jbilcke-hf HF staff commited on
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saving checkpoint-200

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README.md ADDED
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+
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+ ---
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+ base_model: stabilityai/stable-diffusion-xl-base-1.0
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+ instance_prompt: cnmt
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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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+ - jbilcke-hf/cinematic-1
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+ ---
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+
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+ # LoRA DreamBooth - jbilcke-hf/sdxl-cinematic-1
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+ ## MODEL IS CURRENTLY TRAINING ...
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+ Last checkpoint saved: checkpoint-200
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+ These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0 trained on @fffiloni's SD-XL trainer.
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+ The weights were trained on the concept prompt:
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+ ```
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+ cnmt
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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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+ 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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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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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(device)
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+ # This is where you load your trained weights
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+ specific_safetensors = "pytorch_lora_weights.safetensors"
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+ lora_scale = 0.9
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+ pipe.load_lora_weights(
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+ 'jbilcke-hf/sdxl-cinematic-1',
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+ weight_name = specific_safetensors,
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+ # use_auth_token = True
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+ )
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+ prompt = "A majestic cnmt jumping from a big stone at night"
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+ image = pipe(
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+ prompt=prompt,
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+ num_inference_steps=50,
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+ cross_attention_kwargs={"scale": lora_scale}
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+ ).images[0]
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+ ```
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