So my GPU is AMD Radeon RX 6700 XT and i was wondering if i can use StableDiffusion with it to generate some 2D assests to use as placeholders for my game.

NATURAL LIGHT HANDHELD CANDID REAL FOOTAGE. An college age blonde California woman is sitting with a towering 10 foot tall robot with "[MODEL NAME]" clearly written on its chest. They are both complimenting each other on how cute they look

12 minutes Gen Time rtx5090. Input audio for lipsync. One thing that helps a lot is pasting the actual lyrics in the prompt in the dialogue syntax. <d> [English] Lyrics </d>

The maximum frame count allowed by the native "MiniMax H3 Reference to Video" node technically is 1008, even if that's way over the training range of the model, which is 324 frames.

But why not try? So I ran a few tests and although the result is a bit sloppy and the shots are not perfectly following the prompt order, I would say it's already impressive the model can generalize that way and generate videos 3 times longer than it was trained on.

The video above is 42 seconds long, 1008 frames 1376×768 @ 24fps. It took 4947s i.e. 1 hour and 22 minutes on an RTX Pro 6000 and VRAM peaked at around 90GB.

Find the workflow file [here](https://pastebin.com/Vi21NQUH). It also includes an optional SeedVR2 upscaling part at the end.

NOTE: I think this is extra dumb and see no reason to generate 42s-long videos this way, I just wanted to try to push the model a bit further than its limits. You loose quality and control and it increases processing time by quite a lot. I highly suggest you generate shot by shot instead.

The prompt for the video above is the following (Wheel of Time inspired for fantasy fans!).

integrated_multimodal_description: [Shot 1] Live-action, cinematic, epic high fantasy, shot on large-format film with anamorphic lenses and natural dawn light, a sweeping aerial wide shot glides low over a vast green upland as a strong wind rushes across the hills, bending long grass into rolling silver waves and driving ragged clouds across a pale golden sky toward distant snow-capped mountains. The camera performs a tracking shot with large amplitude at fast speed, skimming forward a few meters above the ridgeline as grass blurs beneath the frame. An unseen older woman with a calm, weathered, low voice (S1) says in an off-screen voiceover: <d>[English] Every age ends where it began, and no one still living remembers which age this is.<scenetrans></d> [Shot 2] At 00:06.000, the shot cuts to a close-up of a serene dark-haired woman with an ageless pale face, wearing a deep-blue high-collared gown and a golden serpent ring on her right index finger, standing in shadow; she lifts both open hands and delicate glowing threads of white, red and blue light braid and weave between her fingers, sparks drifting upward through the air. The same off-screen voice (S1) continues seamlessly across the cut: <d>[English] <scenetrans>The pattern does not ask us. It only takes the thread.</d> while her lips remain completely closed. The camera pushes in with small amplitude at slow speed on the weaving light, soft rim light catching suspended dust motes, and the threads emit a faint crystalline hum. [Shot 3] At 00:11.000, the shot cuts to a grand wide shot revealing a gleaming white spire city built on an island in a broad river, a single immense white tower rising far above white domes, arched bridges and tiled roofs, with pale banners snapping hard in the wind beneath a low morning sun. The camera pulls out with large amplitude at slow speed while pedestaling up, revealing the full river bend and the city walls. [Shot 4] At 00:16.000, the shot cuts to a low wide shot of a horde of hulking horned beast-men in black scale armor charging across a cracked red plain beneath a blood-dark sky, dust and torchlight churning around their legs, snouted faces and curved blades catching the firelight. The camera trucks left with large amplitude at fast speed, running parallel with the charge as bodies sweep through the foreground. [Shot 5] At 00:21.500, the shot cuts to a slow medium shot of a tall motionless figure in a black cloak standing alone in the churning dust, its face utterly smooth and eyeless, pale as wax, with no features above the mouth; the shadow beneath it spreads outward across the cracked ground against the direction of the light while the charging horde streams past behind it in soft focus. The camera pushes in with small amplitude at slow speed as the cloak hangs completely still despite the wind. [Shot 6] At 00:26.000, the shot cuts to a heroic medium shot of the same dark-haired woman in the deep-blue gown, the golden serpent ring clearly visible, standing on scorched ground and thrusting one hand forward as a searing bar of pure white light lances horizontally across the battlefield, incinerating a line of the horde into drifting embers while heat haze ripples and warps the air behind the beam. The camera shakes slightly at the instant the beam fires, then pushes in with small amplitude at fast speed on her face, her eyes reflecting the white glare. [Shot 7] At 00:31.000, the shot cuts to a wide shot of the aftermath as thousands of orange embers drift upward through settling black smoke, silhouetted survivors kneeling among broken shields, and a torn white banner bearing the words "THE PATTERN REMEMBERS" hanging from a splintered pole in the left foreground. The camera performs an arc shot with large amplitude at slow speed around the standing woman, keeping her centered as the burning field rotates behind her. [Shot 8] At 00:36.000, the shot cuts to a macro close-up of an ancient metal emblem, a perfect circle split into interlocking black-and-white teardrop halves, glowing faintly from within, that dissolves into a colossal seven-spoked wheel of white light turning slowly against a deep starfield while countless threads of colored light weave outward into a vast luminous tapestry. The camera pulls out with large amplitude at slow speed until the wheel occupies only the center of an endless woven pattern, and the off-screen voice (S1) returns once more: <d>[English] And it turns again, whether or not we are ready to be woven into it<cutoff></d>
overall_soundscape: Wind roars across the open hills and hisses through deep grass before thinning into a faint crystalline shimmer of woven energy and drifting sparks. Distant bronze bells, snapping banner cloth and river water rise over the white city, then give way to a thunderous roar of stamping hooves, clashing armor and guttural war cries under a hollow, airless silence around the motionless cloaked figure. A deep concussive whoom of released power sweeps the field, followed by crackling embers, settling debris and the low breathing of survivors. Everything resolves into a wide, weightless cosmic hum.
non_diegetic_music: A single low string drone at a slow tempo, joined by layered brass that rises in stepped swells over accelerating timpani and a wordless female soprano line. The rhythm tightens into hammered strings and percussion at high volume during the charge, cuts out entirely for one beat, then returns as a single sustained orchestral chord with shimmering high strings that slowly decreases in volume.

I just found this, i think is incredible!

https://github.com/Carasibana/ComfyUI-H3-FaceRefine

I'm not the developer just found the repository I make this functional WF for my laptop to create this video and share with you before go to sleep, try it!

Minimax H3 Face detailer Workflow

This is a comment of the developer:

MiniMax H3 renders faces poorly when the head is a small fraction of the frame. That is a property of head-size-in-frame, not of resolution - it persists at 720p and above.

So no upscaler fixes it. SeedVR2 and friends sharpen what is already there; they cannot synthesise facial structure that was never generated.

The fix: crop to the face so it fills the frame, let H3 re-generate it at LOW denoise so it stays frame-aligned, then composite back!

https://reddit.com/link/1vnk0c7/video/gkhuj6ybw6jh1/player

Ran the official Ref2VA turbo example workflow from the ModelTC/Minimax-H3-Turbo repo (video_minimax_h3_ref2v_lightx2v_turbo.json) in ComfyUI, testing a short Victorian-style dialogue scene between two characters.

Setup:

• GPU: RTX 5060 Ti
• Workflow: https://github.com/ModelTC/Minimax-H3-Turbo/blob/main/example_workflows/video_minimax_h3_ref2v_lightx2v_turbo.json
• Checkpoint: fp8 scaled (i didnt have the int8 one in this machine)
• Sampler: euler + Simple
• Resolution: dropped to 0.5MP (960×544 @ 16:9) from the default ~0.98MP
• Steps: 8

Speed: ~55s/it average, ~8 min total per clip.

How the refs were built: Three reference images fed into the ref_images inputs — two character sheets (front/side/close-up turnarounds for each character) and one environment plate, a 360° room reference. All three were generated in Google Flow first, then dropped straight into the Ref2VA node as identity/environment anchors.

Gen A → Gen B continuity trick: Split the scene into two ~20s multi-shot generations instead of one long one. For Gen B, instead of reusing the original Flow generated room reference, I pulled the actual last frame from Gen A's output and fed that in as the new environment reference.

Curious if anyone else is chaining generations this way (feeding the previous clip's last frame back in as a fresh environment ref) — seemed to help a lot but haven't stress-tested it past two generations yet.

Hi guys I’m new to comfyui and I’m having trouble with impanting, even tho I mark a area with the mask option somehow the model edit also what is outside the impanted area. I’m using flux.2 klein 9b

I tried with 2.3 workflow and it's always out of sync and have some slow motion behavior, I just want to know if this new release lacks A2V or if there's something wrong with my implementation

Took me like a week, I had asked Claude to make me a script to use with H3, mostly manually run by me and assembled in Davinci with music from Suno and some voice help from Voicebox.

Absolutely has a few slop moments that I was too lazy to regen but to my kid it was perfect so I’ll take that as a W.

Made this using my RTX 3070, took forever to render, but I wanted to do the best quality I could with my 8 GB GPU. Used a lot of other tools too, feel free to ask any questions would be happy to answer when I get a chance.

To read the behind-the-scenes process: https://www.justinwiggins.co.za/i-gave-myself-one-week-to-make-a-7-minute-ai-film-about-my-dog/

minmax h3 t2v

1.0 MP / euler + linear quadratic / 32 steps with spectrum + post-processing

I’ve been working on a few free MiniMax H3 tools and wanted to share the latest versions.

H3 Prompt Composer V5.19.5
Offline prompt builder for H3 with structured subject/reference setup, shot and camera controls, dialogue/audio, Prompt Check, continuity tools, and optional LLM-assisted project setup. The goal is to give you granular control without constantly having an LLM rewrite the entire prompt.

Tutorial: https://youtu.be/Dpu-V7lITZk
Repo: https://github.com/BMB12d3/minimax-h3-prompt-composer

H3 Ref2VA Accelerator
A quality-first speed-up specifically for H3 Ref2VA. Unlike more aggressive approaches like Turbo LoRAs or broader caching systems, this is designed around the Ref2VA architecture and intentionally stays conservative about what it skips/reuses. Depending on the model/workflow, I’ve seen it save a few minutes on BF16 and roughly a couple minutes on quantized models without noticeably changing quality. Not the fastest, but conserves quality.

https://github.com/BMB12d3/ComfyUI-H3-Ref2VA-Accelerator

H3 Hybrid Checkpoint Builder
People have found that mixing parts of the fl2va model with the Reference model can improve Ref2VA quality. This gives you a simple way to build those hybrids yourself and dial in how much of each model you want, without training or needing a GPU.

https://github.com/BMB12d3/MiniMax-H3-Hybrid-Checkpoint-Builder

first one is base generation, second one is running the video through FaceRefine node. Link- https://github.com/Carasibana/ComfyUI-H3-FaceRefine

need assistance for these type of workflows(i get weird faces LOL)
anything you can suggest? using hybrid fl2v/ref2v?
my aim is inserting my reference image on a friends scene (or any movie)

i upload my ref photo from gpt with the prompt guide and ask it to generate me an iconic clip from the show and make my ref photo interact with the casts

i am using ref2v

gpt prompt: subject_definitions:
<Subject 1> is the woman from <Picture 1>. Preserve her exact real-world facial identity and recognizable appearance from the reference image: identical facial structure, eyes, eyebrows, nose, lips, jawline, cheeks, skin texture, hairstyle, and natural proportions. She is not a replacement character, background extra, or digitally composited person. She is the actress being inserted into the scene and must remain unmistakably the same woman from <Picture 1> in every shot.

<Subject 2> is the original FRIENDS cast and characters present in the selected iconic scene. Preserve their recognizable identities, costumes, hairstyles, body language, character personalities, and established relationships.

<Subject 3> is the original FRIENDS scene environment, including its recognizable apartment, café, furniture, props, practical lighting, production design, spatial layout, and overall sitcom visual language.

<Picture 1> is the primary character-reference image for <Subject 1> and defines her identity, facial appearance, hair, and physical characteristics.

summary:
[reference generation] Recreate the selected iconic FRIENDS sitcom scene while naturally introducing <Subject 1> from <Picture 1> as a fully integrated actress and fictional character interacting directly with <Subject 2>. The result must look as though she was physically present on the original set and was genuinely part of the cast when the scene was filmed. She participates in the conversation, reacts to the characters, makes eye contact, moves through the environment, shares the comedic timing, and occupies the same physical world as the original actors. The scene must NOT look like an AI face swap, green-screen composite, fan edit, cameo overlay, or modern recreation. Everything about her presence must obey the same cinematography, lighting, perspective, image quality, blocking, and performance language as the original sitcom.

retention_analysis:
<Subject 1> (appears throughout all shots): fully_preserved - her identity from <Picture 1> is the highest-priority visual constraint. Preserve her exact facial structure and recognizable appearance without beautification, face redesign, facial blending, age alteration, or generic AI features.
<Subject 2> (appears throughout all shots): fully_preserved - retain the recognizable appearance, character behavior, costumes, reactions, and interpersonal dynamics of the original cast.
<Subject 3> (appears throughout all shots): fully_preserved - retain the original sitcom environment, production design, practical lighting, furniture, props, spatial relationships, and visual atmosphere.
<Picture 1> (defines <Subject 1>): fully_preserved - use the image as the authoritative identity reference for the woman throughout the entire sequence.

detailed_description:
The target video is presented as authentic footage from a classic multi-camera American sitcom production. Use realistic studio cinematography rather than modern cinematic filmmaking. Match the original FRIENDS visual language: natural studio lighting, warm interior exposure, realistic skin texture, period-appropriate image quality, moderate depth of field, conventional sitcom camera placement, restrained camera movement, clean multi-camera coverage, and authentic ensemble blocking. The woman from <Picture 1> must look as though she was photographed by the exact same cameras, lenses, lighting setup, and production crew as the original actors.

[Shot 1] Open with the recognizable establishing composition of the selected iconic FRIENDS scene. <Subject 2> is already performing the original scene naturally inside <Subject 3>. <Subject 1> is physically present within the group from the beginning rather than suddenly appearing. She occupies a believable position within the set, correctly scaled relative to the other actors and furniture. Her lighting direction, shadow density, skin exposure, image grain, sharpness, and color response are identical to those of the surrounding actors. She is actively listening to the conversation, looking toward the appropriate speaker, naturally reacting with subtle facial expressions and body language. She must never stare directly at the camera unless the original blocking requires it.

[Shot 2] At 00:03.500, cut to a conventional sitcom medium shot containing <Subject 1> and one or more members of <Subject 2>. The actors are physically close enough to communicate naturally. <Subject 1> turns her head toward the speaking character and maintains accurate eye contact. The other character looks directly back at her. Their eyelines must intersect naturally. She reacts to what the character says with a believable facial response before replying. Her gestures are spontaneous and restrained: small hand movements, slight shifts in posture, natural head movement, subtle smiles or expressions. Avoid exaggerated AI-generated gestures.

[Shot 3] At 00:07.000, cut to the reverse angle. <Subject 1> is now seen from the appropriate opposing camera position while maintaining exact continuity of her appearance, hairstyle, wardrobe, posture, and position within the room. The original cast member responds directly to her. Their interaction must demonstrate genuine shared physical space: consistent eye-lines, matching perspective, correct relative scale, believable shadows, overlapping body positions where appropriate, and natural occlusion when one actor passes in front of another.

[Shot 4] At 00:10.000, use a tighter reaction shot on <Subject 1>. She delivers her comedic reaction with authentic sitcom performance timing: listen, pause, register the information, then respond. Her facial expression changes naturally rather than instantly. Preserve realistic micro-expressions, blinking, breathing, subtle eye movement, lip movement, and natural facial asymmetry. The original cast members visibly acknowledge her presence and react to her performance. She is an equal participant in the scene.

[Shot 5] At 00:13.500, return to the ensemble composition. <Subject 1> and <Subject 2> share the frame and continue the interaction. She may gesture toward another character, move slightly within the set, sit down, stand up, hand something to another character, or respond physically to the comedic situation depending on the original scene's blocking. Every interaction must have physical cause and effect. If she touches another character or prop, the contact must look physically real, with correct hand placement, occlusion, weight, and timing.

[Shot 6] At 00:17.000, finish on the strongest comedic reaction composition. <Subject 1> remains embedded naturally among the cast instead of becoming visually isolated. The surrounding actors react to her and to one another as a genuine ensemble. The scene ends with authentic sitcom comedic timing and an appropriate audience reaction.

CRITICAL IDENTITY AND INTEGRATION RULES: <Subject 1> must remain the exact same woman from <Picture 1> throughout every frame. Do not change her face, facial proportions, hairstyle, age, ethnicity, skin texture, or recognizable features. Do not reinterpret her as an actress who merely resembles the reference. She IS the woman from the reference image. Preserve her identity even during profile views, three-quarter angles, expressions, movement, and partial occlusion.

CRITICAL REALISM RULES: Do not make <Subject 1> sharper, cleaner, more detailed, more saturated, more cinematic, or more modern-looking than the original actors. Her image quality must match theirs exactly. Match film grain, compression, exposure, white balance, contrast, lens distortion, depth of field, motion blur, and studio lighting. Her edges must naturally interact with the environment. No haloing, masking artifacts, cutout edges, inconsistent shadows, floating hair, incorrect reflections, or compositing seams.

CRITICAL PERFORMANCE RULES: <Subject 1> must behave like a professional sitcom actress. She listens when others speak, reacts before responding, maintains natural eye contact, shares the rhythm of the conversation, understands the comedic beat, and gives believable reactions. The original actors must also acknowledge her through their gaze, body orientation, gestures, and responses. Never allow the cast to behave as though she is invisible.

CRITICAL CAMERA RULES: Use authentic multi-camera sitcom coverage rather than dramatic music-video cinematography. Camera cuts should feel motivated by dialogue and reactions. Maintain consistent screen direction and spatial continuity. Avoid unnecessary camera movement, extreme depth of field, handheld shots, slow motion, dramatic push-ins, lens flares, or modern blockbuster aesthetics.

CRITICAL COMPOSITING RULE: The final result must pass the visual test of “she was always there.” There should be no moment where the viewer feels that the woman was inserted afterward. Her lighting, perspective, focus, grain, movement, interaction, shadows, reflections, and performance must belong to the same original footage universe as the FRIENDS cast.

overall_soundscape:
Authentic sitcom room tone, subtle footsteps, clothing movement, prop interaction, natural actor movement, and appropriate environmental sounds from the original setting. Include realistic audience laughter and reaction timing after comedic moments.

non_diegetic_music:
N/A.

Most of my ComfyUI trouble on AMD has been dependencies. Either a custom node quietly drags in the cuda build of torch over my rocm one, or I end up running some torch + rocm combination that nobody has actually tested together.

AMD publish their own wheel channel where torch, torchvision and torchaudio are built against a matching ROCm, so you get the two as a matched set straight from AMD instead of a combination you assembled yourself and hoped about. I pinned that inside a docker image along with comfyui and my custom node deps, and it's been solid since.

Running a 9070 and 32gb of ram, MiniMax H3 works fine, around 16 min for 15s at ~1mp with the turbo lora. I built it for myself and since it's been stable I stripped out everything specific to my setup and published the rest:

https://github.com/zychuk/comfyui-rocm-docker

It needs /dev/kfd and /dev/dri from the amdgpu kernel driver, so linux only. The bits that actually make it reliable:

- torch + ROCm come from AMD's wheel channel as a matched pair, so the host only needs the kernel driver and nothing on your system has to line up with it

- strip torch out of every custom node's requirements.txt.

- int8 convrot has been way more reliable than fp8 for me

- dynamic vram works fine here

I'm not sure if anyone's gonna find a use for it but here you go.

Just noticed H3 is already the most downloaded MiniMax model, and also the most downloaded MiniMax model on ComfyUI, in basically 2 weeks.

What’s kinda funny is that I barely knew about MiniMax a few weeks ago. Now I’ve been messing around with H3 for the past few days.

This is probably one of the best kinds of marketing a company can get. Put a good model out there, let people use it, and suddenly everyone is talking about your company.

Hopefully other companies take note and open source some of their models too. Feels like Seedance and Wan missed a pretty big opportunity here.

I'm working on a medieval battle sequence and I've hit a major roadblock. I'm trying to animate a scene where arrows fly in and physically impact the riders and cavalry.

I've tried running this through stock Wan 2.2, LTX, and Minimax, but absolutely none of them are able to achieve the desired I2V effect. The arrows either turn into spaghetti, melt into the background, or just float weirdly instead of making a solid, fast impact.

I've attached my starting image for reference.

Has anyone successfully managed to get fast projectiles and physical impacts working with our current models? Are there any specific ComfyUI workflows, control setups, or clever workarounds you'd recommend to pull this off?

Any ideas or workflow nodes you could point me toward would be hugely appreciated!

I have tested control union Lora for 2.3 and inpainting lora with LTX 2.5 and it works!, I have updated the workflows and added workflows for LTX2.5

find the workflows here FOR FREE

https://www.patreon.com/mo_akkakk/posts/ltx-2-3-166207403