sakshamdembla/sakshamface 🖼️🔢❓📝✓ → 🖼️
About
Example Output
Prompt:
"SAK: Generate a minimalist logo using the front view of my face. Focus exclusively on my beard and hairstyle, capturing their outline and unique shape in black. Remove all facial features (eyes, nose, mouth, etc.) and leave only the beard and hairstyle as clean, bold, black silhouettes. Ensure the design is simple, modern, and suitable for use as a personal logo. "
Output



Performance Metrics
31.81s
Prediction Time
31.87s
Total Time
All Input Parameters
{
"model": "dev",
"prompt": "SAK: Generate a minimalist logo using the front view of my face. Focus exclusively on my beard and hairstyle, capturing their outline and unique shape in black. Remove all facial features (eyes, nose, mouth, etc.) and leave only the beard and hairstyle as clean, bold, black silhouettes. Ensure the design is simple, modern, and suitable for use as a personal logo. ",
"go_fast": false,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 4,
"aspect_ratio": "1:1",
"output_format": "webp",
"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
2024-12-15 02:06:14.690 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2024-12-15 02:06:14.690 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2809.26it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2610.11it/s] 2024-12-15 02:06:14.807 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s free=28670295453696 Downloading weights 2024-12-15T02:06:14Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpzxuk9dij/weights url=https://replicate.delivery/yhqm/gnm2LBDcvfwftE2fRgsf6d3SlNuLzeStpeL0iVkIeIsH7cD3JA/trained_model.tar 2024-12-15T02:06:22Z | INFO | [ Complete ] dest=/tmp/tmpzxuk9dij/weights size="634 MB" total_elapsed=7.242s url=https://replicate.delivery/yhqm/gnm2LBDcvfwftE2fRgsf6d3SlNuLzeStpeL0iVkIeIsH7cD3JA/trained_model.tar Downloaded weights in 7.28s 2024-12-15 02:06:22.094 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/9bc88c9f06645d2e 2024-12-15 02:06:22.242 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2024-12-15 02:06:22.242 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2024-12-15 02:06:22.242 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 92%|█████████▏| 281/304 [00:00<00:00, 2806.83it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2697.48it/s] 2024-12-15 02:06:22.355 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.26s Using seed: 19689 0it [00:00, ?it/s] 1it [00:00, 8.32it/s] 2it [00:00, 5.83it/s] 3it [00:00, 5.32it/s] 4it [00:00, 5.11it/s] 5it [00:00, 4.96it/s] 6it [00:01, 4.90it/s] 7it [00:01, 4.86it/s] 8it [00:01, 4.85it/s] 9it [00:01, 4.84it/s] 10it [00:01, 4.82it/s] 11it [00:02, 4.80it/s] 12it [00:02, 4.80it/s] 13it [00:02, 4.80it/s] 14it [00:02, 4.79it/s] 15it [00:03, 4.79it/s] 16it [00:03, 4.78it/s] 17it [00:03, 4.78it/s] 18it [00:03, 4.79it/s] 19it [00:03, 4.78it/s] 20it [00:04, 4.79it/s] 21it [00:04, 4.78it/s] 22it [00:04, 4.79it/s] 23it [00:04, 4.79it/s] 24it [00:04, 4.78it/s] 25it [00:05, 4.78it/s] 26it [00:05, 4.78it/s] 27it [00:05, 4.78it/s] 28it [00:05, 4.78it/s] 28it [00:05, 4.86it/s] 0it [00:00, ?it/s] 1it [00:00, 4.87it/s] 2it [00:00, 4.81it/s] 3it [00:00, 4.79it/s] 4it [00:00, 4.78it/s] 5it [00:01, 4.77it/s] 6it [00:01, 4.77it/s] 7it [00:01, 4.77it/s] 8it [00:01, 4.77it/s] 9it [00:01, 4.77it/s] 10it [00:02, 4.77it/s] 11it [00:02, 4.77it/s] 12it [00:02, 4.77it/s] 13it [00:02, 4.77it/s] 14it [00:02, 4.77it/s] 15it [00:03, 4.77it/s] 16it [00:03, 4.77it/s] 17it [00:03, 4.77it/s] 18it [00:03, 4.76it/s] 19it [00:03, 4.77it/s] 20it [00:04, 4.77it/s] 21it [00:04, 4.77it/s] 22it [00:04, 4.78it/s] 23it [00:04, 4.78it/s] 24it [00:05, 4.77it/s] 25it [00:05, 4.77it/s] 26it [00:05, 4.77it/s] 27it [00:05, 4.77it/s] 28it [00:05, 4.77it/s] 28it [00:05, 4.77it/s] 0it [00:00, ?it/s] 1it [00:00, 4.82it/s] 2it [00:00, 4.79it/s] 3it [00:00, 4.78it/s] 4it [00:00, 4.78it/s] 5it [00:01, 4.77it/s] 6it [00:01, 4.77it/s] 7it [00:01, 4.78it/s] 8it [00:01, 4.77it/s] 9it [00:01, 4.78it/s] 10it [00:02, 4.78it/s] 11it [00:02, 4.77it/s] 12it [00:02, 4.78it/s] 13it [00:02, 4.77it/s] 14it [00:02, 4.77it/s] 15it [00:03, 4.76it/s] 16it [00:03, 4.77it/s] 17it [00:03, 4.77it/s] 18it [00:03, 4.77it/s] 19it [00:03, 4.77it/s] 20it [00:04, 4.77it/s] 21it [00:04, 4.77it/s] 22it [00:04, 4.77it/s] 23it [00:04, 4.77it/s] 24it [00:05, 4.77it/s] 25it [00:05, 4.77it/s] 26it [00:05, 4.77it/s] 27it [00:05, 4.77it/s] 28it [00:05, 4.76it/s] 28it [00:05, 4.77it/s] 0it [00:00, ?it/s] 1it [00:00, 4.83it/s] 2it [00:00, 4.78it/s] 3it [00:00, 4.78it/s] 4it [00:00, 4.77it/s] 5it [00:01, 4.77it/s] 6it [00:01, 4.77it/s] 7it [00:01, 4.77it/s] 8it [00:01, 4.76it/s] 9it [00:01, 4.77it/s] 10it [00:02, 4.77it/s] 11it [00:02, 4.77it/s] 12it [00:02, 4.77it/s] 13it [00:02, 4.76it/s] 14it [00:02, 4.76it/s] 15it [00:03, 4.76it/s] 16it [00:03, 4.76it/s] 17it [00:03, 4.76it/s] 18it [00:03, 4.76it/s] 19it [00:03, 4.77it/s] 20it [00:04, 4.77it/s] 21it [00:04, 4.77it/s] 22it [00:04, 4.77it/s] 23it [00:04, 4.76it/s] 24it [00:05, 4.76it/s] 25it [00:05, 4.77it/s] 26it [00:05, 4.76it/s] 27it [00:05, 4.77it/s] 28it [00:05, 4.76it/s] 28it [00:05, 4.77it/s] Total safe images: 4 out of 4
Version Details
- Version ID
bad9a1322c80f44a1bbfa73fec88f0af64e293bc70d20014d65e74693b732d3f- Version Created
- November 6, 2024