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fix(sd_local): multi-dir LoRA search + UNet-only load

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LORA_DIRS searches ~/Documents/localmodels/Lora (John's master) then the repose
copy (SD_LORA_DIRS to override). Load via filtered state_dict keeping only
lora_unet_* keys: this diffusers build IndexErrors on these kohya files'
text-encoder half (empty rank_dict in load_lora_into_text_encoder); the UNet
half carries the look. Verified: all 3 anatomy LoRAs load + generate.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
type-two 2026-07-20 18:20:12 +10:00
parent f374465216
commit 3b8c88ce48
1 changed files with 23 additions and 4 deletions
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25
server/operators/sd_local/run.py
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@ -23,7 +23,17 @@ p = json.loads(a.params)
HERE = os.path.expanduser("~/Documents/repose")
BASE = os.path.join(HERE, "models", "Hyper_Realism_1.2_fp16.safetensors")
LORA_DIR = os.path.join(HERE, "models", "lora")
# LoRA search path: John's master dir first, the repose copy second (SD_LORA_DIRS to override)
LORA_DIRS = [os.path.expanduser(d) for d in os.environ.get(
"SD_LORA_DIRS", "~/Documents/localmodels/Lora:~/Documents/repose/models/lora").split(":")]
def find_lora(stem):
for d in LORA_DIRS:
cand = os.path.join(d, stem + ".safetensors")
if os.path.exists(cand):
return cand
return None
if not os.path.exists(BASE):
print(f"ERROR: {BASE} missing — sd_local runs on the primary only")
sys.exit(1)
@ -50,13 +60,22 @@ pipe = pipe.to(dev)
# no attention slicing — it crashes IP-Adapter attn-processor injection (see repose.py)
if p.get("loras"):
from safetensors.torch import load_file
names, weights = [], []
for spec in p["loras"]:
stem, _, w = str(spec).partition("=")
pipe.load_lora_weights(LORA_DIR, weight_name=f"{stem}.safetensors",
adapter_name=stem.replace(" ", "_"))
path = find_lora(stem.strip())
if not path:
print(f"[sd] LORA MISS '{stem}' — searched {LORA_DIRS}", flush=True)
continue
# UNet-only: this diffusers build trips an IndexError on these kohya files' text-encoder
# half (empty rank_dict in load_lora_into_text_encoder). The UNet half carries the look.
sd_l = load_file(path)
unet_only = {k: v for k, v in sd_l.items() if not k.startswith("lora_te")}
pipe.load_lora_weights(unet_only, adapter_name=stem.replace(" ", "_"))
names.append(stem.replace(" ", "_"))
weights.append(float(w or 0.6))
if names:
pipe.set_adapters(names, weights)
print(f"[sd] loras: {dict(zip(names, weights))}", flush=True)