End of training

.gitattributes

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README.md

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+---
+base_model: stabilityai/stable-diffusion-3-medium-diffusers
+library_name: diffusers
+license: other
+instance_prompt: photo of a A@K man
+widget:
+- text: photo of a A@K man in a restaurant
+  output:
+    url: image_0.png
+- text: photo of a A@K man in a restaurant
+  output:
+    url: image_1.png
+- text: photo of a A@K man in a restaurant
+  output:
+    url: image_2.png
+- text: photo of a A@K man in a restaurant
+  output:
+    url: image_3.png
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- template:sd-lora
+- sd3
+- sd3-diffusers
+---
+
+<!-- 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. -->
+
+
+# SD3 DreamBooth - mtreinik/output-20241203_235620
+
+<Gallery />
+
+## Model description
+
+These are mtreinik/output-20241203_235620 DreamBooth weights for stabilityai/stable-diffusion-3-medium-diffusers.
+
+The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md).
+
+Was the text encoder fine-tuned? False.
+
+## Trigger words
+
+You should use `photo of a A@K man` to trigger the image generation.
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+
+```py
+from diffusers import AutoPipelineForText2Image
+import torch
+pipeline = AutoPipelineForText2Image.from_pretrained('mtreinik/output-20241203_235620', torch_dtype=torch.float16).to('cuda')
+image = pipeline('photo of a A@K man in a restaurant').images[0]
+```
+
+## License
+
+Please adhere to the licensing terms as described `[here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/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]

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