End of training

README.md

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+---
+base_model: Tongyi-MAI/Z-Image
+library_name: diffusers
+license: other
+instance_prompt: a photo of sks dog
+widget: []
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- z-image
+- 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. -->
+
+
+# Z Image DreamBooth LoRA - linoyts/trained-z-image-lora
+
+<Gallery />
+
+## Model description
+
+These are linoyts/trained-z-image-lora DreamBooth LoRA weights for Tongyi-MAI/Z-Image.
+
+The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Z Image diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_z_image.md).
+
+Quant training? None
+
+## Trigger words
+
+You should use `a photo of sks dog` to trigger the image generation.
+
+## Download model
+
+[Download the *.safetensors LoRA](linoyts/trained-z-image-lora/tree/main) in the Files & versions tab.
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+
+```py
+from diffusers import AutoPipelineForText2Image
+import torch
+pipeline = AutoPipelineForText2Image.from_pretrained("Tongyi-MAI/Z-Image", torch_dtype=torch.bfloat16).to('cuda')
+pipeline.load_lora_weights('linoyts/trained-z-image-lora', weight_name='pytorch_lora_weights.safetensors')
+image = pipeline('A photo of sks dog in a bucket').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)
+
+## License
+
+Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.2/blob/main/LICENSE.md).
+
+
+## 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]

pytorch_lora_weights.safetensors

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