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config.json

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+{
+  "_class_name": "ControlNetModel",
+  "_diffusers_version": "0.27.2",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
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+    10,
+    20
+  ],
+  "block_out_channels": [
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+    640,
+    1280
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+  "conditioning_channels": 3,
+  "conditioning_embedding_out_channels": [
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+    32,
+    96,
+    256
+  ],
+  "controlnet_conditioning_channel_order": "rgb",
+  "cross_attention_dim": 2048,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": true
+}

coreml_model.mlmodelc/analytics/coremldata.bin

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coreml_model.mlmodelc/coremldata.bin

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coreml_model.mlmodelc/metadata.json

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+  {
+    "shortDescription" : "ControlNet is a neural network structure to control diffusion models by adding extra conditions. Please refer to https:\/\/arxiv.org\/abs\/2302.05543 for details.",
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+        "shortDescription" : "One of the outputs of each downsampling block in ControlNet. The value added to the corresponding resnet output in UNet.",
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+        "shortDescription" : "One of the outputs of each downsampling block in ControlNet. The value added to the corresponding resnet output in UNet.",
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+        "shortDescription" : "One of the outputs of each downsampling block in ControlNet. The value added to the corresponding resnet output in UNet.",
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+    "version" : "TheMistoAI\/MistoLine",
+    "modelParameters" : [
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+    ],
+    "author" : "Please refer to the Model Card available at huggingface.co\/TheMistoAI\/MistoLine",
+    "specificationVersion" : 7,
+    "storagePrecision" : "Mixed (Float16, Palettized (6 bits))",
+    "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)",
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+        "shortDescription" : "The low resolution latent feature maps being denoised through reverse diffusion",
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+        "shortDescription" : "A value emitted by the associated scheduler object to condition the model on a given noise schedule",
+        "shape" : "[1]",
+        "name" : "timestep",
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+        "shortDescription" : "Output embeddings from the associated text_encoder model to condition to generated image on text. A maximum of 77 tokens (~40 words) are allowed. Longer text is truncated. Shorter text does not reduce computation.",
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+    "method" : "predict"
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coreml_model.mlmodelc/weights/weight.bin

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