PseudoTerminal X
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README.md
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---
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license: creativeml-openrail-m
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base_model: "stabilityai/stable-diffusion-3-medium-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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- lora
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- template:sd-lora
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inference: true
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widget:
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- text: 'a studio portrait photograph of emma watson. she looks relaxed and happy.'
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parameters:
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negative_prompt: 'blurry, cropped, ugly'
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output:
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url: ./assets/image_0_0.png
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---
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# sd3-lora-celebrities
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This is a LoRA derived from [stabilityai/stable-diffusion-3-medium-diffusers](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers).
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The main validation prompt used during training was:
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```
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a studio portrait photograph of emma watson. she looks relaxed and happy.
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```
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## Validation settings
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- CFG: `5.0`
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- CFG Rescale: `0.2`
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- Steps: `50`
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- Sampler: `euler`
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- Seed: `2`
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- Resolution: `1280x768`
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Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
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You can find some example images in the following gallery:
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<Gallery />
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The text encoder **was not** trained.
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You may reuse the base model text encoder for inference.
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## Training settings
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- Training epochs: 0
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- Training steps: 200
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- Learning rate: 1e-06
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- Effective batch size: 1
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- Micro-batch size: 1
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- Gradient accumulation steps: 1
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- Number of GPUs: 1
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- Prediction type: v_prediction
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- Rescaled betas zero SNR: True
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- Optimizer: AdamW, stochastic bf16
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- Precision: Pure BF16
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- Xformers: Not used
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- LoRA Rank: 16
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- LoRA Alpha: 16
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- LoRA Dropout: 0.1
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- LoRA initialisation style: default
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## Datasets
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### celebrities-sd3
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- Repeats: 0
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- Total number of images: 1830
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- Total number of aspect buckets: 3
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- Resolution: 0.5 megapixels
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- Cropped: False
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- Crop style: None
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- Crop aspect: None
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## Inference
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```python
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import torch
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from diffusers import StableDiffusion3Pipeline
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model_id = "sd3-lora-celebrities"
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prompt = "a studio portrait photograph of emma watson. she looks relaxed and happy."
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negative_prompt = "malformed, disgusting, overexposed, washed-out"
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pipeline = DiffusionPipeline.from_pretrained(model_id)
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pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
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image = pipeline(
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prompt=prompt,
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negative_prompt='blurry, cropped, ugly',
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num_inference_steps=50,
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generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
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width=1152,
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height=768,
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guidance_scale=5.0,
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guidance_rescale=0.2,
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).images[0]
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image.save("output.png", format="PNG")
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```
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