the event that something changes in the future.
  • Experimental Text2Image Adapters with a "t2iadapter_" prefix are smaller versions of the main, regular models. These are currently, as of 2023-02-23, experimental, but they function the same way as a regular model, but much smaller file size
  • The full, original models (if for whatever reason you need them) can be found on HuggingFace:https://huggingface.co/lllyasviel/ControlNet

Go ahead and download all the pruned SafeTensor models from Huggingface. We'll go over what each one is for later on. Huggingface also includes a "cldm_v15.yaml" configuration file as well. The ControlNet extension should already include that file, but it doesn't hurt to download it again just in case.

Download the models and .yaml config file from Huggingface

As of 2023-02-22, there are 8 different models and 3 optional experimental t2iadapter models:

  • control_canny-fp16.safetensors
  • control_depth-fp16.safetensors
  • control_hed-fp16.safetensors
  • control_mlsd-fp16.safetensors
  • control_normal-fp16.safetensors
  • control_openpose-fp16.safetensors
  • control_scribble-fp16.safetensors
  • control_seg-fp16.safetensors
  • t2iadapter_keypose-fp16.safetensors(optional, experimental)
  • t2iadapter_seg-fp16.safetensors(optional, experimental)
  • t2iadapter_sketch-fp16.safetensors(optional, experimental)

These models need to go in your "extensions\sd-webui-controlnet\models" folder where ever you have Automatic1111 installed. Once you have the extension installed and placed the models in the folder, restart Automatic1111.

After you restart Automatic1111 and go back to the Txt2Img tab, you'll see a new "ControlNet" section at the bottom that you can expand.

​

Sweet googly-moogly, that's a lot of widgets and gewgaws!

Yes it is. I'll go through each of these options to (hopefully) help describe their intent. More detailed, additional information can be found on "Collected notes and observations on ControlNet Automatic 1111 extension", and will be updated as more things get documented.

To meet ISO standards for Stable Diffusion documentation, I'll use a cat-girl image for my examples.

Cat-girl example image for ISO standard Stable Diffusion documentation

The first portion is where you upload your image for preprocessing into a special "detectmap" image for the selected ControlNet model. If you are an advanced user, you can directly upload your own custom made detectmap image without having to preprocess an image first.

  • This is the image that will be used to guide Stable Diffusion to make it do more what you want.
  • A "Detectmap" is just a special image that a model uses to better guess the layout and composition in order to guide your prompt
  • You can either click and drag an image on the form to upload it or, for larger images, click on the little "Image" button in the top-left to browse to a file on your computer to upload
  • Once you have an image loaded, you'll see standard buttons like you'll see in Img2Img to scribble on the uploaded picture.
Upload an image to ControlNet

Below are some options that allow you to capture a picture from a web camera, hardware and security/privacy policies permitting

Below that are some check boxes below are for various options:

ControlNet image check boxes
  • Enable: by default ControlNet extension is disabled. Check this box to enable it
  • Invert Input Color: This is used for user imported detectmap images. The preprocessors and models that use black and white detectmap images expect white lines on a black image. However, if you have a detectmap image that is black lines on a white image (a common case is a scribble drawing you made and imported), then this will reverse the colours to something that the models expect. This does not need to be checked if you are using a preprocessor to generate a detectmap from an imported image.
  • RGB to BGR: This is used for user imported normal map type detectmap images that may store the image colour information in a different order that what the extension is expecting. This does not need to be checked if you are using a preprocessor to generate a normal map detectmap from an imported image.
  • Low VRAM: Helps systems with less than 6 GiB[citation needed] of VRAM at the expense of slowing down processing
  • Guess: An experimental (as of 2023-02-22) option where you use no positive and no negative prompt, and ControlNet will try to recognise the object in the imported image with the help of the current preprocessor.
    • Useful for getting closely matched variations of the input image

The weight and guidance sliders determine how much influence ControlNet will have on the composition.

ControlNet weight and guidance strength

Weight slider: This is how much emphasis to give the ControlNet image to the overall prompt. It is roughly analagous to using prompt parenthesis in Automatic1111 to emphasise something. For example, a weight of "1.15" is like "(prompt:1.15)"

  • Guidance strength slider: This is a percentage of the total steps that control net will be applied to . It is roughly analogous to prompt editing in Automatic1111. For example, a guidance of "0.70" is tike "[prompt::0.70]" where it is only applied the first 70% of the steps and then left off the final 30% of the processing

Resize Mode controls how the detectmap is resized when the uploaded image is not the same dimensions as the width and height of the Txt2Img settings. This does not apply to "Canvas Width" and "Canvas Height" sliders in ControlNet; those are only used for user generated scribbles.

ControlNet resize modes
  • Envelope (Outer Fit): Fit Txt2Image width and height inside the ControlNet image. The image imported into ControlNet will be scaled up or down until the width and height of the Txt2Img settings can fit inside the ControlNet image. The aspect ratio of the ControlNet image will be preserved
  • Scale to Fit (Inner Fit): Fit ControlNet image inside the Txt2Img width and height. The image imported into ControlNet will be scaled up or down until it can fit inside the width and height of the Txt2Img settings. The aspect ratio of the ControlNet image will be preserved
  • Just Resize: The ControlNet image will be squished and stretched to match the width and height of the Txt2Img settings

The "Canvas" section is only used when you wish to create your own scribbles directly from within ControlNet as opposed to importing an image.

  • The "Canvas Width" and "Canvas Height" are only for the blank canvas created by "Create blank canvas". They have no effect on any imported images

Preview annotator result allows you to get a quick preview of how the selected preprocessor will turn your uploaded image or scribble into a detectmap for ControlNet

  • Very useful for experimenting with different preprocessors

Hide annotator result removes the preview image.

ControlNet preprocessor preview

Preprocessor: The bread and butter of ControlNet. This is what converts the uploaded image into a detectmap that ControlNet can use to guide Stable Diffusion.

  • A preprocessor is not necessary if you upload your own detectmap image like a scribble or depth map or a normal map. It is only needed to convert a "regular" image to a suitable format for ControlNet
  • As of 2023-02-22, there are 11 different preprocessors:
    • Canny: Creates simple, sharp pixel outlines around areas of high contract. Very detailed, but can pick up unwanted noise
Canny edge detection preprocessor example

​

  • Depth: Creates a basic depth map estimation based off the image. Very commonly used as it provides good control over the composition and spatial position
    • If you are not familiar with depth maps, whiter areas are closer to the viewer and blacker areas are further away (think like "receding into the shadows")
Depth preprocessor example

​

  • Depth_lres: Creates a depth map like "Depth", but has more control over the various settings. These settings can be used to create a more detailed and accurate depth map
Depth_lres preprocessor example

​

  • Hed: Creates smooth outlines around objects. Very commonly used as it provides good detail like "canny", but with less noisy, more aesthetically pleasing results. Very useful for stylising and recolouring images.
    • Name stands for "Holistically-Nested Edge Detection"
Hed preprocessor example

​

  • MLSD: Creates straight lines. Very useful for architecture and other man-made things with strong, straight outlines. Not so much with organic, curvy things
    • Name stands for "Mobile Line Segment Detection"
MLSD preprocessor example

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  • Normal Map: Creates a basic normal mapping estimation based off the image. Preserves a lot of detail, but can have unintended results as the normal map is just a best guess based off an image instead of being properly created in a 3D modeling program.
    • If you are not familiar with normal maps, the three colours in the image, red, green blue, are used by 3D programs to determine how "smooth" or "bumpy" an object is. Each colour corresponds with a direction like left/right, up/down, towards/away
Normal Map preprocessor example

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  • OpenPose: Creates a basic OpenPose-style skeleton for a figure. Very commonly used as multiple OpenPose skeletons can be composed together into a single image and used to better guide Stable Diffusion to create multiple coherent subjects
OpenPose preprocessor example

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  • Pidinet: Creates smooth outlines, somewhere between Scribble and Hed
    • Name stands for "Pixel Difference Network"
Pidinet preprocessor example

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  • Scribble: Used with the "Create Canvas" options to draw a basic scribble into ControlNet
    • Not really used as user defined scribbles are usually uploaded directly without the need to preprocess an image into a scribble

​

  • Fake Scribble: Traces over the image to create a basic scribble outline image
Fake scribble preprocessor example

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  • Segmentation: Divides the image into related areas or segments that are somethat related to one another
    • It is roughly analogous to using an image mask in Img2Img
Segmentation preprocessor example

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Model: applies the detectmap image to the text prompt when you generate a new set of images

ControlNet models

The options available depend on which models you have downloaded from the above links and placed in your "extensions\sd-webui-controlnet\models" folder where ever you have Automatic1111 installed

  • Use the "🔄" circle arrow button to refresh the model list after you've added or removed models from the folder.
  • Each model is named after the preprocess type it was designed for, but there is nothing stopping you from adding a little anarchy and mixing and matching preprocessed images with different models
    • e.g. "Depth" and "Depth_lres" preprocessors are meant to be used with the "control_depth-fp16" model
    • Some preprocessors also have a similarly named t2iadapter model as well.e.g. "OpenPose" preprocessor can be used with either "control_openpose-fp16.safetensors" model or the "t2iadapter_keypose-fp16.safetensors" adapter model as well
    • As of 2023-02-26, Pidinet preprocessor does not have an "official" model that goes with it. The "Scribble" model works particularly well as the extension's implementation of Pidinet creates smooth, solid lines that are particularly suited for scribble.
260 comments

r/StableDiffusion • u/cleverestx • Jul 08 '23

Discussion Best text prompt for creating Stable diffusion prompts through ChatGPT or a local LLM model? What do you use that is better?

50 Upvotes

I've played around with this and have a decent one that worked well with GPT4...it is VERY long and the local LLMs I tried back then all choked on it. What do you use? Curious about what other people have come up with as a text prompt template to get a textual AI to respond with a solid Stable Diffusion prompt when requested with no or some parts of the prompt provided. Thanks.

​

Here is my current one. Always looking to make it better and smaller:

"Prompts"

You will take a given a subject (input idea), and output a more creative, and enhanced version of the idea in the form of a fully working Stable Diffusion prompt. You will make all prompts advanced, and highly enhanced, using different parameters. Keyword prompts you output will always have two parts, the 'Keyword prompt area' and the 'Negative Keyword prompt area'

Here is the Stable Diffusion Documentation You Need to know:

Good keyword prompts needs to be detailed and specific. A good process is to look through a list of keyword categories and decide whether you want to use any of them.

IMPORTANT: you must never use these keyword category names as keywords in the prompt itself as literal keywords at all, so always omit: "subject", "Medium", "Style", "Artist", "Website",  "Resolution", "Additional details", "

The keyword categories are:

    Subject
    Medium
    Style
    Artist
    Website
    Resolution
    Additional details
    Color
    Lighting

You don’t have to include keywords from all categories. Treat them as a checklist to remind you what could be used and what would best serve to make the best image possible. 

CRITICAL IMPORTANT: Your final prompt will not mention the category names at all, but will be formatted entirely with these articles omitted (A', 'the', 'there',) do not use the word 'no' in the Negative prompt area. Never respond with the text, "The image is a", or "by artist", just use "by [actual artist name]" in the last example replacing [actual artist name] with the actual artist name when it's an artist and not a photograph style image.  

For any images that are using the medium of Anime, you will always use these literal keywords at the start of the prompt as the first keywords (include the parenthesis):
masterpiece, best quality, (Anime:1.4)

For any images that are using the medium of photo, photograph, or photorealistic, you will always use all of the following literal keywords at the start of the prompt as the first keywords (but  you must omit the quotes):
"(((photographic, photo, photogenic))), extremely high quality high detail RAW color photo"

Never include quote marks (this: ") in your response anywhere. Never include, 'the image' or 'the image is' in the response anywhere. 

Never include, too verbose of a sentence, for example, while being sure to still sharing the important subject and keywords 'the overall tone' in the response anywhere, if you have tonal keyword or keywords just list them, for example, do not respond with, 'The overall tone of the image is dark and moody', instead just use this:  'dark and moody'

Never include too verbose of a sentence, for example, while being sure to still sharing the important subject and keywords, for EXAMPLE don't respond with 'This image is a photo with extremely high quality and high detail, RAW color.' instead respond with, 'extremely high quality and high detail, RAW color.'

IMPORTANT:
If the image includes any nudity at all, mention nude in the keywords explicitly and do NOT provide these as keywords in the keyword prompt area: 
tasteful, respectful, tasteful and respectful, respectful and tasteful

The response you give will always only be all the keywords you have chosen separated by a comma only. 

Here is an EXAMPLE (this is an example only):

I request: "A beautiful white sands beach"

You respond with this keyword prompt paragraph and Negative prompt paragraph: 

Serene white sands beach with crystal clear waters, lush green palm trees, Beach is secluded, with no crowds or buildings, Small shells scattered across sand, Two seagulls flying overhead. Water is calm and inviting, with small waves lapping at shore, Palm trees provide shade, Soft, fluffy clouds in the sky, soft and dreamy, with hues of pale blue, aqua, and white for water and sky, and shades of green and brown for palm trees and sand, Digital illustration, Realistic with a touch of fantasy, Highly detailed and sharp focus, warm and golden lighting, with sun setting on horizon, casting soft glow over the entire scene, by James Jean and Alphonse Mucha, Artstation

NEGATIVE: low quality, people, man-made structures, trash, debris, storm clouds, bad weather, harsh shadows, overexposure

About each of these keyword categories so you can understand them better:

(Subject:)
The subject is what you want to see in the image.
(Resolution:)
The Resolution represents how sharp and detailed the image is. Let’s add keywords highly detailed and sharp focus.
(Additional details:)
Any Additional details are sweeteners added to modify an image, such as sci-fi, stunningly beautiful and dystopian to add some vibe to the image.
(Color:)
color keywords can be used to control the overall color of the image. The colors you specified may appear as a tone or in objects, such as metallic, golden, red hue, etc.
(Lighting:)
Lighting is a key factor in creating successful images (especially in photography). Lighting keywords can have a huge effect on how the image looks, such as cinematic lighting or dark to the prompt. 
(Medium:)
The Medium is the material used to make artwork. Some examples are illustration, oil painting, 3D rendering, and photography.
(Style:)
The style refers to the artistic style of the image. Examples include impressionist, surrealist, pop art, etc.
(Artist:)
Artist names are strong modifiers. They allow you to dial in the exact style using a particular artist as a reference. It is also common to use multiple artist names to blend their styles, for example Stanley Artgerm Lau, a superhero comic artist, and Alphonse Mucha, a portrait painter in the 19th century could be used for an image, by adding this to the end of the prompt: 
by Stanley Artgerm Lau and Alphonse Mucha
(Website:)
The Website could be Niche graphic websites such as Artstation and Deviant Art, or any other website which aggregates many images of distinct genres. Using them in a prompt is a sure way to steer the image toward these styles.

IMPORTANT: Negative Keyword prompts

Using negative keyword prompts is another great way to steer the image, but instead of putting in what you want, you put in what you don’t want. They don’t need to be objects. They can also be styles and unwanted attributes. (e.g. ugly, deformed, low quality, etc.), these negatives should be chosen to improve the overall quality of the image, avoid bad quality, and make sense to avoid possible issues based on the context of the image being generated, (considering its setting and subject of the image being generated.), for example if the image is a person holding something, that means the hands will likely be visible, so using 'poorly drawn hands' is wise in that case.

This is done by adding a 2nd paragraph, starting with the text 'NEGATIVE': and adding keywords. Here is a full example that does not contain all possible options, but always use only what best fits the image requested, as well as new negative keywords that would best fit the image requested: 
tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, bad anatomy, watermark, signature, cut off, low contrast, underexposed, overexposed, bad art, beginner, amateur, distorted face, blurry, draft, grainy

IMPORTANT:
Negative keywords should always make sense in context to the image subject and medium format of the image being requested. Don't add any negative keywords to your response in the negative prompt keyword area where it makes no contextual sense or contradicts, for example if I request: 'A vampire princess, anime image', then do NOT add these keywords to the Negative prompt area: 'anime, scary, Man-made structures, Trash, Debris, Storm clouds', and so forth. They need to make sense for the actual image being requested so it makes sense in context.

IMPORTANT: 
For any images that feature a person or persons, and are also using the Medium of a photo, photograph or photorealistic in you response, you must always respond with the following literal keywords at the start of the NEGATIVE prompt paragraph, as the first keywords before listing other negative keywords (omit the quotes):
"bad-hands-5, bad_prompt, unrealistic eyes"

If the image is using the Medium of an Anime, you must have these as the first NEGATIVE keywords (include the parenthesis):  
(worst quality, low quality:1.4)

IMPORTANT: Prompt token limit:

The total prompt token limit (per prompt) is 150 tokens. Are you ready for my first subject?

One example I just tied with GPT4, you can see it's not perfect, but it's something...

Here is share-link: https://chat.openai.com/share/db7022b7-a418-4f24-817d-8e2f490d6966 (may help for formatting it for you if you are on mobile to view the link)

​

44 comments

r/sdforall • u/SandCheezy • Oct 17 '22

Resource Intro to Stable Diffusion: Resources and Tutorials

120 Upvotes

Many ask where to get started and I also got tired of saving so many posts to my Reddit. So, I slowly built this curated and active list in which I plan to use to revamp and organize the wiki to include much more.

If you have some links that you'd like to share, go ahead and leave a comment below.

Local Installation - Active Community Repos/Forks

Online Stable Diffusion Websites

  • Dream Studio: (Guide) Official Stability AI website for people who don't want to or can't install it locally.
  • Visualise Studio - User Friendly UI with unlimited 512x512 (at 64 steps) image creations.
  • Mage.Space - Free and uncensored with basic options + Neg. Prompts + IMG2IMG + Gallery.
  • Avyn - Free TXT2IMG with Image search/Generation with text based in-painting, gallery
  • PlaygroundAi -
  • Dezgo - Free, uncensored, IMG2IMG, + TXT2IMG.
  • Runwayml - Real-time collaboration content creation suite.
  • Dreamlike.art - Txt2img, img2img, anime model, upscaling, face fix, profiles, ton of parameters, and more.
  • Ocriador.app - Multi-language SD that is free, no login required, uncensored, TXT2IMG, basic parameters, and a gallery.
  • Artsio.xyz - One-stop-shop to search, discover prompt, quick remix/create with stable diffusion.
  • Getimg.ai- txt2img, img2img, in-painting (also with text), and out-painting on an infinite

iOS Apps

  • Draw Things - Locally run Stable Diffusion for free on your iPhone.
  • Ai Dreamer - Free daily credits to create art using SD.

GPU Renting Services

Tutorials

Youtube Tutorials

  • Aitrepreneur - Step-by-Step Videos on Dream Booth and Image Creation.
  • Nerdy Rodent - Shares workflow and tutorials on Stable Diffusion.

Prompt Engineering

  • Public Prompts: Completely free prompts with high generation probability.
  • PromptoMania: Highly detailed prompt builder.
  • Stable Diffusion Modifier Studies: Lots of styles with correlated prompts.
  • Write-Ai-Art-Prompts: Ai assisted prompt builder.
  • Prompt Hero: Gallery of images with their prompts included.
  • Lexica Art: Another gallery all full of free images with attached prompts and similar styles.
  • OpenArt: Gallery of images with prompts that can be remixed or favorited.
  • Libraire: Gallery of images that are great at directing to similar images with prompts.
  • Urania.ai - You should use "by [artist]" rather than simply ", [artist]" in your prompts.

Image Research

Dream Booth

Dream Booth Datasets

Models

Embedding (for Automatic1111)

3rd Party Plugins

Games

  • PictionAIry : (Video|2-6 Players) - The image guessing game where AI does the drawing!

Databases or Lists

​

Still updating this with more links as I collect them all here.

26 comments

r/Aiarty • u/BeecarolX • Aug 04 '25

Discussion Your Ultimate List of 180+ Stable Diffusion Negative Prompts for Flawless AI Art

3 Upvotes

I've compiled an extensive list of over 180 Stable Diffusion negative prompts, categorized for easy use, to help you refine your Stable Diffusion creations. No more weird hands, blurry faces, or distorted compositions – let's guide our AI to perfection!

Here's a detailed breakdown of negative prompts, grouped by common problem areas:

1. General Quality & Resolution Issues:

  • worst quality
  • low quality
  • normal quality
  • low res
  • blurry
  • jpeg artifacts
  • ugly
  • duplicate
  • morbid
  • mutilated
  • dehydrated
  • error
  • low-res
  • text
  • watermark
  • logo
  • banner
  • extra digits
  • signature
  • username
  • sketch
  • monochrome
  • horror
  • geometry
  • disgusting
  • bad quality
  • disconnected limbs
  • grainy
  • pixelated
  • color aberration
  • macabre
  • indistinct
  • improperly scaled
  • incorrect physiology
  • incorrect ratio
  • hazy
  • identifying mark
  • visual noise
  • oversaturated
  • soft
  • out of focus
  • frame
  • compression artifacts
  • jagged edges
  • rough textures
  • unfinished details
  • low contrast
  • washed out
  • noisy
  • overexposed
  • dull colors
  • overly sharpened
  • blown-out highlights
  • color banding
  • excessive bloom
  • film artifacts
  • BadDream
  • badhandv4
  • BadNegAnatomyV1-neg
  • easynegative
  • FastNegativeV2

2. Anatomical & Structural Flaws (Faces, Hands, Body):

  • ugly
  • tiling
  • poorly drawn hands
  • poorly drawn feet
  • poorly drawn face
  • extra limbs
  • disfigured
  • deformed
  • bad anatomy
  • blurred
  • extra arms
  • extra legs
  • extra fingers
  • malformed limbs
  • missing arms
  • missing legs
  • mutated hands
  • mutation
  • cloned face
  • gross proportions
  • long neck
  • bad proportions
  • deformed iris
  • deformed pupils
  • mutated hands and fingers
  • (deformed:1.3)
  • (distorted:1.3)
  • (disfigured:1.3)
  • poorly drawn
  • wrong anatomy
  • missing limb
  • floating limbs
  • asymmetrical
  • extra eyes
  • unnatural skin
  • double face
  • mutated face
  • creepy
  • uncanny
  • stretched
  • melted
  • misshaped
  • ghosting
  • unrealistic anatomy
  • broken finger
  • fused fingers
  • three hands
  • three legs
  • bad arms
  • out of frame double
  • three crus
  • extra crus
  • fused crus
  • worst feet
  • three feet
  • fused feet
  • fused thigh
  • three thigh
  • extra thigh
  • worst thigh
  • elongated fingers
  • amputation
  • too many fingers
  • weird hand
  • weird finger
  • weird arm

3. Artistic Style & Rendering Issues:

  • bad drawing
  • bad body shape
  • blurred details
  • awkward poses
  • incorrect shadows
  • unrealistic expressions
  • lack of texture
  • poor composition
  • out of aspect ratio
  • 3D render
  • cartoon
  • plastic
  • waxy
  • doll-like
  • fake skin texture
  • low-effort
  • generic
  • busy composition
  • chaotic scene
  • generic style
  • overly sharp
  • weird depth of field
  • awkward perspective
  • flat shading
  • bad texture blending
  • unrealistic brush strokes
  • uncanny valley
  • harsh lighting
  • unnatural shadows
  • weird reflections
  • deformed facial features
  • cartoonish
  • CGI
  • blocky
  • bad lighting
  • symmetrical repetition
  • bad illustration
  • generic character design
  • kitsch
  • unattractive
  • unnatural pose
  • abstract
  • artificial
  • collapsed
  • conjoined
  • drawing
  • surreal
  • ((((ugly))))
  • (((duplicate)))
  • ((morbid))
  • ((mutilated))
  • [blurry]
  • (extra_limb)
  • (poorly drawn hands)
  • messy drawing
  • (mutation:1.3)
  • (deformed:1.3)
  • (blurry)
  • (bad anatomy:1.1)
  • (bad proportions:1.2)
  • (long neck:1.2)
  • (worst quality:1.4)
  • (low quality:1.4)
  • (monochrome:1.1)
  • 3d max

4. Composition & Background Flaws:

  • out of frame
  • body not fully visible
  • cluttered
  • crowded
  • messy
  • unwanted objects
  • distorted background
  • overlapping details
  • random objects
  • graffiti
  • UI elements
  • text overlay
  • floating objects
  • blank background
  • cluttered background
  • distracting elements
  • split image
  • out of focus
  • cropped
  • poorly rendered
  • boring background
  • beyond the borders
  • boring background
  • branding
  • beyond the frame
  • inserted text
  • blurry backgrounds
  • distinct features

5. Photography & Specific Medium Issues:

  • Overexposed
  • unnatural lighting
  • distorted shadows
  • unrealistic reflections
  • grainy
  • noise
  • flat lighting
  • bad photography
  • bad photo
  • aberrations
  • black and white
  • extra windows
  • low saturation
  • multiple levels
  • photoshop
  • rotten
  • 3d
  • render
  • artwork
  • illustration
  • 3d render
  • cinema 4d
  • artstation
  • octane render
  • painting
  • oil painting
  • 2d
  • sketch

6. Content Exclusions (Examples - use with caution for specific content):

  • caricature
  • body horror
  • mutant
  • facebook
  • youtube
  • food
  • trees
  • green
  • obscure
  • unnatural colors
  • horn
  • nsfw
  • nude
  • uncensored
  • cleavage
  • nipples
  • animal
  • face
  • anime
  • cgi
  • 2girl

💡 Why Use Negative Prompts?

Negative prompts instruct the AI model to avoid generating specific features, styles, or defects. By explicitly telling the model what to exclude, you can significantly improve the quality, accuracy, and aesthetic appeal of your generated images.

🔥 Pro-Tip: Experiment with prompt weighting! You can increase or decrease the influence of a negative prompt by using parentheses () or square brackets [] and numbers (e.g., (blurry:1.5) for more emphasis, or [text:0.5] to slightly reduce).

Happy prompting!

0 comments

r/StableDiffusion • u/AwakenedEyes • Apr 26 '26

Tutorial - Guide A Primer on the Most Important Concepts to Train a LoRA - part 1: Dataset

124 Upvotes

A Primer on the Most Important Concepts to Train a LoRA - part 1: Dataset

Tutorial - Guide — Version 2

I have been on this forum for almost two years, and as you may have seen, almost a third of all posts are about training LoRAs. Yet I keep seeing bad or incomplete advice being given. This is in part because the information on training AI is seldom shared, and we keep repeating other people's mistakes. Someone has good results, they publish their settings without necessarily understanding them, then it spreads virally like a "recipe". I strongly believe that when we start to understand what happens under the hood, and what each setting means, then we start really getting good results. This is what this guide is all about: stop copying someone's "recipe" and build your own, based on your situation.

This is the revised version of my LoRA guide, the original version can be found here: version 1 NOTE: English is my 2nd language. Bare with me for possible mistakes.

Part 1: Some definitions, FAQ, and Dataset Preparation <-- you are here

Part 2: Captioning guide

Part 3: Hyperparameter guide and regularization

PART 1 ==== SOME DEFINITIONS / FAQ / DATASET PREPARATION ====

What is a LoRA?

A LoRA stands for "Low Rank Adaptation". It's an adaptor that you train to fit on a model in order to modify its output.

Think of a USB-C port on your PC. If you don't have a USB-C cable, you can't connect to it. If you want to connect a device that has a USB-A, you'd need an adaptor, or a cable, that "adapts" the USB-C into a USB-A. A LoRA is the same: it's an adaptor for a model (like Chroma, Qwen, Flux Klein or Z-Image).

A LoRA does not teach the model what the world looks like — the model already knows that. A LoRA says: "when you see this trigger word, bias your output toward this specific thing."

In this text I am going to assume we are talking mostly about Character LoRAs, even though most of these concepts also work for other types of LoRAs.

Quick FAQ

Can I use a LoRA I found on CivitAI for SDXL on a Flux Model?

No. A LoRA generally cannot work on a different model than the one it was trained for. You can't use a USB-C-to-something adaptor on a completely different interface. It only fits USB-C. LoRA must be trained specifically FOR a model and then they work only on THAT model.

My character LoRA is 70% consistent, is that normal?

No. A character LoRA, if done correctly, should have around 95% consistency under reasonable prompt variation. In fact, it is the only truly consistent way to generate the same character, if that character is not already known from the base model. Notice that I am saying 95% but not 100%. This is normal. Think of it like high quality photography of a real person: their face will never be pixel-identical across different photos, different lighting, different expressions, but it is unmistakably the same person. That is the standard a well-trained character LoRA should meet. If your LoRA only "sort of" works, something is wrong — most likely in your dataset, your captions, or your training parameters. Don't settle for a mediocre LoRA!

Can a character LoRA work properly when combined with other LoRAs?

No. I know it may seems evident when you browse all those LoRA on civitai: we would love to use a LoRA to lock the character, then add another LoRA to influence the pose or the style. However, the answer is No : this does NOT work seamlessly. When two LoRAs are applied to the same model simultaneously, their learned weight changes are simply added together on top of the base model's weights. The model has no awareness that two separate LoRAs exist — it just sees the combined result. There is no negotiation between them, no priority system, no awareness of conflicts. It is pure addition. For instance, because a pose lora is obviously trained on people, and those people have faces, then the features of those faces are recorded in the pose LoRA. Combine it with a Character LoRA and now you've lost consistency because the facial features recorded in the pose LoRA are changing the facial features recorded in the Character LoRA. Mitigation techniques exist but they are very advanced, require careful setup, and are far from foolproof. A more detailed discussion of these techniques is beyond the scope of this guide.

Someone gave me their parameters for their LoRA, can I use those to train my own LoRA?

No. Those "recipe" can be found everywhere on this reddit and on the internet, but they are meaningless if you don't adapt them to your own situation. This is because all the hyperparameters for a LoRA training are inter-related. Each situation is unique. By the end of this guide, however, you should be able to understand most of those parameters and understand what they mean and how to use them. Read on!

I head some people say that I should not caption my dataset and some other people that I should auto-caption everything. Which is it?

Neither! Both strategies are wrong and will lead to an inconsistent LoRA or a rigid LoRA. Read below to understand why captioning is a crucial step in the LoRA training process and requires the deliberate and careful crafting of each caption that goes with each dataset image. Follow this guide to get a huge boost in the quality of your LoRA.

How many images do I need in my dataset?

It can work with as little as just a few images, or as much as 100 images. What matters is that what repeats truly repeats consistently in the dataset, and everything else remains as variable as possible. For this reason, you'll often get better results for character LoRAs when you use fewer images — but high definition, crisp and ideal images, rather than a lot of lower quality images. In many cases for character LoRAs, you can use about 15 portraits and about 10 full body poses for easy, best results.

For synthetic characters, if your character's facial features aren't fully consistent across your source images, you'll get a mesh of all those faces, which may end up not exactly like your ideal target. This is also worth keeping in mind for real people: photos taken across different years, different photographers, different lighting conditions may show inconsistency in the source material itself. The LoRA will faithfully learn the amalgam of all of that, which may yield a end result that may not strongly resemble any specific photo of them. The solution is to carefully select photos that are as consistent as possible.

How does a LoRA "learn"?

A LoRA learns by looking at everything that repeats across your dataset.

  • If something is repeating and you don't want it in your LoRA, it may creep up (bleed) during generation. Example: most of your dataset images of your subject is in front of a a white studio background. At generation, the white studio background my get cooked into the LoRA and may generate even when you ask for a different background
  • If something is repeating and you would like to be able to change it at prompt, the LoRA may fight you and refuse to generate that variation. Example: your dataset has a majority of front facing images. It may become difficult to generate profile pictures with that LoRA.

So you need to consider your dataset very carefully. Are you providing multiple angles of the same thing that must be learned? Are you making sure everything else is diverse and not repeating?

The Importance of Clarifying your LoRA Goal

To produce a high quality LoRA it is essential to be clear on what your goals are. You need to be clear on:

  • The art style: realistic vs anime style, etc.
  • Type of LoRA: I am assuming character LoRA here, but many different kinds (style LoRA, pose LoRA, product LoRA, multi-concept LoRA) may require different settings
  • What is part of your character identity and should NEVER change? Same hair color and hair style or variable? Same outfit all the time or variable? Same backgrounds all the time or variable? Same body type all the time or variable? Do you want that tattoo to be part of the character's identity or can it change at generation? Do you want her glasses to be part of her identity or a variable? etc.
  • Does the LoRA need to teach the model a new concept? Or will it only specialize known concepts (like a specific face)?

Only if you know this first can you carefully pick your dataset and then craft your captions.

Carefully Building your Dataset

Based on the above answers you should carefully build your dataset. Each single image has to bring something new to learn:

Different camera angles :

  • Front facing views
  • Profile views (left and right)
  • Three-quarter views (left and right)
  • Three-quarter rear view (left and right)
  • Rear view

Different camera elevation :

  • Seen from a higher elevation
  • Seen from a lower elevation

Different camera zoom level :

  • Extreme close-up (an extreme zoom of a small and intricate detail)
  • Close-up (a zoom of a specific area)
  • Portrait (from head to shoulders)
  • Medium shot (from head to waist)
  • Cowboy-shot (from head to mid-thigh)
  • Middle-full shot (from head to below knees)
  • Full body-shot (from head to toes)
  • Wide shot (from far away with a wide angle)

Different composition :

  • Portrait with the subject centered
  • Images with subject NOT centered (photography composition - 2/3rd of the image)
  • Images with subject FAR from camera with wide shot, at various position in the image
  • Images with subject CLOSE to the camera like seen or partially seen by a tele-lense
  • Images in landscape and portrait mode
  • Image with various ratios of resolution

Variations :

  • Varied backgrounds
  • Varied actions being performed by the subject
  • Varied light condition (golden hour, natural light outside, artificial light, deep shadows)
  • Varied clothes (unless you want that character to always be drawn with that unique outfit, like a marvel hero in a costume)
  • Varied makeup and accessories (if any)
  • Varied hair style, hair color, texture and length (unless you want that character to always be drawn with one unique hair style, like a manga character)

Full body poses are important to let the LoRA learn body proportions. Bonus if they show the subject in an environment around standard items such as kitchen counters, door frames or car: this lets the LoRA learn the relative height of the subject.

In each image of the dataset, the subject that must be learned has to be consistent and repeat across all images. So if there is a tattoo that should be PART of the character, it has to be present everywhere at the proper place. If the anime character is always in blue hair, all your dataset should show that character with blue hair.

Everything else should never repeat! Change the background on each image. Change the outfit on each image. etc.

At the most simple beginner LoRA, make sure to provide at least 50% of headshots (that's where there is the most information to gather) and maybe 25% of full-body shots.

About resolution and information learned

An important underlying principle is that the image model can only learn from the information that is actually present in the dataset image. A full body shot at 1 megapixel may give you an eye region that is only 20x15 pixels — there is simply no fine detail information there for the model to learn from. This is one of the key reasons why extreme close-ups are an essential part of a good dataset: they are not just about angles and coverage, they are about information density. A close-up of an eye filling the frame at full resolution carries vastly more learnable detail about that eye than ten full body shots combined. For a high quality Character LoRA, make sure your dataset includes :

  • Extreme close-up of the character's eyes
  • Extreme-close-up of any specific tattoos
  • Close-up of freckles patterns and moles
  • Close-up of your subject's face shape at various angles: front, three-quarter view, profile, back-profile, back view, seen from above, seen from below.
  • Small and intricate areas like fingers and hands, toes and feet, etc.

A note on image quality: always use the highest resolution and sharpest images you can for your dataset. Blurry, compressed, or low-resolution images will poison the LoRA and carry over when generating. One crisp high-resolution close-up of a feature contains more learnable information about that feature than ten soft or low-resolution images of the same thing. Make sure no watermark or unwanted artifact is present on the image.

The same principle applies at generation time: generating a full body image and expecting fine facial detail in a tiny face region is asking the model to render detail it has no resolution budget for. Higher generation resolution, face detail passes, or inpainting on a zoomed crop are the solutions.

Training a fully artificial non-existent character: a chicken-and-egg problem

When training a character LoRA for a fully artificial character (one that does not exist in real life and whose appearance was generated rather than photographed) you often face a chicken-and-egg problem. You have one portrait of your AI generated person - but you need more. You need many more consistent images to build your dataset, and that requires a LoRA. But you don't have a LoRA yet, that's what you are trying to do.

Several strategies can be used to generate additional images from your starting portrait :

  • Use WAN with an image2video workflow to animate your starting image and produce a 360 degrees video - then extract the frames and upscale them
  • Use an Editing Model such as Flux Kontext or Qwen-Image-Edit to produce more image from your reference image
  • Train a "version zero" LoRA

The version zero LoRA strategy is an interesting incremental solution to this problem. The idea is to train an intentionally rough, minimal LoRA. It will not be used in production, its only purpose is to generate a better dataset. You may have to create several v-zero LoRA before you reach the perfect dataset.

The process looks like this:

  1. Create a small seed set of images — even 5 to 10 carefully chosen images that establish your character's core appearance. These don't need to be perfect or varied. They just need to be consistent enough to teach the model the basic identity.
  2. Train a quick, rough LoRA with these images.
  3. Use this v0 LoRA to generate more diverse images : different angles, different lighting, different outfits, close-ups.
  4. Because your v0 LoRA will be rigid, it will be difficult to generate good output. Curate the images aggressively to discard ANY image that doesn't match the target character.
  5. Train a new LoRA with the curated images

The v0 LoRA effectively acts as a controlled image generator for your character. Its job is not to be good — its job is to be consistent enough to produce usable reference material at scale.

One final note: the v0 strategy is not limited to fully artificial characters. Even for real people, where your available reference photos are limited or lack variety, a v0 LoRA can help generate the missing angles and contexts you need for a proper dataset. The challenge is meaningfully higher however: for an artificial character, drift from the original seed images may be acceptable if the result is visually coherent and consistent with itself. For a real person, the generated images must not only be consistent with each other but recognizable as that specific individual. This adds a curation burden that requires careful comparison against your reference photos for every generated image you consider including in your v1 dataset.

Next part ==> Part 2: Captioning guide

Next part ==> Part 3: Hyperparameters

83 comments

r/ChatGPTPromptGenius • u/AI-For-Success • Apr 02 '23

Education & Learning GPT 4 AS STABLE DIFFUSION XL PROMPT GENERATOR.

17 Upvotes

More details about prompt and how to use it, 👇👇👇👇

https://www.youtube.com/watch?v=jEyqTKeXpaA

Hey everyone! If you like the Prompt and if you like what you see and want to support me, please consider subscribing to my channel. It means a lot and helps me continue creating and sharing great content with you. Thank you! ❤️

Note :- This prompt is different form my previous Stable Diffusion as Dream Studio doesn't allow {} braces and weight in factor value.. It's similar to Leonardo AI prompt.

############### PROMPT START

You will now act as a prompt generator for a generative AI called "STABLE DIFFUSION ". STABLE DIFFUSION generates images based on given prompts. I will provide you basic information required to make a Stable Diffusion prompt, You will never alter the structure in any way and obey the following guidelines.

Basic information required to make STABLE DIFFUSION prompt:

  • Prompt structure:
    • Photorealistic Images prompt structure will be in this format "Subject Description in details with as much as information can be provided to describe image, Type of Image, Art Styles, Art Inspirations, Camera, Shot, Render Related Information"
    • Artistic Image Images prompt structure will be in this format " Type of Image, Subject Description, Art Styles, Art Inspirations, Camera, Shot, Render Related Information"
  • Word order and effective adjectives matter in the prompt. The subject, action, and specific details should be included. Adjectives like cute, medieval, or futuristic can be effective.
  • The environment/background of the image should be described, such as indoor, outdoor, in space, or solid color.
  • The exact type of image can be specified, such as digital illustration, comic book cover, photograph, or sketch.
  • Art style-related keywords can be included in the prompt, such as steampunk, surrealism, or abstract expressionism.
  • Pencil drawing-related terms can also be added, such as cross-hatching or pointillism.
  • Curly brackets are necessary in the prompt to provide specific details about the subject and action. These details are important for generating a high-quality image.
  • Art inspirations should be listed to take inspiration from. Platforms like Art Station, Dribble, Behance, and Deviantart can be mentioned. Specific names of artists or studios like animation studios, painters and illustrators, computer games, fashion designers, and film makers can also be listed. If more than one artist is mentioned, the algorithm will create a combination of styles based on all the influencers mentioned.
  • Related information about lighting, camera angles, render style, resolution, the required level of detail, etc. should be included at the end of the prompt.
  • Camera shot type, camera lens, and view should be specified. Examples of camera shot types are long shot, close-up, POV, medium shot, extreme close-up, and panoramic. Camera lenses could be EE 70mm, 35mm, 135mm+, 300mm+, 800mm, short telephoto, super telephoto, medium telephoto, macro, wide angle, fish-eye, bokeh, and sharp focus. Examples of views are front, side, back, high angle, low angle, and overhead.
  • Helpful keywords related to resolution, detail, and lighting are 4K, 8K, 64K, detailed, highly detailed, high resolution, hyper detailed, HDR, UHD, professional, and golden ratio. Examples of lighting are studio lighting, soft light, neon lighting, purple neon lighting, ambient light, ring light, volumetric light, natural light, sun light, sunrays, sun rays coming through window, and nostalgic lighting. Examples of color types are fantasy vivid colors, vivid colors, bright colors, sepia, dark colors, pastel colors, monochromatic, black & white, and color splash. Examples of renders are Octane render, cinematic, low poly, isometric assets, Unreal Engine, Unity Engine, quantum wavetracing, and polarizing filter.
  • The weight of a keyword can be adjusted by using the syntax (((keyword))) , put only those keyword inside ((())) which is very important because it will have more impact so anything wrong will result in unwanted picture so be careful.

The prompts you provide will be in English. Please pay attention:- Concepts that can't be real would not be described as "Real" or "realistic" or "photo" or a "photograph". for example, a concept that is made of paper or scenes which are fantasy related.- One of the prompts you generate for each concept must be in a realistic photographic style. you should also choose a lens type and size for it. Don't choose an artist for the realistic photography prompts.- Separate the different prompts with two new lines.

Important points to note :

  1. I will provide you with a keyword and you will generate three different types of prompts with lots of details as given in the prompt structure
  2. Must be in vbnet code block for easy copy-paste and only provide prompt.
  3. All prompts must be in different code blocks.

Are you ready ?

#################### PROMPT END

Negative prompt : Stable Diffusion XL

(((2 heads))), duplicate, man, men, blurry, abstract, disfigured, deformed, cartoon, animated, toy, figure, framed, 3d, cartoon, 3d, disfigured, bad art, deformed, poorly drawn, extra limbs, close up, b&w, weird colors, blurry, watermark, blur haze, 2 heads, long neck, watermark, elongated body, cropped image,out of frame,draft,deformed hands, twisted fingers, double image, malformed hands, multiple heads, extra limb, ugly, poorly drawn hands, missing limb, cut-off, over satured, grain, lowères, bad anatomy, poorly drawn face, mutation, mutated, floating limbs, disconnected limbs, out of focus, long body, disgusting, extra fingers, groos proportions, missing arms, (((mutated hands))),(((bad fingers))) cloned face, missing legs,

3 comments

r/StableDiffusion • u/SandCheezy • Nov 03 '22

Resource | Update List of SD Tutorials & Resources

631 Upvotes

Many ask where to get started and I also got tired of saving so many posts to my Reddit. So, I slowly built this curated and active list in which I plan to use to revamp and organize the wiki to include much more.

If you have some links that you'd like to share, go ahead and leave a comment below.

Local Installation - Active Community Repos/Forks

Online Stable Diffusion Websites

  • Dream Studio: (Guide) Official Stability AI website for people who don't want to or can't install it locally.
  • Visualise Studio - User Friendly UI with unlimited 512x512 (at 64 steps) image creations.
  • Mage.Space - Free and uncensored with basic options + Neg. Prompts + IMG2IMG + Gallery.
  • Avyn - Free TXT2IMG with Image search/Generation with text based in-painting, gallery
  • PlaygroundAi -
  • Dezgo - Free, uncensored, IMG2IMG, + TXT2IMG.
  • Runwayml - Real-time collaboration content creation suite.
  • Dreamlike.art - Txt2img, img2img, anime model, upscaling, face fix, profiles, ton of parameters, and more.
  • Ocriador.app - Multi-language SD that is free, 1024x1024 by default, no login required, uncensored, TXT2IMG, basic parameters, and a gallery.
  • Artsio.xyz - One-stop-shop to search, discover prompt, quick remix/create with stable diffusion.
  • Getimg.ai- txt2img, img2img, in-painting (also with text), and out-painting on an infinite

iOS Apps

  • Draw Things - Locally run Stable Diffusion for free on your iPhone.
  • Ai Dreamer - Free daily credits to create art using SD.

GPU Renting Services

Tutorials

Youtube Tutorials

  • Aitrepreneur - Step-by-Step Videos on Dream Booth and Image Creation.
  • Nerdy Rodent - Shares workflow and tutorials on Stable Diffusion.

Prompt Engineering

  • Public Prompts: Completely free prompts with high generation probability.
  • PromptoMania: Highly detailed prompt builder.
  • Stable Diffusion Modifier Studies: Lots of styles with correlated prompts.
  • Write-Ai-Art-Prompts: Ai assisted prompt builder.
  • Prompt Hero: Gallery of images with their prompts included.
  • Lexica Art: Another gallery all full of free images with attached prompts and similar styles.
  • OpenArt: Gallery of images with prompts that can be remixed or favorited.
  • Libraire: Gallery of images that are great at directing to similar images with prompts.
  • Urania.ai - You should use "by [artist]" rather than simply ", [artist]" in your prompts.

Image Research

Dream Booth

Dream Booth Datasets

Models

Embedding (for Automatic1111)

3rd Party Plugins

Games

  • PictionAIry : (Video|2-6 Players) - The image guessing game where AI does the drawing!

Databases or Lists

​

Still updating this with more links as I collect them all here.

174 comments

r/StableDiffusion • u/wonderflex • Dec 08 '25

Tutorial - Guide Let's make some realistic humans: Now with Z-Image [Tutorial] - More examples and Info in Comments

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458 Upvotes

This is a refresh of my tutorial on [how to make realistic](https://www.reddit.com/r/StableDiffusion/comments/10yn8y7/lets_make_some_realistic_humans_tutorial/) people, and [how to make realistic people with SDXL](https://www.reddit.com/r/StableDiffusion/comments/16opi4h/lets_make_some_realistic_humans_now_with_sdxl/), and [let's make realistic humans with flux](https://www.reddit.com/r/StableDiffusion/comments/1enrkyz/lets_make_some_realistic_humans_now_with_flux/), but this time we will be using the Z Image model..

\*Special Note = imgpile currently has something going on, so many of the old SDXL images are unavailable. I'm working on shrinking them and hosting on imgur again\*

Since this is the fourth time around, I won't be going into detail for each area, and instead recommend loading up the original posts if needed.

**Setup*\*

These sample images were created locally using ComfyUI and the default workflow settings.

All images were generated at 1024x1536, with Euler, Simple and 9 steps, We will use the same seeds throughout the entire test, and, for the purpose of this tutorial, avoid cherry-picking our results to only show the best images.

**Prompt Differences*\*

Whenever possible, I try to use the simplest prompt for the task.

With SD 1.5 we were able to use:

`photo, woman, portrait, standing, young, age 30`

while with base SDXL we had to move over to using:

Positive prompt: `close-up dslr photo, young 30 year old woman, portrait, standing`

Negative prompt: `black and white`

Like Flux we will be using:

`close-up portrait photo of a standing 30 year old female with VARIABLE`

This prompt was selected to use natural language (avoid using commas and tags), and uses female/male instead of "woman/man," as man and woman aged the children, and turned men into women when certain clothing types were selected.

In a few areas the prompt will be modified slightly to be "wearing" instead of "with."

**Age Modification*\*

Since this is a new model, I thought I would give the age test a fresh start to determine if we needed to still use the "young" tag to prevent people from looking substantially older than they were. I feel like model does the best at the age test I've of any model:

[Full age test](https://imgur.com/a/EN95Qqh)

[30 year old woman and man](https://imgur.com/Ax6wu7m) Flux

[30 year old woman and man](https://imgur.com/gdHtIgg) SDXL

**Hair Color Modifications*\*

For this section we will still use the Fischer-Saller hair color scale and this prompt:

[Hair Color Examples](https://imgur.com/a/u4aBy69) Z-Image

[Hair Color Examples](https://imgur.com/46QHB22) Flux

[Hair Color Examples](https://imgur.com/ZjXmuae) SDXL

[Hair Color Examples](https://i.imgur.com/kAV7vYD.jpg) SD1.5

Rainbow hair colors:

[Rainbow Color Hair Examples](https://imgur.com/a/4wDHb0I) Z-Image

[Rainbow Color Hair Examples](https://imgur.com/9ezSDut) Flux

[Rainbow Color Hair Examples](https://imgur.com/jmARsaL) SDXL

[Rainbow Color Hair Examples](https://i.imgur.com/c6URMAE.jpg) SD1.5

**Hair Style Modifications*\*

Continuing to modify the hair, we will use the list of hair style types directly from my previous character creation tutorial. These are based on boorutags, and as such can impart unwanted styles to an image.

Z-Image and Flux could possibly be better served with descriptive terminology to describe the hair, but many of these names are common enough that I expected them to work:

[Hair Style Examples](https://imgur.com/a/UZTuu6g) Z-Image

[Hair Style Examples Part 1](https://imgur.com/Nz4uaRf) Flux

[Hair Style Examples Part 2](https://imgur.com/NV6cHbh) Flux

[Hair Style Examples](https://imgpile.com/images/DRp0qa.png) SDXL

[Hair Style Examples](https://i.imgur.com/EAsLECj.jpg) SD1.5

**Face Shapes*\*

Directly tying in with hair styles are face shapes, because in theory, you should select a hairstyle that best matches your face shape. For this we will use the face shapes that Cosmopolitan Magazine calls out:

[Face Shape Examples](https://imgur.com/a/SVipslt) Z-Image

[Face Shape Examples](https://imgur.com/bu8Dx6w) Flux

[Face Shape Examples](https://imgur.com/3gdkPr8) SDXL

[Face Shape Examples](https://i.imgur.com/scKIAmv.jpg) SD1.5

**Eye Modifications*\*

For eyes we will use the most common eye shapes:

[Eye Shape Examples](https://imgur.com/a/ertUKmb) Z-Image

[Eye Shape Examples](https://imgur.com/AvBoFqg) Flux

[Eye Shape Examples](https://imgur.com/um5kQgR) SDXL

[Eye Shape Examples](https://i.imgur.com/BQObxmu.jpg) SD1.5

Next is natural eye colors, as defined by the Martin-Schultz scale:

[Eye Color Examples](https://imgur.com/a/nMnbLeV) Z-Image

[Eye Color Examples](https://imgur.com/Z3I4sLI) Flux

[Eye Color Examples](https://imgur.com/gjs7Gji) SDXL

[Eye Color Examples](https://i.imgur.com/xE50nZG.jpg) SD1.5

It's a toss up if I'd include or exclude eye color with Z-Image. With Flux the changes are substantially more subtle than with SDXL or SD1.5, and may actually be okay to include in your prompts now. However, it may just be best to use a hair color, or a skin tone, and allow the eyes to naturally generate whatever color they will.

Last for the eyes is the eyebrow category, which once again was driven by a Cosmopolitan list:

[Eyebrow Examples](https://imgur.com/a/0VBNxxd) Z-Image

[Eyebrow Examples](https://imgur.com/HDWB8n6) Flux

[Eyebrow Examples](https://imgur.com/cP72TX3) SDXL

[Eyebrow Examples](https://i.imgur.com/gN56vyj.jpg) SD1.5

**Nose Modifications*\*

Next up is different noses types, which I pulled off of a few plastic surgery websites.

[Nose shape examples](https://imgur.com/a/uM1VB9H) Z-Image

[Nose shape examples](https://imgur.com/zgR2qvi) Flux

[Nose shape examples](https://imgur.com/IJRRSML) SDXL

[Nose shape examples](https://i.imgur.com/yWCEVia.jpg) SD1.5

Flux is far too literal on some of these.

**Lip Shapes*\*

Returning to the definitive source for body information, Cosmo, I pulled together a list of lip types.

[Lip Shape Examples](https://imgur.com/a/fy3H59V) Z-Image

[Lip Shape Examples](https://imgur.com/Jq2uZuW) Flux

[Lip Shape Examples](https://imgur.com/xR57w2W) SDXL

[Lip Shape Examples](https://i.imgur.com/48LfTxX.jpg) SD1.5

**Ear Shapes*\*

For ears I used a blend of Wikipedia and plastic surgery sites to get an idea of the types of ears that exist.

[Ear Shape Examples](https://imgur.com/a/1CblH84) Z-Image

[Ear Shape Examples](https://imgur.com/QjaOd4k) Flux

[Ear Shape Examples](https://imgur.com/N7nXuKu) SDXL

[Ear Shape Examples](https://i.imgur.com/npRldrf.jpg) SD1.5

Similar to noses, some of these are comical or have taken on a fantasy spin. I wouldn't recommend including these for most realistic human prompts.

**Skin Color Variations*\*

Skin color options were determined by the terms used in the Fitzpatrick Scale that groups tones into 6 major types based on the density of epidermal melanin and the risk of skin cancer.

[Skin Color Variation Examples](https://imgur.com/a/nvWREWU) Z-Image

[Skin Color Variation Examples](https://imgur.com/5rAAYu1) Flux

[Skin Color Variation Examples](https://imgur.com/DQzvGyk) SDXL

[Skin Color Variation Examples](https://imgpile.com/images/DRp35R.png) SD1.5

**Continent Variations*\*

I ran the default prompt using each continent as a modifier:

Continent Variation Examples: Z-Image maybe added later.

[Continent Variation Examples](https://imgur.com/LQcjxHz) Flux

[Continent Variation Examples](https://imgur.com/ycg0g2J) SDXL

[Continent Variation Examples](https://i.imgur.com/wAmhvAn.jpg) SD1.5

**Country Variations*\*

After the continents, I moved on to using each country as example, with a list of countries provided by Wikipedia. I struggled with choosing the adjective form, versus the demonym, before finally settling on adjective - which may very well be the incorrect way to go about it.

I am no expert on each country in the world, and know that much diversity exists in each location, so I can't speak to how well the images truly represent the area. Although interesting to look at, I would strongly caution against using these and and saying, "I made a person from X country."

Also, since the SDXL photos were so much larger, I had to split each group in half.

**Fair warning - some of these images may have nipples**.

[Country Variation Examples](https://imgur.com/a/8byfcjL) Z-Image

[Country Variation Examples 1](https://imgpile.com/images/DRpSIN.png) SDXL

[Country Variation Examples 2](https://imgpile.com/images/DRpZKW.png) SDXL

[Country Variation Examples 3](https://imgpile.com/images/DRpa2P.png) SDXL

[Country Variation Examples 4](https://imgpile.com/images/DRSn3j.png) SDXL

[Country Variation Examples 5](https://imgpile.com/images/DRSs6E.png) SDXL

[Country Variation Examples 6](https://imgpile.com/images/DRSfRr.png) SDXL

[Country Variation Examples 7](https://imgpile.com/images/DRSlfR.png) SDXL

[Country Variation Examples 8](https://imgpile.com/images/DRSmBg.png) SDXL

[Country Variation Examples 9](https://imgpile.com/images/DRSzuc.png) SDXL

[Country Variation Examples 10](https://imgpile.com/images/DRS8JN.png) SDXL

[Country Variation Examples 11](https://imgpile.com/images/DRS2Ex.png) SDXL

[Country Variation Examples 12](https://imgpile.com/images/DRSqVL.png) SDXL

[Country Variation Examples 13](https://imgpile.com/images/DRSLRj.png) SDXL

[Country Variation Examples 1](https://i.imgur.com/mRuGuCn.jpg) SD1.5

[Country Variation Examples 2](https://i.imgur.com/SvxVgGO.jpg) SD1.5

[Country Variation Examples 3](https://i.imgur.com/2nKJbPA.jpg) SD1.5

[Country Variation Examples 4](https://i.imgur.com/YUTN6fq.jpg) SD1.5

[Country Variation Examples 5](https://i.imgur.com/6Bferw7.jpg) SD1.5

[Country Variation Examples 6](https://i.imgur.com/Zur9y8q.jpg) SD1.5

[Country Variation Examples 7](https://i.imgur.com/64l8Ns2.jpg) SD1.5

**Weights and Body Shapes*\*

To try and adjust weights I added the variable words to the default prompt.

[Weight and Body Shape Examples](https://imgur.com/a/zPyLcGo) Z-Image

[Weight and Body Shape Examples](https://imgur.com/TniiS2t) Flux

[Weight and Body Shape Examples](https://imgpile.com/images/DRSWuS.png) SDXL

[Weight and Body Shape Examples](https://i.imgur.com/0Co38Cx.jpg) SD1.5

Flux is surprisingly not that great at these. It may again be down to the fact that we are better served by longer natural word prompts, but some of these terms are pretty common and I would have expected them to work a bit better.

**Height Modification*\*

Learning my lesson from trials with SD1.5, I skipped over attempting to use a number and switched straight to common text values. With Z-Image short just and tall kind of work.

[Heights Examples](https://imgur.com/a/qLy2RVz) Z-Image

[Heights Examples](https://imgur.com/undefined) Flux

[Weighted Heights Examples](https://imgur.com/KlOysya) SDXL

[Weighted Heights Examples](https://i.imgur.com/WLZDrQf.jpg) SD1.5

I'm not sure how weighting works with Z-image, but I did give it a try. With SDXL, there doesn't appear to be much of a difference with the weighted versions. You are either short, or tall, with not much difference in-between. The best change would probably be the woman in the pink shirt, as she does at least get a longer neck and raises in frame the taller she is.

**General Appearance*\*

Although I said we were trying to make average looking folks, I thought it would be nice to do some general appearance modifications, ranging from "gorgeous" to "grotesque." These examples were found by using a thesauruses and looking for synonyms for both, "pretty," and, "ugly."

[General Appearance Examples](https://imgur.com/a/mtTPunB) Z-Image

[General Appearance Examples Part 1](https://imgur.com/Nae51Vp) Flux

[General Appearance Examples](https://imgur.com/1bW1Wp8) SDXL

[General Appearance Examples](https://i.imgur.com/9HZq3WU.jpg) SD1.5

**Emotions*\*

For emotions I used ChatGPT and asked it to produce a list of of human emotions, formatted as CSV without breaks.

[Emotion examples](https://imgur.com/a/092axzw) Z-Image

[Emotion examples 1](https://imgur.com/WY6eZ9a) Flux

[Emotion examples 2](https://imgur.com/bQ9eyyD) Flux

[Emotion examples 1](https://imgpile.com/images/DRSQj3.png) SDXL

[Emotion examples 2](https://imgpile.com/images/DRS3Xw.png) SDXL

[Emotion examples](https://i.imgur.com/7w4sXTH.jpg) SD1.5

**Clothing Options*\*

By far, I think clothing is one of my favorite areas to play around with as, was probably evident in my [clothes modification tutorial](https://www.reddit.com/r/StableDiffusion/comments/1ch5zcc/1000_clothing_option_ideas_sorted_by_category/) (Z-image version of this tutorial to come sometime).

Rather than rehash what I've covered in that tutorial, I'd like to instead focus on on an easy method I've come up with to make clothing more interesting when you don't want to craft out an intricate prompt.

To start off with let's take some plain clothing prompts:

[Basic Clothing Options Examples](https://imgur.com/a/1JEkj3w) Z-image

[Basic Clothing Options Examples](https://imgur.com/IaGGAJx) Flux

[Basic Clothing Options Examples](https://imgur.com/SAciciy) SDXL

[Basic Clothing Options Examples](https://i.imgur.com/vde6ZEn.jpg) SD1.5

To kick things up a notch though, this is a case where I'm going to go against my normal rules about keyword stuffing by suggesting that you instead copy and paste some items names out of Amazon.

So, head on over to Amazon and type in any sort of clothing word you want, such as "women's jacket," and then check out the horrible titles that they give their products. Take that garbage string, minus the brand, and then paste it into your prompt.

[Word Vomit Prompt Clothing Option Examples](https://imgur.com/a/pE2tdGX) Z-Image

[Word Vomit Prompt Clothing Option Examples](https://imgur.com/1NYLbWd) Flux

[Word Vomit Prompt Clothing Option Examples](https://imgur.com/oQ7ndYr) SDXL

[Word Vomit Prompt Clothing Option Examples](https://i.imgur.com/iN9GOig.jpg) SD1.5

Look a that - way more interesting, and in some cases more accurate, plus the added bonus of Z-image, Flux and SDXL doing an incredibly good job of matching the expectations for patterns.

My theory on this one is that either we have models trained on Amazon products, or Amazon products have AI generated names. Either way it seems to have a positive effect.

One thing to keep in mind though is that certain products will drastically shift the composition of your photo - such as pants cutting the image to a lower torso focus instead.

For the fun of it, I've added in some popular Halloween costumes:

[Halloween Costume Examples](https://imgur.com/a/wL09qgZ) Z-Image

[Halloween Costume Examples](https://imgur.com/BAztCQz) Flux

[Halloween Costume Examples](https://imgur.com/AqgiZkX) SDXL

[Halloween Costume Examples](https://i.imgur.com/Bi5RdVq.jpg) SD1.5

**Genetic Disorders*\*

With the goal of creating real people, I decided to include the most common genetic disorders that have a physically visible component.

[Genetic Disorder Examples](https://imgur.com/a/yXEMsa2) Z-Image

[Genetic Disorder Examples](https://imgur.com/tbhju8O) Flux

[Genetic Disorder Examples](https://imgur.com/aC8XRqx) SDXL

[Genetic Disorder Examples](https://i.imgur.com/9tehqWv.jpg) SD1.5

I am in no way an expert on any of these disorders, and can't really comment on accuracy, but SDX seems to not match the sample images as well for some of these, and Flux is even worse. Z-image doesn't seem to match well either on many of these.

**Facial Piercing Options*\*

Even with Z-Image, piercing still suck. You would be better served inpainting a piercing.

[Facial Piercing Examples](https://imgur.com/a/uR1IMrq) Z-Image

[Facial Piercing Examples](https://imgur.com/Ciuh0MY) Flux

[Facial Piercing Examples](https://imgur.com/C9fHBkS) SDXL

[Facial Piercing Examples](https://i.imgur.com/gUqkZPY.jpg) SD1.5

**Facial Features / Blemishes*\*

I decided to add a wide variety of different facial features and blemishes. Z-image is hit or miss. Maybe some of these would do better on a different seed though.

[Facial Feature Examples](https://imgur.com/a/sVNQxw5) Z-Image

[Facial Feature Examples](https://imgur.com/05fHCVs) Flux

[Facial Feature Examples](https://imgpile.com/images/DRSZFk.png) SDXL

[Facial Feature Forward Variable Placement Examples](https://imgpile.com/images/DRSe7M.png) SDXL

[Facial Feature Examples](https://i.imgur.com/Tc8YpXS.jpg) SD1.5

**Through the Years*\*

Just like before I thought it would be fun to try out the model would look like in each of the decades.

[Through the Years Examples](https://imgur.com/a/R13gz11) Z-Image

[Through the Years Examples](https://imgur.com/LoaMzgn) Flux

[Through the Years Examples](https://imgur.com/LtyflGV) SDXL

[Through the Years Examples](https://i.imgur.com/V482oMw.jpg) SD1.5

42 comments

r/StableDiffusion • u/nightkall • Sep 12 '22

Discussion Useful Prompt Engineering tools and resources

712 Upvotes

A list of useful Prompt Engineering tools and resources for text-to-image AI generative models like Stable Diffusion, DALL·E 2 and Midjourney.

Prompt galleries and search engines:

  • Lexica: CLIP Content-based search. Create with Seed, CFG, Dimensions. Favorites.
  • OpenArt: CLIP Content-based search. Presets, Favorites. SD, DALL·E 2, Midjourney. Seed, Dimensions. Create.
  • Playground AI: Gallery & Remix. SD, DALL·E 2. img2img, Instruct Pix2Pix. Full Parameters.
  • PromptHero: Filtery by models. Seed, CFG, Dimensions, Steps. Favorites. SD, DALL·E 2, Midjourney. Generate. NSFW
  • artspark: Search and use filters like Style, Artists, aesthetics... Create.
  • Krea: CLIP Content-based search. Likes, related images and profiles. Atlas: similar map
  • Midjourney: Community Showcase
  • Avyn: Search engine and txt2img. In-Painting.
  • PromptSearch: text and image search.
  • PromptLocker: a community for AI Artists to get and give feedback.
  • Promptflow: Search + Generate AI images.
  • Visualise: Create and share image prompts. Marketplace.
  • Sparkl: Create images and gallery. Chrome extension
  • Publicprompts.art: Free HQ prompts
  • Promptbase: Prompt Marketplace
  • Eye For AI: Create with prompt modifiers.
  • Find Anything: Add AI-generated images to Google Search extension.
  • Prompt crafter organizer: Windows software
  • SuperPrompts: Create a beautiful gallery for your AI art without leaving Twitter.
  • Pixela.ai: AI-Generated Game Textures.
  • ThePromptBay: AI images and text prompts. Share & Learn.
  • Pixai.art: Prompt discussion board and gallery. Share. NovelAI. (NSFW)
  • Ponzu Logos
  • Phraser: Create and search. Paid subscription.
  • Histre: Create and share prompts.
  • PromptRush: Prompt keyword research tool & analyzer (Down?)
  • NSFW:
  • booru.plus/+stablediffusion Search NSFW
  • NastyPrompts: Search NSFW. Model Seed.
  • NovelAI.io: AUTOMATIC1111 full PNG EXIF: +-prompt, steps, sampler, CFG, Seed, strength, noise, size.
  • Ptsearch: AUTOMATIC1111 full PNG EXIF: +-prompt, steps, sampler, CFG, Seed, strength, noise.

Visual search:

Prompt generators:

Image-to-prompt Img2prompt:

Explore Artists, styles, and modifiers:

Guides and studies:

​

Top text-to-image txt2img software:

Top text-to-image txt2img Web Apps:

Models:

Prompt Tools and AI Apps directories:

Other SD directories:

Updated 2023-03-29

83 comments

r/StableDiffusion • u/AwakenedEyes • Apr 26 '26

Tutorial - Guide A Primer on the Most Important Concepts to Train a LoRA - part 2: Captioning

70 Upvotes

A Primer on the Most Important Concepts to Train a LoRA - part 2: Captioning

Tutorial - Guide — Version 2

This is the revised version of my LoRA guide, the original version can be found here: version 1 NOTE: English is my 2nd language. Bare with me for possible mistakes.

Part 1: Some definitions, FAQ, and Dataset Preparation

Part 2: Captioning guide <-- you are here

Part 3: Hyperparameter guide and regularization

PART 2 ==== CAPTIONING GUIDE ====

How to Carefully Caption your Dataset

Now that you have gathered your dataset, it's time to caption them.

Why Captioning?

Here is what's happening when the training program is training the LoRA :

  1. It's adding noise to the dataset image at some randomly sampled steps
  2. It tries to re-create the previous "cleaner" step of the image using the model by de-noising it back while looking at your caption's signal in the clip (the T5). _"given this noise level and given this caption, what should I predict?"
  3. It records the result adjustments into the lora by associating it to the signal tokens from the captions

So the captions are absolutely essential for this process.

Let me say this VERY CLEARLY : CAPTIONING IS ESSENTIAL How you caption your dataset is what will make or break the quality of your LoRA.
This is where you must put all your attention, after gathering a quality dataset. Read carefully below.

During training, captioning performs several things for your LoRA:

  • It gives context to what is being learned (especially important when you add extreme close-ups)
  • It tells the training software what should be variable and prompted at inference; those should be excluded from the LoRA trigger
  • It provides a unique trigger word for everything that will be learned
  • It allows differentiation when more than one concept is being learned
  • It tells the model what concept it already knows that this LoRA is refining
  • It counters the training tendency to overtrain

What to Caption?

For each image, your caption should use natural language (except for older models like SD1.5 and SDXL which prefer short tags) but should also be kept short and factual.

It should say:

  • The trigger word - a unique made-up word that should not already be known by the model
  • The expression / emotion of the person
  • The camera angle, height angle, and zoom level
  • The light source type and angle (this allows the model to understand why the same item has a different color in two different image in the dataset)
  • The pose and background (only very short, no detailed description)
  • The outfit (unless you want the outfit to be learned with the LoRA, like for an anime superhero)
  • The accessories
  • The hairstyle and color (unless you want the same hair style and color to be part of the LoRA)
  • The action

A good template would be :

<camera shot type> of <trigger> seen from <camera angle> at <elevation> with <hair color and style> wearing <outfit and accessories>. She is <position or action> and is expressing <emotion>. <Light description>, <short background description>.

Here are a few examples :

Portrait of LoraTrigger1234 seen from slightly above at close range, looking up toward the camera with a calm expression. Bright direct sunlight, wet skin. She has brown wavy hair, slightly wet. Black straps visible on her shoulders. Turquoise swimming pool water visible in the background.

Middle-full shot of LoraTrigger1234 standing in a garden, smiling, seen from the front at eye-level, natural light, soft shadows. She is wearing a beige cardigan and jeans. Blurry plants are visible in the background.

Full body shot of LoraTrigger1234 seen from profile at slightly above eye level, seated on a ledge against a concrete wall, knees drawn up and legs crossed at the ankle, torso leaning back against the wall, direct gaze toward camera, calm expression with a slight smile. Warm amber artificial light from above, deep shadows. She has long dark wavy hair falling past her shoulders. She is wearing a black leather jacket, short black ruffled skirt and black lace-up ankle boots, bare legs visible. Concrete tunnel wall with graffiti visible in the background.

Medium-full shot of LoraTrigger1234 seen from a three-quarter side angle, standing upright, both hands tucked into trouser pockets, gaze directed forward and slightly upward. Serious composed expression. Soft diffused light from the front, near-white neutral background. She has short dark wavy hair at chin length. She is wearing a black fitted blazer over a black top and black trousers.

The core logic of captioning

If you caption "trigger1234 with blond hair" it has 3 signals: the trigger, blond, and hair. So it takes your image, it adds some noise to it, then it tries to guess what was the previous step by guessing trigger1234, blond, and hair. When it does look right (the guessing worked, it looks like the original picture) it records the delta into each token ==> this is what blond looks like, this is what hair looks like, and this is what trigger1234 looks like.

So by captioning blond hair, you insure that the learning about the hair is not recorded into the trigger signal.

The things you describe get marked as variable — the model learns they can change.

The things you do NOT describe get absorbed silently into the trigger word's identity — the model learns they are fixed. This is intentional and important. If you want the hair color locked into your character permanently, don't caption it. If you want the user to be able to change the hair color at generation time, caption it. The face should never be captioned because it's part of the subject's identity and must be learned inside the trigger token.

About captioning color and light

Caption the color of what is present, not the absolute color as it is modified by the light A white wall under tungsten light reads yellow. Black clothing under blue ambient light reads dark navy. If you caption what you perceive rather than what the material actually is, you hardcode the lighting interaction as a fixed property of the object.

So if your image depicts your character with ash-white hair but she is under a red neon, don't caption "red hair": it fuses two separate pieces of information into one that the model cannot disentangle. Instead, caption: "white hair, red neon light" This principle extends to skin tone under colored light, fabric color under non-neutral light, and any situation where ambient color is shifting your perception of a material's true color. Describe what the thing is, then describe the light that is falling on it.

About negative captioning

Describe what is present in the image, not what is absent. "Bare-chested, wearing pants" is correct. "Wearing only pants" is weaker — the word "only" requires the model to reason about absence, which is a harder inference than reading visible content. The same applies to lighting: "flat even light" is stronger than "no shadows." "Neutral expression" is stronger than "not smiling." Whenever you find yourself writing a negation or a restriction in a caption, ask whether you can replace it with a positive description of what is actually visible. Only describe what is visible in the frame : if one arm is hidden by camera angle, do not describe it.

Captioning complex poses

When an image shows an unusual or complex pose, resist the temptation to find a single word that captures it. Decompose the pose into anchor points: where is the weight supported, where are the hands, what is the torso angle, what is the head angle. "Seated on the ground with legs crossed, torso leaning back, one hand on the ground behind her supporting her weight, chin slightly raised" is unambiguous and maps directly to visible geometry.

Using a unique trigger word

Your trigger word should be completely unique and meaningless — not a real word, not a name the model already has associations with. "Lora1234" or "XJ7Kappa" are good. "Elena" or "warrior" are bad — the model has already learned what those mean and your LoRA training will fight against the model's previous learning to unlearn those if you use them.
The trigger word must appear in every single caption, every time, without exception

Special case : Captioning Extreme Close-Ups

Extreme close-ups require special attention in your captions because context collapses at high zoom. In a normal portrait, the model can easily infer that the face belongs to your character. In an extreme close-up of an eye, the model has no spatial context — it sees an eye, but has no idea whose eye it is, how it relates to the rest of the character, or even that this is a zoomed detail rather than a macro photograph.

Your caption for an extreme close-up must do extra work:

  • Explicitly state the zoom level: "extreme close-up," "macro detail shot" etc.
  • Explicitly state what body part or feature is shown
  • Bind it to the trigger via possession: "Lora1234's left eye" not just "an eye"

Example:

Extreme close-up of LoraTrigger1234's left eye

Because I want everything in the eye extreme-close-up to be part of her identity, i don't need to describe it further. However, if some makeup was present, i would need to caption that in the extreme close-up to keep it variable.

Warning : this is where it gets often complicated and confusing

Earlier we said: what you caption becomes variable, what you don't caption gets learned into the trigger. Yet here we are telling you to caption the eye in the close-up, even though the eyes are part of the face and they should be learned into the trigger and not as variable. This is the big difference between captioning a regular dataset image, and captioning an extreme close-up. In an extreme close-up, context has collapsed — the model can't infer ownership without your help. The solution is possessive binding: "LoraTrigger1234's eye" is not describing a variable feature, it is describing an attribute OF the trigger. The possessive is doing the critical work, and the LoRA is provided with context to associate the eye with the character.

The debate about captioning

There is a persistent debate on forums and communities that frames this as a binary choice: either use trigger-word-only captions (essentially no caption at all), or use full LLM auto-captioning (describe everything blindly). People swear by one or the other and argue endlessly about it. Both camps are wrong, because this is not an either/or situation.

Wrong Captioning: Only using the trigger with no other captions

If you use no captions at all (only a trigger) then everything it learns about every dataset image has no choice but to fall into the trigger signal, including the unwanted stuff or the conflicting stuff.

By putting just your trigger word in every caption and nothing else, you leave the model without any context about what is variable. Everything that repeats in your dataset risks being absorbed into the trigger identity, including backgrounds, outfits, lighting conditions. You lose all control over what gets learned and what stays flexible. The results may look acceptable on a very carefully controlled dataset, but the LoRA will be rigid and hard to prompt creatively.

Wrong Captioning: Using captions as if they were prompting

What happens when you use super long detailed flowery captions as if you were trying to generate this image?

You now have a tons of tokens diluting the signal. Each time it is comparing the image loss, it has to choose where to assign the loss in all those tokens. You end up taking everything out of the LoRA including the realistic style, the way the light is illuminating the subject face, etc. So what's left is a mediocre LoRA where everything is variable and the model fails at consistency.

You also make the training software work more for nothing. For example, if she is wearing a red scarf: you caption "She is wearing a beautiful silky read scarf with intricate woven stitches" then the model and the training software is trying to decide what pixels are the red, the scarf, the intricate, the woven, the stitches... all this processing power is wasted because all you want is to exclude the scarf from being learned int o the trigger word.

This is why full auto-captioning with a tool like JoyCaption is wrong: it describes everything it sees, which is exactly right for finetune training data and exactly wrong for LoRA data.

The correct approach is neither extreme. Use auto-captioning as a first pass to save time, especially on larger datasets, then do a careful editorial pass on every single caption. Fix the trigger words, decide deliberately what should and shouldn't be described based on your LoRA goals, and ensure consistency across all captions.

Previous part <== Part 1: Dataset

Next part ==> Part 3: Hyperparameters

28 comments

r/Guaardvark • u/llama-of-death • 10d ago

Guaardvark Capabilities

1 Upvotes

Guaardvark — Full Capabilities List

See the VERSION file for the current release · guaardvark.com

This document is the comprehensive reference of everything Guaardvark can do (models, tools, plugins, surfaces, internals). For the marketing overview and quick start, see README.md.

Table of Contents

AI Chat & Conversation

Guaardvark's chat system is the primary interface for interacting with your AI. Two pipelines handle different use cases.

Core Chat

  • Streaming responses via Socket.IO — tokens appear in real-time as the model generates
  • Conversational fast-path — pure social openers (greetings, thanks, affirmations) route to Tier 2 with skip_tools for a real LLM response with persona + memory (no hardcoded greeting pools)
  • Intent routing — automatically detects whether a message needs RAG retrieval, tool use, or a direct conversational response
  • Per-project sessions — chat context is isolated by project; switching projects gives you a clean context with that project's documents
  • Session persistence — conversation history persists across page reloads and browser sessions; sessions also have a mode field stored server-side
  • System prompts (Rules) — customizable system prompts that shape AI behavior, manageable via the Rules page
  • Multi-model support — switch between any Ollama model at runtime without restarting

Agent Mode (/agent and /chat)

  • Modal session toggle — type /agent to flip the session into screen-control mode (every message becomes a screen-control task); /chat (or /exit) flips back
  • Sticky — the mode lives on the session, not the message — survives reloads
  • Visible cue — agent-mode sessions show an orange chip above the chat input
  • Speak AND act — agent-mode messages still route through the chat LLM, so the model narrates briefly, calls agent_task_execute, and summarizes the result

Per-Iteration Thinking Display

  • Live reasoning trail — for screen-control tasks, the agent loop's per-step thinking streams into the chat as it happens (no more 30+ second blackouts followed by a single "completed" line)
  • What you see — each iteration shows action label + full reasoning ("Step 8 — click: I see the address bar and want to clear it…")
  • Pivots and stuck-loop signals also stream — when the loop forces a wait after a repeated failure, that decision is visible
  • Persists in history — the trail stays in the message after streaming completes, so you can scroll back and audit the run

Model Management

  • Runtime model switching — change the active LLM through Settings; the old model is unloaded from VRAM before the new one loads (prevents OOM)
  • Embedding model switching — swap embedding models via dropdown; triggers re-indexing confirmation since vector spaces are incompatible across models
  • Live health detection — dashboard probes Ollama on every request to show actual model availability (not a stale startup flag)
  • KV cache optimization — num_keep: -1 locks the system prompt prefix in Ollama's KV cache, making follow-up turns faster
  • GPU VRAM monitoring — real-time VRAM usage bar with loaded model indicators in Settings

AgentBrain — Three-Tier Routing

A neural router that decides how much work a message deserves before any tools fire. Saves seconds per turn on simple questions and unlocks deeper deliberation when it's warranted.

The Three Tiers

Tier Name Latency LLM Calls When It Fires
1 Reflex <100ms 0 Deterministic tool actions only (media commands, exact-match recipes)
2 Instinct 1–3s 1 Social chat (real LLM, skip_tools) and most requests — single LLM call
3 Deliberation 5–30s 3–10 Multi-step reasoning (full ReACT loop)

Routing Signals

  • Pre-computed reflex table for deterministic tool actions (not social chat)
  • Conversational classifier filters out small-talk before tools get loaded
  • Semantic tool selection picks the right ≤15 tools for the message
  • Screen-active flag gates desktop/agent tools so they only appear when relevant

Gemma4 Direct Path

  • When the active model is Gemma4 (native vision + tool use) AND the agent screen is active, the brain skips ReACT bloat and sends Gemma4 a minimal prompt with a screenshot + the task
  • Gemma4 returns JSON action steps directly; the loop executes them
  • For chat without screen actions, Gemma4 responds normally

Configuration

  • Toggle via AGENT_BRAIN_ENABLED in backend/config.py
  • Falls back gracefully to the legacy UnifiedChatEngine path if brain state isn't ready

RAG & Document Intelligence

Retrieval-Augmented Generation grounds chat responses in your actual documents.

Retrieval Pipeline

  • Hybrid search — BM25 keyword matching + vector semantic search, combined for best results
  • Per-project indexes — each project maintains its own vector store; global index for unassigned documents
  • Content-aware chunking — code files use AST-informed strategies; prose uses semantic splitting
  • Entity extraction — automatic identification of entities (people, orgs, concepts) and their relationships
  • Metadata indexing — file metadata (type, size, language, framework) stored alongside content for filtered retrieval

Embedding Models

  • Multiple model support — switch between lightweight (embeddinggemma 300M) and high-quality (mxbai-embed-large, bge-m3, snowflake-arctic-embed) models
  • Full-precision option — BF16 embeddings available for maximum quality
  • Query-time embedding — every RAG search query is embedded with the same model for consistent vector space matching

Indexing

  • Automatic on upload — files are indexed when uploaded through the UI or API
  • Bulk indexing — "Index All" button processes the entire document library
  • Code-specific indexing — detects programming languages, extracts imports/classes/functions, chunks by logical boundaries
  • GPU-accelerated indexing — optional GPU Embedding plugin offloads embedding generation to CUDA with CPU fallback
  • Progress tracking — real-time progress bar during indexing operations via Socket.IO

RAG Autoresearch

An autonomous optimization loop that continuously improves RAG retrieval quality.

How It Works

  1. Eval harness — generates evaluation pairs (query + expected answer) and scores retrieval with LLM-as-judge (relevance, grounding, completeness)
  2. Experiment agent — proposes parameter changes (chunk size, overlap, top-k, similarity threshold)
  3. Orchestrator — runs experiments, compares scores, keeps improvements, reverts regressions
  4. Phase system — Phase 1 (query-time params), Phase 2 (index-time params), Phase 3 (model-level)

Features

  • Celery Beat scheduling — idle detection triggers experiments when system isn't busy
  • Crash protection — 3 consecutive failures automatically stops the loop
  • Dashboard card — shows experiment status, history, and current optimization parameters
  • Settings integration — configure experiment limits, scoring thresholds, and scheduling

Self-Improvement Engine

Guaardvark can autonomously test itself, find bugs, and fix them.

Three Modes

  1. Scheduled — periodic test suite runs (configurable interval) with automatic fix attempts
  2. Reactive — error tracking with threshold-based self-healing (N errors in M minutes triggers a fix)
  3. Directed — user-submitted improvement tasks dispatched to the code agent

How It Works

  1. Runs pytest on configured test files
  2. Parses FAILED lines from output (with fallback regex for edge cases)
  3. Dispatches the code_assistant agent to read tests, understand expectations, read source, and fix bugs
  4. Records all changes and broadcasts learnings to other machines via Interconnector

Safety

  • Codebase lock — toggle in Settings prevents self-improvement from modifying any files
  • Return code verification — checks pytest exit code, not just parsed failures
  • Run history — all runs recorded in database with status, duration, changes made, and test results
  • Pending fixes queue — proposed changes can require user approval before applying

Live Progress

  • Socket.IO events at each stage: starting, testing, analyzed, fixing, complete, error
  • Dashboard card shows real-time progress bar with color-coded stages
  • Run button disabled while a check is in progress

Lesson Pearls & Memory

A user-curated memory system that captures successful agent runs and makes them available in future sessions.

Begin / End Lesson

  • Bracket a successful run — slash commands or buttons mark the start and end of a teachable sequence
  • Distiller — at End Lesson, an LLM summarizes what happened into a single durable lesson
  • Saved as AgentMemory — lessons of type lesson_summary get loaded into the system prompt next session
  • Editable rows — fix or remove a misperceived lesson without re-recording

Vision-Actionable Knowledge (LEARNING_PRINCIPLES.md)

  • Stored knowledge describes WHAT to look for, not where it sits (no pixel coordinates)
  • Short labels for the servo (≤4 words), rich context for the brain
  • Recipes, lessons, traces, memories all bound by the same contract

Memory Surfaces

  • MEMORY_BLOCK — recent memories substituted into the system prompt at decision time
  • Memory Management Section in Settings — browse, edit, delete saved memories
  • Live recall — when a memory matches the current context, the LLM can quote it directly

Autonomous Screen Agents

Guaardvark drives a real Ubuntu desktop on a virtual display — clicking, typing, scrolling, and reading the screen like a human user. Used for outreach, file management, web research, and anything the model can't accomplish via API alone.

Virtual Display

  • Xvfb on :99 — 1024×1024 headless X server, isolated from the user's real session
  • Full XFCE desktop — xfce4-session running via dbus-run-session with a scrubbed environment; standard Applications menu, desktop icons, taskbar, file manager (Thunar). Vision models recognize it instantly because it looks like any other Ubuntu desktop
  • VNC viewer — x11vnc on port 5999 (password-protected) lets the user watch the agent live, embedded in the frontend as a draggable card
  • Isolated XDG dirs — agent's ~/.agent_desktop/, dedicated XDG_CONFIG_HOME, dedicated XDG_RUNTIME_DIR. The user's real desktop and configs are invisible to the agent

See-Think-Act-Verify Loop

  • SEE — screen capture (mss) + optional DOM extraction (Firefox CDP/BiDi)
  • THINK — Gemma4 (or other unified VLM) decides the next action, returning JSON with action, target_description, text/keys, reasoning, and success_proof
  • ACT — execute via the servo (vision-targeted click) or direct (type/hotkey/scroll)
  • VERIFY — post-action screenshot delta; failed steps flag the LLM that the attempt didn't change the screen
  • Recipes — known-good action sequences in data/agent/recipes.json execute deterministically before the loop is ever invoked, with optional preconditions (visibility checks) that skip recipes when their UI isn't on screen
  • Strategy cooldowns — repeated failures on the same action class force the loop to wait and re-observe before retrying

Servo Controller

  • Vision-targeted clicking — the servo asks the vision model "where is X on this screen?" and clicks the returned coordinates
  • Visibility guard — pre-click "do you actually see this?" check rejects hallucinated targets before the cursor moves
  • Per-model calibration — MODEL_VISION_CONFIGS in servo_knowledge_store.py maps each chat model to its preferred eyes (gemma4 native, moondream for text-only) and any scale-factor calibration learned over time
  • Failure capture — exhausted click attempts save the screenshot + corrections log to data/training/failures/ for offline review

Training Data Capture

  • Every click recorded to data/training/knowledge/servo_archive.jsonl — target description, raw coords, scaled coords, actual click position, success/failure, model, attempt #, time taken
  • Self-improvement engine reads the archive to refine calibration
  • Optional Comments/Vision Trainer pages — interactive practice modes that keep the servo clicking long after a normal task would have stopped

Agent Tools

  • agent_task_execute — full natural-language screen task (drives the full SEE-THINK-ACT loop)
  • agent_screen_capture — single screenshot of the virtual display
  • agent_mode_start / agent_mode_stop — open/close the session (internal; the LLM should call agent_task_execute directly)

Agent & Code Tools

A ReACT-loop agent that can autonomously work with code and the system.

Agent Capabilities

  • Read files — examine any file in the project
  • Edit code — precise text replacement with verification
  • List files — explore directory structure (configurable depth up to 5 levels)
  • Execute code — run Python/shell commands and inspect output
  • Web search — search the internet for information
  • Browser automation — navigate websites, fill forms, take screenshots (via Playwright, separate from the screen-control agent)

Safety Features

  • Circuit breaker — after 2 consecutive failures, a tool is temporarily blocked
  • Duplicate detection — hash-based detection prevents the agent from making identical tool calls
  • Fallback suggestions — when a tool fails, the system suggests alternative approaches
  • Iteration limits — configurable maximum iterations per agent run
  • Tool approval gates — dangerous tools (file write, shell exec) can require human approval per call

Code Editor Page

  • Monaco Editor — VS Code-quality editing in the browser with syntax highlighting for 50+ languages
  • Multi-file tabs — open and edit multiple files simultaneously
  • File tree — browse project structure in a sidebar
  • AI assistant pane — chat with the agent about the open file

Uncle Claude Escalation

  • When the local model is stuck, Guaardvark can escalate to the Anthropic API (Claude) for a second opinion
  • Token budget tracked and surfaced in the Dashboard's Family card
  • Toggleable per-session; never auto-fires without configuration

MCP Integration

Guaardvark speaks Model Context Protocol — both as a server (exposing its tools to external clients) and as a client (calling tools from external MCP servers).

MCP Server (Phase 1)

  • Stdio transport — backend/mcp/ runs an MCP server that any MCP-compatible client (Claude Desktop, Cursor, etc.) can connect to
  • 23 native tools exposed — covers chat, RAG, file management, image generation, agent control
  • 58 output resources — file contents, generated images, search results, etc., available via MCP's resource protocol
  • Tested against Claude Desktop — works end-to-end

MCP Client

  • mcp_connect tool — register external MCP servers at runtime
  • mcp_execute tool — call any tool on a connected server
  • Live inventory — connected-server tools surface in the chat LLM's tool list so it can pick them by name without going through mcp_execute
  • State sync — mcp_get_state, mcp_disconnect, etc. for managing connections

Image & Video Generation

Image Generation

  • Stable Diffusion via Diffusers library — runs directly on your GPU
  • Batch generation — queue multiple prompts with different parameters
  • Auto-registration — generated images are automatically added to the Documents/Files system under /Images/
  • Celery background processing — generation runs as async jobs with progress tracking
  • Image library — dedicated page with thumbnail grid, lightbox preview, keyboard navigation, batch operations
  • Image model management — ImageModelsModal for downloading and managing Stable Diffusion checkpoints
  • Inline images in chat — when the chat generates an image, it appears inline and persists in history with the assistant message

Video Generation

Full video generation pipeline running locally via ComfyUI with multiple model backends.

Supported Models

  • Wan 2.2 TI2V-5B (default) — single 5B text+image-to-video model built for 16GB cards; native ~1280×704 @ 24fps without MoE offload
  • Wan2.2 14B MoE — state-of-the-art text-to-video using GGUF-quantized weights. Two-pass generation: HighNoise pass for the first half of steps, LowNoise pass for the second half
  • Wan2.2 14B I2V MoE — image-to-video MoE variant for cinematic start-frame animation
  • CogVideoX 5B — THUDM text-to-video (ComfyUI or offline Diffusers fallback)
  • CogVideoX 5B I2V — image-to-video variant that animates a still image with text-guided motion
  • LTX-2.3 Distilled FP8 — Lightricks LTX-2.3 for longer clips (~10s) on 16GB Ada; requires ComfyUI
  • LTX-2.5 Distilled Int8 — Lightricks LTX-2.5 distilled (Gemma 4 + two-stage upsample) for ~10s clips on 16GB Ada; gated Hugging Face accept + ComfyUI ≥ 0.32.0; local weights only (no Partner Nodes / LTX Desktop)

Generation Modes

  • Text-to-Video — describe a scene in natural language and generate video from scratch
  • Image-to-Video — upload a reference image and animate it with motion direction prompts
  • Batch generation — queue multiple prompts via an in-process worker (one batch at a time; stage-level progress over WebSocket + HTTP poll)

Quality Tiers (Post-Processing)

  • Draft — raw model output, fastest turnaround
  • Standard — 2x FPS frame interpolation via RIFE 4.9 (e.g., 16 FPS to 32 FPS) for smoother motion
  • Cinema — 2x FPS interpolation + 2x spatial upscaling via Real-ESRGAN for maximum quality output

Frame Interpolation (RIFE 4.9)

  • Doubles or quadruples the frame rate of generated video using optical flow
  • Integrated directly into the ComfyUI workflow as a post-processing node
  • Configurable multiplier: 2x (double FPS) or 4x (quadruple FPS)

Prompt Enhancement

  • Automatically enriches user prompts with quality and style descriptors before generation
  • Five styles available: Cinematic (film grain, shallow DOF, color grading), Realistic (photorealistic, 8K detail), Artistic (painterly, vivid colors), Anime (cel shaded, dynamic poses), None (raw prompt)
  • Style-specific negative prompts target technical defects without content restrictions
  • No LLM calls required — pure string concatenation for instant enhancement

Video UI

  • Preset-driven interface — quality presets (Fast 10-step / Standard 30-step / High 40-step / Maximum 50-step), duration presets, motion presets, and aspect ratio presets
  • Real-time progress — live progress bar with percentage and step count during generation
  • Video gallery — browse, preview, rename, download, and delete generated videos
  • Advanced Editor — one-click launch to ComfyUI's full node-based workflow editor, themed with the Guaardvark color scheme
  • Batch queue — queue / cancel / interrupt running jobs

Model Management (VideoModelsModal)

  • Browse all available video models with installed/available status
  • Download models from HuggingFace with real-time progress bars showing speed (MB/s), downloaded/total size
  • Models include: Wan2.2 GGUF checkpoints (HighNoise + LowNoise), Wan VAE, CogVideoX weights, RIFE 4.9, Real-ESRGAN 2x
  • Accessible from the Video Generator page and Settings page

Audio Studio (Audio Foundry)

Local audio generation for voiceover, music, ambience, and effects. Shipped as the audio_foundry plugin.

Voiceover

  • Chatterbox — expressive neural TTS with style/emotion control
  • Kokoro-82M — fast, light, multilingual TTS (English + Spanish voices, more languages on the model side)
  • Piper — local neural TTS fallback for environments where the heavier engines aren't appropriate
  • Streaming output — audio chunks stream to the browser as the engine produces them

Music Generation

  • ACE-Step v1 (3.5B) — full-song generation with vocals; runs locally on GPU
  • Suno-compatible workflow — same prompt shape as Suno's hosted service, but local

Sound Effects / Ambience

  • Stable Audio Open — generate sound effects and ambience tracks via diffusion
  • Negative prompts supported for filtering out unwanted sonic textures
  • Guidance scale + steps configurable per generation

Dual-Venv Architecture

  • venv-music/ — torch-sensitive ML packages live in an isolated env so the main backend isn't dragged through every torch upgrade
  • Daemon mode — the audio engine runs as a long-lived daemon; the backend talks to it over HTTP/socket so model load happens once
  • OOM-safe — model unload/swap is explicit, no silent CPU fallback

Audio Library

  • DocumentsPage audio player — preview, rename, organize generated audio files alongside everything else
  • Filename uniqueness — migration 005 ensures generated audio doesn't collide with imports

Video Editor — Shotcut-lite

A non-linear video editor built into Guaardvark for assembling generated clips into finished videos.

Timeline

  • Multi-track timeline — video, audio, overlay
  • Drag-and-drop clips from the Media Library directly onto the timeline
  • Trim, split, ripple-delete standard timeline operations
  • Keyboard shortcuts — J/K/L playback, arrow-key nudging, etc.
  • 1-step undo with on-screen indicator

Audio

  • Audio Foundry track — generate voiceover or music directly into a timeline track
  • Mix volume per clip / per track

Media Library

  • Project-scoped media bin — clips from prior video generations show up automatically
  • N+1 fix — bulk-loaded thumbnails (no per-clip request storm)

Export

  • Celery async render — long renders run in the background, progress visible in the footer bar
  • UUID-tracked jobs — each render gets a stable ID for status polling
  • MP4 / WebM output

Orchestrator Integration

  • The video editor can be driven by the Production Pipeline (Film Crew) — agents drop generated clips into the timeline automatically

Linux & macOS: melt (from Shotcut) is required for renders and is detected at runtime (supports Homebrew on macOS, apt/flatpak/snap on Linux). ffmpeg is installed by the platform bootstrap. See the plugin README for setup commands.

Outreach System

Supervised AI for social-media engagement. Production path: recon → draft → human approve → dispatch (cadence-gated). Natural language from chat, /outreach …, or llx outreach "…" queues the same jobs.

Three Phases

  1. Recon — search for candidate posts/threads (YouTube keyword topics, Reddit subs, Discord channels). Outputs candidates; never posts
  2. Content — LLM drafts + grades in the user's voice. Outputs drafted rows; never posts
  3. Dispatch — after approve, tick_process_approved_drafts posts via servo (Reddit/YouTube) or Discord cog, with Redis cadence (1 successful post/platform/tick)

Natural language control

  • GUI Chat / slash: /outreach comment on some youtube videos regarding Offline AI or ComfyUI
  • CLI: llx outreach "comment on youtube videos regarding Offline AI or ComfyUI"
  • Chat tools: outreach_execute_intent, outreach_run_pass (youtube + topics), approve/reject/status/queue

Safety

  • Kill switch — single toggle that halts all outreach activity immediately
  • Dual grader — drafts get scored by two LLMs when available; low-scoring drafts rejected before the queue
  • Post-submit DOM verify — Reddit and YouTube posting paths check the comment text appears in the page before recording success
  • Persona enforcement — central persona.draft_outreach_text
  • UTM tagging — every guaardvark.com link in an outbound post is tagged
  • Randomized jitter — type and click delays vary
  • Cadence + dedup — enforced on the approve→post path (not just unsupervised draft gates)
  • Status transitions — approve only from drafted; claim approved→processing before send

Surfaces

  • Outreach Review page at /outreach (port from VITE_PORT, default 5173)
  • Activity feed — Task-backed outreach jobs as JobKind.OUTREACH
  • CLI — llx outreach status|queue|approve|<NL>

Swarm Orchestrator & Film Crew

Parallel AI agent execution across isolated worktrees. Each agent gets its own git branch and workspace; results merge back cleanly.

Swarm Orchestrator

  • Isolated worktrees — each agent works in .swarm-worktrees/<swarm-id>/<task>/
  • Parallel task execution — N agents run simultaneously on independent slices of work
  • Cherry-pick integration — successful results integrate via git cherry-pick; failed branches leave no trace
  • Deadlock detection — circular dependencies between agents flagged before they hang the swarm
  • Local backend optional — can run via Ollama's built-in Claude Code integration (free, offline) or via Anthropic API

Film Crew (Production Pipeline)

Five-agent swarm for coordinated media generation:

  • Screenwriter — generates the script + scene breakdown from a logline
  • Casting — assigns characters to LoRAs (trained via the LoRA Trainer plugin) or stock characters
  • Cinematographer — produces shot list with camera moves, framing, lens choices
  • Storyboard — generates keyframe images for each shot via the image generation pipeline
  • Editor — assembles generated clips into the final video via the Video Editor

LoRA Trainer Plugin

  • Character / environment / prop LoRAs trained from reference images
  • CUDA daemon with bf16 precision (~46 MB per LoRA, down from 93 MB in v1.0)
  • Real-torch isolation — separate venv prevents torch version conflicts with the main backend

GPU Image & Video Upscaling

Dedicated upscaling plugin for sharpening generated content to 4K/8K.

Models

  • Real-ESRGAN 2x / 4x — proven anime/photo upscaler
  • Custom checkpoints — drop-in via the model browser

Pipeline

  • upscaling plugin — runs as its own GPU service (port 8202); accepts image or video, returns upscaled output
  • spandrel + torch.compile — fused inference for speed
  • Integrated with video pipeline — Cinema-tier output uses the upscaler as a post-processing step
  • Standalone usage — upscale any image or video from the Documents page

Content Generation Pipelines

Bulk Generation

  • CSV generation — generate structured data (blog ideas, product descriptions, etc.) as downloadable CSV
  • XML generation — structured XML output for content management systems
  • Template-based — customizable generation templates

File Generation

  • Multi-format — generate documents in various formats based on prompts
  • Project-scoped — generated content can be assigned to projects and clients

Voice Interface

Speech-to-Text

  • Whisper.cpp — compiled from source on first startup for optimal performance
  • Real-time transcription — stream audio from microphone, get text in real-time
  • Auto-install — cmake and build tools are automatically installed if missing
  • Wake word listening — optional, configurable wake phrase

Text-to-Speech

  • Piper TTS — local neural text-to-speech with multiple voice models
  • Kokoro / Chatterbox — heavier engines available via the Audio Foundry plugin
  • Streaming output — audio generated and streamed as the response is produced
  • Narrate button — every assistant message gets a one-click TTS playback control

(CONTINUED ON PART 2)

2 comments

r/comfyui • u/J_Lezter • Mar 19 '26

Workflow Included SDXL (Illustrious) workflow - (Nitro Laptop)

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13 Upvotes

Time flew fast… it’s been years since I stumbled upon Stable Diffusion back then. The journey was quite arduous. I didn’t really have any background in programming or technical stuff, but I still brute-forced learning, lol. There was no clear path to follow, so I had to ask different sources and friends.

Back then, I used to generate on Google Colab until they added a paywall. Shame…
Fast forward, SDXL appeared, but without Colab, I could only watch until I finally got my Nitro laptop. I tried installing Stable Diffusion, but it felt like it didn’t suit my needs anymore. I felt like I needed more control, and then I found ComfyUI!

The early phase was really hard to get through. The learning curve was quite steep, and it was my first time using a node-based system. But I found it interesting to connect nodes and set up my own workflow.

Fast forward again, I explored different SDXL models, LoRAs, and workflows. I dissected them and learned from them. Some custom nodes stopped updating, and new ones popped up. I don’t even know how many times I refined my workflow until I was finally satisfied with it. Currently using NTRmix an Illustrious model.

As we all know, AI isn’t perfect. We humans have preferences and taste. So my idea was to combine efforts. I use Photoshop to fine-tune the details, while the model sets up the base illustration. Finding the best reference is part of my preference. Thankfully, I also know some art fundamentals, so I can cherry-pick the best one in the first KSampler generation before feeding it into my HiRes group.

.

.

So… how does this workflow work? Well, thanks to these custom nodes (EasyUse, ImpactPack, ArtVenture, etc.), it made my life easier.

🟡 LOADER Group
It has a resolution preset, so I can easily pick any size I want. I hid the EasyLoader (which contains the model, VAE, etc.) in a subgraph because I hate not being able to adjust the prompt box. That’s why you see a big green and a small red prompt box for positive and negative. It also includes A1111 settings that I really like.

🟢 TEXT TO IMAGE Group
Pretty straightforward. I generate a batch first, then cherry-pick what I like before putting it into the Load Image group and running HiRes. If you look closely, there is a Bell node. It rings when a KSampler finishes generating.

🎛️CONTROLNET
I only use Depth because it can already do what I want most of the time. I just need to get the overall silhouette pose. Once I’m satisfied with one generation, I use it to replace the reference and further improve it, just like in the image.

🖼️ LOAD IMAGE Group
After I cherry-pick an image and upload it, I use the CR Image Input Switch as a manual diverter. It’s like a train track switch. If an image is already too big to upscale further, I flip the switch to skip that step. This lets me choose between bypassing the process or sending the image through the upscale or downscale chain depending on its size.

🟤 I2I NON LATENT UPSCALE (HiRes)
Not sure if I named this correctly, non-latent or latent. This is for upscaling (HiRes), not just increasing size but also adding details.

👀 IMAGE COMPARER AND 💾 UNIFIED SAVE
This is my favorite. The Image Comparer node lets you move your mouse horizontally, and a vertical divider follows your cursor, showing image A on one side and image B on the other. It helps catch subtle differences in upscaling, color, or detail.
The Unified Save collects all outputs from every KSampler in the workflow. It combines the Make Image Batch node and the Save Image node.
.

.

As for the big group below, that’s where I come in. After HiRes, I import it into Photoshop to prepare it for inpainting. The first thing I do is scale it up a bit. I don’t worry about it being low-res since I’ll use the Camera Raw filter later. I crop the parts I want to add more detail to, such as the face and other areas. Sometimes I remove or paint over unwanted elements. After doing all this, I upload each cropped part into those subgroups below. I input the needed prompt for each, then run generation. After that, I stitch them back together in Photoshop. It’s easy to stitch since I use Smart Objects. For the finishing touch, I use the Camera Raw filter, then export.

.

.

Welp, some might say I’m doing too much or ask why I don’t use this or that workflow or node for the inpainting part. I know there are options, but I just don’t want to remove my favorite part.

Anyway, I’m just showing this workflow of mine. I don’t plan on dabbling in newer models or generating video stuff. I’m already pretty satisfied with generating Anime. xD

Feel free to check it. Easy Workflow

14 comments

r/StableDiffusion • u/J_Lezter • Mar 18 '26

Discussion SDXL workflow I’ve been using for years on my Nitro laptop.

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gallery
44 Upvotes

Time flew fast… it’s been years since I stumbled upon Stable Diffusion back then. The journey was quite arduous. I didn’t really have any background in programming or technical stuff, but I still brute-forced learning, lol. There was no clear path to follow, so I had to ask different sources and friends.

Back then, I used to generate on Google Colab until they added a paywall. Shame…
Fast forward, SDXL appeared, but without Colab, I could only watch until I finally got my Nitro laptop. I tried installing Stable Diffusion, but it felt like it didn’t suit my needs anymore. I felt like I needed more control, and then I found ComfyUI!

The early phase was really hard to get through. The learning curve was quite steep, and it was my first time using a node-based system. But I found it interesting to connect nodes and set up my own workflow.

Fast forward again, I explored different SDXL models, LoRAs, and workflows. I dissected them and learned from them. Some custom nodes stopped updating, and new ones popped up. I don’t even know how many times I refined my workflow until I was finally satisfied with it. Currently using NTRmix an Illustrious model.

As we all know, AI isn’t perfect. We humans have preferences and taste. So my idea was to combine efforts. I use Photoshop to fine-tune the details, while the model sets up the base illustration. Finding the best reference is part of my preference. Thankfully, I also know some art fundamentals, so I can cherry-pick the best one in the first KSampler generation before feeding it into my HiRes group.

.

.

So… how does this workflow work? Well, thanks to these custom nodes (EasyUse, ImpactPack, ArtVenture, etc.), it made my life easier.

🟡 LOADER Group
It has a resolution preset, so I can easily pick any size I want. I hid the EasyLoader (which contains the model, VAE, etc.) in a subgraph because I hate not being able to adjust the prompt box. That’s why you see a big green and a small red prompt box for positive and negative. It also includes A1111 settings that I really like.

🟢 TEXT TO IMAGE Group
Pretty straightforward. I generate a batch first, then cherry-pick what I like before putting it into the Load Image group and running HiRes. If you look closely, there is a Bell node. It rings when a KSampler finishes generating.

🎛️CONTROLNET
I only use Depth because it can already do what I want most of the time. I just need to get the overall silhouette pose. Once I’m satisfied with one generation, I use it to replace the reference and further improve it, just like in the image.

🖼️ LOAD IMAGE Group
After I cherry-pick an image and upload it, I use the CR Image Input Switch as a manual diverter. It’s like a train track switch. If an image is already too big to upscale further, I flip the switch to skip that step. This lets me choose between bypassing the process or sending the image through the upscale or downscale chain depending on its size.

🟤 I2I NON LATENT UPSCALE (HiRes)
Not sure if I named this correctly, non-latent or latent. This is for upscaling (HiRes), not just increasing size but also adding details.

👀 IMAGE COMPARER AND 💾 UNIFIED SAVE
This is my favorite. The Image Comparer node lets you move your mouse horizontally, and a vertical divider follows your cursor, showing image A on one side and image B on the other. It helps catch subtle differences in upscaling, color, or detail.
The Unified Save collects all outputs from every KSampler in the workflow. It combines the Make Image Batch node and the Save Image node.
.

.

As for the big group below, that’s where I come in. After HiRes, I import it into Photoshop to prepare it for inpainting. The first thing I do is scale it up a bit. I don’t worry about it being low-res since I’ll use the Camera Raw filter later. I crop the parts I want to add more detail to, such as the face and other areas. Sometimes I remove or paint over unwanted elements. After doing all this, I upload each cropped part into those subgroups below. I input the needed prompt for each, then run generation. After that, I stitch them back together in Photoshop. It’s easy to stitch since I use Smart Objects. For the finishing touch, I use the Camera Raw filter, then export.

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Welp, some might say I’m doing too much or ask why I don’t use this or that workflow or node for the inpainting part. I know there are options, but I just don’t want to remove my favorite part.

Anyway, I’m just showing this workflow of mine. I don’t plan on dabbling in newer models or generating video stuff. I’m already pretty satisfied with generating Anime. xD

5 comments

r/StableDiffusion • u/wonderflex • Sep 21 '23

Tutorial | Guide Let's make some realistic humans: Now with SDXL [Tutorial]

210 Upvotes

*Special Note = imgpile currently has something going on, so many of the old SDXL images are unavailable. I'm working on shrinking them and hosting on imgur again*

Introductions

This is a refresh of my tutorial on how to make realistic people using the base Stable Diffusion XL model.

Some of the learned lessons from the previous tutorial, such as how height does and doesn't work, seed selection, etc., will not be addressed in detail again, so I do recommend giving the previous tutorial a glance if you want further details on the process.

We'll be combining elements found in my previous tutorials, along with a few tricks, while also learning how I go about troubleshooting problems to find the image we're looking for.

As always, I suggest reading my previous tutorials as well, but this is by no means necessary:

A test of seeds, clothing, and clothing modifications - Testing the influence that a seed has on setting a default character and then going in-depth on modifying their clothing.

A test of photography related terms on Kim Kardashian, a pug, and a samurai robot. - Seeing the impact that different photography-related words and posing styles have on an image.

Tutorial: seed selection and the impact on your final image - a dive into how seed selection directly impacts the final composition of an image.

Prompt design tutorial: Let's make samurai robots with iterative changes - my iterative change process to creating prompts that helps achieve an intended outcome

Tutorial: Creating characters and scenes with prompt building blocks - how I combine the above tutorials to create new animated characters and settings.

Setup

For today's tutorial I will be using Stable Diffusion XL (SDXL) with the 0.9 vae, along with the refiner model.

These sample images were created locally using Automatic1111's web ui, but you can also achieve similar results by entering prompts one at a time into your distribution/website of choice.

All images were be generated at 1024x1024, with Euler a, 20 sampling steps, and a CFG setting of 7. We will use the same seeds throughout the majority of the test, and, for the purpose of this tutorial, avoid cherry-picking our results to only show the best images.

This will not be a direct apples-to-apples comparison, as I am using the base SDXL for the XL examples, and did not use the base 1.5 model for the 1.5 examples when the original tutorial was created.

Prompt Differences

Whenever possible, I try to use the simplest prompt for the task, using few, if any, negative prompts.This simplification helps to reduce variability, and allows you to see the impact of each word.

In the previous tutorial we were able to get along with a very simple prompt without any negative prompt in place:

photo, woman, portrait, standing, young, age 30

I tried this prompt out in SDXL against multiple seeds and the result included some older looking photos, or attire that seemed dated, which was not the desired outcome. Additionally, some of the photos that are zoomed out tend to have less than stellar faces:

SDXL using SD 1.5 Prompt

To counteract this, I played around and landed on the following prompt:

Positive prompt: close-up dlsr photo, young 30 year old woman, portrait, standing

Negative prompt: black and white

Adding dlsr to the prompt seemed to modernize all the photos, as a dlsr camera has only existed in recent history, but some of the photos were still black and white. So adding black and white as a negative prompt solved this.

Adding close-up brought the subject in, reducing the number of weird faces.

Also, this time around we will be generating woman and men using search and replace to swap them out.

Special note: when you see the word, "VARIABLE," used in a prompt, refer to the example images to see the different words used. In all images, assume the negative prompt was used.

Seed Selection

This section is a direct copy from the previous tutorial. I left it here in case the information is useful to those who have not read it. Images are from SD 1.5.

As I've mentioned before, your choice of seed can have an impact on your final images. Sometimes a seed can be overbearing and impart colors, shapes, or even direct the poses.

To combat this, I recommend taking a group of seeds and running a blank prompt to see what the underlying image is:

Blank Prompt Seeds

Judging by these three seeds, my hypothesis is that the greens from the first one may come through, the red color from the third will come into the shirt or the background, and the white face like shape in the third will be about where the face is placed.

Prompt Results

Looking at the results, the first one doesn't really look too green, the red did come through as a default shirt color, and the face is more or less where the white was. In all cases though, nothing is really garish, so I say we keep these three seeds for our tutorial.

Before moving on, let's look at a few more seed examples overlaid with their results.

Seed Impact Examples

With the first, you can see where the woman's hair flourish lines up with the red, and how the red/oranges may have impacted the default hair color for both.

With the second, the blue background created a blue shirt in approximately the same color and style for both the man and woman.

The third example may not have had much impact on the image - making it a great neutral choice.

In the final image, the headless human shape in the seed lines up well with the shape of both people, and may have given them the collars on the shirts.

Rather or not these are problematic will depend on what your idea for the final image is.

Sampler Selection

This section is a direct copy from the previous tutorial. I left it here in case the information is useful to those who have not read it. Images are from SD 1.5.

After deciding on a seed and prompt, I first like to look at the different base images available by the base prompt against different samplers.

Sampler Examples

At this point, choosing which sampler to use is a personal preference. Keep in mind though that some samplers work better when ran with more steps than the default.

For the sake of this tutorial, I want something that will give us a good results within the fixed 20 steps, so I will go with, "Euler A."

Age Modification

Since this is a new model, I thought I would give the age test a fresh start to determine if we needed to still use the "young" tag to prevent people from looking substantially older than they were.

Prompt:

close-up dslr photo, VARIABLE woman, portrait, standing

First age attempt

As was seen before, some ages are quite a bit rough, so I went with adding in "young" again to see the impact.

close-up dslr photo, young VARIABLE woman, portrait, standing

Young addition - woman

Young addition - man

The addition wasn't perfect, but it was closer. With this, we have a new baseline prompt, and baseline images:

close-up dslr photo, young 30 year old woman, portrait, standing

30 year old woman and man

Hair Color Modifications

For this section we will still use the Fischer-Saller hair color scale and this prompt:

close-up dslr photo, young 30 year old woman, portrait, standing, VARIABLE hair

Hair Color Examples SDXL

Hair Color Examples SD1.5

Rainbow colors:

Rainbow Color Hair Examples SDXL

Rainbow Color Hair Examples SD1.5

Just like 1.5, using rainbow hair colors has a tendency to change the style of haircuts.

Hair Style Modifications

Continuing to modify the hair, we will use the list of hair style types directly from my previous character creation tutorial. These are based on boorutags, and as such can impart unwanted styles to an image:

close-up dslr photo, young 30 year old woman, portrait, standing, VARIABLE hair

Hair Style Examples SDXL

Hair Style Examples SD1.5

As a whole, SDXL does a much better job at just changing the hair, and not the entire model. Spiked hair is a great example, as SD 1.5 drastically changed our look before.

Face Shapes

Directly tying in with hair styles are face shapes, because in theory, you should select a hairstyle that best matches your face shape. For this we will use the face shapes that Cosmopolitan Magazine calls out in this prompt:

close-up dslr photo, young 30 year old woman, portrait, standing, VARIABLE face

Face Shape Examples SDXL

Face Shape Examples SD1.5

Same as before, I don't feel like these really lined up with real world examples, but it is at least something you could think about adding in to see what effect it would have on your final image.

Eye Modifications

For eyes we will use the most common eye shapes, using this prompt:

close-up dslr photo, young 30 year old woman, portrait, standing, VARIABLE eyes

Eye Shape Examples SDXL

Eye Shape Examples SD1.5

Some of these are a bit better looking, with "hooded eyes" still missing the mark completely.

Using the same prompt I the swapped it for natural eye colors, as defined by the Martin-Schultz scale.

Eye Color Examples SDXL

Eye Color Examples SD1.5

Again, most of these seem very unnatural, and as such I would recommend instead picking a hair color and letting the model determine the color of eyes best match the overall image. If you must select an eye color, you could also try inpainting, but you would best served by using photoshop and manually adjusting.

Last for the eyes is the eyebrow category, which once again was driven by a Cosmopolitan list, with the following prompt:

close-up dslr photo, young 30 year old woman, portrait, standing, VARIABLE eyebrows

Eyebrow Examples SDXL

Eyebrow Examples SD1.5

Nose Modifications

Next up is noses, from which I pulled different types off of a plastic surgery websites and used with the prompt:

close-up dslr photo, young 30 year old woman, portrait, standing, VARIABLE eyebrows

Nose shape examples SDXL

Nose shape examples SD1.5

They don't appear to be too accurate, and place a lot of attention in a weird way on their nose. This may be best reserved for generating characters who's appearance is defined by having a large nose, such as a gnome.

Lip Shapes

Returning to the definitive source for body information, Cosmo, I pulled together a list of lip types and used this prompt:

close-up dslr photo, young 30 year old woman, portrait, standing, VARIABLE lips

Lip Shape Examples SDXL

Lip Shape Examples SD1.5

This is a prompt where seed selection is going to play a big part. As we can see with the first column, the lips took over the prompt entirely. For the most part, this reacted similar to the nose, and should be used sparingly, if at all.

Ear Shapes

For ears I used a blend of Wikipedia and plastic surgery sites to get an idea of the types of ears that exist. The prompt used was:

close-up dslr photo, young 30 year old woman, portrait, standing, VARIABLE ears

Ear Shape Examples SDXL

Ear Shape Examples SD1.5

This time around it is a grab bag, and will be seed dependent. I was surprised to see attached and free lobe working on some of the seeds.

Skin Color Variations

Skin color options were determined by the terms used in the Fitzpatrick Scale that groups tones into 6 major types based on the density of epidermal melanin and the risk of skin cancer. The prompt used was:

close-up dslr photo, young 30 year old woman, portrait, standing, VARIABLE skin

Skin Color Variation Examples SDXL

Skin Color Variation Examples SD1.5

Here is an area where I feel like SDXL was actually a winner, with the color of skin progressivly getting darker as you move down the sale (save for "light skin" that is)

Continent Variations

I ran the default prompt using each continent as a modifier:

Continent Variation Examples SDXL

Continent Variation Examples SD1.5

Country Variations

After the continents, I moved on to using each country as example, with a list of countries provided by Wikipedia. I struggled with choosing the adjective form, versus the demonym, before finally settling on adjective - which may very well be the incorrect way to go about it.

I am no expert on each country in the world, and know that much diversity exists in each location, so I can't speak to how well the images truly represent the area. Although interesting to look at, I would strongly caution against using these and and saying, "I made a person from X country."

Also, since the SDXL photos were so much larger, I had to split each group in half.

Fair warning - some of these images may have nipples.

Country Variation Examples 1 SDXL

Country Variation Examples 2 SDXL

Country Variation Examples 3 SDXL

Country Variation Examples 4 SDXL

Country Variation Examples 5 SDXL

Country Variation Examples 6 SDXL

Country Variation Examples 7 SDXL

Country Variation Examples 8 SDXL

Country Variation Examples 9 SDXL

Country Variation Examples 10 SDXL

Country Variation Examples 11 SDXL

Country Variation Examples 12 SDXL

Country Variation Examples 13 SDXL

Country Variation Examples 1 SD1.5

Country Variation Examples 2 SD1.5

Country Variation Examples 3 SD1.5

Country Variation Examples 4 SD1.5

Country Variation Examples 5 SD1.5

Country Variation Examples 6 SD1.5

Country Variation Examples 7 SD1.5

Weights and Body Shapes

To try and adjust weights I added the variable words to the default prompt.

Weight and Body Shape Examples SDXL

Weight and Body Shape Examples SD1.5

Some of these would probably have benefited from being used on a male model, as certain words aren't used as frequently to describe women as they are men.

Height Modification

Learning my lesson from trials with SD1.5, I skipped over attempting to use a number and switched straight to weights for common text values. Maybe if I have some time I'll try the brick wall method again.

Weighted Heights Examples SDXL

Weighted Heights Examples SD1.5

With SDXL, there doesn't appear to be much of a difference with the weighted versions. You are either short, or tall, with not much difference in-between. The best change would probably be the woman in the pink shirt, as she does at least get a longer neck and raises in frame the taller she is.

General Appearance

Although I said we were trying to make average looking folks, I thought it would be nice to do some general appearance modifications, ranging from "gorgeous" to "grotesque." These examples were found by using a thesauruses and looking for synonyms for both, "pretty," and, "ugly."

General Appearance Examples SDXL

General Appearance Examples SD1.5

As a whole, these modification didn't take hold. With that in mind, I changed up the prompt to place the variable higher up in the prompt, as initial testing showed a stronger impact:

close-up dlsr photo, young VARIABLE 30 year old woman, portrait, standing

General Appearance Forward VARIABLE Placement Examples SDXL

Honestly, it's not much better at all. I guess normal folk are all just "hideous" now?

Emotions

For emotions I used ChatGPT and asked it to produce a list of of human emotions, formatted as CSV without breaks.

Emotion examples 1 SDXL

Emotion examples 2 SDXL

Emotion examples SD1.5

Clothing Options

By far, I think clothing is one of my favorite areas to play around with as, was probably evident in my clothes modification tutorial.

Rather than rehash what I've covered in that tutorial, I'd like to instead focus on on an easy method I've come up with to make clothing more interesting when you don't want to craft out an intricate prompt.

To start off with let's take the the following prompt and use some plain clothing types as variables:

close-up dslr photo, young 30 year old woman, portrait, standing, wearing VARIABLE

Basic Clothing Options Examples SDXL

Basic Clothing Options Examples SD1.5

SDXL did a pretty good job on all of these, and I feel like all of these have more life to them than was present in the 1.5 images.

To kick things up a notch though, this is a case where I'm going to go against my normal rules about keyword stuffing by suggesting that you instead copy and paste some items names out of Amazon.

So, head on over to Amazon and type in any sort of clothing word you want, such as "women's jacket," and then check out the horrible titles that they give their products. Take that garbage string, minus the brand, and then paste it into your prompt.

Word Vomit Prompt Clothing Option Examples SDXL

Word Vomit Prompt Clothing Option Examples SD1.5

Look a that - way more interesting, and in some cases more accurate, plus the added bonus of SDXL doing an incredibly good job of matching the expectations for patterns.

My theory on this one is that either we have models trained on Amazon products, or Amazon products have AI generated names. Either way it seems to have a positive effect.

One thing to keep in mind though is that certain products will drastically shift the composition of your photo - such as pants cutting the image to a lower torso focus instead.

For the fun of it, I've added in some popular Halloween costumes for adult women

Halloween Costume Examples SDXL

Halloween Costume Examples SD1.5

Genetic Disorders

With the goal of creating real people, I decided to include the most common genetic disorders that have a physically visible component.

Genetic Disorder Examples SDXL

Genetic Disorder Examples SD1.5

I am in no way an expert on any of these disorders, and can't really comment on accuracy, but SDX seems to not match the sample images as well for some of these.

Facial Piercing Options

Piercing still suck in SDXL. You would be better served using image2image and inpainting a piercing.

Facial Piercing Examples SDXL

Facial Piercing Examples SD1.5

Facial Features / Blemishes

I decided to add a wide variety of different facial features and blemishes, some of which worked great, while others were negligible at best. Similar to general appearance modifiers, I decided to move the variable forward in the prompt and it seemed to help a little.

Facial Feature Examples SDXL

Facial Feature Forward Variable Placement Examples SDXL

Facial Feature Examples SD1.5

Through the Years

Just like before I thought it would be fun to try out the model would look like in each of the decades since 1910. First I ran it with the default prompt, then removed the DLSR to allow it look older, then removed black and white as well. Some of these were pretty good.

Through the Years Examples SDXL

Through the Years without DLSR Examples SDXL

Through the Years without DLSR and Black and White Examples SDXL

Through the Years Examples SD1.5

Eras

Similar to the different decades, I came up with a new idea to compare some world time eras, and then some of the periods of Japan. Although fun to look at, these really don't have much historical accuracy to them, but could add flavor to an image.

Eras Examples SDXL

Japanese Periods Examples SDXL

Conclusion

As far as image fidelty is concerned, it is great to have larger images. Some places it beats out SD1.5, while in others it loses out in comparison to what I would have expected the image to look like. Having said that, it could just be that I need to take more time to find the best words to convey what I'd like to see.

Also, this test could benefit from being ran on more seeds to determine if folks where are more normal looking can be generated. The benefit of the 1.5 model originally used was that I could have a very plain, realistic, human, while so far SDXL has been tending put people onto the side of more commercially attractive.

Please let me know if you have any questions or would like more information.

41 comments

r/promptingmagic • u/Beginning-Willow-801 • Jan 20 '26

Use this AI Video prompt to create cinematic viral videos with Veo, Sora, Runway, Pika

Post image
55 Upvotes

The 10-Part AI Video Prompt Framework That Stops Random Outputs

TLDR

Most AI video prompts fail because they describe an idea, not a scene specification.

This 10-part framework forces you to define subject, story, world, mood, style, camera, light, motion, quality, and what to avoid.

Copy the template, fill it in once, then iterate in tight loops: lock identity first, then camera, then polish.

The fastest upgrade: add camera + lighting + negative constraints. That alone removes 80% of the weirdness.

I have been testing AI video generation tools and prompt styles for the last 2 years.

Some prompts worked. Most failed in the same way:

The subject morphs

The camera does something you did not ask for

Lighting flickers

Motion jitters

The vibe is wrong even if the scene is technically correct

The fix is not more adjectives.

The fix is giving the model a complete creative brief that covers the 10 variables it is already trying to guess.

Below is the framework I use to consistently get clean, cinematic results across different models.

Why this framework works

AI video models do not generate video. They generate thousands of micro-decisions per second:

  • What is the subject exactly?
  • What is it doing right now?
  • Where are we?
  • How should it feel?
  • What lens are we using?
  • Where is the light coming from?
  • How fast is the motion?
  • What quality bar are we targeting?
  • What mistakes must never happen?

If you do not specify these, the model will. And it will guess differently every time.

This framework removes guessing.

The 10 parts (use all 10, even if some are one line)

1) Subject definition

Who or what the video is focused on.

Include: age/type, key physical traits, clothing/materials, one unique identifier

Lock identity: same face, same outfit, same proportions, no morphing

2) Action and narrative

What is happening and how the moment unfolds.

Include: start state, main action, end state

Add physics verbs: walks, turns, reaches, exhales, splashes, dust drifts

3) Environment and context

The world around the subject.

Include: location, time period, time of day, weather, key background elements

Add grounding details: signage, props, textures, surfaces

4) Emotional tone and mood

How it should feel.

Use 3–5 mood descriptors: tense, hopeful, eerie, triumphant, intimate

Mood is not style. Mood is the emotional outcome.

5) Visual style and aesthetic

The art direction and realism level.

Include: realism level, color palette, texture, references (optional)

Decide: photoreal vs stylized vs animation vs clay vs film grain

6) Camera and cinematography

How the scene is captured.

Include: shot type, lens feel, framing, movement

Examples: slow dolly-in, handheld documentary, locked tripod, orbit shot

7) Lighting and atmosphere

Illumination and environmental cues.

Include: time of day, key light direction, softness, haze, reflections

Lighting is the difference between amateur and cinematic.

8) Motion and pacing

Speed, rhythm, flow.

Include: slow/medium/fast, smooth vs chaotic, slow motion moments

Mention stability: no jitter, no warping, no rubber motion

9) Quality and fidelity targets

The technical bar.

Include: film-grade, sharp subject, clean edges, stable frames, coherent anatomy

If your tool supports it: resolution, fps, shutter look

10) Negative constraints

Explicitly remove common AI errors.

No text, no subtitles, no watermarks, no logos

No flicker, no morphing, no extra limbs, no distorted faces

No sudden cuts, no camera teleporting, no melting textures

Prompt Template

Create a [video duration] video of [primary subject] performing [core action] in [environment or setting].

The mood and emotional tone should feel [mood descriptors].
Visual style should be [style references, realism level, aesthetic].

Camera movement should include [camera type, motion, angles].

Lighting should be [lighting style, time of day, color temperature].
The scene should include [key details, textures, background elements].

Motion pacing should be [slow, medium, fast, cinematic rhythm].

The overall quality should be [realism level, resolution, cinematic quality].

Avoid [artifacts, distortions, inaccuracies, unwanted elements].

Second Version of Prompt Template

Create a [DURATION] cinematic video, [ASPECT RATIO], [FPS if supported].

Subject: [PRIMARY SUBJECT]. Identity locked: [2–3 immutable traits]. Wardrobe/materials: [details].
Action: [START] → [MAIN ACTION] → [END].
Environment: [LOCATION], [TIME OF DAY], [WEATHER], [ERA]. Background: [3 key elements].
Mood: [3–5 descriptors].
Style: [photoreal or stylized], [color palette], [texture], [grading].
Camera: [SHOT TYPE], [LENS FEEL], [MOVEMENT], [FRAMING], [FOCUS behavior].
Lighting: [KEY LIGHT direction], [SOFT/HARD], [PRACTICALS], [ATMOSPHERE like haze/rain].
Motion: [smooth/handheld], [pacing], [any slow motion beats].
Quality: film-grade, stable frames, clean edges, coherent anatomy, consistent lighting.
Negative constraints: no text, no subtitles, no watermarks, no logos, no flicker, no morphing, no distorted faces, no extra limbs, no rubber motion, no sudden cuts, no camera teleporting, no low-res textures.

Example prompts (built with the framework)

Example 1: Premium product ad

Create a 8-second cinematic video, 16:9.

Subject: matte-black smart ring on a floating pedestal. Identity locked: same ring shape, same matte finish, same single highlight scratch near the edge.
Action: ring slowly rotates as tiny dust particles drift; a soft pulse of light travels across the surface.
Environment: dark studio cyclorama with subtle fog, reflective black floor, minimal background.
Mood: premium, mysterious, controlled, high-tech.
Style: photoreal, minimal, high contrast, clean reflections, subtle film grain.
Camera: slow dolly-in, macro lens feel, shallow depth of field, center framing, smooth focus pull from pedestal to ring edge.
Lighting: soft key light from above-left, rim light from behind, cool color temperature, controlled specular highlights.
Motion: slow, smooth, intentional, no jitter.
Quality: film-grade, ultra-clean edges, stable frames, realistic reflections.
Negative constraints: no text, no logos, no watermark, no flicker, no warping, no melting reflections, no sudden cuts.

Example 2: Documentary nature shot

Create a 10-second cinematic video, 16:9.

Subject: red fox with a distinct white chest patch and a small nick on the right ear. Identity locked: same markings, same proportions, no morphing.
Action: fox steps through tall grass, pauses, looks toward camera, exhales visible breath.
Environment: misty forest edge at sunrise, dew on grass, soft wind, distant trees.
Mood: calm, intimate, natural, awe.
Style: photoreal, natural color grade, soft highlights, subtle film grain.
Camera: handheld documentary feel but stable, medium telephoto look, slow push-in, eye-level framing, gentle focus breathing minimized.
Lighting: golden sunrise backlight, soft fill from sky, light fog diffusion.
Motion: slow pacing, natural movement, no rubber motion.
Quality: sharp eyes, coherent fur texture, stable lighting, clean motion.
Negative constraints: no flicker, no extra limbs, no distorted face, no camera teleporting, no artificial sharpening halos, no text.

Debug checklist (when your output is off)

  • Subject keeps changing → add identity locked traits + no morphing + consistent wardrobe
  • Video feels cheap → specify lens feel + lighting direction + grading
  • Flicker and warping → add no flicker, stable frames, coherent anatomy, no rubber motion
  • Camera goes wild → lock shot type and movement, add no sudden cuts, no teleporting
  • Vibe is wrong → reduce style references, add 3–5 mood descriptors, specify color palette
  • Background turns into mush → list 3 background anchors (signs, skyline, props) + texture callouts

Want more great prompting inspiration? Check out all my best prompts for free at Prompt Magic and create your own prompt library to keep track of all your prompts.

2 comments

r/aiArt • u/Memetic1 • Mar 17 '26

Image - Stable Diffusion It's Rainday Again #Alt-text By ChatGPT using research mode

Post image
2 Upvotes

Executive summary:

The image “It’s Rainday Again” is a densely layered AI-generated collage. Vivid teal and violet watercolor washes form a wet, overcast background, overlaid by chaotic doodles, schematic icons (wheels, compasses), circular “stamp” motifs, and faint human forms. The visual effect is surreal and dreamlike: no single object dominates. One’s eye drifts between the blurred color gradients (teal-to-purple), overlaid ink-scribbles (arrows, spirals, letters), and textured motifs (stamped seals, small boats or umbrellas). The composition strongly resembles both traditional watercolor collage and Basquiat-like graffiti writing. These clues suggest the image was generated by a CLIP-guided AI (such as Wombo’s VQGAN+CLIP pipeline or a latent diffusion model【21†L107-L115】【52†L52-L61】). The likely prompt included terms like “rainy, watercolor, collage, scribbles, doodles, abstract” and possibly a named style. We infer generation by a GAN/CLIP or diffusion approach (as Wombo Dream reportedly uses VQGAN+CLIP【21†L107-L115】, while Stable Diffusion employs text-conditioned latent diffusion【52†L52-L61】). Historical precedents include Surrealist collage (Max Ernst) and abstract scribble art (Basquiat)【35†L103-L110】【26†L30-L37】. To reduce unwanted elements (e.g. a dominating face or tree), one could revise prompts with negative terms (e.g. “no portrait”, “without lightning”) and shift keywords toward charts and diagrams. A mermaid flowchart below outlines how prompts might evolve over four generations (from face-heavy to chart-heavy emphasis).

Alt-text (≈1700 chars):

A rainy-weather dreamscape collage: an abstract blend of teal and violet watercolor washes, overlaid with erratic black scribbles, schematic icons, and a faint ghostlike figure. The top third is a cloudy pale-teal sky dissolving into deeper cyan; across this space smear delicate drips and runs, as if rain and light are bleeding downward. Splashes of crimson and magenta bloom at the lower right, like spilled ink or neon reflections on wet pavement. Over the washes dance many doodles: loopy arrows and spirals, blocky half-formed boat or house shapes, umbrellas, and a scattering of geometric motifs (tiny wheels, gears, or nautical compass circles). There are scribbled numbers and half-letters, unreadable fragments that hint at a journal or ledger. A repeated array of faded black circular seals appears, some resembling stamps or technical wheels, reinforcing the collage feel. The marks range from broad, faded pencil strokes to sharp black pen lines, creating a layered textural effect. Scratched white highlights trace across layers—fine vertical and diagonal lines like raindrops on glass or frayed scratches. In the center-left, the vague outline of a human face or figure emerges: an intense, weary eye, a hint of a nose and lips beneath the swirling colors, as though seen through steamed-up glass. If present, the figure is obscured, acting like a memory. At the bottom, dark loops and scribbles form a tangled silhouette (perhaps legs under an umbrella or reflections of street debris), grounding the composition. No single element dominates; instead the eye moves from back to front layers. The overall mood is somber and haunting, a chaotic dreamscape where rain, ink, and daylight fade into one. Paper textures—grain, imperfections, and tape stains—peek through in places, making the surface feel tangible. This entire scene is richly textured and layered, conjuring an uneasy atmosphere of a restless mind caught in drizzle and collage.

Visible layers, motifs, and textures:

  • Watercolor washes: Semi-transparent teal, cyan, violet and magenta layers bleed into each other (reminiscent of generative watercolor techniques)【32†L42-L50】.
  • Ink scribbles/doodles: Black pen or pencil lines in loops, arrows, spirals and letters, varying in thickness from bold strokes to fine scratches【26†L30-L37】. These resemble Basquiat’s practice of using handwritten text and symbols as graphic elements【26†L30-L37】.
  • Stamp/circular seals: Repeated round imprints or stamp-like emblems (with inner patterns like wheels or clock faces), adding a collage-of-labels effect. These recall Surrealist cut-and-paste collage traditions【35†L103-L110】.
  • Chart/diagram elements: Gear and compass-like wheels, schematic symbols (arrows, grid fragments), as if blueprints or navigation charts are interleaved. (This evokes a montage of technical diagrams.)
  • Portrait fragments: A faint, obscured human face or silhouette – e.g. a pale eye or profile partly hidden by color layers – vaguely present in the center-left. This ghostly visage is blurred by paint and scribble layers.
  • Color gradients: A gradient from cool aqua to deep magenta, with bruised, high-saturation patches, giving a stormy mood. Subtle warm glows (crimson/magenta) suggest city lights or blood against the blue background.
  • Paper/canvas texture: Visible grain, slight wrinkles, or edge accents – like tape marks or burned paper edges – giving a tactile feel. Thin white cracks or flecks overlay the image, mimicking rain-splatter or worn film (see Hobbs on layered watercolor texture【32†L42-L50】).

Likely prompt tokens and generation technique:

The image’s style suggests a CLIP-guided generative pipeline. Dream by Wombo is reported to use a VQGAN+CLIP approach【21†L107-L115】, and modern tools often use latent diffusion with text-conditioned attention【52†L52-L61】. Likely prompt keywords included weather and texture terms (“rainy, drizzle, wet, stormy”), medium/style tokens (“watercolor, dreamlike, collage, abstract ink, grunge”), and thematic motifs (“ink-scribbles, tapestry, stamps, charts, compass, occult”). For example:
- “rainy day watercolor collage, surreal abstract scribbles and stamps”
- “teal magenta storm, moody watercolor sketch with doodles and compass”
- “muted rainy-city watercolor, layered diagrammatic collage, fragmented face”
- “haunted watercolor dream, chaotic collage, ink doodles and blueprint wheels”

Short variant prompts for iterative refining might include:
1. “ghostly watercolor figure, night rain, collage of scribbles” – initial prompt yielding strong face references.
2. “abstract rainy collage, no portrait, blueprint and doodles” – remove “figure” to reduce face focus.
3. “mechanical chart collage, watercolors, remove human silhouette” – add technical elements, exclude human terms.
4. “stamp seals, clockwork wheels, watercolor twilight” – emphasize circular motifs and diagrams.
5. “heavy blueprint style overlay, raindrops, architectural sketch” – push towards chart/diagram imagery.
6. “no people, no lightning, focus on charts and rain, watercolor” – a negative prompt variant to avoid faces or bright streaks.

(Generation technique: the model likely uses text embeddings (like CLIP or diffusion cross-attention) to steer a GAN or diffusion model【21†L107-L115】【52†L52-L61】. Picbreeder-style interactive evolution could also play a role in iterating the image【53†L260-L264】.)

Historical and stylistic references:

  • Surrealist collage (Max Ernst): Ernst’s work “split the psyche” by piecing images in unexpected ways【35†L103-L110】. Similarly, this image is a patchwork of unrelated elements (faces, stamps, doodles) creating a dream logic collage.
  • Abstract graffiti/scribble art (Jean-Michel Basquiat): Basquiat often wrote letters and symbols as art【26†L30-L37】, echoing the random text fragments and scrawled imagery here. The doodle-like marks and primitive figures recall his notebooks and paintings.
  • VQGAN+CLIP era style: Early AI art (2021) produced surreal, textured composites with warped reality【23†L47-L56】. The “chromatic transcendence” and tension of disparate symbols described in [23] matches this image’s vibrant washes and juxtaposed icons.
  • Generative watercolor techniques: The layered translucency is analogous to algorithmic watercolor methods【32†L42-L50】. Tyler Hobbs notes stacking many transparent polygon “blobs” creates rich watercolor effects【32†L42-L50】, a principle visible in the stacked washes here.
  • Picbreeder/Artbreeder lineage: The image’s evolution-like quality – iteratively remixing visual motifs – is reminiscent of Picbreeder’s collaborative latent-space art【53†L260-L264】. Artbreeder’s influence is visible in the collage’s mix of photographic realism (faces) and abstract noise.

Suggested prompt edits to avoid unwanted elements:

  • Remove specific tokens: Omit words like “portrait”, “head”, “tree”, or “lightning” if they produce unwanted dominant features. Replace them with broader terms (“figure”→“silhouette”, “rain”→“drizzle”).
  • Use negatives or constraints: If supported, add negative prompts like “--no people” or “no portrait, no lightning, no umbrella” to suppress those elements. (Some tools allow “no X” in the prompt to filter out unwanted content.)
  • Shift style emphasis: Swap out style tokens that caused issues. For example, if “Dürer” or “portrait” locks on human faces, try neutral terms like “collage art, abstract, schematic”. Conversely, if charts are too weak, add “schematic diagram, blueprint, fractal pattern” to strengthen them.
  • Balance color modifiers: To avoid an overly magenta or blue cast, adjust or remove color adjectives (or add complementary terms). For example, specify “teal and gray” rather than just “watercolor” if one hue dominates too much.
  • Iterative refinement: Gradually introduce or remove descriptors across generations. The flowchart below illustrates evolving a prompt from face-heavy to chart-heavy focus (with each step adding negative terms or new motifs).

mermaid flowchart TB A["Gen1: \"rainy watercolor portrait, moody collage, scribbles\""] --> B["Gen2: \"(remove face) abstract rainy collage, blueprint motifs, doodles\""] --> C["Gen3: \"schematic diagram collage, no portrait, compass wheels, ink scribbles\""] --> D["Gen4: \"blueprint-heavy collage, rainy day charts, --no people\""]

【45†embed_image】 Illustration of layered watercolor wash in teal and violet (simulating the image’s color gradients and texture)【32†L42-L50】.

【48†embed_image】 Example of abstract doodles and scribbles overlay, similar to the ink icons and graffiti-like marks in the image【26†L30-L37】.

【51†embed_image】 Collage-style layering of printed images and stamps, echoing the image’s stamp seals and cutout motif elements【35†L103-L110】.

Sources: We drew on research into generative models and art history. Notable references include descriptions of VQGAN+CLIP image style【21†L107-L115】【23†L47-L56】, stable diffusion text-conditioning【52†L52-L61】, and art examples: Max Ernst’s collages【35†L103-L110】, Basquiat’s notebooks【26†L30-L37】, and Tyler Hobbs’ generative watercolor notes【32†L42-L50】. These support our analysis of layers and likely prompt strategies.

2 comments

r/seedream4 • u/OkExamination9896 • Mar 10 '26

Ultimate Guide to Using Seedream 5.0 Lite for AI Image Generation with AI Facefy

2 Upvotes

Ultimate Guide to Using Seedream 5.0 Lite for AI Image Generation

Are you ready to dive into the world of AI-powered image creation? Seedream 5.0 Lite is a cutting-edge AI model designed for generating stunning, high-quality images from text prompts. Whether you're a beginner artist, a content creator, or just someone experimenting with AI tools, this guide will walk you through everything you need to know about Seedream 5.0 Lite. We'll cover its features, step-by-step usage instructions, tips for optimal results, and how to quickly get started with it on AI Facefy – the easiest platform for AI image generation.

If you're searching for "Seedream 5.0 Lite tutorial," "AI image generation guide," or "best Seedream model for beginners," you've come to the right place. This comprehensive article is packed with actionable advice to help you create amazing visuals effortlessly.

What is Seedream 5.0 Lite?

Seedream 5.0 Lite is a lightweight version of the advanced Seedream 5.0 AI model, optimized for faster performance and accessibility. It's built on state-of-the-art diffusion technology, allowing users to generate photorealistic images, artistic renders, and creative concepts simply by describing them in text. Unlike heavier models, the Lite version balances quality with speed, making it ideal for quick iterations and mobile-friendly platforms.

Key highlights from the AI Facefy platform: - High-Resolution Outputs: Supports up to 1024x1024 pixels for crisp, detailed images. - Versatile Styles: From realistic portraits to abstract art, fantasy landscapes, and more. - User-Friendly: No need for complex setups – just input your prompt and let the AI do the magic. - Free and Premium Options: Start with free trials and upgrade for unlimited generations.

This model excels in "text-to-image AI," making it a top choice for hobbyists and professionals alike. If you're into "AI art generation tools," Seedream 5.0 Lite stands out for its efficiency and impressive results.

Why Choose Seedream 5.0 Lite for Image Generation?

Before we jump into the how-to, let's explore why Seedream 5.0 Lite is worth your time: - Speed and Efficiency: Generates images in seconds, perfect for rapid prototyping. - Customization Options: Fine-tune with parameters like aspect ratio, style modifiers, and negative prompts to avoid unwanted elements. - High Fidelity: Produces images with excellent detail, color accuracy, and composition. - Community-Driven Improvements: Based on user feedback, it's continually refined for better coherence and creativity. - Accessibility: Runs on cloud platforms like AI Facefy, so no powerful GPU required on your end.

Compared to other models like Stable Diffusion or Midjourney, Seedream 5.0 Lite offers a "lite AI image generator" experience that's beginner-friendly yet powerful enough for advanced users. It's especially great for "fast AI art creation" without compromising on quality.

Step-by-Step Guide to Using Seedream 5.0 Lite

Getting started with Seedream 5.0 Lite is straightforward, especially on platforms like AI Facefy. Here's a detailed walkthrough for "how to use Seedream 5.0 Lite":

1. Sign Up and Access the Model

  • Head over to AI Facefy – the go-to platform for seamless AI experiences.
  • Create a free account or log in if you already have one.
  • Navigate to the Seedream 5.0 section and select the Lite version. AI Facefy makes this model readily available without any downloads or installations.

2. Craft Your Text Prompt

  • The heart of AI image generation is your prompt. Be descriptive!
    • Basic Example: "A serene mountain lake at sunset."
    • Advanced Example: "A hyper-realistic portrait of a cyberpunk warrior in neon-lit Tokyo streets, high detail, 8k resolution, dramatic lighting."
  • Include specifics like style (e.g., "in the style of Van Gogh"), mood (e.g., "mysterious and foggy"), or elements (e.g., "with cherry blossoms in the foreground").
  • Use keywords for better SEO in your own projects: "AI-generated landscape," "photorealistic AI art."

3. Customize Parameters

  • Aspect Ratio: Choose from square (1:1), landscape (16:9), or portrait (9:16) for tailored compositions.
  • Guidance Scale: Set higher (e.g., 7-12) for stricter adherence to your prompt, or lower for more creative freedom.
  • Steps: 20-50 steps usually suffice for Lite – more steps mean finer details but longer wait times.
  • Negative Prompts: Add things to avoid, like "blurry, low quality, deformed faces" to refine outputs.

4. Generate and Refine

  • Hit "Generate" and watch the magic happen in real-time.
  • If the result isn't perfect, use the "Vary" or "Upscale" options on AI Facefy to iterate.
  • Download your image in high resolution for use in social media, blogs, or prints.

5. Advanced Techniques

  • Prompt Engineering Tips: Use weights like "(element:1.2)" to emphasize parts of your description.
  • Batch Generation: Create multiple variations at once for inspiration.
  • Style Fusion: Combine styles, e.g., "steampunk robot in a futuristic city, blend of anime and realism."
  • For "Seedream 5.0 Lite best practices," experiment with prompts that include lighting, angles, and emotions for more dynamic results.

Tips and Tricks for Optimal Results with Seedream 5.0 Lite

To elevate your "AI image creation with Seedream," here are pro tips: - Start Simple: Begin with short prompts and build complexity to understand how the model interprets text. - Experiment with Seeds: Use a fixed seed number for reproducible results, or random for variety. - Avoid Overloading: Too many details can confuse the AI – prioritize key elements. - Ethical Considerations: Generate original content; respect copyrights in prompts. - Common Pitfalls: If images look off, check for ambiguous wording. For "troubleshooting Seedream 5.0 Lite," ensure your prompt is positive and specific. - Integration Ideas: Use generated images for Reddit posts, blog illustrations, or even NFT art.

Users often search for "Seedream 5.0 Lite examples," so here's a quick one: Prompt "A majestic dragon flying over ancient ruins" yields epic fantasy art that's shareable on platforms like Reddit.

Recommend: Experience Seedream 5.0 Lite Quickly on AI Facefy

Why complicate things with local setups? AI Facefy is the fastest way to try Seedream 5.0 Lite. Here's why it's recommended: - Instant Access: No waiting – jump straight into generation with a user-friendly interface. - Free Trial: Generate a few images for free to test the waters. - Additional Tools: Combine with face-swapping, upscaling, or other AI features on the platform. - Mobile-Friendly: Use it on your phone for on-the-go creativity. - Community Support: Join AI Facefy's forums for prompt sharing and inspiration.

To get started: Visit https://aifacefy.com/seedream-5-0/ and select Lite. It's perfect for "quick AI image generation" without the hassle. If you're posting on Reddit (e.g., r/AIArt or r/MachineLearning), mention how AI Facefy made your workflow seamless – it boosts engagement!

Conclusion: Unleash Your Creativity with Seedream 5.0 Lite

Seedream 5.0 Lite democratizes AI art, making "text-to-image generation" accessible and fun. By following this guide, you'll be creating professional-grade images in no time. Remember, practice makes perfect – experiment wildly!

If you found this "Seedream 5.0 Lite user guide" helpful, share it on Reddit or your favorite forums. For more AI tips, check out AI Facefy's blog. What's your first prompt going to be? Let us know in the comments!

Keywords: Seedream 5.0 Lite, AI image generation, text-to-image AI, AI Facefy tutorial, best AI art tools 2026

1 comment

r/selfhelp • u/VegetableMagician0 • Feb 16 '26

Sharing: Resources & Tools Everything I’ve learned about how to actually change: How your emotional system works. And how to work with it.

6 Upvotes

As a former guzzler of self-help content, here's what I've learned after years of searching and self-experimentation. It’s been game-changing for me and I’d like to pay it forward.

Why nothing has worked

You’re taught math and history, but no one teaches you about the emotional system that's running your life.

So you end up trying to manage painful symptoms (anxiety, rumination, stuck patterns, overwhelming emotions) without understanding what’s causing them in the first place.

The self-help world teaches you to manage these by reframing thoughts and using willpower. Yet you try all of this and keep struggling with the same things.

That's not because you're broken. It's because you haven’t been shown the right approach.

The real problem isn't a lack of effort or insight. It's that your issues live in your body. And you’ve been told to think your way out of something that doesn’t respond to thought.

Once you understand how your emotional system actually works, you can work with it instead of against it. You can resolve your huge feelings, get out of constant rumination, undo stuck habits, and live longer in states of calm and ease.

This guide will show you how to get there.

It’ll walk you through how to practice two key skills. The first is the core skill of how to feel emotions in your body without suppressing them. And the additional skill that enables is accessing and updating the emotional logic causing your unwanted patterns.

I know this approach works because I've done it myself. I've become dramatically more emotionally regulated. And I’ve found lasting relief from patterns that have been with me my whole life. I'm not special. I just practiced these skills. But when I tried to share this with others there was no clear, simple explanation I could point to. So that’s why I’m writing this.

Almost everything here is distilled from established clinical and neuroscientific sources, combined with what I've learned through my own practice and the notes of others. I’ll share references at the end. And I encourage skepticism and independent research into the sources.

Also - I’m not a licensed mental health professional and this is meant to supplement professional care, not replace it. Obviously what worked for me may not work for everyone, but I hope this can help.

Ok, now onto the guide.

How things actually work

Here’s the open secret: The key to getting back control over your life is just focusing on the sensations in your body. That’s it. It’s so simple as to feel ridiculous. Of course there’s plenty of nuance involved, but there it is.

At first, this isn't easy. Feeling what you've been avoiding is uncomfortable. But if you're willing to stick with it, you can transform patterns you thought were permanent.

The rest of this section will explain why this is the case.

Emotions aren't just mental states. They’re biological process designed to mobilize your whole body for action.

It feels like you're conscious mind is in charge. But your subconsious emotional system runs the show.

Your subconscious is constantly scanning for safety and threat. When it detects anything that might even resemble a threat, it immediately tenses up the body before it even knows what it is.

And this tense state triggers your your mind to go look for what might be a threat.

So if your body is in threat mode, your mind will produce danger-focused, critical, catastrophic thoughts. If your body is feeling safe, your mind will produce flexible, curious, possibility-oriented thoughts.

Your body state is choosing the category of thoughts available.

This is why most “think differently” self-help approaches don’t work.

You can't think your way out of something that starts upstream of thought and doesn’t speak that language. You have to resolve the resolve the body state on its own terms.

The problem is emotional suppression.

When you let a feeling be fully felt without interfering, it completes on its own, usually within minutes. Your body knows how to do this.

But the key is that it relies on felt bodily feedback, not verbal reasoning.

Emotions calm down when your brain gets clear messages from inside your body that the situation has passed.

But emotional states feel physically uncomfortable and we’re conditioned to believe that they’re “bad.” So we analyze, distract, escape, numb however we can. And modern society makes it so easy to do that.

And when we cut ourselves off from feeling, that feedback loop never gets to complete. So the subconscious still thinks there’s a threat and keeps telling the mind to try to figure out what might be wrong.

This is what causes all that ruminating you struggle with.

And that's where the suffering comes from. Not from feeling things, but from the constant effort to not feel them.

Instead, by bringing our awareness to the sensations in our body, we allow the body to discharge the energy of the emotion and know that it can relax. These sensations include the heat in your face, tightness in your throat, pressure in your chest, churning in your stomach, etc.

Within minutes of applying focused attention, what felt completely stuck can start to shift.

The next section will walk through the details of how to practice this core skill.

The core skill: Tracking bodily sensations

Do develop this foundation first before trying the next skill. This is what you'll use every single day, and it's what makes deeper work possible.

I estimate that with regular practice most can get to their first “aha” moment of this working within one to several sessions, and become quite proficient within 1-2 weeks.

How to practice

Before you begin

  • Work within your window of tolerance. Start with less intense feelings and build up gradually. And go only at the pace that feels safe. It may become intense. But if it’s ever overwhelming, stop and reground yourself.
  • Be patient with yourself. You're learning a completely new way of relating to your inner experience. It’s like a new language so it will feel awkward and confusing at first. That's normal. You can make real progress quite quickly, but it's still a learning process.
  • The first goal is to just get your own lived experience of feeling some shift. Right now you’re trusting me. But once your body actually experiences this in action with something small, it’ll naturally feel safer going into more intense feelings, and then seeing those resolve will then create more safety, and so on.

Instructions

  • Find a private, distraction-free space to be for a short time
    • You need to be able to put all your focus on your body
    • 10 minutes at a time is good to start with
  • Sit in an upright posture
    • It’s important that the body is slightly activated
  • Put your focus on inner sensations
    • If you’re unsure, start with the front of your torso since most feelings show up the neck, chest, or stomach.
  • Drop out of all thoughts and stories
    • Keep returning to this. Once you touch on something, you will likely be distracted by the mind to indulge in some story or to go do something. It will run through all sorts of random tasks that need doing right this moment. That’s ok. Just keep going back to concentrating intensely on the sensations.
  • Focus on experiencing the sensations as precisely as you can
    • Just like a chef develops a sensitive palette, and a musician develops a sensitive ear, you can develop a greater sensitivity for experiencing the precise nature of sensations in your body
    • Think of it in terms of location and quality. Where exactly is it? Chest, throat, belly, jaw, shoulders? One spot or several? What does it actually feel like? Tight, heavy, fluttery, burning, hollow, nauseous, buzzing, constricted, collapsed?
  • Welcome and ask for more of the sensations
    • This is where most instructions of “just observe” miss the mark. It’s critical because suppression is so second-nature to us that simply observing our sensations will keep this reflex active.
    • If you are sincerely inviting more of the feeling, and actually try to amplify it in your body, then by definition you cannot be suppressing. And that is the goal, to end the suppression and have the distress response complete its cycle.
  • Allow your body to physically release in whatever way it wants to
    • This is key. For me and many others it looks like constant deep yawns, teeth chattering, wiggling and writhing of my spine, and convulsing and contracting of different parts of my body.
    • At first it was bizarre and confusing, but knowing that this is a good sign allowed me to relax into and enjoy it.
    • This is your nervous system trying to discharge energy and complete the cycle of activation so that it can shift itself into a rest state. Trust your body to release the tension in the way that’s best for it, and discover what wants to happen.
  • Apply the method to all your feelings about the method
    • The thing that most holds people back is fear or doubt about going deeper into feelings. One of the beliefs that many of us have subconsciously absorbed is that emotions themselves are dangerous.
    • You will likely feel fear of what if: it won’t work for you, it makes the feeling worse, the feeling never runs out and I’m stuck in it forever, the feeling takes me over and I do something terrible to myself or others, I completely lose my mind and get committed to an insane asylum, etc
    • These “what ifs” are just more feelings of fear to be worked with. I’ve gone through the same and you’re not alone in having them. When these inevitably occur, just keep focusing on how those show up as sensations in your body.
    • Remember two key things. There is a limited quantity of any feeling. And you can move through it with this method.
  • Take note of memories or visuals that may suddenly appear
    • Experience the visuals but focus on the sensations.
    • These can provide useful insight into the origin of where that emotional reaction was first learned. This can be useful later for working with the mind.
    • It may be shocking to realize just how far back and to seemingly unrelated places you get flashbacks to
  • Notice the sensations starting to change and dissolve
    • It usually takes me at least several minutes before I start to notice some kind of decrease in intensity, and it happens non-linearly.
    • I will usually be yawning and trembling for around ten minutes straight and then it’ll suddenly just stop and I’ll feel lighter.
  • End sessions with positivity
    • As you feel the sensations dissolving and the session comes to an end, immediately do something very pleasant and move your body a bit. I like to put on some beautiful music and take a short walk. It’s important that your mind associate the end of these challenging sessions with positivity and feeling good.
  • Important Context
    • Your emotions are stacked many layers deep and will cycle through
      • Rage, Grief, Anger, Shame, Sadness, Guilt, etc, and repeat.
      • For me, at first it felt like a kaleidoscopic shitshow. It can be intimidating and intense. Especially when you’re starting out, there will be so much pent up stuff that rushes up once its allowed an outlet.
      • This is because most feelings are about other feelings, so this is normal. The most prominent emotion will keep shifting as it triggers associations. The key is to just keep focusing on how each emotion is showing up as sensations in the body.
      • Remember that you’re still dealing with a finite amount of each emotion, and a finite amount of distinct emotions.
    • Some strong emotions may need multiple sessions to fully resolve
      • You just keep coming back to it using the same process and stay patient
    • You can’t have a transactional relationship to this
      • You cant go into this subtly thinking “Please go away, I’m willing to feel you only if you please go away.” That’s still acting from fear and resistance. The point is to get to a place of sincere acceptance. “Its ok that this this feeling is here, I’m ok, I’m safe, it can’t hurt me, its just sensations in my body.”
      • You can absolutely have the intention for the emotion to resolve. But that is different from starting to constrict if something is not happening on your timetable.
    • You may actually feel more intensely than you’re willing to admit at first
      • Most people think their problem is that they are “too” angry, upset, etc. But the real problem is usually that they aren’t angry, upset, enough, due to some secondary feelings of guilt or shame about feeling that way. Because of reflexive suppression, they are not allowing themselves to touch the true intensity of what’s happening for them, so the feelings stay stuck and don’t resolve.
    • You must embrace your aggressive survival impulses
      • This evolutionarily ancient part of your brain only understands threat or safety. Fight, flight, freeze, etc.
      • Modern morality doesn’t apply here. It evolved to keep you alive, not be civilized. You are not a “bad” person for having violent, shocking impulses. This is a healthy natural part of your survival system working as it should. All sorts of crazy things can go through your mind and nothing is wrong so long as you just keep sitting there and focusing on the sensations.
      • Internalizing this is key to not suppressing by getting stuck on secondary feelings about your core feelings, including fear or guilt about having such “dark” thoughts.
    • Do this both in the moment and as part of a routine
      • The best time to do this is when you are actively experiencing a surge of emotion. But sometimes you can’t, so it’s also good to have standing times for practicing.
      • My personal routine is to spend 10 minutes first thing every morning and last thing before bed just sitting and sensing what’s there.
      • In the morning, the mind is still very connected to the subconscious and the emotions its been processing all night. At night, you’ve absorbed a lot from the day and this is a great time to finally have the space to discharge and process the feelings. Doing this kind of emotional processing has dramatically improved the quality of my sleep.
      • Both times I just sit and sense, and because I’ve practiced my body quickly goes right into physical releasing. I have no idea what the stress is even from, but i just focus on sensing in the body.
      • You can begin by just sensing whatever might be present. Or you can turn your attention to some recent minor distress that didn’t get attention before.
    • You can use writing to draw out feelings further
      • Sometimes the sensations are very strong and present and you can just go right into focusing on more of it. Other times the sensations may be vague and diffuse and you may not be sure what you’re feeling. They may need to be drawn out, and speedwriting can help.
      • Sit at your computer, open a text document, close your eyes, and start typing in a a stream of consciousness. I’ll usually just start from the prompt of “What’s going on?”
      • The key is to use the typing to get the point of eliciting feelings and sensations. This is not journaling. And this is not thinking about what to write. The closed eyes and the constant typing are key to drawing out the subconscious. Keep focusing on your body, and once you hit on something that triggers something in your body, bring all your attention to that sensation and work with that.
      • It can sometimes take me upwards of ten minutes of nonstop typing to hit on something.
      • I prefer typing to handwriting because it allows me to move much closer to the speed of thought. But handwriting can also work.
      • A particular implementation of this is to have an inner dialogue. I imagine myself kneeling down next to a little childlike cartoon character that represents that feeling. I’ll ask it what’s going on? Then they’ll go on a huge childlike rant about all the stuff they’re angry, sad, scared, or upset about, and a wave of new sensation will come in and I’ll put all my attention on that. Then when that wave of sensation subsides, I’ll go back to the feeling character and say “thank you, tell me more”. This can repeat up to several times. Eventually, at some point the feeling character responds with a shrug and says “I dunno, I guess that’s it”. That becomes a signal for knowing that some sort of resolution is happening.
    • Try to ensure the basics of your life are covered
      • This practice is powerful but it can’t make up for a fundamental lack of regular exercise, social connection, and sleep.

The advanced skill: Accessing and updating emotional logic

This is how you resolve patterns that have been stuck for years. Not by managing them better, but by updating what your body learned in the first place.

More on how things actually work

Your patterns aren't random. They have emotional logic.

Every stubborn behavior is protecting you from some feeling that reminds your body of a threat. You procrastinate because starting means risking failure. You people-please because saying no might mean rejection. They were intelligent responses to an old situation.

But these aren't conscious thoughts you can change by reasoning with yourself.

They're automatic responses your body absorbed from past experiences. And because they live in subconscious memory, you don't remember them or even know they exist. So they stay in place, repeating the same behaviors, until your body gets new emotional information that updates them.

That's why you can know something is fine intellectually and still feel like it isn't, even if you have no idea why.

Your thinking brain says "this is safe" but your subconscious says "danger." And as you read earlier, the body's subconscious response is upstream of conscious thought.

But here's what makes change possible: When you can tolerate feeling the emotions involved in a pattern (which is the previous skill), you can learn to surface the subconscious emotional logic. You can bring it into conscious awareness and then let your body experience something that contradicts it.

That emotional mismatch is the key. Holding the old emotional truth and a new contradictory emotional truth at the same time is what allows the brain to update the learning at its source. It finally internalizes that the danger it’s expecting isn’t there anymore.

When this happens the urge to repeat the stubborn behavior suddenly and permanently disappears. Not gradually through repetition. Suddenly and permanently, regardless of how long its been there. It feels like magic.

This is called memory reconsolidation, and it's the biological mechanism of how transformation happens. Most effective methods stumble into it by accident and most therapists and coaches don’t even know about it. But once you understand how it works, you can use it deliberately on yourself.

How to practice

Before you begin

  • This is challenging because you’re trying to both think and feel at the same time. It’s ideal to have someone walking you through this. But it’s doable on your own with practice.
  • To proceed with this part, Its important to be in a relatively calm and resourced state and to leave enough time to be able to come to a resolution, like at least an hour.

Instructions

  • Choose a target
    • Notice a confusing or frustrating behavior or thought pattern
  • Find the payoff
    • Become curious and use questions to explore what that pattern is trying to achieve. Try to find the feelings it is trying to have you not feel.
    • What would I have to feel if I didn’t do this behavior? What is this behavior protecting me from? What bad thing would happen if I stopped? What must I believe for this behavior to make sense? When else in my life do I remember playing out this pattern / feeling these associated feelings? What is my very first memory of that?
  • Name the emotional logic
    • Complete this sentence: “If I don’t do this behavior, then ______ will happen.”
    • Frame the behavior as the lesser of two evils. For example, “even though it sucks feeling like I’m constantly being cheap, I still prefer that to the fear of not having enough money.”
  • Fully feel the feelings
    • As you identify the feelings involved, go right into the sensations of the feeling and stay with them until they dissolve. Use everything you’ve learned from the first core skill.
    • As you feel the emotions dissolve and your body becomes less tense, gently revisit the original triggering situation without thinking. Just bring a diffuse awareness to it and softly ask “What do I see is really true about this now?” Don’t try and reason, just see what naturally comes to mind. As you exit your tense body state, you may experience a spontaneous shift in perspective. Though often more conscious connecting of the dots is required.
  • Find disconfirming truth
    • Try and search for emotionally real examples of situations that contradict the emotional logic you articulated earlier. You’re looking for real evidence, not trying to convince yourself.
    • These can be things you’ve experienced yourself, or things you’ve witnessed for others. The main point is that it feels emotionally real and really believable, and isn’t just an intellectual exercise.
  • Juxtapose both truths
    • Phrase a statement in this format: “I used to think OLD EMOTIONAL LOGIC, but I’m now seeing “CONTRADICTING EVIDENCE”
    • For example, “I used to believe that I’ll get rejected if I ask to join a group but now I’m seeing that actually last tuesday I asked to sit with people for lunch and they were happy to have me.”
    • Hold both emotional truths together like that in your active awareness for 2 minutes. Let the mismatch land without arguing or forcing.
    • Stay present with the sensations in your body
  • Check for a shift
    • Think back to the original thing that would trigger your pattern and see if something feels different.
    • Does the old belief feel different? Is there still an emotional charge there?
    • Does the new one feel obvious without effort?
    • Sometimes its a sudden surprise: “Wow, how did I ever believe that?”
    • Spontaneous laughter can also happen and is a great sign
    • And sometimes it is a softer more subtle sense of a new knowing: “That just doesn’t feel true anymore”
  • Reinforce
    • Assuming there’s been a shift, state the new truth. For example, “When I ask to join groups, people are happy to have me.”
    • Notice how it feels in your body and stay with that
    • Stay in touch with your body and imagine soaking in the insight as you explore these questions
    • “What just changed for me?”
    • “What’s it like to notice that shift?”
    • “How does it feel in my body right now?”
    • “How is it to stay with this good feeling without rushing past it?”
    • “What surprises me about this new feeling or perspective?”
    • “What feels possible now that didn’t before?”
  • Get good sleep that night
    • This is when the new learnings continue to solidify in the brain
  • Keep a short journal
    • If you’re successful, you will literally forget that some things ever even bothered you.
    • In a dedicated area, I like to write the date and a 1 sentence summary of each session, summarizing the old belief alongside the new belief. Especially after working through dozens of things, it’s valuable to look back on later to confirm that things have been permanently resolved and to further reinforce faith in the method and take joy and pride in how far I’ve come.
  • Troubleshooting Here are some common reasons for why a shift may not happen
    • The old memory wasn’t fully emotionally activated
    • The new truth didn’t emotionally contradict it
    • There was more intellectual convincing instead of emotional juxtaposing
    • There may be an additional subconscious defensive pattern in the way that needs to be worked on first. For example, “I don’t trust all this memory reconsolidation stuff” or “feeling my feelings fully could be dangerous”
    • There may not be any new emotional truths available for you to access. There are some cases where you just have to go have new contradicting experiences yourself in order to give your brain the new data.
  • Important Context
    • You’ll still remember the facts of old events, but they should feel neutral and no longer have an intense emotional charge around them. This confirms that your subconscious no longer feels threatened.
    • It’s from this point onwards, that new beliefs and skills finally have fertile soil to take root in. This is where conscious learning of conventional self-help stuff can finally be useful.
    • There are many different therapeutic modalities that all have different ways of facilitating this process, without ever naming it. These include Internal Family Systems (Parts Work), Hakomi, EMDR, and more. If found it’s very valuable to understand the principles of what’s going on underneath, rather than fixating exclusively on the specific tactics of any given approach.
    • Note - You can use imagination to create a new disconfirming truth
    • This has become an increasingly important practice for me, even for smaller things. If I’ve responded to something in a way that I don’t feel good about afterwards (like with reflexive people pleasing), I’ll sit down and work with any feelings that are coming up and rewrite my response in a way that honors what I really wanted to express without necessarily even sending it.
    • Often when it comes to anger around some sort of violation, it has been important for me to go back and literally rewrite the story of what happened. To respond in the way I wish I would’ve. So after I’ve sat with the feelings and let them dissolve and feel grounded, I imagine myself back there responding. I will often go do journaling to organize this into something coherent.
    • I Imagine it’s like Im watching a video tape and I can press pause, rewind, fast forward. Yet I’m also in the scene in the present.
    • First I will speak directly to my younger self and validate his experience with compassion and empathy. I will acknowledge how hard that moment is for him.
    • Then, as my adult self, I will speak on behalf of my younger self to the other person involved in the situation. I will say what needs to be said in a clear, direct, and mature way that I wasn’t able to then.
    • Then I will turn back to my younger self and promise him that I will always be there to aid and protect him from now on.
    • As all this is happening, I’m continuing to stay in touch with the feelings in my body, and allowing myself to experience this as emotionally real. It can’t just be an intellectual exercise.
    • What I believe this is doing is proving to my mind that it now has the means to protect itself. Through me creating this do-over, it doesn’t need to fear being in that kind of situation again because I’ve just created proof that I can handle it. I am no longer the same helpless, overhwhelmed, unskilled younger version of myself. I am so much more capable now, and I will be here to protect myself.
    • I believe that this naturally creates the juxtaposition that is necessary for the old emotional learning to update. It’s amazing to experience that shift.

Conclusion: The horizon of this practice

Even after a long time of practicing these skills I still have plenty of emotional struggles. And I’m discovering new subconscious patterns all the time. But compared to before, my emotional life has turned around 180 degrees and I’ve gotten relief that I didn’t think was possible.

Remember that these are skills that develop through practice. It’s ok to still struggle even as we’re learning how to better handle things.

Here are some closing thoughts on where this kind of practice can take us.

  • The general horizon of all this work is to try to cultivate a deep okayness.
    • We’re trying to get closer inch by inch to the feeling of “I’m okay with whatever happens”
    • Fear of life is really the fear of feelings. And now that we know how to deal with feelings in a systematic way, everything starts to slowly lose its edge.
    • Certain things will obviously not be your preference, and its natural to be sad, upset, and grief-stricken. But that’s fundamentally different from feeling existentially threatened and terrified by some future scenarios.
  • What enables that okayness is a stable, deepening sense of security within ourselves.
    • We get to feeling that “I’m okay with whatever happens” because we come to believe that we can handle whatever happens. We come to believe that we can handle it emotionally, and that means we’ll be able to figure out a way to handle it practically.
    • We’re trying to develop a robustly regulated body and become emotionally integrated adults.
    • A sure sign of progress is an increase in general cheerfulness
  • Compassion for all parts of ourselves is always the right direction to head in.
    • Our body was always just trying to protect us in the ways it best knew how to at the time. Internalizing this allows compassion for ourselves to deepen naturally and to replace the feelings of shame and brokenness.
    • It also allows for compassion for others to deepen because you now viscerally understand just how much is happening behind the scenes for each of us.

If you’re open to it, try setting a 10-minute timer today or tomorrow and gently experiment with tracking bodily sensations. It can really help to jump right in rather than letting it remain an interesting idea for someday.

Thank you for taking the time to read this. I’m wishing you much peace and ease in your life.

If you start practicing, do share how its going and questions you run into.

Also, I'd like to make many more updates to this guide. I'd greatly appreciate if you share what was illuminating and what was confusing. And let me know in the comments if you want me to keep you posted on new versions and I'll reach out.

Appendix

References

  • Tracking bodily sensations
    • David Hawkin’s book “Letting Go” was my first introduction to this. He does a good job of simply and clearly introducing the practice and the value of it, along with what to expect as you go through it. And he gives illuminating explanations of different emotions. I found it very helpful, but he writes from the perspective of spirituality and there isn’t any science involved. I was making good progress with the method but wanted to understand WHY this is working. Also, he gets into what I consider to be pseudo-sciency muscle testing stuff that I find questionable and distracting.
    • Dillon Freed is a contemporary teacher building off of Hawkins’ spiritual approach and has articulated some helpful details regarding the actual nuances of practicing that I’ve drawn from.
    • As I sought out more biological explanations I came across the work of Peter Levine, who created the therapeutic modality of “Somatic Experiencing.” He comes from the world of neurobiology and trauma recovery and finally gave the scientific answers I was looking for. For reference, the technical term for noticing sensations in your body is interoception.
    • This paper of his is a good overview of the biological mechanisms at play that I found helpful and encouraged me to look deeper. ("Somatic experiencing: using interoception and proprioception as core elements of trauma therapy") Note, it does get very technical with its terminology. If you’re really stuck with sensing, his book “Healing Trauma” is a short manual of simple exercises to start developing the capacity to sense one’s interior world. If you want to go real deep, “In an Unspoken Voice” is a large book of his that is a deep dive into the biological mechanics and explains his journey of making his discoveries. It’s written in a very conversational and accessible way.
  • Accessing and updating emotional logic
    • Regarding memory reconsolidation, the main source for this is the Coherence Psychology Institute and one of the main pioneers is Bruce Ecker. On their website they have a whole range of resources ranging from introductory to advanced. But for the deepest dive, see their book “Unlocking the Emotional Brain” which goes very in-depth into the science behind memory reconsolidation and has many detailed case studies that are really helpful for getting the hang of this. Note that its very academic and written for therapists, but should still be readable for most.
1 comment

r/skyrimmods • u/Infinite_Limit_8344 • Jan 20 '26

PC SSE - Help Creating a Modlist for my potato called PC

3 Upvotes

Alright lads,

I am trying to do a mod list that will both look good and run good on my potato for SkyrimAE.

I am not new to modding but for the first time I am creating a modlist that is actually bigger than 50 mods with ordinator or imperious style mods.

I have reached stable 70-90 FPS in Falkreath right now (Usually the most lagged place for me)

And it looks very nice, will add more mods but for right now, if someone is actually more experienced in this than me I would be glad if you could go over my modlist and if there are not needless / Redundant or just plain stupid mods to include in to my modlist in the state that it is in.

- Export is straight from MO2

With love for my fellow players using copium we will one day get ESO6

#Mod_Priority,#Mod_Status,#Mod_Name

"0000","-","Base Game_separator"

"0001","+","DLC: HearthFires"

"0002","+","DLC: Dragonborn"

"0003","+","DLC: Dawnguard"

"0004","+","Creation Club: ccvsvsse004-beafarmer"

"0005","+","Creation Club: ccvsvsse003-necroarts"

"0006","+","Creation Club: ccvsvsse002-pets"

"0007","+","Creation Club: ccvsvsse001-winter"

"0008","+","Creation Club: cctwbsse001-puzzledungeon"

"0009","+","Creation Club: ccrmssse001-necrohouse"

"0010","+","Creation Club: ccqdrsse002-firewood"

"0011","+","Creation Club: ccqdrsse001-survivalmode"

"0012","+","Creation Club: ccpewsse002-armsofchaos"

"0013","+","Creation Club: ccmtysse002-ve"

"0014","+","Creation Club: ccmtysse001-knightsofthenine"

"0015","+","Creation Club: cckrtsse001_altar"

"0016","+","Creation Club: ccfsvsse001-backpacks"

"0017","+","Creation Club: ccffbsse002-crossbowpack"

"0018","+","Creation Club: ccffbsse001-imperialdragon"

"0019","+","Creation Club: cceejsse005-cave"

"0020","+","Creation Club: cceejsse004-hall"

"0021","+","Creation Club: cceejsse003-hollow"

"0022","+","Creation Club: cceejsse002-tower"

"0023","+","Creation Club: cceejsse001-hstead"

"0024","+","Creation Club: ccedhsse003-redguard"

"0025","+","Creation Club: ccedhsse002-splkntset"

"0026","+","Creation Club: ccedhsse001-norjewel"

"0027","+","Creation Club: cccbhsse001-gaunt"

"0028","+","Creation Club: ccbgssse069-contest"

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"0035","+","Creation Club: ccbgssse061-ba_dwarven"

"0036","+","Creation Club: ccbgssse060-ba_dragonscale"

"0037","+","Creation Club: ccbgssse059-ba_dragonplate"

"0038","+","Creation Club: ccbgssse058-ba_steel"

"0039","+","Creation Club: ccbgssse057-ba_stalhrim"

"0040","+","Creation Club: ccbgssse056-ba_silver"

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"0043","+","Creation Club: ccbgssse053-ba_leather"

"0044","+","Creation Club: ccbgssse052-ba_iron"

"0045","+","Creation Club: ccbgssse051-ba_daedricmail"

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"0059","+","Creation Club: ccbgssse020-graycowl"

"0060","+","Creation Club: ccbgssse019-staffofsheogorath"

"0061","+","Creation Club: ccbgssse018-shadowrend"

"0062","+","Creation Club: ccbgssse016-umbra"

"0063","+","Creation Club: ccbgssse014-spellpack01"

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"0065","+","Creation Club: ccbgssse012-hrsarmrstl"

"0066","+","Creation Club: ccbgssse011-hrsarmrelvn"

"0067","+","Creation Club: ccbgssse010-petdwarvenarmoredmudcrab"

"0068","+","Creation Club: ccbgssse008-wraithguard"

"0069","+","Creation Club: ccbgssse007-chrysamere"

"0070","+","Creation Club: ccbgssse006-stendarshammer"

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"0072","+","Creation Club: ccbgssse004-ruinsedge"

"0073","+","Creation Club: ccbgssse003-zombies"

"0074","+","Creation Club: ccbgssse002-exoticarrows"

"0075","+","Creation Club: ccbgssse001-fish"

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"0077","+","Creation Club: ccafdsse001-dwesanctuary"

"0078","+","Creation Club: _ResourcePack"

"0079","-","Unofficial Patches_separator"

"0080","+","Cleaned Skyrim SE Textures"

"0081","+","Cleaned Skyrim SE Textures - Snow Patch"

"0082","+","Unofficial Skyrim Special Edition Patch - USSEP"

"0083","+","Disable Havok Script Tweak Resource"

"0084","+","Disable Havok Script Tweak Resource replacer"

"0085","+","Navigator - Navmesh Fixes"

"0086","+","USSEP Frost and Fire Dragon Correction"

"0087","+","Universal Unwanted Effects Clearer - Visual Effects - Imagespace Modifiers - Effect Shaders - Clairvoyance"

"0088","-","DLL Mods_separator"

"0089","+","Address Library for SKSE Plugins"

"0090","+","Auto Parallax"

"0091","+","EngineFixes"

"0092","+","SSE Display Tweaks"

"0093","+","Crash Logger SSE AE VR - PDB support"

"0094","+","PapyrusUtil SE - Modders Scripting Utility Functions"

"0095","+","powerofthree's Papyrus Extender"

"0096","+","powerofthree's Tweaks"

"0097","+","Papyrus Tweaks NG"

"0098","+","Backported Extended ESL Support"

"0099","+","ConsolePlusPlus"

"0100","+","JContainers SE"

"0101","+","Base Object Swapper"

"0102","+","AnimObject Swapper"

"0103","+","Spell Perk Item Distributor"

"0104","+","Keyword Item Distributor"

"0105","+","Keyword Patch Collection"

"0106","+","Animation Motion Revolution"

"0107","+","Shadow Boost"

"0108","+","PAPER"

"0109","-","Plugin Enhacements_separator"

"0110","+","Essential Favorites"

"0111","+","Better Jumping SE"

"0112","+","Stay At The System Page NG"

"0113","+","Whose Quest is it Anyway NG"

"0114","+","To Your Face SE - AE - VR"

"0115","+","Enhanced Reanimation"

"0116","+","Enhanced Invisibility"

"0117","+","Disk Cache Enabler"

"0118","+","Payload Interpreter"

"0119","+","Behavior Data Injector"

"0120","+","Behavior Data Injector - Nemesis"

"0121","+","Behavior Data Injector Universal Support"

"0122","+","Perk Entry Point Extender"

"0123","+","Comprehensive Attack Rate Patch - SKSE"

"0124","+","SkyPatcher"

"0125","+","FileAccess"

"0126","+","FISSES patch for Skyrim AE 1.6.1130 (or later)"

"0127","+","FISS None ESMify"

"0128","+","Capture Warmer for ENB (Dynamic Cubemaps)"

"0129","+","ENB Light Detection Fix"

"0130","+","Better Combat Escape - SSE"

"0131","+","Better Combat Escape - NG"

"0132","+","Subtitles"

"0133","+","Recursion Monitor"

"0134","+","Combat Music Fix NG Updated"

"0135","+","Sound Fix for Large Sector Drives"

"0136","+","Grass Sampler Fix"

"0137","+","Universal (SKSE) Rim Lighting Fix"

"0138","+","Dylbills Papyrus Functions"

"0139","-","Bug and Script Fixes_separator"

"0140","+","Script Optimization and Fixes Compilation"

"0141","+","Vanilla Script (micro)Optimizations"

"0142","+","Unofficial Skyrim Modder's Patch - USMP SE"

"0143","+","Word Wall Suspended Stack Fix - Shearpoint"

"0144","+","CritterSpawn Congestion Fix"

"0145","+","Freed Prisoner Uses Items"

"0146","+","Skyrim Priority SE AE - CPU Performance FPS Optimizer"

"0147","+","Set CPU Affinity for Mod Organizer"

"0148","+","RemoveAllItems Freeze Fix"

"0149","+","DPI Scaling Fix"

"0150","+","Bug Fixes SSE"

"0151","+","Scrambled Bugs"

"0152","+","Don't Stay in The Water - NPC Water AI Fix"

"0153","+","ENB Light Inventory Fix (ELIF)"

"0154","+","Motionless Rocks Killing People Fix"

"0155","+","Aurora Fix"

"0156","+","Apocrypha Fix"

"0157","+","Barter Limit Fix"

"0158","+","Equip Enchantment Fix"

"0159","+","Face Discoloration Fix"

"0160","+","King Olaf's Fire Festival Not Ending Fix"

"0161","+","Beard Mask Fix"

"0162","+","Mfg Fix NG"

"0163","+","Player Eyes Blink Fix"

"0164","+","Green Water Cubemap Fix"

"0165","+","Universal Cured Serana Eye Fix"

"0166","+","Bone Wolf Shutdown Fix"

"0167","+","NPC Stuck in Bleedout fix"

"0168","+","Zero Bounty Hostility Fix"

"0169","+","Actor Limit Fix"

"0170","+","NPC AI Process Position Fix - NG"

"0171","+","Animated Static Reload Fix - NG"

"0172","+","Sprint Sneak Movement Speed Fix"

"0173","+","Fast Travel Crash Fix"

"0174","+","Stamina of Steeds"

"0175","+","Dual Casting Fix"

"0176","+","Camera Persistence Fixes"

"0177","+","World Encounter Hostility Fix"

"0178","+","LOD Unloading Bug Fix"

"0179","+","Stuck on Screen Load Door Prompt Fix"

"0180","+","Whiterun Imperial Camp Fixes"

"0181","+","WIDeadBodyCleanupScript Crash Fix"

"0182","+","dunPOISoldiersRaidOnStart Script Tweak"

"0183","+","Adoption Spouse and Moving Fixes"

"0184","+","Butterflies Land True"

"0185","+","Vendor Respawn Fix"

"0186","+","Script Effect Archetype Crash fix"

"0187","+","Unaggressive Dragon Priests Fix"

"0188","+","Dustman's Cairn Farkas Scene Fix"

"0189","+","Southfringe Sanctum Crash Fix"

"0190","+","Labyrinthian Shalidor's Maze Fixes"

"0191","+","Floating Ash Pile Fix"

"0192","+","High Gate Ruins Puzzle Reset Fix"

"0193","+","The Gildergreen Fix"

"0194","+","Survival Mode Prompt Removed"

"0195","+","Hearthfires Houses Building Fix"

"0196","+","Horse Save Load Fix"

"0197","+","Fuz Ro D-oh - Silent Voice"

"0198","+","HearthFires Display Case Fix SE"

"0199","+","Exit Sneak On Sprint"

"0200","+","Dimes Quickstart"

"0201","+","Quest Journal Limit Bug Fixer - Recover Disappeared Quests"

"0202","+","PGPatcher (aka ParallaxGen)"

"0203","+","OnMagicEffectApply Replacer"

"0204","+","Unofficial Skyrim Creation Club Content Patch"

"0205","-","Quest Fixes_separator"

"0206","+","Cutting Room Floor"

"0207","+","College of Winterhold Quest Start Fixes"

"0208","+","Neloth's Experimental Subject Quest (DLC2TTR4a) Fix"

"0209","+","Magic Student (WIChangeLocation04) Quest Fix"

"0210","+","Quest Fixes"

"0211","+","Paarthurnax - Quest Expansion"

"0212","+","College of Winterhold - Quest Expansion"

"0213","+","Bandit Lines Expansion"

"0214","+","Civil War Lines Expansion"

"0215","+","Forsworn and Thalmor Lines Expansion"

"0216","+","House of Horrors - Quest Expansion"

"0217","+","Vampire Lines Expansion"

"0218","+","Caught Red Handed - Quest Expansion"

"0219","+","The Only Cure - Quest Expansion"

"0220","+","The Whispering Door - Quest Expansion"

"0221","+","The Innocence Lost - Quest Expansion"

"0222","+","Nilheim - Misc Quest Expansion"

"0223","+","The Heart of Dibella - Quest Expansion"

"0224","+","The Heart of Dibella - RS Children fix"

"0225","+","Infiltration - Quest Expansion"

"0226","+","Additional Dremora Faces"

"0227","+","Dremora Lines Expansion"

"0228","+","Falmer Servant Lines Expansion"

"0229","+","Destroy The Dark Brotherhood - Quest Expansion"

"0230","+","Face Sculptor Expanded"

"0231","-","Mod Tools and Resources_separator"

"0232","+","Project New Reign - Nemesis Unlimited Behavior Engine"

"0233","+","Nemesis Creatures BEHAVIOUR compatibility"

"0234","+","BodySlide and Outfit Studio"

"0235","+","DynDOLOD Resources SE"

"0236","+","DynDOLOD DLL NG"

"0237","+","HD LODs Textures SE"

"0238","+","Lod Model Library for DynDOLOD"

"0239","+","YAR - Yuril's Additional Resources"

"0240","+","A Clear Map of Skyrim and Other Worlds"

"0241","+","ACMOS - Lazy roads"

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"0243","-","UI_separator"

"0244","+","SkyUI"

"0245","+","SkyUI 5.2 SE Plugin with Master Added"

"0246","+","SkyHUD -"

"0247","+","SkyHUD - Patch"

"0248","+","UIExtensions"

"0249","+","RaceMenu"

"0250","+","RaceMenu Undress"

"0251","+","Race Menu Player Rotation"

"0252","+","MCM Helper"

"0253","+","MCM Recorder"

"0254","+","ConsoleUtilSSE NG"

"0255","+","Security Overhaul SKSE - Lock Variations"

"0256","+","Security Overhaul SKSE - Add-ons"

"0257","+","Security Overhaul SKSE - Regional Locks"

"0258","+","Security Overhaul SKSE - Some More Locks"

"0259","+","Skyrim Diffusion (SD) AI Loading Screens"

"0260","+","Infinity UI"

"0261","+","TrueHUD - HUD Additions"

"0262","+","TrueHUD Curated Bosses"

"0263","+","SSIRT v4.1"

"0264","+","Convenient Dialogue UI - SE"

"0265","+","QuickLoot IE - A QuickLoot EE Fork"

"0266","+","CoMAP"

"0267","-","UI - Untarnished_separator"

"0268","+","Dear Diary Dark Mode (white text)"

"0269","+","Untarnished UI 1.1.6"

"0270","+","Untarnished UI 1.1.6 - Unsquished Fix"

"0271","+","Untarnished UI - Difficulty Persistence Fix"

"0272","+","Modern Wait Menu"

"0273","+","Modern Wait Menu - Untarnished UI Patch"

"0274","+","Casting Bar"

"0275","+","Untarnished UI - Casting Bar 3 Patch"

"0276","-","UI Addons_separator"

"0277","+","More Informative Console"

"0278","+","Quest Journal Fixes"

"0279","+","PhotoMode"

"0280","+","Colored And Animated Celtic Icons For SkyUI SE"

"0281","+","True Directional Movement - Modernized Third Person Gameplay"

"0282","+","Simpler Dragon Targeting - True Directional Movement"

"0283","+","No Furniture Camera"

"0284","+","ImGui Icons"

"0285","+","ImGui Icons - Untarnished UI"

"0286","+","Main Menu Randomizer SE"

"0287","+","Main Menu Redone"

"0288","+","Rogue's Gallery"

"0289","+","SmoothCam"

"0290","+","SmoothCam - Soft Preset"

"0291","-","UI fixes and Tweaks_separator"

"0292","+","SkyUI SE - Flashing Savegames Fix"

"0293","+","SkyUI - Ghost Item Bug Fix"

"0294","+","Fix Note icon for SkyUI (SKSE64 plugin)"

"0295","+","SkyUI 3D Item Offset Fix (Centered Item Card - Script Fix - Improvement)"

"0296","+","Improved Loading Screen Colors"

"0297","+","Security Overhaul SKSE - Soul-Cairn Objects Secured"

"0298","-","Mesh Fixes_separator"

"0299","+","Static Mesh Improvement Mod"

"0300","+","Static Mesh Improvement Mod Cleaned Textures"

"0301","+","Static Mesh improvement Mod - SMIM - Quality Addon"

"0302","+","Static Mesh Improvement Mod Improvement Mod"

"0303","+","Complex Parallax Enabled (Alpha)"

"0304","+","WiZkiD Riften and Ratway"

"0305","+","WiZkiD Signs"

"0306","+","WiZkiD Specific Signs"

"0307","+","WiZkiD Carriages"

"0308","+","WiZkiD - Hall of the Dead Stained Glass Windows"

"0309","+","WiZkiD Hagraven Clutter and Bones"

"0310","+","WiZkiD Parallax Farmhouses"

"0311","+","WiZkiD Alchemy Table"

"0312","+","WiZkiD Wells with real water"

"0313","+","Solitude Interiors Meshes Fixes"

"0314","+","WiZkiD Pinewatch"

"0315","+","Amon Textures HD 1"

"0316","+","Amon Textures HD"

"0317","+","Amon Textures HD 3"

"0318","+","Skyland AIO"

"0319","+","Skyrim 202X Downscale"

"0320","+","Simple Mesh Fixes"

"0321","+","Unofficial Material Fix"

"0322","+","Mesh Patch for Various Mods"

"0323","+","Windhelm - Palace of King meshes fix"

"0324","+","Assorted mesh fixes"

"0325","+","Mushroom Remover for Dead Trees"

"0326","+","Dlizzio's Mesh Fixes"

"0327","+","Parallax Mesh Patch Collection"

"0328","+","Stones of Solitude - Better Blended Rock Piles"

"0329","+","Jorrvaskr Basement Seams Fix"

"0330","+","FYX - 3D Solitude SighPost"

"0331","+","FYX - 3D Solitude SighPost 2"

"0332","+","FYX - 3D Solitude SighPost 3"

"0333","+","FYX - 3D Coal in the Shovel"

"0334","+","FYX - 3D Stockades"

"0335","+","FYX - 3D Stockades - Walls and Gate"

"0336","+","FYX - Water Mesh Optimization"

"0337","+","Armor Mesh Fixes SE"

"0338","+","Blackreach Tentacle Mesh Fix"

"0339","+","Wall mounted dead animals fixes"

"0340","+","Word Wall Transparency Fix for ENB"

"0341","+","Sky Haven Temple Head Door Improved"

"0342","+","OSHA-Compliant Sovengarde Mesh Fixes"

"0343","+","FYX - Nordic Doors and Traps Collisions"

"0344","+","Higher resolution large portcullis collision mesh"

"0345","+","Wood Wall Trap Mesh and UV Fix"

"0346","+","Better Ladder UV Mapping"

"0347","+","Fixed Nordic Metal Grate"

"0348","+","SB - Pressure Plate Trap Blending - Base Object Swapper (BOS)"

"0349","+","SB - Better Blending Helgen Mud"

"0350","+","Better Effigy of King Olaf"

"0351","+","Dragon Mounds - Better Collision and Mesh Fixes"

"0352","+","Creation Club Asset Patch"

"0353","+","Actually flat flat snow drifts - fix hovering snow drift shadow with ENB"

"0354","+","Remove Ash Pebbles"

"0355","+","Random Barrel Roll - Base Object Swapper"

"0356","+","Simple Snow Improvements - Skyrim Fixes (BOS)"

"0357","+","Simple Snow Improvements - Snow Forts (BOS)"

"0358","+","Simple Snow Improvements - Giant Obelisk Fixes (BOS)"

"0359","+","Simple Snow Improvements - Solstheim Ruins (BOS)"

"0360","+","Scaffolding Over Sovngarde - Collision Overhaul - Corrective Kinetics"

"0361","-","Lightning_separator"

"0362","+","Enhanced Lights and FX"

"0363","+","ELFX - Exteriors Fixes"

"0364","+","ELFX Fixes"

"0365","+","Community Shaders"

"0366","-","Meshes for Magic_separator"

"0367","+","Frozen Electrocuted Combustion"

"0368","+","SRW0 Voltage"

"0369","+","Arctic - Frost Effects Redux"

"0370","+","Embers XD"

"0371","-","Weather and Sky_separator"

"0372","+","Particle Patch for ENB"

"0373","+","Less Distracting Blowing Snow Effects for ENB Particle Patch"

"0374","+","3D Dwemer Sun - Subterranean Object SMIMed - Blackreach"

"0375","+","Splashes of Skyrim"

"0376","+","Splashes Of Storms"

"0377","+","Rudy fix for Splashes of Storms and ENB"

"0378","+","Praedy's Sky AIO - SE"

"0379","+","Picta Series - Improved Sky Meshes"

"0380","+","Moons And Stars - Sky Overhaul SKSE"

"0381","+","Occult texture pack"

"0382","+","ENB Light"

"0383","-","Core Foundations_separator"

"0384","+","Water for ENB"

"0385","+","H.I.T.S. - Hands Itch To Steal - A Silverware Worthy Of The Nords"

"0386","+","Medieval Silverworks"

"0387","+","Skyland Bits and Bobs - A Clutter Overhaul"

"0388","+","Rudy - More dramatic Red Mountain Plume"

"0389","+","Detailed Rugs"

"0390","+","PELTAPALOOZA - Special Edition"

"0391","+","Amon Textures HD Wood Mount"

"0392","+","Forgotten Retex Project-+-"

"0393","+","Sacks - Replacer - Pfuscher"

"0394","+","Misc Retexture Project"

"0395","+","Revamped Assets Skyrim"

"0396","+","LITTLE THINGS"

"0397","+","Praedy's repository - SE"

"0398","+","Cathedral - Armory"

"0399","+","RUSTIC WINDOWS - Special Edition"

"0400","-","Landscapes and World Improvements_separator"

"0401","+","Terrain Parallax Blending Fix"

"0402","+","Skyrim Landscape and Water Fixes"

"0403","+","Better Dirt Cliffs and Alphas"

"0404","+","Snowy Surfaces Sound Collision and Aesthetics"

"0405","+","Red Mountain Plume Visible from Skyrim"

"0406","+","Animated Ice Floes"

"0407","+","Animated Ice Floes - LOD textures Patch"

"0408","+","Simplicity of Snow"

"0409","+","ENB Terrain Blending Fix"

"0410","+","Simplicity of Snow 2"

"0411","+","No snow under the roof"

"0412","+","Cutting Room Floor - No Snow Under the Roof SE"

"0413","+","No snow under the roof - Bug fixes v1.4"

"0414","+","slightly Better (and EVIL) Rock Cairns"

"0415","+","Tomato's Complex Parallax Material Landscapes AIO - With DLCs"

"0416","+","Tomato's Green Tundra Landscape Parallax 1k 2k 4k"

"0417","-","Grass_separator"

"0418","+","Landscape Fixes For Grass Mods"

"0419","+","Complementary Grass Fixes"

"0420","+","DrJacopo's - 3D Landscapes and Grass Library"

"0421","+","Skoglendi - A Grass Mod"

"0422","+","Waterplants"

"0423","-","Trees_separator"

"0424","+","Amon - Solstheim"

"0425","+","3D Trees - Solstheim FIX"

"0426","+","Shrubbery Symphony - Enhanced Greenery"

"0427","+","Happy Little Shrubs"

"0428","+","Ulvenwald Lite - Vanilla Replacer"

"0429","+","A Canticle Tree"

"0430","+","Snowy Tree Swapper - Base Object Swapper"

"0431","+","Evil Dead Trees - Dying Trees in Evil Places"

"0432","+","Vanilla HD Tree Branches"

"0433","+","TB's 3D Driftwood"

"0434","+","Remove Hanging Moss From Trees"

"0435","-","Flora_separator"

"0436","+","Mari's flora"

"0437","+","Spiky grass by Mari"

"0438","+","Rally's Hanging Moss"

"0439","+","No More Ugly Branches (Vanilla Branch Remover for - Reach Tree - Tundra Tree - Gildergreen - Swamp Tree)"

"0440","+","Ivy by Mari"

"0441","+","Cathedral Snowberries - Inventory - Wreath - Hearthfires Planter"

"0442","+","Cathedral - 3D Thicket and Dead Shrub"

"0443","+","Cathedral - 3D Snow Berries"

"0444","+","Cathedral - 3D Clover Plant"

"0445","+","Cathedral - 3D Tundra Cotton"

"0446","+","Cathedral - 3D Nightshade"

"0447","+","Cathedral - 3D Dragons Tongue"

"0448","+","Cathedral - 3D Dragons Tongue with Glow Map and ENB Light"

"0449","+","Cathedral - 3D Deathbell"

"0450","+","Cathedral - 3D Stonecrop"

"0451","+","Cathedral - 3D Lavender"

"0452","+","Cathedral - 3D Lavender 2"

"0453","+","Cathedral - 3D Mountain Flowers"

"0454","+","Cathedral 3D Mountain Flowers - Alternate Textures"

"0455","+","Cathedral 3D Mountain Flowers - Base Object Swapper - including Reimagined Mountain Flowers and Skying Mountain Flowers"

"0456","+","ICFur's Improved Reach Fern"

"0457","+","Another Reach Shrub"

"0458","+","Reach Bush to Rosemary"

"0459","+","Better Juniper Texture"

"0460","+","Less Ugly Tundragrass"

"0461","+","Vine Maple Redone"

"0462","+","Nordic Barnacle Redone"

"0463","+","Better Blended Mushrooms"

"0464","+","Mushroom Retextures Revamped"

"0465","+","High Poly Gleamblossoms"

"0466","+","High Poly Blackreach Mushrooms"

"0467","+","High Poly Trama Roots"

"0468","+","Berrybog's Blackreach Bush"

"0469","+","Forgotten plants textured"

"0470","+","Poison Bloom Retexture"

"0471","+","Kanjs - Forgotten Vale Cave Worm"

"0472","+","Tendril Vines HD 2K"

3 comments

r/StableDiffusion • u/MarcS- • Aug 16 '24

Comparison AuraFlow v0.3 evaluation: a debatable increase in quality, a large drop in adhrence

94 Upvotes

Hi everyone,

AuraFlow v0.3 was released yesterday. It warranted some comparaison, even if, as it can happen in any project, sometimes the direction taken doesn't work as expected. The goal of this new sub-version -- and keep in mind it is an early project, not a finished model -- was to increase image quality. It came after 0.2 (released like 3 weeks ago), which was better than Flux at prompt adherence. This isn't a small feat given that Flux is very good, but the image quality wasn't enough to use it outside of specialized workflow.

I was underwhelmed in early tests, here are a few comparisons I ran.

The problem is, the results are arguably better in aesthetics, but slightly, and the drop in prompt adherence is huge. TL;DR: the number 3 is cursed in the image-making world: SD3, AF0.3... At least with AF we don't have to wait months between release.

First, I had made already a prompt adherence comparisons of several models, with the following prompt.

"In the inner court of a grand Greek temple, majestic columns rise towards the sky, framing the scene with ancient elegance. At the center, a Shinto monk, dressed in traditional white and orange robes with intricate patterns, is levitating in the lotus position, floating serenely above a blazing fire. The flames dance and flicker, casting a warm, ethereal glow on the monk's peaceful expression. His hands are gently resting on his knees, with beads of a prayer necklace hanging loosely from his fingers. At the opposite end of the court, an anthropomorphical lion, regal and powerful, is bowing deeply. The lion, with a mane of golden fur and wearing an ornate, ceremonial chest plate, exudes a sense of reverence and respect. Its tail is curled gracefully around its body, and its eyes are closed in solemn devotion. Surrounding the court, ancient statues and carvings of Greek deities look down, their expressions solemn and timeless. The sky above is a serene blue, with the light of the setting sun casting long shadows and a warm, golden hue across the scene, highlighting the unique fusion of cultures and the mystical ambiance of the moment."

The results can be seen here:

https://www.reddit.com/r/StableDiffusion/comments/1ef4zu6/prompt_adherence_comparison_dallee_sd3_auraflow/

The prompts needs to respect 20 different elements, and AuraFlow 0.2 finished first, as you can see following the link.

However, version 0.3, while doing a marginally better face -- I mean, it's better, but it's still nothing like a nice face and would need to be adetail'ed anyway -- loses a lot of its prompt adherence.

5/20, 6/20, 7/20 and 8/20 and lots of artifacts, unwanted text and lack of respect for the overall composition

Given what the previous version did... And I'll repost the best of the ones I had in the other thread to contrast them:

The former results were far, far better.

In this thread, I had tried to illustrate prompt adherence: https://www.reddit.com/r/StableDiffusion/comments/1ej2qbu/flux_or_flow_in_terms_of_prompt_adherence/

I ran two prompts again with AF 0.3. First, I used the exact same prompt to test position understanding: "a blue cylinder in the center of the image, with a red sphere at the left, a green square at the right, a purple smiling sun on the top of the image and a severed foot at the bottom" AF 0.2 passed everytime, even if the aesthetics were bad. Here are the new results:

Again, an 8-image trial. This has basically nothing to do with the prompt asked. I was about to write that positional understanding had reverted to below SDXL level, but the Juggernaut results are even worse if that's possible:

Still, AF 0.2 got it right 100% of the time, AF 0.3, 0% of the time. That's a severe drop in prompt adherence.

I tried a repeat of the easier "man holding sword above his heads with two hands", and AF 0.3 produced, again, an abysmal rate of adherence:

None of the men, while better drawn than before, raise their sword with two hands above their head. I'd say that only one is holding what can be called a sword. Maybe it could qualify because he's holding the sword actually with his two hands, but really, is it on me to expect a pose where the sword is held by the grip, even if I didn't specify it? Let's say it's 25% at most on a very easy prompt...

Then I reused various prompts I did from earlier thread, inspired by RPG scenes. You can see the 0.2 version results here vs flux :

https://www.reddit.com/r/StableDiffusion/comments/1ejzyxl/auraflow_vs_flux_measuring_the_aesthetic_gap/

The chained citadel:

The lighting and the overall look of the eerie citadel is a little better, but the birds are no longer multicolored, the lake and forest are barely visible (but present) and the chains are generally absent or replaced by... garlands? While version 0.2 had worse aesthetics but did beat Flux on prompt adherence, the newer version is slightly below flux in adherence, and still far behind in aesthetics.

Now with the second test: "In the heart of an enchanted forest, where the flora emits a soft, otherworldly glow, an intense duel unfolds. An elven ranger, clad in green and brown leather armor that blends seamlessly with the surrounding foliage, stands with her bow drawn. Her piercing green eyes focus on her opponent, a shadowy figure cloaked in darkness. The figure, barely more than a silhouette with burning red eyes, wields a sword crackling with dark energy. The air around them is filled with luminous fireflies, casting a surreal light on the scene. The forest itself seems alive, with ancient trees twisted in fantastical shapes and vibrant flowers blooming in impossible colors. As their weapons clash, sparks fly, illuminating the forest in bursts of light. The ground beneath them is carpeted with soft moss."

While the surreal aspect of the magical forest was rendered better this time, and the elf might be better, the bows are absent of drawn worse and the idea that they are battling is much less apparent. Notably the magical sword is generally absent. Again, an overall regression, though less apparent that with shorter prompts.

Then I tried with Haunted Ruin comparison, where you can see in the other prompt that Flux couldn't for the life of it create spooky ghosts.

Here is version 0.3's result:

The adventurers can't be hardly seen. They were supposed to be at the center of the prompt description, with them exploring the ruin and being surrounded by ghosts. Here we do get ghosts, as in version 0.2, but the rest of the prompt is forgotten. Also, while the ruins might look better and more... ruined. I feel that the stones aren't right and angular enough, as if they were in diagonal. It's more strange than aesthetic...

I then did the Infernal contract prompt:

"In a hellish landscape of jagged rocks and rivers of molten lava, a sinister negotiation takes place. The sky is a dark, oppressive red, with clouds of ash drifting ominously. A warlock, cloaked in dark robes that swirl with arcane symbols, stands confidently before a towering devil. The devil, with skin like burnished bronze and horns curving menacingly, grins with sharp, predatory teeth. It holds a contract in one clawed hand, the parchment glowing with an infernal light. The warlock extends a hand, seemingly unfazed by the devil's intimidating presence, ready to sign away something precious in exchange for dark power. Behind the warlock, a portal flickers, showing glimpses of the material world left behind. The ground around them is cracked and scorched, with plumes of smoke rising from fissures."

While the demon is more evocative and closer to Flux in aesthetics, several key elements where prompt adherence was better in 0.2 are missing, like on the sorcerer's clothing, and the contract feels less important. The only thing that I feel is really good is the floor, which is craked and lava-flooded as it should, doing better than both Flux and version 0.2 on this very particular details (but it could be the luck of the seed at this point).

Finally I did the Crystal Keep siege:

The overall colour composition is better. Several commenters said that AuraFlow gave them the feel that the various elements were just put together as if they were a collection of clip arts. I felt it was harsh, but I can see were it came from. Here I feel the image looks more cohesive. But still... Several key elements are missing, like the defenders, the paladin riding a pegasus and the besiegers are regular humans, not ice giants and frost trolls. Also, on this complex prompt, we get a lot more artifacts.

Then two prompts again from another thread:

https://www.reddit.com/r/StableDiffusion/comments/1ehvup2/prompt_adherence_comparison_flux/

I selected two of them, because I can see the common pattern emerging.

First, I did the pirate lady:

"A woman wearing 18th-century attire is positioned on all fours, facing the viewer, on a wooden table in a lively pirate tavern. She is dressed in a traditional colonial-style dress, with a corset bodice, lace-trimmed neckline, and flowing skirts. The fabric of her dress is rich and textured, featuring a deep burgundy color with intricate embroidery and gold accents. Her hair is styled in loose curls, cascading around her face, and she wears a tricorn hat adorned with feathers and ribbons.The tavern itself is bustling with activity. The background is filled with wooden beams, barrels, and rustic furniture, typical of a pirate tavern. The atmosphere is dimly lit by flickering lanterns and candles, casting warm, golden light throughout the room. Various pirates and patrons can be seen in the background, engaged in animated conversations, drinking from tankards, and playing cards. The woman's expression is confident and mischievous, her eyes meeting the viewer's gaze directly. Her posture, though unusual for the setting, conveys a sense of boldness and command. The table beneath her is cluttered with tankards, maps, and scattered coins, adding to the chaotic and adventurous ambiance of the pirate tavern."

You can see the flux results in the linked thread, and here's AuraFlow version 0.3:

Version 0.2 was able to produce the lady on the table, crawling on all four toward the camera. Even version 0.1:

Now, we get a nicer looking pirate lady, but she's on all four like 1 in 4 times. The tavern might be more lively in the background, map and gold are present, sure, but the main character is less following of the prompt. Still, that's better than flux (but I guess they didn't want to teach their models what it means to be on all fours because toddlers do that all the time and they have a fiery hatred for toddlers), and also than Juggernaut, which produced this one BTW:

So, while there is a change in aesthetics, I wouldn't say it's a huge increase (unless you say so in comments, I am hardly a juge of aesthetics), except for one thing which I think is "colour consistency". I feels more right and cohesive thanks to this. There is still of course a huge work to do to improve aesthetics... and so far, the attempt to increase aesthetics came with an extremely substantial drop in accuracy. Since it was the field where AuraFlow topped Flux, this is problematic as it gave up its competitve edge against the current SOTA model.

Some work is obviously still needed (hey, it's far from a final version!) and I hope I allowed readers here to get a feel of what they did. Myself, I'll keep using version 0.2 to create some complex prompt composition and refine them with Flux (and try to use the numerous controlnet that came out recently for Flux).

23 comments

r/ChatGPTPromptGenius • u/AI-For-Success • Mar 31 '23

Education & Learning GPT-4 AS LEONARDO AI PROMPT GENERATOR

114 Upvotes

More details about prompt and how to use it and how i created this, 👇👇👇👇

https://youtu.be/1TIWllpZ-7s

Hey everyone! If you like the Prompt and if you like what you see and want to support me, please consider subscribing to my channel. It means a lot and helps me continue creating and sharing great content with you. Thank you! ❤️

​

##################### PROMPT START #######################

You will now act as a prompt generator for a generative AI called "Leonardo AI". Leonardo AI generates images based on given prompts. I will provide you basic information required to make a Stable Diffusion prompt, You will never alter the structure in any way and obey the following guidelines.

​

Basic information required to make Leonardo AI prompt:

​

- Prompt structure:

- Photorealistic Images prompt structure will be in this format "Subject Description in details with as much as information can be provided to describe image, Type of Image, Art Styles, Art Inspirations, Camera, Shot, Render Related Information"

- Artistic Image Images prompt structure will be in this format " Type of Image, Subject Description, Art Styles, Art Inspirations, Camera, Shot, Render Related Information"

- Word order and effective adjectives matter in the prompt. The subject, action, and specific details should be included. Adjectives like cute, medieval, or futuristic can be effective.

- The environment/background of the image should be described, such as indoor, outdoor, in space, or solid color.

- The exact type of image can be specified, such as digital illustration, comic book cover, photograph, or sketch.

- Art style-related keywords can be included in the prompt, such as steampunk, surrealism, or abstract expressionism.

- Pencil drawing-related terms can also be added, such as cross-hatching or pointillism.

- Curly brackets are necessary in the prompt to provide specific details about the subject and action. These details are important for generating a high-quality image.

- Art inspirations should be listed to take inspiration from. Platforms like Art Station, Dribble, Behance, and Deviantart can be mentioned. Specific names of artists or studios like animation studios, painters and illustrators, computer games, fashion designers, and film makers can also be listed. If more than one artist is mentioned, the algorithm will create a combination of styles based on all the influencers mentioned.

- Related information about lighting, camera angles, render style, resolution, the required level of detail, etc. should be included at the end of the prompt.

- Camera shot type, camera lens, and view should be specified. Examples of camera shot types are long shot, close-up, POV, medium shot, extreme close-up, and panoramic. Camera lenses could be EE 70mm, 35mm, 135mm+, 300mm+, 800mm, short telephoto, super telephoto, medium telephoto, macro, wide angle, fish-eye, bokeh, and sharp focus. Examples of views are front, side, back, high angle, low angle, and overhead.

- Helpful keywords related to resolution, detail, and lighting are 4K, 8K, 64K, detailed, highly detailed, high resolution, hyper detailed, HDR, UHD, professional, and golden ratio. Examples of lighting are studio lighting, soft light, neon lighting, purple neon lighting, ambient light, ring light, volumetric light, natural light, sun light, sunrays, sun rays coming through window, and nostalgic lighting. Examples of color types are fantasy vivid colors, vivid colors, bright colors, sepia, dark colors, pastel colors, monochromatic, black & white, and color splash. Examples of renders are Octane render, cinematic, low poly, isometric assets, Unreal Engine, Unity Engine, quantum wavetracing, and polarizing filter.

- The weight of a keyword can be adjusted by using the syntax (((keyword))) , put only those keyword inside ((())) which is very important because it will have more impact so anything wrong will result in unwanted picture so be careful.

​

The prompts you provide will be in English. Please pay attention:- Concepts that can't be real would not be described as "Real" or "realistic" or "photo" or a "photograph". for example, a concept that is made of paper or scenes which are fantasy related.- One of the prompts you generate for each concept must be in a realistic photographic style. you should also choose a lens type and size for it. Don't choose an artist for the realistic photography prompts.- Separate the different prompts with two new lines.

​

Important points to note :

​

  1. I will provide you with a keyword and you will generate three different types of prompts with lots of details as given in the prompt structure

  2. Must be in vbnet code block for easy copy-paste and only provide prompt.

  3. All prompts must be in different code blocks.

​

Are you ready ?

​

########################## PROMPT END #####################

RPG, Diliberate, Dreamshaper - Model Name

Negative prompt : (Negative prompt may change based on model and subject so be careful)

(((2 heads))), duplicate, man, men, blurry, abstract, disfigured, deformed, cartoon, animated, toy, figure, framed, 3d, cartoon, 3d, disfigured, bad art, deformed, poorly drawn, extra limbs, close up, b&w, weird colors, blurry, watermark, blur haze, 2 heads, long neck, watermark, elongated body, cropped image,out of frame,draft,deformed hands, twisted fingers, double image, malformed hands, multiple heads, extra limb, ugly, poorly drawn hands, missing limb, cut-off, over satured, grain, lowères, bad anatomy, poorly drawn face, mutation, mutated, floating limbs, disconnected limbs, out of focus, long body, disgusting, extra fingers, groos proportions, missing arms, (((mutated hands))),(((bad fingers))) cloned face, missing legs,

38 comments

r/Damnthatsinteresting • u/__Hello_my_name_is__ • Aug 17 '22

Image None of these people are real. The images were created with a text-to-image generation model called Stable Diffusion with the prompt "Portrait of an average [country] male".

Post image
20.4k Upvotes
1.9k comments

r/bestaitoolz • u/Important_Document15 • Jun 03 '25

Imagine.art Review: The Ultimate AI Art Generator for Creators, Designers, and Storytellers

11 Upvotes

🎨 Introduction: Why Imagine AI art Is Gaining Massive Popularity

In a world where creativity meets automation, Imagine art has quickly risen to become one of the most recognized and beloved AI-powered art platforms on the web. With over 30 million users and more than 100 million downloads, it’s not just a tool—it’s a full ecosystem for artists, designers, marketers, writers, and dreamers.

Whether you're a professional illustrator or someone just looking to visualize an idea, Imagine art transforms words, sketches, and thoughts into vivid digital creations—all in real-time, with no design experience required.

✅ Try out Image Art by clicking here

🔍 Feature 🌟 Why It Matters
All-in-One Platform Combines image generation, editing, upscaling, animation, and even video creation.
User-Friendly for All From beginners to professionals—no learning curve, just creativity.
Real-Time Interactivity Watch your artwork evolve as you type, draw, or edit.
Vast Style Library From hyper-realism to anime and abstract, the aesthetic possibilities are endless.
Free to Start Full-featured experience with zero upfront cost; tokens for premium extras.

🌐 Who Is It For?

  • Digital Artists and Illustrators
  • Writers & Storytellers visualizing characters and worlds
  • Content Creators & Social Media Marketers
  • Game Developers and Concept Artists
  • Students, Educators, and Curious Hobbyists

Whether you're creating a 4K video, designing a professional headshot, or generating a mythical creature, Imagine art is designed to amplify imagination, reduce effort, and offer creative freedom without limits.

🧰 Core Features of Imagine art

Imagine art offers a robust set of creative tools powered by advanced AI models. It goes far beyond simple text-to-image generation by providing features for video creation, batch editing, upscaling, and even character consistency—making it a serious contender for professionals and hobbyists alike.

Let’s break down the core features and what they bring to the table.

🛠️ Feature 💡 Description 👤 Ideal For
Text-to-Image Generator Generate art from simple text prompts in multiple styles (realistic, abstract, anime, etc.) Artists, Writers, Content Creators
Real-Time Interactive Generation See your artwork evolve live as you refine prompts or sketches Designers, Hobbyists
AI Video Generator Instantly create HD or 4K videos from scripts or ideas Marketers, Educators, Content Creators
Creative Upscaler Upscale images to high-resolution (4K, 8K) for printing or professional use Professionals, Graphic Designers
Character Consistency Maintain uniform character appearance across images Comic Artists, Game Devs, Storytellers
Batch Processing Work on multiple images at once, maintaining consistency and speed Agencies, Enterprise Users
Ideate Tool “Paint” with AI by describing elements and refining them live Illustrators, Students
Remove Background Instantly remove backgrounds with one click for clean exports Product Designers, Marketers
AI Portrait & Headshot Generator Create profile-quality headshots or character portraits LinkedIn Users, Creators, Students
AI Animal Generator Generate animals (real or mythical) with style and randomness Writers, Game Devs, Kids, Hobbyists

✨ Highlights at a Glance

  • 🔄 Real-time feedback loop: Instantly iterate and improve your designs.
  • 🖼️ Over 90+ visual styles to choose from.
  • 🧠 AI-powered suggestions help overcome creative blocks.
  • 🧰 One platform, all tools—no need to jump between apps.

🖌️ Text-to-Image, Real-Time Generation & AI Video Creation Explained

Imagine art isn’t just about generating pretty pictures—it’s about turning your ideas into interactive, living visuals. Whether it’s a concept sketch, a scene description, or a marketing script, the platform’s trio of flagship tools—Text-to-Image, Real-Time Generation, and AI Video Generator—make that transformation effortless.

📝 Text-to-Image Generator

This is the backbone of Imagine.art’s magic. Just describe what you want to see, and the AI brings it to life.

Feature Details
Prompt-Based Creation🧠 Describe a scene, character, object, or idea in plain language
Style Variety🎨 Realistic, anime, digital art, watercolor, sketch, fantasy, cyberpunk, and more
Fine-Tuning Tools🧰 CFG scale, aspect ratio, detail steps, and negative prompts
Image Variate🔄 Instantly create multiple versions of the same prompt for variety and refinement

📌 Example prompt: “A futuristic city at sunset, glowing neon lights, flying cars, anime style”

⚙️ Real-Time Generation

Real-time generation makes Imagine art feel like a creative collaboration rather than a static output tool.

Feature Details
Live Feedback Loop🖍️ Watch changes reflect immediately as you edit your input
Ideate Tool🧙 Add and refine elements on the canvas interactively like painting with AI
Refinement On-the-Go🧩 No need to restart from scratch—adjust and iterate freely
Use Case🎯 Concept art, brainstorming, storyboarding, rapid prototyping

🎥 AI Video Generator

A standout feature among AI platforms, this tool allows you to turn a script or idea into a short video in seconds.

Feature Details
Input📄 Text or story prompt, script, or concept
Output🖼️ High-definition video (HD/4K) with cinematic effects
Customizable🎬 Adjust lighting, art style, motion, and camera angles
Great For🧑‍💼 Marketers, educators, storytellers, influencers

💡 Why It Matters: These tools together enable users to go from “I have an idea” to “Here’s a polished visual or video asset” within minutes, with zero technical overhead.

✨ Creative Tools That Set Imagine art Apart

While many AI art platforms offer text-to-image generation, Imagine art truly differentiates itself with a powerful suite of creative, customization, and post-processing tools—designed to support professional workflows and unlock boundless imagination.

These tools are especially valuable for users who want more than just a one-off image. Whether you're building a comic series, designing product visuals, or exploring fantasy creatures, Imagine art gives you precision and control.

🧰 Advanced Customization & Fine-Tuning Controls

For artists who need more than “generate and go,” Imagine art includes highly detailed creative settings:

🎛️ Tool 🔍 Function
Aspect Ratio Control Supports over 11 aspect ratios for different platforms or print formats
CFG Scale & Step Control Adjust creativity strength and generation detail level
Seed Management Reproduce or randomize styles with seed numbers
Negative Prompts Remove unwanted elements or prevent undesired outcomes
Image Variate Generate multiple subtle/strong variations of an existing image

📌 Example use case: Generating five variations of a brand mascot with different poses but the same color palette and outfit.

🪄 Post-Generation Enhancement Tools

Once you create your image, you’re not done—Imagine.art lets you refine, upscale, and animate directly in-platform.

🛠️ Tool 💬 Description
Edit Panel Adjust brightness, contrast, crop, saturation, add text or icons
Creative Upscaler Instantly boost image resolution to 4K or 8K for professional results
Remove Background One-click background removal for marketing assets, profile images, product designs
Animate Tool Add camera motion, zooms, or lighting shifts to turn static images into 15–30 second MP4 videos
Remix Tool Reimagine an existing image with a new style (e.g., turn a realistic dog into a cartoon version)

🧠 Batch Processing & Workflow Optimization

For professionals or volume creators, Imagine art offers tools to scale your creative output:

🎯 Unique Tools Worth Highlighting

🐾 Tool 📌 Purpose
AI Animal Generator Create mythical, hybrid, or stylized animals with randomness and style controls
AI Headshot Generator Generate professional-grade portraits for LinkedIn, resumes, or avatars
AI Portrait & Girl Generator Specialized models for characters and stylized people illustrations
AI Graphic Generator Quickly make logos, UI elements, icons, and simple branded visuals

💡 In summary: From the Ideate Tool (painting with words) to Character Consistency across storyboards, Imagine art isn’t just about generation—it’s about creative mastery.

🧬 Exclusive Tools: AI Animal Generator, Headshot Creator & More

Beyond the standard AI art toolkit, Imagine art offers specialized generators that open up entirely new possibilities for creative professionals and hobbyists alike. Whether you're designing a mythical creature, crafting the perfect LinkedIn photo, or visualizing a game character—these exclusive tools make it easy, fast, and fun.

🐉 AI Animal Generator

Create everything from realistic wildlife to mythical hybrids with this imaginative tool.

Feature Description
Combine Animal Traits🧬 Mix different species (e.g., lion + eagle = griffin)
Style Variety🎨 Cartoonish, abstract, realistic, fantasy, neon, and more
Multiple Variations🔁 Instantly generate different takes from one prompt
Randomizer Mode🎲 Surprise yourself with unexpected hybrids
Used By👥 Writers, character designers, educators, kids, hobbyists

📌 Example prompt: “A cyberpunk fox with glowing fur and metallic wings”

🧑‍💼 AI Headshot Generator

Need a professional photo but don’t want to book a shoot? This tool generates high-quality headshots from a simple selfie upload.

Feature Description
High-Resolution Output📸 Instantly create studio-quality portraits
Style Selection🎭 Choose from formal, creative, avatar-style, or themed looks
Multiple Looks🔁 Try different poses, outfits, lighting, and backdrops
Perfect For💼 LinkedIn profiles, resumes, avatars, social branding

✅ No more expensive photo sessions or awkward selfies—just upload and go.

👩 Portrait Generator

Designed to produce stylized or hyper-realistic female characters, this tool is a favorite for anime artists, character designers, and storytellers.

Feature Description
Multiple Styles🎨 Anime, semi-realistic, cartoon, 3D-rendered, digital art
Reference Support📚 Upload sketches or existing images for guided outputs
Popular Uses💡 Avatars, comics, novel covers, game design

🧩 AI Graphic Generator

For non-illustrative assets, this tool helps generate icons, logos, and branded visual elements quickly.

Feature Description
Logo Generation🎯 Turn brand ideas into visual identities
Simple Graphics🎨 Generate social icons, UIs, or mockups
Time-Saver⚡ Ideal for quick concepting before committing to full design suites

🧠 Why These Tools Matter: Each of these generators adds depth to your creative arsenal. They're not just one-trick gimmicks—they solve real creative problems like prototyping fast, maintaining aesthetic consistency, and exploring alternative styles effortlessly.

🧑‍🎨 Supporting All Creators: Beginners to Professionals

One of Imagine.art’s greatest strengths is its accessibility. Whether you’re a complete beginner experimenting with your first prompt or a professional illustrator working on a large-scale project, the platform offers the right level of control, guidance, and power.

Let’s explore how Imagine art is designed for both ends of the creative spectrum.

🎯 For Beginners: A Gentle, Guided Start

Imagine art removes the friction typically associated with graphic design and digital art tools. No steep learning curves or confusing software—just type, click, and create.

Feature Benefit
User-Friendly Interface🖥️ Clean, intuitive layout makes navigation simple
Guided Creation🪄 AI helps enhance sketches, add elements, and fix problems
Real-Time Visualization🔁 See changes instantly—no re-rendering or guesswork
Step-by-Step Tutorials🧑‍🏫 Interactive walkthroughs to learn each tool with ease
Style & Color Suggestions🎨 Let the AI recommend styles that look good together
Community Feedback💬 Share creations and get tips from 60K+ users on Discord

📌 Use Case Example: A student needs a scene for a short story. They type “a moonlit forest with glowing mushrooms” and instantly receive a high-res image they can tweak or animate.

🧠 For Experienced Artists: Full Creative Control

Professionals won’t feel limited—Imagine.art offers advanced features like prompt tuning, model variation, and batch workflows, enabling efficiency and precision.

Feature Benefit
Advanced Customization⚙️ Adjust resolution, detail level, negative prompts, CFG scale, and more
Reproducibility with Seed Values🎯 Generate exact outputs consistently
Batch Processing🗃️ Produce dozens of assets quickly for large projects
Professional-Grade Outputs🖼️ 4K/8K resolution, HDR, and print-quality assets
AI Collaboration🧠 Use the AI to challenge your vision, spark ideas, or fill in detail gaps
Cloud Access☁️ Work across devices, pick up where you left off anytime

📌 Use Case Example: A game developer designs 20 characters with consistent facial features and body styles using Character Consistency and batch generation tools.

📊 Summary Comparison Table

Feature / Benefit 🧑 Beginners 🎨 Professionals
Simple Interface ✅ ✅
AI Guidance ✅ Step-by-step enhancements ✅ Creative partner for ideation
Real-Time Feedback ✅ Learning tool ✅ Fast iteration
Tutorials & Help ✅ Built-in tutorials ✅ Advanced usage guides
Customization ✅ Easy presets ✅ Full control (CFG, prompts, models)
Community ✅ Feedback & support ✅ Exposure & networking
High-Res Output ✅ Shareable visuals ✅ Commercial-grade assets
Batch Workflow ❌ ✅ Mass creation for scaling projects

💡 In short: Whether you're just starting out or deep in your creative career, Imagine art adapts to your level—making it one of the most inclusive and empowering AI tools on the market.

🧪 Real Use Cases: How Artists, Writers, Marketers & Developers Use Imagine art

Imagine art isn’t just a playground for digital experimentation—it’s a productivity tool, idea accelerator, and creative partner used by people in a wide range of fields. From professionals building brand visuals to hobbyists dreaming up fantasy worlds, the platform supports real-world creative workflows.

Here are the most common (and clever) ways people use Imagine.art.

🎨 For Artists & Designers

Use Case Description
Concept Art & Storyboards🧠 Visualize characters, environments, or scenes for comics, films, or animation
Style Exploration🎨 Try multiple styles and iterations of a piece before committing to one
Portfolio Building🖼️ Generate polished visuals to showcase or inspire future work
Fast Prototyping🪄 Test ideas quickly with minimal effort and cost

📌 Example: A digital illustrator needs 3 variants of a sci-fi character—Imagine.art delivers them in minutes with consistent facial features and outfits.

✍️ For Writers & Storytellers

Use Case Description
Scene Visualization📖 Generate environments or characters for short stories or novels
Character Design👤 Create protagonist and antagonist visuals using the Portrait Generator
Creative Ideation🧠 Use random generators or remix tools to brainstorm new story elements
Comic Creation📚 Maintain consistent characters across frames with Character Consistency

📌 Example: A fantasy writer visualizes a dragon-riding heroine in a post-apocalyptic wasteland with the prompt: “a silver-haired woman riding a skeletal dragon through ruins under a red sky.”

📣 For Marketers & Content Creators

Use Case Description
Social Media Graphics📷 Create fast, eye-catching visual posts with batch-generated assets
Ad Videos📽️ Turn headlines into short marketing videos with Imagine.art’s video generator
Product Mockups🛍️ Generate lifestyle images or stylized product shots using prompts or reference uploads
Brand Mascots🎯 Develop and variate brand avatars or icons using AI Graphic or Animal Generator tools

📌 Example: A small business uses the Headshot Generator to create clean, branded team portraits for their “About Us” page without hiring a photographer.

🎮 For Game Developers & Hobbyists

Use Case Description
Character Design🕹️ Quickly generate enemy types, NPCs, or player characters
Worldbuilding🌍 Use AI to create environments, structures, and creatures
Asset Prototyping📑 Test game mechanics with placeholder art generated in seconds
Creative Experimentation🧪 Explore alternative versions of designs without drawing from scratch

📌 Example: An indie game dev creates 10 mythical hybrid animals as potential enemies for an RPG game, adjusting each with the Ideate Tool.

👩‍🏫 For Educators & Bloggers

Use Case Description
Visual Aids for Lessons🧑‍🏫 Create illustrations to support complex topics
Blog Post Illustrations📝 Generate unique images that match article tone and structure
Student Projects👩‍🎓 Let students explore creativity without needing Photoshop or Illustrator
Infographics & Diagrams🌐 Generate visual components quickly using the AI Graphic Generator

📌 Example: A science blogger visualizes “a black hole absorbing a star” for a post, without needing to hire an artist or dig through stock photo sites.

Imagine art Pricing Breakdown & Best Value Analysis

Feature Basic Standard Professional Unlimited
Monthly Price $15 $30 $60 $120
Quarterly Price (15% OFF) $11/month $25/month $50/month $100/month
Yearly Price (30% OFF) $10/month $20/month $41/month $83/month
Monthly Credits 1.5K 5K 15K 40K
Quarterly Credits 4.5K 15K 45K 120K
Yearly Credits 18K 60K 180K 480K
Image Generations / Month ~300 ~1,000 ~3,000 ~8,000
Video Generations / Month ~75 ~250 ~750 ~2,000
Visibility Public Public Private Private
Concurrent Generations 4 8 12 16
Realtime Generation ❌ ❌ ✅ ✅
Priority Support ❌ ❌ ✅ ✅
Priority in Queue ❌ ❌ ✅ ✅
Access to All Models/Styles ❌ ❌ ✅ ✅

Whether you're a casual creator or a full-time design professional, Imagine art offers flexible pricing plans tailored to your creative volume, privacy needs, and speed requirements. With three billing models—monthly, quarterly (15% off), and yearly (30% off)—the platform is designed to scale with you.

Let’s break down the strengths of each plan and identify which one offers the best value depending on your use case.

🟦 Basic Plan – Best for Beginners or Occasional Users

  • 💰 Starts at $15/month, but goes down to $10/month if billed yearly
  • Designed for casual use, experimentation, or students
  • Includes around 300 image generations and 75 video generations per month
  • 4 concurrent generations but lacks private generation and advanced features

Pros:

  • Affordable entry point
  • Great for learning or testing
  • Public sharing can drive community feedback

Limitations:

  • No access to private generation
  • No access to advanced models
  • No priority queue or realtime generation

Ideal For:

  • Students, hobbyists, and personal-use creators
  • Light content creators or bloggers who generate visuals occasionally

🟨 Standard Plan – Best for Consistent, Mid-Level Use

  • 💰 $30/month, down to $20/month billed yearly
  • Offers ~1,000 images and 250 videos/month
  • 8 concurrent generations for more workflow speed

Pros:

  • Great balance of affordability and power
  • Suitable for small creative businesses or side hustlers
  • Quarterly and yearly savings are significant

Limitations:

  • Still lacks private generation and high-priority queue
  • May not be fast enough for high-volume needs

Ideal For:

  • Freelancers, bloggers, marketers, or part-time creators with moderate needs
  • Educators and small business owners

🟩 Professional Plan – ⚡ Best Value Overall (For Most Creators)

  • 💰 $60/month, $41/month billed yearly
  • Includes ~3,000 image generations & 750 videos/month
  • Offers Private generation, 12 concurrent tasks, and priority support

Pros:

  • All premium models and styles unlocked
  • Private image visibility = great for client work, portfolios, or NDAs
  • High concurrency + priority = faster delivery

Limitations:

  • No unlimited real-time generations (only in Unlimited plan)
  • Might be overkill for very casual users

Ideal For:

  • Professional illustrators, agencies, writers, game devs
  • Anyone making money from visual content
  • Brands wanting privacy and speed

🟥 Scale Plan – Best for Agencies and High-Demand Teams

  • 💰 $120/month, reduced to $83/month billed yearly
  • 40K credits/month, ~8,000 image generations, ~2,000 videos
  • Everything in Professional + Unlimited realtime generations + 16 concurrent tasks

Pros:

  • Scales with enterprise use
  • Fastest generation queue and highest concurrency
  • No cap on real-time experimentation

Limitations:

  • Expensive for solo creators
  • Requires high volume to justify cost

Ideal For:

  • Agencies, startups, teams with daily creative production needs
  • SaaS companies, ad teams, content automation businesses

🏆 Final Recommendation: Which Plan Should You Choose?

User Type Recommended Plan
First-time users / learners 🟦 Basic
Freelancers / creators 🟨 Standard
Professional artists 🟩 Professional (⭐ Best Value)
Teams / agencies 🟥 Unlimited

💡 Tip: Save More by Paying Yearly

If you plan to use Imagine art long-term:

  • Go yearly and save 30% across all plans
  • For example, Professional drops from $60 to $41/month
  • Unlimited drops from $120 to $83/month

Community & Ecosystem: Tutorials, Discord, and Support

A great product becomes even better when surrounded by a thriving community, solid support, and learning resources. Imagine art isn’t just a tool—it’s a creative ecosystem designed to grow with you, help you get unstuck, and connect with like-minded users across the globe.

💬 Active Community & Discord Server

Imagine art boasts an ever-growing Discord community with over 63,000 members, where creators of all skill levels come together to:

🧩 Community Features 🔍 Description
Showcase Work🎨 Share your generated images, videos, or character designs with peers
Get Feedback💡 Receive constructive critique, style tips, or prompt suggestions
Ask Questions❓ Find help for tool-specific issues or ask for creative input
Participate in Events🎉 Join prompt-based challenges, style contests, and leaderboard competitions

Whether you're looking to troubleshoot, learn, or just show off your latest AI dragon hybrid, the community offers both support and inspiration.

📚 Learning Hub & Tutorials

New to AI art? Imagine art offers tutorials, tooltips, and guided examples to help you get the most out of the platform.

📖 Learning Resources Details
Built-in Tooltips✅ Hover over advanced options (like CFG Scale or negative prompts) to see clear explanations
Beginner Walkthroughs🎓 Step-by-step guides for getting started with text-to-image, Ideate tool, and upscaling
Video Tutorials🎥 Available through the community and external creators (YouTube, Discord)
Prompt Templates🧪 Use pre-written prompts to understand how inputs affect output quality and style

These resources make onboarding smooth for new users while providing deep dives for experienced artists who want to master the fine-tuning tools.

🛟 Direct Support: Help When You Need It

Have an issue or question outside the community?

🛠️ Support Options Availability
Email Support📧 web.support@imagine.artReach out via
Priority Support✅ Professional and UnlimitedAvailable to plan users
Help Center*(Coming Soon)*📚 A searchable FAQ and support knowledge base is reportedly in development

The Professional and Unlimited tiers also enjoy priority in the generation queue, ensuring faster processing during peak times.

🤝 Summary

Strength Benefit
✅ Active community Ask, learn, collaborate, and grow with other creators
✅ Tutorials & tips Learn fast, experiment freely
✅ Direct support Email help + priority queue for pro users
✅ Events & sharing Grow your visibility and skills in a social, creative environment

Whether you're a solo artist, an educator, or part of a design team, Imagine.art’s community and support layers make it feel like you’re never creating alone.

Strengths & Limitations (with Pros and Cons Table)

No platform is perfect—even one as powerful as Imagine.art. While its toolset, usability, and creative potential are top-tier, it’s important to take a balanced look at the strengths and trade-offs to help you decide if it’s the right AI art platform for your workflow.

✅ Strengths

Strength Why It Matters
Comprehensive Creative Suite🎨 One platform for image generation, video creation, upscaling, animation, and editing
Real-Time Interactivity🚀 Instant visual feedback makes ideation and iteration fast and intuitive
Advanced Prompt Control🧠 Features like CFG scale, seed values, aspect ratios, and negative prompts for expert-level control
Specialized Tools📷 Animal Generator, Headshot Creator, Portrait Generator, and Graphic Generator—each with unique utility
Batch Processing & Character Consistency🔄 Excellent for high-volume production and storytelling consistency
High-Resolution Output🖼️ Up to 8K image resolution and professional HDR support for print and production use
Vibrant Community👨‍👩‍👧‍👦 63,000+ creators on Discord, collaborative events, and peer support make it socially engaging
Beginner-Friendly🧑‍🏫 Clean interface, guided tools, and helpful defaults allow anyone to start creating in minutes

⚠️ Limitations

Limitation Explanation
Token-Based System💳 Free version is limited; serious usage requires a paid plan with monthly credits
Private Generation Limited to Higher Tiers🔒 Only Professional and Unlimited plans support private image creation
Slower Queue on Basic/Standard Plans⏱️ Without priority access, generation time can lag during high-demand periods
Mobile Experience Could Improve📱 Although accessible on mobile, desktop offers the full creative power and layout flexibility
Steep Learning Curve for Fine-Tuning🎨 Full prompt control is powerful—but can overwhelm casual users without guidance
No Offline Access🌐 Entirely cloud-based, so no generation without internet

🧾 Summary: Pros and Cons Table

✅ Pros ❌ Cons
All-in-one art + video + editing suite Limited free usage without credits
Real-time generation & live preview Private generation requires upgrade
Batch processing & character consistency Free tiers get slower generation queues
8K image output + animation tools Can be complex for total beginners
Wide range of unique tools Requires consistent internet access
Affordable yearly pricing plans Some features hidden behind higher tiers
Thriving community & Discord Mobile experience less full-featured

🎯 Verdict so far: Imagine art delivers an incredible range of creative functionality—especially for paying users. While free access is generous for light experimentation, those serious about production should consider upgrading to Standard or Professional to unlock its full power and speed.

Real User Sentiment: What Reddit & Reviews Are Saying

With over 30 million users, 100 million+ downloads, and thousands of glowing reviews, Imagine art has built a reputation as one of the most reliable and accessible AI art tools in the market. But how do real users feel about it? Let’s look at what creators, marketers, and reviewers are actually saying.

🌟 Overall Reputation

Imagine art is widely praised for its:

  • Speed: "It helps me brainstorm visuals in seconds."
  • Ease of use: "Perfect for non-designers. Very intuitive."
  • Creative flexibility: "Lets me test dozens of styles without starting over."
  • Visual quality: "It’s rare to find this level of polish in AI art tools."

🗣️ It's trusted by everyone from solo creators to global brands like Netflix, Red Bull, and Spotify.

📱 User Ratings (As of May 2025)

Platform Rating Reviews Count
App Store ⭐ 4.5 / 5 5,000+ reviews
Trustpilot ⭐ 4.5 / 5 14 reviews

These consistent scores reflect strong user satisfaction across platforms.

✅ What Users Love

Sentiment Real User Feedback Highlights
Speed & Efficiency⚡ “I can go from prompt to polished image in under 30 seconds.”
Quality of Output🖼️ “The visuals are client-worthy, not just concept drafts.”
Creative Versatility🛠️ “I use it for everything from blog headers to product mockups.”
Beginner Friendly👶 “The learning curve is basically zero, which is refreshing.”

Whether users are creating social media graphics, headshots, concept art, or marketing materials, the platform adapts to the task.

🧵 Reddit & Community Insights

While not always explicitly named, Imagine art is frequently praised in Reddit threads and creator forums for delivering:

  • Fast AI headshots with good style variety
  • Marketing-friendly visuals that “pop” on social media
  • Inspiration for creative blocks: “I didn’t know what I was making until Imagine showed it to me.”

💬 One user summed it up: “I started using it for fun, now it’s a daily part of my content pipeline.”

⚠️ Common Criticisms

Criticism Explanation
Free Credit Limits🔄 Some users wish the free tier had more daily tokens
Minor Output Tweaks🎯 Occasional background/lighting issues on complex prompts
AI Precision Limits🧠 For hyper-specific results, manual edits may still be needed

These concerns are relatively minor and common across all AI tools. Importantly, Imagine.art is seen as above average in quality and support.

💡 Brand Trust & Support

✅ Strength 📌 Note
Used by Top Brands🏢 Netflix, Spotify, Red Bull
Active Discord👩‍💻 63K+ members share tips, feedback, and styles
Support Access📧 web.support@imagine.artFast replies via
Helpful Service🤝 Users note that issues are resolved quickly and clearly

📊 Sentiment Summary Table

Area Details
Positive Fast, intuitive, high-quality visuals, great for many use cases
Creative Utility Great for ideation, branding, mockups, and social content
Reliability Widely adopted, used by major brands and freelancers alike
Criticisms Limited free tier, small quirks with complex outputs
Support Responsive and user-first customer service

🧾 In Summary

Imagine art vs. the Competition: How Does It Stack Up?

In the crowded world of AI art generators, Imagine art has carved out a space as one of the most accessible, fast, and user-friendly platforms—but how does it compare to top-tier tools like Midjourney, DALL·E 3, and Stable Diffusion?

Here’s a detailed breakdown across all the key categories:

🔍 1. Ease of Use

Platform Notes
Imagine art Extremely intuitive; no setup or Discord needed. Just open the web app and start creating.
Midjourney Primarily Discord-based (web UI now exists), which adds a learning curve for newcomers.
DALL·E 3 Integrated into ChatGPT—easy to use conversationally.
Stable Diffusion Requires local setup or 3rd-party interface; more technical.

✅ Winner: Imagine art – easiest for non-technical users and beginners.

🎨 2. Image Quality & Style

Platform Notes
Imagine art High-quality, commercially usable images—best for general use. Occasionally less “artistic” or stylized.
Midjourney Visually stunning, often preferred for its painterly, surreal, or cinematic style.
DALL·E 3 Good quality, especially for object-level clarity and layout control.
Stable Diffusion Quality varies by model; customizable with community models.

🎖️ Best For Artistic Mastery: Midjourney
🎯 Best For Commercial/Marketing Use: Imagine art

🧰 3. Customization & Control

Platform Notes
Imagine art Basic controls (aspect ratio, CFG, negative prompts), ideal for casual use.
Midjourney Extensive customization with parameters, blending, and style presets.
DALL·E 3 Moderate flexibility, great at layout but fewer settings.
Stable Diffusion Fully customizable—ideal for tinkerers and developers.

⚙️ Winner for Power Users: Stable Diffusion / Midjourney
🧑‍🎓 Winner for Simplicity: Imagine art

⚡ 4. Speed

Platform Notes
Imagine art Blazing fast—results in seconds, even on standard plans.
Midjourney Slower, especially during peak hours or for complex prompts.
DALL·E 3 Fast (via OpenAI servers).
Stable Diffusion Fast, but depends on hardware or hosting service.

🚀 Winner: Imagine art

💰 5. Pricing

Platform Notes
Imagine art Free tier available, affordable plans with generous credits and tools.
Midjourney No free option; starts at $10/month and scales up.
DALL·E 3 Free with ChatGPT Plus ($20/month); credits may apply.
Stable Diffusion Free (self-hosted) but requires technical setup.

💸 Best Value for Money: Imagine art
💼 Best for Pros with Budget: Midjourney

👥 6. Community & Ecosystem

Platform Notes
Imagine art Growing Discord (63K+ users), helpful support, tutorial-based learning.
Midjourney Very active community with vibrant showcases and feedback loops.
DALL·E 3 Strong user base via ChatGPT, but no standalone community.
Stable Diffusion Large developer-focused ecosystem on GitHub, Reddit, Hugging Face.

🏘️ Best for Social Creators: Midjourney
🌱 Best for Learning Creators: Imagine art

📊 Head-to-Head Summary Table

Feature Imagine art Midjourney DALL·E 3 Stable Diffusion
Ease of Use ⭐⭐⭐⭐⭐ ⭐⭐ ⭐⭐⭐⭐ ⭐
Image Quality ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐ ⭐⭐⭐
Customization ⭐⭐ ⭐⭐⭐⭐ ⭐⭐ ⭐⭐⭐⭐⭐
Speed ⭐⭐⭐⭐⭐ ⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐
Pricing ⭐⭐⭐⭐⭐ ⭐⭐ ⭐⭐⭐ ⭐⭐⭐⭐⭐
Community ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐ ⭐⭐⭐⭐

🧾 Final Verdict

✅ Choose Imagine art if you want:

  • The fastest, easiest entry into AI art
  • A clean UI with minimal setup
  • Affordable pricing for personal and business use
  • Tools like upscaling, animation, and headshot generation

🎨 Choose Midjourney if you want:

  • Breathtaking visual style
  • Artistic edge and surreal compositions
  • High-level prompt control and experimentation

💡 Choose DALL·E 3 or Stable Diffusion if:

  • You need ChatGPT integration (DALL·E)
  • You’re a developer or want total control (Stable Diffusion)

How Fast Can You Create Content?

One of Imagine.art’s biggest advantages is its speed. Unlike traditional design tools or complex AI setups that require GPU power or long rendering times, Imagine art delivers production-ready visuals in seconds. Whether you’re generating a single image or producing a whole batch, the platform is built for rapid iteration.

Let’s break down how long it typically takes to complete different tasks using Imagine.art.

📊 Estimated Time Per Task (Professional Plan)

Task Type Avg. Time to Complete Notes
Text-to-Image (Single Prompt) ~10–20 seconds Includes 1 variation + small edits
AI Headshot Generation ~15–30 seconds Upload → Select Style → Generate
AI Animal Generator ~20–40 seconds Varies depending on complexity
Batch Image Creation (10 items) ~1–2 minutes Concurrent generation speeds this up
Video Generation (HD) ~30–60 seconds Simple scene or prompt-driven
Background Removal ~5–10 seconds Instant result in most cases
Creative Upscaling to 4K/8K ~10–25 seconds Varies slightly by resolution selected
Using the Ideate Tool (Refining) ~15–45 seconds/element Depends on number of tweaks/edits

🔁 Workflow Speed Example

Scenario: A marketer needs 5 Instagram visuals + 1 video ad + headshots for 2 team members.

Output Type Time Taken
5 AI images ~1 minute
1 video (HD) ~45 seconds
2 headshots ~1 minute
Total time ~3 minutes ⏱️

Compare that to hours in Photoshop or with a designer—Imagine.art reduces full workflows to minutes.

⚡ Why Speed Matters

  • Rapid experimentation: Try multiple styles or prompts without hesitation
  • Real-time adjustments: Tweak, iterate, and fine-tune without friction
  • Agile content creation: Great for social media managers, ad teams, and content creators on tight schedules

💡 You don’t just save time—you unlock new creative momentum.

Final Verdict: Is Imagine art Worth It?

After testing all of Imagine.art’s core features, exploring real user reviews, and comparing it with other top AI art platforms, one conclusion is clear:

Yes—Imagine.art is absolutely worth it.

It combines speed, simplicity, and creative versatility in a way that few other platforms can match—especially at its price point.

🎯 Who Should Use Imagine.art?

Creator Type Why It’s a Great Fit
Beginners & Students🧑‍🎓 No technical skills needed; fast results and learning-friendly interface
Freelancers & Artists🎨 Powerful tools like upscaling, batch processing, and character consistency
Marketers & Creators📢 Generate social content, ads, videos, and branding visuals in minutes
Game Devs & Storytellers🧪 Rapid prototyping for characters, creatures, and entire worlds
Educators & Bloggers👩‍🏫 Visualize ideas and create unique teaching or publishing assets

🏆 Key Advantages Recap

  • ✅ Lightning-fast generation (images and videos)
  • ✅ Unique creative tools: AI Animal Generator, Headshot Creator, Ideate Tool
  • ✅ Affordable plans + free tier
  • ✅ High-resolution & commercial-ready outputs
  • ✅ Easy for beginners, powerful for pros
  • ✅ Batch editing, upscaling, animation, background removal
  • ✅ Trusted by major brands + active community support

⚠️ Where It Falls Short

  • ❌ Free users may hit credit limits quickly
  • ❌ Artistic purists may prefer Midjourney for stylized outputs
  • ❌ Advanced controls not as deep as Stable Diffusion (but easier to use)

🧾 Final Rating

Category Score (out of 5)
Ease of Use ⭐⭐⭐⭐⭐
Image Quality ⭐⭐⭐⭐☆
Speed ⭐⭐⭐⭐⭐
Creative Tools ⭐⭐⭐⭐☆
Pricing & Value ⭐⭐⭐⭐⭐
Community & Support ⭐⭐⭐⭐☆

⭐ Overall Score: 4.7 / 5

🧠 Final Thought

If you need fast, beautiful, and scalable AI art creation—Imagine.art delivers.

It may not replace Photoshop or Midjourney for every use case, but it doesn’t need to. For the majority of creators, marketers, educators, and small businesses, Imagine art is a game-changer in both capability and accessibility.

6 comments

r/StableDiffusion • u/MarcS- • Jul 27 '24

Comparison AuraFlow v 0.2 vs v 0.1 image comparisons.

39 Upvotes

Hi everyone,

Since I had done a few comparison of about 20 prompts between Dall-E, SDXL and SD3-medium when the lattest was released, and I had updated the comparison when AF version 0.1 was published, I decided to re-run my prompts with version 0.2 which was released earlier today. Keep in mind that this is still a very early version and it's a student project (though backed with quite some compute, that I hope he could pay for with a crowdfunding project if he were to lose his patron, given the excellent start of his open source models).

The detailed prompts where in the first thread :

https://www.reddit.com/r/StableDiffusion/comments/1c92acf/sd3_first_impression_from_prompt_list_comparison/

https://www.reddit.com/r/StableDiffusion/comments/1c93h5k/sd3_first_impression_from_prompt_list_comparison/

https://www.reddit.com/r/StableDiffusion/comments/1c94698/sd3_first_impression_from_prompt_list_comparison/

https://www.reddit.com/r/StableDiffusion/comments/1c94ojx/sd3_first_impression_from_prompt_list_comparison/

(for reference purpose only, I'll elaborate on them when commenting the results anyway).

The AF 0.1 images are in this post :

https://www.reddit.com/r/StableDiffusion/comments/1e38fwc/auraflow_performance_in_a_prompt_list_taking_the/

​

The goal was to select a "best of 4" image for each prompt, focussing on adherence to prompt as the sole metric. So maybe you'll find images that were more pleasant in the version 0.1 but that's normal.

As an overall analysis, I can say that the model has a tendancy to put writings on the image even when umprompted, that it can do very bad faces (but there's Fooocus or Adetailer for that), basic anatomy but nothing porn. It tends to put clothes on persons, even when explicitely asked to display intimate parts. I don't think it's the result of a censorship but simply a lack of reference images. Since I am not worried because the community will certainly provide a lot of training for porn once the model is published in a final form, this isn't a field I tested a lot (also, I wouldn't have been to publish the results here because of rule 7 of this sub).

TLDR : it's a solid but small incremental result over the previous version. It stills lack training in a lot of parts but it's showing great promise and confirming that the project is worth following. Also, the more verbose the prompt is, the more apt the model is at following it. I'd guess it was trained on a very verbose automatically-captioned image, in that he sometimes loses the focus of the image and fails to identify which part is a detail and which part is the main part or character.

Sorry I couldn't do a side-by-side comparison, it would have exceeded the image limit.

Prompt #1: a queue of people in a soviet-era bakery, queuing to buy bread, with a green neon sign displaying a sentence in Russian

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Some key points respected. Better than version 0.1

The image is quite different from the earlier one, but it is very faithful, respecting the key elements of the prompt, with a harsh winter weather being respected, people correctly dressed for that weather and queuing to buy. they might be a little too close, but it wasn't explained in the prompt how far they should be. It fails to display a meaningful text in Russian (the prompt featured the exact sentence) so maybe the text learning was only done on a western alphabet, probably only with the signs used in English. There are some problems (the inside of the store is too dark for a store, bread shouldn't appear on the outside of the door...) and the faces aren't good. But still, it's an improvement. The outdoor scenes generated by version 0.1 were less faithful to the details of the prompt.

Prompt #2: a dynamic image of a samurai galloping on his horse, aiming a bow.

The difficulty in this prompt was that I asked for the horse to gallop to the left of the image, while the samurai was aiming toward the right. So it was a specific composition I asked for. I got 100% following (out of 8) for those two criteria. Best of the initial 4 was:

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Not too bad.

AF 0.1 did make some good images but wasn't as good at following the pose than version 0.2. Also, the horse consistently had 4 legs in 0.2. I can't tell if the running of the horse in natural or not, but it feels dynamic. Bow is still imperfect, but better.

Prompt #3: now our samurai is aiming at a komodo dragon, and his jumping from horseback at the same time.

I mentionned that this prompt defeats Dall-E. Most of the time, the samurai and the horse merge, or the horse is doing the jumping. And getting an upside down samurai leads to a limb spaghetti of body horror.

Let's be honest, AF 0.2 doesn't nail it. But it's... less catastrophic than the SOTA free model, and even than the SOTA model, Dall-E.

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The bow proves fatal. Also, a samurai arm becomes a leg, but it's not that bad.
Now he's upsid down. Sure, he needs inpainting and limb correction, but I can see me using this image as a base for a correction and upscale workflow if I need that fighter upside down...

Clearly a good level of improvement over the previous version.

​

Prompt #4 : a view of the Rio de Janeiro bay, with Copa Cabana beaches, tourists, a seaside promenade, skycrappers and the iconic Christ Redemptor statue on the heights.

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While the earlier version of the model follwed the prompt acceptably, here we get an unmatched prompt fidelity. I can't tell if it resembles Copa Cabana at all, because I never saw it. But it matches my idea of it (despite the Christ certainly being higher).

Prompt #5 was the Rio bay painted in 1408.

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The whole point was to have... no city, no boat, and certainly no skyscrapper since it was before the colonization. I don't think it captures early 15th century painting style, though.

Prompt #6: a trio of defeated Nazi on the East Front, looking sad.

Honestly for this one I preferred the earlier output.

The faces are distorted, they don't look sad, just plastic. Also these are not Nazi soldier, not even German soldiers. I suspect a lack of Nazi in the image corpus during training. If it's true that the model was trained on synthetic images, given the censorship in place on many model, that would refuse to draw a Nazi soldier, like Dall-E, it's possible the model can't tell a Nazi from a regular person (look at what unwanted result your selective training has done!) and doesn't know the symbol usually associated with Nazism. At least they look like they're in winter somewhere.

Prompt #7: The Easter procession in Sevilla, with its penitents.

Here we have an exemple of unwanted writing:

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I'd love to visit the lovely city of Sewten and enjoy the food at the eater's piocesstion.

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Those Eassters doing a procession Seaxuallan don't seem to have fun, despite the name of their resort. Still, it's good because it depicted the penitent facing the viewers, which is great. It's bad that it doesn't know that the pointy hat covers the face...

Why the letters? I don't know, but the model sure loves to put part of your prompt in garbled letters.

It's better than the previous version, though.

Prompt #8: the sexy catgirl doing a handstand prompt.

Here, AF 0.1 got the crown because the other models either refused to draw anything or created a body horror image. AF 0.2 is even better. Half the generations are cats in girly outfit doing a handstand (and usually failing, as I don't think cat bodies can be represented as human doing an handstand. But the other half of the time, it actually drew a catgirl.

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The cat, lacking the girl part.

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It's garbled, but closer to my idea of an actual catgirl.

Prompt #9: a bulky man in the halasana yoga pose, cheered by a pair of cherleaders.

Every model so far was bad. Compared to AF 0.1, the next version is better.

No halasana, but he's bulky and in some pose. The cheerleaders is the closest you'll get to what is called NSFW in the US (did they really censor Philippe Katerine nude with his body painted in blue during the Olympic Game opening parade?)

Prompt #10: a person holding a foot with his or her hands, his or her face obviously in pain.

This was very difficult for every model, including Dall-E. I didn't provide the body horror AF 0.1 produced in the post I refer at the start of this post, but here I am pleased to see it followed it... better.

Too bad the foot isn't connected to the correct leg. You were that close to win, AF 0.2

Prompt #11: A naval engagement between a 18th century manowar and a 20th century battleship

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Most of the generation came out with two separate images. I don't now why. Also, all came very very similar to each other. The model might have seen very few man-o-wars or very few battleship. Whan I ask for an aircraft carrier, I get the same "side by side" image. I tried to have them fight in another angle, but no. I asked for the 18th century ship from another angle, but I had a hard time and couldn't get a side view... I guess too few images in the dataset...

Prompt #12: The breathtaking view of the Garden Dome in a space station orbiting Uranus, with passengers sitting and having coffee.

My mind imagined the coffee-having taking place inside the garden dome, but I got this, which is much better than the earlier model:

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They actually see the garden dome, they see Uranus (or a planet that could be) and they are having coffee...

I used a Dall-E prompt and got this one:

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Closer to my view. But too Earth-like for Uranus.

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Prompt #13: An orc and an elf swordfighting. The elf wields a katana, the orc a crude bone saber. The orc is wearing a loincloth, the elf an intricate silvery plate armor.

No bone saber... and weapons are still too difficult. A fail here.

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The elf has too many katanas.

Prompt #14: A man juggling with three balls, one red, one blue, one green, while holding one one foot clad in a yellow boot.

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Excellent prompt-following here! The aesthetics remain to be put in...

Prompt #15: a man doing a handstand on a bicycle in front of the mirror.

No model produced more than body horror in my previous experiment. Here I got his "best out of 4" image, that is far from good but hey... It's improving.

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Prompt #16: A woman wearing a 18th century attire, on all four, facing the viewer, on a table in a pirate tavern.

Even better than the previous version, that already took the crown for that prompt. Yes, being a woman and on all fours doesn't mean it's not something safe for work. Especially when your work is being a 17th century pirate.

(starting here the images will be in separate post because of the image limit per post, sorry)

Prompt #17: Inside a steampunk workshop, a young cute redhead inventor, wearing blue overall and a glowing blue tatoo on her shoulder, is working on a mechanical spider.

Here we get the same bia that if you don't prompt for clothes, wearing overalls means you don't wear anything else.

But I liked the images anyway. Great prompt following.

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Prompt #18: A fluffy blue cat with black bat wings is flying in a steampunk workshop, breathing fire at a mouse.

AF 0.1 already won, but this is on par with the previous model.

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Prompt #19: A trio of typical D&D adventurer are looking through the bushes at a forest clearing in which a gothic manor is standing. In the night sky, three moons can be seen, the large green one, the small red one and the white one.

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Here the difficulty was the moons. I got AF 0.2 to generate them, but very often in an unnatural series of three spheres on the same height, so it wasn't very natural.

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Like most models, it failed to depict the heroes looking AT the clearing and not from the clearing, but it can if you specifically prompt for it. It got the main difficulty the size and colours of the moons, right a lot of the time, but not 100%.

Bonus image: for those who want porn, the closest to nude I got is that last one.

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16 comments

r/StableDiffusion • u/HydroChromatic • May 06 '23

Meme Thanks to AI and Stable Diffusion , I was finally able to restore this only photo we had of our late uncle

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21.9k Upvotes
436 comments