shubhamg1242/kriptong πΌοΈπ’βπβ β πΌοΈ
About
Example Output
Prompt:
""KriptonG stands next to a vintage fighter plane on an airfield, dressed in a classic brown leather bomber jacket, aviator sunglasses, and a white silk scarf fluttering in the wind. Behind him, the fiery orange of a setting sun casts long shadows across the tarmac. His confident stance and faint smirk embody the daring spirit of a World War II ace. The ultra-HD resolution captures the weathered leather of his jacket, the gleam of the planeβs metal body, and the nostalgic ambiance of the scene.""
Output



Performance Metrics
26.01s
Prediction Time
26.11s
Total Time
All Input Parameters
{
"model": "dev",
"prompt": "\"KriptonG stands next to a vintage fighter plane on an airfield, dressed in a classic brown leather bomber jacket, aviator sunglasses, and a white silk scarf fluttering in the wind. Behind him, the fiery orange of a setting sun casts long shadows across the tarmac. His confident stance and faint smirk embody the daring spirit of a World War II ace. The ultra-HD resolution captures the weathered leather of his jacket, the gleam of the planeβs metal body, and the nostalgic ambiance of the scene.\"",
"go_fast": false,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 4,
"aspect_ratio": "1:1",
"output_format": "jpg",
"guidance_scale": 3,
"output_quality": 80,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 28
}
Input Parameters
- mask
- Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.
- seed
- Random seed. Set for reproducible generation
- image
- Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.
- model
- Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps.
- width
- Width of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation
- height
- Height of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation
- prompt (required)
- Prompt for generated image. If you include the `trigger_word` used in the training process you are more likely to activate the trained object, style, or concept in the resulting image.
- go_fast
- Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16
- extra_lora
- Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'
- lora_scale
- Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.
- megapixels
- Approximate number of megapixels for generated image
- num_outputs
- Number of outputs to generate
- aspect_ratio
- Aspect ratio for the generated image. If custom is selected, uses height and width below & will run in bf16 mode
- output_format
- Format of the output images
- guidance_scale
- Guidance scale for the diffusion process. Lower values can give more realistic images. Good values to try are 2, 2.5, 3 and 3.5
- output_quality
- Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs
- prompt_strength
- Prompt strength when using img2img. 1.0 corresponds to full destruction of information in image
- extra_lora_scale
- Determines how strongly the extra LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.
- replicate_weights
- Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'
- num_inference_steps
- Number of denoising steps. More steps can give more detailed images, but take longer.
- disable_safety_checker
- Disable safety checker for generated images.
Output Schema
Output
Example Execution Logs
2024-12-16 07:45:16.271 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2024-12-16 07:45:16.272 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 89%|βββββββββ | 270/304 [00:00<00:00, 2694.91it/s] Applying LoRA: 100%|ββββββββββ| 304/304 [00:00<00:00, 2618.72it/s] 2024-12-16 07:45:16.388 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s free=28807090089984 Downloading weights 2024-12-16T07:45:16Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp9jjhktew/weights url=https://replicate.delivery/xezq/qLj44lxhfbUEXyBSSWY9FrUKFeofLGrwPyr0vCp0fhqTYJHPB/trained_model.tar 2024-12-16T07:45:18Z | INFO | [ Complete ] dest=/tmp/tmp9jjhktew/weights size="172 MB" total_elapsed=1.899s url=https://replicate.delivery/xezq/qLj44lxhfbUEXyBSSWY9FrUKFeofLGrwPyr0vCp0fhqTYJHPB/trained_model.tar Downloaded weights in 1.92s 2024-12-16 07:45:18.312 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/0598509dce58f953 2024-12-16 07:45:18.382 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2024-12-16 07:45:18.382 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2024-12-16 07:45:18.382 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 89%|βββββββββ | 270/304 [00:00<00:00, 2697.71it/s] Applying LoRA: 100%|ββββββββββ| 304/304 [00:00<00:00, 2621.51it/s] 2024-12-16 07:45:18.499 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s Using seed: 30132 0it [00:00, ?it/s] 1it [00:00, 8.35it/s] 2it [00:00, 5.86it/s] 3it [00:00, 5.34it/s] 4it [00:00, 5.13it/s] 5it [00:00, 5.01it/s] 6it [00:01, 4.95it/s] 7it [00:01, 4.91it/s] 8it [00:01, 4.89it/s] 9it [00:01, 4.87it/s] 10it [00:01, 4.85it/s] 11it [00:02, 4.84it/s] 12it [00:02, 4.84it/s] 13it [00:02, 4.84it/s] 14it [00:02, 4.83it/s] 15it [00:03, 4.83it/s] 16it [00:03, 4.83it/s] 17it [00:03, 4.83it/s] 18it [00:03, 4.83it/s] 19it [00:03, 4.83it/s] 20it [00:04, 4.83it/s] 21it [00:04, 4.83it/s] 22it [00:04, 4.83it/s] 23it [00:04, 4.83it/s] 24it [00:04, 4.83it/s] 25it [00:05, 4.83it/s] 26it [00:05, 4.83it/s] 27it [00:05, 4.83it/s] 28it [00:05, 4.83it/s] 28it [00:05, 4.90it/s] 0it [00:00, ?it/s] 1it [00:00, 4.86it/s] 2it [00:00, 4.84it/s] 3it [00:00, 4.83it/s] 4it [00:00, 4.83it/s] 5it [00:01, 4.83it/s] 6it [00:01, 4.82it/s] 7it [00:01, 4.82it/s] 8it [00:01, 4.82it/s] 9it [00:01, 4.82it/s] 10it [00:02, 4.82it/s] 11it [00:02, 4.82it/s] 12it [00:02, 4.82it/s] 13it [00:02, 4.82it/s] 14it [00:02, 4.82it/s] 15it [00:03, 4.82it/s] 16it [00:03, 4.82it/s] 17it [00:03, 4.82it/s] 18it [00:03, 4.82it/s] 19it [00:03, 4.82it/s] 20it [00:04, 4.82it/s] 21it [00:04, 4.82it/s] 22it [00:04, 4.82it/s] 23it [00:04, 4.82it/s] 24it [00:04, 4.82it/s] 25it [00:05, 4.82it/s] 26it [00:05, 4.82it/s] 27it [00:05, 4.82it/s] 28it [00:05, 4.82it/s] 28it [00:05, 4.82it/s] 0it [00:00, ?it/s] 1it [00:00, 4.86it/s] 2it [00:00, 4.83it/s] 3it [00:00, 4.83it/s] 4it [00:00, 4.82it/s] 5it [00:01, 4.82it/s] 6it [00:01, 4.82it/s] 7it [00:01, 4.81it/s] 8it [00:01, 4.82it/s] 9it [00:01, 4.81it/s] 10it [00:02, 4.81it/s] 11it [00:02, 4.81it/s] 12it [00:02, 4.80it/s] 13it [00:02, 4.80it/s] 14it [00:02, 4.80it/s] 15it [00:03, 4.80it/s] 16it [00:03, 4.80it/s] 17it [00:03, 4.80it/s] 18it [00:03, 4.80it/s] 19it [00:03, 4.80it/s] 20it [00:04, 4.81it/s] 21it [00:04, 4.80it/s] 22it [00:04, 4.80it/s] 23it [00:04, 4.80it/s] 24it [00:04, 4.80it/s] 25it [00:05, 4.80it/s] 26it [00:05, 4.81it/s] 27it [00:05, 4.81it/s] 28it [00:05, 4.81it/s] 28it [00:05, 4.81it/s] 0it [00:00, ?it/s] 1it [00:00, 4.86it/s] 2it [00:00, 4.81it/s] 3it [00:00, 4.79it/s] 4it [00:00, 4.79it/s] 5it [00:01, 4.79it/s] 6it [00:01, 4.79it/s] 7it [00:01, 4.80it/s] 8it [00:01, 4.80it/s] 9it [00:01, 4.80it/s] 10it [00:02, 4.80it/s] 11it [00:02, 4.79it/s] 12it [00:02, 4.79it/s] 13it [00:02, 4.78it/s] 14it [00:02, 4.79it/s] 15it [00:03, 4.80it/s] 16it [00:03, 4.80it/s] 17it [00:03, 4.80it/s] 18it [00:03, 4.80it/s] 19it [00:03, 4.80it/s] 20it [00:04, 4.79it/s] 21it [00:04, 4.80it/s] 22it [00:04, 4.80it/s] 23it [00:04, 4.80it/s] 24it [00:05, 4.79it/s] 25it [00:05, 4.79it/s] 26it [00:05, 4.80it/s] 27it [00:05, 4.80it/s] 28it [00:05, 4.80it/s] 28it [00:05, 4.80it/s] Total safe images: 4 out of 4
Version Details
- Version ID
d472465eab6d722ec1a5842ba9d7ac1a8e618b06722bd195e577ad32cc97c644- Version Created
- November 17, 2024