Upload app.py

app.py

@@ -65,18 +65,21 @@ REALISM_LORA_FILE = os.environ.get(
 REALISM_LORA_SCALE = float(os.environ.get("H3_REALISM_LORA_SCALE", "0.8"))
 REALISM_LORA_TRIGGER = "r34l1sm"
 
-# Polyhedron: Perfect Eyes / Perfect Skin / Perfect Hands. The Civitai model
-# has multiple versions, so the loader resolves the current version/file at
-# startup instead of hard-coding a version ID that may become stale.
-DETAIL_LORA_REPO = os.environ.get("H3_DETAIL_LORA_REPO", "civitai-model://2899220")
+# Perfect Eyes / Skin / Hands. The original release is on Civitai, but the
+# weights are mirrored in a Hugging Face repository so this Space never needs
+# to contact Civitai at startup. The filename is pinned; no model-page/API lookup.
+DETAIL_LORA_REPO = os.environ.get(
+    "H3_DETAIL_LORA_REPO",
+    "Alex995647/loras-minimax-h3",
+)
 DETAIL_LORA_FILE = os.environ.get(
     "H3_DETAIL_LORA_FILE",
-    "polyhedron_minimax_h3_image_lora_v2__perfe8ct.safetensors",
+    "minimax-h3-t2va-t2v-polyhedron-perfect-eyes-perfect-skin/polyhedron_minimax_h3_image_lora_v2__perfe8ct.safetensors",
 )
 DETAIL_LORA_SCALE = float(os.environ.get("H3_DETAIL_LORA_SCALE", "0.7"))
 
 # Spatial & Physics: object collisions, gravity, stacking, falling, occlusion, and
-# more consistent spatial relationships. The pruned H3 adapter does not require a trigger word.
+# more consistent spatial relationships. The clean pruned H3 adapter has no trigger word.
 SPATIAL_LORA_REPO = os.environ.get(
     "H3_SPATIAL_LORA_REPO",
     "Jojocodex/minimax-h3-spatial-physics-lora",
@@ -87,16 +90,6 @@ SPATIAL_LORA_FILE = os.environ.get(
 )
 SPATIAL_LORA_SCALE = float(os.environ.get("H3_SPATIAL_LORA_SCALE", "0.4"))
 
-BLOWJOB_LORA_REPO = os.environ.get(
-    "H3_BLOWJOB_LORA_REPO",
-    "iamgroot1212/minimax-h3-loras",
-)
-BLOWJOB_LORA_FILE = os.environ.get(
-    "H3_BLOWJOB_LORA_FILE",
-    "MMH3-Hybrid-BlowJob-lora.safetensors",
-)
-BLOWJOB_LORA_SCALE = float(os.environ.get("H3_BLOWJOB_LORA_SCALE", "1.0"))
-
 CONDITIONER_SPACE = os.environ.get("H3_CONDITIONER", "multimodalart/qwen3vl-conditioner")
 # ``pack`` places the transformer at startup (66 GB on disk via the spaces pack); VAEs move on first GPU call.
 PLACEMENT = os.environ.get("H3_PLACEMENT", "pack").lower()
@@ -365,72 +358,10 @@ def _apply_extra_lora(
     from huggingface_hub import hf_hub_download
     from safetensors.torch import load_file
 
-    if repo_id.startswith("civitai-model://"):
-        # Resolve a Civitai model page to its newest model version, then use
-        # the same streaming downloader as the explicit civitai://version-id
-        # path. This keeps the Space independent of a hard-coded version ID.
-        import requests
-        model_id = repo_id.removeprefix("civitai-model://")
-        api_url = f"https://civitai.com/api/v1/models/{model_id}"
-        metadata = requests.get(api_url, timeout=30).json()
-        versions = metadata.get("modelVersions") or []
-        if not versions:
-            raise ValueError(f"{label}: Civitai model {model_id} has no model versions")
-        version = versions[0]
-        model_version_id = str(version["id"])
-        files = [f for f in (version.get("files") or []) if str(f.get("name", "")).lower().endswith(".safetensors")]
-        if not files:
-            raise ValueError(f"{label}: Civitai version {model_version_id} has no .safetensors file")
-        # Prefer the requested filename when it exists; otherwise use the first
-        # safetensors file exposed by the current version.
-        selected = next((f for f in files if f.get("name") == filename), files[0])
-        filename = os.path.basename(selected["name"])
-        download_url = selected.get("downloadUrl") or f"https://civitai.com/api/download/models/{model_version_id}"
-        cache_dir = os.path.join(tempfile.gettempdir(), "h3-lora-cache")
-        os.makedirs(cache_dir, exist_ok=True)
-        path = os.path.join(cache_dir, filename)
-        if not os.path.isfile(path) or os.path.getsize(path) == 0:
-            tmp_path = f"{path}.part"
-            with requests.get(download_url, stream=True, timeout=120, allow_redirects=True) as response:
-                response.raise_for_status()
-                with open(tmp_path, "wb") as handle:
-                    for chunk in response.iter_content(chunk_size=8 * 1024 * 1024):
-                        if chunk:
-                            handle.write(chunk)
-            os.replace(tmp_path, path)
-    elif repo_id.startswith("civitai://"):
-        import requests
-        model_version_id = repo_id.removeprefix("civitai://")
-        download_url = f"https://civitai.com/api/download/models/{model_version_id}"
-        cache_dir = os.path.join(tempfile.gettempdir(), "h3-lora-cache")
-        os.makedirs(cache_dir, exist_ok=True)
-        path = os.path.join(cache_dir, filename)
-        if not os.path.isfile(path) or os.path.getsize(path) == 0:
-            tmp_path = f"{path}.part"
-            with requests.get(download_url, stream=True, timeout=120, allow_redirects=True) as response:
-                response.raise_for_status()
-                with open(tmp_path, "wb") as handle:
-                    for chunk in response.iter_content(chunk_size=8 * 1024 * 1024):
-                        if chunk:
-                            handle.write(chunk)
-            os.replace(tmp_path, path)
-    elif repo_id == "aronzhan/H3-Loras-bucket":
-        import requests
-        bucket_url = f"https://huggingface.co/buckets/{repo_id}/resolve/{filename}"
-        cache_dir = os.path.join(tempfile.gettempdir(), "h3-lora-cache")
-        os.makedirs(cache_dir, exist_ok=True)
-        path = os.path.join(cache_dir, filename)
-        if not os.path.isfile(path) or os.path.getsize(path) == 0:
-            tmp_path = f"{path}.part"
-            with requests.get(bucket_url, stream=True, timeout=60, allow_redirects=True) as response:
-                response.raise_for_status()
-                with open(tmp_path, "wb") as handle:
-                    for chunk in response.iter_content(chunk_size=8 * 1024 * 1024):
-                        if chunk:
-                            handle.write(chunk)
-            os.replace(tmp_path, path)
-    else:
-        path = hf_hub_download(repo_id, filename)
+    # All adapters used by this Space are pinned to repository/file paths.
+    # There is deliberately no Civitai/API fallback here: startup must only
+    # resolve repository files through the Hugging Face Hub.
+    path = hf_hub_download(repo_id, filename)
 
     lora = load_file(path, device="cpu")
 
@@ -598,13 +529,6 @@ def load_and_apply_extra_loras(transformer) -> list[str]:
             "Spatial & Physics LoRA",
             SPATIAL_LORA_SCALE,
         ),
-        _apply_extra_lora(
-            transformer,
-            BLOWJOB_LORA_REPO,
-            BLOWJOB_LORA_FILE,
-            "MiniMax H3 BlowJob LoRA",
-            BLOWJOB_LORA_SCALE,
-        ),
     ]
 
 # ── Model loading ───────────────────────────────────────────────────────────