Commit
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End of training
Browse files- README.md +63 -0
- checkpoint-100/optimizer.bin +3 -0
- checkpoint-100/pytorch_lora_weights.safetensors +3 -0
- checkpoint-100/random_states_0.pkl +3 -0
- checkpoint-100/scaler.pt +3 -0
- checkpoint-100/scheduler.bin +3 -0
- checkpoint-200/optimizer.bin +3 -0
- checkpoint-200/pytorch_lora_weights.safetensors +3 -0
- checkpoint-200/random_states_0.pkl +3 -0
- checkpoint-200/scaler.pt +3 -0
- checkpoint-200/scheduler.bin +3 -0
- checkpoint-300/optimizer.bin +3 -0
- checkpoint-300/pytorch_lora_weights.safetensors +3 -0
- checkpoint-300/random_states_0.pkl +3 -0
- checkpoint-300/scaler.pt +3 -0
- checkpoint-300/scheduler.bin +3 -0
- checkpoint-400/optimizer.bin +3 -0
- checkpoint-400/pytorch_lora_weights.safetensors +3 -0
- checkpoint-400/random_states_0.pkl +3 -0
- checkpoint-400/scaler.pt +3 -0
- checkpoint-400/scheduler.bin +3 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scaler.pt +3 -0
- checkpoint-500/scheduler.bin +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: marcelsamyn
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tags:
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- stable-diffusion-xl
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- stable-diffusion-xl-diffusers
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- text-to-image
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- diffusers
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- lora
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inference: false
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datasets:
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- marcelsamyn/marcelsamyn3
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---
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# LoRA DreamBooth - marcelsamyn/lora-trained-xl-folder
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These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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The weights were trained on the concept prompt:
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`marcelsamyn`
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Use this keyword to trigger your custom model in your prompts.
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LoRA for the text encoder was enabled: False.
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Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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## Usage
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Make sure to upgrade diffusers to >= 0.19.0:
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```
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pip install diffusers --upgrade
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```
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In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
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```
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pip install invisible_watermark transformers accelerate safetensors
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```
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To just use the base model, you can run:
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```python
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import torch
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from diffusers import DiffusionPipeline, AutoencoderKL
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vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16)
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pipe = DiffusionPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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vae=vae, torch_dtype=torch.float16, variant="fp16",
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use_safetensors=True
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)
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# This is where you load your trained weights
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pipe.load_lora_weights('marcelsamyn/lora-trained-xl-folder')
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pipe.to("cuda")
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prompt = "A majestic marcelsamyn jumping from a big stone at night"
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image = pipe(prompt=prompt, num_inference_steps=50).images[0]
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```
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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-500/optimizer.bin
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