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End of training

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  1. README.md +9 -5
  2. pytorch_lora_weights.safetensors +3 -0
README.md CHANGED
@@ -15,19 +15,15 @@ datasets:
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  # LoRA DreamBooth - herve76/bb
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- ## MODEL IS CURRENTLY TRAINING ...
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- Last checkpoint saved: checkpoint-500
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-
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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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  ```
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  bbhf
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- ```
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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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-
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  Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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  ## Usage
@@ -36,24 +32,32 @@ 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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  pipe.to("cuda")
 
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  # This is where you load your trained weights
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  pipe.load_lora_weights('herve76/bb')
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  prompt = "A majestic bbhf 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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  # LoRA DreamBooth - herve76/bb
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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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  ```
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  bbhf
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+ ```
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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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  ```
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  pip install diffusers --upgrade
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  ```
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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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+
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  To just use the base model, you can run:
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+
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  ```python
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  import torch
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  from diffusers import DiffusionPipeline, AutoencoderKL
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+
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  vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16)
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+
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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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+
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  pipe.to("cuda")
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+
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  # This is where you load your trained weights
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  pipe.load_lora_weights('herve76/bb')
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  prompt = "A majestic bbhf jumping from a big stone at night"
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+
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  image = pipe(prompt=prompt, num_inference_steps=50).images[0]
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  ```
pytorch_lora_weights.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ oid sha256:8e5749fa827cd375227d8b05391b2a6ac765bbd54a4fa83b444ec8869ccec999
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+ size 23401064