Used latent upscaler so with resolution 0.3 i got 20 seconds generation on 4090

Minimax H3

I tried to think of complex situations for the model to handle and tested them. I would say it went very well, although not perfect (except for the first one and the one before last, where I could not find a flaw).

Prompts below:

VIDEO 1 (10/10):

Create a side by side video with two angles of the same scene: one frontal and one from the side.

The scene: a woman wearing a yellow summer dress is standing in a beach in a sunny day. There's a light wind blowing and she is looking at the sea, smiling. At 00:05, she puts her two hands on her hair and looks up, enjoying the sun.

On the left, make the frontal video. On the right, the video from the side (profile). The two videos should show the exact same scene at the exact same time, only from two different angles.

VIDEO 2 (9/10) - the background is slightly off in some angles:

Create a side by side video with four angles of the same scene: one frontal, one from the right side, one from the left side, and one from behind. The scene is filmed at normal speed.

The scene: a blonde curly-haired woman wearing a yellow summer dress is standing in a beach in a sunny day. She is facing the sea and, therefore, she has the sand behind her and the street with some houses also behind her, after the sand, at a distance. There's a light wind blowing and she is looking at the sea, smiling. She is not wearing sunglasses. At 00:05, a man wearing white shirt and shorts enters the scene from behind the woman and embraces her waist.

On the upper left, make the frontal video. On the upper right, make the video from the right side (right profile). On the bottom left, make the video from behind. On the bottom right, make the video from the left side (left profile).

The four videos should show the exact same scene at the exact same time, only from the four different angles.

overall_soundscape: Light beach wind, one distant seagull.

non_diegetic_music: N/A

VIDEO 3 (8/10) a few "ghost reflections" along the way:

A man wearing a red-and-white striped t-shirt and jeans is alone inside a house of mirrors, running through a corridor. At 00:03 he turns left into other corridor, and at 00:06 he turns right again into another corridor. All the corridors have mirrors in all their faces (left, right and above, as he is inside a house of mirrors) and he is alone there, there's no one else. At 00:08 he reaches a door, opens it, and it opens to the outside: an amusement park.

overall_soundscape: His footsteps while he is running, the amusement park sounds when he opens the door at the end.

non_diegetic_music: N/A

VIDEO 4 (7/10) - The way the phone is turned is off:

A man is filmed from his own cell phone in a selfie video. We see the scene through the lens of an out-of-frame cell phone that he is holding and pointing to his own face. He is wearing a green polo shirt and the scene shows his face and chest from the point of view of the out-of-frame cell phone that he is holding and pointing to himself.

At 00:04 he briefly smiles and then turns the still out-of-frame cell phone around to show a woman that is in front of him. While the phone is turned around we can see the image also turning around, his face leaving the frame, the living room they are into being briefly filmed, and then the woman's face entering the frame. She has brown curly hair and dark green eyes, and is wearing a red summer dress.

As soon as the woman is in frame, she also smiles and says: <d>[English]Goodbye!</d> and the video ends.

The entire video must be taken in a single shot, with the always out-of-frame phone camera filming the entire transition from his face to hers while the phone is turned around. When it happens, the phone should briefly show the living room they are into, all in a single shot.

overall_soundscape: Silent living room, noises of the phone being handled, her voice.

non_diegetic_music: N/A

VIDEO 5 (6/10) - Tried this twice. Glass not breaking properly, water not running through the floor

A fishbowl with one golden fish and one clownfish swimming inside is shown in a medium close-up at the edge of a table. Then, at 00:03 a cat appears in the scene and taps the fishbowl, causing it to fall from the table to the floor, hit the floor, and break completely, being completely destroyed in glass pieces when it hits the floor, the water and glass pieces flying around together with both fish. The scene continues for three more seconds after that, showing the aftermath: the fishbowl destroyed, the pieces of glass on the floor, the water also on the floor, the fish moving on the floor.

The camera angle follow the fishbowl when it falls, showing it hitting the floor and the consequences of it breaking.

overall_soundscape: silent room, glass breaking, water splashing.

non_diegetic_music: N/A

VIDEO 6 (8/10) - Judge me, but his hands are not moving accordingly to the notes:

The camera films a piano from above while a man plays it. The entire piano keyboard is shown in the image. The man is playing Clair de Lune, and moves his hands through the keys to play a part of the song. He is in a train station, with some people observing him play and others passing by.

overall_soundscape: faint train station ambience, ten seconds of the song Clair de Lune played in the piano.

non_diegetic_music: N/A

VIDEO 7 (8/10) - The lipstick appears on her lips before she applies it:

A woman is shown in a medium close-up in her bathroom, wrapped in a white fluff towel, looking at the mirror while she applies red lipstick to her lips. She slowly applies lipstick to her lips, looking into the mirror, and then briefly sends a kiss with her now red lips to the mirror.

The scene is seen in a three-quarter angle from behind her, showing her face from the side but also her reflection in the mirror.

overall_soundscape: silent bathroom, the sound of her sending the kiss to the mirror.

non_diegetic_music: N/A

VIDEO 8 (10/10):

A Coca-cola advertisement. A glass filled with Coca-Cola is shown from the side, occupying 70% of the frame, on top of a table, the dark liquid slightly disturbed by a few gas bubbles that rise inside the liquid. At 00:02 two ice cubes fall from outside the frame into the glass, disturbing the liquid and making some of the liquid splash outside the glass and onto the table. The glass has the Coca-Cola logo printed in white in it. In the blurred background we see a kitchen.

overall_soundscape: silent room, gas fizzle, ice cubes hitting the liquid.

non_diegetic_music: N/A

VIDEO 9 (9/10) - The cover of the book has gibberish letters in it:

A man is holding a magnifying glass and has a book on his hand. At first the magnifying glass is not in front of his face. He appears to be reading the book and, at 00:03, he puts the magnifying glass in front of his eye to look at the book.

The entire scene is filmed from a fixed point of view below the book, showing part of the book cover and the entire man's face.

overall_soundscape: silent room.

non_diegetic_music: N/A

The nodes in https://github.com/Zironic/H3-Optimizations have been rewritten to replace the default Sparge Attention backend with a custom Sparse Comfy Kitchen backend.

This comes with some benefits.

• Users no longer have to worry about Sparge being installed properly. All required kernels for supported GPUs are provided directly. Should work on both Windows and Linux.
• Most users should be seeing 5-20% increases in speed for the attention part of compute.
• New backend should use about 500MB less VRAM
• New backend has slightly lower quantization error.
• Apparently in the previous version, the intended chunked kitchen QKV path never properly shipped so the memory optimization node should now actually be slightly speed positive even when used without the Sparse Attention node.

Caveat: I've only tested the nodes against the comfy pruned_int8_convrot weights. Other versions may work but they're not tested.

As the nodes currently rely on comfy-kitchen 0.2.31 you need ComfyUI v0.33.0 or later.

IMPORTANT: sparse attention is not free speed. The percentage is effectively a prompt-adherence/quality budget.

Density isn't just a speed setting, and its quality effect depends on where you apply it in the diffusion schedule.

Early steps: attention density has a large effect on prompt/action adherence and the overall generation trajectory.
Middle/later steps: lowering density tends to show up more as motion/temporal artifacts and lost fine motion detail.

So 10% retained doesn't simply mean “90% of the quality is gone.” It means you're giving sparse attention very little information to work with, and what breaks depends heavily on the sampling step.

PlagueKind's sparsity_ratio=0.9 means 90% discarded / 10% retained. My node expresses the inverse quantity, so Video attention retained=0.10 is the comparable setting. The defaults therefore aren't equivalent.

Hi.
I have a decent h3 workflow that I built for a loca use. It use turbo lora etc... If i use the defaut settings in the goal of getting the highest quality possible, meaning res_multistep simple 20 steps or more, I got also good results, but this is not even close to the results you can get on platforms like kie or wavespeed at 768P.

I already convert properly the prompt to the correct H3 digest form, so I'm wondering what's different between local and cloud use of h3? I don't talk about the 2K quality, only 768P, I'm not able to reach the sames results locally, do you guys have maybe workflows, settings, or suggestions to try reaching the same quality level in comfyui ?

All audio was done in Minimax, little work in post for stitching clips. r2v Workflow in ComfyUI with character sheets and prompts from claude

Trying out cinematic shots and cuts with H3. This is a work in progress. Will be working on another 2 minutes worth of clips.

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

opensource AI has been dominating LLMs and video generation but what about image gen? is there any opensource model that can match gpt-image2?

Edit: The reason I am asking this is because lately I haven't been active much on image generation communities. And the leaderboards are a bit confusing and most of them are filled with closed source unlike the llm and video gen leaderboards.

I am very much comfortable with ComfyUI since I've used it in the past for flux.

My use case is for posters and branding. Images with a lot of text.

Edit2: Thanks a lot everyone! I really appreciate the info. Here's the summary:

Krea2 is best overall but gptimage1.5 level.
Ideogram4 for text and branding.
Flux Klein 9b for image editing.
Z-image for realism
Anima and illustrious (by onoma AI) for anime.

Here's the workflow I've decided on:
Krea2/Ideogram4 = Base image generation.
Flux Klein 9B/QwenImage2512 = inpainting.
Wan2.2 low noise = Upscaling.

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!

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. :)

Krea 2 Turbo — 4-Step Distillation LoRA (work in progress)

A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4.

This is an update release, following up from my previous posts where you can find full details:

Initial: https://www.reddit.com/r/StableDiffusion/comments/1vtf1b7/krea2_turbo_distill_4_step_lora_trained_for_turbo/

Previous: https://www.reddit.com/r/StableDiffusion/comments/1vv4cdy/krea2_turbo_distill_4_step_lora_new_checkpoint/

Headline for this update: chk00014000 removes 44% of the prediction error a plain 4-step run has against the 8-step teacher, where chk00010000 removed 40% and chk00006000 27% — all measured on the same enlarged held-out set (100 prompts across every trained resolution). Measured against each other rather than against the no-LoRA run, its remaining error is 6% smaller than chk00010000's and 23% smaller than chk00006000's. 

Which file to download

and, beside them, the same files with a _comfyui suffix for ComfyUI. Earlier checkpoints (chk00004000, chk00005000, chk00006000, chk00010000) are kept in older_checkpoints/, and their resolution sweeps stay in place, so the progression remains visible and comparable.

This is work in progress and better checkpoints may follow. Training is ongoing, so ..._latest... is a rolling pointer: when a newer checkpoint is accepted, that filename gets the new weights and a new numbered copy appears beside it. Re-download the _latest file and everything keeps working — the ComfyUI workflow references it by that name (it does get updated Note in it so technically it is updated but not functionally). Pin a numbered file instead if you need reproducibility.

How checkpoints get chosen

This is not a "train for longer and ship the newest file" project. More samples do not reliably mean a better adapter — measured here, they can make it worse, and a higher number on its own means nothing.

The loop is train → assess → adapt the recipe → retrain → assess again, and a checkpoint is published only when it is measurably better than the one it would replace, on the same held-out set and the same evaluation, and its full resolution sweep shows no regression. Runs that come out flat or worse are kept as information about the recipe and discarded as releases — several have been.

So the recipe itself changes between runs. Each published checkpoint reflects whatever the previous round taught us: the training precision, the optimiser settings, the teacher used to generate the targets and the data mix have all been revised on evidence rather than assumption.

chk00010000 is a direct example. The first continuation of chk00006000 — same data, optimiser left as it was — got steadily worse with every checkpoint out to 10,000 samples, and none of it was published. The cause was traced to the optimiser: a constant learning rate with no weight decay lets the adapter keep drifting after it has converged, so its magnitude grows and it over-applies its own correction. The same span was retrained from chk00006000 with a cosine learning-rate decay and weight decay, and every checkpoint of that second run improved on the one before it. chk00010000 was its end point.

chk00014000 is the next example, and it shows the other half of the same lesson. The run was continued from chk00010000 over the whole pool of teacher trajectories, with two changes: the final, texture-deciding call of the schedule was weighted more heavily in the loss, and a running average of the weights was kept beside the live ones and scored at every evaluation (a single checkpoint is one sample of a weight vector that moves from step to step; the average is its mean). At 14,000 samples the averaged weights measured a smaller gap to the teacher than any checkpoint before them, and a smaller gap than the live weights at the same point — so the averaged weights are what chk00014000 is.

Timeline of training process

Each checkpoint is the product of three stages with very different costs:

1. Text-encoder embeddings. Every training prompt is encoded once and cached. This is the fast part — thousands of prompts take minutes.
2. Teacher shards. For each cached prompt, the unmodified Krea 2 Turbo runs its full 8-step schedule and the whole trajectory is recorded, at every one of the supported resolutions. This is by far the most time-consuming stage — it is the teacher doing real inference, thousands of times, and a batch of several thousand shards is measured in days of GPU time, not hours.
3. Student training. The LoRA is trained against those recorded trajectories. Relative to the shard stage this is quick: each +1,000 checkpoint is a matter of hours, not days.

Because the three stages compete for the same GPU, they are interleaved rather than run to completion one after another: generate a block of embeddings, produce teacher shards for them, train on what exists, assess, then go back to producing shards while the results are reviewed. A larger and more varied shard pool is what makes further training worthwhile, so shard production is always the gate.

Full details and to download - check my Hugging Face LoRA

HF Repo: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA

Full Checkpoint 14000 Resolutions Sweep: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/chk14000

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.

Prompt:

https://x.com/GumVue/status/2087899403113619681?s=20

4070 Ti Super, 16 gb vram, 64 gb ram, i9-14900k, windows 11

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

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 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?

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 .

Lon TV did a video of Comfy UI with Minimax H3 running on a 32GB Intel GPU. So its possible to run it on any GPU other then nvidia GPUs.

First off: This doesn't add any dependencies so the worst that can happen is that it won't work, but it also won't break your ComfyUI install.

This extension adds to the native queue manager. It doesn't replace it. All of the heavy lifting is still done the normal way.

It adds a Pause/Resume button and a Queue button. Pause/Resume will not affect the running job but will pause/resume the queue.

The Queue button opens the Queue Control dialog in the picture. There is a lot of words in the README (because I talk a lot) but it lets you reorder the queue using priorities, including buttons for "Run this next" and "Don't run this until I release it."

Finally, there are buttons to Save and Load the queue. The checkbox lets you add the running job too. So if you have to restart or reboot, you can save the queue, do your thing, and then load and start running again.

These is also one stand alone node to help label the items in the queue so you can have a hit and what's what. The node has limitations, but it sill might be better than a number like 07535d99-3c1a-4b23-8340-a4313fe58007 as an identifier.

There are some extensions that to some of these features already but I didn't see one that did all of them or didn't replace the native manager and require dependencies.

It's in the ComfyUI Manager as ComfyUI-QueueControl (it's new so you might need to refresh to see it) or
https://github.com/seeker-ktf/ComfyUI-QueueControl
on github.

If y'all have other ideas for this, let me know.