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bghira/sd35m-photo-clip_value

sourceHugging Faceotherupdated 2y agoView on Hugging Face
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sd35m-photo-clip_value

This is a LyCORIS adapter derived from stabilityai/stable-diffusion-3.5-medium.

The main validation prompt used during training was:

A photo-realistic image of a cat

Validation settings

  • —CFG: 6.0
  • —CFG Rescale: 0.0
  • —Steps: 30
  • —Sampler: FlowMatchEulerDiscreteScheduler
  • —Seed: 42
  • —Resolution: 1024x1024
  • —Skip-layer guidance:

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery:

<Gallery />

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

  • —Training epochs: 1
  • —Training steps: 112000
  • —Learning rate: 1e-05
  • —Learning rate schedule: constant
  • —Warmup steps: 500
  • —Max grad norm: 1.0
  • —Effective batch size: 9
  • —Micro-batch size: 3
  • —Gradient accumulation steps: 1
  • —Number of GPUs: 3
  • —Gradient checkpointing: True
  • —Prediction type: flow-matching (extra parameters=['fluxscheduleautoshift', 'shift=0.0', 'fluxuseuniformschedule'])
  • —Optimizer: bnb-adamw8bit
  • —Trainable parameter precision: Pure BF16
  • —Caption dropout probability: 10.0%

LyCORIS Config:

json
{
    "bypass_mode": true,
    "algo": "lokr",
    "multiplier": 1.0,
    "full_matrix": true,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 4,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "FeedForward": {
                "factor": 4
            },
            "Attention": {
                "factor": 2
            }
        }
    }
}

Datasets

text-1mp

  • —Repeats: 100
  • —Total number of images: ~13221
  • —Total number of aspect buckets: 5
  • —Resolution: 1.048576 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

signs

  • —Repeats: 150
  • —Total number of images: ~420
  • —Total number of aspect buckets: 11
  • —Resolution: 1.048576 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

moviecollection

  • —Repeats: 0
  • —Total number of images: ~1983
  • —Total number of aspect buckets: 34
  • —Resolution: 1.048576 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

bookcovers

  • —Repeats: 0
  • —Total number of images: ~927
  • —Total number of aspect buckets: 26
  • —Resolution: 1.048576 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

shutterstock

  • —Repeats: 0
  • —Total number of images: ~21111
  • —Total number of aspect buckets: 35
  • —Resolution: 1.048576 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

cinemamix-1mp

  • —Repeats: 0
  • —Total number of images: ~7425
  • —Total number of aspect buckets: 5
  • —Resolution: 1.048576 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

anatomy

  • —Repeats: 5
  • —Total number of images: ~16440
  • —Total number of aspect buckets: 11
  • —Resolution: 1.048576 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

signs-512

  • —Repeats: 0
  • —Total number of images: ~417
  • —Total number of aspect buckets: 12
  • —Resolution: 0.262144 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

moviecollection-512

  • —Repeats: 0
  • —Total number of images: ~1971
  • —Total number of aspect buckets: 19
  • —Resolution: 0.262144 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

bookcovers-512

  • —Repeats: 0
  • —Total number of images: ~918
  • —Total number of aspect buckets: 16
  • —Resolution: 0.262144 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

shutterstock-512

  • —Repeats: 0
  • —Total number of images: ~21096
  • —Total number of aspect buckets: 19
  • —Resolution: 0.262144 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

cinemamix-1mp-512

  • —Repeats: 0
  • —Total number of images: ~7422
  • —Total number of aspect buckets: 3
  • —Resolution: 0.262144 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

anatomy-512

  • —Repeats: 5
  • —Total number of images: ~16437
  • —Total number of aspect buckets: 8
  • —Resolution: 0.262144 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

signs-1440

  • —Repeats: 100
  • —Total number of images: ~423
  • —Total number of aspect buckets: 2
  • —Resolution: 2.0736 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

moviecollection-1440

  • —Repeats: 0
  • —Total number of images: ~2007
  • —Total number of aspect buckets: 45
  • —Resolution: 2.0736 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

bookcovers-1440

  • —Repeats: 0
  • —Total number of images: ~933
  • —Total number of aspect buckets: 28
  • —Resolution: 2.0736 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

shutterstock-1440

  • —Repeats: 0
  • —Total number of images: ~21111
  • —Total number of aspect buckets: 44
  • —Resolution: 2.0736 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

cinemamix-1mp-1440

  • —Repeats: 0
  • —Total number of images: ~7425
  • —Total number of aspect buckets: 5
  • —Resolution: 2.0736 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

anatomy-1440

  • —Repeats: 5
  • —Total number of images: ~16458
  • —Total number of aspect buckets: 9
  • —Resolution: 2.0736 megapixels
  • —Cropped: False
  • —Crop style: None
  • —Crop aspect: None
  • —Used for regularisation data: No

Inference

python
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights


def download_adapter(repo_id: str):
    import os
    from huggingface_hub import hf_hub_download
    adapter_filename = "pytorch_lora_weights.safetensors"
    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
    os.makedirs(path_to_adapter, exist_ok=True)
    hf_hub_download(
        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
    )

    return path_to_adapter_file
    
model_id = 'stabilityai/stable-diffusion-3.5-medium'
adapter_repo_id = 'bghira/sd35m-photo-clip_value'
adapter_filename = 'pytorch_lora_weights.safetensors'
adapter_file_path = download_adapter(repo_id=adapter_repo_id)
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()

prompt = "A photo-realistic image of a cat"
negative_prompt = 'ugly, cropped, blurry, low-quality, mediocre average'

## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
from optimum.quanto import quantize, freeze, qint8
quantize(pipeline.transformer, weights=qint8)
freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=30,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=1024,
    height=1024,
    guidance_scale=6.0,
).images[0]
image.save("output.png", format="PNG")

Exponential Moving Average (EMA)

SimpleTuner generates a safetensors variant of the EMA weights and a pt file.

The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.

The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.