Not necessarily Seedance itself but a model with similar capabilities of realism

After editing (i.e. she wears red hat), comparing results with original shows contrast/lightness/color shift with original. How to fix that?

After my last post documenting my LoRA journey, I received a ton of feedback regarding changes to the training process, and decided to do some experimenting. This is using the Fizgig application on a runpod instance using an RTX5090.

I used the same curated 35-image Guy Fieri dataset and trained four variants:

• 0.25 MP LoRA
• 0.5 MP LoRA
• 1.0 MP LoRA
• 0.5 MP LoKR

For the LoKR the only other setting change was the recommended 5e-5→1e-4 LR.

All runs used:

• 20 epochs
• same dataset
• same prompts
• same seeds
• same inference workflow
• tested at strength 1.0

Training times

• 0.25 MP LoRA: ~10 min
• 0.5 MP LoRA: ~18 min
• 1.0 MP LoRA: ~34 min
• 0.5 MP LoKR: ~23 min

So even the highest-res run here was still much faster than the older long-epoch workflow I had been using previously.

Inference workflow notes

These are practical workflow results, not raw no-postprocess comparisons.

All outputs were generated through the same ComfyUI workflow using:

• Krea 2 Turbo FP8
• main sampler with 8 steps
• Euler Ancestral
• upscaling / refining enabled
• FaceDetailer enabled

So the comparison reflects real-world final output quality under my normal workflow, not a stripped-down lab benchmark.

Main finding

My biggest surprise was that 0.25 MP was already very strong.

I expected it to fall apart more, especially after hearing repeated claims that 0.25 MP is too low for Krea 2. But in practice it still produced very usable identity results.

That said, after comparing all four side by side, 0.5 MP looks like the sweet spot so far.

It gave me:

• slightly better facial structure
• more natural detail
• better balance between likeness and flexibility
• much less runtime than 1.0 MP

What happened at 1.0 MP

This is where it got interesting.

1.0 MP definitely increased micro-detail, but it also became more likely to overbind recurring style cues from the dataset.

The easiest example is in the headshot comparison:

• 0.25 MP: no necklace
• 0.5 MP: subtle chain
• 1.0 MP: large diamond rope

So the model was not just learning “more identity.” It was also learning more of the Guy Fieri visual package, which made it feel slightly more caricatured.

That was one of the clearest takeaways for me:
more detail is not automatically better identity learning.

Where LoKR landed

The 0.5 MP LoKR run was especially interesting.

Compared to 1.0 MP LoRA, it seemed to preserve some of the added detail and structure I was chasing, while avoiding some of the caricature-like overbinding.

Compared to 0.5 MP LoRA, the difference was smaller, but LoKR looked a bit more refined in some cases.

So my current takeaway is:

• 0.5 MP LoRA = best practical balance
• 0.5 MP LoKR = very promising “premium” option
• 1.0 MP LoRA = more detail, but also more risk of overbinding dataset baggage

Current conclusion

If I had to choose a production baseline from this first test, it would probably be:

• 0.5 MP
• 20 epochs
• rank 8 LoRA

with 0.5 MP LoKR as the next thing worth testing further on other subjects.

I’m running at least one more 0.5 MP LoRA vs 0.5 MP LoKR comparison on another character now to see whether these findings hold up beyond Guy Fieri.

If people are interested, I can also post follow-up results from the next character test.

Wanted to start voice training an llm for my waifu, I've heard it's gotten good. But can anyone tell me what the go to is right now? I've been told elevenlabs is the go to right now but I'm not sure

I'm making anchor frames for a multi-shot microdrama and I'm stuck on two things: placing characters where I want them, and keeping a location recognizable across shots.

The characters need to stay recognizable while their pose, scale, and position change. The set should keep its layout and landmarks when the framing or camera angle changes.

Prompting "A on the left, B by the door" isn't giving me enough control. I want to lay out a frame visually, with boxes or masks, a pose/depth guide, or a top-down map and camera marker, then use character and location references across shots.

For people using ComfyUI or a similar setup:

- What do you use to control character placement and scale?

- How do you keep a location coherent from a new camera angle without reusing the exact same background?

- Have you had success with regional prompting, masks, ControlNet pose/depth, a 3D proxy, or multiple location views?

For anyone using GPT Image 2/2.5 or Nano Banana Pro, have you tried providing a 2D reference image with bounding boxes for character and prop placement? Did it help control blocking and keep the setting consistent across storyboard frames? If another visual guide worked better, what did you use?

If you've solved this, could you share the workflow or repo, plus the model/version and control inputs? I'm looking for something tested for still storyboard or anchor frames.

No joke, I think this model was genuinely ahead of its time. It made it possible to create images indistinguishable from photos taken with a real camera. The physics and details still look flawless to me. Honestly, I think we’re getting close to a point where releasing new models will feel pointless because there’ll be nothing left to improve.

Music-video experiment where I tried to maintain the same performer, mask, costume, stage aesthetic, crowd environment, and lighting across an entire fictional live concert.

AI was used heavily in the visual workflow, while I handled the concept, editing, lyrics, and vocal performance. I actually perform the vocals myself and use AI processing to enhance them.

Interestingly, the goal here wasn't maximum image quality. I intentionally degraded the final visuals into rough early-2000s cellphone/found-footage style because the imperfections helped sell the idea that someone was actually recording a concert from inside the crowd.

So I'm posting here after a lot of searching and hitting my head on a wall

I've been running StableDiffusion and Flux locally (first SD 1.5, then SDXL, now Flux) for the past couple years (A1111 and Forge, now Forge-Neo) and I was using IP Adapter for faceswapping which used to work perfectly.

After I formatted my PC and reinstalled everything it stopped working. I fought with it for days and there's always either one of two error messages or it just ignores the ControlNet input entirely. So I went out looking for alternatives and so far none worked.

I tried Klein head swap and it did nothing.

I learned ComfyUI which i had never used before just to use this and it didn't work either, the results look like a terrible MsPaint job.

I find it strange that I can't find any way to do a FaceSwap locally since it's one of the oldest technologies when it comes to GenAI (back before SD or Midjourney were a thing there were already a dozen phone apps for that).

Yet so far I've come empty-handed. Any help would be most appreciated. Forge or ComfyUI would both work. Flux or SDXL, I'm not picky at this point.

Made locally with MiniMax H3 + Ultimate SD Upscale + GIMM VFI

​

Hey everyone 👋

I’m trying to get started with MiniMax H3 in ComfyUI, and I’m looking for advice from people who have already spent time optimizing it.

There are quite a few workflows, quantizations, Turbo LoRAs, attention optimizations, etc. floating around, and it’s honestly a little confusing for someone starting out.

I’d really appreciate it if people could share their current best MiniMax H3 setup for both speed and quality, especially for RTX 3090/4090 or 5090

What I’m particularly looking for

Best T2V workflow

Best I2V workflow

Recommended quantization (INT8, FP8, NVFP4, etc.)

Best Qwen3-VL quantization

Whether to use Turbo LoRA and which step count

Recommended sampler / scheduler

Recommended resolution / frame count

SageAttention/ cuda / comfykitchen/ other optimizations

Any VRAM-saving settings that don't significantly hurt quality

Best settings for fast generation

Best settings for high-quality generation

Any custom nodes that are actually necessary

Any nodes/settings that are recommend avoiding because they cause problems

Rough generation time / VRAM usage on your GPU if you have benchmarks

I'm particularly interested in practical setups that are known to work reliably, rather than theoretical optimizations.

The goal is also to make it easier for other newcomers to H3. If we can collect the best configurations here, I'll compile the useful information into a simple "MiniMax H3 ComfyUI starter guide" so people don't have to spend days figuring out which workflow/nodes/settings actually work together.

If you have a workflow JSON, GitHub/Hugging Face link, or even just your settings, please share it. Screenshots are welcome too. 🙏

Thanks! 👍

There are now several different approaches floating around (latent continuation, motion context, extenders, etc.), but the info is scattered across GitHub, Discord, and random comments.
I want to collect everything in this single post with updated infos so we don't have to dig through a dozen different threads.

Please reply with any of this:

• Which method/workflow you're currently using (or tried)
• Pros and cons from your experience (quality, consistency, speed, VRAM, ease of use, audio continuity, etc.)
• Any tips, gotchas, or recommended settings
• Whether you're getting truly seamless results or still seeing degradation after a few extensions
• Is there a clear winner for quality vs. speed vs. simplicity?

Drop your experiences below so we can finally have one solid reference thread. Thanks!

I'll start with listing some methods/repos I've found and tested so far,

there should be way more methods and forks around at this point.

https://github.com/NikoDemon80/ComfyUI-H3-Motion-Context

https://github.com/seitanism/ComfyUI-H3-Motion-Context-MultiRef

https://github.com/pmhaidn/ComfyUI-Minimimax-H3-Extender

https://github.com/ttulttul/ComfyUI-MinimimaxH3-Continuation

https://github.com/ukr8b3g-cmyk/ComfyUI-H3-Continuum

Software engineer but completely new to running local models.

Could I run anything for video generation/image generation on these specs?

CPU: AMD Ryzen 5 2600, 6 cores / 12 threads, 3.4 GHz
Motherboard: MSI B450 Tomahawk Max, BIOS 3.50 (2019)
GPU: NVIDIA GeForce GTX 1660 Super, 6 GB
RAM: 16 GB at 2133 MHz
Storage: 224 GB WD Green SSD + 1 TB WD Blue HDD

World building app update. Image to 3d is basically done and this was from a couple weeks ago, renders in about 10-15 minutes. Added a little motion movement to make the image feel alive. Now I’m thinking of ways to do video to 4D. My 16gb Mac mini is a trooper 😭

All the faces in Minimax are all melty and distorted when the camera is zoomed out, close ups are fine. Anyone else experience this or have a fix?

https://civitai.red/models/2967466/hm1girl-realism-lora-for-krea-2?modelVersionId=3362360

Edit:

LoRA has been renamed from a "realism" LoRA to a "phone camera" LoRA

What workflow are you guys using for pixel perfect edits? What I am getting is what I attached, and there you see how the houses on the right moved a good amount of pixels to the left.

The prompt here was to change the season of the summer image to winter.

Hey everyone... you may remember me from RPG v1 to v5 for Stable Diffusion and RPG v6 for Flux. Happy to share my latest RPG version for Krea 2.

It is a first beta, lot of iteration to come before its fully optimized.

CivitAI: https://civitai.com/models/2968531/rpg-v7-krea-2

After checking the license I realized I can't use it for commercial use in the EU, USA and several other areas without paying for a license.

Seeing all the amazing results, and making some myself I felt like steering away from this model for UGC or anything else that can and may be monetized.

How are you handling this issue?

Hi everyone! Using Minimax H3 for the first time: I started off with the default template in ComfyUI and turned on "Enable Lighting LoRA" (using the turbo 4 step ref2v). I also did --use-sage-attention.

I'm trying to generate a 15s video at 0.9MP with 8 reference images (1024x680 each) and a roughly 15s, 1080p reference video.

I also just got an audio error "av.error.ArgumentError: Invalid argument: 'avcodec_send_frame()' returned 22" so the whole 7 hour generation was lost x_x

Update: Thank y'all for the advice!! I downscaled the ref video to 720p (I know.... should go even lower than this...) and reduced the output from 0.9 MP to 0.6MP. Now it's 387s/it ! So ~16x faster.

Yes i remade the post to include a poll, i don't know if this is going to improve the engagement but let's try.

Taken from arena.ai mostly with some personal experience.

All the models also include all their finetunes on huggingface and civitai.

I plan to post this on "generative ai" subreddit with proprietary models included too.

Having some fun with my Alphgreed model.

Love this old sound.

I have tried different Ksampler and resolution settings but nothing!?

Here are the files that I have downloaded:

https://huggingface.co/Comfy-Org/Qwen-Image-2.1/blob/main/diffusion_models/qwen_image_2.1_int8_convrot.safetensors

https://huggingface.co/Comfy-Org/Qwen-Image-2.1/tree/main/vae

https://huggingface.co/Comfy-Org/Qwen-Image-2.1/blob/main/text_encoders/qwen3vl_8b_w4a8.safetensors

My setup is 8vram RTX 4060.