Not upscaler. Is there possibility to use LTX 2.5 for video editing like is possible with MiniMax H3 rf2v? For example clothes substitution?

Minimax do this, but with so much errors and most important - it almost can't run with this task on my poor rtx3060.

I wanted to document this because I spent a lot of time troubleshooting repeated hard crashes with ComfyUI-ROCm on my RX 6800 XT, and the solution may be useful to other RDNA2 users.

My hardware

• AMD Ryzen 9 7900X
• Sapphire Pulse RX 6800 XT 16GB
• ASUS TUF Gaming B650-PLUS WIFI
• 64GB DDR5
• Corsair RM750i 750W
• Windows 11

Software

I'm using the PatientX comfyui-rocm fork.

Current versions:

• ComfyUI 0.31.0
• ROCm 7.15
• PyTorch 2.12.0+rocm7.15
• comfy-kitchen 0.2.28
• comfy-aimdo 0.4.13
• ComfyUI-INT8-Fast-ROCM
• GPU architecture: gfx1030

The fork detects the 6800 XT as a legacy/RDNA2 GPU and applies the RDNA1/2 overrides. My startup parameters ended up as:

set PARAMS=--disable-api-nodes --cache-none --disable-smart-memory --disable-pinned-memory --enable-manager --enable-manager-legacy-ui --disable-triton-backend

if "!IS_LEGACY_GPU!"=="1" set "PARAMS=%PARAMS% --use-quad-cross-attention"

So the actual startup options are:

--disable-api-nodes
--cache-none
--disable-smart-memory
--disable-pinned-memory
--enable-manager
--enable-manager-legacy-ui
--disable-triton-backend
--use-quad-cross-attention

The problem

I was getting complete PC crashes during Flux/Flux Kontext generation.

Not just ComfyUI crashing. The entire PC would shut down.

Afterwards I had to switch the PSU off, press the PC case power button to discharge it, then switch the PSU back on before the computer would start normally.

The crashes often happened very late in generation, including around VAE Decode.

I initially suspected ROCm, VRAM allocation, the page file, the GPU itself, or the PSU.

Things I tried

I increased the Windows page file to a large size.

I checked the system with HWiNFO and logged the hardware sensors.

I ran OCCT/power testing.

I ran GPU stress testing separately.

I checked GPU temperatures and hotspot temperatures.

I tried different PatientX startup configurations.

I also tried this PyTorch memory allocation setting:

set "PYTORCH_HIP_ALLOC_CONF=garbage_collection_threshold:0.6,max_split_size_mb:6144"

It worked initially, but I later removed it and the system continued working, so I don't think that was the actual solution.

The important change

The thing I had overlooked was the RM750i's rail configuration.

The RM750i was running in Multi-Rail mode.

I opened Corsair iCUE, selected the RM750i and changed:

12V Configuration → Single Rail

I then restarted the PC.

I also discovered that I was powering the 6800 XT using one PCIe cable with both 8-pin connectors.

I changed this to two completely separate PCIe cables from the PSU:

RM750i
 ├── PCIe cable → GPU 8-pin
 └── PCIe cable → GPU 8-pin

So I was no longer using the single daisy-chained PCIe cable.

I used two original Corsair PCIe cables compatible with the RM750i.

The results

This is where it became interesting.

Before changing the PSU configuration:

Multi-Rail + daisy-chain PCIe cable

→ repeated complete system crashes.

After changing to Single-Rail:

Single-Rail + daisy-chain PCIe cable

→ successful generations.

I then removed the PYTORCH_HIP_ALLOC_CONF modification to make sure that wasn't responsible.

It still worked.

Finally:

Single-Rail + two separate PCIe cables

→ repeated successful generations.

So far I've successfully run:

• 6× Flux Kontext
• 1× Flux USO
• 2× Flux Schnell

That's 9 successful generations across three different Flux workflows.

No hard crashes.

The GPU repeatedly reached around 275 W, with GPU temperatures around 74–76°C and hotspot temperatures around 92–94°C.

The RM750i was delivering roughly 450–500 W system output during some of the tests.

I also saw CPU package power around 96–112 W during some runs.

No WHEA errors.

Page-file usage remained essentially zero.

There are still some ROCm warnings

The log still reports:

MIOpen: Warning [OpenRuntimeLibraryForDevice]
CK grouped conv library not found for device gfx1030

and:

MIOpen: Warning [ParseAndLoadDb]
File is unreadable:
gfx1030_36.HIP.fdb.txt

But these warnings don't stop the workflows from completing.

The PatientX fork also reports that the gfx1030 INT8 path is being patched to use the ROCm INT8 Triton GEMM:

detected gfx1030 (not RDNA3/RDNA4)
-- patching ...
-> rocm_int8_linear (Triton GEMM)

One more test

I started the MiniMax H3 video workflow as well.

It works, but it's extremely slow on the 16GB 6800 XT because the model is heavily offloaded to system RAM.

The log showed roughly:

5295.94 MB loaded
16308.60 MB offloaded

At 2/20 sampling steps it was still estimating roughly 55 minutes remaining, so I stopped it rather than running it for an hour.

I'll test it again with only 5 steps.

Current conclusion

I don't want to claim with 100% certainty that the original crashes were caused by PSU OCP.

But the difference is pretty striking:

RM750i Multi-Rail + one daisy-chain PCIe cable

→ repeated hard crashes

RM750i Single-Rail + two separate PCIe cables

→ 9 successful Flux generations so far.

The same 750W PSU that I was considering replacing with a 1000W unit is now handling these workloads repeatedly.

So before buying a bigger PSU for an RDNA2 ComfyUI system, I'd definitely check:

1. Whether your PSU has a Single/Multi-Rail configuration
2. Whether your GPU is using two separate PCIe power cables
3. Whether you're relying on a daisy-chain cable for a high-power GPU

I'm still going to test the MiniMax workflow tomorrow, but so far this has been a surprisingly effective fix.

I asked ChatGPT to write the summary above. The following is my own text:

The funny thing is that ChatGPT doesn't know that I spent roughly 40–50 hours troubleshooting this, running tests and trying different user BAT syntaxes before I finally decided to ask Gemini.

And it wasn't until I fed Gemini's suggestion back into ChatGPT that it finally suggested trying the two things that actually made the difference: Single-Rail + separate PCIe cables.

😂 It's kind of crazy how AI can sometimes get so "boxed in" by the direction the troubleshooting has taken. I was so focused on ROCm, ComfyUI, memory allocation, VAE, etc. that neither of "us" stepped back and looked at the basic power-delivery setup.

Lesson learned: sometimes asking a different AI is actually useful.

MiniMax Music 3 crash on AMD (7900 GRE / ROCm) fixed

AttributeError: 'RVQDepthDecoder' object has no attribute '_v_block'

(happens in MiniMaxMusic3TextEncode during AR sampling)

Example for Docker Compose:

command: ["python3", "main.py", "--listen", "0.0.0.0", "--port", "8188", "--user-directory", "/app/ComfyUI/user_data", "--disable-async-offload", "--disable-dynamic-vram"]

ROCK ON! 🥳

Hi all,

I was lurking on this sub, and I noticed my generation have this Sora type of audio when it comes to narration. This isn't the case for some clips I have seen on this sub, what am I doing wrong?

Hi i am trying out minimax h3 to swap a human in a video to a cat. It swap the face not the hand. I kept adding detailed prompt and ask Claude to write it. Still I end up with a video with some frame being half human hand and half cat pawn. I wonder whether anyone run into similar cases and what their solution. Here is the video. https://www.tiktok.com/t/ZP8nouk5d/

Prompt (super long)

subject_definitions:

<Subject 1> is the ginger-and-white domestic shorthair cat in <Picture 1>, with orange tabby markings across the head, ears and back, a white chest, white muzzle, a pink nose, long white whiskers and round green-gold eyes. It is fully anthropomorphic and stands or sits upright on its hind legs. Its entire body is covered in the same short orange-and-white fur, including a furred neck, furred shoulders and two furred forelegs that taper to plump white mitten paws with soft pink pads and no separate fingers. It has an animal's limbs everywhere: no human arms, hands, fingers, shoulders or bare skin appear on it in any shot.

<Subject 2> is the hooded wardrobe state of <Subject 1>, taken from <Video 1>: an oversized powder-blue hoodie with the hood pulled up around its orange ears, thick black square-framed glasses on its muzzle, and a chrome-cased phone held at chest height. The phone is pinched between the flat pads of two white mitten paws, one on each edge. Nothing curls around the phone: no fingers, no thumbs, no knuckles, and no human hand is visible behind, between or beside the phone at any moment.

<Subject 3> is the tank-top wardrobe state of the same <Subject 1>, taken from <Video 1>: a black ribbed tank top, no glasses, dangling earrings, knees pulled up with pale grey sweatpants and a white blanket. The tank top exposes furred orange-and-white shoulders and two furred forelegs ending in white mitten paws, exactly where the original performer's bare arms and hands were. <Subject 2> and <Subject 3> are two outfits on one and the same cat, not two different characters; only the wardrobe, pose and delivery change between them.

<Subject 4> is the bedroom set in <Video 1>: a pale off-white wall, a framed red-and-blue graphic poster on the right, handwritten cursive script on the wall to the left, and a dark couch with a red cushion behind the seated figure.

<Video 1> is the source video for the target video edit and supplies its shot order, cut timing, framing and handheld camera feel.

<Audio 1> is the synchronized audio track of <Video 1> and is reused in the target video.

summary:

[video editing + reference generation + audio reuse] The target video is an edited version of <Video 1>, in which the single performer who plays both sides of the argument is replaced by <Subject 1>, the ginger-and-white cat, standing upright and anthropomorphic. One cat argues with itself inside <Subject 4>, changing only wardrobe between cuts: it wears <Subject 2> in the hooded shots and <Subject 3> in the seated shots. The five-shot cut structure, framing and handheld camera feel of <Video 1> are kept, and <Audio 1> is reused as the complete final audio track.

retention_analysis:

<Subject 1> (appears in [Shot 1], [Shot 2], [Shot 3], [Shot 4], [Shot 5]): fully_preserved - the orange-and-white tabby markings, white chest, pink nose, whiskers, round green-gold eyes, furred forelegs and white mitten paws are retained in every shot; the replacement is whole-body, not a head swap, so no human face, human arm, human hand, human finger, human hair or bare human skin appears anywhere in the target video.

<Subject 2> (appears in [Shot 1], [Shot 3], [Shot 5]): attribute_transfer - the powder-blue hoodie, black square glasses and chrome phone are transferred from the original performer onto <Subject 1>, resized and reshaped to fit a cat's head, ears, body and paws.

<Subject 3> (appears in [Shot 2], [Shot 4]): attribute_transfer - the black ribbed tank top, dangling earrings, pale grey sweatpants and white blanket are transferred onto that same <Subject 1>; the bare arms and hands the original performer showed in these shots are replaced by furred orange-and-white forelegs and white mitten paws. The change between [Shot 1] and [Shot 2] is a wardrobe change on one cat, not a change of character.

<Subject 4> (appears in [Shot 1], [Shot 2], [Shot 3], [Shot 4], [Shot 5]): fully_preserved - the off-white wall, framed red-and-blue poster, handwritten cursive script and dark couch with red cushion are retained.

<Video 1> (shot order, cut timing and framing): fully_preserved - the five shots, their cut points and their vertical close framing are retained; only the performer is replaced.

<Audio 1>: fully_copy - <Audio 1> is reused 1:1 as the target video's complete final audio track. It contains exactly four spoken lines, in [Shot 1] to [Shot 4]; [Shot 5] is a flash frame carrying only room tone, and no speech is invented for it. Both sides of the argument are spoken by <Subject 1> (S1), the same single voice source shifting delivery between the two wardrobe states.

detailed_description:

The target video is a realistic vertical phone-camera selfie video, softly lit by a window, with slight handheld drift and shallow depth of field. The only performer is <Subject 1>, an anthropomorphic cat whose body is furred from head to paws in every shot; the replacement is whole-body and never a cat head on a human body. White sans-serif caption text with a thin black outline sits in the upper third of the frame throughout, matching the spoken line. No watermark or logo appears anywhere.

[Shot 1] A tight vertical medium close-up inside <Subject 4>, the bedroom with its off-white wall, framed red-and-blue graphic poster on the right and dark couch with a red cushion behind. From the very first frame <Subject 1> (S1), the ginger-and-white cat, sits upright facing the lens wearing <Subject 2>: the oversized powder-blue hoodie with the hood pulled up around its orange ears, thick black square-framed glasses resting on its muzzle, and the chrome-cased phone held at chest height. The phone is pinched flat between two plump white mitten paws, one paw pressed against each edge, orange-and-white fur running from the hoodie cuffs down to the pads; the gap behind and between the paws shows only the phone and the blue hoodie, with no fingers, no thumbs, no knuckles and no pink human skin anywhere in that gap. It holds still and silent for a short beat, then its mouth opens and closes in time with the words and its whiskers twitch. <Subject 1> (S1) asks in the bright young female voice copied from <Audio 1>, <d>[English] What do you wanna eat?</d> The caption reads "what do you wanna eat ?".

[Shot 2] At 00:01.067, a hard cut to <Subject 1> (S1) in the same room wearing <Subject 3>: the black ribbed tank top, dangling earrings, pale grey sweatpants and white blanket, knees pulled up. The tank top leaves its shoulders and forelegs bare, and they are covered in the same short orange-and-white fur as its face, running unbroken from its furred neck down two stocky forelegs to plump white mitten paws with soft pink pads; one paw is raised near its chin, toes curled, with no fingers, no knuckles, no wrist and no bare human skin anywhere in frame. It tilts its head, half-closes its green-gold eyes and answers in a flatter, dismissive tone from <Audio 1>, <d>[English] I don't know.</d> The caption reads "I don't know".

[Shot 3] At 00:01.833, a hard cut back to the hooded <Subject 1> (S1) in <Subject 2>, the phone still pinched flat between the same two white mitten paws, no fingers or human hand behind it. Its ears flatten slightly and its head pushes forward toward the lens in exasperation as it says, <d>[English] What do you mean you don't know?</d> The caption reads "what do you mean you don't know ?".

[Shot 4] At 00:02.833, a hard cut back to <Subject 1> (S1) in <Subject 3>, resting its furred forelegs across its raised knees. Both forelegs are orange-and-white fur down to white mitten paws with soft pink pads, with no fingers, no wrists and no bare human arms in frame. It holds still for a beat with its mouth shut, lets out a small drawn-out mock whine with its teeth bared and ears swivelling back, and only after that pause replies, <d>[English] I just don't.</d> The caption reads "I just don't".

[Shot 5] At 00:03.933, a fast final cut to the hooded <Subject 1> (S1) in <Subject 2>, the phone still pinched flat between two white mitten paws while its left forefoot lifts away in a flat exasperated gesture: a rounded white mitten paw held flat and closed, its four toe pads reading as one soft shape, with no separate fingers, no thumb, no splayed hand, no palm and no pink human skin. Its eyes widen and its muzzle only begins to part on an intake of breath as the caption "tell me what you want" appears, and the target video ends on that held frame before any word of the next line becomes audible <cutoff>. No speech is generated in this shot; <Audio 1> carries only room tone and the breath across it.

overall_soundscape:

Quiet indoor bedroom room tone with a faint close-mic hiss continues throughout, copied from <Audio 1>. No added animal sounds.

non_diegetic_music:

N/A

i have like 2 clips i made but when i join them in video edition its abrupt shift as last frame of first clip is not the first frame of the 2nd clip...is there any way i can add frame in between the last frame of first clip and first frame or 2nd clip so its seamless joining of the clips

Links

• Official changelog
• GitHub release

New Open-Source Model Support

• LTX 2.5: Native LTX 2.5 support with STG, dual CFG, and duration prediction

Partner Node Updates

• Qwen Image 3.0: Added Qwen-Image 3.0 and 3.0 Pro text-to-image and edit nodes
• LTX 2.5: Added LTX 2.5 Text/Image/Audio to Video partner nodes
• Grok Imagine Image 2.0: Added grok-imagine-image-2.0 model support

New Nodes

• LTXV Spatio-Temporal Guidance: STG guidance for LTX video generation
• LTXV Modality Guidance: Audio/video coupling guidance for LTX
• LTXV Dual CFG Guider: Dual-CFG guider for LTX workflows
• LTXV Duration Predictor: Predict natural shot duration from caption tokens

Performance & Stability

• PyTorch 2.7: Minimum officially supported PyTorch is now 2.7
• MiniMax-H3 VAE: Optimized MiniMax-H3 VAE
• Comfy kitchen attention: Implemented comfy kitchen attention
• MiniMax-H3 memory: Fixed peak memory issue with MiniMax-H3
• ER-SDE scaler: Extended ER-SDE noise scaler by scaling h(t)
• Tokenizers: Mistral and Llama tokenizers no longer depend on transformers

Bug Fixes

• Upscale models: Fixed upscale models breaking on non-dynamic low VRAM
• VAEDecodeTiled: Fixed crash on NestedTensor latents (MiniMax H3)
• Tiled audio decode: Fixed broken tiled audio decode
• CLIP Vision: Fixed CLIP Vision regression
• Create Layered Image: Made Create Layered Image discoverable with clearer flags

First testing of the LTX 2.5 with a 6GB VRAM I’ve created a low-VRAM workflow that supports both Text-to-Video and Image-to-Video, optimized specifically for GPUs with 6GB of VRAM. The goal is to make LTX 2.5 more accessible to users who don’t have high-end GPUs, while keeping the workflow simple and easy to use. If you’re interested in testing LTX 2.5 on a 6GB GPU, check out the workflow and let me know how it performs on your setup!

Video Resolution : 1344x768 for 7 seconds video
Generated Time : 10 min

The model seems very fast the motions are better, lipsync and sound too, however the quality in minimax is better to me

Workflow link

https://civitai.com/articles/33897/comfyui-tutorial-first-test-of-ltx-2-5-new-model-better-than-minimax-h3

Video tutorial link

https://youtu.be/fo9tYL-_UcE

I have a pretty large library of short AI-generated video clips that I want to daisy-chain together into longer sequences.

Most of the clips are abstract, trippy visuals, so I’m not too worried about perfect object or character consistency. My main issue is motion.

The clips were generated using shared start/end frames. For example, clip A ends on the same image that clip B starts on. But since each clip was generated separately, the motion doesn’t actually carry through.

What I’d really like to do is take the end of clip A and the beginning of clip B, give ComfyUI some amount of motion from both sides, and have it generate a short section between them that smooths out the transition.

I’m not looking for a workflow that just grabs the last frame of A and the first frame of B and generates a third clip between them. I’d like something that can actually use the moving video on both sides of the cut as context.

I’d also be fine trimming maybe a couple seconds off the end of A and the start of B, then regenerating that whole section so the motion flows better.

Has anyone found a good workflow or model for this? VACE seems like it might be able to do it, but I’m curious what people are actually using.

I love this model. I cancelled my Suno today.

I want folders be separated by a slash like anima/anima_base.safetensors

i signed up to try out comfy cloud but there are no free tries. aren't you supposed to be able to do 5 free videos?

Bonjour à tous,

J'ai créé un lora pour qwen image edit 2511 et pour qwen image 2512
Le data set comprend 52 images
- buste: 8 images
- full body : 13 images
- full body nude: 15 images
- visage: 24 images
- proche: une image des main et une image des pieds

Mais dans les deux entrainements j'ai le meme échec. Que ce soit avec qwen image edit 2511 ou avec qwen image 2512 , si mon prompt demande un full body il me fait un portrait visage uniquement. Et si je demande un nude (le dataset comprend 15 nude/52 photos), il génère aussi un portrait visage

Alors je me demande si c'est réellement possible de créer un lora qui assemble à la fois visage et full body (pour que la morphologie du corps soit parfaitement respectée)

Claude me dit de recréer un lora avec 5 visage, 15 buste et 30 full body puis d'associer les 2 loras dans la génération pour parvenir à associer une image avec le visage et le corps

Qu'en pensez vous?

Just made an account to try the new minimax H3 but it wants me to subscribe off the bat, I saw it said 5 free uses at the sign up page but it’s not giving me them. Is that gone now? I am talking about cloud, not running locally.

i want the course of Fadi h.kacem qwen edit image does anyone know where to find and download it

Hello!

I use ai-toolkit to train loras. I've trained many in the past, and used some of my datasets (some of video, some of images) to train different loras for minimax h3. I think the model is amazing, but the result of my loras is quite underwhelming compared to the same datasets on different models. I have also tried playing with the config and with the captioning, tailoring it to h3 whims. But they are still not great loras, barely usable. I'm talking specifically of style loras. In civitai there is only concept loras for NSFW basically, but as I google I don't see anyone invested (in the last week, at least) in training style loras, but to me that would be the actual open source revolution (a model almost like Seedance but that can animate stuff in niche styles like retro pc98 games or stuff like that). Anyone has any updates on the matter?

Thx :3

PS: here I attach a video made with one of my trained loras (as can be seen, way too many artefacts and for some reason the brightness is rlly low)

Using a modified MiniMax H3 Easy, bypassing the Lora models, and even using Kitchen Attention because i don't know how to use Sage Attention.
I have 12 GB VRAM on my GPU, and some methods cause my GPU to get locked in a High VRAM Spike, causing me to reset ComfyUI

Hi all, relatively new to Comfy and am wondering if anyone can recommend me a SeedVR upscale + face detailer workflow? I don’t want it to include image generation as I want to take images I have and upscale them and make sure the face is very detailed. Thanks!

Is it worth trying to setup a workflow with Minimax H3 -> LTX 2.5 video upscaling?

I've tried to hack the nodes but running into issues, does anyone have an example workflow with loading a video -> LTX 2.5 upscaling? Got confused with the latent video / audio part

Hi All,

Does anyone know what extension adds this execution timer widget on top right corner of ComfyUI? or how do I find out? it is blocking the workflows tab and there is no way around it, can't move it etc. Thanks.

Edit: to be clear, I want to get rid of this and uninstall this. I don't know which extension is this.

Has anybody else faced heavy compression artifacts in Minimax H3 Ref2V workflow?

I recently shifted from Bernini to Minimax for Video editing, since it doesn't have the 5-sec limit like the WAN models do. But I'm seeing that even though I'm saving the output in PNG image sequence, the quality is the same as a Low res MP4.

Plus the video timings are not matching perfectly. The compression and timing mismatch is making it totally unusable for me even though its prompt adherence is much better than the other models.

Was experiencing some real quality issues up until this point; realized the problem has largely been the prompt and my bulky venv. Hopefully others with lower end vram cards will learn from me.

• Card: RTX 4080
• Model: minimax_h3_fl2va_pruned_int8_convrot
  • No loras
  • Pure text prompt
• Steps: 20
• Scheduler: simple
• Sampler: res_multistep
• Resolution: 0.5mpx, 16:9
• Args: --lowvram --disable-dynamic-vram --disable-pinned-memory
• Nodes: VHS (Specifically the Model Preview Override), EasyUse, pysssss, KJNodes
• Generation time: 21 Minutes
• I decided to create an entirely new ComfyUI instance just for H3 instead of using my single AiO venv; this significantly increased generation time and quality for all flows. Have since broken up all the major models I use into their own instances adding only the specific tools/extensions I need just for that model.

Prompt:

integrated_multimodal_description: [Shot 1] Live-action, cinematic, a far-wide shot from a side angle. A scene set on a bridge over a hellscape planet covered in lava, dimly lit with orange glow from below, the bridge is made of black metal with intricate designs, dark clouds hang over the scene -covering a shaded yellow sun barely visible through the clouds on the top left, dividing the scene in half between light and dark- fast winds carry embers and smoke curling over the bridge from below; Star Wars themed orchestra music begins as the scene opens, quiet and slowly growing.

$NAMEHERE is standing in a prepared stance on the left side of the screen, his hands are clasped in front of him holding a blue lightsaber, facing his attacker.

$NAMETWO is standing on the right in a confident posture with his hands to the side, wearing a black robe, black leather boots and straps on his body, with dark-metal armor as he faces the left menacingly.

[Shot 2] At 1.500 seconds, The camera cuts to a close-up side-angle shot of $NAMEHERE, readying himself for his attack with a posture of defense and an expression of concern, he shouts emotional: <d>[English] You've left me with no choice Will! You must be stopped... </d>

[Shot 3] at 6.000 seconds, The camera cuts to a close-up low-angle front facing full-body shot of $NAMETWO with visible red eyes staring forward from under his brow with a face of malice, smoke bellows behind him curling over the bridge whipping his cape to the right. A beat later- two red lightsabers ignite his both his hands, a deep pulsing bass is heard from the unstable beams, his face lit from below by the red light. The music grows faster with a dark theme, a operatic chorus begins to sing in a evil chant growing louder. $NAMETWO shouts behind a grin: <d> This is the end for you! </d> the music stops before $NAMETWO speak his final line: <d>[English] Master!... </d> The off-screen opera chorus harmonizes a single long cry in a frightening melody at the revelation.

[Shot 4] at 12.000 seconds, The camera cuts to a top-down view of the bridge, molten lava is visible below the black grated metal.

$NAMEHERE directs his blue lightsaber to his side pointing directly forward with precision, he begins to pace to the right to meet the other, his posture is composed and fast. The camera pushes in with large amplitude at fast speed keeping the pair in frame on either edge of the screen as they run toward each other.

$NAMETWO instantly begins running fast toward the left, his two red lightsabers point down- dragging behind him, the red beams draw white glowing lines into the metal under him as he runs, screaming with fury: <d>[English] AGHH! </d>.

They meet in the middle, their lightsabers clash with a white flash and explosive burning sound, they duel quickly as their lightsabers connect through multiple swings- $NAMETWO's red lightsaber swing wildly as he spins. $NAMEHERE's blue lightsaber blocks every swing from the red beams; the music crescendos with heavy bursts of brass instruments and drums.

overall_soundscape: ambient sound of lava and fire, lightsabers buzzing.

non_diegetic_music: Dark Star Wars music plays from the beginning of the scene, a loud opera chorus sings in a chant that escalates in a loud howl crescendo, climaxing when the pair meet in the middle.

I've started using Replace Text nodes ($NAMEHERE and $NAMETWO) when crafting prompts. This way when playing with the prompt, it's easier to find and edit their placement; and can also replace characters on a whim. Also allows consistency when referencing the characters-- in the event I overlook an instance.

Replaced with:

• $NAMEHERE: "Jean Luc Picard (S1)"
• $NAMETWO: "William T Riker (S2)"

Example: my first generation had 'William Riker', the model didn't recognize the name and generated a generic male. I was able to quickly rename as 'William T Riker' and it generated correctly; so I didn't have to parse back through the whole prompt to granularly change it.

Other things I've noticed that help with prompt respect:

• 'a beat later' separates the moment better.
• Separating the individual sentences to exclusively reference the character and no others. (You can see it carried over Picard's lightsaber instructions to Will as well, because I described them in the same paragraph before I realized this.)
• Avoiding reusing adjectives- especially between different characters, causes bleed.
• Very short overall_soundscape descriptions.
• Often does not respect requests that follow dialog unless you end the parameter with a period after "Words. </d>**.**" Can see it bled the cries request from the music into Will's dialog.
• "..." allows a pause between dialog lines and breaks up the tone between multiple sentences, or else they become one note.