anotherjesse/sdxl-civit-lora 🖼️🔢📝❓✓ → 🖼️
Example Output
Performance Metrics
22.45s
Prediction Time
33.32s
Total Time
Input Parameters
- mask
- Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.
- seed
- Random seed. Leave blank to randomize the seed
- image
- Input image for img2img or inpaint mode
- width
- Width of output image
- height
- Height of output image
- prompt
- Input prompt
- refine
- Which refine style to use
- scheduler
- scheduler
- lora_scale
- LoRA additive scale. Only applicable on trained models.
- num_outputs
- Number of images to output.
- lora_weights
- Replicate LoRA weights to use. Leave blank to use the default weights.
- refine_steps
- For base_image_refiner, the number of steps to refine, defaults to num_inference_steps
- guidance_scale
- Scale for classifier-free guidance
- apply_watermark
- Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.
- high_noise_frac
- For expert_ensemble_refiner, the fraction of noise to use
- negative_prompt
- Input Negative Prompt
- prompt_strength
- Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image
- num_inference_steps
- Number of denoising steps
Output Schema
Output
Version Details
- Version ID
492cd9e17c7ebe078af78f78d966234fd8dc339854c028d46b1b5fef785888d8- Version Created
- September 7, 2023