fix(sd_local): multi-dir LoRA search + UNet-only load
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>
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@ -23,7 +23,17 @@ p = json.loads(a.params)
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HERE = os.path.expanduser("~/Documents/repose")
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BASE = os.path.join(HERE, "models", "Hyper_Realism_1.2_fp16.safetensors")
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LORA_DIR = os.path.join(HERE, "models", "lora")
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# LoRA search path: John's master dir first, the repose copy second (SD_LORA_DIRS to override)
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LORA_DIRS = [os.path.expanduser(d) for d in os.environ.get(
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"SD_LORA_DIRS", "~/Documents/localmodels/Lora:~/Documents/repose/models/lora").split(":")]
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def find_lora(stem):
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for d in LORA_DIRS:
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cand = os.path.join(d, stem + ".safetensors")
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if os.path.exists(cand):
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return cand
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return None
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if not os.path.exists(BASE):
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print(f"ERROR: {BASE} missing — sd_local runs on the primary only")
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sys.exit(1)
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@ -50,14 +60,23 @@ pipe = pipe.to(dev)
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# no attention slicing — it crashes IP-Adapter attn-processor injection (see repose.py)
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if p.get("loras"):
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from safetensors.torch import load_file
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names, weights = [], []
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for spec in p["loras"]:
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stem, _, w = str(spec).partition("=")
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pipe.load_lora_weights(LORA_DIR, weight_name=f"{stem}.safetensors",
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adapter_name=stem.replace(" ", "_"))
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path = find_lora(stem.strip())
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if not path:
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print(f"[sd] LORA MISS '{stem}' — searched {LORA_DIRS}", flush=True)
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continue
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# UNet-only: this diffusers build trips an IndexError on these kohya files' text-encoder
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# half (empty rank_dict in load_lora_into_text_encoder). The UNet half carries the look.
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sd_l = load_file(path)
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unet_only = {k: v for k, v in sd_l.items() if not k.startswith("lora_te")}
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pipe.load_lora_weights(unet_only, adapter_name=stem.replace(" ", "_"))
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names.append(stem.replace(" ", "_"))
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weights.append(float(w or 0.6))
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pipe.set_adapters(names, weights)
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if names:
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pipe.set_adapters(names, weights)
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print(f"[sd] loras: {dict(zip(names, weights))}", flush=True)
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if a.input: # reference image → IP-Adapter
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