Add Ideogram 4 family, structured HunyuanImage exclusion, curated Krea 2 LoRAs
Ideogram 4 (diffusers 0.39 Ideogram4Pipeline) as a new image family. The vendor publishes no bf16 checkpoint, so ideogram-ai/ideogram-4-fp8 (raw float8 DiTs, upcast by from_pretrained) is the family base and ideogram-4-nf4-diffusers is the bnb-4bit pipeline artifact (ideogram-4-nf4 is byte-identical and detects to the same family). All three repos join the trusted non-GGUF allowlist and the frontend safetensors catalog. Family specifics handled: - Dual-branch CFG runs through a SEPARATE unconditional_transformer, so the auto-policy size table entry counts two ~9.3B DiTs (37.2 GB bf16), and the pipeline-kind memory plan now takes max(cached bytes, family table) for the family base repo: the fp8 repo's cached bytes undershoot the bf16-resident footprint by ~2x, which would let auto planning pick a resident placement that OOMs. - The pipeline accepts EITHER guidance_scale OR a per-step guidance_schedule (its default: the recommended 45x7.0 + 3x3.0 taper, valid only at 48 steps) and raises when both are set. At the advertised defaults (48 steps, guidance 7) generate() drops the constant so the recommended taper engages; any other request nulls the schedule so the constant broadcasts legally. - Generation defaults per the model card: 48 steps, guidance 7 (both tables). tencent/HunyuanImage-3.0 is deliberately excluded: it has no diffusers pipeline (an 80B autoregressive MoE behind trust_remote_code). A structured exclusion map now surfaces that reason verbatim from validate_load_request instead of the generic unknown-family error. The curated diffusion LoRA catalog gains the nine official krea/Krea-2-LoRA-* style adapters (family-tagged krea-2, explicit weight filenames), so they show up in the picker instead of requiring a typed repo id. Tests: new test_diffusion_more_families.py (detection, trust, defaults, size table, exclusion reason, curated catalog + family filter), two generate() tests for the guidance_scale/guidance_schedule pairing, and the local-scan LoRA test updated for a non-empty curated list. Backend suite + CI-sim (block_diffusers/block_torchao) green; frontend builds.
This commit is contained in:
Daniel Han
parent
04396ec507
commit
cbfc43215d
8 changed files with 304 additions and 8 deletions
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@ -31,9 +31,11 @@ from utils.hardware import clear_gpu_cache
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from .diffusion_families import (
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DIFFUSION_CANCELLED_MSG,
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DIFFUSION_NOT_LOADED_MSG,
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IDEOGRAM4_FAMILY_NAME,
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DiffusionFamily,
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default_generation_params,
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detect_family_for_pick,
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excluded_model_reason,
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resolve_base_repo,
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resolve_local_gguf_child,
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supported_family_names,
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@ -84,7 +86,11 @@ from .diffusion_prequant import (
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load_prequantized_transformer,
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resolve_prequant_source,
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)
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from .diffusion_auto_policy import build_resolved_record, resolve_dense_quant_candidate
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from .diffusion_auto_policy import (
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build_resolved_record,
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family_bf16_components_gb,
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resolve_dense_quant_candidate,
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)
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from .diffusion_transformer_quant import (
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TQ_AUTO,
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DEFAULT_MIN_LINEAR_FEATURES,
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@ -221,6 +227,13 @@ _TRUSTED_NON_GGUF_REPOS = frozenset(
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# training LoRAs on (train on Raw, run adapters on Turbo).
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"krea/krea-2-turbo",
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"krea/krea-2-raw",
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# Ideogram 4: official vendor repos, safetensors-only diffusers pipelines, no
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# remote code. The vendor ships no bf16 checkpoint: -fp8 stores the two DiTs
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# as raw float8 (highest precision available, the family base); the two nf4
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# repos are identical bnb-4bit exports (both listed so either id loads).
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"ideogram-ai/ideogram-4-fp8",
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"ideogram-ai/ideogram-4-nf4",
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"ideogram-ai/ideogram-4-nf4-diffusers",
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}
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)
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@ -508,6 +521,12 @@ class DiffusionBackend:
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kind = resolve_model_kind(gguf_filename, model_kind)
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fam = detect_family_for_pick(repo_id, gguf_filename, family_override)
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if fam is None:
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# A deliberately-excluded model gets its stated reason, not the generic
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# unknown-family message (which reads like a detection gap and invites a
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# family_override retry that would fail deeper and less clearly).
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excluded = excluded_model_reason(repo_id)
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if excluded:
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raise ValueError(f"'{repo_id}' cannot be loaded: {excluded}")
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raise ValueError(
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f"'{repo_id}' is not a supported diffusion image model. Supported families: "
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f"{', '.join(supported_family_names())}. If this is a variant of one of them, "
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@ -1590,6 +1609,19 @@ class DiffusionBackend:
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cached = self._cache_bytes(repo_id) if repo_id else 0
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cached_mib = int(cached // (1024 * 1024)) if cached else None
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model_dense_mib = estimate_safetensors_dense_mib(cached_mib)
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# A repo can store weights in a NARROWER dtype than they occupy after the
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# loader's torch_dtype cast: ideogram-4's base repo ships its two DiTs as
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# raw float8, so the cached bytes undershoot the bf16-resident footprint
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# by ~2x and auto planning would pick a resident placement that OOMs.
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# When the family size table knows the bf16-resident total for THIS repo
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# (the family base -- prequant repos like the bnb-4bit exports have
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# different ids and really do stay compressed), plan against the larger
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# of the two estimates.
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if repo_id and repo_id.strip().lower() == fam.base_repo.lower():
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table = family_bf16_components_gb(fam, fam.base_repo)
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if table is not None and model_dense_mib is not None:
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table_mib = int(sum(table) * (1000.0**3) / (1024.0 * 1024.0))
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model_dense_mib = max(model_dense_mib, table_mib)
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companion_mib = None
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else:
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if transformer_resident_override_mib is not None:
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@ -2118,6 +2150,18 @@ class DiffusionBackend:
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# share this call's seed, drawn sequentially from one generator.
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"num_images_per_prompt": batch_size,
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}
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if state.family.name == IDEOGRAM4_FAMILY_NAME:
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# Ideogram 4 drives CFG through EITHER a constant guidance_scale OR
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# a per-step guidance_schedule; its check_inputs rejects the call
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# when both are set, and the schedule DEFAULTS to the recommended
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# 45x7.0 + 3x3.0 polish taper (valid only at exactly 48 steps). At
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# the family's advertised defaults, drop the constant so the
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# recommended taper engages; any other request nulls the schedule
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# so the constant broadcasts legally to the chosen step count.
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if steps == 48 and abs(float(guidance) - 7.0) < 1e-6:
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kwargs.pop(state.family.cfg_kwarg, None)
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else:
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kwargs["guidance_schedule"] = None
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if init_pil is not None:
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# Reference with extra images passes the whole list (FLUX.2 combines them);
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# every other workflow takes the single image.
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@ -59,6 +59,12 @@ _FAMILY_BF16_GB: dict[str, tuple[float, float, float]] = {
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"qwen-image-edit": (40.9, 16.6, 0.3),
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"z-image": (12.3, 8.0, 0.2),
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"krea-2": (26.3, 8.9, 0.5),
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# Two ~9.3B DiTs (the conditional transformer PLUS the separate
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# unconditional_transformer driving Ideogram's dual-branch CFG), both resident
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# for every generation, and a Qwen3-VL text encoder. The vendor repo stores the
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# DiTs as raw float8 (9.29 GB each); these are the bf16-resident sizes after the
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# loader's dtype cast, per this table's contract.
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"ideogram-4": (37.2, 8.8, 0.2),
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}
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# Base-repo overrides for families whose picker offers multiple sizes under one family
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@ -304,6 +304,25 @@ _FAMILIES: tuple[DiffusionFamily, ...] = (
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# unvalidated upstream, so keep the fp16 fallback off like z-image.
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fp16_incompatible = True,
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),
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# Ideogram 4 (diffusers >= 0.39): a 34-layer single-stream flow-matching DiT PAIR --
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# the conditional transformer plus a separate ``unconditional_transformer`` driving
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# its dual-branch CFG (both ~9B params, so memory planning must count two DiTs) --
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# with a Qwen3-VL text encoder. The vendor publishes no bf16 checkpoint:
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# ideogram-4-fp8 stores the DiTs as raw float8 tensors (from_pretrained upcasts
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# them to the compute dtype) and is the highest-precision artifact, so it is the
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# family base; ideogram-4-nf4-diffusers / ideogram-4-nf4 (identical contents)
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# carry bnb-4bit quantization_configs the pipeline kind re-applies automatically.
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# All three repos are gated="auto" on the Hub, so a load may need the user's HF
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# token. No GGUF variant and no sd.cpp mapping, so the no-GPU route falls back to
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# diffusers. CFG quirk: the pipeline takes EITHER guidance_scale OR a per-step
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# guidance_schedule (see the loader's IDEOGRAM4 branch in diffusion.py).
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DiffusionFamily(
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name = "ideogram-4",
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pipeline_class = "Ideogram4Pipeline",
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transformer_class = "Ideogram4Transformer2DModel",
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base_repo = "ideogram-ai/ideogram-4-fp8",
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aliases = ("ideogram4", "ideogram-v4", "ideogram"),
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),
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# SDXL is the one U-Net family here: the denoiser is ``pipe.unet``
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# (UNet2DConditionModel), not a DiT ``pipe.transformer``, and a single-file
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# ``.safetensors`` is the WHOLE pipeline rather than a transformer-only file.
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@ -343,6 +362,36 @@ def trainable_family_names() -> tuple[str, ...]:
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return tuple(fam.name for fam in _FAMILIES if fam.trainable)
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# The family whose CFG runs through a guidance_scale/guidance_schedule pair rather
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# than a plain guidance_scale (the loader special-cases the call, like krea-2's
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# per-component assembly). Named here so the two modules cannot drift apart.
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IDEOGRAM4_FAMILY_NAME = "ideogram-4"
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# Models Studio deliberately does NOT support, with the reason surfaced verbatim in
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# the load error (instead of the generic unknown-family message, which reads like a
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# detection gap). Keyed by a lowercase substring of the repo id. The bar for support
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# is a diffusers pipeline: HunyuanImage-3.0 is an 80B autoregressive MoE loaded via
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# AutoModelForCausalLM + trust_remote_code -- there is nothing for this backend to
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# assemble, and remote-code execution is out of the question for a load path.
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_EXCLUDED_MODELS: tuple[tuple[str, str], ...] = (
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(
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"hunyuanimage",
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"HunyuanImage-3.0 has no diffusers pipeline (it is an 80B autoregressive MoE "
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"that requires trust_remote_code), so Studio does not support it.",
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),
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)
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def excluded_model_reason(repo_id: str) -> Optional[str]:
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"""The stated reason ``repo_id`` is unsupported, or None when it is simply unknown."""
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needle = (repo_id or "").lower()
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for token, reason in _EXCLUDED_MODELS:
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if token in needle:
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return reason
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return None
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# Editing / inpaint checkpoints share an arch keyword but need a different
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# pipeline and an input image, which this text-to-image backend doesn't drive.
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# "layered" rejects Qwen-Image-Layered: its transformer sets additional_t_cond=True
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@ -456,6 +505,10 @@ _GENERATION_DEFAULTS: tuple[tuple[str, int, float], ...] = (
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("flux.2-klein", 4, 0.0),
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("qwen-image", 20, 4.0),
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("z-image", 20, 4.0),
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# Ideogram 4's model-card settings: 48 steps, guidance 7 (its recommended
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# schedule tapers the last 3 steps to 3.0 -- the loader keeps that taper when
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# the request matches these defaults exactly; see the IDEOGRAM4 branch).
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("ideogram", 48, 7.0),
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)
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# Unrecognised model: distilled few-step / no-CFG shape, matching the UI fallback.
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_GENERATION_DEFAULT_FALLBACK = (9, 0.0)
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@ -66,9 +66,36 @@ class ResolvedLora:
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# Curated, family-tagged catalog of known-good diffusion LoRAs. Kept intentionally small
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# and data-driven; extend as unsloth hosts/curates more. Entries are HF repos with a
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# single-file weight. (Left minimal on purpose -- local discovery is the primary source,
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# and users can also reference any public HF LoRA repo id directly.)
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_CURATED: tuple[LoraCatalogEntry, ...] = ()
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# single-file weight. (Local discovery remains a primary source, and users can also
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# reference any public HF LoRA repo id directly.)
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def _krea2_lora(style: str, display_name: str) -> LoraCatalogEntry:
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"""One official krea/Krea-2-LoRA-* style adapter. All nine follow the same repo
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shape (a single ``{style}.safetensors`` at the root) and are trained on Krea-2-Raw
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for use on Krea-2-Turbo, per Krea's release guidance."""
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return LoraCatalogEntry(
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id = f"krea/Krea-2-LoRA-{style}",
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display_name = display_name,
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source = "hub",
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fmt = "safetensors",
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families = ("krea-2",),
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repo_id = f"krea/Krea-2-LoRA-{style}",
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weight_name = f"{style}.safetensors",
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)
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_CURATED: tuple[LoraCatalogEntry, ...] = (
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_krea2_lora("retroanime", "Krea 2 Retro Anime"),
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_krea2_lora("neondrip", "Krea 2 Neon Drip"),
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_krea2_lora("darkbrush", "Krea 2 Dark Brush"),
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_krea2_lora("softwatercolor", "Krea 2 Soft Watercolor"),
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_krea2_lora("dotmatrix", "Krea 2 Dot Matrix"),
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_krea2_lora("rainywindow", "Krea 2 Rainy Window"),
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_krea2_lora("vintagetarot", "Krea 2 Vintage Tarot"),
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_krea2_lora("sunsetblur", "Krea 2 Sunset Blur"),
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_krea2_lora("kidsdrawing", "Krea 2 Kids Drawing"),
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)
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def loras_dir() -> Path:
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@ -383,6 +383,9 @@ def fake_runtime(monkeypatch):
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diffusers.QwenImageInpaintPipeline = _FakeInpaintPipeline
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# Instruction-editing pipeline (Qwen-Image-Edit): its own pipeline IS the loaded one.
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diffusers.QwenImageEditPlusPipeline = _FakePipeline
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# Ideogram 4, so its guidance_scale/guidance_schedule pairing is exercisable.
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diffusers.Ideogram4Pipeline = _FakePipeline
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diffusers.Ideogram4Transformer2DModel = _FakeTransformer
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# SDXL: a U-Net family. Its single-file checkpoint is the whole pipeline, so the
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# pipeline class carries from_single_file; UNet2DConditionModel is the denoiser
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# class (fetched but unused on the pipeline/single-file-pipeline paths).
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@ -1223,6 +1226,41 @@ def test_generate_qwen_uses_true_cfg_scale(fake_runtime, tmp_path):
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assert call["true_cfg_scale"] == 4.0 and call["guidance_scale"] is None
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def _load_ideogram(backend, tmp_path):
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(tmp_path / "model.gguf").write_bytes(b"weights")
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backend.load_pipeline(
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str(tmp_path),
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gguf_filename = "model.gguf",
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base_repo = "ideogram-ai/ideogram-4-fp8",
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family_override = "ideogram-4",
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)
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def test_generate_ideogram_defaults_keep_recommended_schedule(fake_runtime, tmp_path):
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# Ideogram 4's pipeline defaults to its recommended tapered guidance_schedule
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# (45x7.0 + 3x3.0, valid only at 48 steps) and REJECTS guidance_scale while the
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# schedule is set. At the family's advertised defaults the backend must drop the
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# constant so the recommended taper engages.
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backend = DiffusionBackend()
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_load_ideogram(backend, tmp_path)
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backend.generate(prompt = "a sloth", steps = 48, guidance = 7.0)
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call = backend._state.pipe.last_kwargs
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assert call["guidance_scale"] is None # not passed: the pipe default engages
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assert "guidance_schedule" not in call
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def test_generate_ideogram_custom_guidance_nulls_schedule(fake_runtime, tmp_path):
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# Any non-default request must broadcast the constant legally: guidance_scale set
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# AND guidance_schedule explicitly nulled (the pipeline raises when both are set,
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# and its default schedule is non-None).
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backend = DiffusionBackend()
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_load_ideogram(backend, tmp_path)
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backend.generate(prompt = "a sloth", steps = 20, guidance = 5.0)
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call = backend._state.pipe.last_kwargs
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assert call["guidance_scale"] == 5.0
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assert "guidance_schedule" in call and call["guidance_schedule"] is None
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def test_begin_load_rejects_concurrent(monkeypatch):
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backend = DiffusionBackend()
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# The worker resolves the base + downloads, both over the network; stub them
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@ -153,10 +153,11 @@ def test_list_loras_scans_local(tmp_path, monkeypatch):
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(d / "other.gguf").write_bytes(b"y")
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(d / "ignore.txt").write_bytes(b"z")
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monkeypatch.setattr(dl, "loras_dir", lambda: d)
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ids = {e.id for e in dl.list_loras()}
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assert ids == {"mystyle", "other"}
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fmts = {e.id: e.fmt for e in dl.list_loras()}
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assert fmts["other"] == "gguf" and fmts["mystyle"] == "safetensors"
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# The merged catalog also carries the curated hub entries; the local scan is
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# exactly the weight files dropped in the directory.
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local = {e.id: e for e in dl.list_loras() if e.source == "local"}
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assert set(local) == {"mystyle", "other"}
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assert local["other"].fmt == "gguf" and local["mystyle"].fmt == "safetensors"
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def test_resolve_one_local_and_unknown(tmp_path, monkeypatch):
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112
studio/backend/tests/test_diffusion_more_families.py
Normal file
112
studio/backend/tests/test_diffusion_more_families.py
Normal file
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@ -0,0 +1,112 @@
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# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""Ideogram 4 family registration, the HunyuanImage structured exclusion, and the
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curated krea/Krea-2-LoRA-* catalog entries. Pure-module tests: no torch, no network."""
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import pytest
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from core.inference.diffusion import _is_trusted_diffusion_repo
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from core.inference.diffusion_auto_policy import family_bf16_components_gb
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from core.inference.diffusion_families import (
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IDEOGRAM4_FAMILY_NAME,
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default_generation_params,
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detect_family,
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excluded_model_reason,
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)
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from core.inference.diffusion_lora import _CURATED, list_loras
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# ── ideogram-4 family detection ──────────────────────────────────────────────
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@pytest.mark.parametrize(
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"repo_id",
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[
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"ideogram-ai/ideogram-4-fp8",
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"ideogram-ai/ideogram-4-nf4",
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"ideogram-ai/ideogram-4-nf4-diffusers",
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],
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)
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def test_detect_family_ideogram4_repos(repo_id):
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fam = detect_family(repo_id)
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assert fam is not None and fam.name == IDEOGRAM4_FAMILY_NAME
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assert fam.pipeline_class == "Ideogram4Pipeline"
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assert fam.transformer_class == "Ideogram4Transformer2DModel"
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# The vendor ships no bf16 repo: the raw-float8 export is the family base.
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assert fam.base_repo == "ideogram-ai/ideogram-4-fp8"
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def test_detect_family_ideogram4_override():
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fam = detect_family("some/local-path", override = "ideogram-4")
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assert fam is not None and fam.name == IDEOGRAM4_FAMILY_NAME
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assert detect_family("x", override = "ideogram4").name == IDEOGRAM4_FAMILY_NAME
|
||||
|
||||
|
||||
def test_ideogram4_repos_are_trusted_non_gguf():
|
||||
# The three official vendor pipelines load via from_pretrained, which is gated
|
||||
# to the unsloth org + the explicit allowlist.
|
||||
for rid in (
|
||||
"ideogram-ai/ideogram-4-fp8",
|
||||
"ideogram-ai/ideogram-4-nf4",
|
||||
"ideogram-ai/ideogram-4-nf4-diffusers",
|
||||
):
|
||||
assert _is_trusted_diffusion_repo(rid)
|
||||
assert not _is_trusted_diffusion_repo("ideogram-ai/some-future-repo")
|
||||
|
||||
|
||||
def test_ideogram4_generation_defaults():
|
||||
# Model-card settings: 48 steps, guidance 7 (the backend keeps the pipeline's
|
||||
# recommended tapered schedule when the request matches exactly).
|
||||
assert default_generation_params("ideogram-ai/ideogram-4-fp8") == (48, 7.0)
|
||||
|
||||
|
||||
def test_ideogram4_memory_table_counts_both_dits():
|
||||
fam = detect_family("ideogram-ai/ideogram-4-fp8")
|
||||
components = family_bf16_components_gb(fam)
|
||||
assert components is not None
|
||||
transformer_gb, text_encoders_gb, _vae_gb = components
|
||||
# Two ~9.3B DiTs (conditional + unconditional) at bf16: well above one DiT's
|
||||
# ~18.6 GB. A single-DiT entry here would let auto planning under-reserve and OOM.
|
||||
assert transformer_gb > 30.0
|
||||
assert text_encoders_gb > 5.0
|
||||
|
||||
|
||||
# ── structured exclusions ────────────────────────────────────────────────────
|
||||
def test_hunyuanimage_is_excluded_with_reason():
|
||||
reason = excluded_model_reason("tencent/HunyuanImage-3.0")
|
||||
assert reason is not None and "diffusers" in reason
|
||||
# Not detectable as any family: the exclusion reason is the load error surface.
|
||||
assert detect_family("tencent/HunyuanImage-3.0") is None
|
||||
|
||||
|
||||
def test_excluded_model_reason_none_for_supported_and_unknown():
|
||||
assert excluded_model_reason("unsloth/Z-Image-Turbo-GGUF") is None
|
||||
assert excluded_model_reason("someorg/some-model") is None
|
||||
|
||||
|
||||
def test_validate_load_request_surfaces_exclusion_reason():
|
||||
from core.inference.diffusion import DiffusionBackend
|
||||
|
||||
backend = DiffusionBackend()
|
||||
with pytest.raises(ValueError, match = "trust_remote_code"):
|
||||
backend.validate_load_request("tencent/HunyuanImage-3.0")
|
||||
|
||||
|
||||
# ── curated krea LoRA catalog ────────────────────────────────────────────────
|
||||
def test_curated_krea2_loras_present_and_well_formed():
|
||||
krea = [e for e in _CURATED if e.repo_id and e.repo_id.startswith("krea/Krea-2-LoRA-")]
|
||||
assert len(krea) == 9
|
||||
for entry in krea:
|
||||
assert entry.source == "hub" and entry.fmt == "safetensors"
|
||||
assert entry.families == ("krea-2",)
|
||||
# Every official style repo carries a single "{style}.safetensors" at the root.
|
||||
style = entry.repo_id.split("Krea-2-LoRA-")[-1]
|
||||
assert entry.weight_name == f"{style}.safetensors"
|
||||
|
||||
|
||||
def test_list_loras_family_filter_gates_krea_entries():
|
||||
krea_ids = {e.id for e in _CURATED if e.families == ("krea-2",)}
|
||||
assert krea_ids # curated entries exist
|
||||
listed_for_krea = {e.id for e in list_loras(family = "krea-2")}
|
||||
assert krea_ids <= listed_for_krea
|
||||
listed_for_flux = {e.id for e in list_loras(family = "flux.1")}
|
||||
assert not (krea_ids & listed_for_flux)
|
||||
|
|
@ -100,6 +100,11 @@ const SAFETENSORS_MODELS: Record<string, SafetensorsSpec> = {
|
|||
"unsloth/Z-Image-Turbo-unsloth-bnb-4bit": { kind: "pipeline" },
|
||||
// Krea 2 Turbo: official vendor repo (bf16 pipeline), on the backend allowlist.
|
||||
"krea/Krea-2-Turbo": { kind: "pipeline" },
|
||||
// Ideogram 4: official vendor pipelines, on the backend allowlist. No bf16 repo
|
||||
// exists: -fp8 stores its two DiTs as raw float8 (highest precision; ~46 GB
|
||||
// resident after the bf16 cast); -nf4-diffusers is the bnb-4bit export (~11 GB).
|
||||
"ideogram-ai/ideogram-4-fp8": { kind: "pipeline" },
|
||||
"ideogram-ai/ideogram-4-nf4-diffusers": { kind: "pipeline" },
|
||||
"unsloth/Qwen-Image-2512-unsloth-bnb-4bit": { kind: "pipeline" },
|
||||
"unsloth/Qwen-Image-2512-FP8": {
|
||||
kind: "single_file",
|
||||
|
|
@ -135,6 +140,12 @@ const MODELS: ModelOption[] = [
|
|||
"Safetensors · bnb-4bit",
|
||||
),
|
||||
safetensors("krea/Krea-2-Turbo", "Krea 2 Turbo", "Safetensors · bf16"),
|
||||
safetensors("ideogram-ai/ideogram-4-fp8", "Ideogram 4 (FP8)", "Safetensors · fp8"),
|
||||
safetensors(
|
||||
"ideogram-ai/ideogram-4-nf4-diffusers",
|
||||
"Ideogram 4 (bnb-4bit)",
|
||||
"Safetensors · bnb-4bit",
|
||||
),
|
||||
safetensors(
|
||||
"unsloth/Qwen-Image-2512-unsloth-bnb-4bit",
|
||||
"Qwen-Image 2512 (bnb-4bit)",
|
||||
|
|
@ -223,6 +234,10 @@ const MODEL_DEFAULTS: Array<{ match: string; steps: number; guidance: number }>
|
|||
{ match: "flux.2-dev", steps: 28, guidance: 4 },
|
||||
{ match: "qwen-image", steps: 20, guidance: 4 },
|
||||
{ match: "z-image", steps: 20, guidance: 4 },
|
||||
// Ideogram 4's model-card settings (48 steps, guidance 7). At exactly these
|
||||
// defaults the backend keeps the pipeline's recommended tapered guidance schedule
|
||||
// instead of a flat constant.
|
||||
{ match: "ideogram", steps: 48, guidance: 7 },
|
||||
// SDXL: Turbo is distilled (few steps, no CFG); base/full SDXL wants ~30 steps and
|
||||
// real CFG (~7). "sdxl-turbo" must precede the generic "sdxl" substring match.
|
||||
{ match: "sdxl-turbo", steps: 3, guidance: 0 },
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue