End of training

README.md

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+---
+base_model: CompVis/stable-diffusion-v1-4
+library_name: diffusers
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- textual_inversion
+- diffusers-training
+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. -->
+
+
+# Textual inversion text2image fine-tuning - abhipsabasu/textual_inversion_person_nbf
+These are textual inversion adaption weights for CompVis/stable-diffusion-v1-4. 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]

feature_extractor/preprocessor_config.json

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

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model_index.json

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+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.31.0.dev0",
+  "_name_or_path": "CompVis/stable-diffusion-v1-4",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "image_encoder": [
+    null,
+    null
+  ],
+  "requires_safety_checker": true,
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

safety_checker/config.json

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+    "model_type": "clip_vision_model",
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scheduler/scheduler_config.json

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+  "_diffusers_version": "0.31.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
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+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
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+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
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+}

text_encoder/config.json

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+  "_name_or_path": "CompVis/stable-diffusion-v1-4",
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+  "model_type": "clip_text_model",
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+  "projection_dim": 512,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1",
+  "vocab_size": 49409
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tokenizer/added_tokens.json

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tokenizer/special_tokens_map.json

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tokenizer/tokenizer_config.json

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unet/config.json

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vae/config.json

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