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README.md

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+---
+license: other
+license_name: faipl-1.0-sd
+license_link: https://freedevproject.org/faipl-1.0-sd/
+language:
+- en
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+- text-to-image
+- stable-diffusion
+- stable-diffusion-xl
+- not-for-all-audiences
+- anime
+- trained
+- illustrious
+base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
+---
+
+Original model is [here](https://civitai.com/models/856833?modelVersionId=958667).
+
+> The usage is basically the same as Illustrious XL, you can adjust the art style using the artist list. If you are interested in the artist list but don't know how to write it, you can try using [novelai_300artist](https://github.com/MakkiShizu/stable-diffusion-webui-wildcards/blob/master/wildcards/novelai_300artist.txt)
+
+This model created by [Makki_Shizu](https://civitai.com/user/Makki_Shizu).

model_index.json

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+{
+  "_class_name": "StableDiffusionXLPipeline",
+  "_diffusers_version": "0.31.0.dev0",
+  "feature_extractor": [
+    null,
+    null
+  ],
+  "force_zeros_for_empty_prompt": true,
+  "image_encoder": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "EulerAncestralDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "EulerAncestralDiscreteScheduler",
+  "_diffusers_version": "0.31.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "interpolation_type": "linear",
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "use_karras_sigmas": false
+}

text_encoder/config.json

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+{
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+    "CLIPTextModel"
+  ],
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+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "quick_gelu",
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+  "layer_norm_eps": 1e-05,
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+  "model_type": "clip_text_model",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float16",
+  "transformers_version": "4.44.0",
+  "vocab_size": 49408
+}

text_encoder/model.safetensors

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text_encoder_2/config.json

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+}

text_encoder_2/model.safetensors

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tokenizer/special_tokens_map.json

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tokenizer/tokenizer_config.json

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+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_2/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
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+    "single_word": false
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tokenizer_2/tokenizer_config.json

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+  "pad_token": "!",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

unet/config.json

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+  "_diffusers_version": "0.31.0.dev0",
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unet/diffusion_pytorch_model.safetensors

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vae/config.json

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vae/diffusion_pytorch_model.safetensors

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