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https://github.com/ostris/ai-toolkit.git
synced 2026-01-26 16:39:47 +00:00
Updates to flow matching algo
This commit is contained in:
Jaret Burkett
@@ -329,24 +329,7 @@ class SDTrainer(BaseSDTrainProcess):
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target = self.sd.noise_scheduler.get_velocity(batch.tensor, noise, timesteps)
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elif self.sd.is_flow_matching:
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# only if preconditioning model outputs
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# if not preconditioning, (target = noise - batch.latents)
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# if preconditioning outputs, target latents
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# model_pred = model_pred * (-sigmas) + noisy_model_input
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if self.train_config.target_noise_multiplier != 1.0:
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# we are adjusting the target noise, need to recompute the noisy latents with
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# the noise adjusted above
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with torch.no_grad():
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noisy_latents = self.sd.add_noise(batch.latents, noise, timesteps).detach()
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noise_pred = precondition_model_outputs_flow_match(
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noise_pred,
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noisy_latents,
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timesteps,
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self.sd.noise_scheduler
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)
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target = batch.latents.detach()
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target = (noise - batch.latents).detach()
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else:
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target = noise
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@@ -392,14 +375,8 @@ class SDTrainer(BaseSDTrainProcess):
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loss_per_element = (weighing.float() * (denoised_latents.float() - target.float()) ** 2)
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loss = loss_per_element
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else:
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# handle flow matching ref https://github.com/huggingface/diffusers/blob/ec068f9b5bf7c65f93125ec889e0ff1792a00da1/examples/dreambooth/train_dreambooth_lora_sd3.py#L1485C17-L1495C100
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if self.sd.is_flow_matching and prior_pred is None:
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# outputs should be preprocessed latents
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sigmas = self.sd.noise_scheduler.get_sigmas(timesteps, pred.ndim, dtype, self.device_torch)
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weighting = torch.ones_like(sigmas)
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loss = (weighting.float() * (pred.float() - target.float()) ** 2).reshape(target.shape[0], -1)
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elif self.train_config.loss_type == "mae":
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# handle flow matching ref https://github.com/huggingface/diffusers/blob/ec068f9b5bf7c65f93125ec889e0ff1792a00da1/examples/dreambooth/train_dreambooth_lora_sd3.py#L1485C17-L1495C100
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if self.train_config.loss_type == "mae":
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loss = torch.nn.functional.l1_loss(pred.float(), target.float(), reduction="none")
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else:
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loss = torch.nn.functional.mse_loss(pred.float(), target.float(), reduction="none")
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Submodule repositories/sd-scripts updated: b78c0e2a69...25f961bc77
@@ -13,7 +13,6 @@ from toolkit.paths import SD_SCRIPTS_ROOT
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sys.path.append(SD_SCRIPTS_ROOT)
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from diffusers import (
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StableDiffusionPipeline,
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DDPMScheduler,
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EulerAncestralDiscreteScheduler,
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DPMSolverMultistepScheduler,
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@@ -24,10 +23,8 @@ from diffusers import (
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EulerDiscreteScheduler,
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HeunDiscreteScheduler,
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KDPM2DiscreteScheduler,
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KDPM2AncestralDiscreteScheduler,
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StableDiffusion3Pipeline
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KDPM2AncestralDiscreteScheduler
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)
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from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline
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import torch
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import re
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from transformers import T5Tokenizer, T5EncoderModel, UMT5EncoderModel
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@@ -136,261 +133,6 @@ def match_noise_to_target_mean_offset(noise, target, mix=0.5, dim=None):
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return noise
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def sample_images(
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accelerator,
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args: argparse.Namespace,
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epoch,
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steps,
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device,
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vae,
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tokenizer,
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text_encoder,
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unet,
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prompt_replacement=None,
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force_sample=False
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):
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"""
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StableDiffusionLongPromptWeightingPipelineの改造版を使うようにしたので、clip skipおよびプロンプトの重みづけに対応した
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"""
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if not force_sample:
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if args.sample_every_n_steps is None and args.sample_every_n_epochs is None:
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return
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if args.sample_every_n_epochs is not None:
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# sample_every_n_steps は無視する
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if epoch is None or epoch % args.sample_every_n_epochs != 0:
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return
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else:
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if steps % args.sample_every_n_steps != 0 or epoch is not None: # steps is not divisible or end of epoch
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return
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is_sample_only = args.sample_only
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is_generating_only = hasattr(args, "is_generating_only") and args.is_generating_only
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print(f"\ngenerating sample images at step / サンプル画像生成 ステップ: {steps}")
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if not os.path.isfile(args.sample_prompts):
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print(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}")
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return
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org_vae_device = vae.device # CPUにいるはず
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vae.to(device)
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# read prompts
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# with open(args.sample_prompts, "rt", encoding="utf-8") as f:
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# prompts = f.readlines()
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if args.sample_prompts.endswith(".txt"):
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with open(args.sample_prompts, "r", encoding="utf-8") as f:
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lines = f.readlines()
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prompts = [line.strip() for line in lines if len(line.strip()) > 0 and line[0] != "#"]
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elif args.sample_prompts.endswith(".json"):
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with open(args.sample_prompts, "r", encoding="utf-8") as f:
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prompts = json.load(f)
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# schedulerを用意する
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sched_init_args = {}
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if args.sample_sampler == "ddim":
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scheduler_cls = DDIMScheduler
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elif args.sample_sampler == "ddpm": # ddpmはおかしくなるのでoptionから外してある
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scheduler_cls = DDPMScheduler
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elif args.sample_sampler == "pndm":
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scheduler_cls = PNDMScheduler
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elif args.sample_sampler == "lms" or args.sample_sampler == "k_lms":
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scheduler_cls = LMSDiscreteScheduler
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elif args.sample_sampler == "euler" or args.sample_sampler == "k_euler":
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scheduler_cls = EulerDiscreteScheduler
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elif args.sample_sampler == "euler_a" or args.sample_sampler == "k_euler_a":
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scheduler_cls = EulerAncestralDiscreteScheduler
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elif args.sample_sampler == "dpmsolver" or args.sample_sampler == "dpmsolver++":
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scheduler_cls = DPMSolverMultistepScheduler
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sched_init_args["algorithm_type"] = args.sample_sampler
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elif args.sample_sampler == "dpmsingle":
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scheduler_cls = DPMSolverSinglestepScheduler
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elif args.sample_sampler == "heun":
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scheduler_cls = HeunDiscreteScheduler
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elif args.sample_sampler == "dpm_2" or args.sample_sampler == "k_dpm_2":
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scheduler_cls = KDPM2DiscreteScheduler
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elif args.sample_sampler == "dpm_2_a" or args.sample_sampler == "k_dpm_2_a":
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scheduler_cls = KDPM2AncestralDiscreteScheduler
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else:
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scheduler_cls = DDIMScheduler
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if args.v_parameterization:
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sched_init_args["prediction_type"] = "v_prediction"
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scheduler = scheduler_cls(
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num_train_timesteps=SCHEDULER_TIMESTEPS,
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beta_start=SCHEDULER_LINEAR_START,
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beta_end=SCHEDULER_LINEAR_END,
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beta_schedule=SCHEDLER_SCHEDULE,
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**sched_init_args,
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)
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# clip_sample=Trueにする
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if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False:
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# print("set clip_sample to True")
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scheduler.config.clip_sample = True
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pipeline = StableDiffusionLongPromptWeightingPipeline(
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text_encoder=text_encoder,
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vae=vae,
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unet=unet,
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tokenizer=tokenizer,
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scheduler=scheduler,
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clip_skip=args.clip_skip,
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safety_checker=None,
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feature_extractor=None,
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requires_safety_checker=False,
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)
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pipeline.to(device)
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if is_generating_only:
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save_dir = args.output_dir
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else:
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save_dir = args.output_dir + "/sample"
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os.makedirs(save_dir, exist_ok=True)
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rng_state = torch.get_rng_state()
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cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
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with torch.no_grad():
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with accelerator.autocast():
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for i, prompt in enumerate(prompts):
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if not accelerator.is_main_process:
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continue
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if isinstance(prompt, dict):
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negative_prompt = prompt.get("negative_prompt")
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sample_steps = prompt.get("sample_steps", 30)
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width = prompt.get("width", 512)
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height = prompt.get("height", 512)
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scale = prompt.get("scale", 7.5)
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seed = prompt.get("seed")
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prompt = prompt.get("prompt")
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prompt = replace_filewords_prompt(prompt, args)
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negative_prompt = replace_filewords_prompt(negative_prompt, args)
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else:
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prompt = replace_filewords_prompt(prompt, args)
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# prompt = prompt.strip()
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# if len(prompt) == 0 or prompt[0] == "#":
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# continue
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# subset of gen_img_diffusers
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prompt_args = prompt.split(" --")
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prompt = prompt_args[0]
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negative_prompt = None
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sample_steps = 30
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width = height = 512
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scale = 7.5
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seed = None
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for parg in prompt_args:
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try:
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m = re.match(r"w (\d+)", parg, re.IGNORECASE)
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if m:
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width = int(m.group(1))
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continue
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m = re.match(r"h (\d+)", parg, re.IGNORECASE)
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if m:
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height = int(m.group(1))
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continue
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m = re.match(r"d (\d+)", parg, re.IGNORECASE)
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if m:
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seed = int(m.group(1))
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continue
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m = re.match(r"s (\d+)", parg, re.IGNORECASE)
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if m: # steps
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sample_steps = max(1, min(1000, int(m.group(1))))
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continue
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m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
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if m: # scale
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scale = float(m.group(1))
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continue
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m = re.match(r"n (.+)", parg, re.IGNORECASE)
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if m: # negative prompt
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negative_prompt = m.group(1)
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continue
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except ValueError as ex:
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print(f"Exception in parsing / 解析エラー: {parg}")
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print(ex)
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if seed is not None:
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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if prompt_replacement is not None:
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prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
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if negative_prompt is not None:
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negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
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height = max(64, height - height % 8) # round to divisible by 8
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width = max(64, width - width % 8) # round to divisible by 8
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print(f"prompt: {prompt}")
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print(f"negative_prompt: {negative_prompt}")
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print(f"height: {height}")
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print(f"width: {width}")
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print(f"sample_steps: {sample_steps}")
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print(f"scale: {scale}")
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image = pipeline(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=sample_steps,
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guidance_scale=scale,
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negative_prompt=negative_prompt,
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).images[0]
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ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
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num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}"
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seed_suffix = "" if seed is None else f"_{seed}"
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if is_generating_only:
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img_filename = (
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f"{'' if args.output_name is None else args.output_name + '_'}{ts_str}_{num_suffix}_{i:02d}{seed_suffix}.png"
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)
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else:
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img_filename = (
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f"{'' if args.output_name is None else args.output_name + '_'}{ts_str}_{i:04d}{seed_suffix}.png"
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)
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if is_sample_only:
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# make prompt txt file
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img_path_no_ext = os.path.join(save_dir, img_filename[:-4])
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with open(img_path_no_ext + ".txt", "w") as f:
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# put prompt in txt file
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f.write(prompt)
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# close file
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f.close()
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image.save(os.path.join(save_dir, img_filename))
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# wandb有効時のみログを送信
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try:
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wandb_tracker = accelerator.get_tracker("wandb")
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try:
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import wandb
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except ImportError: # 事前に一度確認するのでここはエラー出ないはず
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raise ImportError("No wandb / wandb がインストールされていないようです")
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wandb_tracker.log({f"sample_{i}": wandb.Image(image)})
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except: # wandb 無効時
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pass
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# clear pipeline and cache to reduce vram usage
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del pipeline
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torch.cuda.empty_cache()
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torch.set_rng_state(rng_state)
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if cuda_rng_state is not None:
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torch.cuda.set_rng_state(cuda_rng_state)
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vae.to(org_vae_device)
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# https://www.crosslabs.org//blog/diffusion-with-offset-noise
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def apply_noise_offset(noise, noise_offset):
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if noise_offset is None or (noise_offset < 0.000001 and noise_offset > -0.000001):
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@@ -591,7 +333,7 @@ def encode_prompts_sd3(
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truncate: bool = True,
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max_length=None,
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dropout_prob=0.0,
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pipeline: StableDiffusion3Pipeline = None,
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pipeline = None,
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):
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text_embeds_list = []
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pooled_text_embeds = None # always text_encoder_2's pool
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