import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Dikshan1234/ImageGen")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]ImageGen — SDXL LoRA (general-purpose)
LoRA fine-tune of stabilityai/stable-diffusion-xl-base-1.0 on a broad, filtered aesthetic dataset
for improved prompt adherence and image quality over stock SDXL.
Training status
- Step: 6,200
- Epoch: 2.00
- Validation loss: 0.1364
- LoRA: rank 32, alpha 64, targets
to_q, to_k, to_v, to_out.0 - Precision: bf16, gradient checkpointing on
Checkpoints
latest/— most recent adapter weightsbest/— lowest validation-loss adapter weights
Dataset sources
Spawning/PD12M(target ~30,000)common-canvas/commoncatalog-cc-by(target ~20,000)laion/laion2B-en-aesthetic(target ~23,000)laion/laion-art(target ~7,500)poloclub/diffusiondb(target ~12,500)kakaobrain/coyo-700m(target ~5,500)
License note: trained on research datasets of scraped image-text pairs. Released under
cc-by-nc-4.0(non-commercial). Verify each source dataset's terms before any downstream commercial use.
Usage
from diffusers import StableDiffusionXLPipeline
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights("Dikshan1234/ImageGen", subfolder="best")
image = pipe("a cozy cabin in a snowy forest, golden hour").images[0]
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Model tree for Dikshan1234/ImageGen
Base model
stabilityai/stable-diffusion-xl-base-1.0