608 lines
23 KiB
Python
608 lines
23 KiB
Python
"""Krea 2 (K2) for ai-toolkit.
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Krea 2 is a single-stream MMDiT text-to-image model:
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- text encoder: Qwen3-VL-4B-Instruct (a stack of 12 hidden-state layers is fed
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in; ``src/text_encoder.py``),
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- autoencoder: the Qwen-Image VAE (f8, 16 latent channels, the same VAE the
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``qwen_image`` arch uses),
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- denoiser: ``SingleStreamDiT`` (``src/mmdit.py``), which fuses the text layers
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with a small ``TextFusionTransformer`` and runs the packed [text | image]
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sequence through ``SingleStreamBlock`` layers.
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Flow-matching convention matches ai-toolkit exactly (t=1 noise -> t=0 clean,
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target = noise - clean), so ``get_noise_prediction`` does no time flip / negation.
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"""
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import os
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from typing import List, Optional
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import torch
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from safetensors.torch import load_file, save_file
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import huggingface_hub
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from huggingface_hub.errors import EntryNotFoundError
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from diffusers import AutoencoderKLQwenImage
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from transformers import (
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AutoTokenizer,
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Qwen2TokenizerFast,
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Qwen3VLForConditionalGeneration,
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)
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from optimum.quanto import freeze, QTensor
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from toolkit.config_modules import GenerateImageConfig, ModelConfig, NetworkConfig
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from toolkit.lora_special import LoRASpecialNetwork
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from toolkit.models.base_model import BaseModel
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from toolkit.basic import flush
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from toolkit.advanced_prompt_embeds import AdvancedPromptEmbeds
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from toolkit.samplers.custom_flowmatch_sampler import (
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CustomFlowMatchEulerDiscreteScheduler,
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)
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from toolkit.accelerator import unwrap_model
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from toolkit.metadata import get_meta_for_safetensors
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from toolkit.util.quantize import quantize, get_qtype, quantize_model
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from toolkit.memory_management import MemoryManager
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from .src.mmdit import (
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DoubleSharedModulation,
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SimpleModulation,
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SingleMMDiTConfig,
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SingleStreamDiT,
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)
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from .src.text_encoder import encode_krea_prompt, SELECT_LAYERS
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from .src.pipeline import Krea2Pipeline, pad_text_features, predict_velocity
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# The reference "single_mmdit_large_wide" architecture (oss_raw / oss_turbo share it).
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KREA2_MMDIT_CONFIG = dict(
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features=6144,
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tdim=256,
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txtdim=2560,
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heads=48,
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kvheads=12,
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multiplier=4,
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layers=28,
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patch=2,
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channels=16,
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txtheads=20,
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txtkvheads=20,
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txtlayers=12,
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)
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# Krea 2's mu schedule is exponential time-shifting whose mu is linearly
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# interpolated in image-token count between (256-res -> 0.5) and (1280-res ->
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# 1.15) -- exactly what CustomFlowMatchEulerDiscreteScheduler's dynamic shifting
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# does, so we mirror those endpoints here for the training timestep distribution.
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# x1 = (256 // (8*2))**2 = 256
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# x2 = (1280 // (8*2))**2 = 6400
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scheduler_config = {
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"base_image_seq_len": 256,
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"max_image_seq_len": 6400,
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"base_shift": 0.5,
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"max_shift": 1.15,
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"num_train_timesteps": 1000,
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"shift": 1.0,
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"use_dynamic_shifting": True,
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"time_shift_type": "exponential",
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}
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# Defaults; both overridable via model.model_kwargs.
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QWEN3_VL_PATH = "Qwen/Qwen3-VL-4B-Instruct"
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QWEN_IMAGE_VAE_PATH = "Qwen/Qwen-Image"
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HF_TOKEN = os.getenv("HF_TOKEN", None)
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def _load_mmdit_state_dict(name_or_path: str, filename: Optional[str]) -> dict:
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"""Load the MMDiT weights from a local safetensors file/dir or the HF hub.
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``name_or_path`` may be: a ``.safetensors`` file, a directory containing one
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(``filename`` or the lone ``.safetensors`` in it), or a hub repo id (the
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file ``filename`` is downloaded, defaulting to ``model.safetensors``).
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"""
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if name_or_path.endswith(".safetensors") and os.path.isfile(name_or_path):
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return load_file(name_or_path)
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if os.path.isdir(name_or_path):
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if filename is not None:
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return load_file(os.path.join(name_or_path, filename))
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candidates = [f for f in os.listdir(name_or_path) if f.endswith(".safetensors")]
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if len(candidates) == 1:
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return load_file(os.path.join(name_or_path, candidates[0]))
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raise FileNotFoundError(
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f"Could not pick an MMDiT checkpoint in {name_or_path}: found "
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f"{candidates}. Set model.model_kwargs.checkpoint_filename."
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)
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# Treat as a hub repo id. When no filename is given, derive it from the repo
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# name's trailing segment (e.g. "krea/Krea-2-Raw" -> "raw.safetensors",
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# "krea/Krea-2-Turbo" -> "turbo.safetensors").
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fname = filename or (
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name_or_path.split("/")[-1].split("-")[-1].lower() + ".safetensors"
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)
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try:
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path = huggingface_hub.hf_hub_download(
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repo_id=name_or_path, filename=fname, token=HF_TOKEN
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)
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except EntryNotFoundError as e:
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raise FileNotFoundError(
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f"Could not find {fname!r} in hub repo {name_or_path!r}. Set "
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"model.model_kwargs.checkpoint_filename to the weight file name."
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) from e
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return load_file(path)
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class Krea2Model(BaseModel):
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arch = "krea2"
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def __init__(
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self,
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device,
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model_config: ModelConfig,
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dtype="bf16",
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custom_pipeline=None,
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noise_scheduler=None,
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**kwargs,
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):
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super().__init__(
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device, model_config, dtype, custom_pipeline, noise_scheduler, **kwargs
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)
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self.is_flow_matching = True
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self.is_transformer = True
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self.target_lora_modules = ["SingleStreamDiT"]
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self.patch_size = KREA2_MMDIT_CONFIG["patch"]
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self.vae_scale_factor = 8 # Qwen-Image VAE is f8
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# Safety cap on prompt token length (truncation only); embeds are stored
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# per-sample at natural length and padded to the batch max at the model call.
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self.max_text_length = int(
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self.model_config.model_kwargs.get("max_text_length", 512)
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)
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# Qwen2TokenizerFast used to tokenize the assistant suffix (matches the
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# reference's separate processor pass).
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self.processor = None
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self.use_old_lokr_format = False
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@staticmethod
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def get_train_scheduler():
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return CustomFlowMatchEulerDiscreteScheduler(**scheduler_config)
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def get_bucket_divisibility(self):
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# 8 for the VAE downsample, 2 for the patch size.
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return self.vae_scale_factor * self.patch_size
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# ------------------------------------------------------------------
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# Loading
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# ------------------------------------------------------------------
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def _load_transformer(self):
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dtype = self.torch_dtype
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self.print_and_status_update("Loading transformer (SingleStreamDiT)")
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mmdit_kwargs = dict(KREA2_MMDIT_CONFIG)
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mmdit_kwargs.update(self.model_config.model_kwargs.get("mmdit_config", {}))
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config = SingleMMDiTConfig(**mmdit_kwargs)
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# Build on meta, then materialize straight from the checkpoint.
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with torch.device("meta"):
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transformer = SingleStreamDiT(config)
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self.print_and_status_update(" - fetching transformer weights")
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state_dict = _load_mmdit_state_dict(
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self.model_config.name_or_path,
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self.model_config.model_kwargs.get("checkpoint_filename", None),
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)
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state_dict = {
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k: (v.to(dtype) if v.is_floating_point() else v)
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for k, v in state_dict.items()
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}
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self.print_and_status_update(" - loading transformer state dict")
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transformer.load_state_dict(state_dict, strict=True, assign=True)
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del state_dict
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flush()
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return transformer
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def _load_text_encoder(self):
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dtype = self.torch_dtype
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te_path = self.model_config.model_kwargs.get("text_encoder_path", QWEN3_VL_PATH)
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self.print_and_status_update(f"Loading Qwen3-VL text encoder from {te_path}")
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tokenizer = AutoTokenizer.from_pretrained(
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te_path, max_length=self.max_text_length, token=HF_TOKEN
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)
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processor = Qwen2TokenizerFast.from_pretrained(
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te_path, max_length=self.max_text_length, token=HF_TOKEN
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)
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text_encoder = Qwen3VLForConditionalGeneration.from_pretrained(
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te_path, torch_dtype=dtype, token=HF_TOKEN
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)
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# We only ever encode text, so the vision tower is dead weight -- drop it to
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# free VRAM and skip loading its (bf16-slow) Conv3d patch_embed onto the GPU.
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if getattr(text_encoder.model, "visual", None) is not None:
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text_encoder.model.visual = None
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text_encoder.eval()
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text_encoder.requires_grad_(False)
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flush()
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return tokenizer, processor, text_encoder
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def _load_vae(self):
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vae_path = self.model_config.model_kwargs.get("vae_path", QWEN_IMAGE_VAE_PATH)
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self.print_and_status_update(f"Loading Qwen-Image VAE from {vae_path}")
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vae = AutoencoderKLQwenImage.from_pretrained(
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vae_path, subfolder="vae", torch_dtype=self.vae_torch_dtype, token=HF_TOKEN
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)
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vae.eval()
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vae.requires_grad_(False)
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return vae
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def load_training_adapter(self, transformer: SingleStreamDiT):
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self.print_and_status_update("Loading assistant LoRA")
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lora_path = self.model_config.assistant_lora_path
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if not os.path.exists(lora_path):
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# assume it is a hub path
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lora_splits = lora_path.split("/")
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if len(lora_splits) != 3:
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raise ValueError(
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f"Assistant LoRA path {lora_path} is not a valid local path or hub path."
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)
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repo_id = "/".join(lora_splits[:2])
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filename = lora_splits[2]
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try:
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lora_path = huggingface_hub.hf_hub_download(
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repo_id=repo_id,
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filename=filename,
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token=HF_TOKEN,
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)
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# upgrade path to the local download
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self.model_config.assistant_lora_path = lora_path
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except Exception as e:
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raise ValueError(
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f"Failed to download assistant LoRA from {lora_path}: {e}"
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)
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# load the adapter and merge it in. We will inference with a -1.0 multiplier so the adapter effects only work during training.
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lora_state_dict = load_file(lora_path)
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# detect the LoRA rank from the first down-projection weight.
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dim_key = next(k for k in lora_state_dict if k.endswith("lora_A.weight"))
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dim = int(lora_state_dict[dim_key].shape[0])
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new_sd = {}
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for key, value in lora_state_dict.items():
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new_key = key.replace("diffusion_model.", "transformer.")
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new_sd[new_key] = value
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lora_state_dict = new_sd
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network_config = {
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"type": "lora",
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"linear": dim,
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"linear_alpha": dim,
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"transformer_only": True,
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}
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network_config = NetworkConfig(**network_config)
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LoRASpecialNetwork.LORA_PREFIX_UNET = "lora_transformer"
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network = LoRASpecialNetwork(
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text_encoder=None,
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unet=transformer,
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lora_dim=network_config.linear,
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multiplier=1.0,
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alpha=network_config.linear_alpha,
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train_unet=True,
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train_text_encoder=False,
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network_config=network_config,
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network_type=network_config.type,
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transformer_only=network_config.transformer_only,
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is_transformer=True,
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target_lin_modules=self.target_lora_modules,
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is_assistant_adapter=True,
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is_ara=True,
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)
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network.apply_to(None, transformer, apply_text_encoder=False, apply_unet=True)
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self.print_and_status_update("Merging in assistant LoRA")
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network.force_to(self.device_torch, dtype=self.torch_dtype)
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network._update_torch_multiplier()
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network.load_weights(lora_state_dict)
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network.merge_in(merge_weight=1.0)
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# mark it as not merged so inference ignores it.
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network.is_merged_in = False
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# add the assistant so sampler will activate it while sampling
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self.assistant_lora: LoRASpecialNetwork = network
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# deactivate lora during training
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self.assistant_lora.multiplier = -1.0
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self.assistant_lora.is_active = False
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# tell the model to invert assistant on inference since we want remove lora effects
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self.invert_assistant_lora = True
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def load_model(self):
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dtype = self.torch_dtype
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self.print_and_status_update("Loading Krea 2 model")
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transformer = self._load_transformer()
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# load assistant lora if specified
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if self.model_config.assistant_lora_path is not None:
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self.load_training_adapter(transformer)
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# set qtype to be float8 if it is qfloat8
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if self.model_config.qtype == "qfloat8":
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self.model_config.qtype = "float8"
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if self.model_config.quantize:
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self.print_and_status_update("Quantizing transformer")
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quantize_model(self, transformer)
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flush()
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if (
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self.model_config.layer_offloading
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and self.model_config.layer_offloading_transformer_percent > 0
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):
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MemoryManager.attach(
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transformer,
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self.device_torch,
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offload_percent=self.model_config.layer_offloading_transformer_percent,
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ignore_modules=[
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module
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for module in transformer.modules()
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if isinstance(module, (SimpleModulation, DoubleSharedModulation))
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],
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)
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if self.model_config.low_vram:
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self.print_and_status_update("Moving transformer to CPU")
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transformer.to("cpu")
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else:
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transformer.to(self.device_torch, dtype=dtype)
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flush()
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tokenizer, processor, text_encoder = self._load_text_encoder()
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if self.model_config.quantize_te:
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self.print_and_status_update("Quantizing text encoder")
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text_encoder.to(self.device_torch)
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quantize(text_encoder, weights=get_qtype(self.model_config.qtype_te))
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freeze(text_encoder)
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flush()
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if (
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self.model_config.layer_offloading
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and self.model_config.layer_offloading_text_encoder_percent > 0
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):
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MemoryManager.attach(
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text_encoder,
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self.device_torch,
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offload_percent=self.model_config.layer_offloading_text_encoder_percent,
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)
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if self.model_config.low_vram:
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self.print_and_status_update("Moving text encoder to CPU")
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text_encoder.to("cpu")
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else:
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text_encoder.to(self.device_torch)
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flush()
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vae = self._load_vae()
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vae.to(self.vae_device_torch, dtype=self.vae_torch_dtype)
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self.noise_scheduler = Krea2Model.get_train_scheduler()
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self.vae = vae
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self.text_encoder = text_encoder
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self.tokenizer = tokenizer
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self.processor = processor
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self.model = transformer
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self.pipeline = Krea2Pipeline(self)
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self.print_and_status_update("Model Loaded")
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# ------------------------------------------------------------------
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# Generation (training previews)
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# ------------------------------------------------------------------
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def get_generation_pipeline(self):
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return Krea2Pipeline(self)
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def generate_single_image(
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self,
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pipeline: Krea2Pipeline,
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gen_config: GenerateImageConfig,
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conditional_embeds: AdvancedPromptEmbeds,
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unconditional_embeds: AdvancedPromptEmbeds,
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generator: torch.Generator,
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extra: dict,
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):
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if self.model.device == torch.device("cpu"):
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self.model.to(self.device_torch)
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sc = self.get_bucket_divisibility()
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gen_config.width = int(gen_config.width // sc * sc)
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gen_config.height = int(gen_config.height // sc * sc)
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img = pipeline(
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conditional_embeds=conditional_embeds,
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unconditional_embeds=unconditional_embeds,
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height=gen_config.height,
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width=gen_config.width,
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num_inference_steps=gen_config.num_inference_steps,
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guidance_scale=gen_config.guidance_scale,
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latents=gen_config.latents,
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generator=generator,
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)[0]
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return img
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# ------------------------------------------------------------------
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# Training hooks
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# ------------------------------------------------------------------
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def get_noise_prediction(
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self,
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latent_model_input: torch.Tensor, # (B, 16, h, w)
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timestep: torch.Tensor, # 0..1000 scale
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text_embeddings: AdvancedPromptEmbeds,
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**kwargs,
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):
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if self.model.device == torch.device("cpu"):
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self.model.to(self.device_torch)
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# toolkit timestep (0..1000, 1000 = pure noise) -> Krea flow time t in
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# [0, 1] with t=1 = pure noise. Same convention -> straight divide.
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t = timestep.to(self.device_torch, dtype=torch.float32) / 1000.0
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if t.dim() == 0:
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t = t.unsqueeze(0)
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if t.shape[0] != latent_model_input.shape[0]:
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t = t.expand(latent_model_input.shape[0])
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context, text_mask = pad_text_features(
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text_embeddings.text_embeds, self.device_torch, self.torch_dtype
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)
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pred = predict_velocity(
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self.transformer,
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latent_model_input.to(self.device_torch, self.torch_dtype),
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t,
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context,
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text_mask,
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)
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return pred
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def get_prompt_embeds(self, prompt) -> AdvancedPromptEmbeds:
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if isinstance(prompt, str):
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prompt = [prompt]
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if self.text_encoder.device == torch.device("cpu"):
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self.text_encoder.to(self.device_torch)
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# Encode each prompt at its natural length and store one (L, 12*2560)
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# tensor per batch item. The (L, 12, 2560) stack is flattened to 2D so the
|
|
# toolkit's batching reads the list length (not the seq length) as the
|
|
# batch size; predict_velocity restores the layer axis. Padding to the
|
|
# batch max is deferred to the model call so caches stay small and any
|
|
# prompts can share a batch.
|
|
features_list = []
|
|
for p in prompt:
|
|
features = encode_krea_prompt(
|
|
self.text_encoder,
|
|
self.tokenizer,
|
|
self.processor,
|
|
p,
|
|
max_length=self.max_text_length,
|
|
select_layers=SELECT_LAYERS,
|
|
)
|
|
# (L, n, d) -> (L, n*d)
|
|
features = features.reshape(features.shape[0], -1)
|
|
features_list.append(features.to(self.torch_dtype))
|
|
|
|
return AdvancedPromptEmbeds(text_embeds=features_list)
|
|
|
|
def get_loss_target(self, *args, **kwargs):
|
|
# Flow-matching velocity target: noise - clean.
|
|
noise = kwargs.get("noise")
|
|
batch = kwargs.get("batch")
|
|
return (noise - batch.latents).detach()
|
|
|
|
def get_model_has_grad(self):
|
|
return False
|
|
|
|
def get_te_has_grad(self):
|
|
return False
|
|
|
|
# ------------------------------------------------------------------
|
|
# VAE (Qwen-Image AutoencoderKLQwenImage -- same handling as qwen_image arch)
|
|
# ------------------------------------------------------------------
|
|
def encode_images(self, image_list: List[torch.Tensor], device=None, dtype=None):
|
|
if device is None:
|
|
device = self.vae_device_torch
|
|
if dtype is None:
|
|
dtype = self.vae_torch_dtype
|
|
|
|
if self.vae.device == torch.device("cpu"):
|
|
self.vae.to(device)
|
|
self.vae.eval()
|
|
self.vae.requires_grad_(False)
|
|
|
|
image_list = [image.to(device, dtype=dtype) for image in image_list]
|
|
images = torch.stack(image_list).to(device, dtype=dtype)
|
|
|
|
# AutoencoderKLQwenImage is a video VAE: add a frame dim.
|
|
images = images.unsqueeze(2)
|
|
latents = self.vae.encode(images).latent_dist.sample()
|
|
|
|
latents_mean = (
|
|
torch.tensor(self.vae.config.latents_mean)
|
|
.view(1, self.vae.config.z_dim, 1, 1, 1)
|
|
.to(latents.device, latents.dtype)
|
|
)
|
|
latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(
|
|
1, self.vae.config.z_dim, 1, 1, 1
|
|
).to(latents.device, latents.dtype)
|
|
|
|
latents = (latents - latents_mean) * latents_std
|
|
latents = latents.squeeze(2) # drop frame dim
|
|
return latents.to(device, dtype=dtype)
|
|
|
|
def decode_latents(self, latents: torch.Tensor, device=None, dtype=None):
|
|
if device is None:
|
|
device = self.vae_device_torch
|
|
if dtype is None:
|
|
dtype = self.vae_torch_dtype
|
|
|
|
if self.vae.device == torch.device("cpu"):
|
|
self.vae.to(device)
|
|
|
|
latents = latents.to(device, dtype=dtype)
|
|
latents = latents.unsqueeze(2) # add frame dim
|
|
|
|
latents_mean = (
|
|
torch.tensor(self.vae.config.latents_mean)
|
|
.view(1, self.vae.config.z_dim, 1, 1, 1)
|
|
.to(latents.device, latents.dtype)
|
|
)
|
|
latents_std = (
|
|
torch.tensor(self.vae.config.latents_std)
|
|
.view(1, self.vae.config.z_dim, 1, 1, 1)
|
|
.to(latents.device, latents.dtype)
|
|
)
|
|
latents = latents * latents_std + latents_mean
|
|
|
|
# Full-resolution decode spikes VRAM; tile it when low on VRAM (decode
|
|
# only -- encode stays untiled).
|
|
tiled = self.model_config.low_vram
|
|
if tiled:
|
|
self.vae.enable_tiling()
|
|
try:
|
|
images = self.vae.decode(latents).sample
|
|
finally:
|
|
if tiled:
|
|
self.vae.disable_tiling()
|
|
images = images.squeeze(2) # drop frame dim
|
|
return images.to(device, dtype=dtype)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Saving / bookkeeping
|
|
# ------------------------------------------------------------------
|
|
def save_model(self, output_path, meta, save_dtype):
|
|
if not output_path.endswith(".safetensors"):
|
|
output_path = output_path + ".safetensors"
|
|
transformer: SingleStreamDiT = unwrap_model(self.model)
|
|
state_dict = transformer.state_dict()
|
|
save_dict = {}
|
|
for k, v in state_dict.items():
|
|
if isinstance(v, QTensor):
|
|
v = v.dequantize()
|
|
save_dict[k] = v.clone().to("cpu", dtype=save_dtype)
|
|
meta = get_meta_for_safetensors(meta, name="krea2")
|
|
save_file(save_dict, output_path, metadata=meta)
|
|
|
|
def get_base_model_version(self):
|
|
return "krea2"
|
|
|
|
def get_transformer_block_names(self) -> Optional[List[str]]:
|
|
return ["blocks"]
|
|
|
|
def convert_lora_weights_before_save(self, state_dict):
|
|
return {
|
|
k.replace("transformer.", "diffusion_model."): v
|
|
for k, v in state_dict.items()
|
|
}
|
|
|
|
def convert_lora_weights_before_load(self, state_dict):
|
|
return {
|
|
k.replace("diffusion_model.", "transformer."): v
|
|
for k, v in state_dict.items()
|
|
}
|