Reset optimized workflows to Flux2 Klein baseline
This commit is contained in:
Morpheus
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# Text-to-Image Masters
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# Text-to-image
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Home for maintained category masters focused on text-to-image generation.
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This category should stay lean. Keep one maintained default unless a second path earns a clearly different role.
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## Current master
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- `zimage-turbo-3070-fast-start`
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## Test submission
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For direct server tests, use:
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`node scripts/submit-workflow-test.mjs --workflow optimized/text-to-image/zimage-turbo-3070-fast-start/workflow.png --prompt "<your prompt>"`
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The first version is intentionally scoped to direct ComfyUI text-to-image submission. It works best with prompt-style JSON workflows and PNG workflows that contain an embedded `prompt` text chunk.
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The optimized text-to-image collection currently focuses on the tested Flux.2 Klein baseline.
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# Flux.2 Klein 9B — RTX 3070 best baseline
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This is the current best tested Flux.2 Klein text-to-image workflow for the 8 GB RTX 3070.
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## Settings
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- Model: `flux-2-klein-9b-fp8mixed.safetensors`
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- Text encoder: `qwen_3_8b_fp8mixed.safetensors` (`flux2`)
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- VAE: `flux2-vae.safetensors`
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- Resolution: 1920×1080
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- Sampler: `euler_cfg_pp`
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- Steps: 6
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- CFG: 1.3
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- Seed: `20260920`
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- Measured generation time: approximately 78 seconds
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The seed and prompt are intentionally fixed so future sampler, scheduler, step, CFG, LoRA, and second-pass experiments can be compared consistently.
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## Optional second pass
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SeedVR2 3B FP8 produced a successful 2560×1440 output from this baseline. A 3840×2160 attempt ran out of the GPU's 8 GB VRAM. The SeedVR2 pass is therefore an optional export step, not part of the fast generation baseline.
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{
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"1": {
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"class_type": "UNETLoader",
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"inputs": {
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"unet_name": "flux-2-klein-9b-fp8mixed.safetensors",
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"weight_dtype": "default"
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}
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},
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"2": {
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"class_type": "CLIPLoader",
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"inputs": {
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"clip_name": "qwen_3_8b_fp8mixed.safetensors",
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"type": "flux2",
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"device": "default"
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}
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},
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"3": {
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"class_type": "VAELoader",
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"inputs": {
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"vae_name": "flux2-vae.safetensors"
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}
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},
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"4": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "Hyper-realistic cinematic documentary photograph inside the canteen of a deep-space exploration spaceship, a spacious lived-in dining hall with brushed titanium bulkheads, warm amber practical lights, cool cyan window spill, a huge panoramic observation window showing a blue nebula, distant stars and the curved limb of a planet, two adult human crew members in the foreground seated across from one another at a scratched metal table playing a tense friendly game of cards, a beautiful adult woman with natural facial features, expressive eyes, shoulder-length dark wavy hair, a fitted practical navy flight jacket over a cream knit top, subtle silver earrings and realistic skin texture, leaning forward holding a playing card, a muscular adult mixed-race man with short curly hair, trimmed beard, broad shoulders and powerful forearms, wearing a charcoal utility shirt with rolled sleeves and a worn leather wristband, studying the table with a half-smile, anatomically correct five-finger hands, orderly readable playing cards and coherent poker chips, steaming coffee, a glass of amber fruit drink, metal cutlery, ceramic bowls, half-eaten noodles, bread, fruit, condiments and scattered crumbs on the table, background crew in varied practical uniforms, one person using a wall-mounted food synthesizer, another carrying a tray, a cook behind a compact galley counter, illuminated panels with abstract non-legible interface graphics, utensils hanging from magnetic rails, food printers, refrigeration drawers, mugs secured in holders, subtle condensation, reflections in polished metal, believable spaceship engineering, cables and access panels, small personal photographs and mission patches, natural candid interaction, rich environmental storytelling, carefully composed wide medium shot at eye level, 35mm cinema lens, realistic perspective, moderate depth of field with both main players and the busy canteen readable, soft volumetric atmosphere, practical motivated lighting, gentle rim light on hair and shoulders, bounced fill light on faces, subtle lens bloom, restrained anamorphic horizontal flares, realistic film grain, high dynamic range, accurate color science, crisp micro-detail, premium science-fiction production design, photorealistic, ultra-detailed, physically plausible materials, cinematic lighting, no text or logos",
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"clip": ["2", 0]
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}
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},
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"5": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "cartoon, illustration, anime, CGI look, plastic skin, waxy faces, beauty filter, over-smoothed skin, duplicate people, extra limbs, missing limbs, malformed hands, fused fingers, unreadable cards, floating objects, distorted faces, crossed eyes, asymmetrical eyes, bad anatomy, warped perspective, overexposure, crushed blacks, muddy detail, extreme saturation, heavy vignette, excessive bloom, excessive lens flare, motion blur, low resolution, artifacts, watermark, caption, subtitle, logo, illegible text, celebrity likeness",
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"clip": ["2", 0]
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}
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},
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"6": {
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"class_type": "CFGGuider",
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"inputs": {
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"cfg": 1.3,
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"model": ["1", 0],
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"positive": ["4", 0],
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"negative": ["5", 0]
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}
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},
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"7": {
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"class_type": "KSamplerSelect",
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"inputs": {
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"sampler_name": "euler_cfg_pp"
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}
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},
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"8": {
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"class_type": "Flux2Scheduler",
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"inputs": {
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"steps": 6,
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"width": 1920,
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"height": 1080
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}
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},
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"9": {
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"class_type": "RandomNoise",
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"inputs": {
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"noise_seed": 20260920
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}
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},
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"10": {
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"class_type": "EmptyFlux2LatentImage",
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"inputs": {
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"width": 1920,
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"height": 1080,
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"batch_size": 1
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}
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},
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"11": {
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"class_type": "SamplerCustomAdvanced",
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"inputs": {
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"noise": ["9", 0],
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"guider": ["6", 0],
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"sampler": ["7", 0],
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"sigmas": ["8", 0],
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"latent_image": ["10", 0]
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}
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},
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"12": {
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"class_type": "VAEDecode",
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"inputs": {
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"samples": ["11", 0],
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"vae": ["3", 0]
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}
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},
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"13": {
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"class_type": "SaveImage",
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"inputs": {
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"filename_prefix": "fluxExperiments/experiment/flux2klein_best_baseline",
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"images": ["12", 0]
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}
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}
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}
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# Z-Image Turbo 3070 Fast Start
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## Summary
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Best current `text-to-image` starter for a lighter local box.
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This master stays close to the official `Z-Image Turbo` example because that is still the cleanest and most portable `Z-Image` graph in the repo.
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## Status
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- `best-current`
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- `not-yet-tested-on-chris-box`
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## Workflow asset
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- `workflow.json`
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- `workflow.png`
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- Prefer `workflow.json` for current testing and editing.
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- `workflow.png` is the original embedded example import.
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## Direct test command
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You can also submit it straight to the live ComfyUI server with:
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`node scripts/submit-workflow-test.mjs --workflow optimized/text-to-image/zimage-turbo-3070-fast-start/workflow.png --prompt "<your prompt>"`
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That path reads either the JSON workflow or the embedded PNG `prompt` chunk, applies text-to-image overrides, submits the job, polls history, and downloads the outputs locally.
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## Built from
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- `models/zimage/z-image-turbo-official-example/`
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## Why this is the current master
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- Official source beats community guesswork for a first baseline.
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- `Z-Image Turbo` looks like one of the strongest fast-image candidates for a `3070`.
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- The graph is simple enough to troubleshoot without drowning in custom-node noise.
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## What I intentionally kept
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- Official model wiring
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- Official `qwen_3_4b` text encoder path
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- The original embedded PNG workflow as a reference import
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## What I intentionally changed
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- switched the maintained test target to `workflow.json`
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- repointed the loader names to the live server filenames
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- lowered the default latent size to `768x768`
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- lowered the default sampler steps to `6`
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## What I intentionally did not add yet
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- community patch nodes
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- extra control branches
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- reference-image complexity
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- "magic" speed hacks without proof
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## Required models
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- `models/text_encoders/Qwen/qwen_3_4b.safetensors`
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- `models/diffusion_models/zImageTurbo_turbo.safetensors`
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- `models/vae/ae.safetensors`
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## Required custom nodes
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- none beyond current official Comfy support
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## Monday test checklist
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- Confirm it imports cleanly from PNG on your current Comfy build.
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- Check whether it really feels lighter than your usual FLUX path.
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- Check whether prompt adherence is good enough to justify keeping it as the `fast-start` master.
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## Next optimization candidates
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- merge in useful edit/reference ideas from the community `Z-Image` graphs
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- build a stronger prompt preset if the base graph is good but inconsistent
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{
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"3": {
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"inputs": {
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"seed": 47447417949230,
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"steps": 6,
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"cfg": 1,
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"sampler_name": "euler",
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"scheduler": "simple",
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"denoise": 1,
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"model": [
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"16",
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0
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],
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"positive": [
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"6",
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0
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],
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"negative": [
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"7",
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0
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],
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"latent_image": [
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"13",
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0
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]
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},
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"class_type": "KSampler",
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"_meta": {
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"title": "KSampler"
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}
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},
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"6": {
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"inputs": {
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"text": "cute anime style girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron, it is a postcard held by a hand in front of a beautiful realistic city at sunset and there is cursive writing that says \"ZImage, Now in ComfyUI\"",
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"clip": [
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"18",
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0
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]
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},
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"class_type": "CLIPTextEncode",
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"_meta": {
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"title": "CLIP Text Encode (Positive Prompt)"
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}
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},
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"7": {
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"inputs": {
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"text": "blurry ugly bad",
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"clip": [
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"18",
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0
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]
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},
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"class_type": "CLIPTextEncode",
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"_meta": {
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"title": "CLIP Text Encode (Negative Prompt)"
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}
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},
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"8": {
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"inputs": {
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"samples": [
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"3",
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0
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],
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"vae": [
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"17",
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0
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]
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},
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"class_type": "VAEDecode",
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"_meta": {
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"title": "VAE Decode"
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}
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},
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"9": {
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"inputs": {
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"filename_prefix": "zimage-3070-fast-start",
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"images": [
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"8",
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0
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]
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},
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"class_type": "SaveImage",
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"_meta": {
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"title": "Save Image"
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}
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},
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"13": {
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"inputs": {
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"width": 768,
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"height": 768,
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"batch_size": 1
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},
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"class_type": "EmptySD3LatentImage",
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"_meta": {
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"title": "EmptySD3LatentImage"
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}
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},
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"16": {
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"inputs": {
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"unet_name": "zImageTurbo_turbo.safetensors",
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"weight_dtype": "default"
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},
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"class_type": "UNETLoader",
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"_meta": {
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"title": "Load Diffusion Model"
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}
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},
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"17": {
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"inputs": {
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"vae_name": "ae.safetensors"
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},
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"class_type": "VAELoader",
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"_meta": {
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"title": "Load VAE"
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}
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},
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"18": {
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"inputs": {
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"clip_name": "Qwen\\qwen_3_4b.safetensors",
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"type": "lumina2",
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"device": "default"
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},
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"class_type": "CLIPLoader",
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"_meta": {
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"title": "Load CLIP"
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}
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}
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}
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