480 lines
17 KiB
Python
480 lines
17 KiB
Python
import os
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from typing import List, Optional
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import huggingface_hub
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import torch
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import yaml
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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.prompt_utils import PromptEmbeds
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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 optimum.quanto import freeze
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from toolkit.util.quantize import (
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quantize,
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get_qtype,
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quantize_model,
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dequantize_if_quantized,
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)
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from toolkit.memory_management import MemoryManager
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from toolkit.metadata import get_meta_for_safetensors
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from safetensors.torch import load_file, save_file
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from transformers import AutoTokenizer, Qwen3ForCausalLM
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from diffusers import AutoencoderKL
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try:
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from diffusers import ZImagePipeline
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# our subclass of the diffusers transformer with the universal loading /
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# quantization mixin (see toolkit/models/classes/_mixin.py)
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from toolkit.models.v2.z_image import ZImageTransformer2DModel
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except ImportError:
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raise ImportError(
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"Diffusers is out of date. Update diffusers to the latest version by doing pip uninstall diffusers and then pip install -r requirements.txt"
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)
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scheduler_config = {
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"num_train_timesteps": 1000,
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"use_dynamic_shifting": False,
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"shift": 3.0,
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}
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# repo to pull the vae / text encoder / tokenizer / config from when loading a
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# single-file checkpoint
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SINGLE_FILE_EXTRAS_REPO = "Tongyi-MAI/Z-Image-Turbo"
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class ZImageModel(BaseModel):
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arch = "zimage"
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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 = ["ZImageTransformer2DModel"]
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# set to True when loading a single-file checkpoint so we save back in that format
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self.is_single_file = False
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# repo to pull config/vae/te from when loading a single-file checkpoint
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self.single_file_extras_repo = SINGLE_FILE_EXTRAS_REPO
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# static method to get the noise scheduler
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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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return 8 * 2 # 8 for the VAE, 2 for patch size
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def load_training_adapter(self, transformer: ZImageTransformer2DModel):
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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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)
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# upgrade path to
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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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dim = int(
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lora_state_dict[
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"diffusion_model.layers.0.attention.to_k.lora_A.weight"
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].shape[0]
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)
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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_transformer(self, model_path, base_model_path, dtype):
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"""Load the ZImage transformer through the OstrisModelMixin universal loader
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(diffusers folder/repo, local or hub single-file checkpoint, pre-quantized
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checkpoint). Returns (transformer, base_model_path) since the base path may
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be redirected to the hub repo for single-file checkpoints."""
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if model_path.endswith(".safetensors"):
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# single-file checkpoint. Load the weights from the file and pull the
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# vae / text encoder / tokenizer / config from the base diffusers repo.
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self.is_single_file = True
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self.print_and_status_update(
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"Model is a single-file checkpoint, loading with safetensors"
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)
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if base_model_path == model_path:
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# extras default to name_or_path which is the single file, fall back to the hub repo
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base_model_path = self.single_file_extras_repo
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elif os.path.exists(model_path):
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# check if the path is a full checkpoint.
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te_folder_path = os.path.join(model_path, "text_encoder")
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# if we have the te, this folder is a full checkpoint, use it as the base
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if os.path.exists(te_folder_path):
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base_model_path = model_path
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# quantization happens inside load_model unless an adapter has to be merged
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# into the full precision weights first (assistant lora / accuracy recovery
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# adapter); those paths quantize after the merge via quantize_model
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qtype = None
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if (
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self.model_config.quantize
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and self.model_config.assistant_lora_path is None
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and self.model_config.accuracy_recovery_adapter is None
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):
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qtype = self.model_config.qtype
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transformer = ZImageTransformer2DModel.load_model(
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model_path,
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dtype=dtype,
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qtype=qtype,
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quantize_device=self.device_torch,
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config_path=base_model_path if self.is_single_file else None,
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)
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flush()
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return transformer, base_model_path
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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 ZImage model")
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model_path = self.model_config.name_or_path
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base_model_path = self.model_config.extras_name_or_path
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self.print_and_status_update("Loading transformer")
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transformer, base_model_path = self.load_transformer(
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model_path, base_model_path, dtype
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)
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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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# already quantized inside load_transformer unless an adapter had to merge
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# into full precision weights first (or the checkpoint was pre-quantized)
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if self.model_config.quantize and not transformer.aitk_is_quantized:
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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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transformer.x_pad_token,
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transformer.cap_pad_token,
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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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flush()
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self.print_and_status_update("Text Encoder")
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tokenizer = AutoTokenizer.from_pretrained(
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base_model_path, subfolder="tokenizer", torch_dtype=dtype
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)
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text_encoder = Qwen3ForCausalLM.from_pretrained(
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base_model_path, subfolder="text_encoder", torch_dtype=dtype
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)
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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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text_encoder.to(self.device_torch, dtype=dtype)
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flush()
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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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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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self.print_and_status_update("Loading VAE")
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vae = AutoencoderKL.from_pretrained(
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base_model_path, subfolder="vae", torch_dtype=dtype
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)
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self.noise_scheduler = ZImageModel.get_train_scheduler()
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self.print_and_status_update("Making pipe")
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kwargs = {}
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pipe: ZImagePipeline = ZImagePipeline(
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scheduler=self.noise_scheduler,
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text_encoder=None,
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tokenizer=tokenizer,
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vae=vae,
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transformer=None,
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**kwargs,
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)
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# for quantization, it works best to do these after making the pipe
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pipe.text_encoder = text_encoder
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pipe.transformer = transformer
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self.print_and_status_update("Preparing Model")
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text_encoder = [pipe.text_encoder]
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tokenizer = [pipe.tokenizer]
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# leave it on cpu for now
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if not self.low_vram:
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pipe.transformer = pipe.transformer.to(self.device_torch)
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flush()
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# just to make sure everything is on the right device and dtype
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text_encoder[0].to(self.device_torch)
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text_encoder[0].requires_grad_(False)
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text_encoder[0].eval()
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flush()
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# save it to the model class
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self.vae = vae
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self.text_encoder = text_encoder # list of text encoders
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self.tokenizer = tokenizer # list of tokenizers
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self.model = pipe.transformer
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self.pipeline = pipe
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self.print_and_status_update("Model Loaded")
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def get_generation_pipeline(self):
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scheduler = ZImageModel.get_train_scheduler()
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pipeline: ZImagePipeline = ZImagePipeline(
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scheduler=scheduler,
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text_encoder=unwrap_model(self.text_encoder[0]),
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tokenizer=self.tokenizer[0],
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vae=unwrap_model(self.vae),
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transformer=unwrap_model(self.transformer),
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)
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pipeline = pipeline.to(self.device_torch)
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return pipeline
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def generate_single_image(
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self,
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pipeline: ZImagePipeline,
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gen_config: GenerateImageConfig,
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conditional_embeds: PromptEmbeds,
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unconditional_embeds: PromptEmbeds,
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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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# CFG is 0 normalized for this model
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guidance = max(0.0, gen_config.guidance_scale - 1.0)
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img = pipeline(
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prompt_embeds=conditional_embeds.text_embeds,
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negative_prompt_embeds=unconditional_embeds.text_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=guidance,
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latents=gen_config.latents,
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generator=generator,
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**extra,
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).images[0]
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return img
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def get_noise_prediction(
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self,
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latent_model_input: torch.Tensor,
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timestep: torch.Tensor, # 0 to 1000 scale
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text_embeddings: PromptEmbeds,
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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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latent_model_input = latent_model_input.unsqueeze(2)
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latent_model_input_list = list(latent_model_input.unbind(dim=0))
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timestep_model_input = (1000 - timestep) / 1000
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model_out_list = self.transformer(
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latent_model_input_list,
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timestep_model_input,
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text_embeddings.text_embeds,
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)[0]
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noise_pred = torch.stack([t.float() for t in model_out_list], dim=0)
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noise_pred = noise_pred.squeeze(2)
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noise_pred = -noise_pred
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return noise_pred
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def get_prompt_embeds(self, prompt: str) -> PromptEmbeds:
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if self.pipeline.text_encoder.device != self.device_torch:
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self.pipeline.text_encoder.to(self.device_torch)
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prompt_embeds, _ = self.pipeline.encode_prompt(
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prompt,
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do_classifier_free_guidance=False,
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device=self.device_torch,
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)
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pe = PromptEmbeds([prompt_embeds, None])
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return pe
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def get_model_has_grad(self):
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return False
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def get_te_has_grad(self):
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return False
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def get_quantization_exclude_modules(self):
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# the patterns live on the transformer class so the mixin quantization
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# uses them too (see toolkit/models/classes/z_image.py)
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return ZImageTransformer2DModel.get_quantization_exclude_modules()
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def save_model(self, output_path, meta, save_dtype):
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transformer: ZImageTransformer2DModel = unwrap_model(self.model)
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if self.is_single_file:
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# loaded from a single-file checkpoint, save back in that format
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sd = transformer.state_dict()
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save_dict = {}
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for key, value in sd.items():
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# dequantize any quantized (e.g. torchao) weights so we save plain tensors
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save_dict[key] = (
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dequantize_if_quantized(value).clone().to("cpu", dtype=save_dtype)
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)
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save_dict = transformer.convert_state_dict_on_save(save_dict)
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if not output_path.endswith(".safetensors"):
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output_path += ".safetensors"
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meta = get_meta_for_safetensors(meta, name=self.arch)
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save_file(save_dict, output_path, metadata=meta)
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else:
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transformer.save_pretrained(
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save_directory=os.path.join(output_path, "transformer"),
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safe_serialization=True,
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)
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meta_path = os.path.join(output_path, "aitk_meta.yaml")
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with open(meta_path, "w") as f:
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yaml.dump(meta, f)
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def get_loss_target(self, *args, **kwargs):
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noise = kwargs.get("noise")
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batch = kwargs.get("batch")
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return (noise - batch.latents).detach()
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def get_base_model_version(self):
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return "zimage"
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def get_transformer_block_names(self) -> Optional[List[str]]:
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return ["layers"]
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def convert_lora_weights_before_save(self, state_dict):
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new_sd = {}
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for key, value in state_dict.items():
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new_key = key.replace("transformer.", "diffusion_model.")
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new_sd[new_key] = value
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return new_sd
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def convert_lora_weights_before_load(self, state_dict):
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new_sd = {}
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for key, value in 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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return new_sd
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