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README.md

@@ -0,0 +1,25 @@
+---
+license: creativeml-openrail-m
+language:
+- en
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+- text-to-image
+- stable-diffusion
+- stable-diffusion-xl
+- realistic
+- photorealistic
+- photo
+- nature
+- art
+- architecture
+- vfx
+- text
+- nsfw
+- commercial-friendly output
+---
+
+Original model is [here](https://civitai.com/models/681249/artisanica-xl?modelVersionId=762508).
+
+> One of the standout features of Artisanica XL is its commercial-friendly output. The graphics generated by this model are free for commercial use, meaning you can confidently use them in your projects without worrying about licensing restrictions. Even more, Artisanica XL supports the commercial use on generation services, making it a versatile tool for professionals in the creative industry.

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",
+    "EDMDPMSolverMultistepScheduler"
+  ],
+  "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": "EDMDPMSolverMultistepScheduler",
+  "_diffusers_version": "0.31.0.dev0",
+  "algorithm_type": "dpmsolver++",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "dynamic_thresholding_ratio": 0.995,
+  "euler_at_final": false,
+  "final_sigmas_type": "zero",
+  "interpolation_type": "linear",
+  "lower_order_final": true,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rho": 7.0,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "sigma_data": 0.5,
+  "sigma_max": 80.0,
+  "sigma_min": 0.002,
+  "sigma_schedule": "karras",
+  "skip_prk_steps": true,
+  "solver_order": 2,
+  "solver_type": "midpoint",
+  "steps_offset": 1,
+  "thresholding": false,
+  "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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+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "quick_gelu",
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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": "4.43.2",
+  "vocab_size": 49408
+}

text_encoder/model.safetensors

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text_encoder_2/config.json

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+  "transformers_version": "4.43.2",
+  "vocab_size": 49408
+}

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tokenizer/special_tokens_map.json

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tokenizer/tokenizer_config.json

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+  "tokenizer_class": "CLIPTokenizer",
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+}

tokenizer_2/special_tokens_map.json

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tokenizer_2/tokenizer_config.json

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

unet/config.json

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

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vae/config.json

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