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StyleRef-styleref-comfyui/workflows/z-image/01-quickstart.json
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BehzadandClaude Sonnet 5 cd36fae865 Run the templates on FLUX, and name the loaded style 
The root templates ran on SDXL base, where every gallery style washed out. A
full compiled style is ~480 tokens and SDXL's CLIP attends to ~77 per chunk, so
the style was truncated before the sampler saw it. No checkpoint swap fixes
that, Juggernaut included: it is an SDXL finetune with the same encoder.

Root set now runs FLUX.1 dev (Comfy-Org fp8 all-in-one, since BFL's own file is
UNET-only and CheckpointLoaderSimple cannot load it), compiled from the flux
target, whose T5 encoder takes the whole prompt. FluxGuidance 3.5, CFG 1.0, and
ConditioningZeroOut on the negative input: FLUX is guidance distilled and has no
negative channel, exactly like Z-Image. Uses EmptySD3LatentImage because FLUX
works in a 16-channel latent space.

The two sets load different gallery styles on purpose. How well a style demos is
a property of the style and the model together: Z-Image reproduces painterly
technique and keeps the Renaissance style, FLUX is strongest at light and
tonality and takes a noir one.

Also:

- Adds the reference-images template the root set was missing, and renumbers
  Facets to 05, so both sets are 01-05 with matching names. Both sets are now
  pinned by test against a shared story list.
- Model download metadata moves to each node's properties.models, where ComfyUI
  reads it, so a missing checkpoint offers a download instead of being silently
  substituted.
- The Load node names the style its slug resolves to, on a read-only row under
  style_ref, via a new /styleref/resolve route.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-07-27 13:20:07 +02:00

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"Z-Image quickstart \u2014 no account needed.\n1. Queue \u2014 the three Z-Image files download on first run.\n2. Change `subject` on StyleRef Apply and queue again (seeds randomize).\nSwap `style_ref` on StyleRef Load for any gallery slug.\n\nZ-Image Turbo (S3-DiT). Three model files download on first use: the UNET (diffusion_models/), the Qwen text encoder (text_encoders/), and the VAE (vae/).\nSampler defaults are turbo: 8 steps, CFG 1.0, euler/simple.\nZ-Image takes no negative prompt. The model runs without classifier-free guidance, so there is no negative encoder on this canvas \u2014 the sampler's negative input gets a zeroed conditioning. StyleRef still compiles the negative and previews it; put anything you need from it into `subject` on Apply as a positive phrase (\"fully clothed\", \"no text or watermark\"). Raising CFG does not bring the negative back.\nStyle prompts use the `diffusion` target \u2014 Z-Image reads the same tag-weighted output SDXL/SD1.5 use."
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