Model card auto-generated by SimpleTuner

README.md

@@ -0,0 +1,176 @@
+---
+license: other
+base_model: "flux/unknown-model"
+tags:
+  - flux
+  - flux-diffusers
+  - text-to-image
+  - diffusers
+  - simpletuner
+  - safe-for-work
+  - lora
+  - template:sd-lora
+  - standard
+inference: true
+widget:
+- text: 'unconditional (blank prompt)'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_0_0.png
+- text: 'A photo-realistic image of a cat'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_1_0.png
+---
+
+# string
+
+This is a standard PEFT LoRA derived from [flux/unknown-model](https://huggingface.co/flux/unknown-model).
+
+
+The main validation prompt used during training was:
+```
+A photo-realistic image of a cat
+```
+
+
+## Validation settings
+- CFG: `3.5`
+- CFG Rescale: `0.0`
+- Steps: `28`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `42`
+- 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: 2
+- Training steps: 500
+- Learning rate: 0.0004
+  - Learning rate schedule: polynomial
+  - Warmup steps: 100
+- Max grad norm: 2.0
+- Effective batch size: 1
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 1
+- Gradient checkpointing: True
+- Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible', 'flux_lora_target=all'])
+- Optimizer: adamw_bf16
+- Trainable parameter precision: Pure BF16
+- Caption dropout probability: 5.0%
+
+
+- LoRA Rank: 16
+- LoRA Alpha: None
+- LoRA Dropout: 0.1
+- LoRA initialisation style: default
+    
+
+## Datasets
+
+### autotrain-512
+- Repeats: 4
+- Total number of images: 6
+- Total number of aspect buckets: 3
+- Resolution: 0.262144 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+### autotrain-crop-512
+- Repeats: 4
+- Total number of images: 6
+- Total number of aspect buckets: 1
+- Resolution: 0.262144 megapixels
+- Cropped: True
+- Crop style: center
+- Crop aspect: square
+- Used for regularisation data: No
+### autotrain-768
+- Repeats: 4
+- Total number of images: 6
+- Total number of aspect buckets: 4
+- Resolution: 0.589824 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+### autotrain-crop-768
+- Repeats: 4
+- Total number of images: 6
+- Total number of aspect buckets: 1
+- Resolution: 0.589824 megapixels
+- Cropped: True
+- Crop style: center
+- Crop aspect: square
+- Used for regularisation data: No
+### autotrain-1024
+- Repeats: 4
+- Total number of images: 6
+- Total number of aspect buckets: 3
+- Resolution: 1.048576 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+### autotrain-crop-1024
+- Repeats: 4
+- Total number of images: 6
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: True
+- Crop style: center
+- Crop aspect: square
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+model_id = '/workspace/models/FLUX.1-dev'
+adapter_id = 'hg-ai-team/string'
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
+pipeline.load_lora_weights(adapter_id)
+
+prompt = "A photo-realistic image of a cat"
+
+
+## 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
+image = pipeline(
+    prompt=prompt,
+    num_inference_steps=28,
+    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
+    width=1024,
+    height=1024,
+    guidance_scale=3.5,
+).images[0]
+image.save("output.png", format="PNG")
+```
+
+
+