--- license: other base_model: "stabilityai/stable-diffusion-3-medium-diffusers" tags: - sd3 - sd3-diffusers - text-to-image - diffusers - simpletuner - lora - template:sd-lora inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'A design of a cute pokemon, featuring a single bird, water type pokemon, on a background' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png --- # sd3-lora-training-rank32-v2 This is a standard PEFT LoRA derived from [stabilityai/stable-diffusion-3-medium-diffusers](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers). The main validation prompt used during training was: ``` A design of a cute pokemon, featuring a single bird, water type pokemon, on a background ``` ## Validation settings - CFG: `5.0` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `1024x1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 81 - Training steps: 2993 - Learning rate: 0.0001 - Effective batch size: 2 - Micro-batch size: 1 - Gradient accumulation steps: 2 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: bf16 - Quantised: No - Xformers: Not used - LoRA Rank: 32 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### pal-rank16 - Repeats: 0 - Total number of images: 73 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: center - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'stabilityai/stable-diffusion-3-medium-diffusers' adapter_id = 'zwloong/sd3-lora-training-rank32-v2' pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt = "A design of a cute pokemon, featuring a single bird, water type pokemon, on a background" negative_prompt = 'blurry, cropped, ugly' pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=20, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=5.0, ).images[0] image.save("output.png", format="PNG") ```