--- license: other base_model: "stabilityai/stable-diffusion-3.5-large" tags: - sd3 - sd3-diffusers - text-to-image - diffusers - simpletuner - safe-for-work - 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: 'A scene from the animated Studio Ghibli movie Spirited Away, where a man with kind eyes and a worn hat sits by a window, lost in thought, as light filters gently through sheer curtains.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'A scene from the animated Studio Ghibli movie Spirited Away, where a woman with long, windswept hair reads a book at an outdoor cafe, surrounded by blooming flowers and soft afternoon light.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'A scene from the animated Studio Ghibli movie Spirited Away, where a fluffy white cat curls up on a windowsill, basking in the warm sunlight, while soft curtains flutter in the breeze.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png - text: 'A scene from the animated Studio Ghibli movie Spirited Away, where a glowing, antlered spirit wanders through a quiet forest, leaving a trail of shimmering footprints among the ferns.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_4_0.png - text: 'A photo-realistic image of a cat' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_5_0.png --- # sd35-spirited-away-lokr This is a LyCORIS adapter derived from [stabilityai/stable-diffusion-3.5-large](https://huggingface.co./stabilityai/stable-diffusion-3.5-large). The main validation prompt used during training was: ``` A photo-realistic image of a cat ``` ## 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: 6 - Training steps: 1200 - Learning rate: 1e-07 - Max grad norm: 0.01 - Effective batch size: 4 - Micro-batch size: 4 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: Pure BF16 - Quantised: Yes: int8-quanto - Xformers: Not used - LyCORIS Config: ```json { "bypass_mode": true, "algo": "lokr", "multiplier": 1.0, "linear_dim": 1000000, "linear_alpha": 1, "factor": 1, "full_matrix": true, "apply_preset": { "target_module": [ "JointTransformerBlock" ], "name_algo_map": { "transformer_blocks.0.norm1*": { "algo": "lokr", "factor": 1, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.0.norm1_context*": { "algo": "lokr", "factor": 1, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.0.ff*": { "algo": "lokr", "factor": 1, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.0.*": { "algo": "lokr", "factor": 2, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.1.norm1*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.1.norm1_context*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.1.ff*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.1.*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.2.norm1*": { "algo": "lokr", "factor": 4, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.2.norm1_context*": { "algo": "lokr", "factor": 4, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.2.ff*": { "algo": "lokr", "factor": 4, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.2.*": { "algo": "lokr", "factor": 8, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.3.norm1*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.3.norm1_context*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.3.ff*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.3.*": { "algo": "lokr", "factor": 14, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.4.norm1*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.4.norm1_context*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.4.ff*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.4.*": { "algo": "lokr", "factor": 12, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.5.norm1*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.5.norm1_context*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.5.ff*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.5.*": { "algo": "lokr", "factor": 14, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.6.norm1*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.6.norm1_context*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.6.ff*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.6.*": { "algo": "lokr", "factor": 14, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.7.norm1*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.7.norm1_context*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.7.ff*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.7.*": { "algo": "lokr", "factor": 14, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.8.norm1*": { "algo": "lokr", "factor": 8, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.8.norm1_context*": { "algo": "lokr", "factor": 8, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.8.ff*": { "algo": "lokr", "factor": 8, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.8.*": { "algo": "lokr", "factor": 16, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.9.norm1*": { "algo": "lokr", "factor": 8, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.9.norm1_context*": { "algo": "lokr", "factor": 8, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.9.ff*": { "algo": "lokr", "factor": 8, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.9.*": { "algo": "lokr", "factor": 16, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.10.norm1*": { "algo": "lokr", "factor": 7, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.10.norm1_context*": { "algo": "lokr", "factor": 7, 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"full_matrix": true }, "transformer_blocks.31.*": { "algo": "lokr", "factor": 2, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.32.norm1*": { "algo": "lokr", "factor": 4, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.32.norm1_context*": { "algo": "lokr", "factor": 4, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.32.ff*": { "algo": "lokr", "factor": 4, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.32.*": { "algo": "lokr", "factor": 8, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.33.norm1*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.33.norm1_context*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.33.ff*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.33.*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.34.norm1*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.34.norm1_context*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.34.ff*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.34.*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.35.norm1*": { "algo": "lokr", "factor": 1, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.35.norm1_context*": { "algo": "lokr", "factor": 1, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.35.ff*": { "algo": "lokr", "factor": 1, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.35.*": { "algo": "lokr", "factor": 2, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.36.norm1*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.36.norm1_context*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.36.ff*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.36.*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.37.norm1*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.37.norm1_context*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.37.ff*": { "algo": "lokr", "factor": 3, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.37.*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true } }, "use_fnmatch": true } } ``` ## Datasets ### screencaps-1024 - Repeats: 0 - Total number of images: 379 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### screencaps-1024-crop - Repeats: 0 - Total number of images: 379 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: random - Crop aspect: square - Used for regularisation data: No ## Inference ```python import torch from diffusers import DiffusionPipeline from lycoris import create_lycoris_from_weights model_id = 'stabilityai/stable-diffusion-3.5-large' 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 = "A photo-realistic image of a cat" 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") ```