End of training

README.md

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

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+    "width": 224
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+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
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+    0.4578275,
+    0.40821073
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+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
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+    0.26130258,
+    0.27577711
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+  "resample": 3,
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+  "size": {
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+mixed_precision: 'no'
+num_train_epochs: 24
+num_validation_images: 4
+num_vectors: 1
+output_dir: /export/home/workspace/dreambooth/baselines/diffusers/examples/textual_inversion/output/poop_emoji
+placeholder_token: <toy>
+pretrained_model_name_or_path: runwayml/stable-diffusion-v1-5
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+repeats: 100
+report_to: tensorboard
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vae/config.json

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+  "_diffusers_version": "0.17.0.dev0",
+  "_name_or_path": "runwayml/stable-diffusion-v1-5",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 512,
+  "scaling_factor": 0.18215,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

vae/diffusion_pytorch_model.bin

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