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README.md

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+---
+license: other
+base_model: "black-forest-labs/FLUX.1-Kontext-dev"
+tags:
+  - flux
+  - flux-diffusers
+  - text-to-image
+  - image-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+
+  - template:sd-lora
+  - standard
+pipeline_tag: text-to-image
+inference: true
+widget:
+- text: 'unconditional (blank prompt)'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_0_0.png
+- text: 'turn this person into a labubu'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_1_0.png
+- text: 'turn this person into a labubu'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_2_0.png
+- text: 'turn this person into a labubu'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_3_0.png
+---
+
+# labubu_dataset
+
+This is a PEFT LoRA derived from [black-forest-labs/FLUX.1-Kontext-dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
+
+The main validation prompt used during training was:
+```
+a photo of a daisy
+```
+
+
+## Validation settings
+- CFG: `2.5`
+- CFG Rescale: `0.0`
+- Steps: `20`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `69`
+- Resolution: `1024x1024`
+- Skip-layer guidance: 
+
+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:
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 15
+- Training steps: 125
+- Learning rate: 1e-05
+  - Learning rate schedule: constant
+  - Warmup steps: 50
+- Max grad value: 2.0
+- Effective batch size: 2
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 2
+  - Number of GPUs: 1
+- Gradient checkpointing: True
+- Prediction type: flow_matching (extra parameters=['flow_schedule_auto_shift', 'shift=0.0', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flux_lora_target=fal'])
+- Optimizer: optimi-lion
+- Trainable parameter precision: Pure BF16
+- Base model precision: `int8-quanto`
+- Caption dropout probability: 0.05%
+
+
+- LoRA Rank: 16
+- LoRA Alpha: 16.0
+- LoRA Dropout: 0.1
+- LoRA initialisation style: default
+    
+
+## Datasets
+
+### my-edited-images
+- Repeats: 0
+- Total number of images: 16
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+model_id = 'black-forest-labs/FLUX.1-Kontext-dev'
+adapter_id = 'playerzer0x/labubu_dataset'
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
+pipeline.load_lora_weights(adapter_id)
+
+prompt = "a photo of a daisy"
+
+
+## Optional: quantise the model to save on vram.
+## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
+from optimum.quanto import quantize, freeze, qint8
+quantize(pipeline.transformer, weights=qint8)
+freeze(pipeline.transformer)
+    
+pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
+model_output = 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(69),
+    width=1024,
+    height=1024,
+    guidance_scale=2.5,
+).images[0]
+
+model_output.save("output.png", format="PNG")
+
+```
+
+
+