End of training
Browse files- .gitattributes +2 -0
- README.md +45 -0
- checkpoint-2000/controlnet/config.json +52 -0
- checkpoint-2000/controlnet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-2000/optimizer.bin +3 -0
- checkpoint-2000/random_states_0.pkl +3 -0
- checkpoint-2000/scaler.pt +3 -0
- checkpoint-2000/scheduler.bin +3 -0
- checkpoint-4000/controlnet/config.json +52 -0
- checkpoint-4000/controlnet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-4000/optimizer.bin +3 -0
- checkpoint-4000/random_states_0.pkl +3 -0
- checkpoint-4000/scaler.pt +3 -0
- checkpoint-4000/scheduler.bin +3 -0
- checkpoint-6000/controlnet/config.json +52 -0
- checkpoint-6000/controlnet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-6000/optimizer.bin +3 -0
- checkpoint-6000/random_states_0.pkl +3 -0
- checkpoint-6000/scaler.pt +3 -0
- checkpoint-6000/scheduler.bin +3 -0
- checkpoint-8000/controlnet/config.json +52 -0
- checkpoint-8000/controlnet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-8000/optimizer.bin +3 -0
- checkpoint-8000/random_states_0.pkl +3 -0
- checkpoint-8000/scaler.pt +3 -0
- checkpoint-8000/scheduler.bin +3 -0
- config.json +52 -0
- diffusion_pytorch_model.safetensors +3 -0
- image_control.png +0 -0
- images_0.png +3 -0
- images_1.png +3 -0
.gitattributes
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*.zst filter=lfs diff=lfs merge=lfs -text
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images_0.png filter=lfs diff=lfs merge=lfs -text
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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-Lam-Hung/controlnet_depth_interior
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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 Bauhaus-inspired living room with a sleek black leather sofa, a tubular steel coffee table exemplifying modernist design, and a geometric patterned rug adding a touch of artistic flair.
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![images_0)](./images_0.png)
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prompt: A glamorous master bedroom in Hollywood Regency style, boasting a plush tufted headboard, mirrored furniture reflecting elegance, luxurious fabrics in rich textures, and opulent gold accents for a touch of luxury.
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![images_1)](./images_1.png)
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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-2000/controlnet/config.json
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{
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"_class_name": "ControlNetModel",
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"_diffusers_version": "0.30.0.dev0",
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"_name_or_path": "lllyasviel/control_v11f1p_sd15_depth",
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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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"attention_head_dim": 8,
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"block_out_channels": [
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"conditioning_channels": 3,
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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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"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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"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-2000/controlnet/diffusion_pytorch_model.safetensors
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checkpoint-4000/controlnet/config.json
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|
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|
checkpoint-4000/controlnet/diffusion_pytorch_model.safetensors
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checkpoint-6000/controlnet/config.json
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"mid_block_type": "UNetMidBlock2DCrossAttn",
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|
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}
|
checkpoint-6000/controlnet/diffusion_pytorch_model.safetensors
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