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README.md

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+---
+license: creativeml-openrail-m
+base_model: "stabilityai/stable-diffusion-xl-base-1.0"
+tags:
+  - sdxl
+  - sdxl-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: 'A photo-realistic image of a cat'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_1_0.png
+- text: 'prompt not found (2)'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_2_0.png
+- text: 'prompt not found (3)'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_3_0.png
+---
+
+# simpletuner-controlnet-sdxl-lora-test
+
+This is a standard PEFT LoRA derived from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
+
+The main validation prompt used during training was:
+```
+A photo-realistic image of a cat
+```
+
+
+## Validation settings
+- CFG: `4.2`
+- CFG Rescale: `0.0`
+- Steps: `20`
+- Sampler: `ddim`
+- Seed: `42`
+- Resolution: `1024x1024`
+
+
+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: 1
+- Training steps: 10
+- Learning rate: 3e-07
+  - Learning rate schedule: constant
+  - Warmup steps: 100
+- Max grad value: 2.0
+- Effective batch size: 3
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 3
+- Gradient checkpointing: True
+- Prediction type: epsilon (extra parameters=['training_scheduler_timestep_spacing=trailing', 'inference_scheduler_timestep_spacing=trailing'])
+- Optimizer: bnb-lion8bit
+- Trainable parameter precision: Pure BF16
+- Base model precision: `no_change`
+- Caption dropout probability: 0.1%
+
+
+- LoRA Rank: 16
+- LoRA Alpha: None
+- LoRA Dropout: 0.1
+- LoRA initialisation style: default
+    
+
+## Datasets
+
+### antelope-data
+- Repeats: 0
+- Total number of images: ~24
+- 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 = 'stabilityai/stable-diffusion-xl-base-1.0'
+adapter_id = 'bghira/simpletuner-controlnet-sdxl-lora-test'
+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"
+negative_prompt = 'blurry, cropped, ugly'
+
+## Optional: quantise the model to save on vram.
+## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
+#from optimum.quanto import quantize, freeze, qint8
+#quantize(pipeline.unet, weights=qint8)
+#freeze(pipeline.unet)
+    
+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,
+    negative_prompt=negative_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(42),
+    width=1024,
+    height=1024,
+    guidance_scale=4.2,
+    guidance_rescale=0.0,
+).images[0]
+
+model_output.save("output.png", format="PNG")
+
+```
+
+
+