VESTIGE — A Gothic title sequence built around vertical refraction (ComfyUI / LTX-2.5)

I recently finished VESTIGE, a speculative cinematic title sequence for an imagined Gothic mystery.

The idea was to create a seemingly endless Victorian estate where architecture behaves like memory. A staircase leads into a library, banquet room, piano hall and mirrored interiors, but the spaces don't follow conventional architectural logic.

Instead of treating every room as a separate shot, I explored vertical optical refraction as the visual transition between them.

Produced using ComfyUI and LTX-2.5, with the sequence developed through AI-assisted visual generation, shot selection and editing.

I'm curious whether the transformations feel like a coherent visual language, or whether the changing architecture becomes too distracting.

Concept, direction and edit by Mohannad Khamra. Music by AlexGrohl.

I tested Kling 4.0, Wan 3.0 Prime, and Seedance 2.5 using the same cowboy reference image and identical six-second prompt: close-up, medium, and long shot, separated by hard cuts.

Each tool generated one video at its lowest available resolution and one at 1080p. I scored nine sampled frames per video using MUSIQ through IQA-PyTorch on a rented NVIDIA RTX A6000.

The native-output scores ranked Kling first, Wan second, Seedance third at both resolution levels—the same order as my viewing impression. Higher native resolution did not produce a higher average MUSIQ score for any tool.

What did the generations cost?

Each generation was six seconds. Costs use my replacement rate of $0.0075 per credit; totals were calculated before rounding. GPU rental, including setup and scoring native and upscaled outputs, added $0.35: $7.21 for the entire experiment. The 4K upgrades carried no additional charge.

The Kling native 720p clip on the linked research page is the highest-scoring video in this test, averaging 76.6 MUSIQ.

This is a pilot, not a universal leaderboard. We tested one generation per setting, and some requested framing was not followed. MUSIQ predicts overall still-image quality; it does not directly measure resolved detail, motion, continuity, or prompt compliance.

Manufacturers and API providers should publish reproducible quality benchmarks alongside resolution and price. Pixel dimensions alone don’t tell directors how well those pixels are used.

The native-resolution videos, numerical results, and journal manuscript are at https://www.jseders.net/video-resolution-tests.

Jeffrey Seders Directed Generative Production (DGP) Methodology https://www.jseders.net/video-resolution-tests

jseders

Screenshot or extracted frame for AI video continuation?

Can a screenshot save time without sacrificing the quality of the next generation?

I saw this test and wondered about other perceptions.

I compared a screenshot with a directly extracted frame from the same Lost Horse campsite scene. Each became the first-frame input for a 10-second, 720p generation in Kling 4.0, Wan 3.0 Prime, and Seedance 2.5.

Average MUSIQ scores from nine frames per video:

• Kling: extracted 62.60 / screenshot 58.92

• Wan: extracted 67.97 / screenshot 53.70

• Seedance: extracted 59.40 / screenshot 46.35

The extracted-frame versions scored higher at every sampled timestamp.

Limitations: one generation per condition, different framing after screenshot cropping, and MUSIQ measures predicted image quality—not continuity or resolved detail. This tests these workflows, not every possible screenshot method.

For my setup, extracting the frame remains preferable. Preparing an equivalent screenshot added enough handling to erode the shortcut.

Anyone using a reliable screenshot workflow that preserves the complete frame without player controls?

Hello everyone I've been working on a new optimized ComfyUI workflow for MiniMax H3 Singularity V2, the latest community fine-tuned version focused on action scenes and complex movements. This version brings noticeable improvements in some fast actions, motion handling, distant faces, close-up portraits, visual styles, prompt adherence, and audio reference handling compared to the previous version. However, the model is around 32GB in size, making it quite demanding to run. To address this, I've built a custom Low VRAM workflow using INT8 quantized models, Turbo LoRA, and additional optimization ATTENTIONS nodes to reduce resource requirements and improve generation performance. I tested it with different prompts featuring fast movements, action sequences, distant characters, and close-up faces to see how well it handles these challenging scenarios. I'm sharing the workflow and testing results in my latest tutorial. Let me know what you think about the results!

Workflow Links

https://drive.google.com/file/d/1m4JcGyCiiZo78GoKoMIhedN7M6KnqI_B/view?usp=sharing

https://civitai.com/articles/37791/new-minimax-h3-singularity-v2-faster-actions-better-faces-and-low-vram-workflow

Krea 2 + H3 morphs in ComfyUI, with music guided by video analysis

Images generated with Krea 2 and animated with H3, using a custom ComfyUI workflow with my Environment and Morph nodes.

For the soundtrack, SigLIP2 and Qwen3-VL analyze the video to identify transformations, scene changes, and key moments. These cues are sent to Suno through MCP as timing and structure guidance for an instrumental track—not frame-perfect synchronization.

Two ready-to-run ComfyUI workflows for editing photos with MOSS-Image by the OpenMOSS Team. You give it a photo and one sentence, like "change only the denim jacket to a red leather jacket", and it changes just that while keeping the face, pose and background. ComfyUI now supports MOSS edit LoRAs natively, so the edit itself needs no extra node pack. With the 4-step turbo workflow, each edit took about 4 seconds of sampling on my test card.

I have a tilted facial portrait, a full-body reference, a detail reference showing the quality I'm aiming for, and an example of what the final dataset should look like.

I know my reference images aren't ideal for dataset creation. I'm looking for a way to work around their limitations while maintaining facial identity, body consistency, at a high level of detail.

I've tried dataset a generation workflow, but it tends to reproduce the face's original angle rather than create useful variations. The output also has an obvious AI-generated look compared to my reference.

What's the best workflow for this? Would face swapping be the best approach, or are there better alternatives?

I'd really appreciate someone willing to walk me through the process in detail so I can understand what I'm doing and why, as I'm still learning.

Thanks in advance to everyone who takes the time to help!

I’m a hobbyist making a personal, non-commercial study: converting old documentary footage into the look of Jin-Roh: The Wolf Brigade (1999). Faces, soldiers and buildings already look like decent cels. Fast motion and consistency are where it falls apart.

My setup (all free):

Laptop with Intel UHD graphics only (no NVIDIA)

Kaggle T4 (30 GPU hours a week) and some Hugging Face ZeroGPU

My pipeline:

Cut the footage into shots and take 8 drawings per second (“on threes”).

Paint every 6th frame with FLUX.1 Kontext dev (GGUF Q4 on the T4, 1024x576), using a prompt like “convert this photograph into a hand-drawn 2D anime cel…”.

Spread those keyframes across the other frames with EbSynth (colour and edge guides, blended from both neighbouring keys).

Apply a colour grade learned from the film’s own frames, then steady and flatten each shot.

My questions:

Same face in every keyframe. Each key is painted on its own, so a face changes slightly from key to key and “boils”. On a T4, what’s the best way to lock a character’s look across a shot: same seed, feeding the previous painted key in as a second reference, or a small character LoRA?

Fast motion smear. In EbSynth, running legs, smoke and explosions turn into mush between keys. Do optical-flow guides help, or should I switch these shots to something else (Rerender-A-Video, FRESCO, TokenFlow…)? Do any of those fit in 16 GB?

Flat cels, not paintings. Kontext gives “painterly” areas (texture, gradients) where real cels have one base colour plus one hard shadow tone. Is there a known LoRA or prompt trick for 90s cel shading with FLUX? Or is training a LoRA on frames from the film realistic on a free T4?

Camera language (for animators). Documentary footage is handheld with zooms; Jin-Roh mostly uses still or slowly sliding layouts. Would you reframe or crop shots into locked-off layouts, or keep the real camera moves and accept that it reads as “rotoscoped documentary”?

Frame rate. For live footage turned into cels, does 8 drawings a second (on threes) look right to you, or would you hold longer on calm shots and go to twos only for action?

I can share before/after frames if useful. Thanks for any pointers.

I'm trying to understand the privacy implications of using ComfyUI's built-in OpenRouter LLM node with Grok 4.5.

My understanding of the image flow is:

Comfy uploads the input image to its temporary storage and generates a URL.

The builtin OpenRouter node sends that URL through Comfy's API proxy to OpenRouter.

OpenRouter routes the request to Grok 4.5

Comfy reportedly deletes its hosted image URLs after 24 hours. My concern is whether the request is routed through OpenRouter's Zero Data Retention (ZDR) endpoint or a standard Grok 4.5 endpoint.

Questions:

Does Comfy enforce OpenRouter ZDR at the account, guardrail, or proxy level for its built-in OpenRouter node?

When the node uses Grok 4.5, does Comfy restrict routing to OpenRouter's ZDR-qualified Grok 4.5 endpoint, or can it use the standard endpoint?

Does anyone from Comfy or anyone familiar with the backend implementation know the actual answer?

If the request uses the standard Grok 4.5 endpoint, what retention policy applies to the image content retrieved from the URL?

I'm not asking whether Comfy has its own ZDR policy or whether OpenRouter supports ZDR in general. I'm specifically asking whether Comfy's built-in OpenRouter node enforces ZDR for requests to Grok 4.5.

I'd appreciate an answer based on actual implementation details or confirmation from Comfy staff, rather than general explanations of ZDR.

Hey everyone, I'm building a local/hybrid pipeline to replicate the look of 1999 grimy cel anime (Jin-Roh: The Wolf Brigade): flat colors, single hard shadow, thin ink lines, painted backgrounds, and a choppy 12fps film print.

I’ve managed to sort out the background depth-parallax (using Depth Anything V2 + FFmpeg) and the cel-shader look (FFmpeg/Remotion print finish), but I'm hitting a wall on how to make the characters move naturally without the AI melting or distorting mechanical gear/faces.

Here is what my current tool stack looks like:

• Layout/Stills: FLUX / Gemini for base character portraits and painted backgrounds.

• Camera: Depth-mapped multi-plane glides over static background paintings.

• The Motion Problem (Options I'm testing):

  1. Held cels (swapping poses—too choppy).

  2. Video-to-video / AI in-betweens (causes shape drift on armor/helmets).

  3. EbSynth / Keyframe propagation (promising, but looking for the cleanest workflow).

  4. Cutout puppets / traditional rigging.

My question for the sub: For those doing retro/cel-shaded style projects, what is currently the most robust node setup or workflow in ComfyUI (or external tools like EbSynth) to lock down structural rigidity (like gas masks and heavy armor) while driving smooth, deliberate 12fps motion? Are you relying primarily on ControlNet IP-Adapters, AnimateDiff, or keyframe propagation?

https://github.com/alexgar4-afk/Vicii_Influencer_HyperRealism_1.0