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

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

Cold

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

Run with an API

Playground API Examples README Versions

Prediction

lucataco/stable-diffusion-3.5-large-lora-trainer:cd6419a53b69fd410a912d945fa481a2a9ecfc4ab93062ed76c53f6e617f89e9

Model

lucataco/stable-diffusion-3.5-large-lora-trainer:cd6419a5

ng14j2cff1rj40cjrr2vbz667m

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

7m 28s

Created

6 months ago by @lucataco

Input

rank: 16
backend: no
optimizer: AdamW
resolution: 768
input_images: 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

{
  "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
}

Install Replicate’s Node.js client library:

npm install replicate

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:cd6419a53b69fd410a912d945fa481a2a9ecfc4ab93062ed76c53f6e617f89e9",
  {
    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
    }
  }
);

// 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

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:cd6419a53b69fd410a912d945fa481a2a9ecfc4ab93062ed76c53f6e617f89e9",
    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
    }
)
print(output)

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

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": "cd6419a53b69fd410a912d945fa481a2a9ecfc4ab93062ed76c53f6e617f89e9",
    "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
    }
  }' \
  https://api.replicate.com/v1/predictions

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

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]
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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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:  50%|█████     | 1/2 [00:04<00:04,  4.98s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:09<00:00,  4.75s/it]
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

Prediction

lucataco/stable-diffusion-3.5-large-lora-trainer:c60e90e9737b8c6b9775e2bc3c167d62da8a7bd7000c9244572b1b5193f3c27a

Model

lucataco/stable-diffusion-3.5-large-lora-trainer:c60e90e9

2vm6qf6cfxrj20cjrxcavwtwb0

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

7m 15s

Created

6 months ago by @lucataco

Input

rank: 16
backend: no
hf_token: ████████████████████
This value was redacted after being sent to the model.
optimizer: AdamW
resolution: 768
hub_model_id: lucataco/SD3.5-Large-queso
input_images: queso2.zip
lr_scheduler: constant
learning_rate: 0.0001
instance_prompt: a photo of QSO dog
max_train_steps: 700
train_batch_size: 1
gradient_accumulation_steps: 1

{
  "rank": 16,
  "backend": "no",
  "hf_token": "[REDACTED]",
  "optimizer": "AdamW",
  "resolution": 768,
  "hub_model_id": "lucataco/SD3.5-Large-queso",
  "input_images": "https://replicate.delivery/pbxt/LlvEhC2BWWxpMKYYzCHHf2TSdJdZ15on8LEkbLY6evEC9bVd/queso2.zip",
  "lr_scheduler": "constant",
  "learning_rate": 0.0001,
  "instance_prompt": "a photo of QSO dog",
  "max_train_steps": 700,
  "train_batch_size": 1,
  "gradient_accumulation_steps": 1
}

Install Replicate’s Node.js client library:

npm install replicate

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:c60e90e9737b8c6b9775e2bc3c167d62da8a7bd7000c9244572b1b5193f3c27a",
  {
    input: {
      rank: 16,
      backend: "no",
      hf_token: "[REDACTED]",
      optimizer: "AdamW",
      resolution: 768,
      hub_model_id: "lucataco/SD3.5-Large-queso",
      input_images: "https://replicate.delivery/pbxt/LlvEhC2BWWxpMKYYzCHHf2TSdJdZ15on8LEkbLY6evEC9bVd/queso2.zip",
      lr_scheduler: "constant",
      learning_rate: 0.0001,
      instance_prompt: "a photo of QSO dog",
      max_train_steps: 700,
      train_batch_size: 1,
      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

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:c60e90e9737b8c6b9775e2bc3c167d62da8a7bd7000c9244572b1b5193f3c27a",
    input={
        "rank": 16,
        "backend": "no",
        "hf_token": "[REDACTED]",
        "optimizer": "AdamW",
        "resolution": 768,
        "hub_model_id": "lucataco/SD3.5-Large-queso",
        "input_images": "https://replicate.delivery/pbxt/LlvEhC2BWWxpMKYYzCHHf2TSdJdZ15on8LEkbLY6evEC9bVd/queso2.zip",
        "lr_scheduler": "constant",
        "learning_rate": 0.0001,
        "instance_prompt": "a photo of QSO dog",
        "max_train_steps": 700,
        "train_batch_size": 1,
        "gradient_accumulation_steps": 1
    }
)
print(output)

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

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": "c60e90e9737b8c6b9775e2bc3c167d62da8a7bd7000c9244572b1b5193f3c27a",
    "input": {
      "rank": 16,
      "backend": "no",
      "hf_token": "[REDACTED]",
      "optimizer": "AdamW",
      "resolution": 768,
      "hub_model_id": "lucataco/SD3.5-Large-queso",
      "input_images": "https://replicate.delivery/pbxt/LlvEhC2BWWxpMKYYzCHHf2TSdJdZ15on8LEkbLY6evEC9bVd/queso2.zip",
      "lr_scheduler": "constant",
      "learning_rate": 0.0001,
      "instance_prompt": "a photo of QSO dog",
      "max_train_steps": 700,
      "train_batch_size": 1,
      "gradient_accumulation_steps": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

trained_model.tar

{
  "completed_at": "2024-10-26T05:49:33.854994Z",
  "created_at": "2024-10-26T05:42:18.495000Z",
  "data_removed": false,
  "error": null,
  "id": "2vm6qf6cfxrj20cjrxcavwtwb0",
  "input": {
    "rank": 16,
    "backend": "no",
    "hf_token": "[REDACTED]",
    "optimizer": "AdamW",
    "resolution": 768,
    "hub_model_id": "lucataco/SD3.5-Large-queso",
    "input_images": "https://replicate.delivery/pbxt/LlvEhC2BWWxpMKYYzCHHf2TSdJdZ15on8LEkbLY6evEC9bVd/queso2.zip",
    "lr_scheduler": "constant",
    "learning_rate": 0.0001,
    "instance_prompt": "a photo of QSO dog",
    "max_train_steps": 700,
    "train_batch_size": 1,
    "gradient_accumulation_steps": 1
  },
  "logs": "Using seed: 3962600180\nExtracted 12 files from zip to input_images\nThe token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\nToken is valid (permission: write).\nYour token has been saved to /root/.cache/huggingface/token\nLogin successful\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', 'a photo of QSO dog', '--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', '3962600180', '--logging_dir', '/tmp/logs', '--push_to_hub', '--hub_token', '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', '--hub_model_id', 'lucataco/SD3.5-Large-queso']\n10/26/2024 05:43:25 - 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{'use_dynamic_shifting', 'max_shift', 'base_shift', 'base_image_seq_len', 'max_image_seq_len'} 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,  3.70s/it]\nLoading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]\nLoading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.57s/it]\n{'dual_attention_layers'} was not found in config. Values will be initialized to default values.\n10/26/2024 05:44:12 - INFO - __main__ - ***** Running training *****\n10/26/2024 05:44:12 - INFO - __main__ -   Num examples = 12\n10/26/2024 05:44:12 - INFO - __main__ -   Num batches each epoch = 12\n10/26/2024 05:44:12 - INFO - __main__ -   Num Epochs = 59\n10/26/2024 05:44:12 - INFO - __main__ -   Instantaneous batch size per device = 1\n10/26/2024 05:44:12 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 1\n10/26/2024 05:44:12 - INFO - __main__ -   Gradient Accumulation steps = 1\n10/26/2024 05:44:12 - INFO - __main__ -   Total optimization steps = 700\nSteps:   0%|          | 0/700 [00:00<?, ?it/s]\nSteps:   0%|          | 1/700 [00:00<07:39,  1.52it/s]\nSteps:   0%|          | 1/700 [00:00<07:39,  1.52it/s, loss=0.139, lr=0.0001]\nSteps:   0%|          | 2/700 [00:01<05:55,  1.96it/s, loss=0.139, lr=0.0001]\nSteps:   0%|          | 2/700 [00:01<05:55,  1.96it/s, loss=0.0678, lr=0.0001]\nSteps: 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100%|█████████▉| 699/700 [05:03<00:00,  2.30it/s, loss=0.0533, lr=0.0001]\nSteps: 100%|█████████▉| 699/700 [05:03<00:00,  2.30it/s, loss=0.101, lr=0.0001] \nSteps: 100%|██████████| 700/700 [05:03<00:00,  2.31it/s, loss=0.101, lr=0.0001]\nSteps: 100%|██████████| 700/700 [05:03<00:00,  2.31it/s, loss=0.0975, 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[ALoaded tokenizer_2 as CLIPTokenizer from `tokenizer_2` subfolder of stable-diffusion-3.5-large.\n{'use_dynamic_shifting', 'max_shift', 'base_shift', 'base_image_seq_len', 'max_image_seq_len'} 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 tokenizer as CLIPTokenizer from `tokenizer` subfolder of stable-diffusion-3.5-large.\nLoading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\u001b[A\nLoading checkpoint shards:  50%|█████     | 1/2 [00:05<00:05,  5.05s/it]\u001b[A\u001b[A\nLoading checkpoint shards: 100%|██████████| 2/2 [00:09<00:00,  4.79s/it]\u001b[A\u001b[A\nLoading checkpoint shards: 100%|██████████| 2/2 [00:09<00:00,  4.83s/it]\nLoaded text_encoder_3 as T5EncoderModel from `text_encoder_3` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  44%|████▍     | 4/9 [00:09<00:12,  2.45s/it]\u001b[ALoaded text_encoder_2 as CLIPTextModelWithProjection from `text_encoder_2` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  56%|█████▌    | 5/9 [00:11<00:08,  2.20s/it]\u001b[ALoaded text_encoder as CLIPTextModelWithProjection from `text_encoder` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  67%|██████▋   | 6/9 [00:11<00:05,  1.70s/it]\u001b[ALoaded tokenizer_3 as T5TokenizerFast from `tokenizer_3` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  78%|███████▊  | 7/9 [00:11<00:02,  1.28s/it]\u001b[ALoaded vae as AutoencoderKL from `vae` subfolder of stable-diffusion-3.5-large.\n{'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...: 100%|██████████| 9/9 [00:13<00:00,  1.14s/it]\u001b[A\nLoading pipeline components...: 100%|██████████| 9/9 [00:13<00:00,  1.53s/it]\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  1.41it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  1.41it/s]\nSteps: 100%|██████████| 700/700 [05:20<00:00,  2.19it/s, loss=0.0975, lr=0.0001]\n./\n./output/\n./output/sd35_large_train_replicate/\n./output/sd35_large_train_replicate/README.md\n./output/sd35_large_train_replicate/lora.safetensors",
  "metrics": {
    "predict_time": 377.992972413,
    "total_time": 435.359994
  },
  "output": "https://replicate.delivery/yhqm/knBxQefxU6pEtUucVGpces44ewBLO2UOIUukO3wf9PovdaUdC/trained_model.tar",
  "started_at": "2024-10-26T05:43:15.862021Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/qoxq-gaqff73bfy2r6kj3352e42p2hvpp34wvryz5jyihe4n7j3u4qhta",
    "get": "https://api.replicate.com/v1/predictions/2vm6qf6cfxrj20cjrxcavwtwb0",
    "cancel": "https://api.replicate.com/v1/predictions/2vm6qf6cfxrj20cjrxcavwtwb0/cancel"
  },
  "version": "c60e90e9737b8c6b9775e2bc3c167d62da8a7bd7000c9244572b1b5193f3c27a"
}

Generated in

6 minutes 18 seconds

Tweak it ShareReport

Using seed: 3962600180
Extracted 12 files from zip to input_images
The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.
Token is valid (permission: write).
Your token has been saved to /root/.cache/huggingface/token
Login successful
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', 'a photo of QSO dog', '--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', '3962600180', '--logging_dir', '/tmp/logs', '--push_to_hub', '--hub_token', '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', '--hub_model_id', 'lucataco/SD3.5-Large-queso']
10/26/2024 05:43:25 - 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.
{'use_dynamic_shifting', 'max_shift', 'base_shift', 'base_image_seq_len', 'max_image_seq_len'} was not found in config. Values will be initialized to default values.
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{'dual_attention_layers'} was not found in config. Values will be initialized to default values.
10/26/2024 05:44:12 - INFO - __main__ - ***** Running training *****
10/26/2024 05:44:12 - INFO - __main__ -   Num examples = 12
10/26/2024 05:44:12 - INFO - __main__ -   Num batches each epoch = 12
10/26/2024 05:44:12 - INFO - __main__ -   Num Epochs = 59
10/26/2024 05:44:12 - INFO - __main__ -   Instantaneous batch size per device = 1
10/26/2024 05:44:12 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 1
10/26/2024 05:44:12 - INFO - __main__ -   Gradient Accumulation steps = 1
10/26/2024 05:44:12 - INFO - __main__ -   Total optimization steps = 700
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Steps: 100%|██████████| 700/700 [05:03<00:00,  2.31it/s, loss=0.0975, 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]Loaded tokenizer_2 as CLIPTokenizer from `tokenizer_2` subfolder of stable-diffusion-3.5-large.
{'use_dynamic_shifting', 'max_shift', 'base_shift', 'base_image_seq_len', 'max_image_seq_len'} 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 tokenizer as CLIPTokenizer from `tokenizer` subfolder of stable-diffusion-3.5-large.
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Loaded text_encoder_3 as T5EncoderModel from `text_encoder_3` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...:  44%|████▍     | 4/9 [00:09<00:12,  2.45s/it]Loaded text_encoder_2 as CLIPTextModelWithProjection from `text_encoder_2` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...:  56%|█████▌    | 5/9 [00:11<00:08,  2.20s/it]Loaded text_encoder as CLIPTextModelWithProjection from `text_encoder` subfolder of stable-diffusion-3.5-large.
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{'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.
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./
./output/
./output/sd35_large_train_replicate/
./output/sd35_large_train_replicate/README.md
./output/sd35_large_train_replicate/lora.safetensors

Prediction

lucataco/stable-diffusion-3.5-large-lora-trainer:6ebda45af5b9c30edee3149cc1624b7f7cae8fab7c692e2c51d82f5fed3198ee

Model

lucataco/stable-diffusion-3.5-large-lora-trainer:6ebda45a

6ty97311w9rj00cjx4pbwngcp0

Status

Succeeded

Source

API

Hardware

A100 (80GB)

Total duration

18m 22s

Created

6 months ago by @lucataco

Input

rank: 16
backend: no
hf_token: ████████████████████
This value was redacted after being sent to the model.
optimizer: prodigy
resolution: 768
hub_model_id: lucataco/SD3.5-Large-yarn-2
input_images: yarn.zip
lr_scheduler: constant
learning_rate: 1
instance_prompt: Frog, yarn art style
max_train_steps: 1500
text_encoder_lr: 1
train_batch_size: 1
train_text_encoder
gradient_accumulation_steps: 1

{
  "rank": 16,
  "backend": "no",
  "hf_token": "[REDACTED]",
  "optimizer": "prodigy",
  "resolution": 768,
  "hub_model_id": "lucataco/SD3.5-Large-yarn-2",
  "input_images": "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
  "lr_scheduler": "constant",
  "learning_rate": 1,
  "instance_prompt": "Frog, yarn art style",
  "max_train_steps": 1500,
  "text_encoder_lr": 1,
  "train_batch_size": 1,
  "train_text_encoder": true,
  "gradient_accumulation_steps": 1
}

Install Replicate’s Node.js client library:

npm install replicate

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",
      hf_token: "[REDACTED]",
      optimizer: "prodigy",
      resolution: 768,
      hub_model_id: "lucataco/SD3.5-Large-yarn-2",
      input_images: "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
      lr_scheduler: "constant",
      learning_rate: 1,
      instance_prompt: "Frog, yarn art style",
      max_train_steps: 1500,
      text_encoder_lr: 1,
      train_batch_size: 1,
      train_text_encoder: true,
      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

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",
        "hf_token": "[REDACTED]",
        "optimizer": "prodigy",
        "resolution": 768,
        "hub_model_id": "lucataco/SD3.5-Large-yarn-2",
        "input_images": "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
        "lr_scheduler": "constant",
        "learning_rate": 1,
        "instance_prompt": "Frog, yarn art style",
        "max_train_steps": 1500,
        "text_encoder_lr": 1,
        "train_batch_size": 1,
        "train_text_encoder": True,
        "gradient_accumulation_steps": 1
    }
)
print(output)

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

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",
      "hf_token": "[REDACTED]",
      "optimizer": "prodigy",
      "resolution": 768,
      "hub_model_id": "lucataco/SD3.5-Large-yarn-2",
      "input_images": "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
      "lr_scheduler": "constant",
      "learning_rate": 1,
      "instance_prompt": "Frog, yarn art style",
      "max_train_steps": 1500,
      "text_encoder_lr": 1,
      "train_batch_size": 1,
      "train_text_encoder": true,
      "gradient_accumulation_steps": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

trained_model.tar

{
  "completed_at": "2024-11-01T19:38:58.822748Z",
  "created_at": "2024-11-01T19:20:36.578000Z",
  "data_removed": false,
  "error": null,
  "id": "6ty97311w9rj00cjx4pbwngcp0",
  "input": {
    "rank": 16,
    "backend": "no",
    "hf_token": "[REDACTED]",
    "optimizer": "prodigy",
    "resolution": 768,
    "hub_model_id": "lucataco/SD3.5-Large-yarn-2",
    "input_images": "https://replicate.delivery/pbxt/LrJveDd3TVKraYSxEWkMl0txKP39KdIBof5EO2IAsuTNIrFU/yarn.zip",
    "lr_scheduler": "constant",
    "learning_rate": 1,
    "instance_prompt": "Frog, yarn art style",
    "max_train_steps": 1500,
    "text_encoder_lr": 1,
    "train_batch_size": 1,
    "train_text_encoder": true,
    "gradient_accumulation_steps": 1
  },
  "logs": "Using seed: 2592116838\nExtracted 16 files from zip to input_images\nThe token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\nToken is valid (permission: write).\nYour token has been saved to /root/.cache/huggingface/token\nLogin successful\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', 'prodigy', '--learning_rate', '1.0', '--text_encoder_lr', '1.0', '--lr_scheduler', 'constant', '--lr_warmup_steps', '0', '--max_train_steps', '1500', '--checkpointing_steps', '1501', '--seed', '2592116838', '--logging_dir', '/tmp/logs', '--push_to_hub', '--hub_token', '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', '--hub_model_id', 'lucataco/SD3.5-Large-yarn-2', '--train_text_encoder']\n11/01/2024 19:21:43 - 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{'max_image_seq_len', 'base_image_seq_len', 'max_shift', 'base_shift', '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,  3.58s/it]\nLoading checkpoint shards: 100%|██████████| 2/2 [00:06<00:00,  3.44s/it]\nLoading checkpoint shards: 100%|██████████| 2/2 [00:06<00:00,  3.46s/it]\n{'dual_attention_layers'} was not found in config. Values will be initialized to default values.\n11/01/2024 19:22:26 - WARNING - __main__ - Learning rates were provided both for the transformer and the text encoder- e.g. text_encoder_lr: 1.0 and learning_rate: 1.0. When using prodigy only learning_rate is used as the initial learning rate.\nUsing decoupled weight decay\n11/01/2024 19:22:27 - INFO - __main__ - ***** Running training *****\n11/01/2024 19:22:27 - INFO - __main__ -   Num examples = 16\n11/01/2024 19:22:27 - INFO - __main__ -   Num batches each epoch = 16\n11/01/2024 19:22:27 - INFO - __main__ -   Num Epochs = 94\n11/01/2024 19:22:27 - INFO - __main__ -   Instantaneous batch size per device = 1\n11/01/2024 19:22:27 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 1\n11/01/2024 19:22:27 - INFO - __main__ -   Gradient Accumulation steps = 1\n11/01/2024 19:22:27 - INFO - __main__ -   Total optimization steps = 1500\nSteps:   0%|          | 0/1500 [00:00<?, ?it/s]\nSteps:   0%|          | 1/1500 [00:01<42:23,  1.70s/it]\nSteps:   0%|          | 1/1500 [00:01<42:23,  1.70s/it, loss=0.0865, lr=1]\nSteps:   0%|          | 2/1500 [00:02<26:52,  1.08s/it, loss=0.0865, lr=1]\nSteps:   0%|          | 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loss=0.186, lr=1]\nSteps: 100%|█████████▉| 1498/1500 [16:10<00:01,  1.55it/s, loss=0.186, lr=1]\nSteps: 100%|█████████▉| 1498/1500 [16:10<00:01,  1.55it/s, loss=0.15, lr=1] \nSteps: 100%|█████████▉| 1499/1500 [16:10<00:00,  1.55it/s, loss=0.15, lr=1]\nSteps: 100%|█████████▉| 1499/1500 [16:10<00:00,  1.55it/s, loss=0.105, lr=1]\nSteps: 100%|██████████| 1500/1500 [16:11<00:00,  1.55it/s, loss=0.105, lr=1]\nSteps: 100%|██████████| 1500/1500 [16:11<00:00,  1.55it/s, loss=0.0747, lr=1]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[ALoaded tokenizer as CLIPTokenizer from `tokenizer` subfolder of stable-diffusion-3.5-large.\nLoaded tokenizer_2 as CLIPTokenizer from `tokenizer_2` subfolder of stable-diffusion-3.5-large.\nLoading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\u001b[A\nLoading checkpoint shards:  50%|█████     | 1/2 [00:05<00:05,  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Values will be initialized to default values.\nLoaded scheduler as FlowMatchEulerDiscreteScheduler from `scheduler` 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...:  67%|██████▋   | 6/9 [00:13<00:05,  1.91s/it]\u001b[ALoaded text_encoder as CLIPTextModelWithProjection from `text_encoder` subfolder of stable-diffusion-3.5-large.\nLoading pipeline components...:  78%|███████▊  | 7/9 [00:14<00:03,  1.53s/it]\u001b[ALoaded vae as AutoencoderKL from `vae` 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...: 100%|██████████| 9/9 [00:14<00:00,  1.07it/s]\u001b[A\nLoading pipeline components...: 100%|██████████| 9/9 [00:14<00:00,  1.59s/it]\nLoading text_encoder.\nLoading text_encoder_2.\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:01<00:00,  1.11s/it]\u001b[A\n100%|██████████| 1/1 [00:01<00:00,  1.11s/it]\nSteps: 100%|██████████| 1500/1500 [16:29<00:00,  1.52it/s, loss=0.0747, lr=1]\n./\n./output/\n./output/sd35_large_train_replicate/\n./output/sd35_large_train_replicate/README.md\n./output/sd35_large_train_replicate/lora.safetensors",
  "metrics": {
    "predict_time": 1045.489331425,
    "total_time": 1102.244748
  },
  "output": "https://replicate.delivery/yhqm/Wq2g9S3byRKRA19jSIgbAe4q6nXEefe2oz8XeQTKPCjXKwldC/trained_model.tar",
  "started_at": "2024-11-01T19:21:33.333417Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/qoxq-7hrdyy752fgxwlpsj3565cc46qad5zjvvcapoigi76xv7ez4rzoa",
    "get": "https://api.replicate.com/v1/predictions/6ty97311w9rj00cjx4pbwngcp0",
    "cancel": "https://api.replicate.com/v1/predictions/6ty97311w9rj00cjx4pbwngcp0/cancel"
  },
  "version": "6ebda45af5b9c30edee3149cc1624b7f7cae8fab7c692e2c51d82f5fed3198ee"
}

Generated in

17 minutes 25 seconds

Tweak it ShareReport

Using seed: 2592116838
Extracted 16 files from zip to input_images
The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.
Token is valid (permission: write).
Your token has been saved to /root/.cache/huggingface/token
Login successful
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', 'prodigy', '--learning_rate', '1.0', '--text_encoder_lr', '1.0', '--lr_scheduler', 'constant', '--lr_warmup_steps', '0', '--max_train_steps', '1500', '--checkpointing_steps', '1501', '--seed', '2592116838', '--logging_dir', '/tmp/logs', '--push_to_hub', '--hub_token', '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', '--hub_model_id', 'lucataco/SD3.5-Large-yarn-2', '--train_text_encoder']
11/01/2024 19:21:43 - 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.
{'max_image_seq_len', 'base_image_seq_len', 'max_shift', 'base_shift', '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]
Loading checkpoint shards:  50%|█████     | 1/2 [00:03<00:03,  3.58s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:06<00:00,  3.44s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:06<00:00,  3.46s/it]
{'dual_attention_layers'} was not found in config. Values will be initialized to default values.
11/01/2024 19:22:26 - WARNING - __main__ - Learning rates were provided both for the transformer and the text encoder- e.g. text_encoder_lr: 1.0 and learning_rate: 1.0. When using prodigy only learning_rate is used as the initial learning rate.
Using decoupled weight decay
11/01/2024 19:22:27 - INFO - __main__ - ***** Running training *****
11/01/2024 19:22:27 - INFO - __main__ -   Num examples = 16
11/01/2024 19:22:27 - INFO - __main__ -   Num batches each epoch = 16
11/01/2024 19:22:27 - INFO - __main__ -   Num Epochs = 94
11/01/2024 19:22:27 - INFO - __main__ -   Instantaneous batch size per device = 1
11/01/2024 19:22:27 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 1
11/01/2024 19:22:27 - INFO - __main__ -   Gradient Accumulation steps = 1
11/01/2024 19:22:27 - INFO - __main__ -   Total optimization steps = 1500
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Steps: 100%|██████████| 1500/1500 [16:11<00:00,  1.55it/s, loss=0.0747, lr=1]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]Loaded tokenizer as CLIPTokenizer from `tokenizer` subfolder of stable-diffusion-3.5-large.
Loaded tokenizer_2 as CLIPTokenizer from `tokenizer_2` subfolder of stable-diffusion-3.5-large.
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Loaded text_encoder_3 as T5EncoderModel from `text_encoder_3` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...:  33%|███▎      | 3/9 [00:10<00:20,  3.36s/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 [00:12<00:14,  2.90s/it]{'max_image_seq_len', 'base_image_seq_len', 'max_shift', 'base_shift', '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_2 as CLIPTextModelWithProjection from `text_encoder_2` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...:  67%|██████▋   | 6/9 [00:13<00:05,  1.91s/it]Loaded text_encoder as CLIPTextModelWithProjection from `text_encoder` subfolder of stable-diffusion-3.5-large.
Loading pipeline components...:  78%|███████▊  | 7/9 [00:14<00:03,  1.53s/it]Loaded vae as AutoencoderKL from `vae` subfolder of stable-diffusion-3.5-large.
Loaded tokenizer_3 as T5TokenizerFast from `tokenizer_3` subfolder of stable-diffusion-3.5-large.
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Loading text_encoder.
Loading text_encoder_2.
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./
./output/
./output/sd35_large_train_replicate/
./output/sd35_large_train_replicate/README.md
./output/sd35_large_train_replicate/lora.safetensors

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