unsloth/studio/backend/core/inference/diffusion_krea2.py

"""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."""
    try:
        local = Path(repo_id).expanduser() / "model_index.json"
        if local.is_file():
            return json.loads(local.read_text())
    except OSError:
        pass
    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)),
    )