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1from diffusers import StableDiffusionPipeline
2import torch
3
4model_id = "runwayml/stable-diffusion-v1-5"
5lora_repo = "abcd2019/artist-sd1.5-lora"
6
7pipe = StableDiffusionPipeline.from_pretrained(
8 model_id,
9 torch_dtype=torch.float16
10).to("cuda")
11
12pipe.load_lora_weights(lora_repo, subfolder="Vincent_van_Gogh")
13
14prompt = "A portrait of a young woman, vincentvangogh style"
15image = pipe(prompt).images[0]
16
17image.save("output.png")1prompts = [
2 "A portrait of a young woman pablo_picasso style",
3 "A portrait of a young woman pablo picasso style",
4 "A portrait of a young woman PabloPicasso style",
5 "A portrait of a young woman pablopicassostyle",
6
7]
1artists = ['Vincent_van_Gogh', "Henri_Matisse", "Pablo_Picasso", "Rembrandt"]
2
3for artist in artists:
4 dataset_dir = f"/kaggle/working/lora_datasets/Rembrandt"
5 output_dir = f"/kaggle/working/lora_output/Rembrandt"
6
7 !accelerate launch train_text_to_image_lora.py --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" --train_data_dir=/kaggle/working/lora_datasets/Rembrandt --resolution=512 --center_crop --random_flip --train_batch_size=2 --gradient_accumulation_steps=4 --max_train_steps=1600 --learning_rate=1e-4 --lr_scheduler="cosine" --lr_warmup_steps=100 --rank=18 --mixed_precision="fp16" --output_dir=/kaggle/working/lora_output/Rembrandt --checkpointing_steps=500
8