End of training

README.md

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+---
+base_model: CompVis/stable-diffusion-v1-4
+library_name: diffusers
+license: creativeml-openrail-m
+inference: true
+instance_prompt: a photo of x-ray knife
+tags:
+- text-to-image
+- dreambooth
+- diffusers-training
+- stable-diffusion
+- stable-diffusion-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. -->
+
+
+# DreamBooth - ZhiguangHan/result
+
+This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of x-ray knife using [DreamBooth](https://dreambooth.github.io/).
+You can find some example images in the following. 
+
+
+
+DreamBooth for the text encoder was enabled: False.
+
+
+## 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]

feature_extractor/preprocessor_config.json

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+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

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+instance_prompt: a photo of x-ray knife
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+output_dir: ./result
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