End of training

README.md

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+---
+license: creativeml-openrail-m
+library_name: diffusers
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- textual_inversion
+- diffusers-training
+inference: true
+base_model: runwayml/stable-diffusion-v1-5
+---
+
+<!-- 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. -->
+
+
+# Textual inversion text2image fine-tuning - hangeol/textual_inversion_dog_2
+These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. 
+
+
+
+
+## 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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+}

tokenizer/tokenizer_config.json

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+{
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+  "bos_token": {
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+    "lstrip": false,
+    "normalized": true,
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+    "single_word": false
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+  "name_or_path": "runwayml/stable-diffusion-v1-5",
+  "pad_token": "<|endoftext|>",
+  "special_tokens_map_file": "./special_tokens_map.json",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+}

unet/config.json

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+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.28.0.dev0",
+  "_name_or_path": "runwayml/stable-diffusion-v1-5",
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+    "CrossAttnUpBlock2D",
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+  ],
+  "upcast_attention": false,
+  "use_linear_projection": false
+}

unet/diffusion_pytorch_model.safetensors

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+size 3438167536

vae/config.json

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+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.28.0.dev0",
+  "_name_or_path": "runwayml/stable-diffusion-v1-5",
+  "act_fn": "silu",
+  "block_out_channels": [
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+    256,
+    512,
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+  "down_block_types": [
+    "DownEncoderBlock2D",
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+  "sample_size": 512,
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+  "up_block_types": [
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+    "UpDecoderBlock2D"
+  ]
+}