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End of training

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README.md ADDED
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+ ---
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+ base_model: cagliostrolab/animagine-xl-3.1
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+ library_name: diffusers
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+ license: openrail++
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+ tags:
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+ - stable-diffusion-xl
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+ - stable-diffusion-xl-diffusers
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+ - text-to-image
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+ - diffusers
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+ - controlnet
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+ - diffusers-training
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+ inference: true
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the training script had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+
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+ # controlnet-watsydney/animagine-result-3000
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+
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+ These are controlnet weights trained on cagliostrolab/animagine-xl-3.1 with new type of conditioning.
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+
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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+ ```python
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+ # TODO: add an example code snippet for running this diffusion pipeline
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+ ```
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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
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+ "_class_name": "ControlNetModel",
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+ "_diffusers_version": "0.30.0",
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+ "act_fn": "silu",
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+ "addition_embed_type": "text_time",
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+ "addition_embed_type_num_heads": 64,
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+ ],
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+ "class_embed_type": null,
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+ "conditioning_channels": 3,
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+ "conditioning_embedding_out_channels": [
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+ 16,
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+ 32,
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+ 96,
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+ 256
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+ ],
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+ "controlnet_conditioning_channel_order": "rgb",
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+ "cross_attention_dim": 2048,
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+ "down_block_types": [
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+ "DownBlock2D",
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+ "CrossAttnDownBlock2D",
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+ "CrossAttnDownBlock2D"
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+ ],
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+ "downsample_padding": 1,
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+ "encoder_hid_dim": null,
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+ "flip_sin_to_cos": true,
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+ "freq_shift": 0,
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+ "in_channels": 4,
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+ "layers_per_block": 2,
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+ "mid_block_scale_factor": 1,
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+ "mid_block_type": "UNetMidBlock2DCrossAttn",
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+ "norm_eps": 1e-05,
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+ "only_cross_attention": false,
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+ "resnet_time_scale_shift": "default",
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+ "upcast_attention": false,
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+ "use_linear_projection": true
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