"""Krea 2 pipeline loader: assembles ``Krea2Pipeline`` from per-component loads. Why not ``Krea2Pipeline.from_pretrained``: the ``krea/Krea-2-Turbo`` repo was exported with transformers 5.2, and two of its configs use 5.x-only conventions that the 4.x line cannot parse: - ``tokenizer/tokenizer_config.json`` declares ``Qwen2Tokenizer`` (slow -- 5.x unified slow/fast under the plain name) but ships only ``tokenizer.json``. 4.x's slow class needs vocab.json/merges.txt (absent), and its fast class trips over ``extra_special_tokens`` stored as a LIST (4.x expects a dict). Loading the fast class with an explicit ``extra_special_tokens = {}`` override is id-identical: every listed token is already registered as an added special token inside tokenizer.json, and the pipeline templates prompts manually (it never uses a chat template). - ``text_encoder/config.json`` keeps the rope settings under ``rope_parameters`` (the 5.x name). 4.x reads ``rope_scaling`` + a top-level ``rope_theta`` and crashes on the missing key (``NoneType.get``). The values are copied across verbatim -- and they equal 4.x's Qwen3-VL defaults (theta 5e6, mrope_section [24, 20, 20], interleaved mrope applied unconditionally), so the rotary embedding is numerically identical. The state dict itself round-trips 1:1 (checkpoint keys == 4.x module keys). ``from_pretrained`` additionally type-checks a passed ``tokenizer`` against the declared SLOW class (a fast tokenizer does not subclass it), so the pipeline is built through its constructor instead, forwarding the ``is_distilled`` / ``text_encoder_select_layers`` / ``patch_size`` init config from model_index.json -- Turbo's fixed mu=1.15 timestep shift rides on ``is_distilled = True``, so dropping it would silently degrade the schedule. Both workarounds are self-disabling on a transformers 5.x runtime: the plain tokenizer load succeeds (no fallback taken) and ``rope_scaling`` parses non-None (no patch). """ from __future__ import annotations import json from pathlib import Path from typing import Any, Optional from loggers import get_logger logger = get_logger(__name__) KREA2_FAMILY_NAME = "krea-2" def load_krea2_tokenizer(repo_id: str, hf_token: Optional[str] = None): """The Krea 2 tokenizer, tolerating the repo's transformers-5.x tokenizer config.""" from transformers import AutoTokenizer kwargs: dict[str, Any] = {"subfolder": "tokenizer"} if hf_token: kwargs["token"] = hf_token try: return AutoTokenizer.from_pretrained(repo_id, **kwargs) except Exception as exc: # noqa: BLE001 -- 4.x config-parse failure, retry with override logger.info("diffusion.krea2 tokenizer compat fallback: %s", exc) return AutoTokenizer.from_pretrained(repo_id, extra_special_tokens = {}, **kwargs) def remap_rope_parameters(text_config) -> None: """Copy 5.x ``rope_parameters`` onto the 4.x ``rope_scaling`` / ``rope_theta`` slots in place. A no-op when ``rope_scaling`` already parsed non-None (a 5.x runtime) or the config carries no ``rope_parameters`` dict.""" rope_parameters = getattr(text_config, "rope_parameters", None) if getattr(text_config, "rope_scaling", None) is None and isinstance(rope_parameters, dict): text_config.rope_scaling = {k: v for k, v in rope_parameters.items() if k != "rope_theta"} if "rope_theta" in rope_parameters: text_config.rope_theta = rope_parameters["rope_theta"] def load_krea2_text_encoder( repo_id: str, dtype, hf_token: Optional[str] = None, ): """The Qwen3-VL text encoder, remapping 5.x ``rope_parameters`` for a 4.x runtime.""" from transformers import AutoConfig, Qwen3VLModel kwargs: dict[str, Any] = {"subfolder": "text_encoder"} if hf_token: kwargs["token"] = hf_token config = AutoConfig.from_pretrained(repo_id, **kwargs) remap_rope_parameters(getattr(config, "text_config", config)) return Qwen3VLModel.from_pretrained(repo_id, config = config, dtype = dtype, **kwargs) def _load_model_index(repo_id: str, hf_token: Optional[str] = None) -> dict[str, Any]: """model_index.json as a dict, from a local path or the Hub cache.""" is_local_dir = False try: root = Path(repo_id).expanduser() is_local_dir = root.is_dir() local = root / "model_index.json" if local.is_file(): return json.loads(local.read_text()) except OSError: pass if is_local_dir: # A local checkpoint dir without the file must fail clearly here: falling through # to hf_hub_download with a filesystem path as the repo id would die with an # opaque HFValidationError instead. raise FileNotFoundError(f"model_index.json not found in local model dir {repo_id}") from huggingface_hub import hf_hub_download path = hf_hub_download(repo_id, "model_index.json", token = hf_token or None) return json.loads(Path(path).read_text()) def load_krea2_pipeline( repo_id: str, dtype, hf_token: Optional[str] = None, transformer = None, with_transformer: bool = True, ): """A ready ``Krea2Pipeline`` for ``repo_id`` (still on CPU; caller places it). ``transformer`` lets the single-file/quant paths hand in a prebuilt denoiser; ``with_transformer = False`` skips the (26 GB) denoiser entirely for a conditioning-only pipeline (the trainer's phased load). The remaining components (VAE, text encoder, tokenizer, scheduler) come from the repo. """ import diffusers # diffusers gained Krea2Pipeline in 0.39; on an older install the getattr chain below # would die with a bare AttributeError mid-load, so fail first with the actionable fix. if not hasattr(diffusers, "Krea2Pipeline"): raise RuntimeError( f"Krea 2 needs diffusers >= 0.39.0 (Krea2Pipeline); this environment has " f"diffusers {getattr(diffusers, '__version__', 'unknown')}. " f"Upgrade with: pip install -U diffusers" ) token = hf_token or None tokenizer = load_krea2_tokenizer(repo_id, hf_token = token) text_encoder = load_krea2_text_encoder(repo_id, dtype, hf_token = token) scheduler = diffusers.FlowMatchEulerDiscreteScheduler.from_pretrained( repo_id, subfolder = "scheduler", token = token ) vae = diffusers.AutoencoderKLQwenImage.from_pretrained( repo_id, subfolder = "vae", torch_dtype = dtype, token = token ) if transformer is None and with_transformer: transformer = diffusers.Krea2Transformer2DModel.from_pretrained( repo_id, subfolder = "transformer", torch_dtype = dtype, token = token ) model_index = _load_model_index(repo_id, hf_token = token) return diffusers.Krea2Pipeline( scheduler = scheduler, vae = vae, text_encoder = text_encoder, tokenizer = tokenizer, transformer = transformer, text_encoder_select_layers = model_index.get("text_encoder_select_layers"), is_distilled = bool(model_index.get("is_distilled", False)), patch_size = int(model_index.get("patch_size", 2)), )