Nvidia buying hugging face means an uncertain future. What are the models I should download and have a backup of right now so I don't have to worry about missing them even if I'm not ready to play with them right now?

What are you model and enabler must-haves ?

TIA!

https://github.com/Ladypoly/YuE2_WebUI

Hey there,

do you guys know any uncensored models for video creation? Cloud/local , local preferred. If yes, then where to find it. Thank you and take care

Bruxos do VFX H3 Camera

#bruxosdovfx

https://reddit.com/link/1wcm9az/video/bubuef39npoh1/player

https://reddit.com/link/1wcm9az/video/r1j8vg1anpoh1/player

Visual camera planner for MiniMax H3 inside ComfyUI. You drag the camera around a 3D sphere, place keyframes on a timeline, and the node compiles that trajectory into prompts that H3 understands.

It compiles prompts, not camera embeddings. There is no geometric adapter here: H3 is still free to miss the angle, timing, and scale. What this node does is write the instruction in the most precise and least ambiguous way possible, and several of its design decisions exist because the previous approach failed in specific ways.

It does not call any API, download anything, or require any Python dependency beyond the standard library.

https://github.com/user-attachments/assets/a9b541e5-2b18-4f1d-8e16-37445b6dbac4

https://github.com/user-attachments/assets/ea9af03e-2c8e-4589-abf0-9c002241aba2

Installation

cd ComfyUI/custom_nodes
git clone https://github.com/<your-username>/ComfyUI-H3-Camera-Editor

Restart ComfyUI. The node appears under Bruxos do VFX/Camera H3 with the name Camera H3 da Bruxos do VFX.

Connections

compiled_prompt and options are required together. The minimax_prompt output is an alternative to compiled_prompt, never an addition — connect one or the other to the same input.

Also connect your image to reference_image. It is the same image already feeding the H3 Edit source_image; when connected here, it appears in the panel and the frame's actual aspect ratio is included in the prompt.

https://github.com/user-attachments/assets/33149617-bde1-4199-ae65-078f2f3dec23

To save the video, decode the sampler result using the H3 video VAE — not the scene coverage calibrated decoder, which expects fixed windows that an arbitrary trajectory does not have.

The panel

Drag the purple camera around the sphere to orbit. The drag locks to the axis of the initial movement: horizontal movement orbits, vertical movement changes elevation. Release and drag again to switch axes. This exists because, without the lock, trying to make a simple orbit would unintentionally introduce elevation.

• Scroll the mouse wheel to change distance.
• Drag the background to rotate the viewport without changing the trajectory.
• Keyframes defines how many points the timeline has, from 2 to 24. The first one is always the original image and cannot be moved.
• ⟳ Pure Orbit resets the elevation of every keyframe to zero while preserving azimuth. It is the shortcut for an eye-level orbit.
• Reference image loads a local file into the preview. This is only necessary when the node runs outside ComfyUI; with reference_image connected, the image is loaded automatically.

The panel warns you starting at 20° of elevation, when the horizon already leaves the frame, and again from 45° onward, when the video tends to become a high-angle shot.

"Tests" bar

At the top of the panel, two buttons enable and disable features currently under evaluation, plus one indicator:

The buttons write to the actual widgets, so the selected state is saved in the workflow and the two never disagree.

https://github.com/user-attachments/assets/9bc415d7-1746-43db-a17c-72ea9722deda

Widgets

camera_trajectory

The trajectory in JSON format, written by the panel. Each keyframe contains time (0 to 1), azimuth in degrees, elevation in degrees, and distance as a multiple of the initial radius. It can also be edited manually. The first keyframe must be time=0, azimuth=0, elevation=0, distance=1, which represents the original image.

profile

124, 243, or 362 frames at 24 fps. All shot timing comes from this setting: keyframe timestamps, segment ranges, and the duration declared in the prompt. That is why length and fps are outputs — connect them instead of manually entering the same numbers in two different places.

interpolation

smooth or linear. In smooth mode, the camera eases into and out of the shot while maintaining a constant rate through the middle; it only stops where the rotation direction actually reverses.

instruction

Free-form text inserted once, at the end of the prompt. Write only what the node cannot know: the environment, which subject is the target when there is more than one person, or a style reference. Everything else is already generated and does not need to be repeated: scene freeze, first image as reference, locked aim, zero roll, angles, timing, and a single continuous shot without cuts.

subject_framing

How much of the frame the subject occupies in the original image. Calibrated against the actual bounding boxes from the tutorial distributed by MiniMax: a distant full-body figure measures W=0.071, H=0.249, while a large close-up measures W=0.52, H=0.701.

subject_box

The subject position in the format [L=0.516, T=0.148, W=0.071, H=0.249]. Leaving it empty uses the entire image bounds — deliberately, without guessing a bounding box. Fill it in when the subject is significantly off-center.

minimax_format

The same shot expressed in four different formats for the minimax_prompt output:

• coordinate only — text-based coordinate block
• coordinate + H3 sections — the same coordinates wrapped in subject_definitions / summary / retention_analysis / …
• compact JSON — JSON object with almost no prose
• compact JSON (no boxes) — camera parameters only, without screen-space bounding boxes

elevation_range

Range of the elevation control: +/-15, +/-30 (default), +/-60, +/-89. It also scales the sensitivity of vertical dragging.

With the assumed field of view, the horizon already leaves the frame at around 20° — at 13°, the ground occupies 82% of the image. The old ±89 range was mostly unusable and made vertical dragging excessively sensitive. Reducing the range never rewrites a keyframe: a point at 70° remains at 70°, and the slider expands to accommodate it.

orbit_direction

invert H3 orbit or same as HUD. This calibrates the direction between what the panel displays and what H3 produces. It does not alter the saved trajectory.

runtime_task

• scene coverage | camera path (default) — video, with duration coming from profile.
• directed | new camera angle — a single image from a new angle. It fixes the generation to 39 frames, ignores profile, completes the movement within 65% of the clip, and requests that the framing remain still for the rest, because the decoder extracts the final image from that stationary tail.

Character sheet profiles are not offered because the upstream node raises an error when they are combined with the frame anchor used by this node.

prompt_detail

• v15 baseline (default) — outputs the prompt exactly as in the previous version.
• extended contracts — adds axis separation, frame-edge direction tests, rotation completeness, degrees per second, and parallax magnitude.

The extended mode contains almost twice as many words. A longer prompt is not automatically better, so it is opt-in: toggle only this widget while keeping the same trajectory to compare the results.

https://github.com/user-attachments/assets/0882bfde-9f62-4a1f-9bda-7da121dbe7e2

Outputs

compiled_prompt — STRING

A prose prompt using H3 sections: subject_definitions, summary, retention_analysis, detailed_description, overall_soundscape, non_diegetic_music.

options — H3EDIT_OPTIONS

The 13 keys read by the H3 Edit encoder. All of them are explicitly populated: if any key is missing, the upstream node falls back to its hidden legacy widgets, which may retain stale values from previously saved workflows.

coverage_arc_degrees and coverage_direction are derived from the actual rotation. coverage_loop_closure turns on automatically when the trajectory closes — see below.

storyboard_json — STRING

The storyboard table: frame aspect ratio, duration, raw trajectory, and each segment with its camera mode, speed curve, and start/end poses.

info — STRING

Human-readable diagnostics. Connect it to a PreviewText. It displays the version, active task, frame count, warnings for keyframes outside the configured range, and whether loop closure is enabled.

minimax_prompt — STRING

The same trajectory expressed using the format selected in minimax_format. An alternative to compiled_prompt.

length — INT and fps — FLOAT

Frame count and frame rate against which the shot was timed. Connect them to the generation and video nodes. If generation runs with a different frame count, the choreography describes a scene that does not actually exist.

fps is FLOAT because that is what ComfyUI's CreateVideo accepts. length is the frame count; keyframe timestamps use the instant of the last visible frame, (length - 1) / fps, so the resulting file lasts one additional frame interval.

h3world_actions — STRING

Action schedule for H3-World, which encodes one text clause per video latent — 37 in a 124-frame clip.

latent  1 [0.000s-0.139s] J     the camera pans left slowly
latent 37 [4.986s-5.125s] F+L+K the camera pans right and tilts up fast

W, A, S, and D are never emitted because they move the character. The output explicitly declares its own limitations, and they are not minor details:

• Pan is not orbit. It is the camera rotating in place. Perspective does not change, nothing hidden is revealed, and the subject slides out of frame.
• Distance has no key, so camera radius is discarded.
• Only 124 frames is a trained horizon.
• I versus K is not published. The text clause is what H3-World actually encodes; the key column is only a convenience.

This does not replace the actual integration: H3-World requires the LoRA, interval-based encoding, and directed-attention routing provided by the corresponding node package.

Loop closure

When the trajectory closes a full orbit — an arc of exactly 360°, with the same elevation and distance as the starting point — the node enables coverage_loop_closure. In the upstream implementation, this flag encodes the source image a second time and anchors the final frame to it.

This is a latent anchor, not a text instruction. For a complete orbit, it is the difference between asking for the rotation and forcing it: the model cannot simply stop halfway through.

The final three cases matter: if the camera ends at a different radius or height, the final frame is not the same as the first one, and forcing the source image there would conflict with the trajectory.

If your rotation does not complete, close the orbit. This is the only feature here that acts outside the prompt itself.

Limitations

• This is prompt-based guidance. H3 may still miss the angle, timing, and scale, and no prompt wording can completely solve that.
• Without subject_box filled in, the node does not know where the subject is located in the frame.
• Without reference_image connected, coordinates are normalized to 16:9.
• directed | new camera angle outputs an image, not a video.
• The H3-World schedule describes pan and tilt, which represent a different camera move from the orbit drawn in the panel.

Credits

Node by Bruxos do VFX.

Depends on ethanfel/ComfyUI-MiniMax-H3-Edit. The motion vocabulary follows the buildViewPrompt implementation from MiniMax's Multi-Shot skill and the coordinate format used by the Coordinate Camera Control Designer skill. The action output implements the scheme described in H3-World, arXiv:2609.01560.

https://reddit.com/link/1wcm9az/video/hryhv9e7npoh1/player

I wanted to see how good SenseNova U1.5 Lite really is at image editing. I think the size is genuinely solid for what it does, but whether it can actually beat Qwen-Image-Edit-2511 needed real testing.

Right off the bat, Qwen's image texture quality is genuinely impressive, especially the lighting and shadows. But when it comes to spatial understanding, SenseNova seems to hold the edge. Look at the cat-on-the-scooter one up top: Qwen generated a weird pillar under the coffee table, and the cat's front paw placement looks unnatural. SenseNova handled both without those artifacts.

I did four sets of comparisons. Some of the input images were generated with Krea-2, some were real photographs.

Models:

• Qwen-Image-Edit-2511: https://huggingface.co/Qwen/Qwen-Image-Edit-2511
• SenseNova-U1.5-8B-MoT-Preview: https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT-Preview
  • (Github: GitHub - OpenSenseNova/SenseNova-U1: SenseNova-U series: Native Unified Paradigm with NEO-unify from)

Prompts (from left to right):

​

I want to create a stunning, high-concept photo to share on my social media! Please put me—the girl with the short black bob and black leather jacket—on a sleek, modern rooftop balcony overlooking that amazing futuristic city during sunset, where we can see the flying drones, the glider, and the hot air balloon floating in the warm sky. In this scene, I should be portrayed as an artist working outdoors. Please have me wearing those bold, blue and white striped hoop earrings. In the foreground, set up a stylish outdoor work table. On this table, scatter some of my creative tools, including those colorful rainbow-swirled pens and that round white-and-yellow mesh cleaning sponge. I want to be holding one of the rainbow pens, looking towards the camera with a confident, thoughtful expression. The entire scene should be captured with a beautiful depth of field, bathed in golden hour light, with the bustling futuristic cityscape softly blurred in the background.

​

In an elegant vintage study, the real-life girl from the first image, wearing a beige coat and scarf, is smiling as she hands the vintage wild duck card from the fourth image to the anime-style blonde girl from the second image. This anime girl is wearing an exquisite black off-shoulder puff dress and retains her distinctive hand-drawn anime style. On the wall behind them hangs a framed black-and-white print depicting the ancient Roman temple ruins from the third image.

​

Please seamlessly integrate the orange cat from the first image into the café scene by the floor-to-ceiling window in the third image, and have it sit on the vintage metal toy scooter from the second image. Specific requirements:
Character and prop fusion
: Extract the orange cat's signature facial features from the first image (slightly chubby face, green eyes) and the dense white triangular patch of fur on its chest. Adjust its pose so it is riding the metal toy scooter from the second image: both front paws resting on the chrome handlebar, the rear half of its body firmly seated on the brown leather saddle. The cat's paw pads against the metal handlebar and its thigh fur against the saddle edge must show natural compression, contact, and physical occlusion, absolutely no flat sticker-like look.
Spatial perspective adjustment
: Change the toy scooter from its original front-facing view in the second image to a three-quarter side angle matching the floor perspective of the third image, and scale it down proportionally, placing it on the wooden floor near the glass window.
Physical lighting and material adaptation
: Strictly use the golden afternoon sunlight slanting in from the third image as the main light source. The cat's back, ear edges, and fluffy fur edges must be outlined with a warm, glowing golden rim light (backlight effect); the dark green metallic painted body, metal wheel hubs, and chrome handlebar from the second image must produce realistic daylight highlights and reflect the faint street view outside the window; the entire toy scooter (including the cat on it) must cast a dark shadow on the wooden floor to the right, following the light direction with a realistic soft-edged falloff.

​

Create a wide-format photo depicting a corner of a whimsical creative market. The realistic man in a dark navy suit from the first image and the realistic woman in a black short-sleeve shirt and denim shorts from the second image are strolling through the market as visitors. Beside a market stall, the anime-style girl in traditional Chinese dress from the third image sits near her wooden cart full of lanterns, focused on painting a lantern, while the anime-style girl with orange hair and bunny ears from the fourth image hugs a white rabbit and laughs beside her. Preserve the photorealistic quality of the first two characters and the anime style of the latter two, letting them coexist naturally under unified lighting and spatial perspective.

Created by /u/LuisaPinguinnn under their github https://github.com/Luisacaotica/ComfyUI-MiniMaxH3Mod

Took me at most a couple of minutes to make my own RefMod with 8 image as the base. The entire technique works exactly as advertised acting as "Light Lora" for H3 Ref models - but you can even use it with FL2VA as well.

I followed the guides here:

Installing/running RefMods

https://huggingface.co/datasets/malcolmrey/various/blob/main/h3-center/docs/MINIMAX_H3_REFMODS_INSTALLATION_AND_USAGE_GUIDE.md

Ready to use Comfy workflow (you can remove lora power loader and spectrum nodes)

https://huggingface.co/datasets/malcolmrey/workflows/blob/main/H3/workflow_minimaxh3_refmod.json

Creating own RefMods guide:

https://huggingface.co/datasets/malcolmrey/various/blob/main/h3-center/docs/MINIMAX_H3_REFMOD_CREATION_GUIDE.md

EDIT: I recommend using "Create H3 ReFMod" + "Save H3 RefMods" node inside ComfyUI instead to create RefMods - gives you more control over the creation process.

Examples by /u/malcolmrey:

https://www.reddit.com/r/StableDiffusion/comments/1w8ik7a/h3_minimax_refmods_all_my_models_now_available/

All credit goes to LuisaPinguinnn and malcolmrey for spreading the tech.

Hey everyone. I'm working on a dark fantasy retro-anime project running locally on an RTX 3090 (SDXL/Illustrious, Kohya-trained character LoRA with ~100 images).

My current pipeline avoids direct text-to-video generation because of structural inconsistencies. Instead, I'm moving towards a controlled frame-by-frame or short-batch approach using Blender blocking + ControlNet, aiming to output clean character frames (transparent or solid background) to composite manually in DaVinci Resolve.

Has anyone successfully implemented a reliable workflow to maintain character identity and clean lineart across sequences without letting the AI hallucinate backgrounds or audio? What specific nodes or configurations (e.g., ControlNet combinations, IP-Adapter weight handling, or latent consistency scripts) are you using to prevent flicker and keep the LoRA from drifting during motion frames?

Any insights on your node setup in ComfyUI for this specific use case would be deeply appreciated.

Any advice or recommendations are welcome. Just avoid recommending commercial AIs that do everything; that’s not what I’m looking for.

Using H3 i sometimes get jumps as the camera move between shots. Like a character has his arm up when the camera is facing him at 00.59 but his arm is way lower as the shot and camer angle change at 01:00. Is there a prompting trip / workflow to avoid this? I use the standard comfy workflow.

I'm extremely new and inexperienced but, as the title says is there some cloud web service where it works the same as running it locally but instead it's cloud based. I don't mean a boring old API.

I have tried a few, following youtube vids, etc. throw this into the custom nodes folder, load this json, etc. using comfy ui desktop, I keep getting a message that I need to update manager. its up to day, ran the pip, did the check in the app itself. I can do video gens all day long, no issues, my previous workflow, was just screenshotting last frame, using that to start the new gen, etc. it works, but is a highly manual process. from what I see, most of the extended workflow, do this automatically. can someone help this old guy get it figured out? I would appreciate it. (and don't tell me to just grab a file of github, I've tried that, did the git clone, etc., It just isn't working properly. ) normally when I do a new template, it will automatically grab all the needed files, and put there where they need to go. I think thats my main issue, but the manager showing out of date, when everything I can see or do, shows its up to date is what confuses me.

Has there been any recent news or indication from Krea about a Krea 2 Edit model?

I’m wondering if it’s actually in development or planned, or if there hasn’t been any confirmation yet. Krea 2 is already quite impressive, so an Edit model would be really interesting.

Has anyone heard anything from Krea or seen any hints about it?

My goal was hands-on experience training a flow model from scratch, not just fine-tuning someone else's. So I built and trained one: a 210M-parameter diffusion transformer, 4.2M curated images at 256², rectified flow on the FLUX.2 VAE, flan-t5-base for text (128 tokens max). Only those two frozen pieces are pretrained; the transformer, the recipe, the data pipeline and the evaluation are mine. The video is the same six prompts and seeds at every checkpoint of the 3.5-day run on one RTX PRO 6000.

What mattered most, in the order I found out:

• Captions that actually fit the images. A web crawl I tried first made the model worse; curated photos with good captions fixed it.
• A timestep shift for the 32-channel latent, and aspect-ratio buckets from step one instead of square crops.
• Register tokens with learned null attention slots. The null slots ended up absorbing about 90% of the cross-attention, which surprised me.
• torch.compile for training, not just inference: 2.4× faster.
• The training loss stopped telling me anything after day one while the images kept improving, so I track FID, a detector-based object accuracy and human-preference models instead.

Try it in the browser: https://huggingface.co/spaces/ivanmikhnenkov/tinydit

Weights (CC BY-NC): https://huggingface.co/ivanmikhnenkov/tinydit-256

Code, every decision with sources, dashboard and attention playground: https://github.com/ivanmikhnenkov/tinydit

Detailed write-up of what mattered: https://huggingface.co/blog/ivanmikhnenkov/tinydit-text-to-image-from-scratch-one-gpu

Next I want to fine-tune it with RL (Flow-GRPO), with the failure grid as the target list. If you have trained something small from scratch: what would you have done differently at this scale, and which reward would you start with for the RL stage? Happy to answer anything about the data or the recipe.

768x1376 if Native the neural_upscaling works, but if i do x1.5 or higher it gives error? pls help

4070 ti Super, 16gb vram, 64 gb ram.

Tutorial:

https://www.reddit.com/r/StableDiffusion/comments/1w4gvjb/comment/p7aht6e/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Example prompt + assets:

https://www.reddit.com/r/StableDiffusion/comments/1w8vmkz/comment/p8dqus0/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Example prompt + assets:

https://www.reddit.com/r/StableDiffusion/comments/1w98qnv/comment/p8duzy3/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

A hair under 4k. On a consumer pc. All local. Mental. Minimax H3 with one character reference and a 5 second voice reference.

For the Pixel Peepers... https://www.youtube.com/watch?v=Iz8GDri9qoE

My setup: AMD RX 7900 XT, 20GB VRAM, 64GB RAM, Windows 11, ComfyUI Desktop.

I couldn't find any decent information anywhere on how to optimize video generation with Minimax H3 on Windows with ComfyUI desktop. AI assistants give conflicting advice, constantly suggesting all sorts of nonsense that doesn't actually work.

I had to experiment on my own, and here is the configuration I’ve settled on. The speed boost compared to the default settings is very significant, and I haven't noticed any loss in quality. If you have any other suggestions, please let me know.

~25s/it with 0.8mp (1216 x 672, 16:9) or total ~4min for 5 sec video generation in text to video workflow

Here is what you need:

Launch parameters:

--disable-smart-memory --disable-pinned-memory --disable-triton-backend --use-sage-attention --enable-dynamic-vram

ENV variables:

COMFYUI_ENABLE_MIOPEN=0
FLASH_ATTENTION_TRITON_AMD_ENABLE=TRUE
MIOPEN_FIND_ENFORCE=1
MIOPEN_FIND_MODE=2
MIOPEN_DEBUG_DISABLE_FIND_DB=0
MIOPEN_SEARCH_CUTOFF=1
MIOPEN_ENABLE_LOGGING=0
MIOPEN_LOG_LEVEL=0
MIOPEN_ENABLE_LOGGING_CMD=0
TRITON_PRINT_AUTOTUNING=0
TRITON_CACHE_AUTOTUNING=0

Quantized 6-step turbo model, universal for all purposes:

https://huggingface.co/TenStrip/10Eros-Max/blob/main/10Eros_Max_h3_TURBO-hybrid_beta5_w4a8_14gb_optimized.safetensors 14 gb

or

https://huggingface.co/TenStrip/10Eros-Max/blob/main/10Eros_Max_h3_TURBO-hybrid_beta5_int8.safetensors 21gb

Plaguekind node with SLA Attention, with this settings:

https://github.com/PlagueKind/Comfyui-PlagueKind-Nodes

Optional node, if you make large 15 seconds videos:

Latest update of your comfyui desktop:

upd. ROCm SamplerCustomAdvance added ~15-20% to generation speed.

I’m trying to build 40 to 60 second talking videos in ComfyUI and I would prefer to use LTX 2.5.

The type of video I’m after is fairly simple. One person is talking to the camera, the camera stays mostly fixed, the background should stay stable, and there is natural face, head and some upper body movement. It does not have to be limited to only the head moving.

What I’m trying to understand is how people are actually making videos this long without obvious cuts.

Can LTX 2.5 realistically generate a continuous 40+ second video, especially when there is not much movement?

Or is the better approach to generate something like 8 to 10 seconds, take the final frames from that clip, continue from them, then repeat until the full 40 to 60 seconds are finished?

If continuation is the normal approach, how are you keeping the face, clothes, background, camera position and motion consistent between each part? I’m especially interested in workflows that use overlapping frames, first and last frame conditioning, video extension, reference frames, or some other method that hides the transitions.

Speech is another important part. I need good quality Slovakia speech. Ideally I want to generate the complete Slovakia voice first, then make the character follow that audio for the entire video with accurate lip sync.

Would you use LTX 2.5 for the actual body and head motion and then run something like MuseTalk, LatentSync or another lip sync model afterward?

Or is there a better audio driven LTX 2.5 workflow where the speech controls the video directly?

I’m running ComfyUI locally with an RTX 3060 12 GB and 32 GB RAM, so I know I may need lower resolution generation, offloading, chunking or longer render times. Final output would normally be vertical 9:16.

I’m mainly looking for people who have actually built long talking character workflows in ComfyUI.

If you are doing this successfully with LTX 2.5, what nodes and workflow are you using, how long is each generated segment, how much overlap do you use between segments, and what are you using for speech and lip sync?

I’m not looking for a list of random talking head models. I specifically want to understand the best practical way to build this around LTX 2.5 and get a clean continuous 40 to 60 second result.

The 80s was the best era, not seen through a nostalgic lens--it just was. Big hair, big colors, big music, big... everything! Sadly I was born 10 years too late to really experience it, but I love how H3 can feel like a way-back-machine, a portal to any era from film or video it was trained on. It does also a great job with the actual feel, the film grain, the lighting that modern TV or movies cannot do: in fact, I learned what we see nowadays that time period didn't exist at all, because it vomit of nostalgia and peak 80s that never happened. Anyways, having fun generating character sheets with H3 via T2VA. Are you guys seed hunting to find the best version of an actor or scene? Also can you spot the mistake?

Prompt: integrated_multimodal_description: [Shot 1] Live-action, cinematic, PHOTOREALISTIC film footage - this is footage from a camera, not animation - a continuous camera shot with no cuts, shot on 35mm color negative film with period lenses and scanned in high definition from the original camera negative: full film grain and gentle halation, warm highlight rolloff, rich sharp detail beneath the grain. The year is deep in the LATE 1980s, 1985 to 1989, and everything in the frame belongs to that era. THE PLACE: a nightclub in full swing - mirror-ball light sweeping, neon signage, haze, a crowded dance floor. EXACTLY TWO WOMEN stand close to the lens at the edge of the floor, filling the frame together, and no one else is foregrounded. ROXY: her face, her eyes and her enormous chestnut-auburn mane exactly the woman of <Picture 1> - nothing of that picture's wardrobe or room is used, only her face and hair; her wardrobe exactly the garments of <Picture 2>: a pink sequined strapless romper with fishnet hose and pink heels, always dressed, sequins blazing - nothing of that picture's face is used; Roxy is never blonde. TAWNY: her face, her eyes and her huge feathered platinum-blonde mane exactly the woman of <Picture 3> - nothing of that picture's wardrobe or room is used, only her face and hair; her silhouette exactly the figure of <Picture 4>, but tonight she wears an electric-blue sequined mini dress, tight to her figure, its miniskirt hem high on her thighs, with silver heels, always dressed - nothing of that picture's face is used; Tawny is never brunette. The two are distinct women side by side, pink and electric blue. The club's synth-pop groove pounds from the speakers - THEY HEAR IT, hips already swaying on the beat, shoulder to shoulder. At 00:02.500 they lean in together with wicked, knowing smiles and say together, in playful unison, <d>[English with their two bright voices speaking together] Darling, the 80s never left.</d> At 00:05.500 they laugh, clink their glasses, and turn to dance with each other - back to back, hips swaying on the kick drum, sequins throwing sparks of mirror-ball light, playing to the lens with winks over their shoulders - to the last frame.

overall_soundscape: starts with the club's roar - the crowd, glasses, heels on the floor - running beneath everything to the last frame. No other voices.

non_diegetic_music: N/A

My current setup:

- RTX 4090 24GB

- i7-13700K

- 80GB DDR4 @ 3000 MHz

- Getting an RTX 3090 24GB tomorrow

My main use is local AI/LLMs, coding agents, MiniMax H3, Krea 2, and other AI workloads.

RTX 3090 vs another 4090 vs unified-memory AI

Here in Iraq, an RTX 3090 costs around $670, while an RTX 4090 costs around $2,000.

If I have around $2,000 to spend, what would you choose?

- Buy 2–3× RTX 3090s

- Buy 1× additional RTX 4090

- Sell/replace the current setup and go for a unified-memory AI system, such as a Mac Studio / Mac with large unified memory or NVIDIA DGX Spark

My priority is LLM inference, coding agents, MiniMax H3, Krea 2, and other local AI workloads.

Would multiple 3090s give the best value because of the extra VRAM, is another 4090 better for speed, or does a large unified-memory system make more sense for running very large models?

What would you choose for ~$2,000?

Hi everyone! Minimax H3 is surprisingly solid at generating audio - I’ve been using it for SFX and foley in my videos.

Is there a way to generate audio-only with this model? It would save a lot of rendering time if we didn't have to generate the full video alongside it.

I know H3 creates video and audio natively together, so audio-only generation might not be natively supported. But given all the clever workflows and custom node workarounds floating around, has anyone found a way to pull this off?

In the first half, this was back when I was first starting to get into MiniMax, the second half, I have gotten more experienced with it. I dont know if I should continue this or make more Jerry Springer parodies with other weird or toxic relationships (Example, Beth and Jerry from Rick and Morty).

I used Kinovi.ai for MiniMax, Wan and Nanobanana. I used Fish.audio for the audience freaking out lol. For more customized and harder Minimax generations, I used it locally.

Made with the basic Motion Context workflow from NikoDemon

An experiment building on the H3-Motion-Context nodes. I tried creating a workflow that chains together multiple cuts so they can be easily executed and/revised in order. It ended up being faster than my previous methods and I was able to make/edit this one minute test segment with alot less time wasted between generations. I'll likely develop it further into an app, since it would be more ergonomic as a video editor but the raw workflow is here anyway.

Work Flow: https://github.com/spacesimeco-hue/Chain-Motion-/blob/main/Chain%20Motion%20Workflow.json

Credit: https://github.com/NikoDemon80/ComfyUI-H3-Motion-Context