mirror of
https://github.com/ostris/ai-toolkit.git
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296 lines
9.4 KiB
Python
296 lines
9.4 KiB
Python
import os
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from typing import TYPE_CHECKING
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import torch
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import yaml
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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 diffusers import AutoencoderKL
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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 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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from transformers import T5TokenizerFast, T5EncoderModel
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from .src import FLitePipeline, DiT
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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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"max_image_seq_len": 4096,
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"max_shift": 1.15,
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"num_train_timesteps": 1000,
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"shift": 3.0,
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"use_dynamic_shifting": True
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}
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class FLiteModel(BaseModel):
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arch = "f-lite"
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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 = ['DiT']
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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 the bucket divisibility for the model
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return 16
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def load_model(self):
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dtype = self.torch_dtype
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# will be updated if we detect a existing checkpoint in training folder
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model_path = self.model_config.name_or_path
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extras_path = self.model_config.extras_name_or_path
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self.print_and_status_update("Loading transformer")
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transformer = DiT.from_pretrained(
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model_path,
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subfolder="dit_model",
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torch_dtype=dtype,
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)
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transformer.to(self.quantize_device, dtype=dtype)
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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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quantization_type = get_qtype(self.model_config.qtype)
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self.print_and_status_update("Quantizing transformer")
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quantize(transformer, weights=quantization_type,
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**self.model_config.quantize_kwargs)
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freeze(transformer)
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transformer.to(self.device_torch)
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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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self.print_and_status_update("Loading T5")
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tokenizer = T5TokenizerFast.from_pretrained(
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extras_path, subfolder="tokenizer", torch_dtype=dtype
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)
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text_encoder = T5EncoderModel.from_pretrained(
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extras_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 T5")
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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.noise_scheduler = FLiteModel.get_train_scheduler()
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self.print_and_status_update("Loading VAE")
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vae = AutoencoderKL.from_pretrained(
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extras_path,
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subfolder="vae",
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torch_dtype=dtype
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)
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vae = vae.to(self.device_torch, dtype=dtype)
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self.print_and_status_update("Making pipe")
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pipe: FLitePipeline = FLitePipeline(
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text_encoder=None,
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tokenizer=tokenizer,
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vae=vae,
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dit_model=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.dit_model = transformer
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pipe.transformer = transformer
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pipe.scheduler = self.noise_scheduler,
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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 = FLiteModel.get_train_scheduler()
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# it has built in scheduler. Basically euler flowmatching
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pipeline = FLitePipeline(
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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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dit_model=unwrap_model(self.transformer)
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)
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pipeline.transformer = pipeline.dit_model
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pipeline.scheduler = scheduler
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return pipeline
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def generate_single_image(
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self,
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pipeline: FLitePipeline,
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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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extra['negative_prompt_embeds'] = unconditional_embeds.text_embeds
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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=gen_config.guidance_scale,
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latents=gen_config.latents,
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generator=generator,
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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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cast_dtype = self.unet.dtype
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noise_pred = self.unet(
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latent_model_input.to(
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self.device_torch, cast_dtype
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),
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text_embeddings.text_embeds.to(
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self.device_torch, cast_dtype
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),
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timestep / 1000,
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)
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if isinstance(noise_pred, QTensor):
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noise_pred = noise_pred.dequantize()
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return noise_pred
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def get_prompt_embeds(self, prompt: str) -> PromptEmbeds:
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if isinstance(prompt, str):
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prompts = [prompt]
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else:
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prompts = prompt
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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, negative_embeds = self.pipeline.encode_prompt(
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prompt=prompts,
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negative_prompt=None,
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device=self.text_encoder[0].device,
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dtype=self.torch_dtype,
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)
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pe = PromptEmbeds(prompt_embeds)
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return pe
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def get_model_has_grad(self):
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# return from a weight if it has grad
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return False
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def get_te_has_grad(self):
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# return from a weight if it has grad
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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: DiT = unwrap_model(self.model)
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# diffusers
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# only save the unet
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transformer: DiT = unwrap_model(self.transformer)
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transformer.save_pretrained(
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save_directory=os.path.join(output_path, 'dit_model'),
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safe_serialization=True,
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)
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# save out meta config
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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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return (batch.latents - noise).detach()
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def convert_lora_weights_before_save(self, state_dict):
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# currently starte with transformer. but needs to start with diffusion_model. for comfyui
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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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# saved as diffusion_model. but needs to be transformer. for ai-toolkit
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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 get_base_model_version(self):
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return "f-lite"
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def get_stepped_pred(self, pred, noise):
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# just used for DFE support
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latents = pred + noise
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return latents
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