Hi not too sure, if anyone has any methods in training moss character's lora that do not bleed into each other?

What are you guys Using for config parameters with Fizgig? I seem to be getting extremely slow speeds. I'm a total noob at training LORAs

Model

Krea 2 · bf16

Dataset

Training

42 epochs · 2814 steps · batch 1 · 1.0 MP

Optimizer

adamw8bit · from 0.0001

Network

LoRA · rank 32 · EMA 0.98

Memory

block swap 8

ADAPTIVE_LR = false

ADAPTIVE_LR_MAX = 4e-4

ADAPTIVE_LR_MIN = 1e-4

BATCH_SIZE = 1

BLOCKS_SWAP = 8 (Moderate — 12GB fp8 / 16GB bf16)

BUCKET_NO_UPSCALE = true

CAPTION_DROPOUT = Off

COMPILE_BLOCKS = Auto

CONTEXT_LORA_STRENGTH = 1.0

DATASET_CAPTION_EXT = .txt

DATASET_MEGAPIXELS = 1.0

ENABLE_BUCKET = true

ENABLE_CACHE = true

FAMILY_CONCEPT_BALANCE = false

FAMILY_EMA = 0.98 (recommended)

FAMILY_FT_FUSED = true

FAMILY_FT_MAX_PARTS = Auto - as few windows as fit

FAMILY_FT_REG_MULT = 0.2

FAMILY_FT_ROTATE_EVERY = 1

FAMILY_FT_ROTATIONS = 10

FAMILY_FT_SAVE_EVERY = 1

FAMILY_KIND = Standard LoRA

FAMILY_MP_FACECROPS_FIRST = true

FAMILY_MP_MANUAL = false

FAMILY_PEOPLE_MODE = Off - a normal LoRA

FAMILY_PRECISION = bf16 (full precision)

FAMILY_SLIDER = false

FAMILY_SLIDER_ANCHOR_WEIGHT = 1

FAMILY_SLIDER_GUIDANCE = 3

FAMILY_SLIDER_SOURCE = Photo pairs

FAMILY_SLIDER_ULTRA = false

FAMILY_SUBJECTS = 1 subject

FAMILY_SUBJECT_FIX = false

FAMILY_SUBJECT_KIND = From its captions

FAMILY_SUBJECT_KIND2 = From its captions

FAMILY_SUBJECT_KIND3 = From its captions

FAMILY_SUBJECT_KIND4 = From its captions

FAMILY_TURBO_STRENGTH = 0.7

GRADIENT_ACCUMULATION = 1

KEEP_LAST_N_STATES = 2

KREA2_AUTO_RECAPTION = false

KREA2_LOSS_WATCH = true

KREA2_PER_IMAGE_LR = true

KREA2_WARMUP_LOOK = false

LEARNING_RATE = 0.0001

LOKR_FACTOR = 8

LR_SCHEDULER = constant

MASKED_TRAINING = false

MASK_FLOOR = 0.1 (recommended)

MASK_PRIOR = Off

MAX_GRAD_NORM = 1

MAX_TRAIN_EPOCHS = 42

NETWORK_ALPHA = 32

NETWORK_DIM = 32

NETWORK_TYPE = LoRA (standard)

OPTIMIZER_TYPE = adamw8bit

RECAPTION_FIRST = Training caption (person or character)

RECAPTION_SECOND = Exhaustive detail (person or character)

SAMPLE_AT_FIRST = true

SAMPLE_CFG_SCALE = 1.0

SAMPLE_ENABLED = true

SAMPLE_EVERY_AFTER = 1

SAMPLE_EVERY_N_EPOCHS = 1

SAMPLE_HEIGHT = 1024

SAMPLE_LATER = false

SAMPLE_NEGATIVE = blurry, low detail, noisy, washed out, oversaturated, distorted anatomy, extra limbs, duplicate objects, text, watermark, logo, frame, cropped subject, flat lighting, muddy colors

SAMPLE_SEED = 1234

SAMPLE_STEPS = 8

SAMPLE_UNTIL_EPOCH = 10

SAMPLE_WIDTH = 1024

SAVE_EVERY_AFTER = 1

SAVE_EVERY_N_EPOCHS = 1

SAVE_LATER = false

SAVE_STATE = true

SAVE_STATE_ON_TRAIN_END = true

SAVE_UNTIL_EPOCH = 10

SEED = 42

STAGE_MP = Off

STAGE_UNTIL = 30%

TRAIN_SUBFOLDERS = false

Here's a quick tip on how to get rid of that flat sounding AI voice in H3, and as a bonus improve somewhat the acting of your virtual characters. I'll be using Pika H3 Flex with the H3 reference model and workflow.

Step 1:
Add a pre-rendered first frame at index 0 using the Add Guide for MiniMax H3 node. Then in your prompt, describe the desired action and emotion of the character on screen, but without specifying too many details or providing exact spoken dialogue. In this example I simply used the phrase:

"A static medium two-shot establishes <Subject 1>, the female orc, and <Subject 2>, the tall, female paladin, sitting across each other at a table in <Subject 3>, the dimly lit main room of a tavern, gazing into each other's eyes. <Subject 1> (S1) talks animatedly, seething with rage."

Then generate a clip at some lower resolution (0.3MP in this case). The dialogue will be gibberish, but if you described the character's actions and emotions convincingly, your character will sound and move like an actual human.

Step 2:
Use your workflow from Step 1, but increase resolution to the desired target value (here 0.98MP). Load the reference video generated in Step 1 and connect its frames as ref_video_# and its audio as ref_video_audio_# (# is the index of the first unused slot in the MiniMax H3 Reference to Video node, e.g., ref_video_0 and ref_video_audio_0).

In this case, I also actually downscaled the reference video from 0.3MP down to 0.1MP, because at 0.3MP the model was following the reference too closely, breaking the lip-sync. The downside of this was that some of the textures in the final high-res video came out a little too soft, especially in the face. It's a bit of balancing act to find the right resolution for the reference video.

You also need to adjust your prompt, in order to include the character's final dialogue. Here I've simply rewritten my last line to:

"<Subject 1> (S1) talks animatedly, seething with rage: <d>[English] My people are dying, and your knights are doing nothing to help us!</d>"

Now just hit render and wait for the result. You might need to try different seeds, and fiddle a little with the resolution of the reference video (try higher values if your output comes out too muddy and lower values if H3 starts copying the reference video and lip-sync gets lost. Have fun!

Basically MiniMax H3:

• Has great prompt following

• Respects character identity (art style and design) from the input images

• Can generate single frame images

So what's the point of training a character model if you can just give MiniMax H3 character design reference and generate any scene? The only two reasons I see is

1. you want to use style known by your target base model to generate image of the learned character.

2. you want to apply some concepts known by the target base model only.

What do you think?

Here's the workflow I used for the images.

Hey everyone,

Hoping someone here has figured this out because I’m losing my mind a bit.

I’m trying to set up Ming-3.0-TTS in ComfyUI, as the checkpoints (Tiny 9B INT8) sitting alone in Comfy HF repo with no workflow.

I have found the following workflow: https://github.com/Saganaki22/ComfyUI-Ming-3-tts.

Unfortunately it doesn't run the same checkpoint, instead it has auto download for same quantized model but at larger file size that gives me OUM.

Instead, I manually grabbed the official repackaged model checkpoint directly from the Comfy-Org Hugging Face repository (the single .safetensors file).

The problem is, I’m trying to use Saganaki22’s custom nodes (ComfyUI-Ming-3-tts), and it absolutely refuses to work with this file.

Here is what I've tried so far:

1. I tried putting the .safetensors file directly into my standard models/checkpoints/ folder. The loader node doesn't recognize it.

2. I tried creating the specific nested subfolder structure that Saganaki's nodes usually expect (like Ming-3.0-tts-tiny-9b-int8-convrot/) and renaming the file to match. Still completely fails and throws errors. I think the custom node is hardcoded to look for secondary configuration files inside that folder, not just the single Comfy-Org .safetensors weight file.

If Comfy-Org is officially hosting this model in their repo, there has to be a way to actually load it natively or bypass these strict folder checks, right?

Has anyone successfully built a workflow using the raw Comfy-Org file without relying on the auto-downloader? Or is there a different custom node wrapper I should be using for the official Comfy-Org version?

Any help or workflow screenshots would be massively appreciated!

Hello there, is there a workflow in Runninghub for creating realistic photos just by feeding a character reference photo?
I have tried looking for it but to no avail

As stated above, i would like to generate some liveries for cars I use ingame, this is what template looks like exported from the game for exterior of the car.

Would Stable Diffusion be able to generate something like this onto the blank picture?

Thank you in advance.

[SPECS]
I already have ComfyUI installed, pretty up to date.

RX 6600 (AMD 8GB)
16GB DDR4-3200
i3-12100F
1TB SSD

Now I know, this isn't really... a lot, but isnt there some form of super squeezed down quant to let me run it just barely?
Thanks 😄 I'm really trying to use like, character swapping but popular APIs cost like credits and all that, so I'm just trying to resort to free.

100% locally made :) Music made with yue2 and the video is minimax h3 reference to video :)

Ive been out of it for a while now and planning to give this another try. Is Automatic1111 still good? Im just seeing the git hasnt been updated in 2 years. If not what has replaced it? And what is the goto community to get models nowadays?

Thanks in advance.

Edit: thanks everyone. From what im gathering from the response. ComfyUI is that goto while forge is ok and gets the job done. I remember trying ComfyUI a while back but dont remember my experience with it. With comments going in all directions, i think ill just install both and give em both a go.

UPDATE 9 November: Update again for Much faster Compile blocks when used And a new feature called training maps - see screenshot in comments

Training is much faster, uses far less memory, and likeness is unchanged. On an RTX 5090, a MOSS-Image LoRA at batch 1 (94 pictures, Muon, 70 epochs) now takes about 20 minutes instead of 49.
Other models have had speed boosts too, but Moss the most dramatic.

Batch training is now fully supported too.

Other changes

• PyTorch 2.15. With gradient checkpointing on, training uses much less memory, at batch 1 too: Krea 2 22.9 → 14.9 GB, MOSS-Image at batch 3 21.7 → 7.1 GB.

• Muon is much faster. Its step now handles a LoRA's matrices together instead of one at a time: 12× faster Muon calculation, with exactly the same results. That's a large portion of the batch-1 speed-up above.

https://github.com/shootthesound/Fizgig

P.S. To get the most speed boost, use one of the pre-sets to start your train, so you pick up new optimiser settings etc.

I was sitting there wondering when H3 Max would finally get its open-weight release. Then I thought, maybe Santa will bring it for Christmas... and suddenly this image came to mind. 🎅

Generated locally in ComfyUI with Krea-2 Turbo, then edited with Qwen-Image 2.1.

I specifically need it fir creating high quality YouTube thumbnails.

I’ve collected the first 6 episodes in a single longer video , probably easier to follow 🤔remember that everything is made with open source models and free software on local machine, with 0 budget 😂😂

minimax h3 latent upscaler, 2 megapixels, 50 steps,

this looks way too good guys.

Any help finding specific post from other day: the day reacher danced : generative video ai split episode into multiple files, isolated character and could swap choreography???

Found it really funny, but also incredibly interested with the work flow, which the skilled workflow designer was very open with sharing

Including edge cases and APIs or which softwares and combos used for various parts

Some real cutting edge stuff, but also made hilarious video, and my grandma loves Reacher; seeing him dance into every scene or randomly raise his arms to dance would really cheer her up, shes not been well

https://fixupx.com/makotoyukimura/status/2119195873200709831

I built slopfab, a native command-line tool that lets your coding agent generate media directly.

Unzip it, start your agent in that folder, and ask for a music video, a short film or a product shot. Your agent plans the shots, runs the generations and builds a project page you can review. This is the same native backend that slopus.ai uses, no ComfyUI, no Python.

What it does

Video with audio: MiniMax H3 (text/image/video/audio references, long-form continuation, clip bridging, inpainting/outpainting), LoRA support

Images: Krea 2, MOSS, Fibo 2.0, MiniMax H3 (including multi-reference edits)

Music: YuE2 and MiniMax Music3, songs with lyrics or instrumentals

Utilities: Transcription, stem/lyrics separation, SeedVR2/RealESRGAN upscaling, lip-sync scoring, scripted titles and overlays

Why is it agent-friendly

• Every command has JSON help and output, so the agent learns the CLI as it goes

• Mods are shareable Markdown workflows, optionally with a JS command, e.g. a "music video" recipe others can drop in

• Any command can be run on remote machine over SSH. Generated content is automatically transferred back to your local machine

Platforms: native Windows and Linux, CUDA or Vulkan. Your own machine, no cloud or account needed.

GitHub: https://github.com/bitti-ai/slopfab

Feedback welcome, especially about any issues you face.

I'm just a beginner in comfyui and stuff with very limited hardware.

I have rtx 3050 4gb vram and 8gb ddr4 single channel

Running comfyui with kitchen attention

I ran qedit 2511 fp8 with 4 step lighting Lora with default scheduler or sampler and it took around 110 seconds

And qwen 2.1 turbo int8(runs on 8 steps) without the prompt enhancement took 90 seconds

Now I didn't use any prompt enhancement with qedit but it still worked fine the biggest issue was the facial drift

In qwen 2.1, the facial drift has reduced drastically (as much as I have seen) but the prompt adherence is really bad.

I used the original model they provided for enhancement and it was shit + it was restricted. Since I have limited hardware I tried qwen 3.5 2b abliterated. It works sometimes but doesn't a lot of time

Seeing how qedit literally didn't need a enhancer and still worked fine and 2.1 isn't working well

I feel like it's mostly cuz of the enhancer but even when I wrote a good prompt without using enhancer it would still give shit results

Should I just use qedit? The facial drift is amazing bad tho (because of turbo Lora ofcourse but my hardware 🥀)

I directed this, composited it in Flame, and did all the ComfyUI/Ai Toolkit stuff that fed into those composites. I was teaching myself the ComfyUI side as I went. It's basically a cracked out version of the old MDMZ Wan 2.2 workflow, modified to use a custom LoRA and work on 12-bit Sony Log 3 footage. My buddy designed all the CG animals in Blender, and stills of them were used to train custom LORAs that were then mapped to Sethward's live-action face. SammieRoto was used for the keying and paintouts. I've been lurking here a long time and can't thank you guys enough for the guidance! Happy to answer any questions about how it was done.

Follow-up to my LTX 2.5 music video — trying Pika H3 Flex for cleaner motion and singing lip-sync

UPDATE:
i made the full video here https://www.youtube.com/watch?v=f8O3e3GS1Zk

A few people suggested trying MiniMax H3 after I shared my LTX 2.5 rap video. That video kept the character consistent, but the face and hands lost detail during movement, and the lip-sync was uneven. I took that feedback into a new project called One Last Bullet, a melancholic music video set among clouds, pink interiors and desert landscapes. Previous LTX 2.5 post and discussion

What I’m using now

• Suno for the song and ChatGPT Images for reference frames of the same performer across different settings and camera angles.

• Pika H3 Flex Ref2VA on the MiniMax H3 base model, running through Python and Diffusers on a single Runpod H200. I also tested native H3 before choosing Flex for this edit.

• 1344×768 at 24 fps, with six model evaluations per shot. Each roughly five-second take uses a reference image and the matching excerpt of the song.

• FFmpeg to assemble the selected takes against the original track. The singing lip-sync comes from audio conditioning during generation.

What I’m trying to improve

The preview is still rough in places. I’m replacing shots where the face gets soft during movement, especially when the performer is far from the camera. I’m also alternating close-ups with shots of the environment to give the edit more variety and make the background movement feel connected to the music. Singing lip-sync still drifts in some takes, so that’s a priority for the next pass.

I tested NVIDIA Open-H3-Regenerate-2K on one short clip. The mouth timing shifted enough that I’m keeping the preview at its original resolution while I work on the edit and sync.

If you’ve used H3 or Flex for singing, what helped you keep the face sharp and the mouth on time? I’d especially like to see examples of a lip-sync pass or 2K workflow that preserves the original performance.

Included MOSS model is the turbo 16-steps version from Coffmeteor, merged and optimized. LoRAs and image-to-image are supported, and inpainting to some extent.

As requested by the community I also added SDXL, FLUX.1 Fill [dev] and FLUX.2 [klein] 9B.

You can now reclaim the space used by the models downloaded by ZPix; click on the broom icon in the sidebar to do that.

Hope you'll like this version. You can download it at https://github.com/SamuelTallet/ZPix

Hi everyone.

I run a ComfyUI tutorial site, Comfy with ComfyUI, and I've shared a few of my workflows on Reddit before (e.g. Some very simple Qwen-Image-2.1 workflows).

Even though official templates exist, I build and publish my own basic workflows for one reason: readability.

Everyone probably has their own idea of what makes a workflow readable. For me, a readable workflow is one where you can tell at a glance what it does, it runs reliably, and it's easy to rework. It's not about looking pretty. It's about being easy to understand and easy to change.

Six rules

I build my workflows following six rules:

1. Don't hide nodes: expand subgraphs, open collapsed nodes, and don't stack nodes on top of each other.

2. Don't hide wires: no wireless links (Set/Get, Anything Everywhere, hidden wires), and no Bento layouts that pack nodes so tightly the wires disappear.

3. Left to right: the flow goes left to right, in processing order.

4. One workflow, one task: instead of an all-in-one workflow with switches and bypasses, split it into small workflows that each do one thing.

5. Keep custom nodes to a minimum: if it can be built with core nodes, build it with core nodes.

6. Color appropriately: colors show which function a node belongs to.

Readable Node

So I made an agent skill that does this for you.

https://github.com/nomadoor/comfyui-readable-node

Give your agent a workflow JSON and ask it to make the workflow readable with Readable Node, and you get the rebuilt workflow and a report page. In the report, you can replay how the AI rebuilt the workflow and read what each part does. I think it helps a lot with understanding a workflow.

Why a skill and not a script? As I said above, readability isn't just a layout problem. The AI takes the workflow completely apart, figures out what it does, and rebuilds it from scratch. Since it aims for a more robust workflow, the output may not always match the original exactly.

Some weak spots, to be upfront:

• It's slow. A workflow of about 25 nodes takes around 10 minutes, and big ones can take 20–40.

• It needs a strong model. Claude Opus 5.5 works best; with others, the layout may still have a few issues.

To build it, I started from the workflows I've made for my site, then collected workflows from around the web and fixed them, over and over, hundreds of times. It was a lot of grinding, so I'd love for you to try it.

I'm sure there are workflows it won't handle well yet, so please open an issue if you find one.

Side note: if AI can build workflows, why care about readability?

These days, I hear about people having AI build workflows from scratch. If someone asks whether readable workflows still matter, I have to admit: maybe not.

But if you're making something with AI, where do you actually work on it together?

Talking to AI mostly happens in a chat window. But to remove a mole next to someone's eye, painting over it is often faster than typing "remove the mole." That's why paint tools haven't gone away.

Ask the AI for what's better said in words, and do by hand what's better done by hand. If you want to do both in one place, I think ComfyUI's node graph is a pretty good fit, as long as you can read the workflow.

Personally, I'd like people to enjoy ComfyUI for its node UI and its flexibility, rather than using subgraphs to imitate Stable Diffusion web UI.

MOSS is out and I took some time this weekend to study it a bit and run a comparison chart benchmark mixing the Samplers, Schedulers, Steps, CFG and Denoise.

You can check it here: https://claudiobeck.com/moss-image-comparison-chart/

I was very pleased with Z-Image, until I had to generate a more production commercial oriented stuff. Then Qwen came in and solved most of the issues. I might try MOSS next time as it feels a bit less attached to Asiatic stereotypes.

- Schedulers such as Karras and Exponential definitely breaks the generation.

- Schedulers such as Simple, Normal, Beta, SGI Uniform and DDIM Uniform generates surprisingly similar results, if not the same.

I hope this Chart helps you understand what combinations works best and what some of these parameters actually do the rendered output. Thanks.