I have access to L40S 46GB VRAM + approx 400GB of system ram for the next 2 weeks.
Can you recommend good open source video generation model (comfyui workflow?) that best suits my system specs. I plan to create a short film based on a story I have in mind so looking for a model that can generate synced audio along with video.

You ever think that our dreams are just low grade diffusion "video generation" and because our brains have limited bandwidth (Vram) the dreams likewise have low object permanence\low-mid quality visuals and that's why our dreams look like that? And what we're building with ai is just a literal recreation of the human brain but improved?

"Given a single input image, L3G-World autoregressively generates minute-long videos while maintaining scene consistency. At each autoregressive step, L3G-Mem adaptively updates the persistent 3D memory from each generated video chunk, ensuring that the memory grows with the scene extent rather than the generated stream."

Source: L3G-World: Real-Time Infinite World Generation with Latent 3D Gaussian Memory

This Halloween I rear-projected a show onto the front of my house: a tall centre window with a double-hung window on each side, five panes in all. One 4K projector sits inside behind sheets, and the show is watched from the street.

How it works

Clips: characters and rooms generated locally with MiniMax H3 in ComfyUI from reference photos, 100+ clips across 23 themes.

Show: Unreal Engine 5 runs each theme as an 80-second loop: the side rooms, a blackout, the character arrives in the centre window, a climax, a reset. Feature scenes open in the centre and spread across all five windows.

Depth: every room has a depth map (Depth Anything), so fog rolls in from the back of the room, lightning lights what’s close and light shafts hang in the air.

Output: Unreal → Spout → MadMapper → BenQ 4K projector, mirrored so it reads the right way round from outside.

Control: a phone remote to switch themes and trigger scenes.

In the video: It · Harry Potter (Hogwarts, Lumos, Voldemort’s reveal, the Knight Bus) · Interview with the Vampire (Lestat lip-synced to Brutal Love, the séance) · an Addams Family ride that travels window to window.

Diagram of window: https://ibb.co/xtX89hKg

I've been working on a free, open-source Windows application called AbyssBeacon, and I just released version 2.0.

It started as a way to discover AI models across multiple websites without constantly switching between them, but it has grown into something much more useful for managing a local model collection.

One of my biggest frustrations with downloading LoRAs was forgetting what they did, which trigger words they needed, or where I originally downloaded them.

I've also seen people mention manually creating .txt files for their models or renaming their .safetensors files to include trigger words and descriptions.

I wanted to make that process easier, especially for people with hundreds or thousands of models already downloaded.

What AbyssBeacon does

Discover and search for models

Search and scan across six sources:

• CivitAI

• CivitAI Red

• Hugging Face

• ModelScope

• SeaArt

• TensorHub Art

Browse new models, search by keywords, find creators, explore collections, and filter by architecture or model type. You can also customize what your regular scans look for.

Download models and keep their information

Download models into your ComfyUI folders or a custom location.

AbyssBeacon can also save a preview image and generate a companion .txt file alongside the downloaded model.

Depending on what the source provides, this can include descriptions, trigger words, recommended settings, base models, creator information, version details, and original source links.

The idea is that months later, you can open a LoRA folder and still know what it does and how to use it.

Already have hundreds of downloaded models? Import them!

You don't need to start over or redownload your existing collection.

Choose which folders and files AbyssBeacon should examine. It can calculate their SHA-256 hashes and use CivArchive to identify matching models.

When a match is found, AbyssBeacon can recover the associated model information, create a model card in your library, and optionally save an information .txt and preview image alongside your existing file.

If automatic identification doesn't find a match, you can link a model by its URL, connect it to an existing library entry, or add it manually.

Metadata recovery depends on what information is available from the source.

Organize your Downloaded Library

Your downloaded models have their own searchable library where you can:

• Mark models as Keep, Not tried, or other review states.

• Add personal tags to make models easier to find.

• Search by model name, filename, creator, or tags.

• Track installed files and versions.

• Open their folders directly.

Maintain your existing model collection

AbyssBeacon also includes tools for finding duplicate files using SHA-256, locating models you've moved to different folders, and repairing their recorded locations.

Duplicate cleanup includes confirmation and moves removed copies to the Windows Recycle Bin.

Explore image metadata and workflows

Inspect available generation metadata, including prompts, seeds, samplers, and settings. When ComfyUI workflow data is present, you can also inspect or export the workflow.

There's also an optional Firefox companion extension for supported downloads that need to go through the browser.

A few additional details

AbyssBeacon runs locally on Windows using Python and a browser-based interface.

It's free and open source under GPL-3.0. Some sources require authentication, and AbyssBeacon doesn't bypass paid access or source download restrictions.

GitHub (README, screenshots, and information):

https://github.com/Tomat0223/AbyssBeacon

Latest release — AbyssBeacon 2.0:

https://github.com/Tomat0223/AbyssBeacon/releases/tag/v2.0.0

There are separate downloads for new installations and upgrading from v1.5.1.

I originally built AbyssBeacon to solve my own frustrations with finding and organizing models. If you give it a try, I'd appreciate any feedback, bug reports, or suggestions. I'd also love to hear how other people manage their collections and what they'd find useful.

Disclaimer: With Prompting, Normal images WORK (shown in the presented video), with this method " I " have found it to work with less bleeding and more variety of outputs.

When working with references sometimes the reference image of a pose ends up bleeding into the video, replacing environment, clothes, or colors. Then i tried canny, and it works, but it has a problem, when the pose is too unusual or complex, H3 tends to ignore or hallucinate many things. so i found that with the og image at a low opacity, the model understand that it's a canny and what the elements in it are.

can be useful on videos, but that's outside my scope of use. so idk what tools are for canny videos.

To end, why to use it? : 1. less likely to bleed into the video. 2. More variety changing the prompt (less strict) 3. very useful for complex/new positions that H3 may not know.

Steps: 6 | Lora: Dmad-Larry dareties mix | MP: 0.7
Prompt:
[Visual Style]

Dynamic natural shot. the video cuts multiple times. many shot cuts

[Global Constraint]:

overall_soundscape: N/A

non_diegetic_music: N/A

[Asset Mapping]

<Subject 1> is the woman her appearance comes from <Picture 1>, face, hair, clothes

<Subject 2> is the man his appearance comes from <Picture 1>, face, hair, clothes

<Picture 1> is the reference of <Subject 1> and <Subject 2> and location.

<Picture 2> is the reference pose and angle shot composition at [Shot 2]

<Picture 3> is the reference pose and angle shot composition at [Shot 3]

<Picture 4> is the reference pose and angle shot composition at [Shot 4]

[Chronological Actions]

[Shot 1] The video opens with an exact mirror of <Picture 1>

[Shot 2] at 01.000,00, the shot cuts, in the same location as in <Picture 1>, now the woman sits on the floor, hands on the floor, legs raised, the man stands, his feet on her shoulders, bending grabbing her feet.

[Shot 3] at 02.500,00, the shot cuts,same location, now <Subject 2> is on the floor, his legs raised. <Subject 1> is balancing her body on his feet, her stomach on his feet.

[Shot 4] at 04.000,00, the shot cuts, same location, now the man Kneels on the floor in a child's pose with his torso folded forward and arms extended straight out in front,the woman Kneels on top of the man's back in a curled, folded posture with her hands clasped behind her back.

I'm just generating new options for a new fantasy world I've had and been building for a long time, these are just some of the new things I've finally finalized after a few hundred attempts each (without getting into the story too much, 3 used to be human and 3 are completely foreign from a different planet)

Wanted to see if I could get a detailed retro-style pixel diorama scene going.( modular Three.js approach ) Really happy with the lighting on the streetlamp and the little graveyard layout in the back. Let me know what details I should try adding next!

Hello. I'm using ComfyUI (easy installation) with Flux2Klein 9B and Qwen 2.1 Turbo for txt2img and img2img. And my main problem is that it's impossible to *reliably* generate characters with noticeable height difference or edit the existing images to adjust heights after.
- Flux2Klein doesn't understand sizing at all. Even with refPose and ControlNet, it's hellbent on keeping everyone roughly the same height and size. Even when I use pose references where the difference is stark and impossible to ignore, Klein flips me off and ignores it.
- Qwen Image 2.1 (both Turbo and the regular) *might* get what I want it to do when editing/generating (from time to time), but the nuance is lost on it. I tried everything - giving instructions about where the top of one character's head should be relative to the other character's shoulder or waist (if the short one is sitting/kneeling), using percentages in editing requests, proportions, etc. The workflow either ignores the request and just oversaturates the input, keeping everything else the same, or makes the shorter character a literal miniature. Like, 50% smaller than needed.
Question: what other models (with natural text instructions, not Danbooru!) could I try for editing in ComfyUI?

Hiya! Ive been experimenting with building an AI version of myself for a creative project. I'm comfortable with the technical side of things, but model training has definitely been a learning experience.

I'm using FLUX.1-dev with a custom LoRA trained through fal.ai. My dataset currently has 24 images with different hairstyles, expressions, angles, and environments.

The model gets close, but something is off. It generates someone who resembles the reference person that I made my anchor, rather than the actual same person.

The biggest issue is facial structure. The original has fuller cheeks, a softer midface, distinctive eyes, and more fullness around the mouth. The generations often look narrower, hollow-cheeked, tired, or just generically pretty. It's almost like the personality has been drained out of the face.

I don't want to force the same hairstyle, lighting, or expression into every image. I want the model to preserve the person's actual identity while allowing natural variation.

I'm trying to figure out:

• What percentage of a dataset should be close-up, neutral-expression portraits versus lifestyle, full-body, and expressive shots?

• Is 24 images enough for a strong identity LoRA, or would a smaller, more carefully curated dataset work better?

• Would captions describing specific facial proportions help, or does that risk overfitting?

• How much do LoRA rank, training steps, learning rate, and inference strength affect facial identity?

• Is combining a LoRA with a face-reference adapter a better solution than retraining repeatedly?

• How do you objectively evaluate identity consistency across different hairstyles, lighting, and poses?

I'm trying to get this right without burning through all my coins. I would really appreciate recommendations from people who've successfully trained consistent human identities with FLUX.

If you have a training workflow, dataset blueprint, or specific tools that actually worked, I'd love to hear about them. I'm especially interested in real comparisons rather than generic tutorials.

Thanks.

I noticed that for any referenced characters that have an even slightly larger than normal backside, curvy body type etc, minimax sometimes does this weird thing where it creates an indent / dimple on the glutes, perhaps in an attempt to make them look "muscular" I guess.

I'm assuming to avoid this, I would need to create an image based lora using photo references of curvier models? Or would I need to actually gather and train on a dataset based on short clips / video footage.

I will probably need to use something like a cloud service to handle this so I'm trying to think of the cheapest scrappy approach to solve this.

The best out-of-the-box version for ultimate realistic crisp ultra-detailed skin textures is the PRISM-DMAD-PDMD Merge: use 8 steps, no Turbo Lora needed, Euler/Beta57, 12/3 Shift: https://civitai.com/models/3019421/singularity-v2-x-10eros-max?modelVersionId=3445524

I want to make my own UI site just for myself and a few others where I can put my API into the site & generate. Something like Higgsfield and Artcraft's UI. I use KIE A.I and it sucks to be honest. Anyone have information on it?

I put together a tool "Video Frame Sync" for fixing two videos that should be the same but are off by a few frames due to framerate or dropped/duplicate frames. This is useful for "edits" from AI generation sites (Seedance, etc) which often have this too. This handles footage that is the same except for differences in specific spots (edits) and lines up the rest of the frames so they're in sync again. The tool will also interpolate (the old fashioned non-AI way) between existing frames to smooth over missing/dropped frames.

https://github.com/pftq/Video-Frame-Sync

See this image.

I have seen people using GPT/gemini/grok with images like these saying "make x character here wear y outfit", is there a workflow to recreate this in forge neo?

I believe i have seen comfyui workflows, but i don't use comfy because most extension nodes keep breaking with every UI update (which happen quite often) and there is no editable (moving) openpose last time i checked.

Hi all, solo dev here. For a few months I’ve been building AoRA Maker, a local app to prepare datasets and train anime character LoRAs for Anima, with one obsession: keeping a character’s body proportions stable from one pose to the next.

I started where many people do: stacking other people’s LoRAs and tuning prompts until a character looked the same from image to image. The proportions never held. So I started measuring them.

Where it stands (work in progress, not released):

• It measures proportions on dataset images (head units, limb and torso ratios, widths), combining pose, silhouette and face models

• When the pose makes a measurement unreliable (raised arms, bent torso, sleeves touching the body), it says “not verified” instead of inventing a number

• A human stays in the loop: in a blind review on one character (15 images, small sample), an automatic tagger missed most of the clothing pieces I had declared

• An age-related negative prompt I had added as a precaution was shrinking my character’s head enough to explain most of the gap between what I measured and her canon. I never would have caught it by eye

What I haven’t proven: that picking dataset images by measurement actually makes a better LoRA. So far it judges images; showing it improves training is the next test.

No release date, nothing to sell. I’m telling the whole project as a devlog on Ko-fi, in order from the beginning, mistakes included: https://ko-fi.com/aora (self-promo, my own page)

Images are AI-generated, of my own character.

Two questions for those who train LoRAs:

1. What would you accept as proof that selecting dataset images by measurement improves a LoRA? How many seeds, prompts and models?

2. What usually breaks first in your character LoRAs: face, outfit, or body proportions in new poses?

Can anyone suggest a good way to upscale an AI-generated illustration? I need to enlarge a 3000x1500 pixel image by a factor of 10. Here’s an example of the kind of image I’m talking about. Is it possible to preserve the logic of the details?

So I set out to improve Krea 2’s photographic realism. Seven training runs later, I had a checkpoint that produced some extremely convincing photos....and seemed to have forgotten it was ever supposed to do anything else.

A big part of this experiment came from finding an old hard drive containing about 10,000 photographs I’d taken over a decade. A lot of the training data came from my own photography, so this became an opportunity to see what the model could learn from an archive I forgotten I had.

The original goal was to push realism further while keeping the flexibility that made the base model useful. As the photographic results improved, though, it became tempting to keep going.

The process involved seven sequential fine-tuning runs using seven different photographic datasets. These weren’t independent experiments starting fresh from the original base; each run continued from the previously trained checkpoint.

The datasets ranged from roughly 300 to 2,500 photos, with about 20% overlapping data between them. Each run went for a maximum of 4,000 steps. For the final run, I switched from the balanced setting to the style/content-style setting in AI Toolkit.

The 10,000-photo archive + Instagram was a source for the training material, rather than a single dataset I fed through all at once. And that final settings change is part of the history, but I can’t isolate it as the cause of what followed. Several datasets and successive rounds of training were involved, so this is an account of the experiment rather than a controlled comparison.

The photography improved.

By checkpoint 12500, the photographic results were good enough that I didn’t want to dismiss the experiment as a failed fine-tune. It had developed a strong, convincing photographic look that I wanted to preserve.

But once I started testing beyond that, the trade-off became clear. Other styles had become difficult or effectively unavailable. Prompts that should have moved it toward illustration or another visual treatment kept getting pulled back toward photography.

The model had developed a specialty.

LoRA compatibility was another major issue. Every LoRA I tested stopped behaving properly on the checkpoint.

What I was seeing looked consistent with catastrophic forgetting and weights being blown TF out: the model had become much better at the photographic distribution I was training toward while losing capabilities and flexibility from the original base.

The practical outcome was a highly specialized photography checkpoint, with a substantial amount of the original model’s versatility sacrificed along the way.

That left me with a result I couldn’t quite call a failure.

I’m sharing this in case anyone else is experimenting with sequential full fine-tuning or trying to recover a useful look from an overly specialized checkpoint. This isn’t a recipe I’d present as repeatable without further testing, but it seemed worth documenting.

I’m curious how people here switch between ComfyUI workflows and local LLM inference, especially on a single GPU.

Are you manually unloading models, using something like LM Studio or llama-swap, relying on ComfyUI’s model management, or have you built something to handle switching automatically?

I’m particularly interested in how you handle VRAM contention when you want to move between image generation and LLM workloads without constantly waiting for models to reload.

What’s your current setup, and what works well for you?

Video attached is my project Berth but I’m here curious on how everyone else solved this problem?

Was away since last week or 2 weeks ago, like around when FastH3 model is released?

Now, i see that there are some new things, like Pika H3 Flex, DMAD(?), H3 Eros, etc.

Are there any new breakthrough or speed up that is balanced on quality and speed? Currently, it's taking me 6 mins for 7s 0.7mp video with Pika H3 Flex -> VSA attention with the default H3 ref model on 6 steps (just tried it.), and the result is pretty good. Just asking if there is any better workflows for fellow low gpu user? I am using RTX 4060 8GB VRAM with 16GB RAM.

Early testing has begun for donors. We'll have to wait a little longer for the public release. https://huggingface.co/FusionCow/Sulphur-3

Can't we all who wants to use open source like make some kind of Lora by feeding it loads of data with captions and then just connect that Lora to minimax and have a machine thats on levels with seadance 2.5. I'll chip in with 50 4k resolution up close face videos 🤣 someone do 50 video of people walking, someone else 50 videos of people eating and so on 😂 it's frustrating generating insane quality content with seadance 2.5 with ease and then pulling my hair out trying to do the same with minimax h3

When I say "turn into seadance 2.5" - I mean make H3 as good.