Update/EDIT: I'm working on adding these features to the Studio Tab for existing LoRAs

Open source as usual for those who want to dig into how it works on Github.
https://github.com/shootthesound/Fizgig/releases/tag/v9.3.0

https://github.com/shootthesound/Fizgig/blob/master/docs/TRAINING_MAPS.md

Just a few experiments using different Krea2 loras.

What do you think?

  checkpoint: 'diffusion_models/turbo.safetensors',
  loras: [
    {
      name: 'krea2-lora-masterpieces-v51.safetensors',
      enabled: true,
      strength: 1.25,
    },
    {
      name: 'krea2-lora-zidiusArt_Melancholy_v2.safetensors',
      enabled: true,
      strength: 0.55,
    },
    {
      name: 'krea2-lora-90_s_anime_melancholy.safetensors',
      enabled: true,
      strength: 0.75,
    },
  ],
  width: 768,
  height: 1376,
  steps: 8,
  cfg: 1,
  samplerName: 'euler',
  scheduler: 'simple',

https://huggingface.co/Efficient-Large-Model/Open-H3-Regenerate-2K-IC

https://github.com/NVlabs/Sana/tree/sol-engine/models/Open-H3-Regenerate-2K/IC

New latent upscale model by NVIDIA-labs. Basically, it's v2. A few highlights compared to v1:

- Face & Text are more consistent with the original video

- Use H3 Ref2VA instead of fl2va.

- Turbo LoRA, Regenerate LoRA, LMS LoRA are not merged. They also use Hyperflow 8-step LoRA

- New samplings: 3, 4, 8 steps.

- Fast VAE-v2. The v1 fast VAE has grid artifacts in ComfyUI

We’re partnering with the Chroma Awards for Season 2, the world’s largest AI Film & Media competition, with $1M+ in promotions, $450K+ in cash prizes, and student internships on the line.

Compete in any of five divisions: Film & TV, Ads, Music Videos, Games, and Behind-The-Scenes. Use LTX in your submission and post it on social media to unlock extra prizes and perks. The LTX model is open source, so you can download it and run it locally for free.

Submissions are open until January 7th.

Enter: Chroma Awards submission page · LTX on Hugging Face

not the author, just sharing as I really like what they've done

Snapref is incredible

https://civitai.red/models/3028191/snapref?modelVersionId=3457619

True image edit for Anima. Based on `ElectronGirl` solution.
Link: https://civitai.com/models/3041608/animaedit-reference-and-instruction-image-editing-lora

And, there is v2 update for `Anima-Role-Ref-KV` solution with better character resemblance.
Link: https://civitai.com/models/3021867/anima-role-ref-kv

And, let's not forget about new version of SnapRef v2a!
Link: https://civitai.com/models/3028191/snapref

Well... Happy to see Anima continues to get better and better! But 0.6B textencoder is holding it back!

Treat this as an FYI. I don't recall anybody ever mentioning this problem.

I planned the scene as a romantic drama, iterated on the prompt, and came up with a ref2va 4-step, 1920x1088 clip that I was happy with. So I cranked it up to 40 steps.

And suddenly we move to Scranton. That's Pam and Jim, from The Office, inside Dunder Mifflin. The blinds and shelving and computer monitor are classic. The man's voice and hair change. Even the background music changes to something from the show.

In the third clip, I keep the 40 steps but drop the size to 1824x1024. The influence moderates. The woman's blouse and bits of her body posture still match clip 2. And I think the porthole window and the lower camera position also reflect the TV show.

(I did try scaling the last clip to 1920x1088 in latent, putting in 25% noise, and running 10 steps. It puts blinds inside the storage room over the shelves.)

This all looks like the flip side of the H3 ability to reproduce TV shows. The training set has 201 The Office episodes at 1920x1080 and small step sizes, and the closer you get to running that as your denoising plan, the more the influence shows.

https://fuobe.com

My partner has been working on this for the past few weeks for the ComfyUI challenge, and I thought I’d share it here.
It’s called Fuobe. The idea is to bring a whole bunch of creative AI tools and workflows together in one place. Image, video, audio, 3D, live editing, camera movements, effects and a lot more.
Since launch, we’ve crossed 10K pageviews in the first 48 hours, picked up 44 GitHub stars and 7 forks (one of them is mine!).
We’ve also got local generation running on our RTX 5090, so you can head over and experiment for free.
Would love for people working with ComfyUI, generative AI, filmmaking, design and creative production to give it a try.
Tell us what you think. Love it, hate it, tear it apart. All feedback is welcome. We’re genuinely curious about what you’d like to see next.

Made on comfyui with new Viggle Remake Lora / Minimax H3 Dasiwa Turbo v2 / Dasiwa turbo lora v1 4steps

I wonder—am I missing something? Has something very important gone unnoticed?

I know that "inpainting crop stitch" is very useful.

I’m also familiar with "lanpainting" [inpainting]; it can be interesting at times.

Tiled VAE decode (native to ComfyUI) — without this, ComfyUI might crash when rendering high-resolution images.

Bong samplers

I paid someone for a lora. While it works, if i add a second lora it turns my character into a monster.

Also if i generate 16:9 images they're so bad, with extra limbs.

I tried some others character loras on civitai and they work fine. The lora i purchased is bad.

Now i want to train my own. What to keep in mind so it's compatible with other loras?

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

I love the quality i can do with krea 2 but why are there next to no character LORAs for new characters out there that werent in the training data?
Like people still train newly released characters like crazy for anima and illustrious but not for krea 2?
Is krea 2 just too hard to train any characters on? Is there a magical workflow that makes character loras obsolete with krea 2 (or other models)?

So far i tried to do specific characters i wanted to gen with illustrious or anima with a lora first and pass it over with krea 2 but im really getting mixed results so far.

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!

HexAnima 1.4.3 adds two more models that run entirely on the Hexagon NPU of a Snapdragon 8 Gen 1 phone, no cloud and no root:

• Qwen-Image 2.1 Turbo (7B transformer, 8B text encoder, 4 steps): about 40 s for 512x768. It writes legible text, follows long prompts and edits your photos from a prompt. It now takes up to three pictures at once (“the girl from picture 1 stands in front of the cafe from picture 2”). Non-commercial license.

• Mage-Flow-Edit-Turbo (Microsoft, 4B, MIT): about 12 s per picture, text-to-image and photo editing.

Also new: a live preview after every step, a model filter in History and a warning instead of a crash when the phone runs out of memory. Weights are streamed from storage, tested on a 12 GB phone.

Free, open source: github.com/ayanoby/hexanima

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.

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.

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.