ambientocclusion
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README.md
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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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widget:
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- text: 'unconditional (blank prompt)'
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parameters:
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negative_prompt: 'blurry, cropped, ugly'
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output:
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url: ./assets/image_0_0.png
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- text: 'a photo of two people in a city street'
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parameters:
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negative_prompt: 'blurry, cropped, ugly'
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output:
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url: ./assets/image_1_0.png
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---
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# meme1
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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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The main validation prompt used during training was:
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```
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a photo of two people in a city street
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```
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## Validation settings
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- CFG: `7.5`
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- CFG Rescale: `0.0`
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- Steps: `20`
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- Sampler: `None`
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- Seed: `42`
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- Resolution: `1024x1024`
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Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
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You can find some example images in the following gallery:
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<Gallery />
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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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## Training settings
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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: 1
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- Micro-batch size: 1
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- Gradient accumulation steps: 1
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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: optimi-lion
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- Precision: bf16
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- Quantised: Yes: int2-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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## Datasets
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### meme1
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- Repeats: 0
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- Total number of images: 1
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- Total number of aspect buckets: 1
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- Resolution: 512 px
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- Cropped: True
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- Crop style: center
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- Crop aspect: square
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## Inference
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```python
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import torch
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from diffusers import DiffusionPipeline
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model_id = 'black-forest-labs/FLUX.1-dev'
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adapter_id = 'ambientocclusion/meme1'
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pipeline = DiffusionPipeline.from_pretrained(model_id)
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pipeline.load_lora_weights(adapter_id)
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prompt = "a photo of two people in a city street"
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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=20,
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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=7.5,
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).images[0]
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image.save("output.png", format="PNG")
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```
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