readme: ---
  model:
    base_learning_rate: 1.0e-04
    target: ldm.models.diffusion.ddpm.LatentDiffusion
    params:
      linear_start: 0.00085
      linear_end: 0.0120
      num_timesteps_cond: 1
      log_every_t: 200
      timesteps: 1000
      first_stage_key: "jpg"
      cond_stage_key: "txt"
      image_size: 64
      channels: 4
      cond_stage_trainable: false   # Note: different from the one we trained before
      conditioning_key: crossattn
      monitor: val/loss_simple_ema
      scale_factor: 0.18215
      use_ema: False
  
      scheduler_config: # 10000 warmup steps
        target: ldm.lr_scheduler.LambdaLinearScheduler
        params:
          warm_up_steps: [ 10000 ]
          cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
          f_start: [ 1.e-6 ]
          f_max: [ 1. ]
          f_min: [ 1. ]
  
      unet_config:
        target: ldm.modules.diffusionmodules.openaimodel.UNetModel
        params:
          image_size: 32 # unused
          in_channels: 4
          out_channels: 4
          model_channels: 320
          attention_resolutions: [ 4, 2, 1 ]
          num_res_blocks: 2
          channel_mult: [ 1, 2, 4, 4 ]
          num_heads: 8
          use_spatial_transformer: True
          transformer_depth: 1
          context_dim: 768
          use_checkpoint: True
          legacy: False
  
      first_stage_config:
        target: ldm.models.autoencoder.AutoencoderKL
        params:
          embed_dim: 4
          monitor: val/rec_loss
          ddconfig:
            double_z: true
            z_channels: 4
            resolution: 256
            in_channels: 3
            out_ch: 3
            ch: 128
            ch_mult:
            - 1
            - 2
            - 4
            - 4
            num_res_blocks: 2
            attn_resolutions: []
            dropout: 0.0
          lossconfig:
            target: torch.nn.Identity
  
      cond_stage_config:
        target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
  ---
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  Model name: H&A 3DKX
  Model versions: 1.0b, 1.1(latest)
  
  ## Changelog:
  V1.1: Minor update based on feedback, containing the following fixes:
  
  -“nsfw”, “nudity” , and “erotica” have been trained into the model and work as Negatives to greatly reduce unintended NSFW content.
  
  - CFG can be pushed a bit higher before the images become burnt. As a result, the model can accommodate more complicated prompts now.
  
  - Oversaturated images will be encountered way less often
  
  ## Description: 
  
  SFW model with limited nsfw capabilities (suggestive nsfw) that is highly versatile for 3D renders.
  The model has the particularity of splitting itself into two different well balanced styles. 
  If you'd like to have your 3D characters have a more "Cartoony" face, you simply start your prompt 
  with "3d cartoon of", and if you want the classic 3D render style, you write "a 3d render of".
  
  Please check the cheat sheet for prompting tips as the structure of the prompt and negatives used has a huge effect.
  Note: Model has an embedded VAE, so do not add one with this model. It will be the best in most cases, and configured for higher resolutions.
  
  ## Model has an embedded VAE, do not use an extra one!
  If you want to chat with us and join our community visit our discord:
  https://discord.gg/CPyqJgXdRG
  ## Dataset:
   - between 140 and 180 pictures of 3D render of all kind
  
  ## PromptGuide/Cheat Sheet
  [3DKX_1.0b/1.1 Guide](https://docs.google.com/document/d/15pJ3TkbmX3LRoSTNsMYsbetvO7A46L60wVOxIL2ZZ6E/)
  
  ## Has a high success rate at:
  - sfw portraits, full body poses, close ups, etc 
  - high versatility in terms of outputs, it isn't locked to perform well on portraits
  - Landscapes, cyberpunk, steampunk, natural, scifi, etc
  - 2B Nier Automata (Don't ask us why)
  - different body types - different ethnicity
  - nsfw portraits, full body poses, close ups, etc 
  
  ## What it "In theory" shouldn't exceed at:
  - anything outside the scope of portraits, people, landscapes, game artworks, 3D sculptures, 3D fantasy, 3D film stills, etc
  - celebrities
  - highly specific animated cartoon characters
  - multiple subjects 
  - highly specific video-game characters
  - pornography, genitalia and highly explicit materials
  
  <img width="768px" src="https://cdn.discordapp.com/attachments/1056287982363086930/1056331346177425438/00011-3928902726-A203d20render20of2020epic20portrait20close20shot20of20beautiful20turkish20woman20wearing20with20angelic20feathered20wings20gold20armour20neckline.png">
  <img width="768px" src="https://cdn.discordapp.com/attachments/323893037379878912/1056823178846031882/00494-2262985444-3d_render_of_a_sharp_focused_detailed_photo_of_a_super_car_with_iridescent_metallic_color_driving_on_a_midnight_road_mul