Model card auto-generated by SimpleTuner

README.md

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+---
+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
+
+---
+
+# lora-training
+
+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:
+
+
+
+```
+xwe hermes handbag, photo of woman lower part posing on city street clad in white holding a xwe hermes handbag
+```
+
+## Validation settings
+- CFG: `7.5`
+- CFG Rescale: `0.0`
+- Steps: `30`
+- Sampler: `None`
+- Seed: `42`
+- Resolution: `1024`
+
+Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
+
+
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 1999
+- Training steps: 2000
+- Learning rate: 1e-06
+- Effective batch size: 8
+  - Micro-batch size: 4
+  - Gradient accumulation steps: 2
+  - Number of GPUs: 1
+- Prediction type: flow-matching
+- Rescaled betas zero SNR: False
+- Optimizer: AdamW, stochastic bf16
+- Precision: Pure BF16
+- Xformers: Not used
+- LoRA Rank: 4
+- LoRA Alpha: None
+- LoRA Dropout: 0.1
+- LoRA initialisation style: default
+
+
+## Datasets
+
+### Hermes-handbag
+- Repeats: 0
+- Total number of images: 8
+- Total number of aspect buckets: 1
+- Resolution: 1 megapixels
+- 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 = 'Fatha/lora-training'
+pipeline = DiffusionPipeline.from_pretrained(model_id)
+pipeline.load_lora_weights(adapter_id)
+
+prompt = "xwe hermes handbag, photo of woman lower part posing on city street clad in white holding a xwe hermes handbag"
+
+
+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=30,
+    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=7.5,
+).images[0]
+image.save("output.png", format="PNG")
+```
+