lucataco / stable-diffusion-3.5-large-lora-trainer

Fine-tune StableDiffusion3.5-Large with Hugging Face Diffusers

Cold

Public
518 runs
A100 (80GB)
GitHub
Weights
Paper
License

Run with an API

Playground API Examples README Versions

Input

input_images

file

A zip file containing the images that will be used for training.

instance_prompt

string

Shift + Return to add a new line

Instance prompt to trigger the image generation

Default: "Frog, yarn art style"

resolution

integer

The resolution for input images, all the images in the train/validation dataset will be resized to this

Default: 768

max_train_steps

integer

(minimum: 100, maximum: 6000)

Total number of training steps to perform

Default: 700

hf_token

secret

Huggingface token (optional) with write access to upload to Hugging Face

hub_model_id

string

Shift + Return to add a new line

Huggingface model location for upload. Requires a HF token with write permissions. Ex: lucataco/SD3.5-Large-queso

Including rank and 11 more...

Run this model in Node.js with one line of code:

npx create-replicate --model=lucataco/stable-diffusion-3.5-large-lora-trainer

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run lucataco/stable-diffusion-3.5-large-lora-trainer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/stable-diffusion-3.5-large-lora-trainer:6ebda45af5b9c30edee3149cc1624b7f7cae8fab7c692e2c51d82f5fed3198ee",
  {
    input: {
      rank: 16,
      backend: "no",
      optimizer: "AdamW",
      resolution: 768,
      input_images: "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
      lr_scheduler: "constant",
      learning_rate: 0.0001,
      instance_prompt: "Frog, yarn art style",
      max_train_steps: 700,
      text_encoder_lr: 0.000005,
      train_batch_size: 1,
      train_text_encoder: false,
      gradient_accumulation_steps: 1
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run lucataco/stable-diffusion-3.5-large-lora-trainer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/stable-diffusion-3.5-large-lora-trainer:6ebda45af5b9c30edee3149cc1624b7f7cae8fab7c692e2c51d82f5fed3198ee",
    input={
        "rank": 16,
        "backend": "no",
        "optimizer": "AdamW",
        "resolution": 768,
        "input_images": "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
        "lr_scheduler": "constant",
        "learning_rate": 0.0001,
        "instance_prompt": "Frog, yarn art style",
        "max_train_steps": 700,
        "text_encoder_lr": 0.000005,
        "train_batch_size": 1,
        "train_text_encoder": False,
        "gradient_accumulation_steps": 1
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run lucataco/stable-diffusion-3.5-large-lora-trainer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "6ebda45af5b9c30edee3149cc1624b7f7cae8fab7c692e2c51d82f5fed3198ee",
    "input": {
      "rank": 16,
      "backend": "no",
      "optimizer": "AdamW",
      "resolution": 768,
      "input_images": "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
      "lr_scheduler": "constant",
      "learning_rate": 0.0001,
      "instance_prompt": "Frog, yarn art style",
      "max_train_steps": 700,
      "text_encoder_lr": 0.000005,
      "train_batch_size": 1,
      "train_text_encoder": false,
      "gradient_accumulation_steps": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/lucataco/stable-diffusion-3.5-large-lora-trainer@sha256:6ebda45af5b9c30edee3149cc1624b7f7cae8fab7c692e2c51d82f5fed3198ee \
  -i 'rank=16' \
  -i 'backend="no"' \
  -i 'optimizer="AdamW"' \
  -i 'resolution=768' \
  -i 'input_images="https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip"' \
  -i 'lr_scheduler="constant"' \
  -i 'learning_rate=0.0001' \
  -i 'instance_prompt="Frog, yarn art style"' \
  -i 'max_train_steps=700' \
  -i 'text_encoder_lr=0.000005' \
  -i 'train_batch_size=1' \
  -i 'train_text_encoder=false' \
  -i 'gradient_accumulation_steps=1'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/lucataco/stable-diffusion-3.5-large-lora-trainer@sha256:6ebda45af5b9c30edee3149cc1624b7f7cae8fab7c692e2c51d82f5fed3198ee
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "rank": 16,
      "backend": "no",
      "optimizer": "AdamW",
      "resolution": 768,
      "input_images": "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
      "lr_scheduler": "constant",
      "learning_rate": 0.0001,
      "instance_prompt": "Frog, yarn art style",
      "max_train_steps": 700,
      "text_encoder_lr": 0.000005,
      "train_batch_size": 1,
      "train_text_encoder": false,
      "gradient_accumulation_steps": 1
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

trained_model.tar

{
  "completed_at": "2024-10-25T23:39:14.239794Z",
  "created_at": "2024-10-25T23:31:46.168000Z",
  "data_removed": false,
  "error": null,
  "id": "ng14j2cff1rj40cjrr2vbz667m",
  "input": {
    "rank": 16,
    "backend": "no",
    "optimizer": "AdamW",
    "resolution": 768,
    "input_images": "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
    "lr_scheduler": "constant",
    "learning_rate": 0.0001,
    "instance_prompt": "Frog, yarn art style",
    "max_train_steps": 700,
    "train_batch_size": 1,
    "gradient_accumulation_steps": 1
  },
  "logs": "Using seed: 3595070789\nExtracted 16 files from zip to input_images\nUsing params: ['accelerate', 'launch', '--dynamo_backend', 'no', 'train_dreambooth_lora_sd3.py', '--pretrained_model_name_or_path', 'stable-diffusion-3.5-large', '--instance_data_dir', 'input_images', '--rank', '16', '--output_dir', '/tmp/train/output/sd35_large_train_replicate', '--mixed_precision', 'bf16', '--instance_prompt', 'Frog, yarn art style', '--resolution', '768', '--train_batch_size', '1', '--gradient_accumulation_steps', '1', '--optimizer', 'AdamW', '--learning_rate', '0.0001', '--lr_scheduler', 'constant', '--lr_warmup_steps', '0', '--max_train_steps', '700', '--checkpointing_steps', '701', '--seed', '3595070789', '--logging_dir', '/tmp/logs']\n10/25/2024 23:33:02 - INFO - __main__ - Distributed environment: DistributedType.NO\nNum processes: 1\nProcess index: 0\nLocal process index: 0\nDevice: cuda\nMixed precision type: bf16\nYou set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers\nYou are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\nYou are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\nYou are using a model of type t5 to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\n{'base_image_seq_len', 'base_shift', 'max_shift', 'max_image_seq_len', 'use_dynamic_shifting'} was not found in config. Values will be initialized to default values.\nLoading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]\nLoading checkpoint shards:  50%|█████     | 1/2 [00:03<00:03,  3.67s/it]\nLoading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]\nLoading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]\n{'dual_attention_layers'} was not found in config. Values will be initialized to default values.\n10/25/2024 23:33:53 - INFO - __main__ - ***** Running training *****\n10/25/2024 23:33:53 - INFO - __main__ -   Num examples = 16\n10/25/2024 23:33:53 - INFO - __main__ -   Num batches each epoch = 16\n10/25/2024 23:33:53 - INFO - __main__ -   Num Epochs = 44\n10/25/2024 23:33:53 - INFO - __main__ -   Instantaneous batch size per device = 1\n10/25/2024 23:33:53 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 1\n10/25/2024 23:33:53 - INFO - __main__ -   Gradient Accumulation steps = 1\n10/25/2024 23:33:53 - INFO - __main__ -   Total optimization steps = 700\nSteps:   0%|          | 0/700 [00:00<?, ?it/s]\nSteps:   0%|          | 1/700 [00:00<07:26,  1.56it/s]\nSteps:   0%|          | 1/700 [00:00<07:26,  1.56it/s, loss=0.132, lr=0.0001]\nSteps:   0%|          | 2/700 [00:01<05:50,  1.99it/s, loss=0.132, lr=0.0001]\nSteps:   0%|          | 2/700 [00:01<05:50,  1.99it/s, loss=0.189, lr=0.0001]\nSteps:  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loss=0.0907, lr=0.0001]\nSteps:  99%|█████████▉| 696/700 [05:02<00:01,  2.31it/s, loss=0.108, lr=0.0001] \nSteps: 100%|█████████▉| 697/700 [05:02<00:01,  2.31it/s, loss=0.108, lr=0.0001]\nSteps: 100%|█████████▉| 697/700 [05:02<00:01,  2.31it/s, loss=0.165, lr=0.0001]\nSteps: 100%|█████████▉| 698/700 [05:02<00:00,  2.31it/s, loss=0.165, lr=0.0001]\nSteps: 100%|█████████▉| 698/700 [05:02<00:00,  2.31it/s, loss=0.194, lr=0.0001]\nSteps: 100%|█████████▉| 699/700 [05:03<00:00,  2.31it/s, loss=0.194, lr=0.0001]\nSteps: 100%|█████████▉| 699/700 [05:03<00:00,  2.31it/s, loss=0.229, lr=0.0001]\nSteps: 100%|██████████| 700/700 [05:03<00:00,  2.31it/s, loss=0.229, lr=0.0001]\nSteps: 100%|██████████| 700/700 [05:03<00:00,  2.31it/s, loss=0.141, lr=0.0001]Model weights saved in /tmp/train/output/sd35_large_train_replicate/pytorch_lora_weights.safetensors\nLoading pipeline components...:   0%|          | 0/9 [00:00<?, ?it/s]\u001b[A{'base_image_seq_len', 'base_shift', 'max_shift', 'max_image_seq_len', 'use_dynamic_shifting'} was not found in config. Values will be initialized to default values.\nLoaded scheduler as FlowMatchEulerDiscreteScheduler from `scheduler` subfolder of stable-diffusion-3.5-large.\nLoaded text_encoder as CLIPTextModelWithProjection from `text_encoder` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  22%|██▏       | 2/9 [00:00<00:01,  5.30it/s]\u001b[A\nLoading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\u001b[A\nLoading checkpoint shards:  50%|█████     | 1/2 [00:04<00:04,  4.98s/it]\u001b[A\u001b[A\nLoading checkpoint shards: 100%|██████████| 2/2 [00:09<00:00,  4.75s/it]\u001b[A\u001b[A\nLoading checkpoint shards: 100%|██████████| 2/2 [00:09<00:00,  4.79s/it]\nLoaded text_encoder_3 as T5EncoderModel from `text_encoder_3` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  33%|███▎      | 3/9 [00:09<00:24,  4.12s/it]\u001b[A{'dual_attention_layers'} was not found in config. Values will be initialized to default values.\nLoaded transformer as SD3Transformer2DModel from `transformer` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  44%|████▍     | 4/9 [00:11<00:16,  3.27s/it]\u001b[ALoaded tokenizer as CLIPTokenizer from `tokenizer` subfolder of stable-diffusion-3.5-large.\nLoaded tokenizer_3 as T5TokenizerFast from `tokenizer_3` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  67%|██████▋   | 6/9 [00:12<00:04,  1.64s/it]\u001b[ALoaded tokenizer_2 as CLIPTokenizer from `tokenizer_2` subfolder of stable-diffusion-3.5-large.\nLoaded text_encoder_2 as CLIPTextModelWithProjection from `text_encoder_2` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  89%|████████▉ | 8/9 [00:13<00:01,  1.30s/it]\u001b[ALoaded vae as AutoencoderKL from `vae` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...: 100%|██████████| 9/9 [00:13<00:00,  1.53s/it]\nSteps: 100%|██████████| 700/700 [05:18<00:00,  2.20it/s, loss=0.141, lr=0.0001]\n./\n./output/\n./output/sd35_large_train_replicate/\n./output/sd35_large_train_replicate/lora.safetensors",
  "metrics": {
    "predict_time": 381.84731858,
    "total_time": 448.071794
  },
  "output": "https://replicate.delivery/yhqm/jIEgASHbbbZOKdpHzeXSs69U7I7EuDmLQMvj0RDp1ONR8O1JA/trained_model.tar",
  "started_at": "2024-10-25T23:32:52.392475Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/qoxq-f7ssj3usyyzpbiefbnvm5arzsinbfancnamm5xjc65jku7zrmela",
    "get": "https://api.replicate.com/v1/predictions/ng14j2cff1rj40cjrr2vbz667m",
    "cancel": "https://api.replicate.com/v1/predictions/ng14j2cff1rj40cjrr2vbz667m/cancel"
  },
  "version": "cd6419a53b69fd410a912d945fa481a2a9ecfc4ab93062ed76c53f6e617f89e9"
}

Generated in

6 minutes 22 seconds

Tweak it ShareReport

Using seed: 3595070789
Extracted 16 files from zip to input_images
Using params: ['accelerate', 'launch', '--dynamo_backend', 'no', 'train_dreambooth_lora_sd3.py', '--pretrained_model_name_or_path', 'stable-diffusion-3.5-large', '--instance_data_dir', 'input_images', '--rank', '16', '--output_dir', '/tmp/train/output/sd35_large_train_replicate', '--mixed_precision', 'bf16', '--instance_prompt', 'Frog, yarn art style', '--resolution', '768', '--train_batch_size', '1', '--gradient_accumulation_steps', '1', '--optimizer', 'AdamW', '--learning_rate', '0.0001', '--lr_scheduler', 'constant', '--lr_warmup_steps', '0', '--max_train_steps', '700', '--checkpointing_steps', '701', '--seed', '3595070789', '--logging_dir', '/tmp/logs']
10/25/2024 23:33:02 - INFO - __main__ - Distributed environment: DistributedType.NO
Num processes: 1
Process index: 0
Local process index: 0
Device: cuda
Mixed precision type: bf16
You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers
You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors.
You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors.
You are using a model of type t5 to instantiate a model of type . This is not supported for all configurations of models and can yield errors.
{'base_image_seq_len', 'base_shift', 'max_shift', 'max_image_seq_len', 'use_dynamic_shifting'} was not found in config. Values will be initialized to default values.
Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
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Loading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
{'dual_attention_layers'} was not found in config. Values will be initialized to default values.
10/25/2024 23:33:53 - INFO - __main__ - ***** Running training *****
10/25/2024 23:33:53 - INFO - __main__ -   Num examples = 16
10/25/2024 23:33:53 - INFO - __main__ -   Num batches each epoch = 16
10/25/2024 23:33:53 - INFO - __main__ -   Num Epochs = 44
10/25/2024 23:33:53 - INFO - __main__ -   Instantaneous batch size per device = 1
10/25/2024 23:33:53 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 1
10/25/2024 23:33:53 - INFO - __main__ -   Gradient Accumulation steps = 1
10/25/2024 23:33:53 - INFO - __main__ -   Total optimization steps = 700
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Steps: 100%|██████████| 700/700 [05:03<00:00,  2.31it/s, loss=0.141, lr=0.0001]Model weights saved in /tmp/train/output/sd35_large_train_replicate/pytorch_lora_weights.safetensors
Loading pipeline components...:   0%|          | 0/9 [00:00<?, ?it/s]{'base_image_seq_len', 'base_shift', 'max_shift', 'max_image_seq_len', 'use_dynamic_shifting'} was not found in config. Values will be initialized to default values.
Loaded scheduler as FlowMatchEulerDiscreteScheduler from `scheduler` subfolder of stable-diffusion-3.5-large.
Loaded text_encoder as CLIPTextModelWithProjection from `text_encoder` subfolder of stable-diffusion-3.5-large.
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Loading checkpoint shards: 100%|██████████| 2/2 [00:09<00:00,  4.79s/it]
Loaded text_encoder_3 as T5EncoderModel from `text_encoder_3` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...:  33%|███▎      | 3/9 [00:09<00:24,  4.12s/it]{'dual_attention_layers'} was not found in config. Values will be initialized to default values.
Loaded transformer as SD3Transformer2DModel from `transformer` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...:  44%|████▍     | 4/9 [00:11<00:16,  3.27s/it]Loaded tokenizer as CLIPTokenizer from `tokenizer` subfolder of stable-diffusion-3.5-large.
Loaded tokenizer_3 as T5TokenizerFast from `tokenizer_3` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...:  67%|██████▋   | 6/9 [00:12<00:04,  1.64s/it]Loaded tokenizer_2 as CLIPTokenizer from `tokenizer_2` subfolder of stable-diffusion-3.5-large.
Loaded text_encoder_2 as CLIPTextModelWithProjection from `text_encoder_2` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...:  89%|████████▉ | 8/9 [00:13<00:01,  1.30s/it]Loaded vae as AutoencoderKL from `vae` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...: 100%|██████████| 9/9 [00:13<00:00,  1.53s/it]
Steps: 100%|██████████| 700/700 [05:18<00:00,  2.20it/s, loss=0.141, lr=0.0001]
./
./output/
./output/sd35_large_train_replicate/
./output/sd35_large_train_replicate/lora.safetensors

This example was created by a different version, lucataco/stable-diffusion-3.5-large-lora-trainer:cd6419a5.

Run time and cost

This model runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

Huggingface Diffusers Dreambooth LoRA Trainer for StableDiffusion3.5-Large

About

Cog implementation of the Diffusers LoRA Trainer for StableDiffusion3.5-Large

Follow me on Twitter/X @lucataco93

How to use

Upload images as a zip file, and hit run to train a LoRA. At this time, it is advised that you also add a HF_token that has write access to Huggingface to upload your trained LoRA.

An example of a trained LoRA style: lucataco/SD3.5-Large-yarn

An example of a trained LoRA object: lucataco/SD3.5-Large-queso

Run your LoRA

Once your LoRA is in Huggingface, go to the model and grab the full URL to the LoRA

An example of a prediction run with a LoRA can be found here: frog

Licensing and commercial use

If you generate images on Replicate with StableDiffusion3.5-Large models and their fine-tunes, then you can use the images commercially.

If you download the weights off Replicate and generate images on your own computer, you can’t use the images commercially.