Wanted try make something a little more substantial with Wan2.1 and multitalk and some Image to Vid workflows in comfy from benjiAI. Ended up taking me longer than id like to admit.

Music is Suno. Used Kontext and Krita to modify and upscale images.

I wanted more slaps in this but A.I is bad at convincing physical violence still. If Wan would be too stubborn I was sometimes forced to use hailuoai as a last resort even though I set out for this be 100% local to test my new 5090.

Chatgpt is better at body morphs than kontext and keeping the characters facial likeness. There images really mess with colour grading though. You can tell whats from ChatGPT pretty easily.

Workflow: https://pastebin.com/HaFydUvK

Came across a bunch of different Kontext workflows and I tried to combine the best of all here!

Notably, u/DemonicPotatox showed us the node "Flux Kontext Diff Merge" that will preserve the quality when the image is reiterated (Output image is taken as input) over and over again.

Another important node is "Set Latent Noise Mask" where you can mask the area you wanna change. It doesnt sit well with Flux Kontext Diff Merge. So I removed the default flux kontext image rescaler (yuck) and replaced it with "Scale Image (SDXL Safe)".

Ofcourse, this workflow can be improved, so if you can think of something, please drop a comment below.

I think the result was good. Of course you can upscale. But in some cases i think unblur has its place.

the Prompt was: turn this photo into a sharp and detailed photo

Really happy with how it turned out. Workflow is in the first image - it produces 3 exposures from a text prompt, which can then be combined in Photoshop into HDR. Works for pretty much anything - sunlight, overcast, indoor, night time

Workflow uses standard nodes, except for GGUF and two WAS suite nodes used to make an overexposed image. For whatever reason, Flux doesn't know what "overexposed" means and doesn't make any changes without it.

LoRA used in the workflow https://civitai.com/models/682349?modelVersionId=763724

Loras destroy anatomy/text etc.

1 year later and no really good fine-tuning

The truth is that it is not possible to train well using a distilled model? Is that it?

Order: Flux sitting on couch with legs crossed (4X) -> Wan sitting on couch with legs crossed (4X), Flux Ballerina with leg up (4X)-> Wan Ballerina with leg up (4X)

I cant speak for anyone else but Wan2.1 as an image model flew clean under my radar until yanokushnir made a post about it yesterday https://www.reddit.com/r/StableDiffusion/comments/1lu7nxx/wan_21_txt2img_is_amazing/

I think it has a much better concept of anatomy because videos contain temporal data on anatomy. Ill tag one example on the end which highlights the photographic differences between the base models (i don't have enough slots to show more)

Additional info: Wan is using a 10 step Lora which i have to assume reduces quality. It takes 500 seconds to generate a single image for Wan2.1 with my 1080 and 1000 for Flux at the same resolution (20 steps)

Hey everyone!

I've been seeing a lot of stunning anime-style images on Pinterest with a very cinematic vibe — like the one I attached below. You know the type: dramatic lighting, volumetric shadows, depth of field, soft glows, and an overall film-like quality. It almost looks like a frame from a MAPPA or Ufotable production.

What I find interesting is that this "cinematic style" stays the same across different anime universes: Jujutsu Kaisen, Bleach, Chainsaw Man, Genshin Impact, etc. Even if the character design changes, the rendering style is always consistent.

I assume it's done using Stable Diffusion — maybe with a specific combination of checkpoint + LoRA + VAE? Or maybe it’s a very custom pipeline?

Does anyone recognize the model or technique behind this? Any insight on prompts, LoRAs, settings, or VAEs that could help achieve this kind of aesthetic?

Thanks in advance 🙏 I really want to understand and replicate this quality myself instead of just admiring it in silence like on Pinterest 😅

More examples with prompts in article: https://civitai.com/articles/16704

TLDR - nothing new, less details, Q8 is closer to BF16. Changing seed has bigger variations. No decrease in instruction following.

Interestingly I found random seed that basicaly destoys backgrounds. Also sometimes FP8 or Q8 performed sligtly better than others.

We’re releasing 3 LoRAs for you to gain precise control of LTX-Video 13B (both Full and Distilled).

The 3 controls are the classics - Pose, Depth and Canny. Controlling human motion, structure and object boundaries, this time in video. You can merge them with style or camera motion LoRAs, as well as LTXV's capabilities like inpainting and outpainting, to get the detailed generation you need (as usual, fast).

But it’s much more than that, we added support in our community trainer for these types of InContext LoRAs. This means you can train your own control modalities.

Check out the updated Comfy workflows: https://github.com/Lightricks/ComfyUI-LTXVideo

The extended Trainer: https://github.com/Lightricks/LTX-Video-Trainer

And our repo with all links and info: https://github.com/Lightricks/LTX-Video

The LoRAs are available now on Huggingface: 💃Pose | 🪩 Depth | ⚞ Canny

Last but not least, for some early access and technical support from the LTXV team Join our Discord server!!

Hi everyone;
I wanted to update you about my last lost about me making an autoregressive colorizer AI model that was so well received (which I thank you for that).

I started with what I thought was an "autoregressive" model but sadly was not really (Still line by line training and inference but was missing the biggest part which is "next line prediction based on previous one").

I saw that with my actual code it's reproducing in-dataset images near perfectly but sadly out-dataset images only makes glitchy "non-sense" images.

I'm making that post because I know my knowledge is very limited (I'm still understanding how all this works) and that I may just be missing a lot here. So I made my code online at github so you (the community) can help me shape it and make it work. (Code Repository)

As it may sounds boring (and FLUX Kontext dev got released and can do the same), I see that "fun" project as a starting point for me to train in the future an open-source "autoregressive" T2I model.

I'm not asking for anything but if you're experienced and wanna help a random guy like me, it would be awesome.

Thank you for taking time to read that useless boring post ^^.

PS: I take all criticism on my work even bad ones as long as It helps me understand more of this world and do better.

My Creature Shock Flux LoRA was trained on approximately 60 images to excel at generating uniquely strange creatures with distinctive features such as fur, sharp teeth, skin details and detailed eyes. While Flux already produces creature images, this LoRA greatly enhances detail, creating more realistic textures like scaly skin and an overall production-quality appearance, making the creatures look truly alive. This one is a lot of a fun and it can do more than you think, prompt adherence is pretty decent, I've included some more details below.

I utilized the Lion optimizer option in Kohya, which proved effective in refining the concept and style without overtraining. The training process involved a batch size of 2, 60 images (no repeats), a maximum of 3000 steps, 35 epochs and a learning rate of 0.0003. The entire training took approximately 4 hours. Images were captioned using Joy Caption Batch, and the model was trained with Kohya and tested in ComfyUI.

The gallery will feature examples with workflows attached, I'm running a very simple 2-pass workflow for most of these, drag and drop the first image into ComfyUI to see the workflow. (It's being analyzed right now, may take a few hours to show up past the filter.)

There are a couple of things with variety that I'd like to improve. I'm still putting the model through its paces, and you can expect v1, trained with some of its generated outputs from v0, to drop soon. I really wanted to share this because I think we, as a community, often get stuck just repeating the same 'recommended' settings without experimenting with how different approaches can break away from default behaviors.

renderartist.com

Download from CivitAI

Download from Hugging Face

Animation of spyro Cartoon made using AI

Look at the fluidity and character consistency. Imagine in 1 year whats gonna happen