--- license: creativeml-openrail-m base_model: "black-forest-labs/FLUX.1-dev" tags: - stable-diffusion - stable-diffusion-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: 'es_style, two figures embraced, one male: red cap, one female: black headdress, standing, hands clasped close, greenish background, earthy tones, muted colors, asymmetrical balance, shadowy harmony, textured brushwork, high contrast, layered application, melancholic, signature bottom-right' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'es_style, 2 figures (woman: brown dress, child: orange clothing), intimate embrace, minimal background, muted tones, textured brushwork, loosely defined, asymmetrical balance, melancholic, signature bottom-left' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'es_style, single figure (man: brown suit, red tie), no background detail, muted tones, asymmetrical balance, textured brushwork, sharp contours, rhythmic patterns, melancholic, signature bottom-left' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png - text: 'es_style, single female figure, red headband, simple draped fabric, looking downward, indoor setting, white background, light beige hues, dark lines, asymmetrical balance, muted tones, sharp contours, dynamic energy, serene, signature bottom-right' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_4_0.png - text: 'mp_style, Street scene, 50 figures (many women: colorful dresses, many men: suits), 23 umbrellas (orange, red, yellow, green), bridge, buildings background, water, boats, Italian flag, steps, lamps, crowd ascending descending bridge, signature bottom-left' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_5_0.png --- # lora-Egon-Schiele-Flux This is a LoRA 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: ``` mp_style, Street scene, 50 figures (many women: colorful dresses, many men: suits), 23 umbrellas (orange, red, yellow, green), bridge, buildings background, water, boats, Italian flag, steps, lamps, crowd ascending descending bridge, signature bottom-left ``` ## Validation settings - CFG: `7.5` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `512` 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: 3 - Training steps: 440 - Learning rate: 0.0004 - Effective batch size: 1 - Micro-batch size: 1 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: AdamW, stochastic bf16 - Precision: Pure BF16 - Quantised: No - Xformers: Not used - LoRA Rank: 64 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### EgonSchiele - Repeats: 0 - Total number of images: 122 - Total number of aspect buckets: 1 - Resolution: 1024 px - Cropped: True - Crop style: center - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'davidrd123/lora-Egon-Schiele-Flux' pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt = "mp_style, Street scene, 50 figures (many women: colorful dresses, many men: suits), 23 umbrellas (orange, red, yellow, green), bridge, buildings background, water, boats, Italian flag, steps, lamps, crowd ascending descending bridge, signature bottom-left" 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=512, height=512, guidance_scale=7.5, ).images[0] image.save("output.png", format="PNG") ```