no module named taming

[1blackbar]

why is that ? i ran everything like in instructions

[nicolai256]

try this? pip install taming-transformers

[nicolai256]

should run fine with just the instructions tho.. multiple people have tested it and it worked. did you install stable diffusion before installing this? if not do that..

[nicolai256]

added some extra things to avoid confusion

[1blackbar]

got it to run but used someone eleses env with GUi then moved your python scripts to it, I gout kicked out for out of memory, so is this thing needs at least 10 gb even for 2 images ? cant someone otpimize the code like with model ? I might try colab im on gtx 1080ti 11gb but it still flips out.

Oh i can see that it creates 16 (!!!!) working processess , what the heck, is this for 3090 only? Where do i change that ?

OK its in v1-finetune.yaml file under num_workers

OK simply lowering worker num wont work, any clue what to lower next for 11gb card to get biggest improvement ? ill tinker with it in meantime

-- Allright got it working !!! i had to lower workers , batch size to 1 and max_images to 5

[nicolai256]

@1blackbar can u send me the config file? i'll put it in my repo send me on discord @MATRIXMANE#1219

[1blackbar]

I added you, in meantime

---

model: base_learning_rate: 5.0e-03 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: image cond_stage_key: caption image_size: 64 channels: 4 cond_stage_trainable: true # Note: different from the one we trained before conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False embedding_reg_weight: 0.0

personalization_config:
  target: ldm.modules.embedding_manager.EmbeddingManager
  params:
    placeholder_strings: ["*"]
    initializer_words: ["sculpture"]
    per_image_tokens: false
    num_vectors_per_token: 1
    progressive_words: False

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

data: target: main.DataModuleFromConfig params: batch_size: 1 num_workers: 10 wrap: false train: target: ldm.data.personalized.PersonalizedBase params: size: 512 set: train per_image_tokens: false repeats: 100 validation: target: ldm.data.personalized.PersonalizedBase params: size: 512 set: val per_image_tokens: false repeats: 10

lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 500 max_images: 15 increase_log_steps: False

trainer: benchmark: True max_steps: 6100

---

cant put it in <> cause reasons

this is my yaml, im sure it can be modified but i dont think it matters how much batchsize you put in cause your gpu is working on max anyway depending if you throw at it big images or smaller , it will get to the end in the same time