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Bug fixes. allow for random negative prompts

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This commit is contained in:
Jaret Burkett
2024-02-21 04:51:52 -07:00
parent 2478554c95
commit 49c41e6a5f
8 changed files with 166 additions and 6 deletions
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31
extensions_built_in/sd_trainer/SDTrainer.py
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@@ -1,3 +1,4 @@
import os
import random
from collections import OrderedDict
from typing import Union, Literal, List, Optional
@@ -51,6 +52,8 @@ class SDTrainer(BaseSDTrainProcess):
self.taesd: Optional[AutoencoderTiny] = None
self._clip_image_embeds_unconditional: Union[List[str], None] = None
self.negative_prompt_pool: Union[List[str], None] = None
self.batch_negative_prompt: Union[List[str], None] = None
def before_model_load(self):
pass
@@ -108,6 +111,16 @@ class SDTrainer(BaseSDTrainProcess):
self._clip_image_embeds_unconditional = unconditional_clip_image_embeds
if self.train_config.negative_prompt is not None:
if os.path.exists(self.train_config.negative_prompt):
with open(self.train_config.negative_prompt, 'r') as f:
self.negative_prompt_pool = f.readlines()
# remove empty
self.negative_prompt_pool = [x.strip() for x in self.negative_prompt_pool if x.strip() != ""]
else:
# single prompt
self.negative_prompt_pool = [self.train_config.negative_prompt]
def process_output_for_turbo(self, pred, noisy_latents, timesteps, noise, batch):
# to process turbo learning, we make one big step from our current timestep to the end
# we then denoise the prediction on that remaining step and target our loss to our target latents
@@ -781,6 +794,18 @@ class SDTrainer(BaseSDTrainProcess):
batch = self.preprocess_batch(batch)
dtype = get_torch_dtype(self.train_config.dtype)
noisy_latents, noise, timesteps, conditioned_prompts, imgs = self.process_general_training_batch(batch)
if self.train_config.do_cfg or self.train_config.do_random_cfg:
# pick random negative prompts
if self.negative_prompt_pool is not None:
negative_prompts = []
for i in range(noisy_latents.shape[0]):
num_neg = random.randint(1, self.train_config.max_negative_prompts)
this_neg_prompts = [random.choice(self.negative_prompt_pool) for _ in range(num_neg)]
this_neg_prompt = ', '.join(this_neg_prompts)
negative_prompts.append(this_neg_prompt)
self.batch_negative_prompt = negative_prompts
else:
self.batch_negative_prompt = ['' for _ in range(batch.latents.shape[0])]
if self.adapter and isinstance(self.adapter, CustomAdapter):
# condition the prompt
@@ -1030,7 +1055,8 @@ class SDTrainer(BaseSDTrainProcess):
if self.train_config.do_cfg:
# todo only do one and repeat it
unconditional_embeds = self.sd.encode_prompt(
["" for _ in range(noisy_latents.shape[0])],
self.batch_negative_prompt,
self.batch_negative_prompt,
dropout_prob=self.train_config.prompt_dropout_prob,
long_prompts=self.do_long_prompts).to(
self.device_torch,
@@ -1050,9 +1076,8 @@ class SDTrainer(BaseSDTrainProcess):
self.device_torch,
dtype=dtype)
if self.train_config.do_cfg:
# todo only do one and repeat it
unconditional_embeds = self.sd.encode_prompt(
["" for _ in range(noisy_latents.shape[0])],
self.batch_negative_prompt,
dropout_prob=self.train_config.prompt_dropout_prob,
long_prompts=self.do_long_prompts).to(
self.device_torch,

4
scripts/make_diffusers_model.py
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@@ -1,5 +1,9 @@
import argparse
from collections import OrderedDict
import sys
import os
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(ROOT_DIR)
import torch

42
scripts/patch_te_adapter.py Normal file
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@@ -0,0 +1,42 @@
import torch
from safetensors.torch import save_file, load_file
from collections import OrderedDict
meta = OrderedDict()
meta["format"] ="pt"
attn_dict = load_file("/mnt/Train/out/ip_adapter/sd15_bigG/sd15_bigG_000266000.safetensors")
state_dict = load_file("/home/jaret/Dev/models/hf/OstrisDiffusionV1/unet/diffusion_pytorch_model.safetensors")
attn_list = []
for key, value in state_dict.items():
if "attn1" in key:
attn_list.append(key)
attn_names = ['down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor', 'down_blocks.0.attentions.1.transformer_blocks.0.attn2.processor', 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.processor', 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.processor', 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.processor', 'down_blocks.2.attentions.1.transformer_blocks.0.attn2.processor', 'up_blocks.1.attentions.0.transformer_blocks.0.attn2.processor', 'up_blocks.1.attentions.1.transformer_blocks.0.attn2.processor', 'up_blocks.1.attentions.2.transformer_blocks.0.attn2.processor', 'up_blocks.2.attentions.0.transformer_blocks.0.attn2.processor', 'up_blocks.2.attentions.1.transformer_blocks.0.attn2.processor', 'up_blocks.2.attentions.2.transformer_blocks.0.attn2.processor', 'up_blocks.3.attentions.0.transformer_blocks.0.attn2.processor', 'up_blocks.3.attentions.1.transformer_blocks.0.attn2.processor', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.processor', 'mid_block.attentions.0.transformer_blocks.0.attn2.processor']
adapter_names = []
for i in range(100):
if f'te_adapter.adapter_modules.{i}.to_k_adapter.weight' in attn_dict:
adapter_names.append(f"te_adapter.adapter_modules.{i}.adapter")
for i in range(len(adapter_names)):
adapter_name = adapter_names[i]
attn_name = attn_names[i]
adapter_k_name = adapter_name[:-8] + '.to_k_adapter.weight'
adapter_v_name = adapter_name[:-8] + '.to_v_adapter.weight'
state_k_name = attn_name.replace(".processor", ".to_k.weight")
state_v_name = attn_name.replace(".processor", ".to_v.weight")
if adapter_k_name in attn_dict:
state_dict[state_k_name] = attn_dict[adapter_k_name]
state_dict[state_v_name] = attn_dict[adapter_v_name]
else:
print("adapter_k_name", adapter_k_name)
print("state_k_name", state_k_name)
for key, value in state_dict.items():
state_dict[key] = value.cpu().to(torch.float16)
save_file(state_dict, "/home/jaret/Dev/models/hf/OstrisDiffusionV1/unet/diffusion_pytorch_model.safetensors", metadata=meta)
print("Done")

65
scripts/repair_dataset_folder.py Normal file
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@@ -0,0 +1,65 @@
import argparse
from PIL import Image
from PIL.ImageOps import exif_transpose
from tqdm import tqdm
import os
parser = argparse.ArgumentParser(description='Process some images.')
parser.add_argument("input_folder", type=str, help="Path to folder containing images")
args = parser.parse_args()
img_types = ['.jpg', '.jpeg', '.png', '.webp']
# find all images in the input folder
images = []
for root, _, files in os.walk(args.input_folder):
for file in files:
if file.lower().endswith(tuple(img_types)):
images.append(os.path.join(root, file))
print(f"Found {len(images)} images")
num_skipped = 0
num_repaired = 0
num_deleted = 0
pbar = tqdm(total=len(images), desc=f"Repaired {num_repaired} images", unit="image")
for img_path in images:
filename = os.path.basename(img_path)
filename_no_ext, file_extension = os.path.splitext(filename)
# if it is jpg, ignore
if file_extension.lower() == '.jpg':
num_skipped += 1
pbar.update(1)
continue
try:
img = Image.open(img_path)
except Exception as e:
print(f"Error opening {img_path}: {e}")
# delete it
os.remove(img_path)
num_deleted += 1
pbar.update(1)
pbar.set_description(f"Repaired {num_repaired} images, Skipped {num_skipped}, Deleted {num_deleted}")
continue
try:
img = exif_transpose(img)
except Exception as e:
print(f"Error rotating {img_path}: {e}")
new_path = os.path.join(os.path.dirname(img_path), filename_no_ext + '.jpg')
img = img.convert("RGB")
img.save(new_path, quality=95)
# remove the old file
os.remove(img_path)
num_repaired += 1
pbar.update(1)
# update pbar
pbar.set_description(f"Repaired {num_repaired} images, Skipped {num_skipped}, Deleted {num_deleted}")
print("Done")

1
toolkit/config_modules.py
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@@ -240,6 +240,7 @@ class TrainConfig:
self.img_multiplier = kwargs.get('img_multiplier', 1.0)
self.latent_multiplier = kwargs.get('latent_multiplier', 1.0)
self.negative_prompt = kwargs.get('negative_prompt', None)
self.max_negative_prompts = kwargs.get('max_negative_prompts', 1)
# multiplier applied to loos on regularization images
self.reg_weight = kwargs.get('reg_weight', 1.0)
self.num_train_timesteps = kwargs.get('num_train_timesteps', 1000)

17
toolkit/ip_adapter.py
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@@ -322,7 +322,7 @@ class IPAdapter(torch.nn.Module):
elif adapter_config.type == 'ip+':
heads = 12 if not sd.is_xl else 20
dim = sd.unet.config['cross_attention_dim'] if not sd.is_xl else 1280
embedding_dim = self.image_encoder.config.hidden_size if not self.config.image_encoder_arch == "convnext" else \
embedding_dim = self.image_encoder.config.hidden_size if not self.config.image_encoder_arch.startswith('convnext') else \
self.image_encoder.config.hidden_sizes[-1]
image_encoder_state_dict = self.image_encoder.state_dict()
@@ -340,6 +340,10 @@ class IPAdapter(torch.nn.Module):
dim = 4096
output_dim = 4096
if self.config.image_encoder_arch.startswith('convnext'):
in_tokens = 16 * 16
embedding_dim = self.image_encoder.config.hidden_sizes[-1]
# ip-adapter-plus
image_proj_model = Resampler(
dim=dim,
@@ -406,6 +410,8 @@ class IPAdapter(torch.nn.Module):
else:
attn_processor_keys = list(sd.unet.attn_processors.keys())
attn_processor_names = []
for name in attn_processor_keys:
cross_attention_dim = None if name.endswith("attn1.processor") or name.endswith("attn.1") else \
sd.unet.config['cross_attention_dim']
@@ -446,6 +452,9 @@ class IPAdapter(torch.nn.Module):
}
attn_procs[name].load_state_dict(weights)
attn_processor_names.append(name)
print(f"Attn Processors")
print(attn_processor_names)
if self.sd_ref().is_pixart:
# we have to set them ourselves
transformer: Transformer2DModel = sd.unet
@@ -690,6 +699,12 @@ class IPAdapter(torch.nn.Module):
else:
clip_image_embeds = clip_output.image_embeds
if self.config.image_encoder_arch.startswith('convnext'):
# flatten the width height layers to make the token space
clip_image_embeds = clip_image_embeds.view(clip_image_embeds.size(0), clip_image_embeds.size(1), -1)
# rearrange to (batch, tokens, size)
clip_image_embeds = clip_image_embeds.permute(0, 2, 1)
if self.config.quad_image:
# get the outputs of the quat
chunks = clip_image_embeds.chunk(quad_count, dim=0)

10
toolkit/models/te_adapter.py
View File

@@ -171,11 +171,19 @@ class TEAdapter(torch.nn.Module):
self.te_ref: weakref.ref = weakref.ref(te)
self.tokenizer_ref: weakref.ref = weakref.ref(tokenizer)
self.token_size = self.te_ref().config.d_model
if self.adapter_ref().config.text_encoder_arch == "t5":
self.token_size = self.te_ref().config.d_model
else:
self.token_size = self.te_ref().config.hidden_size
# init adapter modules
attn_procs = {}
unet_sd = sd.unet.state_dict()
attn_dict_map = {
}
module_idx = 0
attn_processors_list = list(sd.unet.attn_processors.keys())
for name in sd.unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else sd.unet.config['cross_attention_dim']
if name.startswith("mid_block"):

2
toolkit/stable_diffusion_model.py
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@@ -287,7 +287,7 @@ class StableDiffusion:
load_safety_checker=False,
requires_safety_checker=False,
torch_dtype=self.torch_dtype,
safety_checker=False,
safety_checker=None,
**load_args
).to(self.device_torch)
flush()