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sd2-community/stable-diffusion-2-1 base model. Activate the style with the trigger token ukyowood anywhere in the prompt.1from diffusers import StableDiffusionPipeline
2import torch
3
4pipe = StableDiffusionPipeline.from_pretrained(
5 "sd2-community/stable-diffusion-2-1",
6 torch_dtype=torch.float16,
7).to("cuda")
8
9pipe.load_lora_weights("gauravgandhi2411/aetherart-ukiyo-sd21")
10
11img = pipe(
12 "a ukyowood mountain landscape at sunset, traditional woodblock print",
13 negative_prompt="text, watermark, calligraphy, signature, words, letters",
14 num_inference_steps=30,
15 guidance_scale=7.5,
16).images[0]
17img.save("output.png")| Parameter | Value |
|---|---|
| Base model | sd2-community/stable-diffusion-2-1 |
| LoRA rank | 8 |
| Training images | 80 (WikiArt Ukiyo-e) |
| Resolution | 512 × 512 |
| Steps | 1500 |
| Precision | fp16 mixed |
| Batch size | 1 (gradient accumulation = 4, effective batch = 4) |
| Learning rate | 1e-4 |
| Seed | 42 |
| Hardware | NVIDIA RTX 3070 Laptop GPU (8 GB VRAM) |
| Training time | ~2 h 8 min |
text, watermark, calligraphy, signature, words, lettersreports/clip_blindness.md — nine Phase 6b experiments showing CLIP delta <1 SE while LPIPS ranged 0.40–0.73; underfitting paradox; why CLIP cannot guide LoRA training decisions.gauravgandhi2411/aetherart-ukiyo-sdxl — same rank-8, same dataset, trained on GCP L4. Both runs independently select checkpoint-1000.