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: A bike lane with a white-painted buffer zone separating cyclists
+  from moving car traffic.
+widget:
+- text: A photo of a bike lane with a white-painted buffer zone separating cyclists
+    from moving car traffic.
+  output:
+    url: image_0.png
+- text: A photo of a bike lane with a white-painted buffer zone separating cyclists
+    from moving car traffic.
+  output:
+    url: image_1.png
+- text: A photo of a bike lane with a white-painted buffer zone separating cyclists
+    from moving car traffic.
+  output:
+    url: image_2.png
+- text: A photo of a bike lane with a white-painted buffer zone separating cyclists
+    from moving car traffic.
+  output:
+    url: image_3.png
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- 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 LoRA - SteveWCG/trained_buffer
+
+<Gallery />
+
+## Model description
+
+These are SteveWCG/trained_buffer DreamBooth LoRA 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 LoRA for the text encoder enabled? True.
+
+## Trigger words
+
+You should use `A bike lane with a white-painted buffer zone separating cyclists from moving car traffic.` to trigger the image generation.
+
+## Download model
+
+[Download the *.safetensors LoRA](SteveWCG/trained_buffer/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(stabilityai/stable-diffusion-3-medium-diffusers, torch_dtype=torch.float16).to('cuda')
+pipeline.load_lora_weights('SteveWCG/trained_buffer', weight_name='pytorch_lora_weights.safetensors')
+image = pipeline('A photo of a bike lane with a white-painted buffer zone separating cyclists from moving car traffic.').images[0]
+```
+
+### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
+
+- **LoRA**: download **[`diffusers_lora_weights.safetensors` here 💾](/SteveWCG/trained_buffer/blob/main/diffusers_lora_weights.safetensors)**.
+    - Rename it and place it on your `models/Lora` folder.
+    - On AUTOMATIC1111, load the LoRA by adding `<lora:your_new_name:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
+
+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/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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