Would like to know what settings presets are better to train style lora for Krea 2 and Qwen 2.1 in fizgig? Is the fast with adaptive lr ok? Or should we go for the style settings preset?

Valtz v0.1.0.11 is out, with FLUX.3 support!

Things I like so far:

* Supports image generation and editing up to 4K × 4K.

* Preserves pixels outside the target editing region, without unwanted warping or distortion.

That said, I was genuinely blown away by Qwen Image 2.1’s editing quality when it first came out. FLUX.3 hasn’t quite given me that same “wow” moment yet.

Give it a try and let me know what you think! Curious to hear how it compares to Qwen Image for others.

yes, im asking before post because i know we cannot share nsf... content here, but link for workflow, is it ok?

can mods answer me?

Why do I get those weird artifacts in Krea 2 Turbo? I am using the standard workflow from ComfyUI, already tried a different vae and a DeGrid Node but it wont change the result. This is a 20 step 2mp example

really excited about this model. been using it via the api, and the edits are insanely clean. Haven’t noticed any drift or quality loss at all, and it seems to understand prompts really well too. guessing it’s not a small model.

Body anatomy and realism- Krea2 and qwen2.1 just way ahead of flux models. flux 3 is worst in terms of body anatomy.

flux 3 still has best in prompt following i think.

Overall, Flux 3 was probably a capable model, lobotomized by extreme and unnecessary censorship.

Prompts- https://pastebin.com/x5LeaM8u

https://openmoss.ai/MOSS-Image/
https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT
https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base
https://github.com/OpenMOSS/MOSS-Image/blob/main/comfyui/README.md

Examples look amazing, trained on 3.2B images, 5B, apache 2.0. Supports JSON / BBOX but does not require it. Knows both natural language and booru tag based prompting.

Oh. And its the least censored base model so far btw. Here is with a crappy simple prompt: https://files.catbox.moe/6irxjh.png

(Flux.3 klein distilled)

4070s render time: 1mp 10secs
flux3_image_klein_sd_int8_convrot.safetensors
qwen3vl_4b_int8_convrot.safetensors
flux2-vae.safetensors
euler, 16 steps

(Qwen image 2.1 turbo)

4070s render time: 1mp 3secs
qwen_image_2.1_turbo_int8_convrot.safetensors
qwen3vl_8b_int8_convrot.safetensors
qwen_image_2.1_vae_bf16.safetensors
euler, 8 steps

Input source:

https://civitai.com/images/145722153

Prompts:

"Add a small battle-worn robot dog sitting at her left boot, matte gray metal with one glowing cyan eye, looking up at her, reflections on wet asphalt, light rain."

"Re-stage the same woman taking cover crouched behind the left concrete barrier, pistol raised with both hands toward frame-right, coat flaring, rain streaks and neon fog unchanged."

"Change only the lighting to blue hour ambient after sunset, cool diffuse bluish light from the sky with very soft shadows and low highlight contrast, everything else unchanged."

"Change only the lighting to silhouette with strong backlight from behind subjects toward camera and no frontal illumination, subjects render as dark shapes with edge definition against a brighter background, everything else unchanged."

"Redraw as black-and-white manga page, screentone shading, ink lines only, same composition."

"Greatly increase her breast sizes."

"Make an expression sheet of the same woman, six headshots in two rows on light gray: neutral, angry, fearful, sad, smiling, shouting, same bangs, yellow raincoat collar visible, hands out of frame."

"Make an outfit variant sheet of the same woman, three full-body looks side by side on light gray: tactical bodysuit with vest, black formal dress with coat over shoulders, street hoodie and cargo pants, same face and bangs, neutral pose."

"Turn her into a boxed collectible figure: same pose and outfit sealed in a clear plastic window box with cyberpunk street artwork on the backing card, box standing on a desk, soft studio light."

I wasn't expecting there to be a new (non-patch) release so soon after 0.40.0, but here is the next version of ComfyUI.

Additions include Flux3-image model support, MiniMax-H3 LightVAE support, among other fixes/improvements.

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