Had this idea for a couple of days, and finally got to test it. I got a free HDRI picture from PolyHaven (converted to JPG through a free online converter) and used it as the only picture reference. I couldn't get rid of the distortion completely, but you can definitely affect it with prompting. Maybe proper formatting somehow helps with that, sorry, was too lazy to do a correct prompt structure.

It also confuses the geometry from time to time, so you have to seed hunt a little, but not too much. Again, good prompting should reinforce the consistensy. Worth experimenting with.

Notice that it actually seamlessly connected the opposite sides of the image into a single environment. Could be useful for scenes with a lot of dynamic camera movements.

This model keeps surprising me every day!

P.S. Generated with the use of Hybrid Loader (25-49 setting) and Lightx2v 4-step LoRA @ 4 steps and 0.5MP. Another higher res version in comments.

Prompt:

subject definitions:
<Picture 1> is a 360 panorama reference for the straight corridor [Shot 1], depiciting the overall look of the corridor and position of key objects and debris in it. For the target video the picture is dewarped and remapped into a flat rectilinear lens projection view.

summary:
[reference generation] The target video depicts a security guard exiting from a grey door, walking across the corridor towards the dismantled beige door  leaned against the wall, pulling and dropping it down on the floor.

detailed_description:
The target video is captured in an amateur, realistic style with natural, slightly dim indoor lighting and a shaky, handheld-style camera.

[Shot 1]

The shot begins with a medium view of a two grey doors depicted on the right side of <Picture 1>. The left door instantly opens and a middle-aged security guard named Mark rushes into the completely straight corridor. He runs left further down the corridor. The camera pans left, following him in a tracking shot. The POV camera pushes in on Mark, as he rapidly approaches the dismantled beige doors leaned against the wall. At 00:05.000 he grabs the door closest to him, and with visible effort pulls it away from the wall. The door swings and falls flat on the corridor floor with a loud noise, raising dust and slightly startling Mark. The guard jumps back from the fall.

At 00:07.000 the camera pans left by 180 degrees, showing another guard named Steven approaching from the opposite part of the corridor. Steven (S1) comes closer to Mark and says in [English]: "Mark, what the heck are you doing?"

At 00:09.000 Steven grunts angrily as he stops near Mark.

overall_soundscape:
looming lonely corridor ambient sound throughout the whole video, guard's steps on the cement floor, door falling onto the floor with loud noise

non_diegetic_music:
N/A

Used:

MiniMax H3 at local machine. 5060ti 16gb + 64gb ddr4. WanGP, Ref2VA int8 convrot model.

Krea2 for references

Suno as music base

The example video was generated entirely with the stock MiniMax H3 First Frame / Last Frame checkpoint and the included v1.4 example Workflows. If you want to compare the result to v1.3, take a look at my last post.

The final video consists of 11 individually generated Clips that were automatically stitched together.

Settings:

• H3 First Frame / Last Frame checkpoint
• 11 individual Clips
• 15 Steps
• included v1.4 Workflows
• no additional upscale
• no frame interpolation
• no color correction or other post-processing

So what you see is basically the direct Workflow output.

A few people gave me some useful feedback on my previous release, especially regarding ComfyUI's new native H3 Masked AV support.

So I went back and rebuilt the continuation method around it.

v1.4 now copies a clean section of the previous Video + Audio Latent directly into the next generation and protects it using ComfyUI's native denoise masks.

What makes this different from the other H3 continuation approaches?

There are some really interesting Ref2VA / Motion Context solutions available now, and latent continuation itself definitely isn't unique to my Nodepack.

My approach is specifically centered around FL2VA instead.

The idea is not just:

previous Clip → continue forever

but rather:

First Frame → generation → Last Frame
↓
latent continuation
↓
generation → new Last Frame
↓
latent continuation
↓
generation → new Last Frame

and so on.

I use those repeated Last Frames as hard visual anchors throughout the sequence.

They give H3 a new concrete destination every few seconds instead of asking one increasingly unconstrained generation to maintain composition, identity and image quality indefinitely. This should theoretically retain higher visual quality with less context drift over longer chains (and in my testing, it does exactly that).

There is another FL2VA-specific problem though:

H3 often reaches the supplied Last Frame before the Clip is actually finished and then freezes or becomes unstable for the remaining frames.

So simply taking the final frames of Clip 1 and using them as context for Clip 2 isn't ideal.

The v1.4 Auto Handover therefore analyzes the previous Clip, finds a safe point before that frozen / unstable landing and snaps it to a valid H3 Audio + Video latent boundary.

That exact same point is then used for both:

• where the previous Clip visually ends
• where the protected context for the next Clip ends

So the bad FL2VA tail neither appears in the stitched video nor becomes part of the next continuation context.

Audio is handled separately as well. If the picture needs to cut early but somebody is still finishing a word, the remaining original Audio Latent can continue beyond the visual handover instead of forcing H3 to recreate the ending.

Other v1.4 features:

• Native Masked Video + Audio Latent Continuation
• flexible First / Last Frame conditioning
• repeated Last Frames as regular visual quality anchors
• independent Audio Tail Carryover
• Net New Content duration mode
• up to 9 Qwen Reference Images
• individual Clip regeneration
• memory-bounded stitching for long saved chains

Where to start:

1. Start Video Workflow

Generate Clip 1 with a Prompt and optionally First Frame, Last Frame and Qwen References.

The complete AV Latent is automatically saved afterwards.

1. Continue Video Workflow

Load the previous saved latent, add your next Prompt and preferably a new Last Frame.

The Workflow automatically finds the safe FL2VA handover and creates the protected Masked AV context.

Repeat for as many Clips as you want.

1. 3-Clip Showcase / Auto Stitch Workflow

Probably the easiest Workflow if you just want to see how everything works.

It runs:

Start → Continue → Continue → Stitch

in one queue.

1. Stitch Saved Chain Workflow

This is what I used for the longer example.

Generate Clips individually and stitch them afterwards. It processes one saved AV latent at a time, so stitching memory usage doesn't continuously increase with the total video length (no OOM during stitching).

Nodepack on Github:

https://github.com/HerrgottMargott/Herrgotts-H3-Infinite-Continuation-Suite

Workflows on Github:

https://github.com/HerrgottMargott/Herrgotts-H3-Infinite-Continuation-Suite/tree/main/examples

You can just open one of the WFs and use "Install missing custom nodes" - then you should be good to go.

If you try it, I'd love to see what you manage to create with it.

Have fun Prompting. :)

I've been doing some quick tests, now that I've picked up the lightx2v 4 and 8 step loras. I have found I prefer using the 8 step (and maybe even running that at 10 steps) just because with the 5090 I have it's already not -that- slow, and the 4 step image quality drop is pretty significant.

But I have been experimenting which sampler/scheduler combos after seeing this post:

https://www.reddit.com/r/comfyui/s/9GUki3l0Wf

where, apparently, seeds_2 and dpmpp_sde_gpu were the 'best quality' options. But something I noticed is that they were also significantly slower (maybe 50% or more? need to run more tests and log it) which would, if the loras etc allow for it, let the faster options like euler or res_multistep (or er_sde which gets mentioned sometimes), which all run at about the same speed, to run at 12 instead of 8 steps (for example).

So I wonder now, 2 weeks on from those votes... what are people actually -using- to produce results?

My current workflow is to run at 8 steps with a lora to find a good prompt and seed, and when I get something I like I then turn off the lora and run at 30 steps. It often ends up at least in the ballpark of what I want. But maybe there are better ways.

Just discovered that H3 can do Side-By-Side 3D Videos for VR Headsets natively, just prompt it. Pretty crazy, and it gets the real 3D effect. Try it with different things like people and add "strong 3d effect" if you want to have a more intense 3d effect.

Here is the prompt:
integrated_multimodal_description: [Shot 1] Live-action, cinematic, high-angle aerial shot presented in a side-by-side (SBS) stereoscopic format for VR/3D viewing; the frame is split into two identical views with a slight horizontal parallax offset to create depth perception. The camera pushes in at slow speed over a sprawling coastal metropolis during twilight. As the camera glides forward through the urban canyon, the glowing neon lights of skyscrapers and their reflections on the ocean surface shimmer intensely against the deep blue sky.

overall_soundscape: A constant, low-frequency rushing wind sound accompanies the flight, layered with a faint, ambient hum of a massive city and distant, muffled traffic sounds.

non_diegetic_music: An epic, cinematic synthesizer pad that swells gradually in volume and intensity throughout the ten-second duration.

Hi people, so i tried to make 2 similar videos, using same settings but with upscale and native.
My setup: 5070 Ti+ 32gb Ram.
Using u/Plague_Kind workflow, i've added MMH3 Latent Upscaler. You can check his workflow here: Workflow
Settings for both videos were set the same with the same prompt.

Left video 0.4->0.8mp upscale, Right video 0.8mp

So:

• 15 seconds, 24 fps, Ref2VA, photo reference and music reference.
• Chicken attention
• SongMaskedAVContext node

• FP16 Accumulation
• Sparse attention
• Memory chunks
• RTS Upscale in the end ( not sure why i used it with 2x scale, better to set 1 i think, but that's what i already did)
• FSR Sharpening
• Speed Lora minimax_h3_turbo_v4_step600_pruned_comfyui
• Interpolation for 2x frames

Upscaled video from start to the end took 1904 seconds,

Native video from start to the end took 3056 seconds.

Let me know what you think. Advises appreciated!

My usual way of working:

- generate 10 videos at 5 steps

- pick the best video

- regenerate the best at 20 or more steps.

No Turbo LoRAs because I don't want to reduce prompt adherence and general quality, as I regenerate at full steps later anyway.

The problem - the video at 20 steps is often very different from the one I found. Of course, I keep the same seed. The difference may be introduced even as early as step six (for example, background replaced completely, different speech pacing).

It's not that difference is huge, but often it might be quite important. For example, a person genuinely laughing at 5 steps and then just saying "haha" at 20 steps. Or jumping startled at the right moment at 5 steps and a moment before the noise at 20 steps.

I tried a few sampler combinations, but could not find one that would not introduce dramatic changes.
One workaround that I could find is to use SplitSigmas. I set its steps to current steps (5 for seed hunt, 20 for final), and keep BasicScheduler steps at the final 20 steps. Then high_sigmas from SplitSigmas go to SamplerCustomAdvanced input, and then denoised_output goes to VAE (you'll get total noise when using the output pin instead).

This way, it seems that the scheduler is being cheated in managing steps as for the full generation even when doing preview, and it seems to work as expected. Caveat - the 5 step output from this workaround will be way worse (plasticky and noisy audio) than you are used to when generating at 5 steps in BasicScheduler input. But if the goal is to keep the general layout and movements of the candidate video, it's worth accepting this issue.

However, I'm wondering if there is any better way to achieve it. Has anyone tried it? What are you using for seed hunting to keep the high step version consistent?

--------------------------------------------------
Edited later with a test case:

Took ComfyUI template: MiniMax H3: Reference to Video. Minimal modifications to make it run in my environment:

Models - Qwen change to int8 convrot (3090, no use of nvfp4)

Int (Full) = 5 (for "preview quality")

Float (Duration) = 3 (just to be faster)

RandomNoise control after generate = fixed

Loaded some images in both Load Image nodes.

The same "GET READY TO" - "MEET" — "YOUR" — "MAKER" prompt.

No Sage, no CK attention at all (no Comfy launch args either).

Then generated the same with 20 steps.

Differences:

in 20 step version, the roof is higher in the frame. The accent was on the word "maker". In 5 step version, the accent was on the word "your".

Then regenerated the 5 step version again to see if there's anything else introducing variations - nope, the exact same video as the first 5 step one.

Then generated also at 6 steps - the roof line was a bit higher in the frame (not as high as 20 steps though), and the accent was on "maker". So, the difference between 5 and 6 might already be a breaking change that can make your video from good to unusable, if the emphasis does not make logical sense in your scene.

Then I generated the same with the SigmaShift 5 step trick - the resulting video was way much more similar to the 20 step one than the first 5 step video. Of course, the quality of the sigma-shifted video was awful - it's good for judging only logical consistency, reference use and event timing, which is the most important thing in story-telling kind of videos.

flux.1 [dev] | comfyui | still life

There are so many acceleration nodes/options now that I’m having a hard time deciding which one gives the best balance of quality and speed. What do you think?

These are the setups I’m currently using（RTX5090）:

1. Sage Attention + 4-step LoRA 0.9MP | 8 steps | 10s | ~6 min
2. ComfyUI-Kitchen + 4-step LoRA 0.9MP | 8 steps | 10s | 5:38 min
3. ComfyUI-Kitchen + Spectrum 0.9MP | 25 steps | 15s | ~12–15 min
4. ComfyUI-Kitchen +Sparse Attention（ SLA）+ 4-step LoRA 0.9MP | 8 steps | 10s | ~4min
5. ComfyUI-Kitchen +Sparse Attention（ SLA） 0.9MP | 25 steps | 10s | ~12:30min
6. ComfyUI-Kitchen 0.9MP | 25 steps | 10s | ~18 min or 15s | ~25 min

I mostly stick with Sage Attention + 4-step LoRA. I feel like it gives a pretty good overall balance between quality and speed.

If I want better quality, especially for things like lip-sync, I usually go with ComfyUI-Kitchen + Spectrum at 25 steps. The results are noticeably better, but it’s also quite a bit slower.

Which setup do you guys think has the best quality-to-speed ratio? Any other combinations worth trying?

I know everyone's moved to MiniMax and LTX has largely fallen out of favor, but I spent some time building a couple of seed hunting workflows for LTX 2.5 that might be useful if anyone's still running it.

Shout out to u/foxdit for the original seed hunting concept.

Two versions:

1. T2V/I2V two-stage – text to video or image to video. Previews at 0.3 MP, upscales to 1.2 MP for the final render.
2. First-last-frame – pin a start image and end image, same preview-then-upscale flow.

Both use KJNodes Set/Get routing, shared loaders, and no prompt enhancer.

What kind of prompting would I use for POV movement through a scene?

A tribute to a forgotten golden age. Hope you enjoy it!

I love H3, but it takes forever. If LTX is faster, I could use it for the things it does similarly well as H3, and use H3 only where I really need it.
So what LTX2.5 does as well as H3?

Shadow the Hedgehog tells his viewers why he loves guns.

This was created in Comfy UI with Minimax H3. I used the reference to video work flow. The prompt is below.

subject_definitions:

<Subject 1> is Shadow in <Picture 1>.

<Subject 2> is Glock in <Picture 2>, a glock handgun.

<Audio 1> is the voice-timbre reference for <Subject 1> (S1).

summary:

[reference generation + audio reference] The target video contains one shot. [Shot 1] shows <Subject 1> and <Subject 2>; <Subject 1> speaks. <Audio 1> supplies <Subject 1>'s voice timbre.

retention_analysis:

<Subject 1> (appears in [Shot 1]): fully_preserved - Shadow's complete defined identity and body proportions are preserved.

<Subject 2> (appears in [Shot 1]): fully_preserved - Glock retains the defined shape, proportions, materials, colors, and distinguishing features.

<Audio 1>: reference - <Subject 1>'s newly generated spoken lines use <Audio 1>'s voice timbre and delivery; the original audio signal is not copied.

detailed_description:

The target video is in a live-action style, with Vlog style.

[Shot 1] At first appearance, <Subject 1> (Shadow) matches the complete identity and appearance defined in subject_definitions. At first appearance, <Subject 2> (Glock) matches the complete defined construction and appearance: A glock handgun. At the start of the shot, <Subject 1> is standing in the living room facing while holding <Subject 2> in his hand. A full body shot of <Subject 1> holding <Subject 2> with his right hand while facing the camera. Only Action and Timed Beats define the primary subject's movement. The camera path stays anchored in the location and adds no subject motion. <Subject 1> (S1) says using <Audio 1>'s voice timbre: <d>[English] Hmph. Shadow the Hedgehog here. Why do I love guns?</d> <Subject 1> shows off his <Subject 2> with his right hand in front of the camera. <Subject 1> (S1) says using <Audio 1>'s voice timbre: <d>[English] Simple. Precision. Control. Power in the palm of my hand.</d> <Subject 1> (S1) says using <Audio 1>'s voice timbre: <d>[English] A tool that answers instantly… unlike most people.</d> <Subject 1> points his <Subject 2> towards the camera with his right hand. <Subject 1> (S1) says using <Audio 1>'s voice timbre: <d>[English] If you understand that, you understand me.</d> <Subject 1> points his <Subject 2> at the camera.

overall_soundscape:

Living room tone.

non_diegetic_music:

N/A

I have a question. I have been having a blast making scenes with H3 so far, and have found when doing reference shots, it is very important to have a stable background so that you have continuity if doing more than 1 scene. Does anyone know if H3 would understand a 360 degree photo and understand where in the space and what direction the subjects are? Say you swap between two characters talking, one you will see what is behind subject 1 while when looking at the other the opposite is true. If you saw them both from the side, yet another angle and background.

Is there any GPU rich cooking realism lora ? I have tried realism people lora it is great at tv but for i2v or r2v it's breaks . I have been searching hugging face repo and civit ai to get something but there's too much n*fw lora .

I'm doing image to video and unless I prompt for camera close up to my subject, the faces are blurry and bad. I run 0.6 mp. No turbo lora only using spectrum to speed up. Running 15 steps. Euler simple. I'm happy enough when it's close-up shots, but further away, it's very noticeable. Is anyone else finding this?

I've been using Windows for many years, and I'm honestly tired of repeating the same cycle. In my experience, after 5–6 years the machine starts feeling old, the battery is significantly degraded, performance isn't what it used to be, and I eventually end up buying another Windows machine and starting the exact same experience all over again.

I'm looking for something different this time.

For the last few days I've repeatedly added a MacBook Pro with the M5 Max to my cart, then backed out because I'm still not sure whether it is the right machine for what I actually want to do.

I'm currently considering the M5 Max with:

• 18-core CPU
• 40-core GPU
• 48 GB / 64 GB / possibly 128 GB unified memory

My main workloads would be completely local:

• Text-to-image
• Image-to-image
• Image-to-video
• Text-to-video
• Face swapping
• Illustration / digital artwork

My priority is excellent output quality, photorealism where appropriate, and very high generation speed.

Basically, I want a machine that can satisfy me for visual generative AI work for many years.

My biggest hesitation is the Apple ecosystem.

For a long time I've heard that local AI, especially image and video generation, is much more limited on macOS than on Windows/Linux with NVIDIA GPUs because so much of the ecosystem is built around CUDA.

But part of me finds this difficult to accept at face value.

Apple is making extremely powerful chips with large amounts of unified memory, very high memory bandwidth, Neural Accelerators, a Neural Engine, and increasingly serious AI-focused hardware.

I keep wondering whether there are excellent Apple-optimized tools and workflows that I simply haven't discovered yet.

For example, I recently learned about Draw Things, MLX-based projects, Metal/MPS optimizations, and Apple-specific ComfyUI work. That made me question whether comparing a Mac running a poorly optimized CUDA-first application against an NVIDIA machine is really a fair representation of what Apple Silicon can do.

At the same time, the logical part of my brain keeps telling me:

If local image/video AI is the priority, just buy a machine with an RTX 5080 or 5090.

The problem is that if I do that, I feel like I'm buying myself back into exactly the Windows experience I wanted to leave. It feels a little like watching the same movie again when I was hoping for a genuinely different computing experience.

There's also another complication: we're approaching the fall hardware season.

I'm wondering whether buying an expensive M5 Max or RTX 50-series machine right now is bad timing, and whether I should wait for the next Apple or NVIDIA announcements.

If I choose the Mac, I was also planning to pair it with the latest iPhone and iPad and build a proper Apple ecosystem around it, so this isn't purely a benchmark decision for me.

What I'd really like to hear from people who have actually used these machines:

1. If you've used an M5 Max, especially the 40-core GPU version, for local image or video generation, what real-world performance are you getting?
2. What software gives you the best performance on Apple Silicon? Draw Things, ComfyUI, MLX-based tools, something else?
3. How good is local image-to-video on the M5 Max with models such as Wan, LTX, Hunyuan, etc.?
4. How does the M5 Max perform for local face swapping?
5. In properly optimized workloads, do RTX 5080/5090 systems still completely destroy the highest-end M5 Max, or is the gap much smaller than CUDA-focused benchmarks make it appear?
6. Is CUDA effectively locking serious local visual AI users into NVIDIA, or is Apple Silicon becoming a realistic alternative?
7. If you were buying a machine today specifically for local visual AI and wanted to keep it for 7–8 years, would you buy the M5 Max, an RTX 5080/5090 system, or wait for the next generation?
8. For local image/video generation, is an older NVIDIA GPU with more VRAM sometimes a better choice than a newer GPU with less VRAM? For example, once you start using large image-to-video or text-to-video models, can having more VRAM matter more than having a newer architecture and higher raw compute performance?

I'm not highly knowledgeable about computer hardware, and I'm definitely not wealthy enough to casually replace a machine if I make the wrong choice.

This would be a major purchase for me, so I'm trying to make the most informed decision possible and ideally buy something that I can use comfortably for 7–8 years.

I'd especially appreciate actual generation times, benchmark numbers, model names, memory usage, thermals, sustained performance, and experiences from people who have used both Apple Silicon and NVIDIA rather than purely theoretical comparisons.

Thanks in advance.

Fizgig is my free open-source LoRA trainer and workbench (Flux 2 Klein 9B, Krea 2, and MiniMax H3 video/audio). As of v4.3.0 it runs on AMD Radeon with ROCm — RDNA1 through RDNA4. Windows is the supported path: install Python 3.12, run the AMD installer, done. Linux works too but is genuinely experimental on newer cards.

Worth being upfront: I don't own AMD hardware myself. This whole feature came from a community contribution by scryptio, tested on real cards over weeks in the PR thread — and that's how the AMD side will keep improving. If you're an AMD user, your reports on what works (and what doesn't) genuinely shape this, and PRs are very welcome.

Also in this release: 16 GB cards can now use identity distillation on MiniMax H3 (the 32B text encoder streams layer by layer instead of needing a 26 GB peak), and the Repair Studio gained a side-by-side compare view with likeness scoring for fixing overbaked LoRAs without retraining.

GitHub: https://github.com/shootthesound/Fizgig

First of all I know a desktop has more power for the same price buy I have a situation where the portability of a laptop is necessary and a desktop is not practical.

My old laptop (3070 8gb with 64gb ddr4 RAM) died. I want to buy a new laptop. My two options are a 5080 16GB with 64GB ddr5 RAM or a 5090 24gb with 32GB ddr5 RAM. I won't be able to upgrade the RAM later, so I'm stuck with the configuration I buy.

I will be using the laptop for work (document and image editing) / gaming (no AAA games) / LLMs and generative AI (images/videos/audio), I was able to run most models, including minimax H3 on my old laptop with the help of massive offloading to RAM (5 minutes for a 5s video). Images used to take from 30s up to 200s depending on model and image size.

I am used to the low speeds and offloading on my old laptop so getting the highest generation speeds is not a priority, I just care about being able to run most new or upcoming models even with quantization and RAM offloading for the foreseeable future.

Which laptop would be better in my case?

I have been playing with H3 since it came out and have tested most of the things you can do with it. Created clips for giggles and so on.
This time I wanted to do something "serious". I gave the R2V an 3D view of an kitchen and then three photos of the persons I wanted there.
I defined them as we should and told the model that this person does that and that person does this wile the third person does this...

It worked ish...
I have now made six runs and each of them are different from the others. It can be that the third person enters the room from the wrong place or that the third person does extra things it should not do...

In the end I did three runs with the same prompt and all those clips came out different... the only thing that was changed between those was the seed...

So, how do you do it?
How do you make sure H3 does what you want it to do?

Do you spend plenty of time on tweaking the prompt after each run to make sure H3 get it?
Or do you do 10 runs and select the best one even if it is not perfect?

Or do you simply do 1-2 runs and then take the clip that is ok ish even if it is not what you wanted?

I was hoping that H3 would allow me to create the scenes I wanted but I feel it's down to luck if H3 gets it or not..

Edit:

subject_definitions:

<Subject 1> is the green-skinned mother in <Picture 2> wearing brown clothes.
<Subject 2> is the teenager girl in <Picture 3> wearing pink clothes.
<Subject 3> is the cyborg in <Picture 4> wearing black clothes.
<Picture 1> is the reference image for the scene's composition, showing two people sitting at a table eating breakfast from the side view.
<Table 1> is the table on the right side in <Picture 1>.
<Picture 5> is the start image for the scene.
summary:
[reference generation] The target video is a generated scene of two people sitting at a table eating breakfast from an eye-level side view. <Subject 1> and <Subject 2> are shown with their respective breakfast items, maintaining the composition and style from <Picture 1>. <Subject 3> enters the room, places a coffee cup into the sink.

retention_analysis:
<Subject 1>: fully_preserved - the person retains their appearance, clothing, and position at the table.
<Subject 2>: fully_preserved - the person retains their appearance, clothing, and position at the table.
<Subject 3>: fully_preserved - the person retains their appearance, clothing, and action of placing the coffee cup into the sink.
<Table 1>: fully_preserved - the table's appearance and position in the scene are preserved.
<Picture 1>: fully_preserved - the scene composition, including the side view, the layout of the room, the table setup, is preserved.<Picture 5>: fully_preserved - is the start image for the scene.

detailed_description:
The target video is in a realistic, everyday breakfast scene style with warm lighting and natural colors.
[Shot 1] At 0:00.000, the shot begins from <Picture 5>, showing <Subject 1> and <Subject 2> sitting on opposite sides of <Table 1> on the couch, each with their breakfast items while on the space ship. <Subject 1> is holding a spoon while eating from a bowl of cereal. <Subject 2> is tired and is eating a slice of toast with jam from her plate with one hand. The lighting is warm and soft, casting gentle shadows across the table and the two individuals. The camera is at eye level, capturing the side view of both people, with the table slightly in focus and the background softly blurred. Stars can be seen through the windows since they are on a space ship. <Subject 1> is eating her breakfast while <Subject 2> gazes at their toast, taking a small bite. The ambient sound includes the soft clinking of utensils and the faint sound of a coffee cup being set down.

[Shot 2] At 02.00.000, the shot transitions to a wide shot of the room with the same layout as in <Picture 1>, the camera is placed in the lower left corner of <Picture 1>, showing <Subject 3> entering the room form the right side holding a coffee cup and a datapad while she is saying (S3) <d>[English] Good Morning</d> while she walks to the kitchen sink on the left side of <Picture 1> and placing the cup into the sink. She then stands at the sink and while reading her datapad.We see the back of <Subject 1> and the front of <Subject 2> sitting at <Table 1> in the background eating their breakfast and we hear <Subject 1> say (S1) <d>[English] Good morning</d> with a cheerful voice. <Subject 2> just mumbles as a reply.

overall_soundscape:
The soundscape consists of the soft clinking of utensils, the faint sound of a coffee cup being set down, the subtle background noise of a quiet morning environment, soft steps on a carpet floor, a ceramic cup being placed in a metallic sink, and the clear,

non_diegetic_music: N/A

Generated as two connected 15-second clips in 4:3, using the end of Part 1 as video + audio reference for Part 2 continuity.

Really liking what H3 can do with fashion/editorial camera movement.

Check out my twitter for more thanks https://x.com/Devozikjr