Instructions to use runchat/lora-533d7b31-63fd-42a0-be75-b68de7db171f-RunchatShell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use runchat/lora-533d7b31-63fd-42a0-be75-b68de7db171f-RunchatShell with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("runchat/lora-533d7b31-63fd-42a0-be75-b68de7db171f-RunchatShell") prompt = "a photo of RunchatShell style" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
SDXL LoRA: RunchatShell
This is a LoRA (Low-Rank Adaptation) model for Stable Diffusion XL fine-tuned on images with the trigger word RunchatShell.
Usage
from diffusers import StableDiffusionXLPipeline
import torch
# Load base model
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16
)
# Load LoRA weights
pipe.load_lora_weights("runchat/lora-533d7b31-63fd-42a0-be75-b68de7db171f-RunchatShell")
pipe = pipe.to("cuda")
# Generate image
prompt = "a photo of RunchatShell style"
image = pipe(prompt, num_inference_steps=25, guidance_scale=7.5).images[0]
image.save("output.png")
Training Details
- Base model: stabilityai/stable-diffusion-xl-base-1.0
- Training steps: 1000
- Learning rate: 0.0001
- Batch size: 1
- LoRA rank: 32
- Trigger word:
RunchatShell
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Model tree for runchat/lora-533d7b31-63fd-42a0-be75-b68de7db171f-RunchatShell
Base model
stabilityai/stable-diffusion-xl-base-1.0