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To save GPU memory and get more speed, set `torch_dtype=torch.float16` to load and run the model weights directly with half-precision weights.
```Python
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe = pipe.to("cuda")
```
> [!WARNING]
> Don't use [torch.autocast](https://pytorch.org/docs/stable/amp.html#torch.autocast) in any of the pipelines as it can lead to black images and is always slower than pure float16 precision. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/fp16.md | https://huggingface.co./docs/diffusers/en/optimization/fp16/#half-precision-weights | #half-precision-weights | .md | 15_3 |
You could also use a distilled Stable Diffusion model and autoencoder to speed up inference. During distillation, many of the UNet's residual and attention blocks are shed to reduce the model size by 51% and improve latency on CPU/GPU by 43%. The distilled model is faster and uses less memory while generating images of comparable quality to the full Stable Diffusion model.
> [!TIP]
> Read the [Open-sourcing Knowledge Distillation Code and Weights of SD-Small and SD-Tiny](https://huggingface.co./blog/sd_distillation) blog post to learn more about how knowledge distillation training works to produce a faster, smaller, and cheaper generative model.
The inference times below are obtained from generating 4 images from the prompt "a photo of an astronaut riding a horse on mars" with 25 PNDM steps on a NVIDIA A100. Each generation is repeated 3 times with the distilled Stable Diffusion v1.4 model by [Nota AI](https://hf.co/nota-ai).
| setup | latency | speed-up |
|------------------------------|---------|----------|
| baseline | 6.37s | x1 |
| distilled | 4.18s | x1.52 |
| distilled + tiny autoencoder | 3.83s | x1.66 |
Let's load the distilled Stable Diffusion model and compare it against the original Stable Diffusion model.
```py
from diffusers import StableDiffusionPipeline
import torch
distilled = StableDiffusionPipeline.from_pretrained(
"nota-ai/bk-sdm-small", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")
prompt = "a golden vase with different flowers"
generator = torch.manual_seed(2023)
image = distilled("a golden vase with different flowers", num_inference_steps=25, generator=generator).images[0]
image
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co./datasets/huggingface/documentation-images/resolve/main/diffusers/original_sd.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original Stable Diffusion</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co./datasets/huggingface/documentation-images/resolve/main/diffusers/distilled_sd.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">distilled Stable Diffusion</figcaption>
</div>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/fp16.md | https://huggingface.co./docs/diffusers/en/optimization/fp16/#distilled-model | #distilled-model | .md | 15_4 |
To speed inference up even more, replace the autoencoder with a [distilled version](https://huggingface.co./sayakpaul/taesdxl-diffusers) of it.
```py
import torch
from diffusers import AutoencoderTiny, StableDiffusionPipeline
distilled = StableDiffusionPipeline.from_pretrained(
"nota-ai/bk-sdm-small", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")
distilled.vae = AutoencoderTiny.from_pretrained(
"sayakpaul/taesd-diffusers", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")
prompt = "a golden vase with different flowers"
generator = torch.manual_seed(2023)
image = distilled("a golden vase with different flowers", num_inference_steps=25, generator=generator).images[0]
image
```
<div class="flex justify-center">
<div>
<img class="rounded-xl" src="https://huggingface.co./datasets/huggingface/documentation-images/resolve/main/diffusers/distilled_sd_vae.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">distilled Stable Diffusion + Tiny AutoEncoder</figcaption>
</div>
</div>
More tiny autoencoder models for other Stable Diffusion models, like Stable Diffusion 3, are available from [madebyollin](https://huggingface.co./madebyollin). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/fp16.md | https://huggingface.co./docs/diffusers/en/optimization/fp16/#tiny-autoencoder | #tiny-autoencoder | .md | 15_5 |
[xDiT](https://github.com/xdit-project/xDiT) is an inference engine designed for the large scale parallel deployment of Diffusion Transformers (DiTs). xDiT provides a suite of efficient parallel approaches for Diffusion Models, as well as GPU kernel accelerations.
There are four parallel methods supported in xDiT, including [Unified Sequence Parallelism](https://arxiv.org/abs/2405.07719), [PipeFusion](https://arxiv.org/abs/2405.14430), CFG parallelism and data parallelism. The four parallel methods in xDiT can be configured in a hybrid manner, optimizing communication patterns to best suit the underlying network hardware.
Optimization orthogonal to parallelization focuses on accelerating single GPU performance. In addition to utilizing well-known Attention optimization libraries, we leverage compilation acceleration technologies such as torch.compile and onediff.
The overview of xDiT is shown as follows.
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/xDiT/documentation-images/resolve/main/methods/xdit_overview.png">
</div>
You can install xDiT using the following command:
```bash
pip install xfuser
```
Here's an example of using xDiT to accelerate inference of a Diffusers model.
```diff
import torch
from diffusers import StableDiffusion3Pipeline
from xfuser import xFuserArgs, xDiTParallel
from xfuser.config import FlexibleArgumentParser
from xfuser.core.distributed import get_world_group
def main():
+ parser = FlexibleArgumentParser(description="xFuser Arguments")
+ args = xFuserArgs.add_cli_args(parser).parse_args()
+ engine_args = xFuserArgs.from_cli_args(args)
+ engine_config, input_config = engine_args.create_config()
local_rank = get_world_group().local_rank
pipe = StableDiffusion3Pipeline.from_pretrained(
pretrained_model_name_or_path=engine_config.model_config.model,
torch_dtype=torch.float16,
).to(f"cuda:{local_rank}")
# do anything you want with pipeline here
+ pipe = xDiTParallel(pipe, engine_config, input_config)
pipe(
height=input_config.height,
width=input_config.height,
prompt=input_config.prompt,
num_inference_steps=input_config.num_inference_steps,
output_type=input_config.output_type,
generator=torch.Generator(device="cuda").manual_seed(input_config.seed),
)
+ if input_config.output_type == "pil":
+ pipe.save("results", "stable_diffusion_3")
if __name__ == "__main__":
main()
```
As you can see, we only need to use xFuserArgs from xDiT to get configuration parameters, and pass these parameters along with the pipeline object from the Diffusers library into xDiTParallel to complete the parallelization of a specific pipeline in Diffusers.
xDiT runtime parameters can be viewed in the command line using `-h`, and you can refer to this [usage](https://github.com/xdit-project/xDiT?tab=readme-ov-file#2-usage) example for more details.
xDiT needs to be launched using torchrun to support its multi-node, multi-GPU parallel capabilities. For example, the following command can be used for 8-GPU parallel inference:
```bash
torchrun --nproc_per_node=8 ./inference.py --model models/FLUX.1-dev --data_parallel_degree 2 --ulysses_degree 2 --ring_degree 2 --prompt "A snowy mountain" "A small dog" --num_inference_steps 50
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/xdit.md | https://huggingface.co./docs/diffusers/en/optimization/xdit/#xdit | #xdit | .md | 16_0 |
A subset of Diffusers models are supported in xDiT, such as Flux.1, Stable Diffusion 3, etc. The latest supported models can be found [here](https://github.com/xdit-project/xDiT?tab=readme-ov-file#-supported-dits). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/xdit.md | https://huggingface.co./docs/diffusers/en/optimization/xdit/#supported-models | #supported-models | .md | 16_1 |
We tested different models on various machines, and here is some of the benchmark data. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/xdit.md | https://huggingface.co./docs/diffusers/en/optimization/xdit/#benchmark | #benchmark | .md | 16_2 |
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/xDiT/documentation-images/resolve/main/performance/flux/Flux-2k-L40.png">
</div>
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/xDiT/documentation-images/resolve/main/performance/flux/Flux-2K-A100.png">
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/xdit.md | https://huggingface.co./docs/diffusers/en/optimization/xdit/#flux1-schnell | #flux1-schnell | .md | 16_3 |
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/xDiT/documentation-images/resolve/main/performance/sd3/L40-SD3.png">
</div>
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/xDiT/documentation-images/resolve/main/performance/sd3/A100-SD3.png">
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/xdit.md | https://huggingface.co./docs/diffusers/en/optimization/xdit/#stable-diffusion-3 | #stable-diffusion-3 | .md | 16_4 |
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/xDiT/documentation-images/resolve/main/performance/hunuyuandit/L40-HunyuanDiT.png">
</div>
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/xDiT/documentation-images/resolve/main/performance/hunuyuandit/V100-HunyuanDiT.png">
</div>
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/xDiT/documentation-images/resolve/main/performance/hunuyuandit/T4-HunyuanDiT.png">
</div>
More detailed performance metric can be found on our [github page](https://github.com/xdit-project/xDiT?tab=readme-ov-file#perf). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/xdit.md | https://huggingface.co./docs/diffusers/en/optimization/xdit/#hunyuandit | #hunyuandit | .md | 16_5 |
[xDiT-project](https://github.com/xdit-project/xDiT)
[USP: A Unified Sequence Parallelism Approach for Long Context Generative AI](https://arxiv.org/abs/2405.07719)
[PipeFusion: Displaced Patch Pipeline Parallelism for Inference of Diffusion Transformer Models](https://arxiv.org/abs/2405.14430) | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/xdit.md | https://huggingface.co./docs/diffusers/en/optimization/xdit/#reference | #reference | .md | 16_6 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/mps.md | https://huggingface.co./docs/diffusers/en/optimization/mps/ | .md | 17_0 |
|
🤗 Diffusers is compatible with Apple silicon (M1/M2 chips) using the PyTorch [`mps`](https://pytorch.org/docs/stable/notes/mps.html) device, which uses the Metal framework to leverage the GPU on MacOS devices. You'll need to have:
- macOS computer with Apple silicon (M1/M2) hardware
- macOS 12.6 or later (13.0 or later recommended)
- arm64 version of Python
- [PyTorch 2.0](https://pytorch.org/get-started/locally/) (recommended) or 1.13 (minimum version supported for `mps`)
The `mps` backend uses PyTorch's `.to()` interface to move the Stable Diffusion pipeline on to your M1 or M2 device:
```python
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
pipe = pipe.to("mps")
# Recommended if your computer has < 64 GB of RAM
pipe.enable_attention_slicing()
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image
```
<Tip warning={true}>
Generating multiple prompts in a batch can [crash](https://github.com/huggingface/diffusers/issues/363) or fail to work reliably. We believe this is related to the [`mps`](https://github.com/pytorch/pytorch/issues/84039) backend in PyTorch. While this is being investigated, you should iterate instead of batching.
</Tip>
If you're using **PyTorch 1.13**, you need to "prime" the pipeline with an additional one-time pass through it. This is a temporary workaround for an issue where the first inference pass produces slightly different results than subsequent ones. You only need to do this pass once, and after just one inference step you can discard the result.
```diff
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to("mps")
pipe.enable_attention_slicing()
prompt = "a photo of an astronaut riding a horse on mars"
# First-time "warmup" pass if PyTorch version is 1.13
+ _ = pipe(prompt, num_inference_steps=1)
# Results match those from the CPU device after the warmup pass.
image = pipe(prompt).images[0]
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/mps.md | https://huggingface.co./docs/diffusers/en/optimization/mps/#metal-performance-shaders-mps | #metal-performance-shaders-mps | .md | 17_1 |
M1/M2 performance is very sensitive to memory pressure. When this occurs, the system automatically swaps if it needs to which significantly degrades performance.
To prevent this from happening, we recommend *attention slicing* to reduce memory pressure during inference and prevent swapping. This is especially relevant if your computer has less than 64GB of system RAM, or if you generate images at non-standard resolutions larger than 512×512 pixels. Call the [`~DiffusionPipeline.enable_attention_slicing`] function on your pipeline:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True).to("mps")
pipeline.enable_attention_slicing()
```
Attention slicing performs the costly attention operation in multiple steps instead of all at once. It usually improves performance by ~20% in computers without universal memory, but we've observed *better performance* in most Apple silicon computers unless you have 64GB of RAM or more. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/mps.md | https://huggingface.co./docs/diffusers/en/optimization/mps/#troubleshoot | #troubleshoot | .md | 17_2 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/onnx.md | https://huggingface.co./docs/diffusers/en/optimization/onnx/ | .md | 18_0 |
|
🤗 [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with ONNX Runtime. You'll need to install 🤗 Optimum with the following command for ONNX Runtime support:
```bash
pip install -q optimum["onnxruntime"]
```
This guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with ONNX Runtime. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/onnx.md | https://huggingface.co./docs/diffusers/en/optimization/onnx/#onnx-runtime | #onnx-runtime | .md | 18_1 |
To load and run inference, use the [`~optimum.onnxruntime.ORTStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the ONNX format on-the-fly, set `export=True`:
```python
from optimum.onnxruntime import ORTStableDiffusionPipeline
model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]
pipeline.save_pretrained("./onnx-stable-diffusion-v1-5")
```
<Tip warning={true}>
Generating multiple prompts in a batch seems to take too much memory. While we look into it, you may need to iterate instead of batching.
</Tip>
To export the pipeline in the ONNX format offline and use it later for inference,
use the [`optimum-cli export`](https://huggingface.co./docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:
```bash
optimum-cli export onnx --model stable-diffusion-v1-5/stable-diffusion-v1-5 sd_v15_onnx/
```
Then to perform inference (you don't have to specify `export=True` again):
```python
from optimum.onnxruntime import ORTStableDiffusionPipeline
model_id = "sd_v15_onnx"
pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]
```
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/optimum/documentation-images/resolve/main/onnxruntime/stable_diffusion_v1_5_ort_sail_boat.png">
</div>
You can find more examples in 🤗 Optimum [documentation](https://huggingface.co./docs/optimum/), and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/onnx.md | https://huggingface.co./docs/diffusers/en/optimization/onnx/#stable-diffusion | #stable-diffusion | .md | 18_2 |
To load and run inference with SDXL, use the [`~optimum.onnxruntime.ORTStableDiffusionXLPipeline`]:
```python
from optimum.onnxruntime import ORTStableDiffusionXLPipeline
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]
```
To export the pipeline in the ONNX format and use it later for inference, use the [`optimum-cli export`](https://huggingface.co./docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:
```bash
optimum-cli export onnx --model stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/
```
SDXL in the ONNX format is supported for text-to-image and image-to-image. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/onnx.md | https://huggingface.co./docs/diffusers/en/optimization/onnx/#stable-diffusion-xl | #stable-diffusion-xl | .md | 18_3 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/tome.md | https://huggingface.co./docs/diffusers/en/optimization/tome/ | .md | 19_0 |
|
[Token merging](https://huggingface.co./papers/2303.17604) (ToMe) merges redundant tokens/patches progressively in the forward pass of a Transformer-based network which can speed-up the inference latency of [`StableDiffusionPipeline`].
Install ToMe from `pip`:
```bash
pip install tomesd
```
You can use ToMe from the [`tomesd`](https://github.com/dbolya/tomesd) library with the [`apply_patch`](https://github.com/dbolya/tomesd?tab=readme-ov-file#usage) function:
```diff
from diffusers import StableDiffusionPipeline
import torch
import tomesd
pipeline = StableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")
+ tomesd.apply_patch(pipeline, ratio=0.5)
image = pipeline("a photo of an astronaut riding a horse on mars").images[0]
```
The `apply_patch` function exposes a number of [arguments](https://github.com/dbolya/tomesd#usage) to help strike a balance between pipeline inference speed and the quality of the generated tokens. The most important argument is `ratio` which controls the number of tokens that are merged during the forward pass.
As reported in the [paper](https://huggingface.co./papers/2303.17604), ToMe can greatly preserve the quality of the generated images while boosting inference speed. By increasing the `ratio`, you can speed-up inference even further, but at the cost of some degraded image quality.
To test the quality of the generated images, we sampled a few prompts from [Parti Prompts](https://parti.research.google/) and performed inference with the [`StableDiffusionPipeline`] with the following settings:
<div class="flex justify-center">
<img src="https://huggingface.co./datasets/diffusers/docs-images/resolve/main/tome/tome_samples.png">
</div>
We didn’t notice any significant decrease in the quality of the generated samples, and you can check out the generated samples in this [WandB report](https://wandb.ai/sayakpaul/tomesd-results/runs/23j4bj3i?workspace=). If you're interested in reproducing this experiment, use this [script](https://gist.github.com/sayakpaul/8cac98d7f22399085a060992f411ecbd). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/tome.md | https://huggingface.co./docs/diffusers/en/optimization/tome/#token-merging | #token-merging | .md | 19_1 |
We also benchmarked the impact of `tomesd` on the [`StableDiffusionPipeline`] with [xFormers](https://huggingface.co./docs/diffusers/optimization/xformers) enabled across several image resolutions. The results are obtained from A100 and V100 GPUs in the following development environment:
```bash
- `diffusers` version: 0.15.1
- Python version: 3.8.16
- PyTorch version (GPU?): 1.13.1+cu116 (True)
- Huggingface_hub version: 0.13.2
- Transformers version: 4.27.2
- Accelerate version: 0.18.0
- xFormers version: 0.0.16
- tomesd version: 0.1.2
```
To reproduce this benchmark, feel free to use this [script](https://gist.github.com/sayakpaul/27aec6bca7eb7b0e0aa4112205850335). The results are reported in seconds, and where applicable we report the speed-up percentage over the vanilla pipeline when using ToMe and ToMe + xFormers.
| **GPU** | **Resolution** | **Batch size** | **Vanilla** | **ToMe** | **ToMe + xFormers** |
|----------|----------------|----------------|-------------|----------------|---------------------|
| **A100** | 512 | 10 | 6.88 | 5.26 (+23.55%) | 4.69 (+31.83%) |
| | 768 | 10 | OOM | 14.71 | 11 |
| | | 8 | OOM | 11.56 | 8.84 |
| | | 4 | OOM | 5.98 | 4.66 |
| | | 2 | 4.99 | 3.24 (+35.07%) | 2.1 (+37.88%) |
| | | 1 | 3.29 | 2.24 (+31.91%) | 2.03 (+38.3%) |
| | 1024 | 10 | OOM | OOM | OOM |
| | | 8 | OOM | OOM | OOM |
| | | 4 | OOM | 12.51 | 9.09 |
| | | 2 | OOM | 6.52 | 4.96 |
| | | 1 | 6.4 | 3.61 (+43.59%) | 2.81 (+56.09%) |
| **V100** | 512 | 10 | OOM | 10.03 | 9.29 |
| | | 8 | OOM | 8.05 | 7.47 |
| | | 4 | 5.7 | 4.3 (+24.56%) | 3.98 (+30.18%) |
| | | 2 | 3.14 | 2.43 (+22.61%) | 2.27 (+27.71%) |
| | | 1 | 1.88 | 1.57 (+16.49%) | 1.57 (+16.49%) |
| | 768 | 10 | OOM | OOM | 23.67 |
| | | 8 | OOM | OOM | 18.81 |
| | | 4 | OOM | 11.81 | 9.7 |
| | | 2 | OOM | 6.27 | 5.2 |
| | | 1 | 5.43 | 3.38 (+37.75%) | 2.82 (+48.07%) |
| | 1024 | 10 | OOM | OOM | OOM |
| | | 8 | OOM | OOM | OOM |
| | | 4 | OOM | OOM | 19.35 |
| | | 2 | OOM | 13 | 10.78 |
| | | 1 | OOM | 6.66 | 5.54 |
As seen in the tables above, the speed-up from `tomesd` becomes more pronounced for larger image resolutions. It is also interesting to note that with `tomesd`, it is possible to run the pipeline on a higher resolution like 1024x1024. You may be able to speed-up inference even more with [`torch.compile`](torch2.0). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/tome.md | https://huggingface.co./docs/diffusers/en/optimization/tome/#benchmarks | #benchmarks | .md | 19_2 |
<!--Copyright 2024 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
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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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/ | .md | 20_0 |
|
[DreamBooth](https://huggingface.co./papers/2208.12242) is a training technique that updates the entire diffusion model by training on just a few images of a subject or style. It works by associating a special word in the prompt with the example images.
If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. You should have a GPU with >30GB of memory if you want to train faster with Flax.
This guide will explore the [train_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Navigate to the example folder with the training script and install the required dependencies for the script you're using:
<hfoptions id="installation">
<hfoption id="PyTorch">
```bash
cd examples/dreambooth
pip install -r requirements.txt
```
</hfoption>
<hfoption id="Flax">
```bash
cd examples/dreambooth
pip install -r requirements_flax.txt
```
</hfoption>
</hfoptions>
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
<Tip>
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) and let us know if you have any questions or concerns.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#dreambooth | #dreambooth | .md | 20_1 |
<Tip warning={true}>
DreamBooth is very sensitive to training hyperparameters, and it is easy to overfit. Read the [Training Stable Diffusion with Dreambooth using 🧨 Diffusers](https://huggingface.co./blog/dreambooth) blog post for recommended settings for different subjects to help you choose the appropriate hyperparameters.
</Tip>
The training script offers many parameters for customizing your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L228) function. The parameters are set with default values that should work pretty well out-of-the-box, but you can also set your own values in the training command if you'd like.
For example, to train in the bf16 format:
```bash
accelerate launch train_dreambooth.py \
--mixed_precision="bf16"
```
Some basic and important parameters to know and specify are:
- `--pretrained_model_name_or_path`: the name of the model on the Hub or a local path to the pretrained model
- `--instance_data_dir`: path to a folder containing the training dataset (example images)
- `--instance_prompt`: the text prompt that contains the special word for the example images
- `--train_text_encoder`: whether to also train the text encoder
- `--output_dir`: where to save the trained model
- `--push_to_hub`: whether to push the trained model to the Hub
- `--checkpointing_steps`: frequency of saving a checkpoint as the model trains; this is useful if for some reason training is interrupted, you can continue training from that checkpoint by adding `--resume_from_checkpoint` to your training command | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#script-parameters | #script-parameters | .md | 20_2 |
The [Min-SNR](https://huggingface.co./papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
```bash
accelerate launch train_dreambooth.py \
--snr_gamma=5.0
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#min-snr-weighting | #min-snr-weighting | .md | 20_3 |
Prior preservation loss is a method that uses a model's own generated samples to help it learn how to generate more diverse images. Because these generated sample images belong to the same class as the images you provided, they help the model retain what it has learned about the class and how it can use what it already knows about the class to make new compositions.
- `--with_prior_preservation`: whether to use prior preservation loss
- `--prior_loss_weight`: controls the influence of the prior preservation loss on the model
- `--class_data_dir`: path to a folder containing the generated class sample images
- `--class_prompt`: the text prompt describing the class of the generated sample images
```bash
accelerate launch train_dreambooth.py \
--with_prior_preservation \
--prior_loss_weight=1.0 \
--class_data_dir="path/to/class/images" \
--class_prompt="text prompt describing class"
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#prior-preservation-loss | #prior-preservation-loss | .md | 20_4 |
To improve the quality of the generated outputs, you can also train the text encoder in addition to the UNet. This requires additional memory and you'll need a GPU with at least 24GB of vRAM. If you have the necessary hardware, then training the text encoder produces better results, especially when generating images of faces. Enable this option by:
```bash
accelerate launch train_dreambooth.py \
--train_text_encoder
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#train-text-encoder | #train-text-encoder | .md | 20_5 |
DreamBooth comes with its own dataset classes:
- [`DreamBoothDataset`](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L604): preprocesses the images and class images, and tokenizes the prompts for training
- [`PromptDataset`](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L738): generates the prompt embeddings to generate the class images
If you enabled [prior preservation loss](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L842), the class images are generated here:
```py
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
```
Next is the [`main()`](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L799) function which handles setting up the dataset for training and the training loop itself. The script loads the [tokenizer](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L898), [scheduler and models](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L912C1-L912C1):
```py
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
if model_has_vae(args):
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
)
else:
vae = None
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
```
Then, it's time to [create the training dataset](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L1073) and DataLoader from `DreamBoothDataset`:
```py
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
class_num=args.num_class_images,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
encoder_hidden_states=pre_computed_encoder_hidden_states,
class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,
tokenizer_max_length=args.tokenizer_max_length,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
num_workers=args.dataloader_num_workers,
)
```
Lastly, the [training loop](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L1151) takes care of the remaining steps such as converting images to latent space, adding noise to the input, predicting the noise residual, and calculating the loss.
If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#training-script | #training-script | .md | 20_6 |
You're now ready to launch the training script! 🚀
For this guide, you'll download some images of a [dog](https://huggingface.co./datasets/diffusers/dog-example) and store them in a directory. But remember, you can create and use your own dataset if you want (see the [Create a dataset for training](create_dataset) guide).
```py
from huggingface_hub import snapshot_download
local_dir = "./dog"
snapshot_download(
"diffusers/dog-example",
local_dir=local_dir,
repo_type="dataset",
ignore_patterns=".gitattributes",
)
```
Set the environment variable `MODEL_NAME` to a model id on the Hub or a path to a local model, `INSTANCE_DIR` to the path where you just downloaded the dog images to, and `OUTPUT_DIR` to where you want to save the model. You'll use `sks` as the special word to tie the training to.
If you're interested in following along with the training process, you can periodically save generated images as training progresses. Add the following parameters to the training command:
```bash
--validation_prompt="a photo of a sks dog"
--num_validation_images=4
--validation_steps=100
```
One more thing before you launch the script! Depending on the GPU you have, you may need to enable certain optimizations to train DreamBooth.
<hfoptions id="gpu-select">
<hfoption id="16GB">
On a 16GB GPU, you can use bitsandbytes 8-bit optimizer and gradient checkpointing to help you train a DreamBooth model. Install bitsandbytes:
```py
pip install bitsandbytes
```
Then, add the following parameter to your training command:
```bash
accelerate launch train_dreambooth.py \
--gradient_checkpointing \
--use_8bit_adam \
```
</hfoption>
<hfoption id="12GB">
On a 12GB GPU, you'll need bitsandbytes 8-bit optimizer, gradient checkpointing, xFormers, and set the gradients to `None` instead of zero to reduce your memory-usage.
```bash
accelerate launch train_dreambooth.py \
--use_8bit_adam \
--gradient_checkpointing \
--enable_xformers_memory_efficient_attention \
--set_grads_to_none \
```
</hfoption>
<hfoption id="8GB">
On a 8GB GPU, you'll need [DeepSpeed](https://www.deepspeed.ai/) to offload some of the tensors from the vRAM to either the CPU or NVME to allow training with less GPU memory.
Run the following command to configure your 🤗 Accelerate environment:
```bash
accelerate config
```
During configuration, confirm that you want to use DeepSpeed. Now it should be possible to train on under 8GB vRAM by combining DeepSpeed stage 2, fp16 mixed precision, and offloading the model parameters and the optimizer state to the CPU. The drawback is that this requires more system RAM (~25 GB). See the [DeepSpeed documentation](https://huggingface.co./docs/accelerate/usage_guides/deepspeed) for more configuration options.
You should also change the default Adam optimizer to DeepSpeed’s optimized version of Adam [`deepspeed.ops.adam.DeepSpeedCPUAdam`](https://deepspeed.readthedocs.io/en/latest/optimizers.html#adam-cpu) for a substantial speedup. Enabling `DeepSpeedCPUAdam` requires your system’s CUDA toolchain version to be the same as the one installed with PyTorch.
bitsandbytes 8-bit optimizers don’t seem to be compatible with DeepSpeed at the moment.
That's it! You don't need to add any additional parameters to your training command.
</hfoption>
</hfoptions>
<hfoptions id="training-inference">
<hfoption id="PyTorch">
```bash
export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
export INSTANCE_DIR="./dog"
export OUTPUT_DIR="path_to_saved_model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=400 \
--push_to_hub
```
</hfoption>
<hfoption id="Flax">
```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="./dog"
export OUTPUT_DIR="path-to-save-model"
python train_dreambooth_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=5e-6 \
--max_train_steps=400 \
--push_to_hub
```
</hfoption>
</hfoptions>
Once training is complete, you can use your newly trained model for inference!
<Tip>
Can't wait to try your model for inference before training is complete? 🤭 Make sure you have the latest version of 🤗 Accelerate installed.
```py
from diffusers import DiffusionPipeline, UNet2DConditionModel
from transformers import CLIPTextModel
import torch
unet = UNet2DConditionModel.from_pretrained("path/to/model/checkpoint-100/unet")
# if you have trained with `--args.train_text_encoder` make sure to also load the text encoder
text_encoder = CLIPTextModel.from_pretrained("path/to/model/checkpoint-100/checkpoint-100/text_encoder")
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", unet=unet, text_encoder=text_encoder, dtype=torch.float16,
).to("cuda")
image = pipeline("A photo of sks dog in a bucket", num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
```
</Tip>
<hfoptions id="training-inference">
<hfoption id="PyTorch">
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("path_to_saved_model", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
image = pipeline("A photo of sks dog in a bucket", num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
```
</hfoption>
<hfoption id="Flax">
```py
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained("path-to-your-trained-model", dtype=jax.numpy.bfloat16)
prompt = "A photo of sks dog in a bucket"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
image.save("dog-bucket.png")
```
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#launch-the-script | #launch-the-script | .md | 20_7 |
LoRA is a training technique for significantly reducing the number of trainable parameters. As a result, training is faster and it is easier to store the resulting weights because they are a lot smaller (~100MBs). Use the [train_dreambooth_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py) script to train with LoRA.
The LoRA training script is discussed in more detail in the [LoRA training](lora) guide. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#lora | #lora | .md | 20_8 |
Stable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the [train_dreambooth_lora_sdxl.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py) script to train a SDXL model with LoRA.
The SDXL training script is discussed in more detail in the [SDXL training](sdxl) guide. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#stable-diffusion-xl | #stable-diffusion-xl | .md | 20_9 |
DeepFloyd IF is a cascading pixel diffusion model with three stages. The first stage generates a base image and the second and third stages progressively upscales the base image into a high-resolution 1024x1024 image. Use the [train_dreambooth_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py) or [train_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) scripts to train a DeepFloyd IF model with LoRA or the full model.
DeepFloyd IF uses predicted variance, but the Diffusers training scripts uses predicted error so the trained DeepFloyd IF models are switched to a fixed variance schedule. The training scripts will update the scheduler config of the fully trained model for you. However, when you load the saved LoRA weights you must also update the pipeline's scheduler config.
```py
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", use_safetensors=True)
pipe.load_lora_weights("<lora weights path>")
# Update scheduler config to fixed variance schedule
pipe.scheduler = pipe.scheduler.__class__.from_config(pipe.scheduler.config, variance_type="fixed_small")
```
The stage 2 model requires additional validation images to upscale. You can download and use a downsized version of the training images for this.
```py
from huggingface_hub import snapshot_download
local_dir = "./dog_downsized"
snapshot_download(
"diffusers/dog-example-downsized",
local_dir=local_dir,
repo_type="dataset",
ignore_patterns=".gitattributes",
)
```
The code samples below provide a brief overview of how to train a DeepFloyd IF model with a combination of DreamBooth and LoRA. Some important parameters to note are:
* `--resolution=64`, a much smaller resolution is required because DeepFloyd IF is a pixel diffusion model and to work on uncompressed pixels, the input images must be smaller
* `--pre_compute_text_embeddings`, compute the text embeddings ahead of time to save memory because the [`~transformers.T5Model`] can take up a lot of memory
* `--tokenizer_max_length=77`, you can use a longer default text length with T5 as the text encoder but the default model encoding procedure uses a shorter text length
* `--text_encoder_use_attention_mask`, to pass the attention mask to the text encoder
<hfoptions id="IF-DreamBooth">
<hfoption id="Stage 1 LoRA DreamBooth">
Training stage 1 of DeepFloyd IF with LoRA and DreamBooth requires ~28GB of memory.
```bash
export MODEL_NAME="DeepFloyd/IF-I-XL-v1.0"
export INSTANCE_DIR="dog"
export OUTPUT_DIR="dreambooth_dog_lora"
accelerate launch train_dreambooth_lora.py \
--report_to wandb \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a sks dog" \
--resolution=64 \
--train_batch_size=4 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--scale_lr \
--max_train_steps=1200 \
--validation_prompt="a sks dog" \
--validation_epochs=25 \
--checkpointing_steps=100 \
--pre_compute_text_embeddings \
--tokenizer_max_length=77 \
--text_encoder_use_attention_mask
```
</hfoption>
<hfoption id="Stage 2 LoRA DreamBooth">
For stage 2 of DeepFloyd IF with LoRA and DreamBooth, pay attention to these parameters:
* `--validation_images`, the images to upscale during validation
* `--class_labels_conditioning=timesteps`, to additionally conditional the UNet as needed in stage 2
* `--learning_rate=1e-6`, a lower learning rate is used compared to stage 1
* `--resolution=256`, the expected resolution for the upscaler
```bash
export MODEL_NAME="DeepFloyd/IF-II-L-v1.0"
export INSTANCE_DIR="dog"
export OUTPUT_DIR="dreambooth_dog_upscale"
export VALIDATION_IMAGES="dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"
python train_dreambooth_lora.py \
--report_to wandb \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a sks dog" \
--resolution=256 \
--train_batch_size=4 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-6 \
--max_train_steps=2000 \
--validation_prompt="a sks dog" \
--validation_epochs=100 \
--checkpointing_steps=500 \
--pre_compute_text_embeddings \
--tokenizer_max_length=77 \
--text_encoder_use_attention_mask \
--validation_images $VALIDATION_IMAGES \
--class_labels_conditioning=timesteps
```
</hfoption>
<hfoption id="Stage 1 DreamBooth">
For stage 1 of DeepFloyd IF with DreamBooth, pay attention to these parameters:
* `--skip_save_text_encoder`, to skip saving the full T5 text encoder with the finetuned model
* `--use_8bit_adam`, to use 8-bit Adam optimizer to save memory due to the size of the optimizer state when training the full model
* `--learning_rate=1e-7`, a really low learning rate should be used for full model training otherwise the model quality is degraded (you can use a higher learning rate with a larger batch size)
Training with 8-bit Adam and a batch size of 4, the full model can be trained with ~48GB of memory.
```bash
export MODEL_NAME="DeepFloyd/IF-I-XL-v1.0"
export INSTANCE_DIR="dog"
export OUTPUT_DIR="dreambooth_if"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=64 \
--train_batch_size=4 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-7 \
--max_train_steps=150 \
--validation_prompt "a photo of sks dog" \
--validation_steps 25 \
--text_encoder_use_attention_mask \
--tokenizer_max_length 77 \
--pre_compute_text_embeddings \
--use_8bit_adam \
--set_grads_to_none \
--skip_save_text_encoder \
--push_to_hub
```
</hfoption>
<hfoption id="Stage 2 DreamBooth">
For stage 2 of DeepFloyd IF with DreamBooth, pay attention to these parameters:
* `--learning_rate=5e-6`, use a lower learning rate with a smaller effective batch size
* `--resolution=256`, the expected resolution for the upscaler
* `--train_batch_size=2` and `--gradient_accumulation_steps=6`, to effectively train on images wiht faces requires larger batch sizes
```bash
export MODEL_NAME="DeepFloyd/IF-II-L-v1.0"
export INSTANCE_DIR="dog"
export OUTPUT_DIR="dreambooth_dog_upscale"
export VALIDATION_IMAGES="dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"
accelerate launch train_dreambooth.py \
--report_to wandb \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a sks dog" \
--resolution=256 \
--train_batch_size=2 \
--gradient_accumulation_steps=6 \
--learning_rate=5e-6 \
--max_train_steps=2000 \
--validation_prompt="a sks dog" \
--validation_steps=150 \
--checkpointing_steps=500 \
--pre_compute_text_embeddings \
--tokenizer_max_length=77 \
--text_encoder_use_attention_mask \
--validation_images $VALIDATION_IMAGES \
--class_labels_conditioning timesteps \
--push_to_hub
```
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#deepfloyd-if | #deepfloyd-if | .md | 20_10 |
Training the DeepFloyd IF model can be challenging, but here are some tips that we've found helpful:
- LoRA is sufficient for training the stage 1 model because the model's low resolution makes representing finer details difficult regardless.
- For common or simple objects, you don't necessarily need to finetune the upscaler. Make sure the prompt passed to the upscaler is adjusted to remove the new token from the instance prompt. For example, if your stage 1 prompt is "a sks dog" then your stage 2 prompt should be "a dog".
- For finer details like faces, fully training the stage 2 upscaler is better than training the stage 2 model with LoRA. It also helps to use lower learning rates with larger batch sizes.
- Lower learning rates should be used to train the stage 2 model.
- The [`DDPMScheduler`] works better than the DPMSolver used in the training scripts. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#training-tips | #training-tips | .md | 20_11 |
Congratulations on training your DreamBooth model! To learn more about how to use your new model, the following guide may be helpful:
- Learn how to [load a DreamBooth](../using-diffusers/loading_adapters) model for inference if you trained your model with LoRA. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/dreambooth.md | https://huggingface.co./docs/diffusers/en/training/dreambooth/#next-steps | #next-steps | .md | 20_12 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/instructpix2pix.md | https://huggingface.co./docs/diffusers/en/training/instructpix2pix/ | .md | 21_0 |
|
[InstructPix2Pix](https://hf.co/papers/2211.09800) is a Stable Diffusion model trained to edit images from human-provided instructions. For example, your prompt can be "turn the clouds rainy" and the model will edit the input image accordingly. This model is conditioned on the text prompt (or editing instruction) and the input image.
This guide will explore the [train_instruct_pix2pix.py](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py) training script to help you become familiar with it, and how you can adapt it for your own use case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
```bash
cd examples/instruct_pix2pix
pip install -r requirements.txt
```
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
<Tip>
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py) and let us know if you have any questions or concerns.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/instructpix2pix.md | https://huggingface.co./docs/diffusers/en/training/instructpix2pix/#instructpix2pix | #instructpix2pix | .md | 21_1 |
The training script has many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L65) function. Default values are provided for most parameters that work pretty well, but you can also set your own values in the training command if you'd like.
For example, to increase the resolution of the input image:
```bash
accelerate launch train_instruct_pix2pix.py \
--resolution=512 \
```
Many of the basic and important parameters are described in the [Text-to-image](text2image#script-parameters) training guide, so this guide just focuses on the relevant parameters for InstructPix2Pix:
- `--original_image_column`: the original image before the edits are made
- `--edited_image_column`: the image after the edits are made
- `--edit_prompt_column`: the instructions to edit the image
- `--conditioning_dropout_prob`: the dropout probability for the edited image and edit prompts during training which enables classifier-free guidance (CFG) for one or both conditioning inputs | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/instructpix2pix.md | https://huggingface.co./docs/diffusers/en/training/instructpix2pix/#script-parameters | #script-parameters | .md | 21_2 |
The dataset preprocessing code and training loop are found in the [`main()`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L374) function. This is where you'll make your changes to the training script to adapt it for your own use-case.
As with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the InstructPix2Pix relevant parts of the script.
The script begins by modifying the [number of input channels](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L445) in the first convolutional layer of the UNet to account for InstructPix2Pix's additional conditioning image:
```py
in_channels = 8
out_channels = unet.conv_in.out_channels
unet.register_to_config(in_channels=in_channels)
with torch.no_grad():
new_conv_in = nn.Conv2d(
in_channels, out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding
)
new_conv_in.weight.zero_()
new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
unet.conv_in = new_conv_in
```
These UNet parameters are [updated](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L545C1-L551C6) by the optimizer:
```py
optimizer = optimizer_cls(
unet.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
```
Next, the edited images and edit instructions are [preprocessed](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L624) and [tokenized](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L610C24-L610C24). It is important the same image transformations are applied to the original and edited images.
```py
def preprocess_train(examples):
preprocessed_images = preprocess_images(examples)
original_images, edited_images = preprocessed_images.chunk(2)
original_images = original_images.reshape(-1, 3, args.resolution, args.resolution)
edited_images = edited_images.reshape(-1, 3, args.resolution, args.resolution)
examples["original_pixel_values"] = original_images
examples["edited_pixel_values"] = edited_images
captions = list(examples[edit_prompt_column])
examples["input_ids"] = tokenize_captions(captions)
return examples
```
Finally, in the [training loop](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L730), it starts by encoding the edited images into latent space:
```py
latents = vae.encode(batch["edited_pixel_values"].to(weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
```
Then, the script applies dropout to the original image and edit instruction embeddings to support CFG. This is what enables the model to modulate the influence of the edit instruction and original image on the edited image.
```py
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
original_image_embeds = vae.encode(batch["original_pixel_values"].to(weight_dtype)).latent_dist.mode()
if args.conditioning_dropout_prob is not None:
random_p = torch.rand(bsz, device=latents.device, generator=generator)
prompt_mask = random_p < 2 * args.conditioning_dropout_prob
prompt_mask = prompt_mask.reshape(bsz, 1, 1)
null_conditioning = text_encoder(tokenize_captions([""]).to(accelerator.device))[0]
encoder_hidden_states = torch.where(prompt_mask, null_conditioning, encoder_hidden_states)
image_mask_dtype = original_image_embeds.dtype
image_mask = 1 - (
(random_p >= args.conditioning_dropout_prob).to(image_mask_dtype)
* (random_p < 3 * args.conditioning_dropout_prob).to(image_mask_dtype)
)
image_mask = image_mask.reshape(bsz, 1, 1, 1)
original_image_embeds = image_mask * original_image_embeds
```
That's pretty much it! Aside from the differences described here, the rest of the script is very similar to the [Text-to-image](text2image#training-script) training script, so feel free to check it out for more details. If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/instructpix2pix.md | https://huggingface.co./docs/diffusers/en/training/instructpix2pix/#training-script | #training-script | .md | 21_3 |
Once you're happy with the changes to your script or if you're okay with the default configuration, you're ready to launch the training script! 🚀
This guide uses the [fusing/instructpix2pix-1000-samples](https://huggingface.co./datasets/fusing/instructpix2pix-1000-samples) dataset, which is a smaller version of the [original dataset](https://huggingface.co./datasets/timbrooks/instructpix2pix-clip-filtered). You can also create and use your own dataset if you'd like (see the [Create a dataset for training](create_dataset) guide).
Set the `MODEL_NAME` environment variable to the name of the model (can be a model id on the Hub or a path to a local model), and the `DATASET_ID` to the name of the dataset on the Hub. The script creates and saves all the components (feature extractor, scheduler, text encoder, UNet, etc.) to a subfolder in your repository.
<Tip>
For better results, try longer training runs with a larger dataset. We've only tested this training script on a smaller-scale dataset.
<br>
To monitor training progress with Weights and Biases, add the `--report_to=wandb` parameter to the training command and specify a validation image with `--val_image_url` and a validation prompt with `--validation_prompt`. This can be really useful for debugging the model.
</Tip>
If you’re training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.
```bash
accelerate launch --mixed_precision="fp16" train_instruct_pix2pix.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_ID \
--enable_xformers_memory_efficient_attention \
--resolution=256 \
--random_flip \
--train_batch_size=4 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=15000 \
--checkpointing_steps=5000 \
--checkpoints_total_limit=1 \
--learning_rate=5e-05 \
--max_grad_norm=1 \
--lr_warmup_steps=0 \
--conditioning_dropout_prob=0.05 \
--mixed_precision=fp16 \
--seed=42 \
--push_to_hub
```
After training is finished, you can use your new InstructPix2Pix for inference:
```py
import PIL
import requests
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline
from diffusers.utils import load_image
pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained("your_cool_model", torch_dtype=torch.float16).to("cuda")
generator = torch.Generator("cuda").manual_seed(0)
image = load_image("https://huggingface.co./datasets/sayakpaul/sample-datasets/resolve/main/test_pix2pix_4.png")
prompt = "add some ducks to the lake"
num_inference_steps = 20
image_guidance_scale = 1.5
guidance_scale = 10
edited_image = pipeline(
prompt,
image=image,
num_inference_steps=num_inference_steps,
image_guidance_scale=image_guidance_scale,
guidance_scale=guidance_scale,
generator=generator,
).images[0]
edited_image.save("edited_image.png")
```
You should experiment with different `num_inference_steps`, `image_guidance_scale`, and `guidance_scale` values to see how they affect inference speed and quality. The guidance scale parameters are especially impactful because they control how much the original image and edit instructions affect the edited image. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/instructpix2pix.md | https://huggingface.co./docs/diffusers/en/training/instructpix2pix/#launch-the-script | #launch-the-script | .md | 21_4 |
Stable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the [`train_instruct_pix2pix_sdxl.py`](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py) script to train a SDXL model to follow image editing instructions.
The SDXL training script is discussed in more detail in the [SDXL training](sdxl) guide. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/instructpix2pix.md | https://huggingface.co./docs/diffusers/en/training/instructpix2pix/#stable-diffusion-xl | #stable-diffusion-xl | .md | 21_5 |
Congratulations on training your own InstructPix2Pix model! 🥳 To learn more about the model, it may be helpful to:
- Read the [Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co./blog/instruction-tuning-sd) blog post to learn more about some experiments we've done with InstructPix2Pix, dataset preparation, and results for different instructions. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/instructpix2pix.md | https://huggingface.co./docs/diffusers/en/training/instructpix2pix/#next-steps | #next-steps | .md | 21_6 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/text2image.md | https://huggingface.co./docs/diffusers/en/training/text2image/ | .md | 22_0 |
|
<Tip warning={true}>
The text-to-image script is experimental, and it's easy to overfit and run into issues like catastrophic forgetting. Try exploring different hyperparameters to get the best results on your dataset.
</Tip>
Text-to-image models like Stable Diffusion are conditioned to generate images given a text prompt.
Training a model can be taxing on your hardware, but if you enable `gradient_checkpointing` and `mixed_precision`, it is possible to train a model on a single 24GB GPU. If you're training with larger batch sizes or want to train faster, it's better to use GPUs with more than 30GB of memory. You can reduce your memory footprint by enabling memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing, gradient accumulation or xFormers. A GPU with at least 30GB of memory or a TPU v3 is recommended for training with Flax.
This guide will explore the [train_text_to_image.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
<hfoptions id="installation">
<hfoption id="PyTorch">
```bash
cd examples/text_to_image
pip install -r requirements.txt
```
</hfoption>
<hfoption id="Flax">
```bash
cd examples/text_to_image
pip install -r requirements_flax.txt
```
</hfoption>
</hfoptions>
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/text2image.md | https://huggingface.co./docs/diffusers/en/training/text2image/#text-to-image | #text-to-image | .md | 22_1 |
<Tip>
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) and let us know if you have any questions or concerns.
</Tip>
The training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L193) function. This function provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.
For example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:
```bash
accelerate launch train_text_to_image.py \
--mixed_precision="fp16"
```
Some basic and important parameters include:
- `--pretrained_model_name_or_path`: the name of the model on the Hub or a local path to the pretrained model
- `--dataset_name`: the name of the dataset on the Hub or a local path to the dataset to train on
- `--image_column`: the name of the image column in the dataset to train on
- `--caption_column`: the name of the text column in the dataset to train on
- `--output_dir`: where to save the trained model
- `--push_to_hub`: whether to push the trained model to the Hub
- `--checkpointing_steps`: frequency of saving a checkpoint as the model trains; this is useful if for some reason training is interrupted, you can continue training from that checkpoint by adding `--resume_from_checkpoint` to your training command | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/text2image.md | https://huggingface.co./docs/diffusers/en/training/text2image/#script-parameters | #script-parameters | .md | 22_2 |
The [Min-SNR](https://huggingface.co./papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
```bash
accelerate launch train_text_to_image.py \
--snr_gamma=5.0
```
You can compare the loss surfaces for different `snr_gamma` values in this [Weights and Biases](https://wandb.ai/sayakpaul/text2image-finetune-minsnr) report. For smaller datasets, the effects of Min-SNR may not be as obvious compared to larger datasets. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/text2image.md | https://huggingface.co./docs/diffusers/en/training/text2image/#min-snr-weighting | #min-snr-weighting | .md | 22_3 |
The dataset preprocessing code and training loop are found in the [`main()`](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L490) function. If you need to adapt the training script, this is where you'll need to make your changes.
The `train_text_to_image` script starts by [loading a scheduler](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L543) and tokenizer. You can choose to use a different scheduler here if you want:
```py
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
tokenizer = CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
```
Then the script [loads the UNet](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L619) model:
```py
load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
```
Next, the text and image columns of the dataset need to be preprocessed. The [`tokenize_captions`](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L724) function handles tokenizing the inputs, and the [`train_transforms`](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L742) function specifies the type of transforms to apply to the image. Both of these functions are bundled into `preprocess_train`:
```py
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["pixel_values"] = [train_transforms(image) for image in images]
examples["input_ids"] = tokenize_captions(examples)
return examples
```
Lastly, the [training loop](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L878) handles everything else. It encodes images into latent space, adds noise to the latents, computes the text embeddings to condition on, updates the model parameters, and saves and pushes the model to the Hub. If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/text2image.md | https://huggingface.co./docs/diffusers/en/training/text2image/#training-script | #training-script | .md | 22_4 |
Once you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀
<hfoptions id="training-inference">
<hfoption id="PyTorch">
Let's train on the [Naruto BLIP captions](https://huggingface.co./datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `dataset_name` to the model and the dataset (either from the Hub or a local path). If you're training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.
<Tip>
To train on a local dataset, set the `TRAIN_DIR` and `OUTPUT_DIR` environment variables to the path of the dataset and where to save the model to.
</Tip>
```bash
export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
export dataset_name="lambdalabs/naruto-blip-captions"
accelerate launch --mixed_precision="fp16" train_text_to_image.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$dataset_name \
--use_ema \
--resolution=512 --center_crop --random_flip \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--enable_xformers_memory_efficient_attention \
--lr_scheduler="constant" --lr_warmup_steps=0 \
--output_dir="sd-naruto-model" \
--push_to_hub
```
</hfoption>
<hfoption id="Flax">
Training with Flax can be faster on TPUs and GPUs thanks to [@duongna211](https://github.com/duongna21). Flax is more efficient on a TPU, but GPU performance is also great.
Set the environment variables `MODEL_NAME` and `dataset_name` to the model and the dataset (either from the Hub or a local path).
<Tip>
To train on a local dataset, set the `TRAIN_DIR` and `OUTPUT_DIR` environment variables to the path of the dataset and where to save the model to.
</Tip>
```bash
export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
export dataset_name="lambdalabs/naruto-blip-captions"
python train_text_to_image_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$dataset_name \
--resolution=512 --center_crop --random_flip \
--train_batch_size=1 \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--output_dir="sd-naruto-model" \
--push_to_hub
```
</hfoption>
</hfoptions>
Once training is complete, you can use your newly trained model for inference:
<hfoptions id="training-inference">
<hfoption id="PyTorch">
```py
from diffusers import StableDiffusionPipeline
import torch
pipeline = StableDiffusionPipeline.from_pretrained("path/to/saved_model", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
image = pipeline(prompt="yoda").images[0]
image.save("yoda-naruto.png")
```
</hfoption>
<hfoption id="Flax">
```py
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained("path/to/saved_model", dtype=jax.numpy.bfloat16)
prompt = "yoda naruto"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
image.save("yoda-naruto.png")
```
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/text2image.md | https://huggingface.co./docs/diffusers/en/training/text2image/#launch-the-script | #launch-the-script | .md | 22_5 |
Congratulations on training your own text-to-image model! To learn more about how to use your new model, the following guides may be helpful:
- Learn how to [load LoRA weights](../using-diffusers/loading_adapters#LoRA) for inference if you trained your model with LoRA.
- Learn more about how certain parameters like guidance scale or techniques such as prompt weighting can help you control inference in the [Text-to-image](../using-diffusers/conditional_image_generation) task guide. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/text2image.md | https://huggingface.co./docs/diffusers/en/training/text2image/#next-steps | #next-steps | .md | 22_6 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/overview.md | https://huggingface.co./docs/diffusers/en/training/overview/ | .md | 23_0 |
|
🤗 Diffusers provides a collection of training scripts for you to train your own diffusion models. You can find all of our training scripts in [diffusers/examples](https://github.com/huggingface/diffusers/tree/main/examples).
Each training script is:
- **Self-contained**: the training script does not depend on any local files, and all packages required to run the script are installed from the `requirements.txt` file.
- **Easy-to-tweak**: the training scripts are an example of how to train a diffusion model for a specific task and won't work out-of-the-box for every training scenario. You'll likely need to adapt the training script for your specific use-case. To help you with that, we've fully exposed the data preprocessing code and the training loop so you can modify it for your own use.
- **Beginner-friendly**: the training scripts are designed to be beginner-friendly and easy to understand, rather than including the latest state-of-the-art methods to get the best and most competitive results. Any training methods we consider too complex are purposefully left out.
- **Single-purpose**: each training script is expressly designed for only one task to keep it readable and understandable.
Our current collection of training scripts include:
| Training | SDXL-support | LoRA-support | Flax-support |
|---|---|---|---|
| [unconditional image generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) | | | |
| [text-to-image](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) | 👍 | 👍 | 👍 |
| [textual inversion](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb) | | | 👍 |
| [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb) | 👍 | 👍 | 👍 |
| [ControlNet](https://github.com/huggingface/diffusers/tree/main/examples/controlnet) | 👍 | | 👍 |
| [InstructPix2Pix](https://github.com/huggingface/diffusers/tree/main/examples/instruct_pix2pix) | 👍 | | |
| [Custom Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion) | | | |
| [T2I-Adapters](https://github.com/huggingface/diffusers/tree/main/examples/t2i_adapter) | 👍 | | |
| [Kandinsky 2.2](https://github.com/huggingface/diffusers/tree/main/examples/kandinsky2_2/text_to_image) | | 👍 | |
| [Wuerstchen](https://github.com/huggingface/diffusers/tree/main/examples/wuerstchen/text_to_image) | | 👍 | |
These examples are **actively** maintained, so please feel free to open an issue if they aren't working as expected. If you feel like another training example should be included, you're more than welcome to start a [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) to discuss your feature idea with us and whether it meets our criteria of being self-contained, easy-to-tweak, beginner-friendly, and single-purpose. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/overview.md | https://huggingface.co./docs/diffusers/en/training/overview/#overview | #overview | .md | 23_1 |
Make sure you can successfully run the latest versions of the example scripts by installing the library from source in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then navigate to the folder of the training script (for example, [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)) and install the `requirements.txt` file. Some training scripts have a specific requirement file for SDXL, LoRA or Flax. If you're using one of these scripts, make sure you install its corresponding requirements file.
```bash
cd examples/dreambooth
pip install -r requirements.txt
# to train SDXL with DreamBooth
pip install -r requirements_sdxl.txt
```
To speedup training and reduce memory-usage, we recommend:
- using PyTorch 2.0 or higher to automatically use [scaled dot product attention](../optimization/torch2.0#scaled-dot-product-attention) during training (you don't need to make any changes to the training code)
- installing [xFormers](../optimization/xformers) to enable memory-efficient attention | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/overview.md | https://huggingface.co./docs/diffusers/en/training/overview/#install | #install | .md | 23_2 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/distributed_inference.md | https://huggingface.co./docs/diffusers/en/training/distributed_inference/ | .md | 24_0 |
|
On distributed setups, you can run inference across multiple GPUs with 🤗 [Accelerate](https://huggingface.co./docs/accelerate/index) or [PyTorch Distributed](https://pytorch.org/tutorials/beginner/dist_overview.html), which is useful for generating with multiple prompts in parallel.
This guide will show you how to use 🤗 Accelerate and PyTorch Distributed for distributed inference. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/distributed_inference.md | https://huggingface.co./docs/diffusers/en/training/distributed_inference/#distributed-inference | #distributed-inference | .md | 24_1 |
🤗 [Accelerate](https://huggingface.co./docs/accelerate/index) is a library designed to make it easy to train or run inference across distributed setups. It simplifies the process of setting up the distributed environment, allowing you to focus on your PyTorch code.
To begin, create a Python file and initialize an [`accelerate.PartialState`] to create a distributed environment; your setup is automatically detected so you don't need to explicitly define the `rank` or `world_size`. Move the [`DiffusionPipeline`] to `distributed_state.device` to assign a GPU to each process.
Now use the [`~accelerate.PartialState.split_between_processes`] utility as a context manager to automatically distribute the prompts between the number of processes.
```py
import torch
from accelerate import PartialState
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
)
distributed_state = PartialState()
pipeline.to(distributed_state.device)
with distributed_state.split_between_processes(["a dog", "a cat"]) as prompt:
result = pipeline(prompt).images[0]
result.save(f"result_{distributed_state.process_index}.png")
```
Use the `--num_processes` argument to specify the number of GPUs to use, and call `accelerate launch` to run the script:
```bash
accelerate launch run_distributed.py --num_processes=2
```
<Tip>
Refer to this minimal example [script](https://gist.github.com/sayakpaul/cfaebd221820d7b43fae638b4dfa01ba) for running inference across multiple GPUs. To learn more, take a look at the [Distributed Inference with 🤗 Accelerate](https://huggingface.co./docs/accelerate/en/usage_guides/distributed_inference#distributed-inference-with-accelerate) guide.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/distributed_inference.md | https://huggingface.co./docs/diffusers/en/training/distributed_inference/#-accelerate | #-accelerate | .md | 24_2 |
PyTorch supports [`DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html) which enables data parallelism.
To start, create a Python file and import `torch.distributed` and `torch.multiprocessing` to set up the distributed process group and to spawn the processes for inference on each GPU. You should also initialize a [`DiffusionPipeline`]:
```py
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from diffusers import DiffusionPipeline
sd = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
)
```
You'll want to create a function to run inference; [`init_process_group`](https://pytorch.org/docs/stable/distributed.html?highlight=init_process_group#torch.distributed.init_process_group) handles creating a distributed environment with the type of backend to use, the `rank` of the current process, and the `world_size` or the number of processes participating. If you're running inference in parallel over 2 GPUs, then the `world_size` is 2.
Move the [`DiffusionPipeline`] to `rank` and use `get_rank` to assign a GPU to each process, where each process handles a different prompt:
```py
def run_inference(rank, world_size):
dist.init_process_group("nccl", rank=rank, world_size=world_size)
sd.to(rank)
if torch.distributed.get_rank() == 0:
prompt = "a dog"
elif torch.distributed.get_rank() == 1:
prompt = "a cat"
image = sd(prompt).images[0]
image.save(f"./{'_'.join(prompt)}.png")
```
To run the distributed inference, call [`mp.spawn`](https://pytorch.org/docs/stable/multiprocessing.html#torch.multiprocessing.spawn) to run the `run_inference` function on the number of GPUs defined in `world_size`:
```py
def main():
world_size = 2
mp.spawn(run_inference, args=(world_size,), nprocs=world_size, join=True)
if __name__ == "__main__":
main()
```
Once you've completed the inference script, use the `--nproc_per_node` argument to specify the number of GPUs to use and call `torchrun` to run the script:
```bash
torchrun run_distributed.py --nproc_per_node=2
```
> [!TIP]
> You can use `device_map` within a [`DiffusionPipeline`] to distribute its model-level components on multiple devices. Refer to the [Device placement](../tutorials/inference_with_big_models#device-placement) guide to learn more. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/distributed_inference.md | https://huggingface.co./docs/diffusers/en/training/distributed_inference/#pytorch-distributed | #pytorch-distributed | .md | 24_3 |
Modern diffusion systems such as [Flux](../api/pipelines/flux) are very large and have multiple models. For example, [Flux.1-Dev](https://hf.co/black-forest-labs/FLUX.1-dev) is made up of two text encoders - [T5-XXL](https://hf.co/google/t5-v1_1-xxl) and [CLIP-L](https://hf.co/openai/clip-vit-large-patch14) - a [diffusion transformer](../api/models/flux_transformer), and a [VAE](../api/models/autoencoderkl). With a model this size, it can be challenging to run inference on consumer GPUs.
Model sharding is a technique that distributes models across GPUs when the models don't fit on a single GPU. The example below assumes two 16GB GPUs are available for inference.
Start by computing the text embeddings with the text encoders. Keep the text encoders on two GPUs by setting `device_map="balanced"`. The `balanced` strategy evenly distributes the model on all available GPUs. Use the `max_memory` parameter to allocate the maximum amount of memory for each text encoder on each GPU.
> [!TIP]
> **Only** load the text encoders for this step! The diffusion transformer and VAE are loaded in a later step to preserve memory.
```py
from diffusers import FluxPipeline
import torch
prompt = "a photo of a dog with cat-like look"
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
transformer=None,
vae=None,
device_map="balanced",
max_memory={0: "16GB", 1: "16GB"},
torch_dtype=torch.bfloat16
)
with torch.no_grad():
print("Encoding prompts.")
prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(
prompt=prompt, prompt_2=None, max_sequence_length=512
)
```
Once the text embeddings are computed, remove them from the GPU to make space for the diffusion transformer.
```py
import gc
def flush():
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
del pipeline.text_encoder
del pipeline.text_encoder_2
del pipeline.tokenizer
del pipeline.tokenizer_2
del pipeline
flush()
```
Load the diffusion transformer next which has 12.5B parameters. This time, set `device_map="auto"` to automatically distribute the model across two 16GB GPUs. The `auto` strategy is backed by [Accelerate](https://hf.co/docs/accelerate/index) and available as a part of the [Big Model Inference](https://hf.co/docs/accelerate/concept_guides/big_model_inference) feature. It starts by distributing a model across the fastest device first (GPU) before moving to slower devices like the CPU and hard drive if needed. The trade-off of storing model parameters on slower devices is slower inference latency.
```py
from diffusers import FluxTransformer2DModel
import torch
transformer = FluxTransformer2DModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
device_map="auto",
torch_dtype=torch.bfloat16
)
```
> [!TIP]
> At any point, you can try `print(pipeline.hf_device_map)` to see how the various models are distributed across devices. This is useful for tracking the device placement of the models. You can also try `print(transformer.hf_device_map)` to see how the transformer model is sharded across devices.
Add the transformer model to the pipeline for denoising, but set the other model-level components like the text encoders and VAE to `None` because you don't need them yet.
```py
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
text_encoder=None,
text_encoder_2=None,
tokenizer=None,
tokenizer_2=None,
vae=None,
transformer=transformer,
torch_dtype=torch.bfloat16
)
print("Running denoising.")
height, width = 768, 1360
latents = pipeline(
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
num_inference_steps=50,
guidance_scale=3.5,
height=height,
width=width,
output_type="latent",
).images
```
Remove the pipeline and transformer from memory as they're no longer needed.
```py
del pipeline.transformer
del pipeline
flush()
```
Finally, decode the latents with the VAE into an image. The VAE is typically small enough to be loaded on a single GPU.
```py
from diffusers import AutoencoderKL
from diffusers.image_processor import VaeImageProcessor
import torch
vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="vae", torch_dtype=torch.bfloat16).to("cuda")
vae_scale_factor = 2 ** (len(vae.config.block_out_channels))
image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor)
with torch.no_grad():
print("Running decoding.")
latents = FluxPipeline._unpack_latents(latents, height, width, vae_scale_factor)
latents = (latents / vae.config.scaling_factor) + vae.config.shift_factor
image = vae.decode(latents, return_dict=False)[0]
image = image_processor.postprocess(image, output_type="pil")
image[0].save("split_transformer.png")
```
By selectively loading and unloading the models you need at a given stage and sharding the largest models across multiple GPUs, it is possible to run inference with large models on consumer GPUs. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/distributed_inference.md | https://huggingface.co./docs/diffusers/en/training/distributed_inference/#model-sharding | #model-sharding | .md | 24_4 |
<!--Copyright 2024 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
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|
<Tip warning={true}>
This script is experimental, and it's easy to overfit and run into issues like catastrophic forgetting. Try exploring different hyperparameters to get the best results on your dataset.
</Tip>
Kandinsky 2.2 is a multilingual text-to-image model capable of producing more photorealistic images. The model includes an image prior model for creating image embeddings from text prompts, and a decoder model that generates images based on the prior model's embeddings. That's why you'll find two separate scripts in Diffusers for Kandinsky 2.2, one for training the prior model and one for training the decoder model. You can train both models separately, but to get the best results, you should train both the prior and decoder models.
Depending on your GPU, you may need to enable `gradient_checkpointing` (⚠️ not supported for the prior model!), `mixed_precision`, and `gradient_accumulation_steps` to help fit the model into memory and to speedup training. You can reduce your memory-usage even more by enabling memory-efficient attention with [xFormers](../optimization/xformers) (version [v0.0.16](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212) fails for training on some GPUs so you may need to install a development version instead).
This guide explores the [train_text_to_image_prior.py](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py) and the [train_text_to_image_decoder.py](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py) scripts to help you become more familiar with it, and how you can adapt it for your own use-case.
Before running the scripts, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
```bash
cd examples/kandinsky2_2/text_to_image
pip install -r requirements.txt
```
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
<Tip>
The following sections highlight parts of the training scripts that are important for understanding how to modify it, but it doesn't cover every aspect of the scripts in detail. If you're interested in learning more, feel free to read through the scripts and let us know if you have any questions or concerns.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/kandinsky.md | https://huggingface.co./docs/diffusers/en/training/kandinsky/#kandinsky-22 | #kandinsky-22 | .md | 25_1 |
The training scripts provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py#L190) function. The training scripts provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.
For example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:
```bash
accelerate launch train_text_to_image_prior.py \
--mixed_precision="fp16"
```
Most of the parameters are identical to the parameters in the [Text-to-image](text2image#script-parameters) training guide, so let's get straight to a walkthrough of the Kandinsky training scripts! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/kandinsky.md | https://huggingface.co./docs/diffusers/en/training/kandinsky/#script-parameters | #script-parameters | .md | 25_2 |
The [Min-SNR](https://huggingface.co./papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
```bash
accelerate launch train_text_to_image_prior.py \
--snr_gamma=5.0
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/kandinsky.md | https://huggingface.co./docs/diffusers/en/training/kandinsky/#min-snr-weighting | #min-snr-weighting | .md | 25_3 |
The training script is also similar to the [Text-to-image](text2image#training-script) training guide, but it's been modified to support training the prior and decoder models. This guide focuses on the code that is unique to the Kandinsky 2.2 training scripts.
<hfoptions id="script">
<hfoption id="prior model">
The [`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py#L441) function contains the code for preparing the dataset and training the model.
One of the main differences you'll notice right away is that the training script also loads a [`~transformers.CLIPImageProcessor`] - in addition to a scheduler and tokenizer - for preprocessing images and a [`~transformers.CLIPVisionModelWithProjection`] model for encoding the images:
```py
noise_scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2", prediction_type="sample")
image_processor = CLIPImageProcessor.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_processor"
)
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="tokenizer")
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_encoder", torch_dtype=weight_dtype
).eval()
text_encoder = CLIPTextModelWithProjection.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="text_encoder", torch_dtype=weight_dtype
).eval()
```
Kandinsky uses a [`PriorTransformer`] to generate the image embeddings, so you'll want to setup the optimizer to learn the prior mode's parameters.
```py
prior = PriorTransformer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
prior.train()
optimizer = optimizer_cls(
prior.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
```
Next, the input captions are tokenized, and images are [preprocessed](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py#L632) by the [`~transformers.CLIPImageProcessor`]:
```py
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["clip_pixel_values"] = image_processor(images, return_tensors="pt").pixel_values
examples["text_input_ids"], examples["text_mask"] = tokenize_captions(examples)
return examples
```
Finally, the [training loop](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py#L718) converts the input images into latents, adds noise to the image embeddings, and makes a prediction:
```py
model_pred = prior(
noisy_latents,
timestep=timesteps,
proj_embedding=prompt_embeds,
encoder_hidden_states=text_encoder_hidden_states,
attention_mask=text_mask,
).predicted_image_embedding
```
If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.
</hfoption>
<hfoption id="decoder model">
The [`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py#L440) function contains the code for preparing the dataset and training the model.
Unlike the prior model, the decoder initializes a [`VQModel`] to decode the latents into images and it uses a [`UNet2DConditionModel`]:
```py
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
vae = VQModel.from_pretrained(
args.pretrained_decoder_model_name_or_path, subfolder="movq", torch_dtype=weight_dtype
).eval()
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_encoder", torch_dtype=weight_dtype
).eval()
unet = UNet2DConditionModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder="unet")
```
Next, the script includes several image transforms and a [preprocessing](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py#L622) function for applying the transforms to the images and returning the pixel values:
```py
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["pixel_values"] = [train_transforms(image) for image in images]
examples["clip_pixel_values"] = image_processor(images, return_tensors="pt").pixel_values
return examples
```
Lastly, the [training loop](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py#L706) handles converting the images to latents, adding noise, and predicting the noise residual.
If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.
```py
model_pred = unet(noisy_latents, timesteps, None, added_cond_kwargs=added_cond_kwargs).sample[:, :4]
```
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/kandinsky.md | https://huggingface.co./docs/diffusers/en/training/kandinsky/#training-script | #training-script | .md | 25_4 |
Once you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀
You'll train on the [Naruto BLIP captions](https://huggingface.co./datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters, but you can also create and train on your own dataset by following the [Create a dataset for training](create_dataset) guide. Set the environment variable `DATASET_NAME` to the name of the dataset on the Hub or if you're training on your own files, set the environment variable `TRAIN_DIR` to a path to your dataset.
If you’re training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.
<Tip>
To monitor training progress with Weights & Biases, add the `--report_to=wandb` parameter to the training command. You’ll also need to add the `--validation_prompt` to the training command to keep track of results. This can be really useful for debugging the model and viewing intermediate results.
</Tip>
<hfoptions id="training-inference">
<hfoption id="prior model">
```bash
export DATASET_NAME="lambdalabs/naruto-blip-captions"
accelerate launch --mixed_precision="fp16" train_text_to_image_prior.py \
--dataset_name=$DATASET_NAME \
--resolution=768 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--checkpoints_total_limit=3 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--validation_prompts="A robot naruto, 4k photo" \
--report_to="wandb" \
--push_to_hub \
--output_dir="kandi2-prior-naruto-model"
```
</hfoption>
<hfoption id="decoder model">
```bash
export DATASET_NAME="lambdalabs/naruto-blip-captions"
accelerate launch --mixed_precision="fp16" train_text_to_image_decoder.py \
--dataset_name=$DATASET_NAME \
--resolution=768 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--checkpoints_total_limit=3 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--validation_prompts="A robot naruto, 4k photo" \
--report_to="wandb" \
--push_to_hub \
--output_dir="kandi2-decoder-naruto-model"
```
</hfoption>
</hfoptions>
Once training is finished, you can use your newly trained model for inference!
<hfoptions id="training-inference">
<hfoption id="prior model">
```py
from diffusers import AutoPipelineForText2Image, DiffusionPipeline
import torch
prior_pipeline = DiffusionPipeline.from_pretrained(output_dir, torch_dtype=torch.float16)
prior_components = {"prior_" + k: v for k,v in prior_pipeline.components.items()}
pipeline = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", **prior_components, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt="A robot naruto, 4k photo"
image = pipeline(prompt=prompt, negative_prompt=negative_prompt).images[0]
```
<Tip>
Feel free to replace `kandinsky-community/kandinsky-2-2-decoder` with your own trained decoder checkpoint!
</Tip>
</hfoption>
<hfoption id="decoder model">
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("path/to/saved/model", torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()
prompt="A robot naruto, 4k photo"
image = pipeline(prompt=prompt).images[0]
```
For the decoder model, you can also perform inference from a saved checkpoint which can be useful for viewing intermediate results. In this case, load the checkpoint into the UNet:
```py
from diffusers import AutoPipelineForText2Image, UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("path/to/saved/model" + "/checkpoint-<N>/unet")
pipeline = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", unet=unet, torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()
image = pipeline(prompt="A robot naruto, 4k photo").images[0]
```
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/kandinsky.md | https://huggingface.co./docs/diffusers/en/training/kandinsky/#launch-the-script | #launch-the-script | .md | 25_5 |
Congratulations on training a Kandinsky 2.2 model! To learn more about how to use your new model, the following guides may be helpful:
- Read the [Kandinsky](../using-diffusers/kandinsky) guide to learn how to use it for a variety of different tasks (text-to-image, image-to-image, inpainting, interpolation), and how it can be combined with a ControlNet.
- Check out the [DreamBooth](dreambooth) and [LoRA](lora) training guides to learn how to train a personalized Kandinsky model with just a few example images. These two training techniques can even be combined! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/kandinsky.md | https://huggingface.co./docs/diffusers/en/training/kandinsky/#next-steps | #next-steps | .md | 25_6 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/wuerstchen.md | https://huggingface.co./docs/diffusers/en/training/wuerstchen/ | .md | 26_0 |
|
The [Wuerstchen](https://hf.co/papers/2306.00637) model drastically reduces computational costs by compressing the latent space by 42x, without compromising image quality and accelerating inference. During training, Wuerstchen uses two models (VQGAN + autoencoder) to compress the latents, and then a third model (text-conditioned latent diffusion model) is conditioned on this highly compressed space to generate an image.
To fit the prior model into GPU memory and to speedup training, try enabling `gradient_accumulation_steps`, `gradient_checkpointing`, and `mixed_precision` respectively.
This guide explores the [train_text_to_image_prior.py](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
```bash
cd examples/wuerstchen/text_to_image
pip install -r requirements.txt
```
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
<Tip>
The following sections highlight parts of the training scripts that are important for understanding how to modify it, but it doesn't cover every aspect of the [script](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) in detail. If you're interested in learning more, feel free to read through the scripts and let us know if you have any questions or concerns.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/wuerstchen.md | https://huggingface.co./docs/diffusers/en/training/wuerstchen/#wuerstchen | #wuerstchen | .md | 26_1 |
The training scripts provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L192) function. It provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.
For example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:
```bash
accelerate launch train_text_to_image_prior.py \
--mixed_precision="fp16"
```
Most of the parameters are identical to the parameters in the [Text-to-image](text2image#script-parameters) training guide, so let's dive right into the Wuerstchen training script! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/wuerstchen.md | https://huggingface.co./docs/diffusers/en/training/wuerstchen/#script-parameters | #script-parameters | .md | 26_2 |
The training script is also similar to the [Text-to-image](text2image#training-script) training guide, but it's been modified to support Wuerstchen. This guide focuses on the code that is unique to the Wuerstchen training script.
The [`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L441) function starts by initializing the image encoder - an [EfficientNet](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/modeling_efficient_net_encoder.py) - in addition to the usual scheduler and tokenizer.
```py
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
pretrained_checkpoint_file = hf_hub_download("dome272/wuerstchen", filename="model_v2_stage_b.pt")
state_dict = torch.load(pretrained_checkpoint_file, map_location="cpu")
image_encoder = EfficientNetEncoder()
image_encoder.load_state_dict(state_dict["effnet_state_dict"])
image_encoder.eval()
```
You'll also load the [`WuerstchenPrior`] model for optimization.
```py
prior = WuerstchenPrior.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
optimizer = optimizer_cls(
prior.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
```
Next, you'll apply some [transforms](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L656) to the images and [tokenize](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L637) the captions:
```py
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["effnet_pixel_values"] = [effnet_transforms(image) for image in images]
examples["text_input_ids"], examples["text_mask"] = tokenize_captions(examples)
return examples
```
Finally, the [training loop](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L656) handles compressing the images to latent space with the `EfficientNetEncoder`, adding noise to the latents, and predicting the noise residual with the [`WuerstchenPrior`] model.
```py
pred_noise = prior(noisy_latents, timesteps, prompt_embeds)
```
If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/wuerstchen.md | https://huggingface.co./docs/diffusers/en/training/wuerstchen/#training-script | #training-script | .md | 26_3 |
Once you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀
Set the `DATASET_NAME` environment variable to the dataset name from the Hub. This guide uses the [Naruto BLIP captions](https://huggingface.co./datasets/lambdalabs/naruto-blip-captions) dataset, but you can create and train on your own datasets as well (see the [Create a dataset for training](create_dataset) guide).
<Tip>
To monitor training progress with Weights & Biases, add the `--report_to=wandb` parameter to the training command. You’ll also need to add the `--validation_prompt` to the training command to keep track of results. This can be really useful for debugging the model and viewing intermediate results.
</Tip>
```bash
export DATASET_NAME="lambdalabs/naruto-blip-captions"
accelerate launch train_text_to_image_prior.py \
--mixed_precision="fp16" \
--dataset_name=$DATASET_NAME \
--resolution=768 \
--train_batch_size=4 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--dataloader_num_workers=4 \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--checkpoints_total_limit=3 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--validation_prompts="A robot naruto, 4k photo" \
--report_to="wandb" \
--push_to_hub \
--output_dir="wuerstchen-prior-naruto-model"
```
Once training is complete, you can use your newly trained model for inference!
```py
import torch
from diffusers import AutoPipelineForText2Image
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
pipeline = AutoPipelineForText2Image.from_pretrained("path/to/saved/model", torch_dtype=torch.float16).to("cuda")
caption = "A cute bird naruto holding a shield"
images = pipeline(
caption,
width=1024,
height=1536,
prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
prior_guidance_scale=4.0,
num_images_per_prompt=2,
).images
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/wuerstchen.md | https://huggingface.co./docs/diffusers/en/training/wuerstchen/#launch-the-script | #launch-the-script | .md | 26_4 |
Congratulations on training a Wuerstchen model! To learn more about how to use your new model, the following may be helpful:
- Take a look at the [Wuerstchen](../api/pipelines/wuerstchen#text-to-image-generation) API documentation to learn more about how to use the pipeline for text-to-image generation and its limitations. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/wuerstchen.md | https://huggingface.co./docs/diffusers/en/training/wuerstchen/#next-steps | #next-steps | .md | 26_5 |
There are many datasets on the [Hub](https://huggingface.co./datasets?task_categories=task_categories:text-to-image&sort=downloads) to train a model on, but if you can't find one you're interested in or want to use your own, you can create a dataset with the 🤗 [Datasets](https://huggingface.co./docs/datasets) library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. Another dataset structure may be a directory of images and a text file containing their corresponding text captions for tasks like text-to-image generation.
This guide will show you two ways to create a dataset to finetune on:
- provide a folder of images to the `--train_data_dir` argument
- upload a dataset to the Hub and pass the dataset repository id to the `--dataset_name` argument
<Tip>
💡 Learn more about how to create an image dataset for training in the [Create an image dataset](https://huggingface.co./docs/datasets/image_dataset) guide.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/create_dataset.md | https://huggingface.co./docs/diffusers/en/training/create_dataset/#create-a-dataset-for-training | #create-a-dataset-for-training | .md | 27_0 |
For unconditional generation, you can provide your own dataset as a folder of images. The training script uses the [`ImageFolder`](https://huggingface.co./docs/datasets/en/image_dataset#imagefolder) builder from 🤗 Datasets to automatically build a dataset from the folder. Your directory structure should look like:
```bash
data_dir/xxx.png
data_dir/xxy.png
data_dir/[...]/xxz.png
```
Pass the path to the dataset directory to the `--train_data_dir` argument, and then you can start training:
```bash
accelerate launch train_unconditional.py \
--train_data_dir <path-to-train-directory> \
<other-arguments>
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/create_dataset.md | https://huggingface.co./docs/diffusers/en/training/create_dataset/#provide-a-dataset-as-a-folder | #provide-a-dataset-as-a-folder | .md | 27_1 |
<Tip>
💡 For more details and context about creating and uploading a dataset to the Hub, take a look at the [Image search with 🤗 Datasets](https://huggingface.co./blog/image-search-datasets) post.
</Tip>
Start by creating a dataset with the [`ImageFolder`](https://huggingface.co./docs/datasets/image_load#imagefolder) feature, which creates an `image` column containing the PIL-encoded images.
You can use the `data_dir` or `data_files` parameters to specify the location of the dataset. The `data_files` parameter supports mapping specific files to dataset splits like `train` or `test`:
```python
from datasets import load_dataset
# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="path_to_zip_file")
# example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset(
"imagefolder",
data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip",
)
# example 4: providing several splits
dataset = load_dataset(
"imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]}
)
```
Then use the [`~datasets.Dataset.push_to_hub`] method to upload the dataset to the Hub:
```python
# assuming you have ran the huggingface-cli login command in a terminal
dataset.push_to_hub("name_of_your_dataset")
# if you want to push to a private repo, simply pass private=True:
dataset.push_to_hub("name_of_your_dataset", private=True)
```
Now the dataset is available for training by passing the dataset name to the `--dataset_name` argument:
```bash
accelerate launch --mixed_precision="fp16" train_text_to_image.py \
--pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5" \
--dataset_name="name_of_your_dataset" \
<other-arguments>
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/create_dataset.md | https://huggingface.co./docs/diffusers/en/training/create_dataset/#upload-your-data-to-the-hub | #upload-your-data-to-the-hub | .md | 27_2 |
Now that you've created a dataset, you can plug it into the `train_data_dir` (if your dataset is local) or `dataset_name` (if your dataset is on the Hub) arguments of a training script.
For your next steps, feel free to try and use your dataset to train a model for [unconditional generation](unconditional_training) or [text-to-image generation](text2image)! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/create_dataset.md | https://huggingface.co./docs/diffusers/en/training/create_dataset/#next-steps | #next-steps | .md | 27_3 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/sdxl.md | https://huggingface.co./docs/diffusers/en/training/sdxl/ | .md | 28_0 |
|
<Tip warning={true}>
This script is experimental, and it's easy to overfit and run into issues like catastrophic forgetting. Try exploring different hyperparameters to get the best results on your dataset.
</Tip>
[Stable Diffusion XL (SDXL)](https://hf.co/papers/2307.01952) is a larger and more powerful iteration of the Stable Diffusion model, capable of producing higher resolution images.
SDXL's UNet is 3x larger and the model adds a second text encoder to the architecture. Depending on the hardware available to you, this can be very computationally intensive and it may not run on a consumer GPU like a Tesla T4. To help fit this larger model into memory and to speedup training, try enabling `gradient_checkpointing`, `mixed_precision`, and `gradient_accumulation_steps`. You can reduce your memory-usage even more by enabling memory-efficient attention with [xFormers](../optimization/xformers) and using [bitsandbytes'](https://github.com/TimDettmers/bitsandbytes) 8-bit optimizer.
This guide will explore the [train_text_to_image_sdxl.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py) training script to help you become more familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
```bash
cd examples/text_to_image
pip install -r requirements_sdxl.txt
```
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/sdxl.md | https://huggingface.co./docs/diffusers/en/training/sdxl/#stable-diffusion-xl | #stable-diffusion-xl | .md | 28_1 |
<Tip>
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py) and let us know if you have any questions or concerns.
</Tip>
The training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L129) function. This function provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.
For example, to speedup training with mixed precision using the bf16 format, add the `--mixed_precision` parameter to the training command:
```bash
accelerate launch train_text_to_image_sdxl.py \
--mixed_precision="bf16"
```
Most of the parameters are identical to the parameters in the [Text-to-image](text2image#script-parameters) training guide, so you'll focus on the parameters that are relevant to training SDXL in this guide.
- `--pretrained_vae_model_name_or_path`: path to a pretrained VAE; the SDXL VAE is known to suffer from numerical instability, so this parameter allows you to specify a better [VAE](https://huggingface.co./madebyollin/sdxl-vae-fp16-fix)
- `--proportion_empty_prompts`: the proportion of image prompts to replace with empty strings
- `--timestep_bias_strategy`: where (earlier vs. later) in the timestep to apply a bias, which can encourage the model to either learn low or high frequency details
- `--timestep_bias_multiplier`: the weight of the bias to apply to the timestep
- `--timestep_bias_begin`: the timestep to begin applying the bias
- `--timestep_bias_end`: the timestep to end applying the bias
- `--timestep_bias_portion`: the proportion of timesteps to apply the bias to | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/sdxl.md | https://huggingface.co./docs/diffusers/en/training/sdxl/#script-parameters | #script-parameters | .md | 28_2 |
The [Min-SNR](https://huggingface.co./papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting either `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
```bash
accelerate launch train_text_to_image_sdxl.py \
--snr_gamma=5.0
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/sdxl.md | https://huggingface.co./docs/diffusers/en/training/sdxl/#min-snr-weighting | #min-snr-weighting | .md | 28_3 |
The training script is also similar to the [Text-to-image](text2image#training-script) training guide, but it's been modified to support SDXL training. This guide will focus on the code that is unique to the SDXL training script.
It starts by creating functions to [tokenize the prompts](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L478) to calculate the prompt embeddings, and to compute the image embeddings with the [VAE](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L519). Next, you'll a function to [generate the timesteps weights](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L531) depending on the number of timesteps and the timestep bias strategy to apply.
Within the [`main()`](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L572) function, in addition to loading a tokenizer, the script loads a second tokenizer and text encoder because the SDXL architecture uses two of each:
```py
tokenizer_one = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
)
tokenizer_two = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
)
text_encoder_cls_one = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
)
```
The [prompt and image embeddings](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L857) are computed first and kept in memory, which isn't typically an issue for a smaller dataset, but for larger datasets it can lead to memory problems. If this is the case, you should save the pre-computed embeddings to disk separately and load them into memory during the training process (see this [PR](https://github.com/huggingface/diffusers/pull/4505) for more discussion about this topic).
```py
text_encoders = [text_encoder_one, text_encoder_two]
tokenizers = [tokenizer_one, tokenizer_two]
compute_embeddings_fn = functools.partial(
encode_prompt,
text_encoders=text_encoders,
tokenizers=tokenizers,
proportion_empty_prompts=args.proportion_empty_prompts,
caption_column=args.caption_column,
)
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
train_dataset = train_dataset.map(
compute_vae_encodings_fn,
batched=True,
batch_size=args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps,
new_fingerprint=new_fingerprint_for_vae,
)
```
After calculating the embeddings, the text encoder, VAE, and tokenizer are deleted to free up some memory:
```py
del text_encoders, tokenizers, vae
gc.collect()
torch.cuda.empty_cache()
```
Finally, the [training loop](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L943) takes care of the rest. If you chose to apply a timestep bias strategy, you'll see the timestep weights are calculated and added as noise:
```py
weights = generate_timestep_weights(args, noise_scheduler.config.num_train_timesteps).to(
model_input.device
)
timesteps = torch.multinomial(weights, bsz, replacement=True).long()
noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
```
If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/sdxl.md | https://huggingface.co./docs/diffusers/en/training/sdxl/#training-script | #training-script | .md | 28_4 |
Once you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀
Let’s train on the [Naruto BLIP captions](https://huggingface.co./datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `DATASET_NAME` to the model and the dataset (either from the Hub or a local path). You should also specify a VAE other than the SDXL VAE (either from the Hub or a local path) with `VAE_NAME` to avoid numerical instabilities.
<Tip>
To monitor training progress with Weights & Biases, add the `--report_to=wandb` parameter to the training command. You’ll also need to add the `--validation_prompt` and `--validation_epochs` to the training command to keep track of results. This can be really useful for debugging the model and viewing intermediate results.
</Tip>
```bash
export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
export VAE_NAME="madebyollin/sdxl-vae-fp16-fix"
export DATASET_NAME="lambdalabs/naruto-blip-captions"
accelerate launch train_text_to_image_sdxl.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--pretrained_vae_model_name_or_path=$VAE_NAME \
--dataset_name=$DATASET_NAME \
--enable_xformers_memory_efficient_attention \
--resolution=512 \
--center_crop \
--random_flip \
--proportion_empty_prompts=0.2 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=10000 \
--use_8bit_adam \
--learning_rate=1e-06 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--mixed_precision="fp16" \
--report_to="wandb" \
--validation_prompt="a cute Sundar Pichai creature" \
--validation_epochs 5 \
--checkpointing_steps=5000 \
--output_dir="sdxl-naruto-model" \
--push_to_hub
```
After you've finished training, you can use your newly trained SDXL model for inference!
<hfoptions id="inference">
<hfoption id="PyTorch">
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("path/to/your/model", torch_dtype=torch.float16).to("cuda")
prompt = "A naruto with green eyes and red legs."
image = pipeline(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
image.save("naruto.png")
```
</hfoption>
<hfoption id="PyTorch XLA">
[PyTorch XLA](https://pytorch.org/xla) allows you to run PyTorch on XLA devices such as TPUs, which can be faster. The initial warmup step takes longer because the model needs to be compiled and optimized. However, subsequent calls to the pipeline on an input **with the same length** as the original prompt are much faster because it can reuse the optimized graph.
```py
from diffusers import DiffusionPipeline
import torch
import torch_xla.core.xla_model as xm
device = xm.xla_device()
pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0").to(device)
prompt = "A naruto with green eyes and red legs."
start = time()
image = pipeline(prompt, num_inference_steps=inference_steps).images[0]
print(f'Compilation time is {time()-start} sec')
image.save("naruto.png")
start = time()
image = pipeline(prompt, num_inference_steps=inference_steps).images[0]
print(f'Inference time is {time()-start} sec after compilation')
```
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/sdxl.md | https://huggingface.co./docs/diffusers/en/training/sdxl/#launch-the-script | #launch-the-script | .md | 28_5 |
Congratulations on training a SDXL model! To learn more about how to use your new model, the following guides may be helpful:
- Read the [Stable Diffusion XL](../using-diffusers/sdxl) guide to learn how to use it for a variety of different tasks (text-to-image, image-to-image, inpainting), how to use it's refiner model, and the different types of micro-conditionings.
- Check out the [DreamBooth](dreambooth) and [LoRA](lora) training guides to learn how to train a personalized SDXL model with just a few example images. These two training techniques can even be combined! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/sdxl.md | https://huggingface.co./docs/diffusers/en/training/sdxl/#next-steps | #next-steps | .md | 28_6 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/unconditional_training.md | https://huggingface.co./docs/diffusers/en/training/unconditional_training/ | .md | 29_0 |
|
Unconditional image generation models are not conditioned on text or images during training. It only generates images that resemble its training data distribution.
This guide will explore the [train_unconditional.py](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then navigate to the example folder containing the training script and install the required dependencies:
```bash
cd examples/unconditional_image_generation
pip install -r requirements.txt
```
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/unconditional_training.md | https://huggingface.co./docs/diffusers/en/training/unconditional_training/#unconditional-image-generation | #unconditional-image-generation | .md | 29_1 |
<Tip>
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py) and let us know if you have any questions or concerns.
</Tip>
The training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L55) function. It provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.
For example, to speedup training with mixed precision using the bf16 format, add the `--mixed_precision` parameter to the training command:
```bash
accelerate launch train_unconditional.py \
--mixed_precision="bf16"
```
Some basic and important parameters to specify include:
- `--dataset_name`: the name of the dataset on the Hub or a local path to the dataset to train on
- `--output_dir`: where to save the trained model
- `--push_to_hub`: whether to push the trained model to the Hub
- `--checkpointing_steps`: frequency of saving a checkpoint as the model trains; this is useful if training is interrupted, you can continue training from that checkpoint by adding `--resume_from_checkpoint` to your training command
Bring your dataset, and let the training script handle everything else! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/unconditional_training.md | https://huggingface.co./docs/diffusers/en/training/unconditional_training/#script-parameters | #script-parameters | .md | 29_2 |
The code for preprocessing the dataset and the training loop is found in the [`main()`](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L275) function. If you need to adapt the training script, this is where you'll need to make your changes.
The `train_unconditional` script [initializes a `UNet2DModel`](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L356) if you don't provide a model configuration. You can configure the UNet here if you'd like:
```py
model = UNet2DModel(
sample_size=args.resolution,
in_channels=3,
out_channels=3,
layers_per_block=2,
block_out_channels=(128, 128, 256, 256, 512, 512),
down_block_types=(
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D",
"DownBlock2D",
),
up_block_types=(
"UpBlock2D",
"AttnUpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
),
)
```
Next, the script initializes a [scheduler](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L418) and [optimizer](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L429):
```py
# Initialize the scheduler
accepts_prediction_type = "prediction_type" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
if accepts_prediction_type:
noise_scheduler = DDPMScheduler(
num_train_timesteps=args.ddpm_num_steps,
beta_schedule=args.ddpm_beta_schedule,
prediction_type=args.prediction_type,
)
else:
noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)
# Initialize the optimizer
optimizer = torch.optim.AdamW(
model.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
```
Then it [loads a dataset](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L451) and you can specify how to [preprocess](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L455) it:
```py
dataset = load_dataset("imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train")
augmentations = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
```
Finally, the [training loop](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L540) handles everything else such as adding noise to the images, predicting the noise residual, calculating the loss, saving checkpoints at specified steps, and saving and pushing the model to the Hub. If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/unconditional_training.md | https://huggingface.co./docs/diffusers/en/training/unconditional_training/#training-script | #training-script | .md | 29_3 |
Once you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀
<Tip warning={true}>
A full training run takes 2 hours on 4xV100 GPUs.
</Tip>
<hfoptions id="launchtraining">
<hfoption id="single GPU">
```bash
accelerate launch train_unconditional.py \
--dataset_name="huggan/flowers-102-categories" \
--output_dir="ddpm-ema-flowers-64" \
--mixed_precision="fp16" \
--push_to_hub
```
</hfoption>
<hfoption id="multi-GPU">
If you're training with more than one GPU, add the `--multi_gpu` parameter to the training command:
```bash
accelerate launch --multi_gpu train_unconditional.py \
--dataset_name="huggan/flowers-102-categories" \
--output_dir="ddpm-ema-flowers-64" \
--mixed_precision="fp16" \
--push_to_hub
```
</hfoption>
</hfoptions>
The training script creates and saves a checkpoint file in your repository. Now you can load and use your trained model for inference:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128").to("cuda")
image = pipeline().images[0]
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/unconditional_training.md | https://huggingface.co./docs/diffusers/en/training/unconditional_training/#launch-the-script | #launch-the-script | .md | 29_4 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/lora.md | https://huggingface.co./docs/diffusers/en/training/lora/ | .md | 30_0 |
|
<Tip warning={true}>
This is experimental and the API may change in the future.
</Tip>
[LoRA (Low-Rank Adaptation of Large Language Models)](https://hf.co/papers/2106.09685) is a popular and lightweight training technique that significantly reduces the number of trainable parameters. It works by inserting a smaller number of new weights into the model and only these are trained. This makes training with LoRA much faster, memory-efficient, and produces smaller model weights (a few hundred MBs), which are easier to store and share. LoRA can also be combined with other training techniques like DreamBooth to speedup training.
<Tip>
LoRA is very versatile and supported for [DreamBooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py), [Kandinsky 2.2](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py), [Stable Diffusion XL](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora_sdxl.py), [text-to-image](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py), and [Wuerstchen](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py).
</Tip>
This guide will explore the [train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Navigate to the example folder with the training script and install the required dependencies for the script you're using:
<hfoptions id="installation">
<hfoption id="PyTorch">
```bash
cd examples/text_to_image
pip install -r requirements.txt
```
</hfoption>
<hfoption id="Flax">
```bash
cd examples/text_to_image
pip install -r requirements_flax.txt
```
</hfoption>
</hfoptions>
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
<Tip>
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/text_to_image_lora.py) and let us know if you have any questions or concerns.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/lora.md | https://huggingface.co./docs/diffusers/en/training/lora/#lora | #lora | .md | 30_1 |
The training script has many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/dd9a5caf61f04d11c0fa9f3947b69ab0010c9a0f/examples/text_to_image/train_text_to_image_lora.py#L85) function. Default values are provided for most parameters that work pretty well, but you can also set your own values in the training command if you'd like.
For example, to increase the number of epochs to train:
```bash
accelerate launch train_text_to_image_lora.py \
--num_train_epochs=150 \
```
Many of the basic and important parameters are described in the [Text-to-image](text2image#script-parameters) training guide, so this guide just focuses on the LoRA relevant parameters:
- `--rank`: the inner dimension of the low-rank matrices to train; a higher rank means more trainable parameters
- `--learning_rate`: the default learning rate is 1e-4, but with LoRA, you can use a higher learning rate | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/lora.md | https://huggingface.co./docs/diffusers/en/training/lora/#script-parameters | #script-parameters | .md | 30_2 |
The dataset preprocessing code and training loop are found in the [`main()`](https://github.com/huggingface/diffusers/blob/dd9a5caf61f04d11c0fa9f3947b69ab0010c9a0f/examples/text_to_image/train_text_to_image_lora.py#L371) function, and if you need to adapt the training script, this is where you'll make your changes.
As with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the LoRA relevant parts of the script.
<hfoptions id="lora">
<hfoption id="UNet">
Diffusers uses [`~peft.LoraConfig`] from the [PEFT](https://hf.co/docs/peft) library to set up the parameters of the LoRA adapter such as the rank, alpha, and which modules to insert the LoRA weights into. The adapter is added to the UNet, and only the LoRA layers are filtered for optimization in `lora_layers`.
```py
unet_lora_config = LoraConfig(
r=args.rank,
lora_alpha=args.rank,
init_lora_weights="gaussian",
target_modules=["to_k", "to_q", "to_v", "to_out.0"],
)
unet.add_adapter(unet_lora_config)
lora_layers = filter(lambda p: p.requires_grad, unet.parameters())
```
</hfoption>
<hfoption id="text encoder">
Diffusers also supports finetuning the text encoder with LoRA from the [PEFT](https://hf.co/docs/peft) library when necessary such as finetuning Stable Diffusion XL (SDXL). The [`~peft.LoraConfig`] is used to configure the parameters of the LoRA adapter which are then added to the text encoder, and only the LoRA layers are filtered for training.
```py
text_lora_config = LoraConfig(
r=args.rank,
lora_alpha=args.rank,
init_lora_weights="gaussian",
target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
)
text_encoder_one.add_adapter(text_lora_config)
text_encoder_two.add_adapter(text_lora_config)
text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))
text_lora_parameters_two = list(filter(lambda p: p.requires_grad, text_encoder_two.parameters()))
```
</hfoption>
</hfoptions>
The [optimizer](https://github.com/huggingface/diffusers/blob/e4b8f173b97731686e290b2eb98e7f5df2b1b322/examples/text_to_image/train_text_to_image_lora.py#L529) is initialized with the `lora_layers` because these are the only weights that'll be optimized:
```py
optimizer = optimizer_cls(
lora_layers,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
```
Aside from setting up the LoRA layers, the training script is more or less the same as train_text_to_image.py! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/lora.md | https://huggingface.co./docs/diffusers/en/training/lora/#training-script | #training-script | .md | 30_3 |
Once you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀
Let's train on the [Naruto BLIP captions](https://huggingface.co./datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `DATASET_NAME` to the model and dataset respectively. You should also specify where to save the model in `OUTPUT_DIR`, and the name of the model to save to on the Hub with `HUB_MODEL_ID`. The script creates and saves the following files to your repository:
- saved model checkpoints
- `pytorch_lora_weights.safetensors` (the trained LoRA weights)
If you're training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.
<Tip warning={true}>
A full training run takes ~5 hours on a 2080 Ti GPU with 11GB of VRAM.
</Tip>
```bash
export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
export OUTPUT_DIR="/sddata/finetune/lora/naruto"
export HUB_MODEL_ID="naruto-lora"
export DATASET_NAME="lambdalabs/naruto-blip-captions"
accelerate launch --mixed_precision="fp16" train_text_to_image_lora.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_NAME \
--dataloader_num_workers=8 \
--resolution=512 \
--center_crop \
--random_flip \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=15000 \
--learning_rate=1e-04 \
--max_grad_norm=1 \
--lr_scheduler="cosine" \
--lr_warmup_steps=0 \
--output_dir=${OUTPUT_DIR} \
--push_to_hub \
--hub_model_id=${HUB_MODEL_ID} \
--report_to=wandb \
--checkpointing_steps=500 \
--validation_prompt="A naruto with blue eyes." \
--seed=1337
```
Once training has been completed, you can use your model for inference:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
pipeline.load_lora_weights("path/to/lora/model", weight_name="pytorch_lora_weights.safetensors")
image = pipeline("A naruto with blue eyes").images[0]
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/lora.md | https://huggingface.co./docs/diffusers/en/training/lora/#launch-the-script | #launch-the-script | .md | 30_4 |
Congratulations on training a new model with LoRA! To learn more about how to use your new model, the following guides may be helpful:
- Learn how to [load different LoRA formats](../using-diffusers/loading_adapters#LoRA) trained using community trainers like Kohya and TheLastBen.
- Learn how to use and [combine multiple LoRA's](../tutorials/using_peft_for_inference) with PEFT for inference. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/lora.md | https://huggingface.co./docs/diffusers/en/training/lora/#next-steps | #next-steps | .md | 30_5 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/controlnet.md | https://huggingface.co./docs/diffusers/en/training/controlnet/ | .md | 31_0 |
|
[ControlNet](https://hf.co/papers/2302.05543) models are adapters trained on top of another pretrained model. It allows for a greater degree of control over image generation by conditioning the model with an additional input image. The input image can be a canny edge, depth map, human pose, and many more.
If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing`, `gradient_accumulation_steps`, and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. You should have a GPU with >30GB of memory if you want to train faster with Flax.
This guide will explore the [train_controlnet.py](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
<hfoptions id="installation">
<hfoption id="PyTorch">
```bash
cd examples/controlnet
pip install -r requirements.txt
```
</hfoption>
<hfoption id="Flax">
If you have access to a TPU, the Flax training script runs even faster! Let's run the training script on the [Google Cloud TPU VM](https://cloud.google.com/tpu/docs/run-calculation-jax). Create a single TPU v4-8 VM and connect to it:
```bash
ZONE=us-central2-b
TPU_TYPE=v4-8
VM_NAME=hg_flax
gcloud alpha compute tpus tpu-vm create $VM_NAME \
--zone $ZONE \
--accelerator-type $TPU_TYPE \
--version tpu-vm-v4-base
gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \
```
Install JAX 0.4.5:
```bash
pip install "jax[tpu]==0.4.5" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
```
Then install the required dependencies for the Flax script:
```bash
cd examples/controlnet
pip install -r requirements_flax.txt
```
</hfoption>
</hfoptions>
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
<Tip>
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet.py) and let us know if you have any questions or concerns.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/controlnet.md | https://huggingface.co./docs/diffusers/en/training/controlnet/#controlnet | #controlnet | .md | 31_1 |
The training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L231) function. This function provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.
For example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:
```bash
accelerate launch train_controlnet.py \
--mixed_precision="fp16"
```
Many of the basic and important parameters are described in the [Text-to-image](text2image#script-parameters) training guide, so this guide just focuses on the relevant parameters for ControlNet:
- `--max_train_samples`: the number of training samples; this can be lowered for faster training, but if you want to stream really large datasets, you'll need to include this parameter and the `--streaming` parameter in your training command
- `--gradient_accumulation_steps`: number of update steps to accumulate before the backward pass; this allows you to train with a bigger batch size than your GPU memory can typically handle | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/controlnet.md | https://huggingface.co./docs/diffusers/en/training/controlnet/#script-parameters | #script-parameters | .md | 31_2 |
The [Min-SNR](https://huggingface.co./papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
Add the `--snr_gamma` parameter and set it to the recommended value of 5.0:
```bash
accelerate launch train_controlnet.py \
--snr_gamma=5.0
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/controlnet.md | https://huggingface.co./docs/diffusers/en/training/controlnet/#min-snr-weighting | #min-snr-weighting | .md | 31_3 |
As with the script parameters, a general walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the relevant parts of the ControlNet script.
The training script has a [`make_train_dataset`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L582) function for preprocessing the dataset with image transforms and caption tokenization. You'll see that in addition to the usual caption tokenization and image transforms, the script also includes transforms for the conditioning image.
<Tip>
If you're streaming a dataset on a TPU, performance may be bottlenecked by the 🤗 Datasets library which is not optimized for images. To ensure maximum throughput, you're encouraged to explore other dataset formats like [WebDataset](https://webdataset.github.io/webdataset/), [TorchData](https://github.com/pytorch/data), and [TensorFlow Datasets](https://www.tensorflow.org/datasets/tfless_tfds).
</Tip>
```py
conditioning_image_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
]
)
```
Within the [`main()`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L713) function, you'll find the code for loading the tokenizer, text encoder, scheduler and models. This is also where the ControlNet model is loaded either from existing weights or randomly initialized from a UNet:
```py
if args.controlnet_model_name_or_path:
logger.info("Loading existing controlnet weights")
controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)
else:
logger.info("Initializing controlnet weights from unet")
controlnet = ControlNetModel.from_unet(unet)
```
The [optimizer](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L871) is set up to update the ControlNet parameters:
```py
params_to_optimize = controlnet.parameters()
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
```
Finally, in the [training loop](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L943), the conditioning text embeddings and image are passed to the down and mid-blocks of the ControlNet model:
```py
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
controlnet_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
down_block_res_samples, mid_block_res_sample = controlnet(
noisy_latents,
timesteps,
encoder_hidden_states=encoder_hidden_states,
controlnet_cond=controlnet_image,
return_dict=False,
)
```
If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/controlnet.md | https://huggingface.co./docs/diffusers/en/training/controlnet/#training-script | #training-script | .md | 31_4 |
Now you're ready to launch the training script! 🚀
This guide uses the [fusing/fill50k](https://huggingface.co./datasets/fusing/fill50k) dataset, but remember, you can create and use your own dataset if you want (see the [Create a dataset for training](create_dataset) guide).
Set the environment variable `MODEL_NAME` to a model id on the Hub or a path to a local model and `OUTPUT_DIR` to where you want to save the model.
Download the following images to condition your training with:
```bash
wget https://huggingface.co./datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png
wget https://huggingface.co./datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
```
One more thing before you launch the script! Depending on the GPU you have, you may need to enable certain optimizations to train a ControlNet. The default configuration in this script requires ~38GB of vRAM. If you're training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.
<hfoptions id="gpu-select">
<hfoption id="16GB">
On a 16GB GPU, you can use bitsandbytes 8-bit optimizer and gradient checkpointing to optimize your training run. Install bitsandbytes:
```py
pip install bitsandbytes
```
Then, add the following parameter to your training command:
```bash
accelerate launch train_controlnet.py \
--gradient_checkpointing \
--use_8bit_adam \
```
</hfoption>
<hfoption id="12GB">
On a 12GB GPU, you'll need bitsandbytes 8-bit optimizer, gradient checkpointing, xFormers, and set the gradients to `None` instead of zero to reduce your memory-usage.
```bash
accelerate launch train_controlnet.py \
--use_8bit_adam \
--gradient_checkpointing \
--enable_xformers_memory_efficient_attention \
--set_grads_to_none \
```
</hfoption>
<hfoption id="8GB">
On a 8GB GPU, you'll need to use [DeepSpeed](https://www.deepspeed.ai/) to offload some of the tensors from the vRAM to either the CPU or NVME to allow training with less GPU memory.
Run the following command to configure your 🤗 Accelerate environment:
```bash
accelerate config
```
During configuration, confirm that you want to use DeepSpeed stage 2. Now it should be possible to train on under 8GB vRAM by combining DeepSpeed stage 2, fp16 mixed precision, and offloading the model parameters and the optimizer state to the CPU. The drawback is that this requires more system RAM (~25 GB). See the [DeepSpeed documentation](https://huggingface.co./docs/accelerate/usage_guides/deepspeed) for more configuration options. Your configuration file should look something like:
```bash
compute_environment: LOCAL_MACHINE
deepspeed_config:
gradient_accumulation_steps: 4
offload_optimizer_device: cpu
offload_param_device: cpu
zero3_init_flag: false
zero_stage: 2
distributed_type: DEEPSPEED
```
You should also change the default Adam optimizer to DeepSpeed’s optimized version of Adam [`deepspeed.ops.adam.DeepSpeedCPUAdam`](https://deepspeed.readthedocs.io/en/latest/optimizers.html#adam-cpu) for a substantial speedup. Enabling `DeepSpeedCPUAdam` requires your system’s CUDA toolchain version to be the same as the one installed with PyTorch.
bitsandbytes 8-bit optimizers don’t seem to be compatible with DeepSpeed at the moment.
That's it! You don't need to add any additional parameters to your training command.
</hfoption>
</hfoptions>
<hfoptions id="training-inference">
<hfoption id="PyTorch">
```bash
export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
export OUTPUT_DIR="path/to/save/model"
accelerate launch train_controlnet.py \
--pretrained_model_name_or_path=$MODEL_DIR \
--output_dir=$OUTPUT_DIR \
--dataset_name=fusing/fill50k \
--resolution=512 \
--learning_rate=1e-5 \
--validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
--validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--push_to_hub
```
</hfoption>
<hfoption id="Flax">
With Flax, you can [profile your code](https://jax.readthedocs.io/en/latest/profiling.html) by adding the `--profile_steps==5` parameter to your training command. Install the Tensorboard profile plugin:
```bash
pip install tensorflow tensorboard-plugin-profile
tensorboard --logdir runs/fill-circle-100steps-20230411_165612/
```
Then you can inspect the profile at [http://localhost:6006/#profile](http://localhost:6006/#profile).
<Tip warning={true}>
If you run into version conflicts with the plugin, try uninstalling and reinstalling all versions of TensorFlow and Tensorboard. The debugging functionality of the profile plugin is still experimental, and not all views are fully functional. The `trace_viewer` cuts off events after 1M, which can result in all your device traces getting lost if for example, you profile the compilation step by accident.
</Tip>
```bash
python3 train_controlnet_flax.py \
--pretrained_model_name_or_path=$MODEL_DIR \
--output_dir=$OUTPUT_DIR \
--dataset_name=fusing/fill50k \
--resolution=512 \
--learning_rate=1e-5 \
--validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
--validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
--validation_steps=1000 \
--train_batch_size=2 \
--revision="non-ema" \
--from_pt \
--report_to="wandb" \
--tracker_project_name=$HUB_MODEL_ID \
--num_train_epochs=11 \
--push_to_hub \
--hub_model_id=$HUB_MODEL_ID
```
</hfoption>
</hfoptions>
Once training is complete, you can use your newly trained model for inference!
```py
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.utils import load_image
import torch
controlnet = ControlNetModel.from_pretrained("path/to/controlnet", torch_dtype=torch.float16)
pipeline = StableDiffusionControlNetPipeline.from_pretrained(
"path/to/base/model", controlnet=controlnet, torch_dtype=torch.float16
).to("cuda")
control_image = load_image("./conditioning_image_1.png")
prompt = "pale golden rod circle with old lace background"
generator = torch.manual_seed(0)
image = pipeline(prompt, num_inference_steps=20, generator=generator, image=control_image).images[0]
image.save("./output.png")
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/controlnet.md | https://huggingface.co./docs/diffusers/en/training/controlnet/#launch-the-script | #launch-the-script | .md | 31_5 |
Stable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the [`train_controlnet_sdxl.py`](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet_sdxl.py) script to train a ControlNet adapter for the SDXL model.
The SDXL training script is discussed in more detail in the [SDXL training](sdxl) guide. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/controlnet.md | https://huggingface.co./docs/diffusers/en/training/controlnet/#stable-diffusion-xl | #stable-diffusion-xl | .md | 31_6 |
Congratulations on training your own ControlNet! To learn more about how to use your new model, the following guides may be helpful:
- Learn how to [use a ControlNet](../using-diffusers/controlnet) for inference on a variety of tasks. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/controlnet.md | https://huggingface.co./docs/diffusers/en/training/controlnet/#next-steps | #next-steps | .md | 31_7 |
<!--Copyright 2024 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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/text_inversion.md | https://huggingface.co./docs/diffusers/en/training/text_inversion/ | .md | 32_0 |
|
[Textual Inversion](https://hf.co/papers/2208.01618) is a training technique for personalizing image generation models with just a few example images of what you want it to learn. This technique works by learning and updating the text embeddings (the new embeddings are tied to a special word you must use in the prompt) to match the example images you provide.
If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. With the same configuration and setup as PyTorch, the Flax training script should be at least ~70% faster!
This guide will explore the [textual_inversion.py](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Navigate to the example folder with the training script and install the required dependencies for the script you're using:
<hfoptions id="installation">
<hfoption id="PyTorch">
```bash
cd examples/textual_inversion
pip install -r requirements.txt
```
</hfoption>
<hfoption id="Flax">
```bash
cd examples/textual_inversion
pip install -r requirements_flax.txt
```
</hfoption>
</hfoptions>
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co./docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
<Tip>
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py) and let us know if you have any questions or concerns.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/text_inversion.md | https://huggingface.co./docs/diffusers/en/training/text_inversion/#textual-inversion | #textual-inversion | .md | 32_1 |