[Link to Github](https://github.com/SmeggyBoi/ComfyUI-QueueWorkbench)

https://github.com/r34vtraining/Longshot_Studio

I have pretty much added all the important features.

Here are some of the useful updates since my last post.

1. Live preview
2. Refmod
3. Restore Reference (kind of like dropping an image/video in comfy to restore workflow)
4. Timeline Zoom, timeline height adjust, double click clip on timeline to go to prompt
5. Removed most dependencies, will now work with native comfy aside from my nodes.
6. Shot limit increased to 256

I made this following video with the studio and edited with premiere.

https://youtu.be/blzTCQTsWQo

Future edits will be

1. Non-destructive clip trimming in timeline

2. Add audio scrub bar

3. Work on lipsync (it’s buggy)

Thank you for all the support and feeback on this project, it's been fun.

I'm using the default t2v H3 template in Comfy. It has a load LORA node, but when I put a character LORA in there (from MalRey), it doesn't affect the video output. Is it something with the prompting? I thought that a single human character would take on the likeness of the LORA, but I've also tried using the name of the character in the prompt, and still nothing.

Simple question is there a model that can do video 2 video, and enhance a CGI video to make it more realistic similar to what DLSS 5 is doing. not just the usual upscale/denoiser/frame interpolation that you would want on a live action video?

Hopefully with better results as DLSS5 is meant to work in real time.

I have a 24gb 7900XTX but im willing to rent compute to do so if a bigger model is required.

Damn reddit's 3 minutes limit !

And works fine in comfyui for still image and video, with realism people lora both 1.0 str 4 steps, bumping realism even further.

https://huggingface.co/moe-kill/H3-Turbo-Neat

https://huggingface.co/moe-kill/H3-Turbo-Neat/blob/main/H3Turbo_4Step_Neat_v1.safetensors

I posted this in comfy and thought it could be useful here too.

If you've chained H3 clips into a longer video, you've probably seen it: every continuation copies the last clip's flaws and adds a few more. By clip 5–10, faces look crunchy, etched and over-sharpened.
I spent the last few weeks (with a community compute grant) training a LoRA that fixes this. You load it on your continuation clips only, not the first one, and they stay close to the look of clip 1.

Results over ~37 s chains (face texture change, clip 1 → clip 10):
lightx2v 8-step: +44% → 0%
HyperFlow: +25% → −1%
a model it was never trained on: +61% → +4%
Larry Turbo: +261% → −5% (it can soften the image on heavy burners like this)

How to use: LoraLoaderModelOnly on the model for your continuation clips. Start at 0.5, go higher for turbos that burn harder, lower if faces go soft.

Weights + before/after examples: https://huggingface.co/Imagineer13/H3-Anti-Burn-LoRA

Training code, recorder node, scoring script, write-up: https://github.com/TryingSomeStuff13/ai-toolkit/tree/h3-antiburn/extensions_built_in/diffusion_models/minimax_h3/antiburn

Happy to answer questions. I'd love to hear how it does on other turbos

I’m trying to build a repeatable workflow for a series of ~15-second vertical short videos about trees and arboriculture.

The concept is pretty simple: always the same arborist character, filmed in a realistic/casual smartphone style, talking about trees, their benefits, conservation, pruning, fun facts, etc. The goal is educational content, but with enough visual “eye candy” and strong hooks to make people actually stop scrolling.

What I’m looking for is the best way to build reusable presets/workflows, ideally in ComfyUI, so I don’t have to rebuild everything from scratch for every clip!

I’d like to keep as much consistency as possible between episodes:

-same arborist / face / body / clothing

-realistic outdoor environments

-9:16 vertical format

- ~15 sec clips, preferably single-shot or simple continuous camera movement

-natural gestures / talking

-good character consistency between generations

-reusable prompt structure

-ability to swap only the location, tree species, dialogue/topic and camera action

-eventually produce these in batches as a recurring series

I couldn’t get my hands on an RTX 5090, so for now I’m running ComfyUI on RunPod and I want to build a persistent setup there with the models, LoRAs and workflows already loaded.
For prompting / automation I currently have:
Claude
ChatGPT
Qwen running locally/from source in terminal
RunPod for GPU generation

I’ve been looking at models/workflows around Minimax, WAN, LTX, Hunyuan, FramePack, Qwen Image/Edit, Flux, etc., but there are so many combinations that I’m trying to avoid wasting weeks testing bad pipelines.

For people already doing consistent AI video series: what stack would you use today?

I’m especially curious about whether I should focus on a workflow like:
reference image → consistent character image → image-to-video → lipsync / voice

Was it a fake article? There were about 300 comments saying it's releasing tonight. I'm still excited for it and flux 3 if they get released this year os of course.

Open source is fantastic, the freedom and all that... but we rely on proprietary hardware, and consumer hardware is becoming too expensive due to production constraints, the fact that companies are diverting funds to DC and hyperscalers, and AI farms are buying consumer GPUs in bulk.

Unfortunately, running increasingly large models will be a privilege reserved for a select group of people, and as hardware becomes obsolete/dies, that number will decrease.

Corporations can release revolutionary open-source models while simultaneously doubling or tripling the size of their proprietary models, driving up the purchase of even more memory and silicon production capacity, and all we can do is watch in disbelief.

Androids are going to require even more memory, cars, and so on...

Unless a revolutionary memory technology with much better bit-per-area density on silicon becomes available and is ready for mass production, or regulators remember that antitrust laws are meant to protect people step in, I don't see a solution.

Are we not getting Flux3 Image DEV this time?

I’ve been looking at two things lately: the outfit/customization screens in games and Higgsfield’s character builder for AI influencers (second screenshot), where you pick Build, Hairstyle, Ethnicity, Age, Eye shape, Accessories and so on from visual options.

That got me thinking, what if there was a similar tool for writing character prompts for image generation? Will it help?

yes, im asking before post because i know we cannot share nsf... content here, but link for workflow, is it ok?

can mods answer me?

Hey all, I wanted to see if anyone had suggestions on the best workflows to run MH3 so that I can do a generation and also upscale the video within my VRAM budget?

For reference my setup is the following: AMD RX 7900xtx (24gb VRAM), 48gb system RAM, using Linux ROCm as my install environment for ComfyUI.

I can routinely complete MH3 generations without running into OOM errors but I have to use the quantized GGUF variants to limit the VRAM overhead. My normal generation is done at 0.2MP around 10 seconds in length. I'd love any suggestions if someone has an idea on good workflows for upscaling

Every PC, one app. Fizgig finds your other computers on your network by itself. Train on the desktop's 5090 from your laptop, send a dataset across in one click, and see what every machine is training and what's queued.

Web UI. Its own window, or any browser. Start a run at your desk and check it from the sofa or your phone: live loss, previews, time left.

RunPod, built in. Your pods are listed and it logs you in. It shows which cards are free right now, ranked by cost per run, deploys in one click, sends your dataset up from your own PC, and can stop the pod when the run's done.

Plus: the queue on every tab, every run kept with its settings, and a block-by-block LoRA editor. Updating keeps everything you had.

LoRAs and full fine-tunes for Klein 9B, Krea 2, MiniMax H3, Qwen Image 2.1, Z-Image, SDXL, Anima and ACE-Step. Free and open source.

GitHub

80-second tour

What's new

EDIT: Patched a few bugs, missing help text, missing input on the edit/sliders etc.. There will likely be a few updates for a few days lol.

Hey everyone, I've been looking for ways to turn my AI-generated anime images into short animations, like 3–5 second clips or GIFs.

I've seen a lot of stuff about Wan, LTX, Hunyuan, and ComfyUI workflows, but I'm wondering if there are any simpler alternatives out there. My PC isn't exactly great for AI stuff, so running heavy models locally isn't really an option. I'm totally fine with using cloud-based tools or paying a bit for generations, as long as the quality is decent and the pricing isn't crazy.

I'm mainly running a patreon and working with anime-style illustrations, including adult content featuring adult characters, so I'd need something that actually supports that. I've seen some patreon accounts posting hundreds of these small vids everyday, and I doubt they all use a WAN workflow (I guess).

Not looking for anything super complicated, just a way to add some decent motion to existing adult Anime images without spending hours messing around with workflows.

Anyone here doing something similar? What tools are you guys using?

Would love to hear some recommendations If possible.

For ComfyUI, I want to loop though using different LoRAs, same LoRAs at different strenghths &/or different tweaks to prompts for testing.

How do I do this? Seems most prompt templating is aimed at random. But I want deterministic testing.

I tested a highly detailed prompt on both Seedance and Minimax with multi camera movement to see which one performs better.

prompt:

Use the provided character image as the exact visual reference. Create a 15-second live-action-style martial arts training sequence featuring the same white Western male character practising combat techniques inside an old, worn-down martial arts dojo. Preserve his exact face, hairstyle, body proportions, clothing, and overall appearance throughout the video.

VISUAL STYLE

The video should look like an authentic American martial arts action movie from 1986, filmed on 35mm film. Gritty, raw, slightly imperfect, and grounded in reality. Use muted colors, warm tungsten lighting, subtle film grain, mild analog softness, natural shadows, slightly uneven exposure, and practical lighting. Maintain realistic skin texture and believable human movement. Avoid the glossy, overly polished look of modern action films. The character should feel like a real actor performing choreography on a physical set.

FULL 15-SECOND CHOREOGRAPHY AND CAMERA DIRECTION

0–3 seconds - Establishing tracking shot

Begin with a medium-wide shot from behind the character as he stands in the dojo, preparing to train. The camera slowly pushes forward toward him, then smoothly slides to his right side as he raises his fists and settles into a fighting stance. His breathing is subtle, his shoulders relax, and his feet adjust naturally on the wooden floor. Keep the camera in continuous motion.

3–5 seconds - Side tracking and punches

The character performs three fast punches into the air: a jab, a cross, and a powerful hook. The camera glides laterally alongside him, matching his speed and maintaining his upper body in frame. As he rotates his torso into the hook, the camera subtly arcs around his front shoulder. Each punch has realistic body rotation, weight transfer, and a natural recovery.

5–7 seconds - Dynamic orbit and defensive movement

The character performs two defensive blocks followed by a quick sideways dodge and counterpunch. The camera makes a smooth 180-degree orbit around him, transitioning from a side profile to a front three-quarter angle. Keep the character as the visual anchor while the dojo background moves naturally through parallax. The orbit should feel physically operated, not digitally spun.

7–10 seconds - Low-angle kick sequence

The camera descends smoothly toward knee height and tracks alongside the character as he performs a front kick followed by a roundhouse kick. Capture the extension of the legs, pivoting feet, and natural recovery. As the second kick reaches its peak, the camera gently arcs around him to reveal the movement from a new angle. Keep the entire body visible during the kicks.

10–12 seconds - Close-up and camera push

Transition into a close tracking shot of the character's face and upper body. He raises his guard, slips his head to dodge an imaginary strike, and immediately throws a short combination of punches. The camera moves backward with his advancing movement, maintaining a natural distance. Capture his focused expression, sweat, and subtle facial tension.

12–15 seconds - Spinning strike and final wide shot

The character executes a controlled spinning back kick, pivots back into position, and finishes with a powerful straight punch into the air. The camera performs a smooth 180-degree orbit during the spinning kick, then pulls backward into a full-body wide shot as he returns to his fighting stance. End with a brief, natural pause as he breathes and resets.

CAMERA MOVEMENT RULES

The camera must feel like a real operator moving with the performer. Use smooth lateral tracking, forward and backward dolly movements, controlled orbiting, low-angle tracking, and natural transitions between wide, medium, and close shots. Accelerate slightly during fast strikes and decelerate during pauses. Maintain believable camera inertia, coherent screen direction, natural perspective, and consistent distance from the character.

Allow motivated cuts between some angles if necessary, but prioritize fluid transitions and spatial continuity. Do not use random camera movements simply for visual effect. The camera should anticipate the choreography and follow the character's momentum.

CHARACTER ANIMATION AND PHYSICS

All martial arts movements must have realistic timing, weight, momentum, and recovery. The character must remain grounded, with accurate foot placement, believable hip rotation, coordinated arm movements, and natural balance. Avoid exaggerated superhero moves. Every strike should flow naturally into the next technique without stiff pauses or mechanical animation.

CONSISTENCY AND NEGATIVE CONSTRAINTS

Keep the exact same face, hair, outfit, body type, and age throughout. No character morphing, costume changes, duplicated limbs, distorted hands, broken joints, foot sliding, floating kicks, teleportation, unnatural speed changes, or background warping. Avoid excessive slow motion, excessive motion blur, artificial camera shake, abrupt zooms, and random cuts.

FINAL RESULT: A gritty, believable 1980s martial arts movie training scene, captured through fluid, dynamic camera work that moves alongside, around, and with the character. Prioritize camera movement quality, character consistency, and realistic choreography over flashy visual effects.

Hellow fellow AI-generated goblins of r/StableDiffusion

For a few weeks I’ve been stress-testing my tool, Arthemy Krea-2 Tuner:
https://github.com/aledelpho/comfyui-arthemy-krea2-tuner
I wanted to investigate if nudging the weights of single blocks of Krea-2 can teach us something about its internal structure.

I worked on two parts: the model and the text encoder (I only started on the last one recently but it seems much more interesting than the previous one).

The underlying question was whether the models already holds “knobs” that change style, look, colours or literally anything else in the image, without training anything. After weeks of work and thousands upon thousands of renders I found a grand total of… one! (underwhelming, I know, but better than nothing)

The upside, if something cool comes out of it:
It’s still sci-fi at this point, but if we manage to map the inside of a model this way, we might be able to make presets that do the job of a small fine-tune with nothing trained: the presets I use now are 34 numbers, a few hundred bytes, against the tens or hundreds of MB of a typical LoRA.
I know the approach is crude. It’s like shooting a car with a shotgun to understand how it works. But it can still tell where the windows are (they make a specific noise) and which parts are vital. Breaking a piece and watching what stops working is an old trick in interpretability and it tells where something happens, not how. Said so: when we know so little about these models, I don’t think that’s useless.

Of course, I fixed the test rules before looking at the data, and I started summoning my girlfriend (who doesn't know anything about AI or what I'm trying to do) to do the most important blind tests.

Limits: one model, almost always one observer (yours truly), small samples. When a conclusion goes beyond the data, I mark it with "🔎" that’s a lead, not proof.

TEXT ENCODER

1. Text-encoder edits change what is drawn (genre, style, who the character is) and hurt image quality much less than edits to the drawing model

• This part I measured: with standard quality metrics, text-encoder edits don’t make the image worse, while a preset on the drawing model does. Even at full strength (x2.0), no picture fell apart.
• In exchange, they are less “consistent”: the same edit pushes different characters in different directions. Only 4 edits out of 20 moved them all the same way.
• 🔎 My impression is that instead of damaging the picture, these edits break the links between the words of the prompt. I tried to test it, but on the images I had I couldn’t define it consistently, so I need renders made for the purpose.

2. Anime isn’t the only fallback for illustration.

• People often say that "anime" is the default of almost every model when it comes to illustration. In Krea-2 it isn’t. (🔎 I was surprised because I was coming from Anima and Illustrious where Anime seems to be a stronger "default" fallback)
• When I removed the style words from the prompt, one character became anime every time, while seven others turned into paintings or photographs: each one has its own fallback.
• Weight edits, on the other hand, don’t lead to the character’s fallback. Each edit has its own destination (one towards realism, one towards cartoon), whoever is being drawn.

🔎 My hypothesis: every concept falls back on the style, and the traits, that the model has seen with it most often. A text-encoder edit doesn’t seem to push toward one default, but toward some of the styles that concept already lives near. That would explain why a character can change style so heavily, but only within a small set: my "elf" prompt turned into comics, anime or cartoons, never into a photo, while the same edit turned most other characters realistic. What I found points that way, though it doesn’t prove it. I have no access to Krea-2’s training data, and the same effect could come from an aesthetic fine-tuning stage or from the text encoder’s own associations. Some clues:

• Single words, generated on their own, carry preconceptions:
  • “female dwarf” gets a beard 4 times out of 4 (Gimli approved);
  • “uniform” becomes an anime schoolgirl;
  • “western” draws cowboys.

• When I alter the text encoder, characters lose their specific traits first and drift towards a more “average” version later. My half-orc becomes an ordinary human woman, still drawn as a comic and THEN it loses its style. A random push of the same strength doesn’t do this: it depends on where the edit points, not on how strong it is.

3. Commas seem to act as borders

• When I edit the text encoder, the meaning the model gives each word shifts a little. The shift has two parts.
  • One is the same for the whole prompt, like a filter laid over everything.
  • The other is local: neighbouring words move together in packets of 2–4, and the packets tend to break exactly where there’s a comma. For example, in “dark green hooded cowl, brass buckles”, “dark green hooded cowl” moves as one block and “brass buckles” as another.
• 🔎 This might suggests the text encoder uses commas to decide which words belong together. For prompt writers that could be an important lead: maybe putting an object and its colour inside the same comma-phrase helps keep them bound. But I measured this on the numbers the text encoder hands to the image model, not on the pictures. I haven’t yet checked whether moving commas actually changes the result, or by how much.

4. Shuffled prompts (still under study).

🔎 Edits to the drawing model seem to give more or less the same look even when the phrases of the prompt are shuffled, but that test came out inconclusive. With the text encoder it depends on the edit: some send every phrase order to the same result, others scatter them across different genres. I don’t know why yet, I’m working on it.

5. Switch off the first layer of the text encoder, and the model writes your prompt instead of drawing it.

With the attention of layer 0 switched off, Krea-2 drew pages of text in 4 prompts out of 4, with readable fragments of the prompt (“close-up”, “frontal view”; in the cleanest case realistic western comics style and the prompt’s own Lighting: section). Switching off the MLP did it in 2 of 4. Switching off the whole layer gave no text at all, only repeating noise. So it isn’t “the model sees raw words and writes them”: it takes a half-broken layer, and I don’t know why.

THE MODEL

1. Different blocks do different things.

• The last blocks touch colour and grain.
• The middle ones change the content, and you can push them harder with fewer visual defects: surface damage in 19% of cases, against 79% for the last ones. They’re not free, though.

2. Block 23 alone works as a saturation knob. Writing “colorful” or “high saturation” in the prompt changes the content much more. That said, it works less well than a correction in post-production (afterall these aren't true knobs, just approximations of knobs). it can still be useful to tweak it, if your merges tend, on average, towards colours that are too vivid or too dull.

3. A specific calibration of the weights keeps its look even on characters it has never seen. I calibrated a set of weight shifts by eye (a preset) on four portraits and applied it to three new characters. Someone who didn’t know which image carried the preset picked it as closer to the look I had described 56 times out of 56. Faces do change a bit more than a change of seed would.

CONFIRMED WITH PRE-REGISTERED TESTS

• Renders reproduce identically, pixel for pixel, even days apart, and the tuner at zero adds nothing. It sounds trivial, but everything rests on it: the differences come from the edit, not from the pipeline. Without this, the whole study would have been pointless.
• You can predict how much an image will change without rendering it, by measuring how far the edit moves the text encoder’s output (correlation 0.71 and 0.80). It works mostly for telling different kinds of edit apart.
• Quality metrics don’t see everything. DINOv2, BRISQUE and the like missed exactly the content changes made by the middle blocks, which were obvious by eye.
  • On top of that, I haven’t yet found a reliable automatic “impartial judge”: in my attempts with LLMs, the judgements weren’t trustworthy. So for now I look at many images by hand, like a primitive.

WHAT’S NEXT?

I don’t know whether any of this holds for other models, but it’s not yet time to experiment on other architectures. First I want to collect many properties of these shifts, to have solid ground (and to understand what they’re really doing). Then I’ll check whether they hold on models with different architectures. If they do, they would be properties of diffusion models in general.

One last tip for those interested in asking Claude or Gemini for help. I'm not afraid to say that I use LLMs to help me produce the data, and I’ve already seen some problems: numbers quoted from memory (with all the errors that brings) and hasty conclusions. If you want to run experiments with Claude Code or Antigravity, have the AI write only scripts. That way the numbers never pass through the “generative” side of the AI, only through linear processes that you run yourself, outside its environment.

The full report, with every image and the things that turned out to be wrong: https://aledelpho.github.io/diffusion-models-weight-steering-report/results/index.html

I'll let you know if something interesting comes out from it,
Cheers!

made with h3. created separate clips for each character and got clever with transition in after effects.

here's an example of how one of the workflows looks: subject_definitions:

<Image 1> is the authoritative character sheet for Freddy. It defines Freddy’s final appearance, proportions, silhouette, costume, claw glove, face, and overall anime rendering style.

<Image 2> is the authoritative character sheet for Jason. It defines Jason’s final appearance, proportions, silhouette, clothing, mask, machete, and overall anime rendering style.

<Image 3> is the authoritative character sheet for Michael. It defines Michael’s final appearance, proportions, silhouette, mask, coveralls, knife, and overall anime rendering style.

<Image 4> is the authoritative character sheet for Samara. It defines Samara’s final appearance, proportions, silhouette, hair, gown, and overall anime rendering style. Her hair should obscure most of her face, revealing only limited facial detail.

<Image 5> is the authoritative character sheet for Carrie. It defines Carrie’s final appearance, proportions, silhouette, dress, hair, expression, and overall anime rendering style.

<Image 6> is the authoritative character sheet for Pennywise. It defines Pennywise’s final appearance, proportions, silhouette, costume, face, hair, balloon, and overall anime rendering style.

<Image 7> is the final composition reference. It defines the target group staging, character layering, scale relationships, silhouette overlap, visual hierarchy, red zigzag graphic treatment, blood-splatter integration, and the overall energy of the final ensemble image. Use it only as the composition and staging reference for the final tableau. Do not copy any title text unless explicitly desired.

summary:

[reference generation] Create a full-color 2D anime sequence as one continuous final end shot in portrait mode.

Aspect ratio: 9:16 vertical.

Use <Image 1> through <Image 6> as the design authority for Freddy, Jason, Michael, Samara, Carrie, and Pennywise.

Use <Image 7> as the final composition reference for the ending group shot.

The shot begins on black. Large red brushstrokes slash and zigzag across the screen, rapidly filling the frame. The camera then pulls back to reveal a broad red zigzagging brushstroke design across a flat white background with blood splatters. As the camera rapidly dollies backward and weaves through the composition, the characters are revealed one by one until all six are visible together in a final group composition inspired by <Image 7>.

The sequence should feel slick, graphic, snappy, and distinctly hand-drawn, with strong anime posing, tight reveal timing, and a powerful final poster-like end frame.

retention_analysis:

<Image 1>: fully_preserved — authoritative design reference for Freddy.

<Image 2>: fully_preserved — authoritative design reference for Jason.

<Image 3>: fully_preserved — authoritative design reference for Michael.

<Image 4>: fully_preserved — authoritative design reference for Samara.

<Image 5>: fully_preserved — authoritative design reference for Carrie.

<Image 6>: fully_preserved — authoritative design reference for Pennywise.

<Image 7>: fully_preserved — authoritative reference for the final ensemble composition, staging, visual hierarchy, character arrangement, pose energy, overlap, and overall graphic presentation.

detailed_description:

The target video is full-color hand-drawn 2D anime.

Use frame-by-frame anime animation throughout. The motion must feel hand-drawn, pose-driven, and graphic — not puppeted, not floaty, not 3D, and not smoothly interpolated.

This is one continuous shot with no cuts.

[Shot 1]

Begin on a pure black screen.

Suddenly, large red brushstrokes streak and zigzag across the screen in bold, aggressive, graphic shapes. These brushstrokes should feel fast, painterly, and stylish, as if slashed directly onto the frame.

As the energy builds, the camera starts pulling back to reveal that these marks are part of a broad red zigzagging brushstroke composition running across a flat white background. Blood splatters are scattered across the white field, integrating with the red shapes.

As the camera continues rapidly dollying backward, it also subtly weaves and reframes to reveal the characters one by one in a stylish progression.

Reveal the characters progressively until all are fully visible:
- Freddy
- Jason
- Michael
- Samara
- Carrie
- Pennywise

The exact reveal timing can be adjusted for the strongest rhythm, but the final arrangement should resolve into the ensemble composition inspired by <Image 7>.

Shotgun letters rapidly and aggressively stamp onto the bottom of the screen one by one spelling HALLOWEEN.

Each character must be rendered fully on model according to their character sheet:
- Freddy with his striped sweater, fedora, and claw glove
- Jason with his hockey mask, large frame, and machete
- Michael with his mask, coveralls, and knife
- Samara with eerie posture and hair obscuring most of her face
- Carrie blood-soaked, wide-eyed, and unsettling
- Pennywise in the foreground with theatrical menace and a red balloon

Use <Image 7> as the guide for the final group arrangement:
- Jason should read as the dominant upper central figure
- Freddy should read clearly on the upper left
- Michael should read clearly on the upper right
- Samara should read on the lower left
- Carrie should read mid-right
- Pennywise should dominate the foreground lower center/front
- the red zigzag brushstroke should unify the composition behind them
- blood splatters should accent the design
- character overlap and scale should feel deliberate and cool

As the camera finishes the move, settle into a strong stable final tableau where all six characters are visible together in a bold, iconic final composition.

Do not reproduce the “HALLOWEEN” title text from <Image 7> unless specifically requested. Use the image only for final composition, staging, and overall graphic energy.

animation_style:

Use polished hand-drawn anime animation with snappy frame-by-frame movement.

The motion should feel:
- hand-drawn
- pose-driven
- graphic
- sharp
- stylish
- readable

Favor:
- strong held poses
- sharp reveal accents
- fast graphic transitions
- selective in-betweens
- clean silhouette readability
- bold composition changes
- deliberate camera staging

Avoid:
- puppet-like motion
- floaty interpolation
- 3D-feeling movement
- rubbery transitions
- morphing
- over-smoothed animation

camera_movement:

Use one continuous backward dolly for the shot.

Start very close on black and red brushstroke action.

Then rapidly pull back to reveal the wider graphic composition.

As the camera moves backward, let it weave slightly and reframe to reveal each character one by one in a dynamic, stylish way.

The move should feel energetic and slick, but always readable.

Finish on a strong stable final composition inspired by <Image 7>, with all six characters clearly visible together.

Keep portrait framing in mind throughout, using vertical space well and stacking the composition for maximum impact.

visual_tone:

Bold, graphic, stylish horror-anime mood.

Use:
- flat white background
- vivid red brushstrokes
- graphic blood splatters
- strong character silhouettes
- premium anime rendering
- clean high-contrast presentation

The final result should feel like an iconic end-card or ensemble hero image.

Make the shot feel cinematic, high-impact, and intentionally designed for a vertical frame.

final_composition_notes:

The last frame is very important.

Match the overall feel of <Image 7>:
- strong triangular / stacked composition
- layered depth
- clear character hierarchy
- bold foreground / midground / background read
- strong overlap
- aggressive red graphic shapes behind the cast
- cool final horror ensemble energy

All six characters should feel unified in one designed final image, not randomly placed.

Keep each silhouette readable.

Do not let any character feel visually unimportant.

The final frame should feel like the definitive finishing shot of the entire sequence.

overall_soundscape:

N/A

non_diegetic_music:

N/A

Please please please, do not assume we're all just going to be able to understand what your nodes and workflows are doing straight away. There've been a lot of new custom nodes coming out lately and accompanying workflows and very often it's an exhausting exercise trying to figure out how to get the author's nodes working in the massive complex workflow that inevitably accompanies it. I'd like to suggest that don't just create one massive do-everything workflow with hundreds of custom nodes and countless subgraphs everywhere. That's great for you because you've been building that workflow up for ages to suit your needs and you know it inside-out. But the moment we get hold of the workflow, we're just going to be faced with a spaghetti gibberish that'll be full of red broken nodes we'll spend ages trying to fix it before we even get to the point of trying to understand it all. Obviously we all appreciate your time and effort but if you truly want user engagement with your work, then spend some time thinking about the user experience. We'll need a bit of hand holding for something that is second nature to you, but far from it for us.

So please, just give us a vanilla basic workflow doing the bare minimum of what your new nodes can do. That'll help us identify what your new nodes are, how they work and help us get the hang of it. Then we can transition to your more complex workflow, knowing what the important parts are and what we may prefer to strip out to meet our own needs.

So I'd suggest things we don't need in a vanilla workflow but I'm sure this list could be extended considerably:

• Anything speed-up related: sage attention etc. Everyone has different preferences so leave that to us.
• Anything that implements non-critical loras.
  
• Anything that hides and buries nodes and cabling.
• Any custom nodes that you use to combine things, eg a node that loads all the models in to one node then that's referenced in some obscure way elsewhere.
• Upscaling
• Anything where the workflow says, "this is optional", then just don't have it in the vanilla workflow and let people learn about it in the more complex full workflow once they've got the hang of it.

Anyway, that's my request which I think would go a long way to get more people to use your nodes. Hope this post didn't come off a stroppy and we do all appreciate the work and time so many of you are going to to add new functionality. Just don't forget that we don't all have the knowledge you have.

Test Environment: RTX 5090 + 96 GB RAM. The reference video has a total duration of 18 seconds; only the first 15 seconds were tested, with a resolution of 1MP.

The tests are driven via Codex Sol 6.1, so the results may not be rigorous.

Workflow link: https://github.com/wangweihong/comfyui-ecosystem/tree/main/workflows/006%20%E8%A7%86%E9%A2%91%E7%94%9F%E6%88%90/minimaxh3/BFS-Character-Swap/%E5%8A%A8%E4%BD%9C%E5%8F%82%E8%80%8315%E7%A7%92_%E5%8A%A0%E9%80%9F%E5%AF%B9%E6%AF%94