End of training
Browse files- README.md +53 -0
- checkpoint-20000/controlnet/config.json +51 -0
- checkpoint-20000/controlnet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-20000/optimizer.bin +3 -0
- checkpoint-20000/random_states_0.pkl +3 -0
- checkpoint-20000/scheduler.bin +3 -0
- checkpoint-40000/controlnet/config.json +51 -0
- checkpoint-40000/controlnet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-40000/optimizer.bin +3 -0
- checkpoint-40000/random_states_0.pkl +3 -0
- checkpoint-40000/scheduler.bin +3 -0
- checkpoint-60000/controlnet/config.json +51 -0
- checkpoint-60000/controlnet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-60000/optimizer.bin +3 -0
- checkpoint-60000/random_states_0.pkl +3 -0
- checkpoint-60000/scheduler.bin +3 -0
- checkpoint-80000/controlnet/config.json +51 -0
- checkpoint-80000/controlnet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-80000/optimizer.bin +3 -0
- checkpoint-80000/random_states_0.pkl +3 -0
- checkpoint-80000/scheduler.bin +3 -0
- config.json +51 -0
- diffusion_pytorch_model.safetensors +3 -0
- image_control.png +0 -0
- images_0.png +0 -0
- images_1.png +0 -0
- images_2.png +0 -0
- images_3.png +0 -0
- images_4.png +0 -0
- images_5.png +0 -0
README.md
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---
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license: creativeml-openrail-m
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library_name: diffusers
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tags:
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- stable-diffusion
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- stable-diffusion-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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base_model: runwayml/stable-diffusion-v1-5
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inference: true
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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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# controlnet-louistichelman/controlnet_streetview_safety_sd15
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These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
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You can find some example images below.
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prompt: A realistic and safe-looking street view in the USA.
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
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prompt: A realistic and dangerous-looking street view in the USA.
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
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prompt: A realistic street view in Israel.
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
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prompt: A realistic street view in Japan.
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
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prompt: A realistic and safe-looking street view in Mexico.
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
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prompt: A realistic and dangerous-looking street view in Mexico.
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
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## Intended uses & limitations
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#### How to use
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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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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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checkpoint-20000/controlnet/config.json
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{
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"_class_name": "ControlNetModel",
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"_diffusers_version": "0.29.0.dev0",
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"act_fn": "silu",
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"addition_embed_type": null,
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"addition_embed_type_num_heads": 64,
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"addition_time_embed_dim": null,
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"attention_head_dim": 8,
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"block_out_channels": [
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320,
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1280,
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1280
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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": 768,
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"down_block_types": [
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D"
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],
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"downsample_padding": 1,
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"encoder_hid_dim": null,
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"encoder_hid_dim_type": null,
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"flip_sin_to_cos": true,
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"freq_shift": 0,
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"global_pool_conditions": false,
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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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"norm_num_groups": 32,
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"num_attention_heads": null,
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"num_class_embeds": null,
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"only_cross_attention": false,
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"projection_class_embeddings_input_dim": null,
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"resnet_time_scale_shift": "default",
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"transformer_layers_per_block": 1,
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"upcast_attention": false,
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"use_linear_projection": false
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}
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checkpoint-20000/controlnet/diffusion_pytorch_model.safetensors
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checkpoint-20000/random_states_0.pkl
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checkpoint-40000/controlnet/config.json
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"_class_name": "ControlNetModel",
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"controlnet_conditioning_channel_order": "rgb",
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"cross_attention_dim": 768,
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"down_block_types": [
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D"
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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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"global_pool_conditions": false,
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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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"upcast_attention": false,
|
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"use_linear_projection": false
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}
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checkpoint-40000/controlnet/diffusion_pytorch_model.safetensors
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checkpoint-60000/controlnet/config.json
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"controlnet_conditioning_channel_order": "rgb",
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"cross_attention_dim": 768,
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"down_block_types": [
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D"
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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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"use_linear_projection": false
|
51 |
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}
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checkpoint-60000/controlnet/diffusion_pytorch_model.safetensors
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checkpoint-60000/optimizer.bin
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checkpoint-80000/controlnet/config.json
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