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Model card auto-generated by SimpleTuner

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+ ---
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+ license: creativeml-openrail-m
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+ base_model: "black-forest-labs/FLUX.1-dev"
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+ tags:
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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+ - text-to-image
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+ - diffusers
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+ - simpletuner
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+ - lora
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+ - template:sd-lora
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+ inference: true
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+
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+ ---
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+
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+ # ohwx-man
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+
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+ This is a LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).
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+
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+
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+
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+ The main validation prompt used during training was:
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+
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+
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+
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+ ```
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+ a photograph of ohwx man
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+ ```
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+
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+ ## Validation settings
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+ - CFG: `3.5`
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+ - CFG Rescale: `0.0`
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+ - Steps: `28`
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+ - Sampler: `None`
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+ - Seed: `42`
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+ - Resolution: `1024`
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+
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+ Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
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+
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+
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+
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+
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+ <Gallery />
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+
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+ The text encoder **was not** trained.
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+ You may reuse the base model text encoder for inference.
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+
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+
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+ ## Training settings
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+
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+ - Training epochs: 999
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+ - Training steps: 1000
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+ - Learning rate: 0.0001
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+ - Effective batch size: 4
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+ - Micro-batch size: 1
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+ - Gradient accumulation steps: 4
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+ - Number of GPUs: 1
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+ - Prediction type: flow-matching
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+ - Rescaled betas zero SNR: False
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+ - Optimizer: AdamW, stochastic bf16
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+ - Precision: Pure BF16
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+ - Quantised: Yes: int8-quanto
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+ - Xformers: Not used
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+ - LoRA Rank: 16
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+ - LoRA Alpha: None
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+ - LoRA Dropout: 0.1
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+ - LoRA initialisation style: default
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+
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+
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+ ## Datasets
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+
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+ ### ohwx-man
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+ - Repeats: 0
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+ - Total number of images: 4
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+ - Total number of aspect buckets: 1
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+ - Resolution: 512 px
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+ - Cropped: False
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+ - Crop style: None
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+ - Crop aspect: None
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+
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+
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+ ## Inference
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+
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+
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+ ```python
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+ import torch
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+ from diffusers import DiffusionPipeline
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+
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+ model_id = 'black-forest-labs/FLUX.1-dev'
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+ adapter_id = 'AA12312424/ohwx-man'
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+ pipeline = DiffusionPipeline.from_pretrained(model_id)
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+ pipeline.load_lora_weights(adapter_id)
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+
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+ prompt = "a photograph of ohwx man"
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+
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+
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+ pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
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+ image = pipeline(
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+ prompt=prompt,
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+ num_inference_steps=28,
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+ generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
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+ width=1024,
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+ height=1024,
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+ guidance_scale=3.5,
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+ ).images[0]
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+ image.save("output.png", format="PNG")
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+ ```
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+