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feat(workflow): support Flux.2 Klein 9B checkpoints (CLIPLoader type=flux2)

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Adds a `flux2_klein` model family for Black Forest Labs' Flux.2 Klein 9B
release. Different architecture from Flux.1 D — required a separate
workflow rather than extending flux_unet.

Architecture differences from Flux.1 D:
- Single Qwen3-8B text encoder via CLIPLoader(type=flux2), producing
  12288-dim conditioning (3 stacked hidden layers). NOT DualCLIPLoader.
- EmptyFlux2LatentImage instead of EmptySD3LatentImage (different
  latent shape).
- Custom-sampling pipeline: Flux2Scheduler -> SIGMAS, fed into
  SamplerCustomAdvanced together with BasicGuider + RandomNoise +
  KSamplerSelect. No standalone KSampler node, so the caller's
  scheduler argument is ignored.
- Dedicated VAE: flux2-vae.safetensors (not Flux.1's ae.safetensors).

Detection:
- New FLUX2_KLEIN_PATTERNS constant lists known Klein filenames
  ("lust_", "moodydesire"). _is_flux2_klein() checks base_model field
  first ("flux.2 klein" / "flux2 klein") then filename pattern.
- detect_model_family() runs the Klein check BEFORE flux_unet, so
  Klein checkpoints that also match UNet-only patterns (lust_v10,
  moodyDesireMix) correctly route to flux2_klein.

Affected checkpoints reclassified from flux_unet -> flux2_klein:
- lust_v10.safetensors (Flux.2 Klein 9B-base per CivitAI DB)
- moodyDesireMix_v20PRO.safetensors (Flux.2 Klein 9B)

Still flux_unet (genuinely Flux.1 D UNet-only):
- cyberrealisticFlux_v25, fcFluxPonyPerfectBase,
  getphatFLUXReality_v11Softcore.

Required ComfyUI host setup (one-time):
- /home/madcat/comfyui/models/text_encoders/qwen_3_8b_fp8mixed.safetensors
  (8.1 GB, from Comfy-Org/vae-text-encorder-for-flux-klein-9b on HF)
- /home/madcat/comfyui/models/vae/flux2-vae.safetensors (321 MB)

Verified end-to-end on madcat: lust_v10 generated successfully through
the new flux2_klein workflow (~85s per image at 1024x1024, 20 steps).

7 new tests; 253 -> 259 total. Existing flux_unet tests retargeted to
genuine Flux.1 D checkpoints (getphat, fcFluxPony, cyberrealisticFlux).
This commit is contained in:
Adam Ladachowski
2026-05-17 18:22:23 +02:00
parent 3e04929515
commit b7263d1229
4 changed files with 367 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tensors/cli.py
+1 -1
View File
@@ -788,7 +788,7 @@ def generate( # noqa: PLR0915
help=(
"Override detected model family "
"(pony, illustrious, sdxl, sdxl_lightning, sdxl_turbo, "
"sd15, sd15_lcm, flux, flux_schnell, flux_unet, zimage)"
"sd15, sd15_lcm, flux, flux_schnell, flux_unet, flux2_klein, zimage)"
),
),
] = None,
tensors/comfyui.py
+185
View File
@@ -830,6 +830,83 @@ FLUX_UNET_WORKFLOW_TEMPLATE: dict[str, Any] = {
}
# Flux.2 Klein 9B workflow template — different architecture from Flux.1:
# - Single Qwen3-8B text encoder via CLIPLoader(type=flux2), produces 12288-dim
# conditioning (3 stacked hidden layers)
# - EmptyFlux2LatentImage instead of EmptySD3LatentImage (different latent shape)
# - Custom-sampling pipeline (Flux2Scheduler + BasicGuider + RandomNoise +
# KSamplerSelect + SamplerCustomAdvanced) instead of plain KSampler
# - Dedicated VAE (flux2-vae.safetensors), not the Flux.1 ae.safetensors
# Verified end-to-end against ComfyUI on madcat with lust_v10.safetensors.
FLUX2_KLEIN_WORKFLOW_TEMPLATE: dict[str, Any] = {
"100": {
"class_type": "UNETLoader",
"inputs": {"unet_name": "", "weight_dtype": "default"},
},
"101": {
"class_type": "CLIPLoader",
"inputs": {
"clip_name": "qwen_3_8b_fp8mixed.safetensors",
"type": "flux2",
},
},
"102": {
"class_type": "VAELoader",
"inputs": {"vae_name": "flux2-vae.safetensors"},
},
"130": {
"class_type": "CLIPTextEncode",
"inputs": {"text": "", "clip": ["101", 0]},
},
"131": {
"class_type": "CLIPTextEncode",
"inputs": {"text": "", "clip": ["101", 0]},
},
"140": {
"class_type": "FluxGuidance",
"inputs": {"conditioning": ["130", 0], "guidance": 3.5},
},
"150": {
"class_type": "EmptyFlux2LatentImage",
"inputs": {"width": 1024, "height": 1024, "batch_size": 1},
},
"151": {
"class_type": "RandomNoise",
"inputs": {"noise_seed": 0},
},
"152": {
"class_type": "KSamplerSelect",
"inputs": {"sampler_name": "euler"},
},
"153": {
"class_type": "Flux2Scheduler",
"inputs": {"steps": 20, "width": 1024, "height": 1024},
},
"154": {
"class_type": "BasicGuider",
"inputs": {"model": ["100", 0], "conditioning": ["140", 0]},
},
"160": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"noise": ["151", 0],
"guider": ["154", 0],
"sampler": ["152", 0],
"sigmas": ["153", 0],
"latent_image": ["150", 0],
},
},
"170": {
"class_type": "VAEDecode",
"inputs": {"samples": ["160", 0], "vae": ["102", 0]},
},
"180": {
"class_type": "SaveImage",
"inputs": {"filename_prefix": "flux2", "images": ["170", 0]},
},
}
# Default SDXL/Illustrious/Pony compatible workflow template
# Uses separate VAE loader for better quality with modern models
DEFAULT_WORKFLOW_TEMPLATE: dict[str, Any] = {
@@ -1056,6 +1133,93 @@ def _build_flux_unet_workflow(
return workflow
def _build_flux2_klein_workflow(
prompt: str,
model: str | None,
seed: int,
steps: int,
sampler: str,
width: int,
height: int,
batch_size: int,
lora_name: str | None,
lora_strength: float,
vae: str | None,
guidance: float,
clip_encoder: str,
clip_type: str,
) -> dict[str, Any]:
"""Build a Flux.2 Klein 9B workflow (single Qwen3 encoder, custom sampling).
The graph differs from Flux.1 in three ways:
1. Single-encoder ``CLIPLoader`` (type=flux2) instead of ``DualCLIPLoader``.
2. ``EmptyFlux2LatentImage`` for the Flux2-specific latent shape.
3. Custom-sampling pipeline: ``Flux2Scheduler`` produces SIGMAS, fed into
``SamplerCustomAdvanced`` along with ``BasicGuider``/``RandomNoise``/
``KSamplerSelect``. There is no standalone ``KSampler`` node, so the
caller-provided ``scheduler`` is ignored (Flux2Scheduler is the only
supported sigma source).
"""
workflow = copy.deepcopy(FLUX2_KLEIN_WORKFLOW_TEMPLATE)
actual_seed = seed if seed >= 0 else random.randint(0, 2**32 - 1)
# UNet checkpoint
if model:
workflow["100"]["inputs"]["unet_name"] = model
# Text encoder (Qwen3-8B variant — fp8 default, callers can override via
# family preset's clip_encoder field if a different quantization is desired).
workflow["101"]["inputs"]["clip_name"] = clip_encoder
workflow["101"]["inputs"]["type"] = clip_type
# External VAE — fall back to flux2-vae.safetensors from the template if unset
if vae:
workflow["102"]["inputs"]["vae_name"] = vae
# Positive prompt (negative is unused for Flux — guidance is distilled)
workflow["130"]["inputs"]["text"] = prompt
# FluxGuidance carries the real prompt-adherence dial
workflow["140"]["inputs"]["guidance"] = guidance
# Latent dimensions
workflow["150"]["inputs"]["width"] = width
workflow["150"]["inputs"]["height"] = height
workflow["150"]["inputs"]["batch_size"] = batch_size
# Noise seed (separate node in custom-sampling pipeline)
workflow["151"]["inputs"]["noise_seed"] = actual_seed
# Sampler selection
workflow["152"]["inputs"]["sampler_name"] = sampler
# Flux2Scheduler — must receive matching width/height for correct sigma schedule
workflow["153"]["inputs"]["steps"] = steps
workflow["153"]["inputs"]["width"] = width
workflow["153"]["inputs"]["height"] = height
# Optional LoRA: injected between UNet/CLIP loaders and BasicGuider/
# CLIPTextEncode consumers. Mirrors the flux_unet wiring pattern.
if lora_name:
workflow["110"] = {
"class_type": "LoraLoader",
"inputs": {
"model": ["100", 0],
"clip": ["101", 0],
"lora_name": lora_name,
"strength_model": lora_strength,
"strength_clip": lora_strength,
},
}
# Re-route consumers from raw loaders (100/101) to LoRA outputs (110).
workflow["154"]["inputs"]["model"] = ["110", 0]
workflow["130"]["inputs"]["clip"] = ["110", 1]
workflow["131"]["inputs"]["clip"] = ["110", 1]
return workflow
def _build_workflow(
prompt: str,
negative_prompt: str = "",
@@ -1143,6 +1307,27 @@ def _build_workflow(
guidance=_resolve_flux_guidance(guidance, cfg, defaults),
)
# Flux.2 Klein 9B: different architecture (single Qwen3 encoder, custom
# sampling pipeline, Flux2 latent format). Must dispatch BEFORE flux_unet
# since Klein checkpoints also set external_clip=True.
if family == "flux2_klein":
return _build_flux2_klein_workflow(
prompt=prompt,
model=model,
seed=seed,
steps=resolved_steps,
sampler=resolved_sampler,
width=resolved_width,
height=resolved_height,
batch_size=batch_size,
lora_name=lora_name,
lora_strength=lora_strength,
vae=resolved_vae,
guidance=_resolve_flux_guidance(guidance, cfg, defaults),
clip_encoder=defaults.get("clip_encoder", "qwen_3_8b_fp8mixed.safetensors"),
clip_type=defaults.get("clip_type", "flux2"),
)
# UNet-only Flux checkpoints (no baked-in CLIP/T5/VAE): use the split-loader
# variant. Triggered by family="flux_unet" — also covers any family whose
# preset opts in via external_clip=True.
tensors/config.py
+54 -1
View File
@@ -692,6 +692,29 @@ MODEL_FAMILY_DEFAULTS: dict[str, dict[str, Any]] = {
"clip_l": "clip_l.safetensors",
"clip_t5": "t5xxl_fp16.safetensors",
},
# Flux.2 Klein 9B — newer Black Forest Labs release. Different architecture
# from Flux.1: single Qwen3-8B text encoder (12288-dim conditioning, 3 stacked
# hidden layers), Flux2 latent format, custom Flux2Scheduler, dedicated VAE
# (flux2-vae.safetensors). Workflow uses CLIPLoader (type=flux2) instead of
# DualCLIPLoader, and the custom-sampling pipeline (SamplerCustomAdvanced +
# BasicGuider + Flux2Scheduler) instead of plain KSampler.
"flux2_klein": {
"quality_prefix": "",
"negative_prompt": "",
"width": 1024,
"height": 1024,
"portrait": (832, 1216),
"landscape": (1216, 832),
"cfg": 1.0,
"guidance": 3.5,
"sampler": "euler",
"scheduler": "simple", # unused — Flux2Scheduler provides sigmas
"steps": 20,
"vae": "flux2-vae.safetensors",
"external_clip": True,
"clip_encoder": "qwen_3_8b_fp8mixed.safetensors",
"clip_type": "flux2",
},
"zimage": {
"quality_prefix": "",
"negative_prompt": "",
@@ -730,6 +753,28 @@ def _is_flux_unet_only(name_lower: str) -> bool:
return any(p in name_lower for p in FLUX_UNET_ONLY_PATTERNS)
# Flux.2 Klein 9B filename substrings (case-insensitive). These checkpoints are
# UNet-only AND require the Flux.2 architecture (Qwen3-8B encoder, Flux2
# scheduler). Detection is primarily via base_model field ("Flux.2 Klein"); the
# filename patterns are a fallback for checkpoints with missing/wrong DB
# metadata. Filename match wins over base_model.
FLUX2_KLEIN_PATTERNS: tuple[str, ...] = (
"lust_", # lust_v10.safetensors
"moodydesire", # moodyDesireMix_v20PRO.safetensors
)
def _is_flux2_klein(name_lower: str, base_lower: str) -> bool:
"""True if the model is a Flux.2 Klein 9B checkpoint.
Detects via base_model field ("flux.2 klein", "flux2 klein") first,
then filename pattern fallback.
"""
if "flux.2 klein" in base_lower or "flux2 klein" in base_lower:
return True
return any(p in name_lower for p in FLUX2_KLEIN_PATTERNS)
def detect_model_family(model_name: str, base_model: str | None = None) -> str | None: # noqa: PLR0911
"""Detect model family from filename or CivitAI base_model field.
@@ -739,11 +784,19 @@ def detect_model_family(model_name: str, base_model: str | None = None) -> str |
Returns:
Model family key (pony, illustrious, sdxl, sdxl_lightning, sdxl_turbo,
sd15, sd15_lcm, flux, flux_schnell, flux_unet, zimage) or None if unknown
sd15, sd15_lcm, flux, flux_schnell, flux_unet, flux2_klein, zimage)
or None if unknown
"""
name_lower = model_name.lower()
base_lower = (base_model or "").lower()
# Flux.2 Klein 9B override: must run BEFORE flux_unet (Klein patterns like
# "lust_" and "moodydesire" also appear in FLUX_UNET_ONLY_PATTERNS) AND
# before the generic flux check. Detection prefers base_model field but
# falls back to filename pattern for checkpoints with missing metadata.
if _is_flux2_klein(name_lower, base_lower):
return "flux2_klein"
# UNet-only Flux override: must run BEFORE the generic flux check below,
# since some patterns ("cyberrealisticflux", "getphatflux", "fcfluxpony")
# also contain the substring "flux". Filename wins over base_model
tests/test_tensors.py
+127 -15
View File
@@ -321,11 +321,16 @@ class TestModelFamilyDetection:
assert detect_model_family("model.safetensors", "Flux.1 S schnell") == "flux_schnell"
def test_detect_flux_unet_lust(self) -> None:
"""lust_*.safetensors → flux_unet (no 'flux' in name, custom pattern)."""
"""lust_*.safetensors → flux2_klein (Klein detection wins via filename pattern).
Originally classified as flux_unet, but lust_v10 is actually Flux.2 Klein 9B
(per CivitAI base_model). Klein detection runs before flux_unet, so the
lust_ pattern in FLUX2_KLEIN_PATTERNS takes precedence.
"""
from tensors.config import detect_model_family
assert detect_model_family("lust_v10.safetensors") == "flux_unet"
assert detect_model_family("LUST_v10.safetensors") == "flux_unet"
assert detect_model_family("lust_v10.safetensors") == "flux2_klein"
assert detect_model_family("LUST_v10.safetensors") == "flux2_klein"
def test_detect_flux_unet_cyberrealistic(self) -> None:
"""cyberrealisticFlux_*.safetensors → flux_unet (intercepts generic 'flux' match)."""
@@ -340,10 +345,14 @@ class TestModelFamilyDetection:
assert detect_model_family("getphatFLUXReality_v11Softcore.safetensors") == "flux_unet"
def test_detect_flux_unet_moody(self) -> None:
"""moodyDesireMix_*.safetensors → flux_unet (no 'flux' in name)."""
"""moodyDesireMix_*.safetensors → flux2_klein (Klein, not Flux.1 D).
Originally classified as flux_unet, but moodyDesireMix is Flux.2 Klein
9B per CivitAI. Klein detection wins via the moodydesire filename pattern.
"""
from tensors.config import detect_model_family
assert detect_model_family("moodyDesireMix_v20PRO.safetensors") == "flux_unet"
assert detect_model_family("moodyDesireMix_v20PRO.safetensors") == "flux2_klein"
def test_detect_flux_unet_fcfluxpony(self) -> None:
"""fcFluxPony*.safetensors → flux_unet (intercepts flux + fluxpony)."""
@@ -358,11 +367,15 @@ class TestModelFamilyDetection:
"""Filename UNet-only pattern wins over a (likely wrong) CivitAI base_model tag."""
from tensors.config import detect_model_family
# Even if CivitAI claims "SDXL 1.0", the filename pattern wins.
assert detect_model_family("lust_v10.safetensors", "SDXL 1.0") == "flux_unet"
# cyberrealisticFlux: filename pattern wins over wrong "Pony" tag → flux_unet.
assert (
detect_model_family("cyberrealisticFlux_v25.safetensors", "Pony") == "flux_unet"
)
# getphat: filename pattern wins over wrong "SDXL 1.0" tag → flux_unet.
assert (
detect_model_family("getphatFLUXReality_v11.safetensors", "SDXL 1.0")
== "flux_unet"
)
def test_flux_unet_family_defaults_has_external_clip(self) -> None:
"""flux_unet preset advertises external_clip + clip filenames."""
@@ -381,12 +394,106 @@ class TestModelFamilyDetection:
"""flux_unet model resolves to the flux_unet preset with external_clip set."""
from tensors.config import get_model_generation_defaults
defaults = get_model_generation_defaults("lust_v10.safetensors")
# getphat is genuinely Flux.1 D UNet-only (not Klein).
defaults = get_model_generation_defaults("getphatFLUXReality_v11.safetensors")
assert defaults["family"] == "flux_unet"
assert defaults["external_clip"] is True
assert defaults["sampler"] == "euler"
assert defaults["scheduler"] == "simple"
# ---- Flux.2 Klein 9B detection + workflow ----
def test_detect_flux2_klein_from_base_model(self) -> None:
"""base_model='Flux.2 Klein 9B-base' → flux2_klein."""
from tensors.config import detect_model_family
assert detect_model_family("anything.safetensors", "Flux.2 Klein 9B-base") == "flux2_klein"
assert detect_model_family("anything.safetensors", "Flux.2 Klein 9B") == "flux2_klein"
# Compact variant ("flux2 klein" without the dot) — also accepted.
assert detect_model_family("anything.safetensors", "flux2 Klein") == "flux2_klein"
def test_detect_flux2_klein_from_filename(self) -> None:
"""Filename fallback: lust_ and moodydesire → flux2_klein even without DB metadata."""
from tensors.config import detect_model_family
assert detect_model_family("lust_v10.safetensors") == "flux2_klein"
assert detect_model_family("moodyDesireMix_v20PRO.safetensors") == "flux2_klein"
def test_detect_flux2_klein_overrides_flux_unet(self) -> None:
"""Klein detection runs BEFORE flux_unet, so Klein patterns win."""
from tensors.config import detect_model_family
# lust_ matches both FLUX2_KLEIN_PATTERNS and FLUX_UNET_ONLY_PATTERNS.
# Klein check runs first → flux2_klein.
assert detect_model_family("lust_v10.safetensors") == "flux2_klein"
# Even with wrong base_model, Klein filename wins.
assert detect_model_family("lust_v10.safetensors", "SDXL 1.0") == "flux2_klein"
def test_flux2_klein_family_defaults(self) -> None:
"""flux2_klein preset has external_clip + Qwen3 encoder + Flux.2 VAE."""
from tensors.config import MODEL_FAMILY_DEFAULTS
defaults = MODEL_FAMILY_DEFAULTS["flux2_klein"]
assert defaults["external_clip"] is True
assert defaults["clip_encoder"] == "qwen_3_8b_fp8mixed.safetensors"
assert defaults["clip_type"] == "flux2"
assert defaults["vae"] == "flux2-vae.safetensors"
assert defaults["cfg"] == 1.0
assert defaults["guidance"] == 3.5
def test_build_workflow_flux2_klein_uses_cliploader(self) -> None:
"""Flux.2 Klein workflow uses CLIPLoader(type=flux2) + EmptyFlux2LatentImage +
custom-sampling pipeline (no plain KSampler, no DualCLIPLoader)."""
from tensors.comfyui import _build_workflow
wf = _build_workflow(prompt="test", model="lust_v10.safetensors", seed=42)
class_types = {node["class_type"] for node in wf.values()}
# Required Flux.2-specific nodes
assert "CLIPLoader" in class_types
assert "EmptyFlux2LatentImage" in class_types
assert "Flux2Scheduler" in class_types
assert "BasicGuider" in class_types
assert "SamplerCustomAdvanced" in class_types
assert "RandomNoise" in class_types
# Forbidden — these belong to Flux.1 / SDXL paths
assert "DualCLIPLoader" not in class_types
assert "KSampler" not in class_types
assert "EmptySD3LatentImage" not in class_types
assert "CheckpointLoaderSimple" not in class_types
assert "ModelSamplingFlux" not in class_types
# Verify CLIPLoader is configured correctly
clip_nodes = [n for n in wf.values() if n["class_type"] == "CLIPLoader"]
assert len(clip_nodes) == 1
assert clip_nodes[0]["inputs"]["type"] == "flux2"
assert clip_nodes[0]["inputs"]["clip_name"] == "qwen_3_8b_fp8mixed.safetensors"
# VAE is the Flux.2 one, not Flux.1's ae.safetensors
vae_nodes = [n for n in wf.values() if n["class_type"] == "VAELoader"]
assert vae_nodes[0]["inputs"]["vae_name"] == "flux2-vae.safetensors"
def test_build_workflow_flux2_klein_with_lora(self) -> None:
"""LoRA injection inserts LoraLoader and reroutes BasicGuider + text encoders."""
from tensors.comfyui import _build_workflow
wf = _build_workflow(
prompt="test",
model="lust_v10.safetensors",
seed=42,
lora_name="some_flux_lora.safetensors",
lora_strength=0.8,
)
# LoRA node added at "110"
assert "110" in wf
assert wf["110"]["class_type"] == "LoraLoader"
assert wf["110"]["inputs"]["lora_name"] == "some_flux_lora.safetensors"
assert wf["110"]["inputs"]["strength_model"] == 0.8
# BasicGuider (model consumer) now wired to LoRA output
assert wf["154"]["inputs"]["model"] == ["110", 0]
# Both text encoders re-routed to LoRA clip output
assert wf["130"]["inputs"]["clip"] == ["110", 1]
assert wf["131"]["inputs"]["clip"] == ["110", 1]
def test_detect_sdxl_variants(self) -> None:
"""Test detecting SDXL family variants."""
from tensors.config import detect_model_family
@@ -607,7 +714,9 @@ class TestFluxUnetWorkflowBuilder:
"""flux_unet checkpoints emit UNETLoader + DualCLIPLoader + VAELoader and NO CheckpointLoaderSimple."""
from tensors.comfyui import _build_workflow
wf = _build_workflow(prompt="a cat", model="lust_v10.safetensors")
# getphat is genuinely Flux.1 D UNet-only. lust_v10 used to live here
# but is actually Flux.2 Klein — see TestFamilyDetection.
wf = _build_workflow(prompt="a cat", model="getphatFLUXReality_v11.safetensors")
# Three split loaders at the canonical IDs
assert wf["100"]["class_type"] == "UNETLoader"
@@ -619,7 +728,7 @@ class TestFluxUnetWorkflowBuilder:
assert node["class_type"] != "CheckpointLoaderSimple"
# UNet filename plumbed through
assert wf["100"]["inputs"]["unet_name"] == "lust_v10.safetensors"
assert wf["100"]["inputs"]["unet_name"] == "getphatFLUXReality_v11.safetensors"
# DualCLIPLoader configured for flux with both encoders
clip_inputs = wf["101"]["inputs"]
@@ -642,7 +751,7 @@ class TestFluxUnetWorkflowBuilder:
from tensors.comfyui import _build_workflow
wf = _build_workflow(
prompt="a cat", model="lust_v10.safetensors", cfg=7.5
prompt="a cat", model="getphatFLUXReality_v11.safetensors", cfg=7.5
)
assert wf["160"]["inputs"]["cfg"] == 1.0
# The caller's cfg=7.5 should re-route to FluxGuidance (same precedence as plain flux)
@@ -654,7 +763,7 @@ class TestFluxUnetWorkflowBuilder:
wf = _build_workflow(
prompt="a cat",
model="lust_v10.safetensors",
model="getphatFLUXReality_v11.safetensors",
lora_name="my_style.safetensors",
lora_strength=0.6,
)
@@ -675,7 +784,7 @@ class TestFluxUnetWorkflowBuilder:
wf = _build_workflow(
prompt="a cat",
model="lust_v10.safetensors",
model="getphatFLUXReality_v11.safetensors",
vae="other_vae.safetensors",
)
assert wf["102"]["inputs"]["vae_name"] == "other_vae.safetensors"
@@ -692,8 +801,11 @@ class TestFluxUnetWorkflowBuilder:
"""
from tensors.comfyui import _build_workflow
# moodyDesireMix has no "flux" in name but must route to the UNet workflow.
wf = _build_workflow(prompt="a cat", model="moodyDesireMix_v20PRO.safetensors")
# fcFluxPony is genuinely Flux.1 D (UNet-only) — moodyDesire was Klein.
wf = _build_workflow(
prompt="a cat",
model="fcFluxPonyPerfectBase_fcFluxPerfectBase.safetensors",
)
assert wf["100"]["class_type"] == "UNETLoader"
assert wf["101"]["class_type"] == "DualCLIPLoader"