Tag: generative-art

A ComfyUI workflow that re-skins a photographed image sequence into a completely different material world, while keeping the original architecture, perspective and camera motion intact.

Built for a 4-wall projection piece: the output tiles seamlessly on the X axis, so the left and right edges of every frame match up when the image is wrapped around a room.

This workflow produced one of the scenes in Liquid Horizons (2026), a 360° looped panoramic projection in a 4.2 × 4.2 m enclosed room, exhibited at F|X|R Médialabor, Veszprém. Released as-is, with the settings that were actually used.

(Magyar leírás: GitHub → README.hu.md)

The stairwell’s arches, openings and depth layering survive the transfer — they just become tissue instead of concrete.

1. What this actually does

It is not img2img with a high denoise and hope. Four independent systems constrain the render, and each one is there to solve a specific failure mode:

Plus a sequenced batch loader so a 5000-frame shot can be rendered 64 frames at a time without running out of VRAM, and an image-sequence saver so the result stays as frames (no video encoding, no generation loss) for the edit.

2. Pipeline, in order

3. Why each block is in there

3.1 Sequenced loader (the part everyone gets wrong)

PrimitiveInt is set to increment. Its value goes through MathExpression: a * 64 into the loader’s skip_first_frames. frame_load_cap is 64.

So each time you press Queue, ComfyUI renders the next 64 frames:

Combined with Auto Queue this walks through an arbitrarily long sequence at constant VRAM.

Reset PrimitiveInt to 0 before starting a new sequence, otherwise you silently resume from wherever the last job stopped. If you change frame_load_cap, change the * 64 multiplier to match — they must be identical or you skip or duplicate frames.

The loader also does the first downscale (1920×1080), and ImageScale takes it to the actual render resolution of 768×432. SD 1.5 is trained at 512², so 768×432 is roughly the largest 16:9 frame you can push before geometry starts repeating; the upscaler recovers the resolution at the end.

3.2 Two ControlNets, deliberately unequal

Two design decisions worth copying:

• The canny ControlNet is fed LineArt, not Canny. Canny on a mossy, noisy concrete photo produces thousands of texture edges, and the model dutifully draws every one of them — you get a traced photo. LineArt returns clean structural contours and ignores surface grain, which is exactly what you want when the surface is about to be replaced anyway.
• Neither starts at 0.0. Depth kicks in at 10 %, lineart at 20 %. The first steps are left free so the model can commit to the material before structure is imposed. Start both at 0 and the output looks like painted concrete instead of tissue.
• Depth ends at 0.80 so the last 20 % of sampling is unconstrained — that is where the organic detail actually forms.

ScaledSoftControlNetWeights (0.825) feeds both loaders. It ramps ControlNet influence across the UNet blocks instead of applying it flat, which is the main defence against the “AI traced over my photo” look.

3.3 IPAdapter — the actual style transfer

ip-adapter-plus_sd15 + CLIP-ViT-H-14, weight 0.8, ease in-out, end_at 0.8, embeds scaling V only.

The text prompt can describe what (fleshy tendons, Voronoi cells, turquoise veins) but it cannot pin down the exact palette, the sheen on the bubbles, or the specific way the membranes catch light. The reference image does that. Weight 0.8 is high — this workflow is style-led, the prompt only steers.

ease in-out weights the reference more in the middle of sampling, less at the start and end, which keeps it from fighting the ControlNets early or muddying the fine detail late.

PrepImageForClipVision (LANCZOS, center crop, 0.01 sharpening) is not optional — CLIP Vision wants 224², and feeding it a raw 5504×3072 image without a proper centre crop throws away most of the reference.

3.4 Seamless X tiling for 4-wall projection

Three nodes work together:

• Apply Circular Padding Model — swaps the UNet’s convolutions to circular padding
• Apply Circular Padding VAE — same for the VAE, so the encode/decode doesn’t re-introduce a seam
• CircularVAEDecode with x_only — restricts the wrap to the horizontal axis

Horizontal only, because the image wraps around four walls but the floor and ceiling are not continuous. Enable Y as well and you get a visible smear where the ceiling tries to meet the floor.

Both padding nodes must be active — patching only the model leaves a faint seam that the VAE reintroduces at decode, and it is very hard to spot until it is projected two metres wide.

3.5 Motion

• ADE_AnimateDiffLoaderWithContext — mm_sd_v15_v2.ckpt, beta schedule avg(sqrt_linear,linear), motion_scale 0.8
• ADE_LoopedUniformContextOptions — context length 16, stride 1, overlap 8, fuse flat
• ADE_AnimateDiffLoRALoader — v2_lora_ZoomIn.ckpt at 0.45

AnimateDiff here is not generating the motion — the source footage already has a moving camera. Its job is temporal coherence: the 16-frame context window with 8 frames of overlap means every frame is denoised with knowledge of its neighbours, so the tissue textures stay attached to the same surfaces instead of boiling.

motion_scale 0.8 (below the default 1.0) deliberately damps AnimateDiff’s own motion so it doesn’t fight the camera move that is already in the plate.

The ZoomIn motion LoRA at 0.45 adds a slow inward push on top of the source camera move. It is subtle at that weight — a drift, not a zoom. Set it to 0 if your source already moves enough.

3.6 Sampler settings

35 steps · cfg 8.5 · dpmpp_2m · karras · denoise 0.90

Note that the latent comes from VAEEncode of the source frames, not from an empty latent — this is img2img. denoise 0.90 leaves 10 % of the original image in the mix, which is enough to anchor the lighting and contrast to the plate without letting the concrete show through.

cfg 8.5 is high for SD 1.5, but it is compensating for two ControlNets and an IPAdapter all pulling in different directions.

more_details LoRA at 0.6/0.6 is on the base model — it is there because the render resolution (768×432) is low and the model needs help producing texture that survives a 4× upscale.

3.7 Output

Image Save (WAS Node Suite) writes a numbered JPG sequence at quality 100, 5-digit padding: styleTransfer_00001.jpg, styleTransfer_00002.jpg, …

Frames, not video, on purpose: – the batch loader produces the shot in chunks; a video node would produce a pile of clips to splice – no inter-frame compression before the edit – a failed chunk can be re-rendered and dropped in without touching the rest

4x-UltraSharp runs before the save, taking 768×432 to 3072×1728.

4. Requirements

Custom nodes

Models

The MiDaS preprocessor downloads its own weights on first run.

The checkpoint path in the workflow is SD1.5\dreamshaper_8.safetensors — it expects a SD1.5 subfolder inside models/checkpoints/. Either create it or re-pick the checkpoint in the node.

5. Setup

ComfyUI/
├── input/
│   ├── stairway_seq/                   <- your source frames (jpg or png, numbered)
│   └── neuronWithBubbles_ref.jpeg      <- the style reference
└── output/
    └── styleTransfer/                  <- created on first save

All paths in the workflow are relative to the ComfyUI root, so it loads and runs on any machine without editing. If your LoadVideosFromFolder build insists on an absolute path, replace input/stairway_seq with the full path — that is the only node that may need it.

6. Running it

1. Drop the source frames into ComfyUI/input/stairway_seq/.
2. Load LCM_ip_ctrl_transfer.json.
3. Set PrimitiveInt to 0.
4. Queue once. Check the first 64 frames before committing to the whole shot.
5. Happy with it? Turn on Auto Queue and let it walk the sequence.
6. Reset PrimitiveInt to 0 before the next shot.

Do a lookdev pass first. Bypass the batch stepping, set frame_load_cap to 16, and iterate on prompt / IPAdapter weight / denoise until a 16-frame chunk looks right. A full shot at these settings is not something you want to discover was wrong at frame 3000.

7. What to tune

8. Prompt variations

Four Note nodes in the workflow carry the prompt variants that were tried. The active one is the oxygen bubble version; the others are kept because they change the result substantially:

• base — fleshy tendons, Zaha Hadid fluid architecture, Voronoi cellular pattern, metal datacables, turquoise veins, hazy blue void, dreamcore / Y2K surrealism
• macro — pushes toward close-up biological texture, subsurface scattering, matte non-glowing bio-hardware
• oxygen bubbles (active) — adds iridescent translucent bubbles trapped in the tissue, chromatic aberration, prismatic reflections

Recurring negatives: grainy, noise, grit, dots, dither, low-res textures (1.5) and glow, neon, vibrant lights (1.4). The anti-glow weight matters — SD 1.5 will make anything described as “organic network” glow like a screensaver unless you actively suppress it.

9. Troubleshooting

10. License

• Workflow and documentation — MIT. Use it, change it, ship it commercially, no attribution required (though it is appreciated).
• Example images (stairway_seq_000000.jpg, neuronWithBubbles_ref.jpeg, stairway_brained_out.jpg) — CC BY 4.0. Share and adapt with credit.
• Models, LoRAs and custom nodes referenced by the workflow are not covered by either — each has its own terms. Check them before commercial use.

See LICENSE for the full text.

11. Notes

Built and rendered on ComfyUI 0.3.14 / frontend 0.6.0, RTX 4090. Roughly 20 GB VRAM at 64 frames × 768×432.

The workflow ships with embed_workflow enabled on the saver, so every output JPG carries the graph that produced it — drag one into ComfyUI to get the exact settings back.