Add Arcane Diffusion for Diffusers (#11)

- add diffusers weights (3c52108e99834093febedf88561cba85d99d6e75)


Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>

README.md

@@ -7,6 +7,29 @@ Use the tokens **_arcane style_** in your prompts for the effect.
 
 If you enjoy this model, please check out my other models on [Huggingface](https://huggingface.co/nitrosocke)
 
+### 🧨 Diffusers
+
+This model can be used just like any other Stable Diffusion model. For more information,
+please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
+
+You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
+
+```python
+from diffusers import StableDiffusionPipeline
+import torch
+
+model_id = "nitrosocke/Arcane-Diffusion"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "arcane style, a magical princess with golden hair"
+image = pipe(prompt).images[0]
+
+image.save("./magical_princess.png")
+```
+
+![img](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/magical_princess.png)
+
 ### Sample images from v3:
 ![output Samples v3](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-v3-samples-01.jpg)
 ![output Samples v3](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-v3-samples-02.jpg)
@@ -20,4 +43,4 @@ If you enjoy this model, please check out my other models on [Huggingface](https
 **Version 2** (arcane-diffusion-v2): This uses the diffusers based dreambooth training and prior-preservation loss is way more effective. The diffusers where then converted with a script to a ckpt file in order to work with automatics repo.
 Training was done with 5k steps for a direct comparison to v1 and results show that it needs more steps for a more prominent result. Version 3 will be tested with 11k steps.
 
-**Version 1** (arcane-diffusion-5k): This model was trained using _Unfrozen Model Textual Inversion_ utilizing the _Training with prior-preservation loss_ methods. There is still a slight shift towards the style, while not using the arcane token.
+**Version 1** (arcane-diffusion-5k): This model was trained using _Unfrozen Model Textual Inversion_ utilizing the _Training with prior-preservation loss_ methods. There is still a slight shift towards the style, while not using the arcane token.

feature_extractor/preprocessor_config.json

@@ -0,0 +1,20 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

model_index.json

@@ -0,0 +1,32 @@
+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.7.0.dev0",
+  "feature_extractor": [
+    "transformers",
+    "CLIPFeatureExtractor"
+  ],
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "LMSDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

safety_checker/config.json

@@ -0,0 +1,179 @@
+{
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+  "_name_or_path": "CompVis/stable-diffusion-safety-checker",
+  "architectures": [
+    "StableDiffusionSafetyChecker"
+  ],
+  "initializer_factor": 1.0,
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+  "model_type": "clip",
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+}

safety_checker/pytorch_model.bin

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scheduler/scheduler_config.json

@@ -0,0 +1,9 @@
+{
+  "_class_name": "LMSDiscreteScheduler",
+  "_diffusers_version": "0.7.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "num_train_timesteps": 1000,
+  "trained_betas": null
+}

text_encoder/config.json

@@ -0,0 +1,25 @@
+{
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+  "architectures": [
+    "CLIPTextModel"
+  ],
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+  "transformers_version": "4.24.0.dev0",
+  "vocab_size": 49408
+}

text_encoder/pytorch_model.bin

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tokenizer/special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": true,
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+}

tokenizer/tokenizer_config.json

@@ -0,0 +1,34 @@
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+    "single_word": false
+  }
+}

unet/config.json

@@ -0,0 +1,36 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.7.0.dev0",
+  "act_fn": "silu",
+  "attention_head_dim": 8,
+  "block_out_channels": [
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+  "down_block_types": [
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+    "DownBlock2D"
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+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
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+  "out_channels": 4,
+  "sample_size": 32,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ]
+}

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vae/config.json

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+  "_diffusers_version": "0.7.0.dev0",
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+  ]
+}

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