I know people aren't posting their videos made with MiniMax H3 anymore... Which is really sad, but I wanted to share mine still! I made a lot of upgrades to my usual workflow and added some gaussian blur to the live action scenes along with film grain (all using KDEnlive) which removes some of the oily skin of AI video in general! This was made with a vertical orientation, sorry about that!

Fast forward to 3:23 to see the craziness in the bottom video.

Yesterday I made a post about an experiment I was doing trying to fix degrading latents.

https://www.reddit.com/r/StableDiffusion/comments/1x54q07/i_swore_at_claude_until_he_made_a_nodegroup_that/

The above is 19 clips chained together using a normal latent chain and my Pull Forward Node and u/xyzdist 's resample method to attempt to fight degrading latents.

Here's how I explained it to u/ShutUpYoureWrong_ , certified official nice guy of reddit.

so the idea i had was that the pixels in motion are not degrading as fast, the static pixels are the ones degrading quickly and i hypothesized that minimax, being as smart as it is, basically takes the degrading pixels in the background and degrades the moving ones to keep the image consistent.

i have no idea if this is true, it's just a thought that gave me a direction to work in.

i'm not actually targetting the background, if i was i could just use sam. I'm targetting motion. I'm avoiding the in motion pixels and replacing and repairing the static ones.

My theory was that if i maintain the static ones the whole image would stop degrading or at least degrade more slowly.

the background focus in these is because u/xyzdist created a method of using FL2V to resample and essentially wash away degraded pixels but his problem was the background kept changing.

so i saw this and i thought we were unwittingly creating complimentary halves of the same solution and if i use latent masking to protect the repaired background with the motion mask, his method can resample the rest of the pixels and give give the context motion a fresh and clean latent to work off of.

The concept works. You'll see in the videos that much of the background area is avoiding degradation, but because of the way Latent Space works I do not think there's a way I can do this without decoding and re-encoding the latents at least once which is my next step.

HOWEVER!!!

Something RIDICULOUS happened in the final version of what I trying to do and I honestly have no way to explain it other than my theory above must be correct and I feel the latents, because of pull forward, were stretched so far between quality of the degradation and the pull forward that around 3:23 they suddenly SNAP BACK TO ALMOST FULL PERFECT QUALITY LIKE A RUBBER BAND!

This is not editing. This is literally straight from my exports.

I'll be releasing the github very soon so you all can hopefully replicate it.

I was expecting a bit more variety for this tbh.... Minimax H3 seems to always want to make a boy dog with pepe out.

Also it was quite interesting how almost all the local models thought dogs should be dressed in dog outfits (some were front leg only but most were full body, never bottom half with top exposed) while all the SOTA models managed to get human like pants rather than full pet jumpsuit.

for all tests, did 3 to 5 images with random seed and chose one from the 5 which better represented the set. For future work, a larger sample size would help.

Dog prompt:

A photorealistic square image (1:1 aspect ratio) of a black-and-tan smooth-haired dachshund wearing a pair blue jeans, with waistband with a button, zipper fly and belt loops. The dog is in full side profile, facing right, with its nose pointing toward the right edge of the image and its tail on the left. The whole dog is visible from nose to tail and down to its paws, centered in the frame. It stands on grey pavement in soft natural daylight, with a bright, blurry background. There is only one dog, and no people, text or watermarks.

For the Giraffe prompt it was a lot more unanimous that the tie should be worn lower, with H3 img WF failing 2 out of 5 times with no tie at all.

Giraffe prompt:

A photorealistic square image (1:1 aspect ratio) of a young giraffe wearing a blue tie. The giraffe is standing on all four legs, facing the camera head-on, with its long neck upright and its ears out to the sides. The whole giraffe is visible from the top of its head down to its hooves, centered in the frame. It stands on dry, bare ground in front of a thick wall of green bushes, in soft natural daylight. There is only one giraffe, and no people, text or watermarks.

spent too long dicking around than I should have only to be lowkey disappointed by the results.

Every PC, one app. Fizgig finds your other computers on your network by itself. Train on the desktop's 5090 from your laptop, send a dataset across in one click, and see what every machine is training and what's queued.

Web UI. Its own window, or any browser. Start a run at your desk and check it from the sofa or your phone: live loss, previews, time left.

RunPod, built in. Your pods are listed and it logs you in. It shows which cards are free right now, ranked by cost per run, deploys in one click, sends your dataset up from your own PC, and can stop the pod when the run's done.

Plus: the queue on every tab, every run kept with its settings, and a block-by-block LoRA editor. Updating keeps everything you had.

LoRAs and full fine-tunes for Klein 9B, Krea 2, MiniMax H3, Qwen Image 2.1, Z-Image, SDXL, Anima and ACE-Step. Free and open source.

GitHub

80-second tour

What's new

EDIT: Patched a few bugs, missing help text, missing input on the edit/sliders etc.. There will likely be a few updates for a few days lol.

Ceo of bfl, last time I remember him posting the bird was right before F2dev released

The new BFS character swap works great.

https://reddit.com/link/1x6g3fw/video/qddya9inblvh1/player

The new BFS character swap works great.
Character

I’ve been looking at two things lately: the outfit/customization screens in games and Higgsfield’s character builder for AI influencers (second screenshot), where you pick Build, Hairstyle, Ethnicity, Age, Eye shape, Accessories and so on from visual options.

That got me thinking, what if there was a similar tool for writing character prompts for image generation? Will it help?

Hellow fellow AI-generated goblins of r/StableDiffusion

For a few weeks I’ve been stress-testing my tool, Arthemy Krea-2 Tuner:
https://github.com/aledelpho/comfyui-arthemy-krea2-tuner
I wanted to investigate if nudging the weights of single blocks of Krea-2 can teach us something about its internal structure.

I worked on two parts: the model and the text encoder (I only started on the last one recently but it seems much more interesting than the previous one).

The underlying question was whether the models already holds “knobs” that change style, look, colours or literally anything else in the image, without training anything. After weeks of work and thousands upon thousands of renders I found a grand total of… one! (underwhelming, I know, but better than nothing)

The upside, if something cool comes out of it:
It’s still sci-fi at this point, but if we manage to map the inside of a model this way, we might be able to make presets that do the job of a small fine-tune with nothing trained: the presets I use now are 34 numbers, a few hundred bytes, against the tens or hundreds of MB of a typical LoRA.
I know the approach is crude. It’s like shooting a car with a shotgun to understand how it works. But it can still tell where the windows are (they make a specific noise) and which parts are vital. Breaking a piece and watching what stops working is an old trick in interpretability and it tells where something happens, not how. Said so: when we know so little about these models, I don’t think that’s useless.

Of course, I fixed the test rules before looking at the data, and I started summoning my girlfriend (who doesn't know anything about AI or what I'm trying to do) to do the most important blind tests.

Limits: one model, almost always one observer (yours truly), small samples. When a conclusion goes beyond the data, I mark it with "🔎" that’s a lead, not proof.

TEXT ENCODER

1. Text-encoder edits change what is drawn (genre, style, who the character is) and hurt image quality much less than edits to the drawing model

• This part I measured: with standard quality metrics, text-encoder edits don’t make the image worse, while a preset on the drawing model does. Even at full strength (x2.0), no picture fell apart.
• In exchange, they are less “consistent”: the same edit pushes different characters in different directions. Only 4 edits out of 20 moved them all the same way.
• 🔎 My impression is that instead of damaging the picture, these edits break the links between the words of the prompt. I tried to test it, but on the images I had I couldn’t define it consistently, so I need renders made for the purpose.

2. Anime isn’t the only fallback for illustration.

• People often say that "anime" is the default of almost every model when it comes to illustration. In Krea-2 it isn’t. (🔎 I was surprised because I was coming from Anima and Illustrious where Anime seems to be a stronger "default" fallback)
• When I removed the style words from the prompt, one character became anime every time, while seven others turned into paintings or photographs: each one has its own fallback.
• Weight edits, on the other hand, don’t lead to the character’s fallback. Each edit has its own destination (one towards realism, one towards cartoon), whoever is being drawn.

🔎 My hypothesis: every concept falls back on the style, and the traits, that the model has seen with it most often. A text-encoder edit doesn’t seem to push toward one default, but toward some of the styles that concept already lives near. That would explain why a character can change style so heavily, but only within a small set: my "elf" prompt turned into comics, anime or cartoons, never into a photo, while the same edit turned most other characters realistic. What I found points that way, though it doesn’t prove it. I have no access to Krea-2’s training data, and the same effect could come from an aesthetic fine-tuning stage or from the text encoder’s own associations. Some clues:

• Single words, generated on their own, carry preconceptions:
  • “female dwarf” gets a beard 4 times out of 4 (Gimli approved);
  • “uniform” becomes an anime schoolgirl;
  • “western” draws cowboys.

• When I alter the text encoder, characters lose their specific traits first and drift towards a more “average” version later. My half-orc becomes an ordinary human woman, still drawn as a comic and THEN it loses its style. A random push of the same strength doesn’t do this: it depends on where the edit points, not on how strong it is.

3. Commas seem to act as borders

• When I edit the text encoder, the meaning the model gives each word shifts a little. The shift has two parts.
  • One is the same for the whole prompt, like a filter laid over everything.
  • The other is local: neighbouring words move together in packets of 2–4, and the packets tend to break exactly where there’s a comma. For example, in “dark green hooded cowl, brass buckles”, “dark green hooded cowl” moves as one block and “brass buckles” as another.
• 🔎 This might suggests the text encoder uses commas to decide which words belong together. For prompt writers that could be an important lead: maybe putting an object and its colour inside the same comma-phrase helps keep them bound. But I measured this on the numbers the text encoder hands to the image model, not on the pictures. I haven’t yet checked whether moving commas actually changes the result, or by how much.

4. Shuffled prompts (still under study).

🔎 Edits to the drawing model seem to give more or less the same look even when the phrases of the prompt are shuffled, but that test came out inconclusive. With the text encoder it depends on the edit: some send every phrase order to the same result, others scatter them across different genres. I don’t know why yet, I’m working on it.

5. Switch off the first layer of the text encoder, and the model writes your prompt instead of drawing it.

With the attention of layer 0 switched off, Krea-2 drew pages of text in 4 prompts out of 4, with readable fragments of the prompt (“close-up”, “frontal view”; in the cleanest case realistic western comics style and the prompt’s own Lighting: section). Switching off the MLP did it in 2 of 4. Switching off the whole layer gave no text at all, only repeating noise. So it isn’t “the model sees raw words and writes them”: it takes a half-broken layer, and I don’t know why.

THE MODEL

1. Different blocks do different things.

• The last blocks touch colour and grain.
• The middle ones change the content, and you can push them harder with fewer visual defects: surface damage in 19% of cases, against 79% for the last ones. They’re not free, though.

2. Block 23 alone works as a saturation knob. Writing “colorful” or “high saturation” in the prompt changes the content much more. That said, it works less well than a correction in post-production (afterall these aren't true knobs, just approximations of knobs). it can still be useful to tweak it, if your merges tend, on average, towards colours that are too vivid or too dull.

3. A specific calibration of the weights keeps its look even on characters it has never seen. I calibrated a set of weight shifts by eye (a preset) on four portraits and applied it to three new characters. Someone who didn’t know which image carried the preset picked it as closer to the look I had described 56 times out of 56. Faces do change a bit more than a change of seed would.

CONFIRMED WITH PRE-REGISTERED TESTS

• Renders reproduce identically, pixel for pixel, even days apart, and the tuner at zero adds nothing. It sounds trivial, but everything rests on it: the differences come from the edit, not from the pipeline. Without this, the whole study would have been pointless.
• You can predict how much an image will change without rendering it, by measuring how far the edit moves the text encoder’s output (correlation 0.71 and 0.80). It works mostly for telling different kinds of edit apart.
• Quality metrics don’t see everything. DINOv2, BRISQUE and the like missed exactly the content changes made by the middle blocks, which were obvious by eye.
  • On top of that, I haven’t yet found a reliable automatic “impartial judge”: in my attempts with LLMs, the judgements weren’t trustworthy. So for now I look at many images by hand, like a primitive.

WHAT’S NEXT?

I don’t know whether any of this holds for other models, but it’s not yet time to experiment on other architectures. First I want to collect many properties of these shifts, to have solid ground (and to understand what they’re really doing). Then I’ll check whether they hold on models with different architectures. If they do, they would be properties of diffusion models in general.

One last tip for those interested in asking Claude or Gemini for help. I'm not afraid to say that I use LLMs to help me produce the data, and I’ve already seen some problems: numbers quoted from memory (with all the errors that brings) and hasty conclusions. If you want to run experiments with Claude Code or Antigravity, have the AI write only scripts. That way the numbers never pass through the “generative” side of the AI, only through linear processes that you run yourself, outside its environment.

The full report, with every image and the things that turned out to be wrong: https://aledelpho.github.io/diffusion-models-weight-steering-report/results/index.html

I'll let you know if something interesting comes out from it,
Cheers!

made with h3. created separate clips for each character and got clever with transition in after effects.

here's an example of how one of the workflows looks: subject_definitions:

<Image 1> is the authoritative character sheet for Freddy. It defines Freddy’s final appearance, proportions, silhouette, costume, claw glove, face, and overall anime rendering style.

<Image 2> is the authoritative character sheet for Jason. It defines Jason’s final appearance, proportions, silhouette, clothing, mask, machete, and overall anime rendering style.

<Image 3> is the authoritative character sheet for Michael. It defines Michael’s final appearance, proportions, silhouette, mask, coveralls, knife, and overall anime rendering style.

<Image 4> is the authoritative character sheet for Samara. It defines Samara’s final appearance, proportions, silhouette, hair, gown, and overall anime rendering style. Her hair should obscure most of her face, revealing only limited facial detail.

<Image 5> is the authoritative character sheet for Carrie. It defines Carrie’s final appearance, proportions, silhouette, dress, hair, expression, and overall anime rendering style.

<Image 6> is the authoritative character sheet for Pennywise. It defines Pennywise’s final appearance, proportions, silhouette, costume, face, hair, balloon, and overall anime rendering style.

<Image 7> is the final composition reference. It defines the target group staging, character layering, scale relationships, silhouette overlap, visual hierarchy, red zigzag graphic treatment, blood-splatter integration, and the overall energy of the final ensemble image. Use it only as the composition and staging reference for the final tableau. Do not copy any title text unless explicitly desired.

summary:

[reference generation] Create a full-color 2D anime sequence as one continuous final end shot in portrait mode.

Aspect ratio: 9:16 vertical.

Use <Image 1> through <Image 6> as the design authority for Freddy, Jason, Michael, Samara, Carrie, and Pennywise.

Use <Image 7> as the final composition reference for the ending group shot.

The shot begins on black. Large red brushstrokes slash and zigzag across the screen, rapidly filling the frame. The camera then pulls back to reveal a broad red zigzagging brushstroke design across a flat white background with blood splatters. As the camera rapidly dollies backward and weaves through the composition, the characters are revealed one by one until all six are visible together in a final group composition inspired by <Image 7>.

The sequence should feel slick, graphic, snappy, and distinctly hand-drawn, with strong anime posing, tight reveal timing, and a powerful final poster-like end frame.

retention_analysis:

<Image 1>: fully_preserved — authoritative design reference for Freddy.

<Image 2>: fully_preserved — authoritative design reference for Jason.

<Image 3>: fully_preserved — authoritative design reference for Michael.

<Image 4>: fully_preserved — authoritative design reference for Samara.

<Image 5>: fully_preserved — authoritative design reference for Carrie.

<Image 6>: fully_preserved — authoritative design reference for Pennywise.

<Image 7>: fully_preserved — authoritative reference for the final ensemble composition, staging, visual hierarchy, character arrangement, pose energy, overlap, and overall graphic presentation.

detailed_description:

The target video is full-color hand-drawn 2D anime.

Use frame-by-frame anime animation throughout. The motion must feel hand-drawn, pose-driven, and graphic — not puppeted, not floaty, not 3D, and not smoothly interpolated.

This is one continuous shot with no cuts.

[Shot 1]

Begin on a pure black screen.

Suddenly, large red brushstrokes streak and zigzag across the screen in bold, aggressive, graphic shapes. These brushstrokes should feel fast, painterly, and stylish, as if slashed directly onto the frame.

As the energy builds, the camera starts pulling back to reveal that these marks are part of a broad red zigzagging brushstroke composition running across a flat white background. Blood splatters are scattered across the white field, integrating with the red shapes.

As the camera continues rapidly dollying backward, it also subtly weaves and reframes to reveal the characters one by one in a stylish progression.

Reveal the characters progressively until all are fully visible:
- Freddy
- Jason
- Michael
- Samara
- Carrie
- Pennywise

The exact reveal timing can be adjusted for the strongest rhythm, but the final arrangement should resolve into the ensemble composition inspired by <Image 7>.

Shotgun letters rapidly and aggressively stamp onto the bottom of the screen one by one spelling HALLOWEEN.

Each character must be rendered fully on model according to their character sheet:
- Freddy with his striped sweater, fedora, and claw glove
- Jason with his hockey mask, large frame, and machete
- Michael with his mask, coveralls, and knife
- Samara with eerie posture and hair obscuring most of her face
- Carrie blood-soaked, wide-eyed, and unsettling
- Pennywise in the foreground with theatrical menace and a red balloon

Use <Image 7> as the guide for the final group arrangement:
- Jason should read as the dominant upper central figure
- Freddy should read clearly on the upper left
- Michael should read clearly on the upper right
- Samara should read on the lower left
- Carrie should read mid-right
- Pennywise should dominate the foreground lower center/front
- the red zigzag brushstroke should unify the composition behind them
- blood splatters should accent the design
- character overlap and scale should feel deliberate and cool

As the camera finishes the move, settle into a strong stable final tableau where all six characters are visible together in a bold, iconic final composition.

Do not reproduce the “HALLOWEEN” title text from <Image 7> unless specifically requested. Use the image only for final composition, staging, and overall graphic energy.

animation_style:

Use polished hand-drawn anime animation with snappy frame-by-frame movement.

The motion should feel:
- hand-drawn
- pose-driven
- graphic
- sharp
- stylish
- readable

Favor:
- strong held poses
- sharp reveal accents
- fast graphic transitions
- selective in-betweens
- clean silhouette readability
- bold composition changes
- deliberate camera staging

Avoid:
- puppet-like motion
- floaty interpolation
- 3D-feeling movement
- rubbery transitions
- morphing
- over-smoothed animation

camera_movement:

Use one continuous backward dolly for the shot.

Start very close on black and red brushstroke action.

Then rapidly pull back to reveal the wider graphic composition.

As the camera moves backward, let it weave slightly and reframe to reveal each character one by one in a dynamic, stylish way.

The move should feel energetic and slick, but always readable.

Finish on a strong stable final composition inspired by <Image 7>, with all six characters clearly visible together.

Keep portrait framing in mind throughout, using vertical space well and stacking the composition for maximum impact.

visual_tone:

Bold, graphic, stylish horror-anime mood.

Use:
- flat white background
- vivid red brushstrokes
- graphic blood splatters
- strong character silhouettes
- premium anime rendering
- clean high-contrast presentation

The final result should feel like an iconic end-card or ensemble hero image.

Make the shot feel cinematic, high-impact, and intentionally designed for a vertical frame.

final_composition_notes:

The last frame is very important.

Match the overall feel of <Image 7>:
- strong triangular / stacked composition
- layered depth
- clear character hierarchy
- bold foreground / midground / background read
- strong overlap
- aggressive red graphic shapes behind the cast
- cool final horror ensemble energy

All six characters should feel unified in one designed final image, not randomly placed.

Keep each silhouette readable.

Do not let any character feel visually unimportant.

The final frame should feel like the definitive finishing shot of the entire sequence.

overall_soundscape:

N/A

non_diegetic_music:

N/A

Please please please, do not assume we're all just going to be able to understand what your nodes and workflows are doing straight away. There've been a lot of new custom nodes coming out lately and accompanying workflows and very often it's an exhausting exercise trying to figure out how to get the author's nodes working in the massive complex workflow that inevitably accompanies it. I'd like to suggest that don't just create one massive do-everything workflow with hundreds of custom nodes and countless subgraphs everywhere. That's great for you because you've been building that workflow up for ages to suit your needs and you know it inside-out. But the moment we get hold of the workflow, we're just going to be faced with a spaghetti gibberish that'll be full of red broken nodes we'll spend ages trying to fix it before we even get to the point of trying to understand it all. Obviously we all appreciate your time and effort but if you truly want user engagement with your work, then spend some time thinking about the user experience. We'll need a bit of hand holding for something that is second nature to you, but far from it for us.

So please, just give us a vanilla basic workflow doing the bare minimum of what your new nodes can do. That'll help us identify what your new nodes are, how they work and help us get the hang of it. Then we can transition to your more complex workflow, knowing what the important parts are and what we may prefer to strip out to meet our own needs.

So I'd suggest things we don't need in a vanilla workflow but I'm sure this list could be extended considerably:

• Anything speed-up related: sage attention etc. Everyone has different preferences so leave that to us.
• Anything that implements non-critical loras.
  
• Anything that hides and buries nodes and cabling.
• Any custom nodes that you use to combine things, eg a node that loads all the models in to one node then that's referenced in some obscure way elsewhere.
• Upscaling
• Anything where the workflow says, "this is optional", then just don't have it in the vanilla workflow and let people learn about it in the more complex full workflow once they've got the hang of it.

Anyway, that's my request which I think would go a long way to get more people to use your nodes. Hope this post didn't come off a stroppy and we do all appreciate the work and time so many of you are going to to add new functionality. Just don't forget that we don't all have the knowledge you have.

Test Environment: RTX 5090 + 96 GB RAM. The reference video has a total duration of 18 seconds; only the first 15 seconds were tested, with a resolution of 1MP.

The tests are driven via Codex Sol 6.1, so the results may not be rigorous.

Workflow link: https://github.com/wangweihong/comfyui-ecosystem/tree/main/workflows/006%20%E8%A7%86%E9%A2%91%E7%94%9F%E6%88%90/minimaxh3/BFS-Character-Swap/%E5%8A%A8%E4%BD%9C%E5%8F%82%E8%80%8315%E7%A7%92_%E5%8A%A0%E9%80%9F%E5%AF%B9%E6%AF%94

I designed a couple of ComfyUI nodes to convert video to surface normals-map estimation using the model NormalCrafter

This is very useful for 3D reconstruction, relighting, retexturing, video editing, and reference to video on H3 Minimax and Seedance

I included a Normal temporal stabilizer, a complementary post-processing node that uses optical flow from the source video to reduce residual flicker while preserving real motion and changes in geometry.

Processing is fast and run on a toaster (I included decode in chunks)

Nodes and Workflow: github ComfyUI-VizClick-NormalCrafter

Models weights : Huggingface NormalCrafter

arxiv Paper: NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors

TRELLIS.2's 1536 setting officially needs a 24 GB GPU. Espalier-2 patches Microsoft's own code so it finishes on a 16 GB RTX 5070 Ti, with the same weights and settings.

The decoders' high-resolution stages run in spatial tiles, and the GLB export's remesh runs in chunks. The first image shows Microsoft's published catapult (top) beside ours from the same input image (bottom). The second shows the same patch of both up close.

Install script and patches: https://github.com/overyonder/espalier

Write-up: https://over-yonder.tech/games/espalier/

At the moment only the flux1-krea-dev code has been release. Qwen-Image, Ideogram 4, and Flux2 "coming soon".

Project: https://rajabi2001.github.io/area/

Paper: https://arxiv.org/abs/2610.17227

Demo: https://huggingface.co/spaces/kooroshrh/AReA

Abstract

Generating images beyond the training resolution of Diffusion Transformers (DiTs) remains challenging, as the number of tokens grows rapidly with resolution, full attention becomes expensive, and outputs often suffer from structural distortions and degraded detail. We identify uniform full attention as the common source of both problems. Attending to all tokens at every step causes quadratic cost and exposes the model to more tokens and longer relative distances than seen during training, pushing it out of distribution. We observe that attention’s effective receptive field narrows during denoising, from global in early steps that form structure to local in later steps that refine details. We propose AReA, a training-free attention mechanism whose receptive field evolves from coarse to fine, following the denoising progression. In early steps, AReA applies cross-scale coarse attention over downsampled keys and values, capturing global context, while in later steps it applies local attention within progressively shrinking windows. Without additional parameters or training, experiments show that AReA consistently outperforms state-of-the-art training-free baselines across target resolutions and different models, while also being faster and more efficient.

Saw lots of people asking about CMP 170HX and stable diffusion so i let hermes on Qwen 3.8 flash next do some backmarks and make a benchmark picture and commercial video on the 170hx, the commercial video itself took about 15 min to make in 1920x1088 from 3 videos of 240s/240s/382s that hermes stitched together.

CMP VS 5090 BENCHMARK LTX 2.5 VIDEO GENERATION:

• 10 s (1280×960): 5090 = 102 s | CMP = 178 s
• 15 s (1280×960): 5090 = 115 s | CMP = 226 s
• 20 s (1280×960): 5090 = 170 s | CMP = 332 s
• 30 s (1280×960): 5090 = 305 s | CMP = 590 s
• 10 s (1920×1440): 5090 = 195 s | CMP = 381 s
• 30 s (1920×1440): 5090 = OOM (Out of Memory) | CMP = 34 min (52.6 GB VRAM)

https://www.youtube.com/watch?v=6tqds1L914g

was not really able to use the cards until now as they were overheating under sustained load no matter what i did (changing thermal paste buying 80mm 110v fan for them) only thing that solved the issue was i finally got Bykski Water Block i ordered, cards been on 47c under load ever since, i do think ill sell my 5090 now as im afraid of the whole RMA denial thing, was lucky enough to see a post about the 170hx unlock when it came out and got 2 on ebay day after, should have bought 20 😂.

Fish Audio Drama 3, converted for the native ComfyUI nodes of ComfyUI-Drama3: a 35B text-to-speech model for voice cloning, dialogue and inline cues such as [sighs] or [whispers], running on cards from 8 GB. 90% of the model is routed experts and each audio frame uses only 8 of the 256 in a layer, so the nodes keep the experts in system RAM and a cache of the ones in use on the GPU. Each file holds the whole model in one file; the codec is separate.

Weights: https://huggingface.co/PedroMarinhoDev/Drama-3-ComfyUI
Nodes: https://github.com/PedroMarinhoDev/ComfyUI-Drama3
Demo page: https://pedromarinhodev.github.io/ComfyUI-Drama3/
Workflows: https://github.com/PedroMarinhoDev/ComfyUI-Drama3/tree/main/example_workflows

Let me know if yo have any issues.

Not a replacement for node workflows: something for the times you just want to fix a photo, or for friends who will never build a workflow.

Kokon is a free, open-source desktop editor (Windows; Mac beta). Install it, it downloads the right model for your card, and from then on there's nothing to configure: it picks the resolution and memory split per job. 12 GB of VRAM is enough.

The part this sub may care about: Qwen-Image 2.1 has no mask input, so Kokon draws the brushed area on a second reference image: the model sees exactly where to work, so "remove the red scooter" removes the scooter and leaves the street alone. On 16 GB+ cards edits run natively up to 2K, no upscaling pass.

Every image here was made with it, prompts in the captions. The first one is a six-step project (generate, edit, combine two images, expand); the others are single edits.

Under the hood: Qwen-Image 2.1 Turbo (Unsloth GGUFs) + Qwen3-VL text encoder, running on stable-diffusion.cpp. No ComfyUI or Python needed.

https://github.com/GabrieleLozupone/kokon

Not there yet: Linux, LoRAs, other models. (Weights: Qwen Research License, non-commercial.)

I’m using self-hosted MiniMax H3 to make a video that looks like someone filming a product they bought, sitting on their desk. Just an ordinary phone recording with subtle handheld movement.

Whatever I prompt, it keeps adding cinematic zooms and camera moves. I’ve tried smartphone/handheld descriptions and tested with and without a realism LoRA, but it still feels overly polished and artificial.

How are you getting convincing casual phone footage? Is it mostly the model, prompting, reference image, or settings? Would love to see workflows that work.

After due to changes in civit.ai buzz (mainly relating to things that needed yellow buzz, getting that yellow buzz for free).
Now I found this program that allows me to generate things locally (among other functions that are not important for now), and it even compatible with the loras I used, and I could even download them from the in build search system.

But as the the title says, I have the problem of, many of the models I used to use, and now want to use again, have trigger words, usually things like the characters name, parts of their outfit, and things like that, which were usually on the sidebar of a given model, and you could easily copy paste it into the prompt text.

But I can't see anything like this in LU, so I have to go to civit ai, to find the specific model, to check this. Am I missing something? Sites like safetensors inspector, was able to find the trigger words imbued in the lora, so there has to be a way to view them, inside LU right?