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---
base_model: HiDream-ai/HiDream-I1-Full
library_name: diffusers
license: mit
instance_prompt: a photo of sks dog
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- hidream
- hidream-diffusers
- template:sd-lora
- text-to-image
- diffusers-training
- diffusers
- lora
- hidream
- hidream-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# HiDream Image DreamBooth LoRA - linoyts/dog-hidream-lora
<Gallery />
## Model description
These are linoyts/dog-hidream-lora DreamBooth LoRA weights for HiDream-ai/HiDream-I1-Full.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [HiDream Image diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_hidream.md).
## Trigger words
You should use `a photo of sks dog` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](linoyts/dog-hidream-lora/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
>>> import torch
>>> from transformers import PreTrainedTokenizerFast, LlamaForCausalLM
>>> from diffusers import UniPCMultistepScheduler, HiDreamImagePipeline
>>> scheduler = UniPCMultistepScheduler(
... flow_shift=3.0, prediction_type="flow_prediction", use_flow_sigmas=True
... )
>>> tokenizer_4 = PreTrainedTokenizerFast.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
>>> text_encoder_4 = LlamaForCausalLM.from_pretrained(
... "meta-llama/Meta-Llama-3.1-8B-Instruct",
... output_hidden_states=True,
... output_attentions=True,
... torch_dtype=torch.bfloat16,
... )
>>> pipe = HiDreamImagePipeline.from_pretrained(
... "HiDream-ai/HiDream-I1-Full",
... scheduler=scheduler,
... tokenizer_4=tokenizer_4,
... text_encoder_4=text_encoder_4,
... torch_dtype=torch.bfloat16,
... )
>>> pipe.enable_model_cpu_offload()
>>> pipe.load_lora_weights(f"linoyts/dog-hidream-lora")
>>> image = pipe(f"a photo of sks dog").images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |