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Krea 2 is a 12.9B single-stream flow-matching DiT trained from scratch, using a Qwen3-VL-4B text encoder and the Qwen-Image VAE. The transformer is vendored as a diffusers ModelMixin whose module tree mirrors the checkpoint, so weights load with no key conversion; the pipeline ports the reference encode, flow-matching denoise, and VAE decode. The text encoder is shared at runtime via the existing dedup registry, so Base and Turbo reuse one Qwen3-VL-4B copy. Covers text-to-image, image-to-image, native LoRA, and the single-file UNET override. Also completes SD.Next's partial Qwen-Image VAE support (5D decode input and TAESD preview mapping) that K2 shares.
54 lines
2.6 KiB
Python
54 lines
2.6 KiB
Python
import diffusers
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import transformers
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from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
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from modules.logger import log
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from pipelines import generic
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def load_krea2(checkpoint_info, diffusers_load_config=None):
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if diffusers_load_config is None:
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diffusers_load_config = {}
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repo_id = sd_models.path_to_repo(checkpoint_info)
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sd_models.hf_auth_check(checkpoint_info)
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load_args, _ = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
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log.debug(f'Load model: type=Krea2 repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
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from pipelines.krea2.transformer_krea2 import Krea2Transformer2DModel
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from pipelines.krea2.pipeline_krea2 import Krea2Pipeline, Krea2Img2ImgPipeline
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from pipelines.krea2 import KREA2_SPEC
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diffusers.Krea2Transformer2DModel = Krea2Transformer2DModel
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diffusers.Krea2Pipeline = Krea2Pipeline
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diffusers.Krea2Img2ImgPipeline = Krea2Img2ImgPipeline
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generic.set_pipeline('Krea2', Krea2Pipeline)
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# One class per task so get_diffusers_task defaults to text2image and set_diffuser_pipe switches
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# to the img2img variant cleanly (matches the Chroma/Qwen per-task-class pattern).
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from diffusers.pipelines import auto_pipeline
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auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING['krea2'] = Krea2Pipeline
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auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING['krea2'] = Krea2Img2ImgPipeline
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if repo_id is None or repo_id.lower() == 'none':
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return None
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# Keep small/sensitive layers in compute dtype. `first` (in=64) and `txtfusion.projector`
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# (in=12) are below the int8 GEMM's minimum K; `last` is the output projection; `tmlp`/`tproj`
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# produce the global per-block modulation, too int8-sensitive to quantize (its error compounds
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# across blocks and steps). All are tiny next to the 28 blocks, so the memory cost is small.
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transformer = generic.load_transformer(repo_id, cls_name=Krea2Transformer2DModel, load_config=diffusers_load_config, native_spec=KREA2_SPEC, modules_to_not_convert=['first', 'last', 'projector', 'tmlp', 'tproj'])
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text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen3VLModel, load_config=diffusers_load_config)
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pipe = Krea2Pipeline.from_pretrained(
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repo_id,
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cache_dir=shared.opts.diffusers_dir,
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transformer=transformer,
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text_encoder=text_encoder,
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**load_args,
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)
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generic.load_vae_override(pipe, diffusers_load_config)
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del transformer
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del text_encoder
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sd_hijack_te.init_hijack(pipe)
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sd_hijack_vae.init_hijack(pipe)
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devices.torch_gc(force=True, reason='load')
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return pipe
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