scale_factor in yaml

[miasik]

I asked Grok to analyze the difference between the default "sd_xl_base.yaml" and a yaml that OT generates for me(at the bottom).

Its answer called new questions:

• What is the reason of using different scale_factor?
• Does OT load and use this setting later, when I use the model as the base for a new session? Does Cloudy OT do that if the base model set as cloud://Models/Model.safetensor?

Key Differences Between OneTrainer Config and Base SDXL Config

This page outlines the key differences between the YAML configuration file generated by OneTrainer after fine-tuning and the assumed base SDXL configuration (e.g., sd_xl_base.yaml from Stability AI’s generative-models repository).

Section	OneTrainer Config	Base Config (Assumed)	Key Difference
scale_factor	0.13025	0.18215	Adjusted latent scaling, likely optimized for the fine-tuned dataset. This affects how the VAE maps pixel values to latents, potentially improving reconstruction for the specific task but reducing compatibility with the original SDXL refiner.
disable_first_stage_autocast	true	false (or unspecified)	Forces full precision (FP32) for the VAE instead of mixed precision (e.g., FP16). Likely set during fine-tuning for stability or quality, increasing memory usage and possibly slowing inference.
first_stage_config	resolution: 256, lossconfig: torch.nn.Identity, monitor: val/rec_loss	Likely similar structure, no monitor field	resolution: 256 may be a legacy or misaligned value (SDXL typically operates at 1024x1024, 128x128 latents). lossconfig and monitor suggest a training-derived config adapted for inference, with no functional impact.

Analysis of Differences

1. scale_factor: 0.13025

• Change: Lowered from the base SDXL value of 0.18215.
• Impact: Indicates OneTrainer optimized the VAE’s latent space scaling for the fine-tuned dataset. This custom value may require a matching VAE or adjustments when using the original SDXL refiner.

2. disable_first_stage_autocast: true

• Change: Explicitly disables autocasting for the VAE, unlike the base config’s default or false.
• Impact: Ensures full precision during VAE operations, possibly to avoid numerical issues during fine-tuning. Increases computational cost compared to the base setup.

3. first_stage_config Tweaks

• Change: Includes resolution: 256 and training-related fields (monitor: val/rec_loss) not typically emphasized in inference configs.
• Impact: Minor metadata differences reflecting OneTrainer’s training process. The resolution value is ambiguous and likely non-functional for SDXL’s 1024x1024 output pipeline.

Implications

• Compatibility: The scale_factor shift may misalign this model with the original SDXL refiner unless it was fine-tuned similarly.
• Performance: Full-precision VAE (disable_first_stage_autocast: true) could enhance quality but at a higher memory and speed cost.
• Usage: Test inference with adjusted timesteps or schedulers, and verify GPU performance impacts from autocast settings.

My yaml:

model:
  params:
    conditioner_config:
      params:
        emb_models:
        - input_key: txt
          is_trainable: false
          params:
            layer: hidden
            layer_idx: 11
          target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
        - input_key: txt
          is_trainable: false
          params:
            always_return_pooled: true
            arch: ViT-bigG-14
            freeze: true
            layer: penultimate
            legacy: false
            version: laion2b_s39b_b160k
          target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
        - input_key: original_size_as_tuple
          is_trainable: false
          params:
            outdim: 256
          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
        - input_key: crop_coords_top_left
          is_trainable: false
          params:
            outdim: 256
          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
        - input_key: target_size_as_tuple
          is_trainable: false
          params:
            outdim: 256
          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
      target: sgm.modules.GeneralConditioner
    denoiser_config:
      params:
        discretization_config:
          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
        num_idx: 1000
        scaling_config:
          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
        weighting_config:
          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
    disable_first_stage_autocast: true
    first_stage_config:
      params:
        ddconfig:
          attn_resolutions: []
          attn_type: vanilla-xformers
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          - 4
          double_z: true
          dropout: 0.0
          in_channels: 3
          num_res_blocks: 2
          out_ch: 3
          resolution: 256
          z_channels: 4
        embed_dim: 4
        lossconfig:
          target: torch.nn.Identity
        monitor: val/rec_loss
      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
    network_config:
      params:
        adm_in_channels: 2816
        attention_resolutions:
        - 4
        - 2
        channel_mult:
        - 1
        - 2
        - 4
        context_dim: 2048
        in_channels: 4
        legacy: false
        model_channels: 320
        num_classes: sequential
        num_head_channels: 64
        num_res_blocks: 2
        out_channels: 4
        spatial_transformer_attn_type: softmax-xformers
        transformer_depth:
        - 1
        - 2
        - 10
        use_checkpoint: true
        use_linear_in_transformer: true
        use_spatial_transformer: true
      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
    scale_factor: 0.13025
  target: sgm.models.diffusion.DiffusionEngine

[miasik]

Looks like I've already found the answer for the first question. The source of scale_factor is:
https://huggingface.co/SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep/blob/main/vae/config.json
So only the second question is still active:
Does OT load and use this setting later, from the yaml, when I use the model as the base for a new session? Does Cloudy OT do that if the base model is set as cloud://Models/Model.safetensor?