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Add local FLUX image editing

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Mikei386
2026-08-30 10:07:40 +02:00
parent b18bc8964a
commit 6af25cf30c
13 changed files with 209 additions and 64 deletions
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.env.example
+1 -1
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@@ -3,7 +3,7 @@ AI_BIND_ADDRESS=10.77.0.2
MODEL_DIR=/data/models
ROUTER_API_KEY=GENERATED_BY_INSTALLER
CONTROLLER_TOKEN=GENERATED_BY_INSTALLER
Z_IMAGE_MODEL_DIR=/data/models/Z-Image-Turbo
FLUX_MODEL_DIR=/data/models/FLUX.2-klein-4B
IMAGE_GPU_DEVICES=1
PIPER_TTS_VERSION=1.6.0
PIPER_VOICE=de_DE-thorsten-high
ATHENA.md
+2 -2
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@@ -10,7 +10,7 @@ Sie betreibt:
- llama.cpp mit genau einem aktiven Qwen-Profil,
- den OpenAI-kompatiblen Profile Router,
- Z-Image-Turbo für Bilder,
- FLUX.2-klein-4B für Textbilder und Referenzbild-Bearbeitung,
- XTTS und Piper für Sprache,
- das Athena-Dashboard,
- WireGuard-Gateway und Datenbackup,
@@ -48,7 +48,7 @@ Qwen-Profil wird vom Profile Controller verwaltet.
- Fast: kurze, interaktive Aufgaben
- Medium/Large/Ultra: steigende Kontextgrößen desselben lokalen Qwen-Modells
- Uncensored: separates lokales Profil
- Z-Image-Turbo: Bildgenerierung; Qwen wird dafür kurz entladen und danach
- FLUX.2-klein-4B: Bildgenerierung und Editing; Qwen wird dafür kurz entladen und danach
automatisch wiederhergestellt
- XTTS: RTX 3060; Piper bleibt CPU-Fallback
README.md
+1 -1
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@@ -10,7 +10,7 @@ Bild- und Sprachausgabe. **Hermes und die Fach-MCPs laufen auf Unraid.**
- genau ein aktives llama.cpp-Profil: Fast, Medium, Large, Ultra oder Uncensored
- Profile Router auf Port 8081
- Z-Image-Turbo als exklusiver Bild-Worker auf der RTX 5080
- FLUX.2-klein-4B für Textbilder und Referenzbild-Bearbeitung auf der RTX 5080
- XTTS auf der RTX 3060 mit Piper als CPU-Fallback
- Live-Dashboard mit 21 Tagen Detailhistorie auf Port 8099
- WireGuard-Gateway, Datenbackup und Athena-Operator
compose.yaml
+3 -3
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@@ -573,7 +573,7 @@ services:
IMAGE_DIR: /data/images
IMAGE_WORKER_URL: http://image-worker:8086
IMAGE_WORKER_TOKEN: "${CONTROLLER_TOKEN:?CONTROLLER_TOKEN is required}"
IMAGE_MODEL_NAME: Z-Image-Turbo
IMAGE_MODEL_NAME: FLUX.2-klein-4B
CHAT_IMAGE_ALLOW_REMOTE_URLS: "false"
ENABLE_IMAGE_GENERATION: "true"
ENABLE_TTS: "true"
@@ -628,13 +628,13 @@ services:
read_only: true
tmpfs: ["/tmp:size=1g,mode=1777"]
volumes:
- "${Z_IMAGE_MODEL_DIR:-/data/models/Z-Image-Turbo}:/models/Z-Image-Turbo:ro"
- "${FLUX_MODEL_DIR:-/data/models/FLUX.2-klein-4B}:/models/FLUX.2-klein-4B:ro"
- router-images:/data/images
environment:
NVIDIA_VISIBLE_DEVICES: ${IMAGE_GPU_DEVICES:-1}
NVIDIA_DRIVER_CAPABILITIES: compute,utility
WORKER_TOKEN: "${CONTROLLER_TOKEN:?CONTROLLER_TOKEN is required}"
Z_IMAGE_MODEL_DIR: /models/Z-Image-Turbo
FLUX_MODEL_DIR: /models/FLUX.2-klein-4B
IMAGE_DIR: /data/images
networks: [inference]
security_opt: ["no-new-privileges:true"]
config/install.env.example
+1 -1
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@@ -16,7 +16,7 @@ NVIDIA_MIN_DRIVER_MAJOR=570
TEXT_GPU_DEVICES=0
SECONDARY_GPU_DEVICES=1
IMAGE_GPU_DEVICES=1
Z_IMAGE_MODEL_DIR=/data/models/Z-Image-Turbo
FLUX_MODEL_DIR=/data/models/FLUX.2-klein-4B
# Headless remote reachability. Firmware power-loss recovery is configured
# separately once at the physical machine.
deploy/install.sh
+5 -4
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@@ -15,7 +15,7 @@ OLD_SERVICE=mike-ai-local-llm-router.service
OLD_DIR=/opt/mike-ai/local-llm-router
BACKUP_DIR=/opt/mike-ai/.backup-ai-profile-router-$(date +%Y%m%d-%H%M%S)
VENV="$INSTALL_DIR/venv"
MODEL_DIR=/opt/mike-ai/models/FLUX.2-klein-base-4B
MODEL_DIR=/opt/mike-ai/models/FLUX.2-klein-4B
IMAGE_DIR="$INSTALL_DIR/images"
XTTS_VENV=/opt/mike-ai/xtts/venv
XTTS_CACHE=/opt/mike-ai/xtts/.cache
@@ -72,13 +72,14 @@ echo "-- Installiere festgeschriebene Bild-Abhängigkeiten"
if [ -f "$MODEL_DIR/model_index.json" ]; then
echo "-- FLUX-Modell vorhanden: $MODEL_DIR"
else
echo "-- Lade FLUX.2-klein-base-4B nach $MODEL_DIR (kann dauern)"
echo "-- Lade FLUX.2-klein-4B nach $MODEL_DIR (kann dauern)"
"$VENV/bin/python" - <<'PY'
import os
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="black-forest-labs/FLUX.2-klein-base-4B",
local_dir="/opt/mike-ai/models/FLUX.2-klein-base-4B",
repo_id="black-forest-labs/FLUX.2-klein-4B",
revision="e7b7dc27f91deacad38e78976d1f2b499d76a294",
local_dir="/opt/mike-ai/models/FLUX.2-klein-4B",
local_dir_use_symlinks=False,
)
print("Modell-Download abgeschlossen")
dev/test_local.sh
+27
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@@ -343,6 +343,33 @@ CTYPE=$(grep -i content-type /tmp/hdr12.txt | tr -d "\r")
[ "$CODE" = "200" ] && [ -s /tmp/test_dl.png ] && echo "$CTYPE" | grep -qi "image/png" \
&& ok "PNG-Download (200, $CTYPE)" || bad "PNG-Download (Code $CODE, $CTYPE)"
# --- 12b. Referenzbild-Bearbeitung ------------------------------------------------------
echo "== Test 12b: POST /v1/images/edits mit lokalem Referenzbild"
python3 - <<'PY' >/tmp/edit-request.json
import base64, json
png = open('/tmp/test_dl.png', 'rb').read()
print(json.dumps({
'prompt': 'Behalte die Person bei und ändere nur den Hintergrund',
'size': '1024x1024',
'steps': 4,
'guidance': 1.0,
'response_format': 'b64_json',
'image_b64': base64.b64encode(png).decode(),
}))
PY
rm -f /tmp/test_worker_requests.jsonl
RESP=$(curl -sf "$BASE/v1/images/edits" -H "Content-Type: application/json" \
--data-binary @/tmp/edit-request.json)
echo "$RESP" | python3 -c '
import base64,json,sys
d=json.load(sys.stdin)
assert base64.b64decode(d["data"][0]["b64_json"])[:4] == b"\x89PNG"
' || bad "Bildbearbeitung liefert kein PNG"
REFS=$(tail -1 /tmp/test_worker_requests.jsonl | python3 -c \
'import json,sys; print(len(json.load(sys.stdin).get("source_files", [])))')
[ "$REFS" = "1" ] && ! find /tmp/test-images -name '.edit-*.ref' | grep -q . \
&& ok "Referenzbild übergeben und danach gelöscht" || bad "Referenzbild-Pfad/Cleanup"
# --- 13. Bild-Liste ---------------------------------------------------------------------
echo "== Test 13: GET /images"
RESP=$(curl -sf "$BASE/images")
docs/ARCHITECTURE.md
+2 -2
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@@ -6,7 +6,7 @@ flowchart LR
H -->|OpenAI API| R[Profile Router<br/>Athena :8081]
R --> P[Profile Controller]
P --> Q[genau ein llama.cpp-Profil<br/>Qwen Fast / Medium / Large / Ultra / Uncensored]
R --> I[Z-Image-Turbo<br/>RTX 5080, bei Bedarf]
R --> I[FLUX.2-klein-4B<br/>RTX 5080, Text + Editing]
R --> T[XTTS RTX 3060<br/>Piper CPU-Fallback]
H --> U[MUA / Unraid MCP]
@@ -49,4 +49,4 @@ Kontextgröße:
Die visuelle Fassung liegt als `athena-architecture-map.png` neben dieser Datei.
Eine zweite Detailkarte, `athena-gpu-allocation-map.png`, zeigt die
profilabhängige Layer-Verteilung auf RTX 5080 und RTX 3060 sowie die festen
GPU-Zuordnungen von Z-Image, Vision-Projektor und XTTS.
GPU-Zuordnungen von FLUX.2, Vision-Projektor und XTTS.
install.sh
+7 -7
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@@ -341,7 +341,7 @@ UNCENSORED_TENSOR_SPLIT=${UNCENSORED_TENSOR_SPLIT:-90,10}
UNCENSORED_MTP_MAX=${UNCENSORED_MTP_MAX:-2}
EXPERIMENTAL_GPU_DEVICES=${TEXT_GPU_DEVICES:-0}
IMAGE_GPU_DEVICES=${IMAGE_GPU_DEVICES:-${TEXT_GPU_DEVICES:-0}}
Z_IMAGE_MODEL_DIR=${Z_IMAGE_MODEL_DIR:-/data/models/Z-Image-Turbo}
FLUX_MODEL_DIR=${FLUX_MODEL_DIR:-/data/models/FLUX.2-klein-4B}
LLAMA_THREADS=${LLAMA_THREADS:-6}
LLAMA_THREADS_BATCH=${LLAMA_THREADS_BATCH:-6}
EOF
@@ -460,14 +460,14 @@ build_and_start() {
docker build --progress=plain --build-arg LLAMA_CPP_COMMIT="$commit" \
-f platform/docker/llama-cpp/Dockerfile -t mike-ai/llama.cpp:local .
docker compose --env-file "$SECRETS_DIR/stack.env" --profile image build image-worker
if [[ ! -s ${Z_IMAGE_MODEL_DIR:-/data/models/Z-Image-Turbo}/model_index.json ]]; then
log "Z-Image-Turbo laden"
install -d -m 0755 "${Z_IMAGE_MODEL_DIR:-/data/models/Z-Image-Turbo}"
if [[ ! -s ${FLUX_MODEL_DIR:-/data/models/FLUX.2-klein-4B}/model_index.json ]]; then
log "FLUX.2-klein-4B laden"
install -d -m 0755 "${FLUX_MODEL_DIR:-/data/models/FLUX.2-klein-4B}"
docker run --rm --entrypoint python \
-v "${Z_IMAGE_MODEL_DIR:-/data/models/Z-Image-Turbo}:/download" \
-v "${FLUX_MODEL_DIR:-/data/models/FLUX.2-klein-4B}:/download" \
mike-ai/image-worker:local -c \
"from huggingface_hub import snapshot_download; snapshot_download('Tongyi-MAI/Z-Image-Turbo', revision='f332072aa78be7aecdf3ee76d5c247082da564a6', local_dir='/download')"
chmod -R a-w "${Z_IMAGE_MODEL_DIR:-/data/models/Z-Image-Turbo}"
"from huggingface_hub import snapshot_download; snapshot_download('black-forest-labs/FLUX.2-klein-4B', revision='e7b7dc27f91deacad38e78976d1f2b499d76a294', local_dir='/download')"
chmod -R a-w "${FLUX_MODEL_DIR:-/data/models/FLUX.2-klein-4B}"
fi
# Creates the tools network and deploys the only host-bound MCP: Operator.
# Portable MCPs and Hermes live on Unraid and are restored through Appdata.
platform/docker/image-worker/image_worker.py
+42 -18
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@@ -1,5 +1,10 @@
#!/usr/bin/env python3
"""Private Z-Image-Turbo worker used only during a GPU hot swap."""
"""Private FLUX.2 Klein 4B worker used only during a GPU hot swap.
The same pipeline handles text-to-image and local reference-image editing.
Reference images are exchanged with the router through the shared image
volume; request bodies therefore never contain private image bytes here.
"""
from __future__ import annotations
@@ -14,7 +19,7 @@ from pathlib import Path
HOST = os.environ.get("WORKER_HOST", "0.0.0.0")
PORT = int(os.environ.get("WORKER_PORT", "8086"))
TOKEN = os.environ.get("WORKER_TOKEN", "").strip()
MODEL_DIR = os.environ.get("Z_IMAGE_MODEL_DIR", "/models/Z-Image-Turbo")
MODEL_DIR = os.environ.get("FLUX_MODEL_DIR", "/models/FLUX.2-klein-4B")
OUTPUT_DIR = Path(os.environ.get("IMAGE_DIR", "/data/images")).resolve()
PIPE = None
LOAD_SECONDS = 0.0
@@ -33,15 +38,13 @@ def load_pipeline() -> None:
if PIPE is not None:
return
import torch
from diffusers import ZImagePipeline
from diffusers import Flux2KleinPipeline
started = time.monotonic()
PIPE = ZImagePipeline.from_pretrained(
PIPE = Flux2KleinPipeline.from_pretrained(
MODEL_DIR, torch_dtype=torch.bfloat16, low_cpu_mem_usage=False)
# The Qwen text encoder and the DiT do not fit together in the usable
# 16 GiB of the RTX 5080. Sequential offload keeps only the active
# submodule on CUDA. This is slower than a fully resident pipeline, but
# deterministic and leaves the RTX 3060 available for XTTS.
PIPE.enable_sequential_cpu_offload()
# Officially supported low-VRAM path. It keeps the complete pipeline
# within the usable 16 GiB of the RTX 5080 and leaves the RTX 3060 alone.
PIPE.enable_model_cpu_offload()
if hasattr(PIPE, "enable_vae_slicing"):
PIPE.enable_vae_slicing()
if hasattr(PIPE, "enable_vae_tiling"):
@@ -51,6 +54,7 @@ def load_pipeline() -> None:
def generate(data: dict) -> dict:
import torch
from PIL import Image
prompt = data.get("prompt")
filename = data.get("filename")
if not isinstance(prompt, str) or not prompt.strip() or len(prompt) > 8000:
@@ -62,17 +66,37 @@ def generate(data: dict) -> dict:
if (width, height) not in {(1024, 1024), (1536, 1024), (1024, 1536),
(1920, 1088), (1088, 1920)}:
raise ValueError("unsupported image size")
steps = int(data.get("steps", 9))
guidance = float(data.get("guidance", 0.0))
if steps != 9 or guidance != 0.0:
raise ValueError("Z-Image-Turbo requires steps=9 and guidance=0.0")
steps = int(data.get("steps", 4))
guidance = float(data.get("guidance", 1.0))
if steps != 4 or guidance != 1.0:
raise ValueError("FLUX.2-klein-4B requires steps=4 and guidance=1.0")
source_files = data.get("source_files") or []
if not isinstance(source_files, list) or len(source_files) > 4:
raise ValueError("invalid source image list")
source_images = []
for source_name in source_files:
if not isinstance(source_name, str) or Path(source_name).name != source_name:
raise ValueError("invalid source image filename")
source = (OUTPUT_DIR / source_name).resolve()
if source.parent != OUTPUT_DIR or not source.is_file():
raise ValueError("source image not found")
with Image.open(source) as opened:
source_images.append(opened.convert("RGB"))
seed = data.get("seed")
generator = None if seed is None else torch.Generator(device="cuda").manual_seed(int(seed))
load_pipeline()
started = time.monotonic()
image = PIPE(prompt=prompt, height=height, width=width,
num_inference_steps=9, guidance_scale=0.0,
generator=generator).images[0]
kwargs = {
"prompt": prompt,
"height": height,
"width": width,
"num_inference_steps": 4,
"guidance_scale": 1.0,
"generator": generator,
}
if source_images:
kwargs["image"] = source_images[0] if len(source_images) == 1 else source_images
image = PIPE(**kwargs).images[0]
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
output = OUTPUT_DIR / filename
image.save(output)
@@ -84,7 +108,7 @@ def generate(data: dict) -> dict:
class Handler(BaseHTTPRequestHandler):
def log_message(self, fmt: str, *args: object) -> None:
# Never log request bodies/prompts.
print(f"[z-image-worker] {self.client_address[0]} {fmt % args}", flush=True)
print(f"[flux-image-worker] {self.client_address[0]} {fmt % args}", flush=True)
def reply(self, status: int, payload: dict) -> None:
body = json.dumps(payload, separators=(",", ":")).encode()
@@ -113,7 +137,7 @@ class Handler(BaseHTTPRequestHandler):
raise ValueError("invalid request size")
self.reply(200, generate(json.loads(self.rfile.read(length))))
except Exception as exc:
print(f"[z-image-worker] generation failed: "
print(f"[flux-image-worker] generation failed: "
f"{type(exc).__name__}: {str(exc)[:1000]}", flush=True)
self.reply(400, {"status": "error", "message": str(exc)})
platform/models/manifest.example.yaml
+3 -2
View File
@@ -28,8 +28,9 @@ models:
sha256: "REPLACE_AFTER_VERIFICATION"
image:
role: image-generation
source: Tongyi-MAI/Z-Image-Turbo
target: /data/models/Z-Image-Turbo
source: black-forest-labs/FLUX.2-klein-4B
revision: e7b7dc27f91deacad38e78976d1f2b499d76a294
target: /data/models/FLUX.2-klein-4B
revision: "f332072aa78be7aecdf3ee76d5c247082da564a6"
xtts:
role: text-to-speech
router/ai_profile_router.py
+95 -17
View File
@@ -18,8 +18,9 @@ Virtuelle Modelle: qwen-fast, qwen-medium, qwen-large, qwen-ultra,
Kommandos: POST /fast, /medium, /large, /ultra, /uncensored
GET /status (Zustand)
Bildgenerierung (Z-Image-Turbo):
Bildgenerierung und Editing (FLUX.2-klein-4B):
POST /v1/images/generations (OpenAI-kompatibel)
POST /v1/images/edits (lokal, Referenzbilder)
GET /images (Liste)
GET /images/<datei> (PNG-Download)
@@ -37,7 +38,7 @@ Der Router leitet /v1/audio/speech und /v1/audio/transcriptions
per HTTP an die Worker weiter.
Der Router agiert als Modell-Orchestrator: vor der Generierung wird
llama.cpp gestoppt, der Bild-Worker lädt Z-Image, generiert und entlädt
llama.cpp gestoppt, der Bild-Worker lädt FLUX.2, generiert/bearbeitet und entlädt
das Modell wieder; danach wird das vorherige Qwen-Profil wiederher-
gestellt und erst dann geantwortet (try/finally – Qwen wird auch bei
Fehlgeschlagener Generierung wiederhergestellt).
@@ -116,7 +117,7 @@ CONNECT_TIMEOUT = float(os.environ.get("CONNECT_TIMEOUT", "10")) # s, Connect
POLL_INTERVAL = float(os.environ.get("POLL_INTERVAL", "2")) # s, Polling-Intervall
MAX_GENERATION_TOKENS = int(os.environ.get("MAX_GENERATION_TOKENS", "8192"))
# --- Bildgenerierung (Z-Image-Turbo) ---
# --- Bildgenerierung und Referenzbild-Bearbeitung (FLUX.2 Klein 4B) ---
LLAMA_SERVICE = os.environ.get("LLAMA_SERVICE", "mike-ai-llama-ui.service")
SYSTEMCTL_BIN = os.environ.get("SYSTEMCTL_BIN", "systemctl")
IMAGE_WORKER = os.environ.get(
@@ -125,7 +126,7 @@ IMAGE_PYTHON = os.environ.get(
"IMAGE_PYTHON", "/opt/mike-ai/ai-profile-router/venv/bin/python")
IMAGE_WORKER_URL = os.environ.get("IMAGE_WORKER_URL", "").rstrip("/")
IMAGE_WORKER_TOKEN = os.environ.get("IMAGE_WORKER_TOKEN", "").strip()
IMAGE_MODEL_NAME = os.environ.get("IMAGE_MODEL_NAME", "Z-Image-Turbo")
IMAGE_MODEL_NAME = os.environ.get("IMAGE_MODEL_NAME", "FLUX.2-klein-4B")
IMAGE_DIR = os.environ.get(
"IMAGE_DIR", "/opt/mike-ai/ai-profile-router/images")
IMAGE_WORKER_LOG = os.environ.get(
@@ -153,8 +154,8 @@ IMAGE_SIZES = {
"1920x1088": (1920, 1088),
"1088x1920": (1088, 1920),
}
# Z-Image-Turbo nutzt neun Scheduler-Schritte (acht DiT-Forwards) ohne CFG.
IMAGE_QUALITY = {"standard": 9, "high": 9}
# Das destillierte FLUX.2-klein-4B ist auf vier Schritte ausgelegt.
IMAGE_QUALITY = {"standard": 4, "high": 4}
IMAGE_DEFAULT_QUALITY = "standard"
IMAGE_MAX_N = 4
@@ -742,7 +743,7 @@ def switch_profile(profile: str, implicit: bool = False) -> None:
# ---------------------------------------------------------------------------
# Bildgenerierung (Z-Image-Turbo)
# Bildgenerierung und Editing (FLUX.2-klein-4B)
# ---------------------------------------------------------------------------
class _Worker:
@@ -986,7 +987,8 @@ def _restore_qwen(profile: str) -> None:
def generate_image(prompt: str, width: int, height: int, steps: int,
guidance: float, seed: int | None, n: int,
quality: str = "standard"
quality: str = "standard",
source_files: list[str] | None = None,
) -> tuple[list[str], str | None]:
"""Orchestriert die Bildgenerierung inkl. Qwen-Hotswap.
@@ -1047,6 +1049,7 @@ def generate_image(prompt: str, width: int, height: int, steps: int,
"guidance": guidance,
"seed": seed,
"output": output,
"source_files": source_files or [],
}, timeout=IMAGE_GEN_TIMEOUT)
if resp.get("status") != "ok":
raise RuntimeError(
@@ -1065,6 +1068,8 @@ def generate_image(prompt: str, width: int, height: int, steps: int,
"steps": steps,
"guidance": guidance,
"quality": quality,
"mode": "image-edit" if source_files else "text-to-image",
"reference_images": len(source_files or []),
"seconds": resp.get("seconds"),
"model": IMAGE_MODEL_NAME,
"created": time.strftime("%Y-%m-%dT%H:%M:%S"),
@@ -1403,6 +1408,12 @@ class Handler(BaseHTTPRequestHandler):
else:
self._send_error(503, "Bildgenerierung ist nicht installiert",
"server_error", "feature_disabled")
elif path == "/v1/images/edits" and self.command == "POST":
if ENABLE_IMAGE_GENERATION:
self._image_edit()
else:
self._send_error(503, "Bildbearbeitung ist nicht installiert",
"server_error", "feature_disabled")
elif path == "/v1/audio/speech" and self.command == "POST":
if ENABLE_TTS:
self._speech()
@@ -1627,22 +1638,89 @@ class Handler(BaseHTTPRequestHandler):
# ---------- Bildgenerierung ----------
def _image_generate(self) -> None:
data = self._read_image_request()
if data is not None:
self._image_request(data, [])
def _image_edit(self) -> None:
"""Edit with local image bytes supplied by the private Hermes plugin."""
data = self._read_image_request()
if data is None:
return
encoded: list[str] = []
primary = data.pop("image_b64", None)
if isinstance(primary, str) and primary:
encoded.append(primary)
references = data.pop("reference_images_b64", [])
if references is None:
references = []
if not isinstance(references, list) or any(
not isinstance(item, str) for item in references):
self._send_error(400, "'reference_images_b64' muss eine Liste sein",
"invalid_request_error", "invalid_references")
return
encoded.extend(references)
if not encoded:
self._send_error(400, "Referenzbild fehlt",
"invalid_request_error", "missing_image")
return
if len(encoded) > 4:
self._send_error(400, "höchstens vier Referenzbilder erlaubt",
"invalid_request_error", "too_many_images")
return
source_files: list[str] = []
try:
for item in encoded:
if item.startswith("data:"):
header, separator, item = item.partition(",")
if not separator or not header.lower().startswith("data:image/"):
raise ValueError("ungültige Bild-Data-URI")
try:
raw = base64.b64decode(item, validate=True)
except Exception as exc:
raise ValueError("ungültige Base64-Bilddaten") from exc
if not raw or len(raw) > CHAT_IMAGE_MAX_BYTES:
raise ValueError(
f"Referenzbild muss 1..{CHAT_IMAGE_MAX_BYTES} Bytes groß sein")
name = f".edit-{os.urandom(12).hex()}.ref"
os.makedirs(IMAGE_DIR, exist_ok=True)
with open(os.path.join(IMAGE_DIR, name), "xb") as output:
output.write(raw)
source_files.append(name)
self._image_request(data, source_files)
except ValueError as exc:
self._send_error(400, str(exc),
"invalid_request_error", "invalid_image")
finally:
for name in source_files:
try:
os.unlink(os.path.join(IMAGE_DIR, name))
except FileNotFoundError:
pass
except OSError as exc:
log.warning("temporäres Referenzbild nicht gelöscht: %s", exc)
def _read_image_request(self) -> dict | None:
try:
body = self._read_body()
except ValueError as e:
self._send_error(400, str(e),
"invalid_request_error", "invalid_body")
return
return None
try:
data = json.loads(body)
except ValueError:
self._send_error(400, "ungültiges JSON",
"invalid_request_error", "invalid_json")
return
return None
if not isinstance(data, dict):
self._send_error(400, "Request muss ein JSON-Objekt sein",
"invalid_request_error", "invalid_request")
return
return None
return data
def _image_request(self, data: dict, source_files: list[str]) -> None:
prompt = data.get("prompt")
if not isinstance(prompt, str) or not prompt.strip():
@@ -1679,19 +1757,19 @@ class Handler(BaseHTTPRequestHandler):
"invalid_request_error", "invalid_quality")
return
steps = data.get("steps", IMAGE_QUALITY[quality])
if not isinstance(steps, int) or isinstance(steps, bool) or steps != 9:
self._send_error(400, "Z-Image-Turbo erfordert 'steps'=9",
if not isinstance(steps, int) or isinstance(steps, bool) or steps != 4:
self._send_error(400, "FLUX.2-klein-4B erfordert 'steps'=4",
"invalid_request_error", "invalid_steps")
return
guidance = data.get("guidance", 0.0)
guidance = data.get("guidance", 1.0)
try:
guidance = float(guidance)
except (TypeError, ValueError):
self._send_error(400, "'guidance' muss eine Zahl sein",
"invalid_request_error", "invalid_guidance")
return
if guidance != 0.0:
self._send_error(400, "Z-Image-Turbo erfordert 'guidance'=0.0",
if guidance != 1.0:
self._send_error(400, "FLUX.2-klein-4B erfordert 'guidance'=1.0",
"invalid_request_error", "invalid_guidance")
return
@@ -1719,7 +1797,7 @@ class Handler(BaseHTTPRequestHandler):
try:
results, warning = generate_image(
prompt.strip(), width, height, steps, guidance, seed, n,
quality)
quality, source_files)
except (ValueError, RuntimeError) as e:
self._send_error(503, str(e), "server_error", "image_generation_failed")
return
router/image_worker.py
+20 -6
View File
@@ -1,5 +1,5 @@
#!/usr/bin/env python3
"""FLUX.2 [klein] 4B Base – Bild-Worker.
"""FLUX.2 [klein] 4B – Bild-Worker with reference-image editing.
Protokoll: zeilenbasiertes JSON über stdin/stdout.
@@ -32,7 +32,7 @@ os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
MODEL_DIR = os.environ.get(
"FLUX_MODEL_DIR", "/opt/mike-ai/models/FLUX.2-klein-base-4B")
"FLUX_MODEL_DIR", "/opt/mike-ai/models/FLUX.2-klein-4B")
_pipe = None # geladene Pipeline (None = entladen)
_load_seconds = 0.0 # Dauer des letzten Ladens
@@ -83,12 +83,13 @@ def _unload() -> None:
def _generate(req: dict) -> dict:
import torch
from PIL import Image
prompt = req["prompt"]
width = int(req.get("width", 1024))
height = int(req.get("height", 1024))
steps = int(req.get("steps", 50))
guidance = float(req.get("guidance", 4.0))
steps = int(req.get("steps", 4))
guidance = float(req.get("guidance", 1.0))
seed = req.get("seed")
output = req["output"]
@@ -98,14 +99,27 @@ def _generate(req: dict) -> dict:
generator = None
if seed is not None:
generator = torch.Generator(device="cuda").manual_seed(int(seed))
image = _pipe(
kwargs = dict(
prompt=prompt,
height=height,
width=width,
guidance_scale=guidance,
num_inference_steps=steps,
generator=generator,
).images[0]
)
source_files = req.get("source_files") or []
if not isinstance(source_files, list) or len(source_files) > 4:
raise ValueError("invalid source image list")
sources = []
for source in source_files:
if not isinstance(source, str):
raise ValueError("invalid source image filename")
path = os.path.join(os.path.dirname(output), source)
with Image.open(path) as opened:
sources.append(opened.convert("RGB"))
if sources:
kwargs["image"] = sources[0] if len(sources) == 1 else sources
image = _pipe(**kwargs).images[0]
os.makedirs(os.path.dirname(output) or ".", exist_ok=True)
image.save(output)