File size: 5,523 Bytes
8b7a3d1 db0cd98 8b7a3d1 ecfdc8b 400839c ecfdc8b 400839c 8b7a3d1 ecfdc8b a3f0757 ecfdc8b a3f0757 8b7a3d1 ecfdc8b 8b7a3d1 ecfdc8b 07a02e5 ecfdc8b a3f0757 ecfdc8b a3f0757 ecfdc8b 07a02e5 a3f0757 ecfdc8b 07a02e5 ecfdc8b a3f0757 ecfdc8b a3f0757 ecfdc8b 8b7a3d1 ecfdc8b 8b7a3d1 ecfdc8b 07a02e5 5a1aaa1 ecfdc8b 8b7a3d1 ecfdc8b 07a02e5 8b7a3d1 ecfdc8b 8b7a3d1 ecfdc8b 8b7a3d1 ecfdc8b 67d646a 8b7a3d1 db0cd98 |
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 |
#!/usr/bin/env python
from __future__ import annotations
import os
import gradio as gr
from constants import UploadTarget
from inference import InferencePipeline
from trainer import Trainer
def create_training_demo(
trainer: Trainer, pipe: InferencePipeline | None = None, disable_run_button: bool = False
) -> gr.Blocks:
def read_log() -> str:
with open(trainer.log_file) as f:
lines = f.readlines()
return "".join(lines[-10:])
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
with gr.Box():
gr.Markdown("Training Data")
training_video = gr.File(label="Training video")
training_prompt = gr.Textbox(label="Training prompt", max_lines=1, placeholder="A man is surfing")
gr.Markdown(
"""
- Upload a video and write a `Training Prompt` that describes the video.
"""
)
with gr.Column():
with gr.Box():
gr.Markdown("Training Parameters")
with gr.Row():
base_model = gr.Text(label="Base Model", value="CompVis/stable-diffusion-v1-4", max_lines=1)
resolution = gr.Dropdown(
choices=["512", "768"], value="512", label="Resolution", visible=False
)
hf_token = gr.Text(
label="Hugging Face Write Token", type="password", visible=os.getenv("HF_TOKEN") is None
)
with gr.Accordion(label="Advanced options", open=False):
num_training_steps = gr.Number(label="Number of Training Steps", value=300, precision=0)
learning_rate = gr.Number(label="Learning Rate", value=0.000035)
gradient_accumulation = gr.Number(
label="Number of Gradient Accumulation", value=1, precision=0
)
seed = gr.Slider(label="Seed", minimum=0, maximum=100000, step=1, randomize=True, value=0)
fp16 = gr.Checkbox(label="FP16", value=True)
use_8bit_adam = gr.Checkbox(label="Use 8bit Adam", value=False)
checkpointing_steps = gr.Number(label="Checkpointing Steps", value=1000, precision=0)
validation_epochs = gr.Number(label="Validation Epochs", value=100, precision=0)
gr.Markdown(
"""
- The base model must be a Stable Diffusion model compatible with [diffusers](https://github.com/huggingface/diffusers) library.
- Expected time to train a model for 300 steps: ~20 minutes with T4
- You can check the training status by pressing the "Open logs" button if you are running this on your Space.
"""
)
with gr.Row():
with gr.Column():
gr.Markdown("Output Model")
output_model_name = gr.Text(label="Name of your model", placeholder="The surfer man", max_lines=1)
validation_prompt = gr.Text(
label="Validation Prompt", placeholder="prompt to test the model, e.g: a dog is surfing"
)
with gr.Column():
gr.Markdown("Upload Settings")
with gr.Row():
upload_to_hub = gr.Checkbox(label="Upload model to Hub", value=True)
use_private_repo = gr.Checkbox(label="Private", value=True)
delete_existing_repo = gr.Checkbox(label="Delete existing repo of the same name", value=False)
upload_to = gr.Radio(
label="Upload to",
choices=[_.value for _ in UploadTarget],
value=UploadTarget.MODEL_LIBRARY.value,
)
pause_space_after_training = gr.Checkbox(
label="Pause this Space after training",
value=False,
interactive=bool(os.getenv("SPACE_ID")),
visible=False,
)
run_button = gr.Button("Start Training", interactive=not disable_run_button)
with gr.Box():
gr.Text(label="Log", value=read_log, lines=10, max_lines=10, every=1)
if pipe is not None:
run_button.click(fn=pipe.clear)
run_button.click(
fn=trainer.run,
inputs=[
training_video,
training_prompt,
output_model_name,
delete_existing_repo,
validation_prompt,
base_model,
resolution,
num_training_steps,
learning_rate,
gradient_accumulation,
seed,
fp16,
use_8bit_adam,
checkpointing_steps,
validation_epochs,
upload_to_hub,
use_private_repo,
delete_existing_repo,
upload_to,
pause_space_after_training,
hf_token,
],
)
return demo
if __name__ == "__main__":
trainer = Trainer()
demo = create_training_demo(trainer)
demo.queue(api_open=False, max_size=1).launch()
|