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DISCO ELYSIUM
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IDENTITY
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Base model Ideogram 4
Generation mode not declared by the author
Checkpoint not determined β€” verify before use
Category Visual style
Author wystanbelov785
Source https://civitai.com/models/2487755
Provenance CivitAI Β· model 2487755 Β· version 3129598
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WEIGHTS
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File MixedElysium_Ideogram.safetensors
Version Ideogram
Size 201.9 MB
Format SafeTensor / unknown precision
SHA256 e762d7696337f6e50f78a7316a37ef2bcea847ab9a712f5ac5f1fa91697c0caa
Published unknown
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TRIGGER WORDS
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No trigger word. The LoRA applies as soon as it is loaded β€”
strength is the only way to dial the effect up or down.
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RECOMMENDED SETTINGS
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The author did not publish specific settings.
No author settings were published. Community baseline: steps 48 is the
reference default: docs/inference.md says "`V4_QUALITY_48` is the default"
and the pipeline's `num_steps` default is 48; diffusers documents the same
48-step default. 20 is the practical ComfyUI default β€” the `V4_DEFAULT_20`
preset and the value the shipped template `image_ideogram4_t2i.json`
selects (its preset combo defaults to "Default"). 12 = `V4_TURBO_12`. With
the TurboTime LoRA: 8 in the nomadoor community workflow; the model card
claims usable output down to 2., cfg A guidance SCHEDULE, not one number,
and the negative branch comes from a second model rather than a negative
prompt. Verified from src/ideogram4/sampler_configs.py: V4_QUALITY_48 = 45
steps @ gw 7 then 3 polish steps @ gw 3; V4_DEFAULT_20 = 18 @ 7 + 2 @ 3;
V4_TURBO_12 = 11 @ 7 + 1 @ 3. Pipeline default `guidance_scale` = 7.0 when
no schedule is given. In ComfyUI the shipped template sets
`DualModelGuider` cfg 7 plus a `CFGOverride` node with widgets [3, 0.7, 1]
(cfg 3 from 0.7β†’1.0 of the run); kijai recommends starting the override at
0.7, not 0.9. TurboTime: cfg 1.0 / no CFG., sampler Euler flow-matching. In
ComfyUI: `KSamplerSelect` β†’ euler, driven by `SamplerCustomAdvanced`, with
the model-specific `Ideogram4Scheduler` (logit-normal sigma schedule). Its
params are `mu` and `std`, not the usual `shift`. Reference presets: mu 0.0
/ std 1.5 (48-step), mu 0.0 / std 1.75 (20-step), mu 0.5 / std 1.75
(12-step). The Ideogram4Scheduler node's own widget defaults are steps 20,
mu 0.5, std 1.75, but the shipped template overrides mu/std/steps from the
preset JSON, so out of the box it runs 20 / mu 0.0 / std 1.75. mu is auto-
shifted by resolution in both implementations: mean = mu +
0.5*log((w*h)/(512*512))., resolution 1024Γ—1024 default (pipeline
`height`/`width` default 1024). Any multiple of 16 per side from 256 to
2048, aspect ratios up to 6:1 or 1:6 β€” one model covers all of it.
2048Γ—2048 with V4_QUALITY_48 for maximum quality. Buckets documented in
docs/inference.md: 1536Γ—1024 (3:2), 1024Γ—1536 (2:3), 1920Γ—1088 (~16:9),
2048Γ—768 (~21:9), 1024Γ—1792 (~9:16), 1600Γ—400 (4:1)., lora_strength No
single official number; these are author-stated values. DeverStyle Archer:
0.6, and explicitly "on the model only, not on the model_negative".
zjourney v2: 0.55 standalone (range 0.35–0.6), or v1 @ 0.35 + v2 @ 0.45
stacked. Furry Enhancer: the model page says 0.6–0.8, but its own version
notes ask for 0.7–0.9 (v1.94) and 0.8–0.95 (v2.8). TurboTime: 0.6 in the
nomadoor workflow (the author gives no figure). So ~0.5–0.9 is the
realistic band, read the specific card..
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ADOPTION & POPULARITY
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No statistics available.
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COMMUNITY NOTES
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Archive summary:
Disco Elysium is a style LoRA designed for Ideogram 4.0 checkpoints,
published by [wystanbelov785](/users/wystanbelov785) on CivitAI in
March 2026, now on version 3. Load a compatible checkpoint first, then
apply this LoRA on top via your UI's LoRA loader. It has attracted 37
downloads. It's intended for digital art, oil painting, illustration,
and cartoon content. The model is flagged NSFW on CivitAI and can
produce explicit content.
Source: https://civarchive.com/models/2487755?modelVersionId=3129598
Comments are other users' words, reproduced as written.
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CHAINING / STACKING
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Stacks with others not stated
The author gave no chaining guidance. The rules below are the
general Ideogram 4 ones, not anything specific to this LoRA.
General Ideogram 4 rules that apply here (see REFERENCE.txt):
β€’ Chain `LoraLoaderModelOnly` nodes in series off the CONDITIONAL model
loader, ahead of the guider. DeverStyle states its samples were made
"by setting the LORA @ 0.6 strength on the model only, not on the
model_negative".
β€’ Model-only loaders throughout. The text encoder is a frozen Qwen3-VL-8B
with no LoRA-patchable CLIP branch, so the model+clip `LoraLoader` has
nothing useful to do.
β€’ To affect the unconditional/negative branch you must load a second,
physically distinct model file and patch that one. Passing the same
patched model object into both DualModelGuider inputs is a verified no-
op (Comfy-Org issue #14521; kijai confirmed it is expected behaviour).
β€’ Trigger words go INSIDE the JSON caption, in the semantically matching
field, not prepended to a prose string. DeverStyle instructs putting
`dvr_arch_tv` in `style_description.art_style` (and in the content
field); the Kiki LoRA card describes using `art_style: "Studio Ghibli
style"` with `medium: "anime_screencap"`.
β€’ Two versions of the same LoRA can be stacked at reduced strengths:
zjourney's author recommends v1 @ 0.35 + v2 @ 0.45 over either alone at
full strength.
β€’ TurboTime changes the graph, not just the strength: it runs at cfg 1.0
and the reference community workflow swaps `DualModelGuider` for a
single-model `CFGGuider`, dropping the unconditional model. Re-tune any
stacked style LoRA downward.
β€’ A LoRA can be applied to only part of the run. The Gray Screen bypass
LoRA is used at NEGATIVE weight (about -0.25) and, in its shipped
workflows, only on the first step and then disabled; its author warns
that "LoRA being active through the whole generation could lead to
degradation".
β€’ The author never states which weight family this targets. Silent
degradation, not hard errors, in every case observed. (1) Loading a
conditional-trained LoRA onto the unconditional transformer succeeds
silently because the architectures match; it perturbs the negative
branch instead of the positive one. (2) Verified ComfyUI trap: in `Du
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PROS
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+ Compact at 202 MB β€” cheap to stack with other LoRAs. [source: file
size]
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CONS & CAVEATS
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- No trigger word β€” the LoRA is always on once loaded, so strength is the
only control. [source: model metadata]
- Author never states which generation mode it was trained for; test
against your own workflow before relying on it. [source: description]
- Thin documentation β€” expect trial and error. [source: description
length]
- Modest adoption (251 downloads) β€” less community feedback to rely on.
[source: source stats]
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COMPATIBILITY WARNING
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Silent degradation, not hard errors, in every case observed. (1) Loading a
conditional-trained LoRA onto the unconditional transformer succeeds
silently because the architectures match; it perturbs the negative branch
instead of the positive one. (2) Verified ComfyUI trap: in
`DualModelGuider`, passing the SAME model object as both the conditional
model and the LoRA-patched unconditional model is a no-op. Comfy-Org issue
#14521 tabulates it β€” case 3 (DualModelGuider, conditional model as
positive, conditional model + LoRA as negative) was "exactly identical" to
plain conditional-only CFG, while case 4 with the real unconditional model
differed. kijai confirmed it is expected: "there's no (working) mechanic to
swap LoRA on each step on the same model, it would only work if the model
was a different one." To patch the negative branch you must load a
genuinely separate model file. (3) A diffusers/PEFT-prefixed LoRA in
ComfyUI is expected to produce only a "lora key not loaded" console warning
and no visible effect β€” this follows from the absent key mapping (see
checkpoint_families) rather than from a published Ideogram-4 bug report.
(4) Cross-quantization loading (fp8-trained LoRA onto nvfp4/nf4/GGUF)
generally works with some precision loss; int8_convrot had a specific, now-
fixed bug (see gotchas).
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AUTHOR'S DESCRIPTION
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(the author left no description)
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EXAMPLE MEDIA
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6 preview(s) in ./example_images/ β€” community results for
this LoRA. Videos keep a matching _poster.jpg still frame.
No prompts are recorded below: CivitAI's public API flags these posts as
having generation metadata but does not return it, so the prompts cannot be
scraped. Open the model page to read them.
01_image.jpeg (image, 768x1376)
02_image.jpeg (image, 768x1376)
03_image.jpeg (image, 1024x1024)
04_image.jpeg (image, 1024x1024)
05_image.jpeg (image, 1024x1024)
06_image.jpeg (image, 1024x1024)
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HOW TO USE (COMFYUI)
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Use ComfyUI's NATIVE Ideogram 4 support (Comfy-Org/ComfyUI PR #14259 "feat:
Add Ideogram4 model support", merged 2026-06-03) β€” not the official
`ComfyUI-Ideogram4` custom-node wrapper, whose nodes (Magic Prompt /
Pipeline Loader / Generate) wrap Ideogram's reference pipeline and contain
no LoRA input at all (verified: no occurrence of "lora" in its nodes.py).
1. Update ComfyUI to a build containing `comfy_extras/nodes_ideogram4.py`
(which supplies `Ideogram4Scheduler`); the same PR added the generic
`DualModelGuider` and `CFGOverride` nodes, which live in
`comfy_extras/nodes_custom_sampler.py`.
2. From `Comfy-Org/Ideogram-4` download into
`ComfyUI/models/diffusion_models/`: `ideogram4_fp8_scaled.safetensors`
(9.28 GB) AND `ideogram4_unconditional_fp8_scaled.safetensors` (9.28 GB).
Lower-VRAM alternatives in the same folder: `*_nvfp4_mixed.safetensors`
(5.49 GB each) or `*_int8_convrot.safetensors` (9.58 GB each).
3. Download `qwen3vl_8b_fp8_scaled.safetensors` (10.59 GB; or
`qwen3vl_8b_nvfp4.safetensors`, 6.31 GB) into
`ComfyUI/models/text_encoders/`. The shipped template also lists an
optional `gemma4_e4b_it_fp8_scaled.safetensors` from `Comfy-Org/gemma-4`
for local prompt building.
4. Download `flux2-vae.safetensors` (0.34 GB, from Comfy-Org/Ideogram-4 or
Comfy-Org/flux2-dev) into `ComfyUI/models/vae/`.
5. Put LoRA .safetensors in `ComfyUI/models/loras/` (no subfolder
convention enforced).
6. Load the built-in template `image_ideogram4_t2i` (Workflow β†’ Browse
Templates), or the workflow from
docs.comfy.org/tutorials/image/ideogram/ideogram-v4.
7. Insert a `LoraLoaderModelOnly` between the main `UNETLoader` and the
`DualModelGuider`'s conditional model input. There is no CLIP-side LoRA
here β€” the text encoder is a frozen Qwen3-VL β€” so model-only is the right
loader. Strength ~0.5–0.8.
8. Do NOT reuse one patched model object for both guider inputs; it
silently does nothing. Patching the negative branch requires a second,
physically separate model file with its own loader.
9. Write the prompt as a JSON caption into `CLIPTextEncode` (the CLIPLoader
type is `ideogram4`); keep `ConditioningZeroOut` feeding the unconditional
branch.
Non-ComfyUI paths: diffusers β€”
`Ideogram4Pipeline.from_pretrained("ideogram-ai/ideogram-4-nf4")` (the
docs' own example; `ideogram-ai/ideogram-4-nf4-diffusers` also exists),
then `pipeline.load_lora_weights(...)`. Training β€” `ostris/ai-toolkit`
(built-in `extensions_built_in/diffusion_models/ideogram4/` plus an
`Ideogram4Captioner` that writes JSON captions) or diffusers
`examples/dreambooth/train_dreambooth_lora_ideogram4.py`. The official HF
weights are GATED (`gated: auto`): accept the license on the model page and
authenticate (`hf auth login` / `HF_TOKEN`) or downloads fail.
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Scraped by lorakit on 2026-09-05 03:47 UTC.
Stats and description are the author's; pros/cons are derived from the
evidence tagged beside each line.
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