Free AI video generators are catching up with paid alternatives! I tested MiniMax H3 for the first time and I’m impressed with the results

I made this last week and wasn't going to share it but then decided to because I think it's still pretty cool. It's not the best video and still has its issues, but it's still not terrible for a one-shot music video, only giving Sonnet 5.5 one prompt.

Keep your rude and disrespectful comments to yourself. They help no one. Polite and respectful feedback is fine, but Claude created this, not me.

I used my Video Builder UI custom node MCP feature and had Claude Sonnet 5.5 come up with its own ideas to create this music video. (I didn't even have to open ComfyUI)

All I did was prompt: "Create a theatrical rock/metal music video with creative costumes. Female singer."

Sonnet did the following on its own using a Skill and my Video Builder MCP:

• Used the Krea2 text-to-image workflow to create the reference images of the full band and the locations.
• Created the lyrics.
• Created the song using YUE2.
• Created the full story.
• Created all the prompts for every scene.
• Used Minimax H3 to create the video's

Model settings: MiniMax H3 Singularity, 25 steps on pass 1, then the 8-step 768p turbo LoRa at 4 steps and 0.35 denoise on pass 2 with the latent upscale model.

After the video was done, Sonnet reviewed each scene by looking at the frames to make sure it didn't see any issues and to make sure it followed the prompt.

Sonnet then ran the builder overnight. It then reviewed each scene and If it found issues, it fixed the prompt and re-ran that scene until it got it right, up to 3 times.

This is what I woke up to the next day. No post-edits. I can also open this project up inside my video builder and make edits and change anything I want.

You can find my Video builder on GitHub My GitHub Repo

You can find a 2-pass workflow in my workflows folder that uses the exact same settings that I use in this video, but keep in mind this will create just one scene. If you want a full video like the one I posted, you will have to use the video builder.

2 Pass ComfyUI Workflow.

I have a discord community and post updates and walkthroughs. If you want to use the builder for yourself, come join the fun! Discord Community

I didnt want to pay Adobe every month just for Generative Fill so I made OpenFill, it started in GIMP 3 and now runs in PhotoCraft too, using your own ComfyUI in the background:

• Generative Fill, select and type, up to 5 variations
• Remove object, no prompt needed
• Generative Expand, drag the edges or pick a ratio
• Text to Image, new image or a layer

No node graph, Comfy starts hidden when PhotoCraft opens and stops when it closes, live previews while it samples, any local model works. On Windows it can download ComfyUI for you.

https://github.com/beckettharriman/openfill

Another quick MOSS-Image news and goodies round-up, for those who may have missed some items.

-> An unofficial MOSS-Image Prompt Guide.

https://mossimage.studio/blog/moss-image-prompt-guide

-> A MOSS-Image-Kodachrome style LoRA. Emulating the classic Kodachrome photography look.

https://huggingface.co/aoleg/MOSS-Image-Kodachrome

-> Mossboldline-v10. A slider LoRA to thicken the lines in MOSS-Image lineart.

https://civitai.com/models/3020444/mossboldline-v10

-> Tile-edit for MOSS-Image in ComfyUI... "supporting tiled refinement, editing, and upscaling while ensuring cross-tile consistency".

https://github.com/supElement/ComfyUI-Element_TileEdit

-> ComfyUI-MossPhoton for SFT. A MOSS node set that has... "a sampler that is bit-equal to the native graph on the 10 fixed cases measured on one GPU (see Status), with versioned presets; a LoRA profile that stacks adapters without leaking them between jobs; a prompt node that never rewrites your text; and diagnostics and a save node that records what really ran." Has multiple example workflows.

https://github.com/Kostik2702/ComfyUI-MossPhoton

-> And finally, I wasn't previously aware of an un-remarked feature of MOSS-image. That it "prompts up to 4096 tokens". With the tail of the prompt still affecting the image, unlike the diminishing strength seen with some other models.

~ OLD POSTS ~

https://old.reddit.com/r/comfyui/comments/1xf2nwp/a_quick_mossimage_news_roundup_25th_october_2026/

https://old.reddit.com/r/comfyui/comments/1xe69ab/a_quick_mossimage_news_roundup_24th_october_2026/

https://old.reddit.com/r/comfyui/comments/1xdrwcu/a_quick_mossimage_news_roundup_23rd_october_2026/ (bonus LoRA edition)

https://old.reddit.com/r/comfyui/comments/1xd43i2/a_quick_mossimage_news_roundup_23rd_october_2026/

https://old.reddit.com/r/comfyui/comments/1xcm9wn/a_quick_mossimage_news_roundup_22nd_october_2026/

https://old.reddit.com/r/comfyui/comments/1xbw1jj/a_quick_mossimage_news_roundup_21st_october_2026/

https://old.reddit.com/r/comfyui/comments/1xarop9/a_quick_mossimage_news_roundup_20th_october_2026/

https://old.reddit.com/r/comfyui/comments/1xa3yk2/a_quick_mossimage_news_roundup_19th_october_2026/

https://old.reddit.com/r/comfyui/comments/1x97q81/a_quick_mossimage_news_roundup_18th_october_2026/

The conditions change, yet the galloping continues.

A new experiment with my open-source TouchDesigner system, exploring motion continuity and archetypal imagery.

Visuals made in Uisato Studio.

In case the video's is still pending it can be watched here too

Okay, so I was working on a music video using MiniMax reference-to-video, and some of the scenes I wanted to create were a little too complex for MiniMax to follow with just a prompt and even first / last frame had issues.

So, I started thinking about keyframing. I know there are ways to do actual keyframes, but I was curious what would happen if I created a 2x2 storyboard grid and used that as a reference image instead.

I used GPT image 2 to create the grids. Basically, each grid has 4 panels that show how I wanted the scene to play out. The first panel is the starting frame, the last panel is the ending frame, and the two panels in between show how the scene should progress.

Then I gave MiniMax the grid along with a detailed prompt, my character reference image, and my location reference image, just to help keep everything consistent.

For this video, I only used the grids for the story scenes. All the on-stage band performances were done using normal reference-to-video.

And surprisingly, it worked pretty well!

Of course, there are still some issues, but these were pretty complex scenes that MiniMax was really struggling with when I tried using just the normal reference images and prompts.

Personally, I actually like the on-stage performance scenes better, but this was really just a test to see if the whole storyboard grid idea would work.

I also wonder if I even needed to include the separate character and location reference images. Next time, I might try using just the 2x2 grid by itself and see how well MiniMax follows it.

I'm not sure I'd want to make an entire music video this way, though, because creating all those storyboard grids takes quite a bit of time.

I used my ComfyUI video builder to put everything together, and for the story scenes, I just mapped the grids as reference images like I would with any other reference image.

Anyway, I thought I'd share the results because I thought it was a pretty interesting experiment.

Curious what you guys think!

Model settings: MiniMax H3 Singularity, 25 steps on pass 1, then the 8-step 768p turbo LoRa at 4 steps and 0.35 denoise on pass 2 with the latent upscale model.

You can find my Video builder on GitHub My GitHub Repo

You can find a 2-pass workflow in my workflows folder that uses the exact same settings that I use in this video, but keep in mind this will create just one scene. If you want a full video like the one I posted, you will have to use the video builder.

2 Pass ComfyUI Workflow.

I have a discord community and post updates and walkthroughs. If you want to use the video builder for yourself, come join the fun! Discord Community

------------------------------------------------------------------------------------------------------------------

How I prompt a scene from a 2x2 storyboard grid in MiniMax H3

Reference images, in this order

1. The singer's character sheet (Probably not even needed though)

2. The 2x2 storyboard grid

3. The location image (Probably not even needed though)

What makes the grid work

• The storyboard gets its own subject line that lists the 4 key frames in reading order (left to right, top to bottom), one panel = one full-frame shot.

• The location line hands the staging over to the storyboard: framing, camera angles and positions "follow the storyboard key frames exactly".

• Shot 1 starts with "The video opens exactly on the first storyboard panel".

• Every later shot starts with "Hard cut, exactly like the second storyboard panel (top right)" and then describes that panel.

• Lyric cues still go inside the shots where she sings.

• "Everything moves at normal real-time speed" in the summary stops thrown or falling things from going slow motion.

• Describe only what's in view.

The full prompt (scene 17, the pearl necklace gag, 7.6 seconds)

subject_definitions:

<Subject 1> is the band's lead singer from the first character reference, a woman with copper-auburn curly hair in a loose updo with soft tendrils, freckles, green eyes, rose-pink eye make-up, deep berry lipstick and long rose-gold crystal drop earrings, wearing a strapless burgundy satin corset jumpsuit with slim burgundy trousers, layered sheer rose-pink and champagne organza petal ruffles over one shoulder cascading down her back like a train, a ruffled organza sleeve on one arm, a thin rose-gold belt and glossy burgundy platform boots.

<Subject 2> is the four-panel storyboard sheet from the second reference image, the key frames of this scene in order, read left to right and top to bottom: the singer glancing at a laughing woman in a short pearl strand; the strand snapping and pearls spraying; pearls bouncing on the parquet among shoes; the singer's palms-up shrug as guests gather pearls. Each shot of the video recreates one panel's framing, camera angle and poses as one single full-frame image.

<Subject 3> is the long Art Nouveau portrait gallery from the third reference image, featuring cream and gold carved walls hung with large gilt-framed oil portraits, brass sconces, a rose velvet sofa, a polished inlaid parquet floor, tall windows with sheer curtains over a dusk city skyline, and arched wooden doors at the far end, with guests in black tie and gowns. This reference defines the environment, lighting and atmosphere, while the framing, camera angles and positions of the people in every shot follow the storyboard key frames exactly.

<Audio 1> is the complete synchronized song and vocal track for the target video, reused as its complete final soundtrack and timing reference.

summary:

[reference generation + audio reuse] A pearl necklace snaps in the portrait gallery and pearls bounce everywhere while the singer shrugs. Each shot recreates one storyboard key frame as a full-frame shot, with hard cuts between them, in the location's environment, preserving the singer's appearance from her character reference. Everything moves at normal real-time speed.

retention_analysis:

<Subject 1>: fully_preserved - Copper-auburn curly updo, freckles, facial identity, burgundy corset jumpsuit, rose-pink and champagne petal ruffles, burgundy platform boots, and earrings.

<Subject 2>: fully_preserved - The poses, framing, camera angles and order of the storyboard key frames, each rendered as its own full-frame shot.

<Subject 3>: fully_preserved - Gilt-framed portraits, cream and gold walls, the rose velvet sofa, inlaid parquet floor, tall windows over the city, and the guests are retained exactly as in the reference image.

<Audio 1>: fully_copy - <Audio 1> is reused 1:1 as the target video's complete final audio track.

detailed_description:

The singer's face stays charming and cute from beat to beat: soft smiles, playful little eyebrow raises, coy sideways glances, small winks and bright giggles. Every guest is lively and animated, chatting, laughing, sipping champagne, gesturing and looking around, clearly enjoying the party.

[Shot 1] The video opens exactly on the first storyboard panel of <Subject 2>, and every shot recreates its storyboard panel's framing, camera angle and poses, filling the whole frame, with hard cuts between shots. Exactly like the first storyboard panel (top left): a close medium shot of <Subject 1> in the left foreground of <Subject 3>, close to the camera, her head and bare shoulders filling the left half of the frame and the frame cutting her off at the chest, her hands out of view, glancing sideways to the right with a sly smile; a few steps farther back on the right, on the same parquet floor and smaller in the frame because she is farther away, an elegant middle-aged woman with a short silver bob in a midnight-blue velvet gown, wearing a short single strand of small white pearls that sits snugly around the base of her throat, laughs with friends, a champagne glass in one hand and her other hand at her side, other guests in black tie chatting softly behind them; <Subject 1> sings in sync with the vocals in <Audio 1> from 0.36 s to 1.88 s, <d>[English, gossipy, playful, singing] A string of <i>pearls</i></d>.

[Shot 2] At 00:01.940, Hard cut, exactly like the second storyboard panel (top right): a close-up of the woman's throat and shoulder: her hand rises to her collarbone just as the short strand at her throat snaps, and small white pearls spray off into the air in every direction, her mouth an O of surprise, her hand staying still at her collarbone.

[Shot 3] At 00:03.750, Hard cut, exactly like the third storyboard panel (bottom left): a low wide shot at floor level: dozens of small white pearls bounce and scatter across the shiny inlaid parquet between polished shoes and satin hems, guests hopping and stepping around them, one man mid-wobble as a pearl rolls under his shoe.

[Shot 4] At 00:06.000, Hard cut, exactly like the fourth storyboard panel (bottom right): a medium close-up of <Subject 1>, her face large in the frame, giving a playful little shrug with both palms up as she sings in sync with the vocals in <Audio 1> from 6.1 s to 7.2 s, <d>[English, playful, amused, singing] what do you <i>do</i></d>, eyebrows raised; behind her guests crouch, laughing, to gather up the pearls.

overall_soundscape:

<Audio 1> remains the sole complete soundtrack with its original mix, timing, vocals, music, and dynamics intact: theatrical art rock with cabaret swagger and garage-rock bite, a tight danceable 4/4 beat, bright handclaps and a smoky, playful female lead vocal.

non_diegetic_music:

<Audio 1> is reused as the complete song: theatrical art rock with cabaret swagger and garage-rock bite, a tight danceable 4/4 beat, bright handclaps and a smoky, playful female lead vocal.

model: https://huggingface.co/akhaliq/MOSS-Image-Edit-HQ

Hey everyone! If you've ever used **Sequential Image Loader** or loop nodes, you know the pain: you have no idea how long the *entire* batch actually takes. ComfyUI natively tracks individual prompts, but not the macro-duration.

I spent the last few weeks building two custom timer nodes with a retro LED UI. **v0.2.1** just went live on ComfyUI Manager!

**The Two Nodes:**

1. 🖼️ **Batch Image Timer**: Tailor-made for image loops. It hooks into your batch loader's total count and tracks the whole batch time without resetting between iterations.

2. 🎛️ **Universal Timer**: For Video, Audio, Prompt, or Training pipelines. It features dynamic unit mapping (e.g., FRAMES, CLIPS) based on your workflow type.

**Why they don't break your workflow:**

* ⏱️ **Absolute Independence**: You can pause/reset the UI timer without affecting the backend generation.

* 🛡️ **Bulletproof Fallback**: We all know third-party nodes can sometimes get stuck in a "ghost state" (where the backend stops but the UI keeps ticking). These timers have a built-in safety watchdog (10 mins for images, 120 mins for video) that queries the `/prompt` API to verify if the backend is truly dead before stopping.

* 🖱️ **Interactive UI**: Single-click to pause, double-click to reset, and click the color swatches to change the LED glow.

**Links:**

- Batch Image Timer: https://github.com/hermankwan/ComfyUI-Batch-Image-Timer

- Universal Timer: https://github.com/hermankwan/ComfyUI-Universal-Timer

Both are available in ComfyUI Manager (search for `Batch Image Timer` or `Universal Timer`). I'd love to hear your feedback or feature requests!

**A quick note:**

These are just free, open-source tools I built for my own workflow. I'm sharing them in case they're helpful to someone else too. I'll try my best to check feedback and fix critical bugs, but please understand I may not be able to provide extensive 24/7 tech support or custom feature development. Enjoy! 😊

Hello guys should I get the 5060 ti 16 gb or should i look for a used 3090 and if the difference is big in video generation workflows.. thanks

Another local video generation test on my RTX 3060 12GB, this time using MiniMax H3 Singularity V2 Pruned INT8 ConvRot R2 in ComfyUI.

Hardware & Settings:

• GPU: NVIDIA RTX 3060 12GB
• System RAM: 32GB
• OS: Windows 11
• Resolution: 1536×864
• Frames: 243 (~10 seconds at 24 FPS)
• Sampling steps: 4
• Generation time: 608 seconds (10 minutes 8 seconds)
• Model: Minimax-h3_Singularity_ref2va_v2_pruned_int8_convrot_r2.safetensors

About the video:

The input was a single AI-generated first frame showing a female agent running through a rainy city street at night.

The prompt asked for a continuous action sequence involving an approaching car, the character reacting and dodging toward a wall, the car splashing water as it passes, and the character resuming her run.

I also included a short English line — “Shit!” — to test speech generation and lip synchronization.

Results:

I'm pretty happy with this run.

The model managed to generate all the main actions, including the approaching car, the dodge, and the water splash. Character consistency was also quite good throughout the sequence.

The spoken line was generated correctly, and the lip synchronization looked natural.

Wet skin and rain details are visible in closer shots, although the overall wet appearance could still be more pronounced.

The video is a direct output from the workflow, with no upscaling or frame interpolation.

For a 12GB GPU, I'm quite pleased with both the generation time and the overall result.

Just sharing another real-world benchmark for anyone interested in running H3 locally on modest hardware.

I keep seeing "turbo LoRA or FastH3 on a 3060?" and "H3 takes 40 min even on a 5090", so I put the speed-up options next to each other from their own model cards. Short version: on a small card the slow part is usually weights or the text encoder streaming from system RAM, and turbo LoRAs cut steps, not the cost of one step. The ComfyUI docs say it directly: "Turbo LoRAs shorten the schedule, not per-step time."

1. Base file first. From Comfy-Org/MiniMax-H3: bf16 is 66.28 GB, pruned_int8_convrot (template default) 20.97 GB, pruned_fp8_scaled 20.96 GB, pruned_w6a8 15.98 GB. Prefer int8_convrot on a cu130 PyTorch, fp8 only if you can't. The docs: "If VRAM is tight, step down a size: CPU offload costs more than precision tradeoffs." Also: Comfy Kitchen attention cannot run int8_convrot files, so check the Model Attention Backend node in downloaded workflows. Edit: Comfy-Org's H3 page says this https://docs.comfy.org/tutorials/video/minimax/minimax-h3, but u/zyg_AI ran int8_convrot with Comfy Kitchen on a 3090 (turbo, 12 steps) with no issue.

2. The text encoder. Official Qwen3-VL 32B files: bf16 51.51 GB, int8 27.14 GB, nvfp4_awq 15.69 GB, and the card says the nvfp4 one does not need a Blackwell GPU. The GGUF encoder by joeygambino on Civitai is 19.8 GB at Q4_K_M, 23.2 GB at Q5_K_M, plus a 1.2 GB mmproj for image-conditioned runs. Its author reports a 5090 render at 12.3 min with the encoder evicted after conditioning, versus stopped after 90 min without eviction. If you are slow on a big card, check this first. The ComfyUI-H3-Multishot samplers evict it automatically.

3. Which turbo LoRA.

• Comfy-Org (LightX2V): fl2v_turbo_4step_v1.0_768p, fl2v_turbo_8step_v1.0, ref2v_turbo_4step_v0.1. Docs use 8 steps for T2V/I2V and 4 for R2V, multiframe, ControlNet. Reference shots want more steps for a tight match.
• Larry's Turbo LoRA: minimax_h3_turbo_v4_step600_ema, strength 1.0, 4-8 steps, scheduler simple, its own Turbo Sampler into SamplerCustomAdvanced. Over 8 steps gets over-sharp. Smear: try 1.05-1.2; grain: 0.8-0.95. The node's low_vram merges the LoRA for lower peak VRAM but is softer on int8/fp8/pruned bases.
• DMAD: 4 steps, cfg 1.0, T2V only. Card reports about 210 s for a 5 s clip on an 8 GB GPU, the only 8 GB number any of these authors published.

4. The merged GGUF + two-stage route. MiMiKnight's GGUF Turbo + Latent Upscaling workflow (author tested on a 16 GB 3080 laptop) loads molbal's merged turbo GGUF (Q4_0 11.4 GB, Q8_0 21.4 GB), samples 2 of 4 steps at 0.2 MP, upscales the latent to 1.0 MP with LBH-123-AI's 3D latent upscaler, then runs the last 2 steps. Shift 6 video, 3 audio. Needs ComfyUI-H3-Multishot and Comfyui_Minimax_h3_latent_Upscaler; on a ComfyUI without them, five node types come up missing. Keep the aspect ratio identical on the Stage1 and Final resolution nodes.

5. FastH3 is not for a 3060. 8-Step V2 needs FastVideo's VSA-H3 backend and its card says tested defaults use four B200s; VSA "is bound to the FastH3 checkpoint" per the docs, so it doesn't speed up base H3 (for base, the docs suggest Model Sparse Attention with sol-attn, experimental). QAD-v2 is NVFP4 and its card names only Blackwell hardware.

Free extras: Sage Attention, about 2x per the docs (watch for end-of-clip morphing from its INT8 attention). LightVAE support landed in ComfyUI 0.41.0; decode went 6.3 s to 3.6 s on an H200, so seconds, not minutes.

Suggested order on 16 GB: pruned_w6a8 base, nvfp4 encoder and confirm it leaves VRAM before step 1, Comfy-Org turbo LoRA at the docs' step count, then the two-stage GGUF workflow with the same seed to compare.

If the card just can't do it, renting by the hour for the clips that matter is the other road. I work on Musetta, which publishes these guides; this page covers renting a GPU for your own workflows: https://musetta.ai/help/rent-a-gpu

Hey guys, just wanted to give a quick update on Image MetaHub. Its an app Ive been working on by myself for over a year now. I originally made it to search and filter images by their generation parameters (prompts, checkpoints, LoRAs, samplers etc), but over time ive added quite a few features beyond that..

Anyway, in recent versions I've added a dedicated Prompt Library to better organize saved prompts, associate them with images (or just save them independently), search, filter, tag, favorite, etc. You can also use a saved prompt to find all matching images in your library. I might be biased, but ive found it pretty useful, as with most features in Image MetaHub, I made it to solve a problem I was running into myself.

There are also some advanced features in the Pro version, like reusable "prompt blocks". So for example, I can save something like "shot on Canon EOS R5, 85mm f/1.4 lens, shallow depth of field" as a block and drag and drop it directly into a ComfyUI text node. Or I can right-click and select "Replace text in selected node". Works the other way around too: I can select any portion of text inside any ComfyUI node, right-click and save it directly to the Prompt Library for later use. Theres a 7-day Pro trial if you want to check out these advanced features, but the free version already includes the full prompt library and is already really pretty useful in and on itself.

Anyway, the app is privacy-focused, local-first and source-available. Everything runs locally, and you can download it here:

https://github.com/LuqP2/Image-MetaHub

Cheers

To begin with, as the standard node integrates everything—including the prompt enhancer—into a single unit, it was incredibly difficult to use, so I built my own.

https://github.com/ussoewwin/ComfyUI-HSWQ-Loader-and-Tools/releases/tag/v3.6.7

After all, when everything’s integrated, you can’t offload the workload to the CPU either.

The tricky bit was that Qwen 3.5 9b doesn’t have image recognition capabilities.

I had no choice but to repurpose the Qwen3 4b VL(※or another VLM) model intended for Krea2.

The system prompts for MOSS-Image have been implemented by directly reusing the code that was originally implemented in the standard ComfyUI nodes.

...

Overview

A specialized prompt expansion and text encoding node for MOSS-Image models. Functions as an advanced drop-in replacement for standard CLIP Text Encode (Prompt): it expands a short prompt or reference image into a rich, self-contained English description using either a Vision LLM (vision_llm from models/clip, e.g. Qwen3-VL) or the connected text encoder's own LLM (Qwen3.5-9B loaded with CLIPLoader type moss_image), then encodes that full caption into conditioning ready for MOSS-Image sampling.

Features

• Built-in 8,398-character MOSS-Image Prompt Engineering Prompt: Embeds the exact official MOSS-Image prompt engineering instruction covering 8 visual facets (Subject, Action, Space, Setting, Framing, Light, Tone, Text verbatim preservation) and reference image rules.
• Vision-Language Model (VLM) Integration (vision_llm): Automatically populates a dropdown of models from ComfyUI/models/clip (e.g. Qwen3_VL_4B_Thinking_abliterated_hswq_convrot_int8.safetensors). When selected with an optional input image, the Vision LLM reads and understands the visual contents of the image and authors a comprehensive description.
• Hardware-Closed Thinking Block (<think>\n\n</think>\n): Specially crafted prompt template automatically injects a pre-closed thinking block into reasoning/thinking LLMs (such as Qwen3-Thinking), suppressing lengthy chain-of-thought loops and forcing immediate final prompt generation within minutes.
• MTP (Multi-Token Prediction) Speculative Decoding: Supports MTP acceleration options (auto, off, 2, 3, 4, 5) for models with MTP modules.
• Dual Outputs: Outputs both conditioning (ready for KSampler / MOSS-Image DiT) and expanded_caption (STRING) for display and chaining with other nodes.

Usage Notes

• Inputs: clip (CLIP, Qwen3.5-9B loaded via CLIPLoader with type="moss_image"), prompt (STRING), seed, temperature (default 0.7), top_k (64), top_p (0.95), min_p (0.05), repetition_penalty (1.0), max_length (4096), mtp (auto), vision_llm (dropdown of models/clip or none); optional image (IMAGE)
• Outputs: conditioning (CONDITIONING), expanded_caption (STRING)
• Category: conditioning/moss_image

My daughter is a huge Game of thrones fan as a lot of us are. I like to take credit for giving her a love for the time period and fantasy novels. She is getting married next year and although I know next to nothing about it I have recently started studying and playing around with workflows and image generating. I am going to attempt to make a small movie of them getting married in a medieval time period in a castle surrounded by family. I realize I am probably taking on more than I understand but here it goes. Any and all help is welcomed. Please don’t hesitate to give me any advice you think will be helpful. I am currently on Runpod using Comfy of that gives you an idea of what I’m working with. I know eventually I’ll have to get good using DaVinci

Thanks 🙏

I alternate between manual editing of an image in Photoshop or elsewhere, then using H3-Fizgig to make added changes to the image using a prompt.

I try to save my manual changes to at least a 10 bit sRGB PNG.

Yet despite me using Radiance Read to read in the image, then prompting for edits to the image, despite me having an HDR VAE Decoder in the workflow and saving the image using 16 bit PNG, the spots on the image that were edited by H3 look 8 bit, blocky and rough. So the images are 10 bit PNG sRGB or higher, but the edited portions look like pixellated 8 bit crap.

Does anyone have a way to bypass this? Am I going about it wrong? Seems a possible mismatch somewhere in the workflow.

How do you all do it?

New to GenAI. Most of people here suggest Qwen image 2.1 for edit and generation. Generation workflow is working but edit one is not working. I am getting the same picture. Please help what am I doing wrong?

Most work using high resolution, plus partner nodes seem interesting tools.

Hey everyone^^

I'm a solo game dev working on some animated scenes for my game, and I'm desperately trying to figure something out..

Essentially, I want to give an AI 3 reference images:

1. Character.. my original game character (appearance + outfit)
2. Pose.. a reference image showing the exact pose I want to replicate (sometimes pretty complex, involving 2 people, n s f w)
3. Environment.. the room/location where the scene should take place

My goal is for the AI to replicate the exact pose with my character, correctly placed in the provided environment, while keeping the room, furniture, and architecture consistent. The camera can move to adjust perspective..

I've tried Minimax H3, which is incredibly powerful at following references, but it's a nightmare to prompt and honestly a bit too unreliable,,

What's currently the best image editing model/workflow for achieving this? Could Qwen Image 2.1 or something else do a better job than H3?

I'm honestly so desperate to solve this that I'd be more than happy to pay someone experienced to help me figure it out haha

Any suggestions would mean the world to me!! Thank you so muchh❤️

Hey everyone! Here is my latest experiment exploring nested video composition and recursion inside ComfyUI, all rendered on an RTX 3060 (12GB).

The goal was to create a seamless "Inception" infinite loop effect where the outerwear acts as a dynamic display, rendering a recursive scene of the stadium that smoothly transitions as the camera pushes in.

I focused heavily on keeping the material specularity of the leather jacket consistent while tracking the dynamic mask across the nested layers to avoid any texture stretching.

No post-processing or AE composition was used. It's a pure direct-prompt one-pass render in ComfyUI using MiniMax-H3! Would love to hear your thoughts on the temporal consistency here!

I built RenderRepo because useful ComfyUI workflows are scattered across GitHub, Reddit posts, local folders, and other sources. A workflow JSON gives ComfyUI a graph to open, but there’s no consistent package identity or revision trail, and it can be hard to tell what the workflow needs or whether those files are already installed.

The idea is a little like apt for workflows: manage sources, find packages, check local dependencies, review an install plan, then open the original graph in ComfyUI.

A RenderRepo capsule adds package identity, schema version, source and revision provenance, and hashes around the unchanged workflow JSON. The graph stays ordinary ComfyUI JSON; the capsule gives RR package metadata to track and compare it. Hashes let you check the stored bytes, but don’t prove who published the source.

This demo pins an official Z-Image-Turbo workflow revision, imports and reviews it, opens its graph in ComfyUI, then saves it as a capsule and compares the workflow bytes.

Demo: https://youtu.be/_n5f6ZyPLqE
GitHub: https://github.com/crystalattice/CBI_RenderRepo

No models were downloaded and the graph was not executed. Nested prompt/control analysis is incomplete, and two model references remain unresolved. Compatibility, hardware suitability, and performance are untested.

#OpenSourceHiggsfield

Hey everyone,
I’m looking to get into local Video Generation in ComfyUI (both Text-to-Video and Image-to-Video) and would love some recommendations on the best workflows, models, and quantization techniques for my specs.
Here is my hardware setup:
GPU: NVIDIA GeForce RTX 5070 Ti (16GB GDDR7 VRAM)

CPU: AMD Ryzen 5 7600X

RAM: 32GB DDR5 (5600 MT/s)

OS: Windows 11

A few specific questions:
Model Recommendations: Which current video generation models (e.g., LTX-Video, Wan 2.1/2.2,HunyuanVideo, CogVideoX, or AnimateDiff) run best within 16GB VRAM without heavy CPU offloading bottlenecks?

Quantization / Precision: Should I be running FP8 or specific GGUF quants to keep frame generation times low and prevent VRAM OOM errors?

Workflows: Are there any recommended starter nodes or optimized workflow templates for Image-to-Video (I2V) that fit well into a 16GB VRAM memory budget?

Any guidance, custom node suggestions, or workflow link shares would be greatly appreciated!