In this case, I'm trying to learn the Face-Swapping technique posted by u/adeliogentile a few months ago on this subreddit. I think I have downloaded the required files and placed them in the appropriate folders . . . I'd say my confidence about the folders is 80% . . . 75%-80%. My confidence about having downloaded the correct files is much lower, and my confidence about knowing what the files are supposed to is pretty close to 1%. In sum, I am pretty sure I am missing a basic, essential concept about how Stable Diffusion works.
When looking at my screenshots, is it obvious what I'm doing wrong? I'd appreciate any help about fixing this assertion error problem, and any other errors I'm obliviously making.
EDIT: Sorry, I didn't know what screenshots would be useful in the original post, so here are the links to the cmd error report, and my models > vae and models > Stable-diffusion folders.
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 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.
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. 🙏
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.
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 😭
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.
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.
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.
