End of training

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+val_imgs_grid.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: creativeml-openrail-m
+library_name: diffusers
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- diffusers-training
+base_model: runwayml/stable-diffusion-v1-5
+inference: true
+---
+
+<!-- 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. -->
+
+
+# Text-to-image finetuning - Peachman/sd-hubble-model
+
+This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **Supermaxman/esa-hubble** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Hubble image of a colorful ringed nebula: A new vibrant ring-shaped nebula was imaged by the NASA/ESA Hubble Space Telescope', 'Pink-tinted plumes in the Large Magellanic Cloud: The aggressively pink plumes seen in this image are extremely uncommon, with purple-tinted currents and nebulous strands reaching out into the surrounding space']: 
+
+![val_imgs_grid](./val_imgs_grid.png)
+
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained("Peachman/sd-hubble-model", torch_dtype=torch.float16)
+prompt = "Hubble image of a colorful ringed nebula: A new vibrant ring-shaped nebula was imaged by the NASA/ESA Hubble Space Telescope"
+image = pipeline(prompt).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Epochs: 1
+* Learning rate: 1e-05
+* Batch size: 1
+* Gradient accumulation steps: 4
+* Image resolution: 512
+* Mixed-precision: bf16
+
+
+
+## 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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+    "images",
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+    "resample",
+    "do_center_crop",
+    "crop_size",
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+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "do_convert_rgb",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
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+    "width": 224
+  },
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+  "do_convert_rgb": true,
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+  "do_resize": true,
+  "image_mean": [
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+    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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