Update app.py

app.py

@@ -267,8 +267,6 @@ class LTX23DistilledA2VPipeline(DistilledPipeline):
 # Model repos
 LTX_MODEL_REPO = "Lightricks/LTX-2.3"
 GEMMA_REPO ="Lightricks/gemma-3-12b-it-qat-q4_0-unquantized"
-GEMMA_ABLITERATED_REPO = "Sikaworld1990/gemma-3-12b-it-abliterated-sikaworld-high-fidelity-edition-Ltx-2"
-GEMMA_ABLITERATED_FILE = "gemma-3-12b-it-abliterated-sikaworld-high-fidelity-edition.safetensors"
 
 # Download model checkpoints
 print("=" * 80)
@@ -293,72 +291,7 @@ checkpoint_path = hf_hub_download(
     local_dir_use_symlinks=False,
 )
 spatial_upsampler_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-spatial-upscaler-x2-1.1.safetensors")
-
-print("[Gemma] Setting up abliterated Gemma text encoder...")
-MERGED_WEIGHTS = "/tmp/abliterated_gemma_merged.safetensors"
-gemma_root = "/tmp/abliterated_gemma"
-os.makedirs(gemma_root, exist_ok=True)
-
-gemma_official_dir = snapshot_download(
-    repo_id=GEMMA_REPO,
-    ignore_patterns=["*.safetensors", "*.safetensors.index.json"],
-)
-
-for fname in os.listdir(gemma_official_dir):
-    src = os.path.join(gemma_official_dir, fname)
-    dst = os.path.join(gemma_root, fname)
-    if os.path.isfile(src) and not fname.endswith(".safetensors") and fname != "model.safetensors.index.json":
-        if not os.path.exists(dst):
-            os.symlink(src, dst)
-
-if os.path.exists(MERGED_WEIGHTS):
-    print("[Gemma] Using cached merged weights")
-else:
-    abliterated_weights_path = hf_hub_download(
-        repo_id=GEMMA_ABLITERATED_REPO,
-        filename=GEMMA_ABLITERATED_FILE,
-    )
-    index_path = hf_hub_download(
-        repo_id=GEMMA_REPO,
-        filename="model.safetensors.index.json"
-    )
-    with open(index_path) as f:
-        weight_index = json.load(f)
-
-    vision_keys = {}
-    for key, shard in weight_index["weight_map"].items():
-        if "vision_tower" in key or "multi_modal_projector" in key:
-            vision_keys[key] = shard
-    needed_shards = set(vision_keys.values())
-
-    shard_paths = {}
-    for shard_name in needed_shards:
-        shard_paths[shard_name] = hf_hub_download(
-            repo_id=GEMMA_REPO,
-            filename=shard_name
-        )
-
-    _fp8_types = {torch.float8_e4m3fn, torch.float8_e5m2}
-    raw = load_file(abliterated_weights_path)
-    merged = {}
-    for key, tensor in raw.items():
-        t = tensor.to(torch.bfloat16) if tensor.dtype in _fp8_types else tensor
-        merged[f"language_model.{key}"] = t
-    del raw
-
-    for key, shard_name in vision_keys.items():
-        with safe_open(shard_paths[shard_name], framework="pt") as f:
-            merged[key] = f.get_tensor(key)
-
-    save_file(merged, MERGED_WEIGHTS)
-    del merged
-    gc.collect()
-
-weight_link = os.path.join(gemma_root, "model.safetensors")
-if os.path.exists(weight_link):
-    os.remove(weight_link)
-os.symlink(MERGED_WEIGHTS, weight_link)
-print(f"[Gemma] Root ready: {gemma_root}")
+gemma_root = snapshot_download(repo_id=GEMMA_REPO)
 
 # ---- Insert block (LoRA downloads) between lines 268 and 269 ----
 # LoRA repo + download the requested LoRA adapters
@@ -396,6 +329,7 @@ print(f"Transition LoRA: {transition_lora_path}")
 
 print(f"Checkpoint: {checkpoint_path}")
 print(f"Spatial upsampler: {spatial_upsampler_path}")
+print(f"Gemma root: {gemma_root}")
 
 # Initialize pipeline WITH text encoder and optional audio support
 # ---- Replace block (pipeline init) lines 275-281 ----