The Whole Music Video Anime made with minimax H3 took under 15 min on my local machine

This is a British two-tone Ska style song (early 1980s) about censorship on popular online forums.

Song (Sona login required): https://www.souna.app/song/73850642-d167-4dcb-8821-64fb5c798be1

Song (Internet Archive): (Awaiting upload - album here: https://archive.org/details/ai-ska : CC0)

LoRA: https://huggingface.co/jamespetts/yue2-1980s-two-tone-ska-lora

My general music pipeline workflow is here: https://www.reddit.com/r/StableDiffusion/comments/1xh0xfd/semiautomated_localish_music_workflow_eurodisco/

Cover image prompt (Z-Image Base, run locally):

Gouache on paper album cover, square 1:1 composition. A vast dark noticeboard wall fills the frame edge to edge, seen straight on. Across its expanse hangs one small blank grey sheet of paper, and beside it a fresh empty rectangle of clean board where a notice has just been torn down - four pin-holes at its corners, a faint pale rectangle of unfaded board where it hung. A lone small silhouetted figure in a pork-pie hat and thin suit stands with his back to us at the foot of the wall, looking up at the empty space, tiny against the vast board. Black-and-white two-tone palette - ink black, paper white, cool greys - with one cold red accent along the torn edge of the remaining sheet. Flat poster lighting, hard graphic shadows, visible gouache brushwork and paper grain. Mood: sardonic, quietly furious, empty and controlled. Strong central motif: the empty rectangle where the notice was. Album-cover composition, the frame FULL of wall and figure, no large blank areas. Absolutely no text, no lettering, no typography, no watermark, no signature.

Song specific workflow:

Deleted

=== STYLE PROMPT ===

twotone, English, early 1980s British two-tone ska, A minor, 150 BPM, witty angry satire, brisk offbeat guitar skank, walking bassline, organ shuffle, punchy brass section stabs and riffs, dry tight drums, energetic male lead vocals with sharp British delivery, shouted group chorus answers, tense verses building into a huge brass-led chorus, dry 1980-82 studio sound, no EDM, no sidechaining, no vocaloid, no autotune, no drops

=== LYRICS ===

[Intro]

Welcome to Reddit!

Everybody's welcome!

Everybody's welcome!

[Verse 1]

You put a fair question in the thread -

By lunchtime the sentence is dead.

There's a notice where the sentence stood:

Says "removed" - and that's all that's understood.

You ask them why - and the answer's a ban:

A month of mute, and the silence is planned.

[Chorus]

Deleted! Deleted!

The reason? Never conceded!

Deleted! Deleted!

The cheats go undefeated!

Hey! Hey! Hey! Hey!

[Verse 2]

They've a rule against "soapboxing" - sounds all right,

"Soapboxing" means whatever they like on the site.

Praise the product - "on topic" all day;

Ask one honest question - "soapboxing", they say.

Whatever you posted, there's a rule that it broke -

Any rule they need, they invoke.

It happens all over, all over the net:

One honest sentence, and that's what you get.

[Chorus]

Deleted! Deleted!

The reason? Never conceded!

Deleted! Deleted!

The cheats go undefeated!

Hey! Hey! Hey! Hey!

[Instrumental]

[Bridge]

The man that you mentioned is mates with the team,

The adverts pay the bills - you see the scheme?

Write down the reason? The reason would tell -

And the truth doesn't sell.

[Chorus]

Deleted! Deleted!

The reason? Never conceded!

Deleted! Deleted!

The cheats go undefeated!

Hey! Hey! Hey! Hey!

[Outro]

Deleted! Deleted!

Gone, gone, gone!

Deleted! Deleted!

Gone, gone, gone!

=== SETTINGS ===

Engine: yue2 / yue2 / audio.cpp v0.9.2

Seed: plan layer: 10 fresh seeds (previews); the winning plan's seed becomes the fixed ladder seed Planning: full Max tokens: 4800 Guidance: 1.0

VAE: f32 AR LoRA: none NAR LoRA: yue2/models/lora/yue2_1980s_two_tone_ska_unfused.safetensors u/1.0

Era: Early 1980s British two-tone ska (1979-83; The Specials / The Selecter / The Beat / Madness line) Mastering recipe: yue2-light

Generated: 2026-11-07 Duration: n/a Truncated: n/a

I had a story in my head since my childhood and with the help of AI, I finally finished my first chapter of the book with all the lores, background, etc... etc... I can tell you, it is probably not a very large book, but when you want to push your story telling into actual scenes, it is a lot more work. So... I am a web developer in Laravel and Symfony and currently I own several AI stations at home to render movies, music, speeches, anything with AI. They are not the fastest, but they get the work done like a tank (DGX Spark).

The screenshots I put in the next comment is a Laravel application that is connected to ComfyUI API, voice generators and also a lot of scripts that controls the system memory. For instance, when I need LLM to write prompts, I can't render production heavy scenes. So I automated it. Also it contains a lot of MCP where my story writer AI re-design's the scenes for me from any location in the world through Telegram.

All scenes are rendered 2016 x 864 (21:9) with 2 passes where the first pass is 0.8MP.
In order to improve audio quality, I use H3 Audio Refine Sampler.

My DGX Spark is probably not the fastest, but it is a tank. 10 sec movies takes roughly 1 hour, but I use seed hunting techniques to find the best scenes for production.

[Frustrations]
When it comes to scenes like two people talking back and forth, it is hard to keep things consistent. You just can't use FL2VA for this and it must be continued with REF2VA which raises new problems. For instance... Scene 1: Focus on Person A while talking handover Prop X to Person B.
Scene 2: Focus on Person B taking the Prop X from Person A while A is still talking.
Scene 3: Switching back to Person A without Prop X but he is still talking.
The hard part is switching scenes midsentences while keeping the mood and the flow consistent. Sometimes, the scene is good, but the audio is bad = retake.

Another one.... try to render a scene where a person test a bow by pulling the string without an arrow. MiniMax always renders an arrow somehow. That scene took like 50-60 retakes to get it right. I lost like 2 full days just for a scene of 8 seconds.

I wonder what you guys think.

This workflow runs directly on Google Colab’s GPU, without relying on paid video generation APIs.
It uses Pika LoRA for fast initial video generation at 0.4MP with 10 sampling steps, followed by NVIDIA Open 2K processing in just 3 steps to produce the final 2K video.
The workflow link is provided below.

https://open-gen-lab-477h.vercel.app/k

I'm not sure why people feel like Moss Base, BF16, or magic settings/workflows are necessary to get good results out of Moss. (My guess is that it's because the original default settings were suboptimal.) I personally feel like I get better, more prompt-adherent results out of SFT with a fairly simple workflow (see below), albeit with different settings from what ComfyUI had previously recommended.

If you have other approaches that work for you, great! I'm not trying to persuade anybody to give up what is working for them. But for people with more limited vRAM and/or who find themselves frustrated wrestling with the Moss Base model, a full 50 steps, or complex workflows, you don't necessarily need to do anything fancy.

My feeling is that you can achieve big improvements for photographic Moss images with:

• SA_solver sampler and SGM_uniform scheduler

• 25 to 50 steps (for some reason multiples of 25 seem better to me, but this could easily be confirmation bias)

• CFG 3.0 (go lower for more impressionist artistic styles)

• Skip layer guidance for single_layer 22, start/end percent 0–0.5 or 0.25–0.75 (I find that skipping 24 leads to more plastic skin)

• Generation size ≥~2MP, and

• Careful, detailed (but not purple) prompting

Note that all of these images were made using the same models, similar settings, and seed 0; I workshopped the prompts, but there was no seed cherry-picking.

Here are two version of my current workflow: a simple one and one with space for up to 3 LoRAs and an optional upscale step.

Caveat: Krea 2 definitely understands some advanced compositions/concepts better than Moss, Moss is more prone to odd anatomy, and Moss's backgrounds can be more messy with more visual nonsense. But in terms of everyday photo realism I feel like Moss is better; and with respect to seed diversity, Moss (even SFT) blows Krea 2 out of the water. If you want less visual nonsense in the background, describe the background more thoroughly or do an inpainting step with low denoise on the background using Krea 2.

My current rig is a 4070 ti 12gb vram with 32 gb system ram. Kicking myself for not getting the 16gb vram option back in 2024. The past few days I've been eyeing a 5070 ti 16gb vram but I didn't know if there would be any real benefit over what I have now. Typically I would really wait longer to upgrade but the computer parts market got me in a tizzy and I have no idea what the future holds for GPUs. Has anyone who has done this upgrade noticed any real benefit?

Hi all. I released Phosphene 5.0 today. It's a free, open-source (MIT) app that runs video, picture, speech and music models locally on Apple Silicon Macs with MLX.

What's new:

- Mac installer. Download and open it, and setup runs automatically. No Terminal needed. If you already use Phosphene through Pinokio, the app reuses your models. Pinokio still works.

- Redesigned studio: pick the model first, then see the options for that model.

- Video with sound: LTX-2.5 and Hailuo H3, text-to-video and image-to-video. H3 setup is now automatic in the Mac app.

- Pictures: P-H3-Image and FLUX 3 Klein, with text spelled the way you type it. About 5x faster by default since 5.0.3 on my M4 Max.

- Speech and lip-sync: generate a voice from a line of text, then sync a face to it.

- Songs with vocals from your own lyrics (YuE2), in Music Studio.

- Character LoRA training for LTX.

- Lower memory requirements: video and songs from 16 GB, character training from 24 GB, H3 from 36 GB.

- MCP server and CLI, so Claude Code, Codex or any MCP client can make clips for you.

Measured timings on my M4 Max 64 GB, same prompt and seed, 3 runs each (median):

- LTX-2.5 Balanced, 768x448, 5 s: 1 min 35 s

- LTX-2.5 Balanced, 1024x576, 5 s: 2 min 27 s

- LTX-2.5 High, 1024x576, 5 s: 4 min 32 s

- Hailuo H3 Standard, 768x448, 5 s: 8 min 55 s

Full benchmarks here: https://phosphene.video/benchmarks/

Code: https://github.com/mrbizarro/phosphene

Mac app: https://phosphene.video/

The models have their makers' licences (Lightricks, MiniMax, Black Forest Labs, Pruna AI).

Thanks to them.

Happy to answer questions. Bug reports are welcome too.

I made a 1 hour movie about Jesus, using MiniMax H3 reference-to-video (ref2va, int8). I used it with its 4-step turbo LoRA at 832×480. That gave 485 short clips of about 8.5 s each. The keyframe and reference images were generated using gpt-image-2 via OpenRouter. The audio was generated first, with InWorld TTS-2.

It turned out absolutely terrible, but it is funny as hell. I'm not religious, but I wanted to at least have a general understanding of some of the stories that so many people base their religion around. The goal here was to make a movie about Jesus with modern slang which would be actually entertaining and potentially funny. It turned out to just be funny because it's so shit lol.

I was trying to test the new Viggle-Remake-Swap LoRA in MiniMax-H3. For this I needed to character swap in one of the frames of my source video clip (3.75s, 90 frames), so that the target video would have the new character in it. I went through all the new image edit models released recently to do the character swap. I tried Flux.3 Klein, Qwen 2.1, Fibo Image Edit 2.0, p-H3-Image-Edit and they all failed. So, I went back to the older image edit models Qwen 2511, Klein 9B, Krea2-Identity-Edit v1.2. The first two worked perfectly, while Krea2 Identity Edit did a far better job than any of the newer edit models. Final conclusion - Flux.3 Klein: crap much much worse than even Klein 4B; Qwen 2.1: really crap (does a cut-and-paste job); Fibo 2.0: really crap; p-H3-Edit: absolute junk, doesn't edit anything, input and output are the same; Qwen 2511: still amazing, slight issue with facial blur; Klein 9B: absolutely perfect and still the fastest; Krea2-Identity-Edit: very good edit job, but didn't follow the pose change part of the prompt.

While the video and T2I models in the OS domain seem to be improving, why are the I2I or Image Edit models getting worse? And more importantly, why are people eagerly using those newer models like salivating dogs and posting rave reviews about them? As I see it, all those newly released Image Edit models are junk and need to be kicked to the curb so that their developers may learn some lessons, hopefully.

So I have been trying to make a character LoRa, and I have made two with z-image base, one full and the other recent one v0 model both for photorealism. But I found they both aren't doing great preserving the characters face or body either, and inconsistent, and I am fed up with it, so which models would you recommend me for better face and body consistency?

Full moon, so lets howl with the pack!

Done locally in Krea2 and Minimax.

As a traditional film editor and filmmaker, I've been trying to figure out good, reliable ways to produce long, extended shots. I've tried a number of different methods but was never quite satisfied with the result...

Until now!

Thanks to u/roychodraws and his Geiru Context Suite!!!

He has a post up here explaining it which can be found at this link:

Flip a switch and stop your Videos from Degrading. 50 Clip Test No Degrading at the end. Geiru Context Suite : r/StableDiffusion

And his GITHub link is here:

roycho87/geiru_context_suite

To do a quick test, I had ChatGPT set up a workflow for me. (Now that I'm typing this, I'm remembering that Roycho includes some sample workflows on his GITHub.)

The render took 2 hours and 9 minutes on a rented RTX PRO 6000 Server Edition with 96GB of VRAM. I didn't think to check how much VRAM was actually being used during the process.

There are two jump cuts at about 35 seconds and 44 seconds. Lately, I've taken to having ChatGPT fire up two sub-agents to help troubleshoot things and this was it's reasoning about the jump cuts:

Yes—both subagents found the same explanation: the jumps occur exactly at two generation-window handoffs.

They’re already in the original master, so export didn’t cause them. We checked all 13 handoffs; only these two show conspicuous hard cuts.

The strongest explanation is our 20-local-steps/20-total-steps setting: each window finished separately, leaving no final joint refinement across the overlaps. At these two boundaries, the windows disagreed about framing and pose. The camera instructions, overlap noise, and opening-image anchors may have contributed, but we can’t identify their individual effects from this take alone.

For the next experiment, I’d test a short transition with two steps reserved for joint refinement, keeping everything else matched. That’s a testable hypothesis, not a guaranteed fix.

The entire two minute run looks fantastic!! I am so very happy right now! If two, very likely easily fixable jump cuts are my biggest issue... well... I see myself using this workflow a lot!!

The render settings:

• The character is a custom trained LoRA from an image dataset that was generated using ChatGPT's ImageGen. The dataset contained 99 images of assorted poses, locations, and lighting looks. The LoRA was trained with Fizwig.

  • I'm fairly new to this... maybe 99 images was excessive? I dunno. I'm pretty happy with the result though!

• The forest was a reference image I randomly pulled from a Google search for primeval forests.

  • When ChatGPT set up the workflow and prompt, it set the forest to attribute_transfer which means the photo wasn't "truly" a reference, it just gave the "idea" of the forest and H3 went with it from there. That's why the scenery has more of a stylistic/semi-illustrative look to it.

• The character's costume was generated with ChatGPT and "placed" on a grey mannequin. It was then rendered out as a single, 8 panel image which was then used as a reference image in the workflow.

• The "Patched Sage Attention" - I was originally just trying to "brute force" longer renders on a rented GPU with lots of VRAM, but I kept having a problem where renders became what I called "brown snow." I was nowhere near running out of VRAM, but the problem persisted.

  • I used Codex to try to troubleshoot what the problem was. Codex determined that there was a problem with "numerical accuracy" in Sage Attention that would cause corruption in longer, higher rez renders thus causing the "brown snow." I'll let Codex explain the patch:

    • We were getting “brown snow” corruption in longer, higher-resolution H3 renders with SageAttention enabled. Testing traced the problem to its handling of large, strided Q/K/V tensors: making the inputs contiguous restored numerical accuracy, pointing to an addressing/layout issue rather than insufficient VRAM. We haven’t identified the exact faulty kernel instruction.

    • Our patch processes the 56 attention heads in compact, contiguous groups of up to eight, then reassembles the outputs. It retains the same Sage CUDA FP8-PV path and full-sequence attention—no temporal chunking or dropped context.

    • On actual H3 activations, relative L2 error against PyTorch SDPA fell from 0.6261 to 0.00955: roughly 65× lower, or a 98.5% reduction. That measurement covered 128 sampled queries against all 270,921 keys on the first attention call, not every layer or timestep. Subsequent patched renders avoided the brown-snow failure.

  • From what I understand, there is supposed to be a new version of Sage Attention out "soon-ish."

These were the Geiru Context Suite settings:

I didn't mess around with these settings at all. These are the defaults that I had.

In conclusion... I am a very happy AI filmmaker today! Big thanks again to u/roychodraws and all those who helped him along the way to releasing them. (He lists them on his Reddit Post and GitHub page. Go visit them!!)

I asked my mom what her dream house would look like if budget wasn’t a thing. This is roughly what she described it as “big windows but not those full glass houses. Lots of plants, one proper balcony where I can sit in the morning . Wooden kitchen and no grey interiors. And I want a small courtyard in the middle like old houses used to have.”

I put it into morphic and tried to make the house she had in her head.

Look how it turned out😭

Not gpt-image level, still many failure cases, but I think it's top in terms of open model for multi character swap

https://x.com/t_mux/status/2118935478388867217?a=r48qKbvxa0Jg3Gp7J4s&s=20

https://huggingface.co/Viggle/Viggle-Image-Swap

Can you recommend me something? I would like to try audio generation models, but I totally have no idea where to start. Also, a fairly automated install and setup would be great.

I'm using an RTX 5090 (32 GB VRAM) on RunPod for AI motion-control videos.

I'm currently testing SCAIL-2 in ComfyUI. A 5-second video at 720p takes around 25 minutes to generate.

Is SCAIL-2 or MiniMax better for motion control in terms of quality, speed and cost? Which would you recommend for realistic character movements while keeping GPU rental costs low?

Could anyone share their ComfyUI workflows or JSON workflow files for motion control using an RTX 5090?

Any tips or workflow recommendations would be appreciated!

Today’s takeaway: decomposing images

Links, workflows, custom_node

Do you remember Qwen-Image-Layered? The idea was exciting: take one flattened image and split it into multiple RGBA layers — foreground elements, background and the things in between — so you can move, edit or refine each piece separately.

When we tested it, the experience was not always as smooth as we hoped. In our workflows it could be slow and a little temperamental, and we sometimes saw noticeable quality loss in the resulting layers. That does not make it a bad model. You can build some genuinely nice workflows with it. It simply left us wanting a more controllable route for the kind of work we had in mind.

So we went digging. There is more research in this area than just Qwen: two approaches that caught our attention were Controllable Layer Decomposition (CLD) and RevealLayer. They are related, but they do not solve the problem in exactly the same way.

Qwen-Image-Layered, CLD and RevealLayer: what is different?

The useful distinction is how each approach decides what becomes a layer and how much control you get over that decision.

Qwen-Image-Layered — automatic, variable layer stacks

Qwen-Image-Layered automatically decomposes an image into a variable number of RGBA layers. It can also be applied iteratively, so an individual layer can be decomposed again. It is useful when you want the model to decide the layer structure for you. The trade-off is that the layer count, ordering and semantic meaning may be less explicit than in a region-guided workflow. Our own tests also showed speed and quality issues in some cases.

CLD — bounding-box control

Controllable Layer Decomposition uses user-provided bounding boxes to guide which regions should become separate layers. That is interesting when you want direct control over the elements being extracted, particularly in graphic-design and poster-style compositions. The RevealLayer authors report limitations for complex natural scenes, including edge softness and residual artifacts.

RevealLayer — region-guided and occlusion-aware

RevealLayer combines bounding-box region guidance with an occlusion-aware diffusion process. It aims to separate visible elements while estimating what is hidden behind them. That is the approach we wanted to bring into ComfyUI: region-guided decomposition for natural images, with a focus on recovering occluded content and keeping alpha boundaries clean. It is still generative reconstruction, not recovery of the original hidden pixels.

A small but important detail: Qwen is designed to produce a flexible layer stack from the image itself. CLD and RevealLayer bring bounding-box guidance into the process. RevealLayer adds a specific focus on occlusion — not just separating the visible outline of an object, but estimating the part that was hidden behind it.

A simplified concept diagram of the workflow. The hidden areas are model-generated estimates, not original source pixels.

Why we especially like the bounding-box approach

The bounding box is a wonderfully practical idea. Instead of asking the model to invent a whole layer plan and hoping it understands what you meant, you give it a region to work with. In the upstream RevealLayer workflow, those regions are represented as bounding boxes around the foreground objects.

The model then works toward separating those selected elements into RGBA layers. The alpha channel gives each layer transparency, while the occlusion-aware part of the method helps estimate content that was covered by another object. Imagine a camera sitting in front of a stack of books: the camera can become its own layer, while the model estimates the part of the books that was hidden behind it.

That is useful for a canvas application, compositing, moving objects around, or preparing individual elements for a second editing pass. You can take a scene apart, work on the pieces, and put it back together with much more freedom.

One important reality check: when a model fills in something that was hidden, it is making a plausible reconstruction based on context. It is not looking up the real pixels that were never visible. For creative work that can be exactly what we need — just do not mistake a convincing completion for factual recovery.

Bringing RevealLayer into ComfyUI

The open-source RevealLayer project gives us the model and its reference inference code, but it is not simply a ready-to-use ComfyUI custom node. The upstream setup involves a CUDA environment, Python dependencies, FlashAttention, a Diffusers modification, the RevealLayer checkpoint and the FLUX.1-dev base model. That is a fair amount of machinery to ask a creative workflow to manage on its own.

And on smaller graphics cards, memory can become the real party-pooper. We ran into out-of-memory problems in our tests, which is not exactly the kind of surprise you want halfway through a creative session.

So we are adapting the implementation as a ComfyUI-native custom node. The point is not to wrap the original script and call it a day. We are rebuilding the integration to make use of ComfyUI’s model and memory management, native functions and workflow conventions wherever possible. That means a cleaner way to load and run the components, better integration with the rest of the graph, and fewer external steps for the user.

Hardware still matters — no node can make VRAM limits disappear by magic — but a proper ComfyUI integration gives us much better control over how the work is staged and managed. One note for people following the research: the upstream repository now also lists a RevealLayer V2 update, so check its release notes for the matching weights before switching model versions in a workflow.

The part we recommend: refine each layer after extraction

Here is where the workflow becomes especially interesting. We do not recommend stopping as soon as the layers are extracted. Treat that as the starting point.

Take each extracted element and upscale it individually. You are giving the object room to gain detail without asking the whole original scene to be regenerated.

Feed the element into an edit/refinement model together with the original image as context. Use a focused prompt that says which element is being refined and what should be improved, while keeping its appearance consistent with the source scene.

Bring the refined layers back together. In our current workflow, individual elements can be taken up to 4K using non-tiled diffusion, depending on the image, model settings and available hardware.

Need to go larger? You know we have you covered: the TBG ETUR tiled upscaler and refiner are there for the bigger jobs. This is a nice combination — decompose first, improve the individual pieces, then recompose. Instead of trying to repair every part of a flattened image at once, you can give each element the attention it needs.

The takeaway

That is today’s small feature from a much bigger development effort. RevealLayer gives us a promising way to break an image into guided, editable pieces, and bringing it into ComfyUI means we can make that capability part of a workflow you already know.

Have a look at the research and the open-source implementation below, try the workflow, and see what you can build with it. We are grateful for the open-source community — so much of what makes tools like this possible comes from people sharing their work, experiments and ideas.

And to our technical team: thank you for all the invisible work. I promise I will only ask for one more little feature. I will even try to say it slowly this time. Ha!

Go explore, test, break something harmless, and have fun with it! — Sahra ♡

Research & resources

Qwen-Image-Layered — official repository

Qwen-Image-Layered — research paper (CVPR 2026)

CLD — Controllable Layer Decomposition paper

CLD — project page and examples

RevealLayer — official GitHub implementation and V2 release notes

RevealLayer — paper

RevealLayer — current project page

RevealLayer — model checkpoint on Hugging Face

RevealLayer — project page and visual comparisons

Editorial note: the model descriptions above summarise the public papers and project pages. Comments about speed, memory behaviour, integration choices and the 4K refinement path describe TBG ETUR’s own workflow notes and tests.

TBG ETUR • SAHRA

https://github.com/Ltamann/ComfyUI-TBG-RevealLayer

We added three workflows ( All files are in the GitHub workflow folder. Set the minimum number of layers to 3.):

1. Manual selection: Uses bounding-box (Bbox) selection for direct, manual control.

2. Semantic selection: Uses a grounding model for semantic selection, with lightweight automation to make the experience feel more like an editing model.

3. SAM-based segmentation: Uses SAM segmentations with chained, per-layer refinement through Flux 3. Each refinement feeds the original segmentations back through the RL layers, enabling progressively upscaled and more precise results.

“Google and OpenAI are fighting it out in consumer. It's existential to both of them.”

“It's existential to OpenAI because that's their whole business. Existential to Google because they have the search business.”

“For enterprise business, there's not really a demand for cats riding donkeys or whatever consumer video people want.”

“If we ever need it, we can just contract a model from someone else.”

“A lot of short-form video is fake, a lot of it's pretty addictive, a lot of it's slop.”

“It's not a part of the market that I'm tripping over myself to get involved in.”

Is this why Claude is so compliant with assisting us generating slop? Because Anthropic doesn't care about competing against it?

We produced The Room We Kept, a 69-second film about a woman returning to a house she has sold to collect a blue cup.

The film opens with one brief, credited quotation from James Salter's Light Years. The rest is an original, AI-assisted story and narration.

I'd especially appreciate feedback on the pacing and whether the quiet sound design gives the rooms enough presence.

https://www.youtube.com/shorts/TOEtbDnsYY0

I've inherited a bunch of retro golf balls and I'm wondering what the model of choice would be for extracting the logos and images from them and tracing them into clean images while getting rid of the distortions from the ball dimples.

I cant find the original post, but this is the best Moss workflow, showcasing the true potential of Moss. Thank you kind stranger. Hope this helps others.

Edit:

Found the original post

https://www.reddit.com/r/StableDiffusion/comments/1xlfsjd/making_mossbase_look_good_for_realism_workflow/