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Merge branch 'dev' into dev_device_support

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Kohya S
2024-02-11 19:51:28 +09:00
parent 74fe0453b2 f897d55781
commit 75ecb047e2
1 changed files with 132 additions and 101 deletions
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125
library/train_util.py
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@@ -19,7 +19,7 @@ from typing import (
Tuple,
Union,
)
from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs
from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs, PartialState
import glob
import math
import os
@@ -4655,7 +4655,6 @@ def line_to_prompt_dict(line: str) -> dict:
return prompt_dict
def sample_images_common(
pipe_class,
accelerator: Accelerator,
@@ -4673,6 +4672,7 @@ def sample_images_common(
"""
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
"""
if steps == 0:
if not args.sample_at_first:
return
@@ -4692,8 +4692,10 @@ def sample_images_common(
print(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}")
return
distributed_state = PartialState() # for multi gpu distributed inference. this is a singleton, so it's safe to use it here
org_vae_device = vae.device # CPUにいるはず
vae.to(device)
vae.to(distributed_state.device)
# unwrap unet and text_encoder(s)
unet = accelerator.unwrap_model(unet)
@@ -4703,10 +4705,6 @@ def sample_images_common(
text_encoder = accelerator.unwrap_model(text_encoder)
# read prompts
# with open(args.sample_prompts, "rt", encoding="utf-8") as f:
# prompts = f.readlines()
if args.sample_prompts.endswith(".txt"):
with open(args.sample_prompts, "r", encoding="utf-8") as f:
lines = f.readlines()
@@ -4719,12 +4717,11 @@ def sample_images_common(
with open(args.sample_prompts, "r", encoding="utf-8") as f:
prompts = json.load(f)
schedulers: dict = {}
# schedulers: dict = {} cannot find where this is used
default_scheduler = get_my_scheduler(
sample_sampler=args.sample_sampler,
v_parameterization=args.v_parameterization,
)
schedulers[args.sample_sampler] = default_scheduler
pipeline = pipe_class(
text_encoder=text_encoder,
@@ -4737,23 +4734,55 @@ def sample_images_common(
requires_safety_checker=False,
clip_skip=args.clip_skip,
)
pipeline.to(device)
pipeline.to(distributed_state.device)
save_dir = args.output_dir + "/sample"
os.makedirs(save_dir, exist_ok=True)
rng_state = torch.get_rng_state()
cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
with torch.no_grad():
# with accelerator.autocast():
for i, prompt_dict in enumerate(prompts):
if not accelerator.is_main_process:
continue
# preprocess prompts
for i in range(len(prompts)):
prompt_dict = prompts[i]
if isinstance(prompt_dict, str):
prompt_dict = line_to_prompt_dict(prompt_dict)
prompts[i] = prompt_dict
assert isinstance(prompt_dict, dict)
# Adds an enumerator to the dict based on prompt position. Used later to name image files. Also cleanup of extra data in original prompt dict.
prompt_dict["enum"] = i
prompt_dict.pop("subset", None)
# save random state to restore later
rng_state = torch.get_rng_state()
cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None # TODO mps etc. support
if distributed_state.num_processes <= 1:
# If only one device is available, just use the original prompt list. We don't need to care about the distribution of prompts.
with torch.no_grad():
for prompt_dict in prompts:
sample_image_inference(accelerator, args, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=controlnet)
else:
# Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available)
# prompt_dicts are assigned to lists based on order of processes, to attempt to time the image creation time to match enum order. Probably only works when steps and sampler are identical.
per_process_prompts = [] # list of lists
for i in range(distributed_state.num_processes):
per_process_prompts.append(prompts[i::distributed_state.num_processes])
with torch.no_grad():
with distributed_state.split_between_processes(per_process_prompts) as prompt_dict_lists:
for prompt_dict in prompt_dict_lists[0]:
sample_image_inference(accelerator, args, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=controlnet)
# clear pipeline and cache to reduce vram usage
del pipeline
with torch.cuda.device(torch.cuda.current_device()):
torch.cuda.empty_cache()
torch.set_rng_state(rng_state)
if cuda_rng_state is not None:
torch.cuda.set_rng_state(cuda_rng_state)
vae.to(org_vae_device)
def sample_image_inference(accelerator: Accelerator, args: argparse.Namespace, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=None):
assert isinstance(prompt_dict, dict)
negative_prompt = prompt_dict.get("negative_prompt")
sample_steps = prompt_dict.get("sample_steps", 30)
@@ -4765,31 +4794,32 @@ def sample_images_common(
prompt: str = prompt_dict.get("prompt", "")
sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler)
if seed is not None:
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
scheduler = schedulers.get(sampler_name)
if scheduler is None:
scheduler = get_my_scheduler(
sample_sampler=sampler_name,
v_parameterization=args.v_parameterization,
)
schedulers[sampler_name] = scheduler
pipeline.scheduler = scheduler
if prompt_replacement is not None:
prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
if negative_prompt is not None:
negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
if seed is not None:
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
else:
# True random sample image generation
torch.seed()
torch.cuda.seed()
scheduler = get_my_scheduler(
sample_sampler=sampler_name,
v_parameterization=args.v_parameterization,
)
pipeline.scheduler = scheduler
if controlnet_image is not None:
controlnet_image = Image.open(controlnet_image).convert("RGB")
controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS)
height = max(64, height - height % 8) # round to divisible by 8
width = max(64, width - width % 8) # round to divisible by 8
print(f"prompt: {prompt}")
print(f"\nprompt: {prompt}")
print(f"negative_prompt: {negative_prompt}")
print(f"height: {height}")
print(f"width: {width}")
@@ -4810,15 +4840,21 @@ def sample_images_common(
controlnet_image=controlnet_image,
)
with torch.cuda.device(torch.cuda.current_device()):
torch.cuda.empty_cache()
image = pipeline.latents_to_image(latents)[0]
# adding accelerator.wait_for_everyone() here should sync up and ensure that sample images are saved in the same order as the original prompt list
# but adding 'enum' to the filename should be enough
ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}"
seed_suffix = "" if seed is None else f"_{seed}"
i: int = prompt_dict["enum"]
img_filename = (
f"{'' if args.output_name is None else args.output_name + '_'}{ts_str}_{num_suffix}_{i:02d}{seed_suffix}.png"
f"{'' if args.output_name is None else args.output_name + '_'}{num_suffix}_{i:02d}_{ts_str}{seed_suffix}.png"
)
image.save(os.path.join(save_dir, img_filename))
# wandb有効時のみログを送信
@@ -4832,19 +4868,14 @@ def sample_images_common(
wandb_tracker.log({f"sample_{i}": wandb.Image(image)})
except: # wandb 無効時
pass
# clear pipeline and cache to reduce vram usage
del pipeline
clean_memory()
torch.set_rng_state(rng_state)
if cuda_rng_state is not None:
torch.cuda.set_rng_state(cuda_rng_state)
vae.to(org_vae_device)
# endregion
# # clear pipeline and cache to reduce vram usage
# del pipeline
# torch.cuda.empty_cache()
# region 前処理用