animacustoms/ernestamod 🖼️🔢❓📝✓ → 🖼️
About
Example Output
Prompt:
"(stunning 18 years old woman), confidently sitting in luxurious Bentley Continental GT, dressed in elegant (sleek black dress with thigh-high slit and open back), detailed facial features and expression, moody evening lighting, high-definition, warm and inviting ambiance, showcasing opulence and style, surrounding luxurious interior details, ultra-detailed, capturing confidence and youth in a sophisticated atmosphere."
Output



Performance Metrics
27.89s
Prediction Time
28.20s
Total Time
All Input Parameters
{
"model": "dev",
"width": 1440,
"height": 1440,
"prompt": "(stunning 18 years old woman), confidently sitting in luxurious Bentley Continental GT, dressed in elegant (sleek black dress with thigh-high slit and open back), detailed facial features and expression, moody evening lighting, high-definition, warm and inviting ambiance, showcasing opulence and style, surrounding luxurious interior details, ultra-detailed, capturing confidence and youth in a sophisticated atmosphere.",
"go_fast": false,
"lora_scale": 0.94,
"megapixels": "1",
"num_outputs": 4,
"aspect_ratio": "3:2",
"output_format": "webp",
"guidance_scale": 2.06,
"output_quality": 100,
"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
2024-11-30 03:07:36.232 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys 2024-11-30 03:07:36.233 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 92%|█████████▏| 279/304 [00:00<00:00, 2766.12it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2680.84it/s] 2024-11-30 03:07:36.346 | SUCCESS | fp8.lora_loading:unload_loras:559 - LoRAs unloaded in 0.11s free=29028307935232 Downloading weights 2024-11-30T03:07:36Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpw04xloi1/weights url=https://replicate.delivery/xezq/NrbQj3eO212WMq2frewfgxa0MiADDziOFPk4E3f3lcqhQQweE/trained_model.tar 2024-11-30T03:07:37Z | INFO | [ Complete ] dest=/tmp/tmpw04xloi1/weights size="172 MB" total_elapsed=1.625s url=https://replicate.delivery/xezq/NrbQj3eO212WMq2frewfgxa0MiADDziOFPk4E3f3lcqhQQweE/trained_model.tar Downloaded weights in 1.65s 2024-11-30 03:07:37.996 | INFO | fp8.lora_loading:convert_lora_weights:493 - Loading LoRA weights for /src/weights-cache/d06f933a2f66b9da 2024-11-30 03:07:38.067 | INFO | fp8.lora_loading:convert_lora_weights:514 - LoRA weights loaded 2024-11-30 03:07:38.067 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys 2024-11-30 03:07:38.067 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 92%|█████████▏| 279/304 [00:00<00:00, 2772.31it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2686.80it/s] 2024-11-30 03:07:38.181 | SUCCESS | fp8.lora_loading:load_lora:534 - LoRA applied in 0.18s Using seed: 55771 0it [00:00, ?it/s] 1it [00:00, 8.78it/s] 2it [00:00, 6.10it/s] 3it [00:00, 5.57it/s] 4it [00:00, 5.36it/s] 5it [00:00, 5.19it/s] 6it [00:01, 5.11it/s] 7it [00:01, 5.08it/s] 8it [00:01, 5.06it/s] 9it [00:01, 5.06it/s] 10it [00:01, 5.04it/s] 11it [00:02, 5.02it/s] 12it [00:02, 5.02it/s] 13it [00:02, 5.02it/s] 14it [00:02, 5.02it/s] 15it [00:02, 5.02it/s] 16it [00:03, 5.01it/s] 17it [00:03, 5.01it/s] 18it [00:03, 5.00it/s] 19it [00:03, 5.00it/s] 20it [00:03, 5.00it/s] 21it [00:04, 5.01it/s] 22it [00:04, 5.01it/s] 23it [00:04, 5.02it/s] 24it [00:04, 5.02it/s] 25it [00:04, 5.02it/s] 26it [00:05, 5.01it/s] 27it [00:05, 5.00it/s] 28it [00:05, 5.00it/s] 28it [00:05, 5.09it/s] 0it [00:00, ?it/s] 1it [00:00, 5.05it/s] 2it [00:00, 5.01it/s] 3it [00:00, 5.01it/s] 4it [00:00, 5.00it/s] 5it [00:00, 5.00it/s] 6it [00:01, 5.01it/s] 7it [00:01, 5.01it/s] 8it [00:01, 5.01it/s] 9it [00:01, 5.00it/s] 10it [00:01, 5.01it/s] 11it [00:02, 5.01it/s] 12it [00:02, 5.02it/s] 13it [00:02, 5.01it/s] 14it [00:02, 5.01it/s] 15it [00:02, 5.00it/s] 16it [00:03, 5.00it/s] 17it [00:03, 5.01it/s] 18it [00:03, 5.00it/s] 19it [00:03, 5.02it/s] 20it [00:03, 5.02it/s] 21it [00:04, 5.01it/s] 22it [00:04, 5.01it/s] 23it [00:04, 5.01it/s] 24it [00:04, 5.01it/s] 25it [00:04, 5.00it/s] 26it [00:05, 5.00it/s] 27it [00:05, 4.99it/s] 28it [00:05, 5.00it/s] 28it [00:05, 5.01it/s] 0it [00:00, ?it/s] 1it [00:00, 5.06it/s] 2it [00:00, 5.04it/s] 3it [00:00, 5.02it/s] 4it [00:00, 5.01it/s] 5it [00:00, 5.00it/s] 6it [00:01, 5.00it/s] 7it [00:01, 5.01it/s] 8it [00:01, 5.01it/s] 9it [00:01, 5.01it/s] 10it [00:01, 5.00it/s] 11it [00:02, 5.00it/s] 12it [00:02, 4.99it/s] 13it [00:02, 4.99it/s] 14it [00:02, 5.01it/s] 15it [00:02, 5.01it/s] 16it [00:03, 5.01it/s] 17it [00:03, 5.00it/s] 18it [00:03, 5.01it/s] 19it [00:03, 5.01it/s] 20it [00:03, 5.01it/s] 21it [00:04, 5.00it/s] 22it [00:04, 5.00it/s] 23it [00:04, 5.00it/s] 24it [00:04, 5.00it/s] 25it [00:04, 4.99it/s] 26it [00:05, 4.99it/s] 27it [00:05, 4.99it/s] 28it [00:05, 4.99it/s] 28it [00:05, 5.00it/s] 0it [00:00, ?it/s] 1it [00:00, 5.04it/s] 2it [00:00, 5.01it/s] 3it [00:00, 5.01it/s] 4it [00:00, 4.99it/s] 5it [00:01, 4.99it/s] 6it [00:01, 4.97it/s] 7it [00:01, 4.97it/s] 8it [00:01, 4.99it/s] 9it [00:01, 4.99it/s] 10it [00:02, 4.99it/s] 11it [00:02, 4.99it/s] 12it [00:02, 4.98it/s] 13it [00:02, 4.97it/s] 14it [00:02, 4.98it/s] 15it [00:03, 4.99it/s] 16it [00:03, 4.99it/s] 17it [00:03, 4.99it/s] 18it [00:03, 4.99it/s] 19it [00:03, 4.99it/s] 20it [00:04, 4.99it/s] 21it [00:04, 4.99it/s] 22it [00:04, 4.99it/s] 23it [00:04, 4.99it/s] 24it [00:04, 4.99it/s] 25it [00:05, 4.99it/s] 26it [00:05, 4.99it/s] 27it [00:05, 4.99it/s] 28it [00:05, 4.99it/s] 28it [00:05, 4.99it/s] Total safe images: 4 out of 4
Version Details
- Version ID
e811b8022bf1886d6edef817bef9b40e876c79377eec652db351431fc1859901- Version Created
- November 30, 2024