feat(flux_local): z-image / z-image-turbo + qwen model families

Each family gets its own mflux CLI — they do not accept each other's flags.
Z-Image 4-bit runs in ~6GB, viable on the 16-32GB boxes.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
John King 2026-08-24 11:51:35 +10:00
parent bc8f5e7e23
commit eed95c45f6
4 changed files with 278 additions and 17 deletions

View File

@ -2,23 +2,97 @@
"id": "flux_local",
"name": "FLUX (local, MLX)",
"category": "generate",
"description": "Prompt → image entirely on this Mac via mflux 0.18 (MLX). flux2-klein-4b = Apache/UNGATED, works out of the box, better than FLUX.1-schnell (~15GB first download). schnell-4bit = ungated community quant. schnell/dev/krea-dev/klein-9b are HF-gated (license accept + HF token in Settings). No API cost, no cloud. Elo context: klein ~1083-1119, FLUX.1-dev ~1027, nano-banana ~1154.",
"description": "Prompt -> image entirely on this Mac via mflux (MLX). Covers FLUX.1, FLUX.2 klein, Z-Image Turbo and Qwen-Image, with LoRA support. flux2-klein-4b/schnell-4bit/z-image-turbo are ungated. Z-Image Turbo is the pick for anatomy (correct hands at baseline). Cannot load SD/SDXL LoRAs - those need ComfyUI (comfyui_sd).",
"accepts": [],
"produces": ["image"],
"produces": [
"image"
],
"resources": "gpu",
"entry": "run.py",
"python": "venvs/mflux/bin/python",
"params_schema": {
"type": "object",
"properties": {
"prompt": {"type": "string", "default": "", "description": "What to generate"},
"model": {"type": "string", "enum": ["flux2-klein-4b", "flux2-klein-9b", "schnell", "schnell-4bit", "dev", "krea-dev"], "default": "flux2-klein-4b", "description": "klein-4b + schnell-4bit are ungated; the rest need the HF token"},
"steps": {"type": "integer", "default": 4, "minimum": 1, "maximum": 50, "description": "klein/schnell: 2-4; dev/krea-dev: 20-28"},
"width": {"type": "integer", "default": 1024, "minimum": 256, "maximum": 2048},
"height": {"type": "integer", "default": 1024, "minimum": 256, "maximum": 2048},
"seed": {"type": "integer", "default": 42, "description": "Fixed seed = reproducible A/B tests"},
"quantize": {"type": "string", "enum": ["none", "8", "4"], "default": "8", "description": "FLUX.1 models only: 8-bit ≈ full quality at half memory"},
"guidance": {"type": "number", "default": 3.5, "minimum": 0, "maximum": 10, "description": "dev/krea-dev only"}
"prompt": {
"type": "string",
"default": "",
"description": "What to generate"
},
"model": {
"type": "string",
"enum": [
"flux2-klein-4b",
"flux2-klein-9b",
"schnell",
"schnell-4bit",
"dev",
"krea-dev",
"z-image-turbo",
"z-image",
"qwen"
],
"default": "flux2-klein-4b",
"description": "flux2-klein-4b + schnell-4bit + z-image-turbo are UNGATED (work out of the box). schnell/dev/krea-dev/klein-9b need an HF license accept + token. z-image-turbo = Alibaba Z-Image (DiT): best anatomy/hands, few-step (6-10), 4-bit ~6GB. qwen = Qwen-Image. NOTE these are DIFFERENT ARCHITECTURES \u2014 a LoRA only works on its own."
},
"steps": {
"type": "integer",
"default": 4,
"minimum": 1,
"maximum": 50,
"description": "klein/schnell: 2-4; dev/krea-dev: 20-28"
},
"width": {
"type": "integer",
"default": 1024,
"minimum": 256,
"maximum": 2048
},
"height": {
"type": "integer",
"default": 1024,
"minimum": 256,
"maximum": 2048
},
"seed": {
"type": "integer",
"default": 42,
"description": "Fixed seed = reproducible A/B tests"
},
"quantize": {
"type": "string",
"enum": [
"none",
"8",
"4"
],
"default": "8",
"description": "FLUX.1: 4 or 8. Z-Image/Qwen: 3-8 (4 recommended, ~6GB). Ignored for klein."
},
"guidance": {
"type": "number",
"default": 3.5,
"minimum": 0,
"maximum": 10,
"description": "dev/krea-dev only"
},
"lora": {
"type": [
"string",
"array"
],
"default": "",
"description": "LoRA file(s): full path, or a bare name resolved against MB_LORA_DIRS (localmodels/Lora, civit-lib/{sd15,sdxl}/Lora). Accepts 'name=0.7' shorthand. MUST match the model's architecture \u2014 a mismatched LoRA is a SILENT no-op, not an error."
},
"lora_weight": {
"type": [
"number",
"array"
],
"default": 0.8,
"minimum": 0,
"maximum": 2,
"description": "LoRA strength, scalar or per-LoRA list. 0.6-1.0 is the useful range."
}
}
}
}
}

View File

@ -0,0 +1,24 @@
{
"id": "flux_local",
"name": "FLUX (local, MLX)",
"category": "generate",
"description": "Prompt → image entirely on this Mac via mflux 0.18 (MLX). flux2-klein-4b = Apache/UNGATED, works out of the box, better than FLUX.1-schnell (~15GB first download). schnell-4bit = ungated community quant. schnell/dev/krea-dev/klein-9b are HF-gated (license accept + HF token in Settings). No API cost, no cloud. Elo context: klein ~1083-1119, FLUX.1-dev ~1027, nano-banana ~1154.",
"accepts": [],
"produces": ["image"],
"resources": "gpu",
"entry": "run.py",
"python": "venvs/mflux/bin/python",
"params_schema": {
"type": "object",
"properties": {
"prompt": {"type": "string", "default": "", "description": "What to generate"},
"model": {"type": "string", "enum": ["flux2-klein-4b", "flux2-klein-9b", "schnell", "schnell-4bit", "dev", "krea-dev"], "default": "flux2-klein-4b", "description": "klein-4b + schnell-4bit are ungated; the rest need the HF token"},
"steps": {"type": "integer", "default": 4, "minimum": 1, "maximum": 50, "description": "klein/schnell: 2-4; dev/krea-dev: 20-28"},
"width": {"type": "integer", "default": 1024, "minimum": 256, "maximum": 2048},
"height": {"type": "integer", "default": 1024, "minimum": 256, "maximum": 2048},
"seed": {"type": "integer", "default": 42, "description": "Fixed seed = reproducible A/B tests"},
"quantize": {"type": "string", "enum": ["none", "8", "4"], "default": "8", "description": "FLUX.1 models only: 8-bit ≈ full quality at half memory"},
"guidance": {"type": "number", "default": 3.5, "minimum": 0, "maximum": 10, "description": "dev/krea-dev only"}
}
}
}

View File

@ -1,6 +1,7 @@
import argparse
import json
import os
import shlex
import subprocess
import sys
import time
@ -23,11 +24,30 @@ outdir = Path(a.outdir)
out_png = outdir / f"flux_{model.replace('/', '_')}_s{p.get('seed', 42)}.png"
bindir = Path(sys.executable).parent
# Each family needs a different mflux CLI, and they do NOT accept each other's flags.
# z-image/qwen are separate architectures from FLUX despite living in the same venv.
if model.startswith("flux2-"):
cli = bindir / "mflux-generate-flux2"
model_args = ["--model", model]
quant_args = [] # klein is small; skip quantization flags
guidance_args = [] # distilled klein: guidance fixed at 1.0
elif model in ("z-image", "z-image-turbo"):
# Alibaba Z-Image (DiT). filipstrand's 4-bit MLX build runs in ~6GB, which is what
# makes it viable on the 16-32GB boxes. Turbo is few-step: 6-10, not 25.
cli = bindir / ("mflux-generate-z-image-turbo" if model.endswith("turbo")
else "mflux-generate-z-image")
model_args = ["--base-model", model]
if model == "z-image-turbo":
model_args += ["-m", p.get("hf_repo", "filipstrand/Z-Image-Turbo-mflux-4bit")]
quant = str(p.get("quantize", "4"))
quant_args = ["-q", quant] if quant in ("3", "4", "5", "6", "8") else []
guidance_args = []
elif model == "qwen":
cli = bindir / "mflux-generate-qwen"
model_args = ["--base-model", "qwen"]
quant = str(p.get("quantize", "4"))
quant_args = ["-q", quant] if quant in ("3", "4", "5", "6", "8") else []
guidance_args = []
elif model == "schnell-4bit":
cli = bindir / "mflux-generate"
# ungated community pre-quantized weights (~10GB) — no HF license wall
@ -44,9 +64,66 @@ else:
if model in ("dev", "krea-dev") else [])
if not cli.exists():
print(f"ERROR: mflux not installed ({cli} missing). Run scripts/install_mflux.sh")
print(f"ERROR: mflux CLI not installed ({cli} missing). Run scripts/install_mflux.sh")
sys.exit(1)
# ---- LoRA -------------------------------------------------------------------
# THE RULE: a LoRA only works on its own base architecture. Loading an SD1.5 LoRA on
# FLUX, or a FLUX.1 LoRA on FLUX.2, is a SILENT no-op — mflux does not error, the image
# just comes back unchanged and you blame the LoRA. So we fail loudly on a missing file
# and log exactly what was applied.
#
# `lora` accepts a path, a bare filename resolved against LORA_DIRS, or a list. Weights
# come from `lora_weight` (scalar or list, default 0.8).
lora_dirs = [Path(d).expanduser() for d in
os.environ.get("MB_LORA_DIRS",
"~/Documents/localmodels/Lora:~/Documents/civit-lib/sd15/Lora:"
"~/Documents/civit-lib/sdxl/Lora:~/Documents/loras").split(":")]
def resolve_lora(name):
q = Path(name).expanduser()
if q.is_file():
return q
for d in lora_dirs:
c = d / name
if c.is_file():
return c
if not name.endswith(".safetensors"):
c = d / f"{name}.safetensors"
if c.is_file():
return c
return None
lora_args = []
raw = p.get("lora") or p.get("loras") or []
if isinstance(raw, str):
raw = [raw] if raw.strip() else []
if raw:
weights = p.get("lora_weight", p.get("lora_scales", 0.8))
if not isinstance(weights, list):
weights = [weights] * len(raw)
paths, scales = [], []
for i, nm in enumerate(raw):
# allow "name=0.7" shorthand
if isinstance(nm, str) and "=" in nm and not Path(nm).exists():
nm, _, w = nm.rpartition("=")
try:
weights[i] = float(w)
except ValueError:
pass
hit = resolve_lora(nm)
if not hit:
print(f"ERROR: LoRA not found: {nm}")
print(f" searched: {', '.join(str(d) for d in lora_dirs)}")
sys.exit(1)
paths.append(str(hit))
scales.append(str(weights[i] if i < len(weights) else 0.8))
lora_args = ["--lora-paths", *paths, "--lora-scales", *scales]
for pth, sc in zip(paths, scales):
print(f"[lora] {Path(pth).name} @ {sc}", flush=True)
cmd = [str(cli), *model_args,
"--prompt", p["prompt"],
"--steps", str(steps),
@ -54,9 +131,9 @@ cmd = [str(cli), *model_args,
"--height", str(p.get("height", 1024)),
"--seed", str(p.get("seed", 42)),
"--output", str(out_png),
*quant_args, *guidance_args]
*quant_args, *guidance_args, *lora_args]
print("+", " ".join(cmd), flush=True)
print("+", " ".join(shlex.quote(c) for c in cmd), flush=True)
print("(first run per model downloads weights from HuggingFace)", flush=True)
env = os.environ.copy()
# hf_xet's chunked downloader intermittently fails ("Unable to parse string as
@ -66,8 +143,8 @@ t0 = time.time()
res = subprocess.run(cmd, stdout=sys.stdout, stderr=subprocess.STDOUT, env=env)
if res.returncode != 0:
print("HINT: gated-repo/auth errors mean this model needs an HF license accept "
"+ token (Settings → HuggingFace token). flux2-klein-4b and schnell-4bit "
"are ungated and need nothing.")
"+ token (Settings → HuggingFace token). flux2-klein-4b, schnell-4bit and "
"z-image-turbo are ungated and need nothing.")
sys.exit(res.returncode)
elapsed = round(time.time() - t0, 1)
@ -81,5 +158,6 @@ if not out_png.exists():
(outdir / "result.json").write_text(json.dumps({"outputs": [
{"path": out_png.name,
"meta": {"tool": "mflux", "model": model, "steps": steps,
"seed": p.get("seed", 42), "seconds": elapsed}}]}))
"seed": p.get("seed", 42), "seconds": elapsed,
"loras": [Path(x).name for x in lora_args[1:1 + len(raw)]] if raw else []}}]}))
print(f"done in {elapsed}s: {out_png.name}", flush=True)

View File

@ -0,0 +1,85 @@
import argparse
import json
import os
import subprocess
import sys
import time
from pathlib import Path
ap = argparse.ArgumentParser()
ap.add_argument("--input", action="append", default=[])
ap.add_argument("--outdir", required=True)
ap.add_argument("--params", default="{}")
a = ap.parse_args()
p = json.loads(a.params)
if not p.get("prompt"):
print("ERROR: prompt is required")
sys.exit(1)
model = p.get("model", "flux2-klein-4b")
steps = int(p.get("steps", 4))
outdir = Path(a.outdir)
out_png = outdir / f"flux_{model.replace('/', '_')}_s{p.get('seed', 42)}.png"
bindir = Path(sys.executable).parent
if model.startswith("flux2-"):
cli = bindir / "mflux-generate-flux2"
model_args = ["--model", model]
quant_args = [] # klein is small; skip quantization flags
guidance_args = [] # distilled klein: guidance fixed at 1.0
elif model == "schnell-4bit":
cli = bindir / "mflux-generate"
# ungated community pre-quantized weights (~10GB) — no HF license wall
model_args = ["--model", "dhairyashil/FLUX.1-schnell-mflux-4bit",
"--base-model", "schnell"]
quant_args = []
guidance_args = []
else:
cli = bindir / "mflux-generate"
model_args = ["--model", model]
quant = str(p.get("quantize", "8"))
quant_args = ["--quantize", quant] if quant in ("4", "8") else []
guidance_args = (["--guidance", str(p.get("guidance", 3.5))]
if model in ("dev", "krea-dev") else [])
if not cli.exists():
print(f"ERROR: mflux not installed ({cli} missing). Run scripts/install_mflux.sh")
sys.exit(1)
cmd = [str(cli), *model_args,
"--prompt", p["prompt"],
"--steps", str(steps),
"--width", str(p.get("width", 1024)),
"--height", str(p.get("height", 1024)),
"--seed", str(p.get("seed", 42)),
"--output", str(out_png),
*quant_args, *guidance_args]
print("+", " ".join(cmd), flush=True)
print("(first run per model downloads weights from HuggingFace)", flush=True)
env = os.environ.copy()
# hf_xet's chunked downloader intermittently fails ("Unable to parse string as
# hex hash value"); the plain HTTP path is reliable.
env["HF_HUB_DISABLE_XET"] = "1"
t0 = time.time()
res = subprocess.run(cmd, stdout=sys.stdout, stderr=subprocess.STDOUT, env=env)
if res.returncode != 0:
print("HINT: gated-repo/auth errors mean this model needs an HF license accept "
"+ token (Settings → HuggingFace token). flux2-klein-4b and schnell-4bit "
"are ungated and need nothing.")
sys.exit(res.returncode)
elapsed = round(time.time() - t0, 1)
if not out_png.exists():
pngs = sorted(outdir.glob("*.png"))
if not pngs:
print("ERROR: no image produced")
sys.exit(1)
out_png = pngs[-1]
(outdir / "result.json").write_text(json.dumps({"outputs": [
{"path": out_png.name,
"meta": {"tool": "mflux", "model": model, "steps": steps,
"seed": p.get("seed", 42), "seconds": elapsed}}]}))
print(f"done in {elapsed}s: {out_png.name}", flush=True)