DreamBooth

Train custom image models with DreamBooth on Clore.ai GPUs

Train Stable Diffusion to generate images of specific subjects.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is DreamBooth?

DreamBooth fine-tunes SD on your images:

• Train on 5-20 images

• Generate new images of your subject

• Any style or context

• Works with SD 1.5 and SDXL

Requirements

Model	VRAM	Training Time
SD 1.5	12GB	15-30 min
SDXL	24GB	30-60 min
SD 1.5 + LoRA	8GB	10-20 min

Quick Deploy

Docker Image:

pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel

Ports:

22/tcp
7860/http

Command:

pip install diffusers transformers accelerate bitsandbytes && \
pip install xformers peft && \
python dreambooth_train.py

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Installation

pip install diffusers transformers accelerate
pip install bitsandbytes xformers peft

Prepare Training Data

1. Collect 5-20 images of your subject

2. Crop to face/subject

3. Resize to 512x512 (or 1024x1024 for SDXL)

4. Remove backgrounds if needed

from PIL import Image
import os

def prepare_images(input_dir, output_dir, size=512):
    os.makedirs(output_dir, exist_ok=True)

    for filename in os.listdir(input_dir):
        if filename.endswith(('.jpg', '.png', '.jpeg')):
            img = Image.open(os.path.join(input_dir, filename))
            img = img.convert('RGB')

            # Center crop to square
            min_dim = min(img.size)
            left = (img.width - min_dim) // 2
            top = (img.height - min_dim) // 2
            img = img.crop((left, top, left + min_dim, top + min_dim))

            # Resize
            img = img.resize((size, size), Image.LANCZOS)
            img.save(os.path.join(output_dir, filename))

prepare_images("./raw_photos", "./training_data")

DreamBooth with LoRA (Recommended)

Memory-efficient training:

from diffusers import StableDiffusionPipeline, DDPMScheduler
from diffusers.loaders import LoraLoaderMixin
import torch

# Training script
from accelerate import Accelerator
from diffusers import AutoencoderKL, UNet2DConditionModel
from transformers import CLIPTextModel, CLIPTokenizer
from peft import LoraConfig, get_peft_model

# Load models
model_id = "runwayml/stable-diffusion-v1-5"
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet")

# Add LoRA to UNet
lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["to_q", "to_k", "to_v", "to_out.0"],
    lora_dropout=0.1,
)

unet = get_peft_model(unet, lora_config)

Using diffusers Training Script

# Clone training scripts
git clone https://github.com/huggingface/diffusers
cd diffusers/examples/dreambooth

# Install requirements
pip install -r requirements.txt

# Train with LoRA
accelerate launch train_dreambooth_lora.py \
    --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \
    --instance_data_dir="./training_data" \
    --instance_prompt="a photo of sks person" \
    --output_dir="./dreambooth_model" \
    --resolution=512 \
    --train_batch_size=1 \
    --gradient_accumulation_steps=1 \
    --learning_rate=1e-4 \
    --lr_scheduler="constant" \
    --lr_warmup_steps=0 \
    --max_train_steps=500 \
    --seed=42

Training Parameters

Parameter	Recommended	Effect
learning_rate	1e-4 to 5e-6	Higher = faster, lower = stable
max_train_steps	400-1000	More = better fit
train_batch_size	1-2	Higher needs more VRAM
resolution	512 (SD1.5) / 1024 (SDXL)	Training size

Instance Prompt

Choose a unique identifier:

# Good prompts
"a photo of sks person"      # sks = unique token
"a photo of xyz dog"
"a photo of abc car"

# The token (sks, xyz, abc) should be rare

With Class Preservation

Prevent overfitting:

accelerate launch train_dreambooth_lora.py \
    --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \
    --instance_data_dir="./my_dog_photos" \
    --instance_prompt="a photo of sks dog" \
    --class_data_dir="./regular_dog_photos" \
    --class_prompt="a photo of dog" \
    --with_prior_preservation \
    --prior_loss_weight=1.0 \
    --num_class_images=200 \
    --output_dir="./dreambooth_dog" \
    --max_train_steps=800

SDXL DreamBooth

accelerate launch train_dreambooth_lora_sdxl.py \
    --pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \
    --instance_data_dir="./training_data" \
    --instance_prompt="a photo of sks person" \
    --output_dir="./dreambooth_sdxl" \
    --resolution=1024 \
    --train_batch_size=1 \
    --gradient_accumulation_steps=4 \
    --learning_rate=1e-4 \
    --max_train_steps=500 \
    --mixed_precision="fp16"

Using Trained Model

Load LoRA

from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16
).to("cuda")

# Load your trained LoRA
pipe.load_lora_weights("./dreambooth_model")

# Generate
image = pipe(
    "a photo of sks person as an astronaut on mars",
    num_inference_steps=30,
    guidance_scale=7.5
).images[0]

image.save("astronaut.png")

Full Fine-tune

pipe = StableDiffusionPipeline.from_pretrained(
    "./dreambooth_model",
    torch_dtype=torch.float16
).to("cuda")

image = pipe("a photo of sks person in a suit").images[0]

Gradio Interface

import gradio as gr
from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16
).to("cuda")

pipe.load_lora_weights("./dreambooth_model")

def generate(prompt, negative_prompt, steps, guidance, seed):
    generator = torch.Generator("cuda").manual_seed(seed) if seed > 0 else None

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=steps,
        guidance_scale=guidance,
        generator=generator
    ).images[0]

    return image

demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(label="Prompt (use 'sks' for your subject)"),
        gr.Textbox(label="Negative Prompt", value="blurry, ugly"),
        gr.Slider(20, 50, value=30, step=1, label="Steps"),
        gr.Slider(5, 15, value=7.5, step=0.5, label="Guidance"),
        gr.Number(value=-1, label="Seed")
    ],
    outputs=gr.Image(label="Generated Image"),
    title="DreamBooth Portrait Generator"
)

demo.launch(server_name="0.0.0.0", server_port=7860)

Training Tips

For People

• Use varied angles (front, side, 3/4)

• Different lighting conditions

• Various expressions

• Clear, high-resolution photos

For Objects

• Multiple angles

• Different backgrounds

• Consistent lighting

• No occlusion

For Styles

• 10-20 example images

• Consistent artistic style

• Various subjects in that style

Troubleshooting

Overfitting

• Reduce max_train_steps

• Lower learning_rate

• Use prior preservation

• More training images

Underfitting

• Increase max_train_steps

• Higher learning_rate

• More training images

• Check image quality

Style Not Learned

• Increase LoRA rank (r=16 or 32)

• Train longer

• Use more examples

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use the Spot market for interruptible work — about a third of servers price spot below on-demand (median ~13% off), the rest match it

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• Kohya Training - Advanced training

• Stable Diffusion WebUI - Use models

• LoRA Fine-tuning - LLM training