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import os
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from typing import TYPE_CHECKING, List, Optional
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import torch
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import yaml
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from toolkit import train_tools
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from toolkit.config_modules import GenerateImageConfig, ModelConfig
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from PIL import Image
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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 CustomFlowMatchEulerDiscreteScheduler
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from toolkit.dequantize import patch_dequantization_on_save
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from toolkit.accelerator import get_accelerator, unwrap_model
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from optimum.quanto import freeze, QTensor
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from toolkit.util.quantize import quantize, get_qtype
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import torch.nn.functional as F
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from diffusers import QwenImagePipeline, QwenImageTransformer2DModel, AutoencoderKLQwenImage
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from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer
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from tqdm import tqdm
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if TYPE_CHECKING:
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from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
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scheduler_config = {
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"base_image_seq_len": 256,
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"base_shift": 0.5,
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"invert_sigmas": False,
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"max_image_seq_len": 8192,
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"max_shift": 0.9,
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"num_train_timesteps": 1000,
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"shift": 1.0,
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"shift_terminal": 0.02,
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"stochastic_sampling": False,
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"time_shift_type": "exponential",
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"use_beta_sigmas": False,
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"use_dynamic_shifting": True,
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"use_exponential_sigmas": False,
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"use_karras_sigmas": False
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}
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class QwenImageModel(BaseModel):
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arch = "qwen_image"
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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,
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model_config,
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dtype,
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custom_pipeline,
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noise_scheduler,
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**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 = ['QwenImageTransformer2DModel']
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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 16 * 2 # 16 for the VAE, 2 for patch size
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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 Qwen Image 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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transformer_path = model_path
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transformer_subfolder = 'transformer'
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if os.path.exists(transformer_path):
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transformer_subfolder = None
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transformer_path = os.path.join(transformer_path, 'transformer')
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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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self.print_and_status_update("Loading transformer")
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transformer = QwenImageTransformer2DModel.from_pretrained(
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transformer_path,
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subfolder=transformer_subfolder,
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torch_dtype=dtype
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)
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if self.model_config.quantize:
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# patch the state dict method
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patch_dequantization_on_save(transformer)
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# move and quantize only certain pieces at a time.
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quantization_type = get_qtype(self.model_config.qtype)
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all_blocks = list(transformer.transformer_blocks)
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self.print_and_status_update(" - quantizing transformer blocks")
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for block in tqdm(all_blocks):
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block.to(self.device_torch, dtype=dtype)
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quantize(block, weights=quantization_type)
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freeze(block)
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block.to('cpu')
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# flush()
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self.print_and_status_update(" - quantizing extras")
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transformer.to(self.device_torch, dtype=dtype)
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quantize(transformer, weights=quantization_type)
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freeze(transformer)
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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 = Qwen2Tokenizer.from_pretrained(
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base_model_path, subfolder="tokenizer", torch_dtype=dtype
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)
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text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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base_model_path, subfolder="text_encoder", torch_dtype=dtype
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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(
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self.model_config.qtype))
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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 = AutoencoderKLQwenImage.from_pretrained(
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base_model_path, subfolder="vae", torch_dtype=dtype)
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self.noise_scheduler = QwenImageModel.get_train_scheduler()
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self.print_and_status_update("Making pipe")
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pipe: QwenImagePipeline = QwenImagePipeline(
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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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)
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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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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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pipe.transformer = pipe.transformer.to(self.device_torch)
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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 = QwenImageModel.get_train_scheduler()
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pipeline: QwenImagePipeline = QwenImagePipeline(
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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: QwenImagePipeline,
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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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control_img = None
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if gen_config.ctrl_img is not None:
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raise NotImplementedError(
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"Control image generation is not supported in Qwen Image model... yet"
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)
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control_img = Image.open(gen_config.ctrl_img)
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control_img = control_img.convert("RGB")
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# resize to width and height
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if control_img.size != (gen_config.width, gen_config.height):
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control_img = control_img.resize(
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(gen_config.width, gen_config.height), Image.BILINEAR
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)
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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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prompt_embeds=conditional_embeds.text_embeds,
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prompt_embeds_mask=conditional_embeds.attention_mask.to(self.device_torch, dtype=torch.int64),
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negative_prompt_embeds=unconditional_embeds.text_embeds,
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negative_prompt_embeds_mask=unconditional_embeds.attention_mask.to(self.device_torch, dtype=torch.int64),
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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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true_cfg_scale=gen_config.guidance_scale,
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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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batch_size, num_channels_latents, height, width = latent_model_input.shape
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latent_model_input = latent_model_input.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
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latent_model_input = latent_model_input.permute(0, 2, 4, 1, 3, 5)
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latent_model_input = latent_model_input.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
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prompt_embeds_mask = text_embeddings.attention_mask.to(self.device_torch, dtype=torch.int64)
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img_shapes = [(1, height // 2, width // 2)] * batch_size
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noise_pred = self.transformer(
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hidden_states=latent_model_input.to(self.device_torch, self.torch_dtype),
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timestep=timestep / 1000,
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guidance=None,
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encoder_hidden_states=text_embeddings.text_embeds.to(self.device_torch, self.torch_dtype),
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encoder_hidden_states_mask=prompt_embeds_mask,
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img_shapes=img_shapes,
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txt_seq_lens=prompt_embeds_mask.sum(dim=1).tolist(),
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return_dict=False,
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**kwargs,
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)[0]
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# unpack the noise prediction
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noise_pred = noise_pred.view(batch_size, height // 2, width // 2, num_channels_latents, 2, 2)
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noise_pred = noise_pred.permute(0, 3, 1, 4, 2, 5)
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noise_pred = noise_pred.reshape(batch_size, num_channels_latents, height, width)
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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, prompt_embeds_mask = self.pipeline.encode_prompt(
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prompt,
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device=self.device_torch,
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num_images_per_prompt=1,
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)
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pe = PromptEmbeds(
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prompt_embeds
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)
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pe.attention_mask = prompt_embeds_mask
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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 save_model(self, output_path, meta, save_dtype):
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# only save the unet
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transformer: QwenImageTransformer2DModel = unwrap_model(self.model)
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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 "qwen_image"
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def get_transformer_block_names(self) -> Optional[List[str]]:
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return ['transformer_blocks']
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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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def encode_images(
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self,
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image_list: List[torch.Tensor],
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device=None,
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dtype=None
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):
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if device is None:
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device = self.vae_device_torch
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if dtype is None:
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dtype = self.vae_torch_dtype
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# Move to vae to device if on cpu
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if self.vae.device == 'cpu':
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self.vae.to(device)
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self.vae.eval()
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self.vae.requires_grad_(False)
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# move to device and dtype
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image_list = [image.to(device, dtype=dtype) for image in image_list]
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images = torch.stack(image_list).to(device, dtype=dtype)
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# it uses wan vae, so add dim for frame count
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images = images.unsqueeze(2)
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latents = self.vae.encode(images).latent_dist.sample()
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latents_mean = (
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torch.tensor(self.vae.config.latents_mean)
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.view(1, self.vae.config.z_dim, 1, 1, 1)
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.to(latents.device, latents.dtype)
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)
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latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
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latents.device, latents.dtype
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)
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latents = (latents - latents_mean) * latents_std
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latents = latents.to(device, dtype=dtype)
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latents = latents.squeeze(2) # remove the frame count dimension
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return latents
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Jaret Burkett