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29b0094 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | # Copyright 2023 metric-space, The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Total Batch size = 128 = 4 (num_gpus) * 8 (per_device_batch) * 4 (accumulation steps)
Feel free to reduce batch size or increasing truncated_rand_backprop_min to a higher value to reduce memory usage.
CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/scripts/alignprop.py \
--num_epochs=20 \
--train_gradient_accumulation_steps=4 \
--sample_num_steps=50 \
--train_batch_size=8 \
--tracker_project_name="stable_diffusion_training" \
--log_with="wandb"
"""
from dataclasses import dataclass, field
import numpy as np
from transformers import HfArgumentParser
from trl import AlignPropConfig, AlignPropTrainer, DefaultDDPOStableDiffusionPipeline
from trl.models.auxiliary_modules import aesthetic_scorer
@dataclass
class ScriptArguments:
pretrained_model: str = field(
default="runwayml/stable-diffusion-v1-5", metadata={"help": "the pretrained model to use"}
)
pretrained_revision: str = field(default="main", metadata={"help": "the pretrained model revision to use"})
hf_hub_model_id: str = field(
default="alignprop-finetuned-stable-diffusion", metadata={"help": "HuggingFace repo to save model weights to"}
)
hf_hub_aesthetic_model_id: str = field(
default="trl-lib/ddpo-aesthetic-predictor",
metadata={"help": "HuggingFace model ID for aesthetic scorer model weights"},
)
hf_hub_aesthetic_model_filename: str = field(
default="aesthetic-model.pth",
metadata={"help": "HuggingFace model filename for aesthetic scorer model weights"},
)
use_lora: bool = field(default=True, metadata={"help": "Whether to use LoRA."})
# list of example prompts to feed stable diffusion
animals = [
"cat",
"dog",
"horse",
"monkey",
"rabbit",
"zebra",
"spider",
"bird",
"sheep",
"deer",
"cow",
"goat",
"lion",
"frog",
"chicken",
"duck",
"goose",
"bee",
"pig",
"turkey",
"fly",
"llama",
"camel",
"bat",
"gorilla",
"hedgehog",
"kangaroo",
]
def prompt_fn():
return np.random.choice(animals), {}
def image_outputs_logger(image_pair_data, global_step, accelerate_logger):
# For the sake of this example, we will only log the last batch of images
# and associated data
result = {}
images, prompts, _ = [image_pair_data["images"], image_pair_data["prompts"], image_pair_data["rewards"]]
for i, image in enumerate(images[:4]):
prompt = prompts[i]
result[f"{prompt}"] = image.unsqueeze(0).float()
accelerate_logger.log_images(
result,
step=global_step,
)
if __name__ == "__main__":
parser = HfArgumentParser((ScriptArguments, AlignPropConfig))
script_args, training_args = parser.parse_args_into_dataclasses()
training_args.project_kwargs = {
"logging_dir": "./logs",
"automatic_checkpoint_naming": True,
"total_limit": 5,
"project_dir": "./save",
}
pipeline = DefaultDDPOStableDiffusionPipeline(
script_args.pretrained_model,
pretrained_model_revision=script_args.pretrained_revision,
use_lora=script_args.use_lora,
)
trainer = AlignPropTrainer(
training_args,
aesthetic_scorer(script_args.hf_hub_aesthetic_model_id, script_args.hf_hub_aesthetic_model_filename),
prompt_fn,
pipeline,
image_samples_hook=image_outputs_logger,
)
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
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