End of training

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+image_0.png filter=lfs diff=lfs merge=lfs -text
+image_1.png filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: openrail++
+library_name: diffusers
+tags:
+- text-to-image
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- template:sd-lora
+- stable-diffusion-xl
+- stable-diffusion-xl-diffusers
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+instance_prompt: a photo of SCARMOZA cheese
+widget:
+- text: A small yellow ball of SCARMOZA cheese on a rustic wooden table.
+  output:
+    url: image_0.png
+- text: A small yellow ball of SCARMOZA cheese on a rustic wooden table.
+  output:
+    url: image_1.png
+- text: A small yellow ball of SCARMOZA cheese on a rustic wooden table.
+  output:
+    url: image_2.png
+- text: A small yellow ball of SCARMOZA cheese on a rustic wooden table.
+  output:
+    url: image_3.png
+---
+
+<!-- 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. -->
+
+
+# SDXL LoRA DreamBooth - JawadC/scarmoza
+
+<Gallery />
+
+## Model description
+
+These are JawadC/scarmoza LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
+
+The weights were trained  using [DreamBooth](https://dreambooth.github.io/).
+
+LoRA for the text encoder was enabled: False.
+
+Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
+
+## Trigger words
+
+You should use a photo of SCARMOZA cheese to trigger the image generation.
+
+## Download model
+
+Weights for this model are available in Safetensors format.
+
+[Download](JawadC/scarmoza/tree/main) them in the Files & versions tab.
+
+
+
+## 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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