108M parameter denoiser text to image model. Meant as a research experiement but still produces very good images for it's size.

Samples in the readme alongside how to run.

Flux 3 klien make me remember SD3 body horror. but for other things look nice. will need further testing

I'm using the default t2v H3 template in Comfy. It has a load LORA node, but when I put a character LORA in there (from MalRey), it doesn't affect the video output. Is it something with the prompting? I thought that a single human character would take on the likeness of the LORA, but I've also tried using the name of the character in the prompt, and still nothing.

Simple question is there a model that can do video 2 video, and enhance a CGI video to make it more realistic similar to what DLSS 5 is doing. not just the usual upscale/denoiser/frame interpolation that you would want on a live action video?

Hopefully with better results as DLSS5 is meant to work in real time.

I have a 24gb 7900XTX but im willing to rent compute to do so if a bigger model is required.

Really appreciate all the love and feedback on V1 and V2. I've been working on the next release, and this time it's a full checkpoint: a merge of my custom Realism Checkpoint and V3 LoRA.

The focus is still on natural skin textures and a wider range of skin tones, without that overly smooth, plastic look. It also gives you a bit more creative freedom now… let's just say being specific about clothing in your prompts is a good idea

A few tips before you try it:

• Natural language works best. Describe the scene, lighting, clothing, and expression you want. Short prompts work too, but leave more room for interpretation.
• Generate at 2MP if you can. It was trained on high-resolution images and looks best there. At 1MP, you might see some minor noise; RTX upscaling nodes or similar post-processing can help.
• Euler + Simple, 4–8 steps. Six steps is my sweet spot.

You can drop it into your existing Krea 2 pipeline as a checkpoint replacement.

I'm also putting together a full ComfyUI workflow and a custom node for some prompting techniques I've been developing. I'll share those soon. For now, there are example images on the model pages so you can see what it can do.

Give it a try and share your results! Feedback on the earlier versions helped a lot, so I'd love to hear what works for you and what still needs improvement. Cheers!

Links:

• Previous Reddit post — V2
• Download on Hugging Face
• Civitai — checkpoint and example images

Drama3 support in audio.cpp was contributed by https://github.com/JarodMica. Many thanks for the great work!

So far, follow-up optimizations have primarily focused on CUDA. The model also runs on Vulkan and Metal. There's still plenty of room for improvement in performance and VRAM efficiency and correctness. On Vulkan, one request hit the token limit. I also found some unexpected silence in multi-speaker generation.

There will be more iterations before the model makes it into a minor release.

If you're interested in helping us further optimize the model, contributions are very welcome! Please ensure that any changes touching the shared framework are additive and opt-in, so we don't have to spend time testing for potential regressions across the other 100+ models that could be affected by changes to shared code.

RTX 5090, Q4_k:

I tested a highly detailed prompt on both Seedance and Minimax with multi camera movement to see which one performs better.

prompt:

Use the provided character image as the exact visual reference. Create a 15-second live-action-style martial arts training sequence featuring the same white Western male character practising combat techniques inside an old, worn-down martial arts dojo. Preserve his exact face, hairstyle, body proportions, clothing, and overall appearance throughout the video.

VISUAL STYLE

The video should look like an authentic American martial arts action movie from 1986, filmed on 35mm film. Gritty, raw, slightly imperfect, and grounded in reality. Use muted colors, warm tungsten lighting, subtle film grain, mild analog softness, natural shadows, slightly uneven exposure, and practical lighting. Maintain realistic skin texture and believable human movement. Avoid the glossy, overly polished look of modern action films. The character should feel like a real actor performing choreography on a physical set.

FULL 15-SECOND CHOREOGRAPHY AND CAMERA DIRECTION

0–3 seconds - Establishing tracking shot

Begin with a medium-wide shot from behind the character as he stands in the dojo, preparing to train. The camera slowly pushes forward toward him, then smoothly slides to his right side as he raises his fists and settles into a fighting stance. His breathing is subtle, his shoulders relax, and his feet adjust naturally on the wooden floor. Keep the camera in continuous motion.

3–5 seconds - Side tracking and punches

The character performs three fast punches into the air: a jab, a cross, and a powerful hook. The camera glides laterally alongside him, matching his speed and maintaining his upper body in frame. As he rotates his torso into the hook, the camera subtly arcs around his front shoulder. Each punch has realistic body rotation, weight transfer, and a natural recovery.

5–7 seconds - Dynamic orbit and defensive movement

The character performs two defensive blocks followed by a quick sideways dodge and counterpunch. The camera makes a smooth 180-degree orbit around him, transitioning from a side profile to a front three-quarter angle. Keep the character as the visual anchor while the dojo background moves naturally through parallax. The orbit should feel physically operated, not digitally spun.

7–10 seconds - Low-angle kick sequence

The camera descends smoothly toward knee height and tracks alongside the character as he performs a front kick followed by a roundhouse kick. Capture the extension of the legs, pivoting feet, and natural recovery. As the second kick reaches its peak, the camera gently arcs around him to reveal the movement from a new angle. Keep the entire body visible during the kicks.

10–12 seconds - Close-up and camera push

Transition into a close tracking shot of the character's face and upper body. He raises his guard, slips his head to dodge an imaginary strike, and immediately throws a short combination of punches. The camera moves backward with his advancing movement, maintaining a natural distance. Capture his focused expression, sweat, and subtle facial tension.

12–15 seconds - Spinning strike and final wide shot

The character executes a controlled spinning back kick, pivots back into position, and finishes with a powerful straight punch into the air. The camera performs a smooth 180-degree orbit during the spinning kick, then pulls backward into a full-body wide shot as he returns to his fighting stance. End with a brief, natural pause as he breathes and resets.

CAMERA MOVEMENT RULES

The camera must feel like a real operator moving with the performer. Use smooth lateral tracking, forward and backward dolly movements, controlled orbiting, low-angle tracking, and natural transitions between wide, medium, and close shots. Accelerate slightly during fast strikes and decelerate during pauses. Maintain believable camera inertia, coherent screen direction, natural perspective, and consistent distance from the character.

Allow motivated cuts between some angles if necessary, but prioritize fluid transitions and spatial continuity. Do not use random camera movements simply for visual effect. The camera should anticipate the choreography and follow the character's momentum.

CHARACTER ANIMATION AND PHYSICS

All martial arts movements must have realistic timing, weight, momentum, and recovery. The character must remain grounded, with accurate foot placement, believable hip rotation, coordinated arm movements, and natural balance. Avoid exaggerated superhero moves. Every strike should flow naturally into the next technique without stiff pauses or mechanical animation.

CONSISTENCY AND NEGATIVE CONSTRAINTS

Keep the exact same face, hair, outfit, body type, and age throughout. No character morphing, costume changes, duplicated limbs, distorted hands, broken joints, foot sliding, floating kicks, teleportation, unnatural speed changes, or background warping. Avoid excessive slow motion, excessive motion blur, artificial camera shake, abrupt zooms, and random cuts.

FINAL RESULT: A gritty, believable 1980s martial arts movie training scene, captured through fluid, dynamic camera work that moves alongside, around, and with the character. Prioritize camera movement quality, character consistency, and realistic choreography over flashy visual effects.

I’m trying to build a repeatable workflow for a series of ~15-second vertical short videos about trees and arboriculture.

The concept is pretty simple: always the same arborist character, filmed in a realistic/casual smartphone style, talking about trees, their benefits, conservation, pruning, fun facts, etc. The goal is educational content, but with enough visual “eye candy” and strong hooks to make people actually stop scrolling.

What I’m looking for is the best way to build reusable presets/workflows, ideally in ComfyUI, so I don’t have to rebuild everything from scratch for every clip!

I’d like to keep as much consistency as possible between episodes:

-same arborist / face / body / clothing

-realistic outdoor environments

-9:16 vertical format

- ~15 sec clips, preferably single-shot or simple continuous camera movement

-natural gestures / talking

-good character consistency between generations

-reusable prompt structure

-ability to swap only the location, tree species, dialogue/topic and camera action

-eventually produce these in batches as a recurring series

I couldn’t get my hands on an RTX 5090, so for now I’m running ComfyUI on RunPod and I want to build a persistent setup there with the models, LoRAs and workflows already loaded.
For prompting / automation I currently have:
Claude
ChatGPT
Qwen running locally/from source in terminal
RunPod for GPU generation

I’ve been looking at models/workflows around Minimax, WAN, LTX, Hunyuan, FramePack, Qwen Image/Edit, Flux, etc., but there are so many combinations that I’m trying to avoid wasting weeks testing bad pipelines.

For people already doing consistent AI video series: what stack would you use today?

I’m especially curious about whether I should focus on a workflow like:
reference image → consistent character image → image-to-video → lipsync / voice

Uncompressed image: https://postimg.cc/gallery/6WBdT03 (To view the uncompressed image, click on it a second time after opening it, or simply download the entire image album)

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After these tests, I’m confused, and I don’t understand why the Flux 3 Klein model exists or why it turned out the way it did.

Our test subjects

Flux 2 Klein (9B) (Distill) (BF16)

Flux 3 Klein (D) - Distill (BF16)

Flux 3 Klein (B) - Base, NoDistill (BF16)

I took equivalent models for comparison, Flux 3 Klein Distill and Flux 2 Klein (9B), and it’s more correct and fair to compare exactly these two with each other. Still, I decided to add Flux 3 Klein (Base) to understand what this model is capable of overall in its maximum configuration.

For the tests, I used the most standard ComfyUI workflows. I also didn’t use the prompt enhancement feature: my tests include both good, correct prompts and overly abstract, deliberately badly written ones, in order to check how the model handles normal prompts and bad ones, and how well it can generate from short prompts that don’t contain enough information.

Test categories

I split the tests into several categories with different goals:

• Images 1-3: complex prompts with lots of details and different poses. These show how well a model follows the prompt and how often it loses details.
• Images 4-6: typography, mixing typography with real-world imagery, and banner creation.
• Images 7-11: styles. Pay attention to how well each model handles different styles.
• Images 12-13: level of detail. This is similar to tests 1–3, but with a stronger focus on small details.
• Image 14: character understanding.
• Images 15-17: your favorite (or most hated) category, 1girl. Here you can judge overall realism and the ability to generate in an amateur-photo style.

Settings (RTX 3090)

Flux 2 Klein (9B) - 8 steps, 1 CFG (1MP) - generation time (00:07)

Flux 3 Klein (D) - 15 steps, 1 CFG (1MP) - generation time (00:12)

Flux 3 Klein (B) - 50 steps, 3.5 CFG (1MP) - generation time (1:22 - 1:24)

By the way, Flux 2 has a very good VAE, and it’s really sad that Black Forest didn’t update the VAE to version 3 as well :(

My opinion

Flux 3 has the same problems as Flux 2, namely limb mutations. Of course, I didn’t include images with mutations in the tests, but believe me: I ran each generation 2-5 times to get a more or less normal image, and what you see in the tests is the best I got out of those 2-5 generations. For example, for the Nike advertisement image I had to generate about 10 times, at least, to get a normal image without mutations and with physically correct objects in the frame.

Judging by the tests, I can say that the Flux 3 Klein model, specifically the base one without distillation, produces some of the worst images I’ve ever seen, not counting Z-image Base. I assume Flux 3 Klein (Base) exists only for training LoRAs on it, because it’s simply not suitable for T2I tasks. Perhaps it would perform better in an I2I (Edit) scenario, but I’m not sure about that.

Flux 3 Klein Distill gives better results than Flux 3 Base, but it still performs worse than Flux 2 Klein (9B). That’s exactly why I wrote at the beginning that I consider the release of Flux 3 pointless, at least in the form it’s in now, especially when we have such decent models as Krea2, Qwen 2.1 and even BooguImage in the T2I space.

With Flux 3 Klein (Distill), we have anatomy problems, even worse than on F2K, poor detail in eyes and faces, and generally poor detail in some places (Artifacts and blurring in areas with excessive detail). Perhaps the low detail in eyes and other areas could be fixed by raising the megapixel count from 1 to 2-3 during generation, but I think 1MP is the most basic setting and it should always work well, especially since Flux 2 Klein 9B had no problems at all with the “1MP” setting when generating content. Continuing the comparison with Flux 2: on some images Flux 3 has slightly better stylization, but it still has far more drawbacks than advantages to choose it as a replacement for Flux 2 Klein.

I could write a lot more here, but I think the test images will give more information, since everything is pretty obvious anyway. But again, the final choice and the final opinion always remain yours, so I’m looking forward to your comments to hear your thoughts on Flux 3 Klein.

tried making 4 30 second video using minmax H3 turbo mode switched on it generator videos much faster and the correct people are saying the correct lines and nor sometimes talking gibberish in bits of the video

is there a way to fix this when i turn turbo mode off because without turbo mode on the people look better in the video just sometime audio is bad

Just threw some of my usual testing prompts into it to get a feel for the model - I have to say, it's not entirely without merit in terms of atmosphere, most models struggle quite a bit more with concepts like snowstorms.

But the details tend to be pretty messed up compared to other current models and when it comes to anything living ... yeah, I don't think the model has ever seen proportions.

I'm using ComfyUI with Krea 2 RAW and an identity LoRA. I'm looking for the best realism LoRAs that make AI-generated images look almost indistinguishable from real photographs, especially realistic skin texture, visible pores, fine facial details, natural lighting, and realistic fabric texture.

Which realism LoRAs do you recommend, and what model architectures do they work best with? I'd especially appreciate recommendations that improve realism without making skin look plastic or changing my model's facial identity.

Please share the LoRA names, links, and recommended weights if possible.

https://github.com/r34vtraining/Longshot_Studio

I have pretty much added all the important features.

Here are some of the useful updates since my last post.

1. Live preview
2. Refmod
3. Restore Reference (kind of like dropping an image/video in comfy to restore workflow)
4. Timeline Zoom, timeline height adjust, double click clip on timeline to go to prompt
5. Removed most dependencies, will now work with native comfy aside from my nodes.
6. Shot limit increased to 256

I made this following video with the studio and edited with premiere.

https://youtu.be/blzTCQTsWQo

Future edits will be

1. Non-destructive clip trimming in timeline

2. Add audio scrub bar

3. Work on lipsync (it’s buggy)

Thank you for all the support and feeback on this project, it's been fun.

Hey everyone, I've been looking for ways to turn my AI-generated anime images into short animations, like 3–5 second clips or GIFs.

I've seen a lot of stuff about Wan, LTX, Hunyuan, and ComfyUI workflows, but I'm wondering if there are any simpler alternatives out there. My PC isn't exactly great for AI stuff, so running heavy models locally isn't really an option. I'm totally fine with using cloud-based tools or paying a bit for generations, as long as the quality is decent and the pricing isn't crazy.

I'm mainly running a patreon and working with anime-style illustrations, including adult content featuring adult characters, so I'd need something that actually supports that. I've seen some patreon accounts posting hundreds of these small vids everyday, and I doubt they all use a WAN workflow (I guess).

Not looking for anything super complicated, just a way to add some decent motion to existing adult Anime images without spending hours messing around with workflows.

Anyone here doing something similar? What tools are you guys using?

Would love to hear some recommendations If possible.

Been using Krea 2 for a project. I keep on using it the same way using similar prompts and the same Moodboard. Since Krea 3 Beta dropped, I noticed it got SO much worse. Every now and then an anime creature, cute or simply with anime proportions, will drop in without any prompting. It happened before, but much less often and the moodboard was the main source of inspiration. Now it seems as if it's less coherent with my prompts overall.

Edit: I'm running it on Krea's official website, I don't have the powerful GPU needed to run it locally.

https://civitai.red/models/2997793/kroma-krea-2

It does not get enough attention imo even though its good at everything. The only thing it lacks is knowing artist names compared to Anima.

My miniscule experience (started 3 weeks ago):
Learnt little editing in invokeAI canvas (inpainting - destroying and creating new pixels, outpainting - fill out cropped images like hairbun/stretch background scenery, make more room for extra character, objects etc). Yet to learn controlnet, ipadapter, image to image generation etc

Generated 2000+ images, got rough idea about danbooru tags the use of ++, style prompts(anime), year prompts, order of tag priority but advanced tagging using brackets, paranthesis weightage are beyond my experience so used AI to generate a long list of prompts to fix things I wanted to add or remove. A lot of rng to get some decent images, some silliness like cat ears, donkey ears, blonde hair still persist even after adding those respective tags in negative prompt

Generated custom lora (total 2 attempts raised epoch steps to 50 in 2nd attempt instead of 10). Used AI guided recommended settings like rank,alpha,removing tags which should be permanent lora features - used 39 images of varying zoom, background, angle, pose etc (some included generic faceless male character but those images were tagged 1boy or 1male so it shouldnt matter i think?)

Current apprehensions/confusions:

Pose accuracy especially with multiple character lora, bleeding into other character/background, missing/extra fingers - Will I be able to avoid these issues with control net?

Character consistency outside vertical character sprites - Particularly CG images of character, will it be consistent enough? Did i mess up my custom lora?

Danbooru prompt mess vs natural language prompt:

Newer models generate anime movie level quality images with details natural language prompt but will I be able to replicate wai illustrious 2010-2015 era style anime?

I am aware some of them heavily lean towards realism like flux but I also see the potential. Is there some other model or style modifiers you recommend to add with base model to replicate wai illustrious version 14

My biggest issues so far are : extra/missing fingers, character consistency even with custom lora, editing workaround (due to my inexperience), danbooru vs natural language prompt, pose accuracy, bleeding in wai illustrious

Newer models that use natural prompt - Tend to produce shiny plastic skin (which gets really pronounced on my oled monitor), hard or near impossible to replicate wai illustrious 2d flat cel shaded sharp line art anime style

TLDR : wai illustrious consistency worries, seeking alternative model with natural prompt instead of danbooru that can replicate flat 2d images like illustrious. I mostly followed AI based advice, read some of the basics (invoke tutorial page)

P.S - The vertical sprite images were created with a resolution of 896x1152 which is not yet upscaled to 1.5x to be fitted in dialogic 2/godot 4 engine under 1920x1080 project. Sorry if my inexperienced questions annoys you but I need guidance from you experienced lot. My system in 4090 rtx, 9800x3d and ddr5 64gb ram 6000 MTS (not sure if ram matters for image generation compared to 24gb vram).

Allocated 20gb vram dedicated for invoke, remaining to keep explorer up and avoid OOM crash. Its gone now after doing some fixes like changing page file to the ssd drive i am running invoke on and changing vae to fp32 from fp16. Mostly following recommended steps,cfg values and using only dpm++2m karras or euler ancestral. Custom lora weight around 0.7-0.85. Removed custom lora and other prompts small edits, confine bounding box to inpaint region.

For ComfyUI, I want to loop though using different LoRAs, same LoRAs at different strenghths &/or different tweaks to prompts for testing.

How do I do this? Seems most prompt templating is aimed at random. But I want deterministic testing.