--- license: creativeml-openrail-m base_model: "stabilityai/stable-diffusion-xl-base-1.0" tags: - sdxl - sdxl-diffusers - text-to-image - diffusers - simpletuner - not-for-all-audiences - lora - template:sd-lora - lycoris inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'your prompt to validate on' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'another prompt to validate on' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'ggn_style, A nude woman sits on a patterned cloth with crossed legs. She holds a small object in her hand. A low table with fruit is in front of her. Pink flowers are in the background, with mountains and palm trees beyond. Text is in the bottom left corner.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png --- # paul-gaugin-sdxl-lora-03 This is a LyCORIS adapter derived from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). The main validation prompt used during training was: ``` ggn_style, A nude woman sits on a patterned cloth with crossed legs. She holds a small object in her hand. A low table with fruit is in front of her. Pink flowers are in the background, with mountains and palm trees beyond. Text is in the bottom left corner. ``` ## Validation settings - CFG: `4.2` - 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: 0 - Training steps: 400 - Learning rate: 5e-05 - Effective batch size: 4 - Micro-batch size: 4 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: epsilon - Rescaled betas zero SNR: False - Optimizer: optimi-lionweight_decay=1e-3 - Precision: Pure BF16 - Quantised: Yes: int8-quanto - Xformers: Not used - LyCORIS Config: ```json { "algo": "lokr", "multiplier": 1.0, "linear_dim": 10000, "linear_alpha": 1, "factor": 16, "apply_preset": { "target_module": [ "Attention", "FeedForward" ], "module_algo_map": { "Attention": { "factor": 16 }, "FeedForward": { "factor": 8 } } } } ``` ## Datasets ### paul-gaugin-sdxl-512 - Repeats: 10 - Total number of images: 85 - Total number of aspect buckets: 8 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None ### paul-gaugin-sdxl-1024 - Repeats: 10 - Total number of images: 85 - Total number of aspect buckets: 6 - Resolution: 1.048576 megapixels - Cropped: False - Crop style: None - Crop aspect: None ### paul-gaugin-sdxl-512-crop - Repeats: 10 - Total number of images: 85 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### paul-gaugin-sdxl-1024-crop - Repeats: 10 - Total number of images: 85 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: random - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline from lycoris import create_lycoris_from_weights model_id = 'stabilityai/stable-diffusion-xl-base-1.0' adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually lora_scale = 1.0 wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer) wrapper.merge_to() prompt = "ggn_style, A nude woman sits on a patterned cloth with crossed legs. She holds a small object in her hand. A low table with fruit is in front of her. Pink flowers are in the background, with mountains and palm trees beyond. Text is in the bottom left corner." 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=4.2, guidance_rescale=0.0, ).images[0] image.save("output.png", format="PNG") ```