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--- |
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license: other |
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base_model: stabilityai/stable-diffusion-3.5-large |
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tags: |
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- sd3 |
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- sd3-diffusers |
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- text-to-image |
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- diffusers |
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- simpletuner |
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- safe-for-work |
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- lora |
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- template:sd-lora |
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- standard |
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inference: true |
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widget: |
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- text: grey shirt with a small logo of a bunny painted in the alebrijeros style |
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output: |
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url: images/example_wr1vsmbx0.png |
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- text: hoodie with flowers painted in the alebrijeros style |
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output: |
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url: images/example_8srfnn37z.png |
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--- |
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# sd3-lora-alebrijeros-final |
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Este es un LoRA derivado del modelo más reciente de stable diffusion: |
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[stabilityai/stable-diffusion-3.5-large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large). |
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El prompt usado durante el entrenamiento: |
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``` |
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sweatshirt painted in the alebrijeros style |
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``` |
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You can find some example images in the following gallery: |
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<Gallery /> |
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## Configuración del entrene |
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- Training epochs: 4 |
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- Training steps: 2600 |
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- Learning rate: 5e-05 |
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- Max grad norm: 0.01 |
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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: flow-matching |
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- Rescaled betas zero SNR: False |
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- Optimizer: adamw_bf16 |
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- Precision: Pure BF16 |
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- Quantised: Yes: int8-quanto |
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- Xformers: Not used |
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- LoRA Rank: 64 |
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- LoRA Alpha: None |
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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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Imágenes variadas obtenidas de google que incluyen desde un mercedes pintado por alebrijeros hasta miss universo que fue con un vestido estilo alebrijero |
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## Inference |
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```python |
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import torch |
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from diffusers import DiffusionPipeline |
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model_id = 'stabilityai/stable-diffusion-3.5-large' |
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adapter_id = 'CarlosRiverMe/sd3-lora-alebrijeros-final' |
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pipeline = DiffusionPipeline.from_pretrained(model_id) |
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pipeline.load_lora_weights(adapter_id) |
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prompt = "sweatshirt painted in the alebrijeros style" |
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negative_prompt = 'blurry, cropped, ugly' |
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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=negative_prompt, |
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num_inference_steps=20, |
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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=512, |
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height=512, |
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guidance_scale=5.0, |
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).images[0] |
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image.save("output.png", format="PNG") |
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``` |
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