saving checkpoint-100

README.md

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+
+---
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+instance_prompt: bbhf
+tags:
+- stable-diffusion-xl
+- stable-diffusion-xl-diffusers
+- text-to-image
+- diffusers
+- lora
+inference: false
+datasets:
+- herve76/bb
+---
+    
+# LoRA DreamBooth - herve76/bb
+
+## MODEL IS CURRENTLY TRAINING ...
+Last checkpoint saved: checkpoint-100
+
+These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. 
+
+The weights were trained on the concept prompt: 
+```
+bbhf
+```
+Use this keyword to trigger your custom model in your prompts. 
+
+LoRA for the text encoder was enabled: False.
+
+Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
+
+## Usage
+
+Make sure to upgrade diffusers to >= 0.19.0:
+```
+pip install diffusers --upgrade
+```
+In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
+```
+pip install invisible_watermark transformers accelerate safetensors
+```
+To just use the base model, you can run:
+```python
+import torch
+from diffusers import DiffusionPipeline, AutoencoderKL
+vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16)
+pipe = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    vae=vae, torch_dtype=torch.float16, variant="fp16",
+    use_safetensors=True
+)
+pipe.to("cuda")
+# This is where you load your trained weights
+pipe.load_lora_weights('herve76/bb')
+
+prompt = "A majestic bbhf jumping from a big stone at night"
+image = pipe(prompt=prompt, num_inference_steps=50).images[0]
+```

checkpoint-100/optimizer.bin

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checkpoint-100/pytorch_lora_weights.safetensors

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checkpoint-100/random_states_0.pkl

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checkpoint-100/scaler.pt

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checkpoint-100/scheduler.bin

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