--- license: other base_model: "black-forest-labs/FLUX.1-dev" tags: - flux - flux-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 rocky Maine coastline with bold, geometric shapes representing cliffs and waves. Strong colors and simplified forms dominate the composition, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'An abstract composition inspired by Berlin''s urban life. Fragmented shapes, numbers, and symbols arranged in a Cubist-influenced style, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'A still life of flowers in a vase, rendered with thick brushstrokes and vibrant, non-naturalistic colors. Simplified forms show Cubist influence, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png - text: 'A stark New Mexico landscape with stylized mountains and desert flora. Bold outlines and earthy colors capture the essence of the Southwest, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_4_0.png - text: 'A portrait of a WWI German soldier, composed of geometric shapes and military symbols. Strong, emotive use of color and form, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_5_0.png - text: 'Mount Katahdin in Maine, depicted with sharp angles and bold colors. The landscape is reduced to its essential forms, emphasizing its rugged nature, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_6_0.png - text: 'Modern New York skyscrapers rendered in Hartley''s style. Geometric shapes and bold colors create a dynamic urban composition, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_7_0.png - text: 'An orbiting space station viewed through a Modernist lens. Fragmented forms and symbolic elements represent the futuristic structure, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_8_0.png - text: 'An electric car charging station, depicted with Cubist-inspired fragmentation. Bold colors and geometric shapes represent energy and technology, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_9_0.png - text: 'A composition of social media icons and symbols, arranged in a Modernist style reminiscent of Hartley''s German officer paintings, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_10_0.png - text: 'An abstract representation of climate change, using Hartley''s bold style to depict melting ice caps, rising seas, and changing weather patterns, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_11_0.png - text: 'A person wearing a VR headset, surrounded by fragmented, Cubist-inspired virtual elements. Bold colors and geometric forms dominate the composition, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_12_0.png - text: 'hamster, in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_13_0.png - text: 'hamster in the style of MRSDN' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_14_0.png --- # Flux-Marsden-Hartley-LoKr-SimpleTuner-03 This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). The main validation prompt used during training was: ``` hamster in the style of MRSDN ``` ## Validation settings - CFG: `3.0` - 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: 12 - Training steps: 7400 - Learning rate: 0.0004 - Effective batch size: 2 - Micro-batch size: 2 - 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 { "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 ### marsden-hartley-Flux-CC-512 - Repeats: 10 - Total number of images: 25 - Total number of aspect buckets: 4 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None ### marsden-hartley-Flux-CC-1024 - Repeats: 10 - Total number of images: 25 - Total number of aspect buckets: 8 - Resolution: 1.048576 megapixels - Cropped: False - Crop style: None - Crop aspect: None ### marsden-hartley-Flux-CC-512-crop - Repeats: 10 - Total number of images: 25 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### marsden-hartley-Flux-CC-1024-crop - Repeats: 10 - Total number of images: 25 - 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 = 'black-forest-labs/FLUX.1-dev' 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 = "hamster in the style of MRSDN" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=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=3.0, ).images[0] image.save("output.png", format="PNG") ```