dgtlcorp / tero
- Public
- 58 runs
-
H100
Prediction
dgtlcorp/tero:cc45fa51d9570923a2466eecb8544b44bc20a81f40a79f5d14e4de87cff0d897IDh5ba46z1zsrm80cm8zbtekbyz0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A cozy winter coffee shop setting with warm, inviting lighting and rustic wooden interiors. Large windows frame a snowy outdoor scene, with frosted glass and soft natural light streaming in. The Blonde model, with a curvy, hourglass figure enhanced by a BBL, stands confidently near a wooden table. She is wearing beige tero leggings with fleece lining, paired with a long, tailored wool coat that drapes elegantly to mid-thigh. Beneath the coat, she wears a fitted turtleneck top, adding a sophisticated and polished touch to her winter ensemble. Stylish knee-high boots complement the look, ensuring practicality and flair for the colder months. Her hand lightly rests on the back of the chair, while the other holds a steaming latte, exuding warmth and sophistication. Her face features soft, striking Latina features with long wavy hair cascading over her shoulders. The table beside her is decorated with a croissant on a plate, an open journal, and a branded coffee cup, completing the Starbucks-inspired ambiance. Warm caramel, cream, and forest green tones dominate the scene, with fairy lights and candles subtly glowing in the background. The beige tero leggings, designed for ultimate winter comfort, are the centerpiece, with lighting emphasizing their sleek fit and cozy fleece texture, perfectly balancing style and warm
- go_fast
- 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
{ "model": "dev", "prompt": "A cozy winter coffee shop setting with warm, inviting lighting and rustic wooden interiors. Large windows frame a snowy outdoor scene, with frosted glass and soft natural light streaming in. The Blonde model, with a curvy, hourglass figure enhanced by a BBL, stands confidently near a wooden table. She is wearing beige tero leggings with fleece lining, paired with a long, tailored wool coat that drapes elegantly to mid-thigh. Beneath the coat, she wears a fitted turtleneck top, adding a sophisticated and polished touch to her winter ensemble. Stylish knee-high boots complement the look, ensuring practicality and flair for the colder months.\n\nHer hand lightly rests on the back of the chair, while the other holds a steaming latte, exuding warmth and sophistication. Her face features soft, striking Latina features with long wavy hair cascading over her shoulders. The table beside her is decorated with a croissant on a plate, an open journal, and a branded coffee cup, completing the Starbucks-inspired ambiance. Warm caramel, cream, and forest green tones dominate the scene, with fairy lights and candles subtly glowing in the background. The beige tero leggings, designed for ultimate winter comfort, are the centerpiece, with lighting emphasizing their sleek fit and cozy fleece texture, perfectly balancing style and warm", "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 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run dgtlcorp/tero using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "dgtlcorp/tero:cc45fa51d9570923a2466eecb8544b44bc20a81f40a79f5d14e4de87cff0d897", { input: { model: "dev", prompt: "A cozy winter coffee shop setting with warm, inviting lighting and rustic wooden interiors. Large windows frame a snowy outdoor scene, with frosted glass and soft natural light streaming in. The Blonde model, with a curvy, hourglass figure enhanced by a BBL, stands confidently near a wooden table. She is wearing beige tero leggings with fleece lining, paired with a long, tailored wool coat that drapes elegantly to mid-thigh. Beneath the coat, she wears a fitted turtleneck top, adding a sophisticated and polished touch to her winter ensemble. Stylish knee-high boots complement the look, ensuring practicality and flair for the colder months.\n\nHer hand lightly rests on the back of the chair, while the other holds a steaming latte, exuding warmth and sophistication. Her face features soft, striking Latina features with long wavy hair cascading over her shoulders. The table beside her is decorated with a croissant on a plate, an open journal, and a branded coffee cup, completing the Starbucks-inspired ambiance. Warm caramel, cream, and forest green tones dominate the scene, with fairy lights and candles subtly glowing in the background. The beige tero leggings, designed for ultimate winter comfort, are the centerpiece, with lighting emphasizing their sleek fit and cozy fleece texture, perfectly balancing style and warm", 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 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun dgtlcorp/tero using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "dgtlcorp/tero:cc45fa51d9570923a2466eecb8544b44bc20a81f40a79f5d14e4de87cff0d897", input={ "model": "dev", "prompt": "A cozy winter coffee shop setting with warm, inviting lighting and rustic wooden interiors. Large windows frame a snowy outdoor scene, with frosted glass and soft natural light streaming in. The Blonde model, with a curvy, hourglass figure enhanced by a BBL, stands confidently near a wooden table. She is wearing beige tero leggings with fleece lining, paired with a long, tailored wool coat that drapes elegantly to mid-thigh. Beneath the coat, she wears a fitted turtleneck top, adding a sophisticated and polished touch to her winter ensemble. Stylish knee-high boots complement the look, ensuring practicality and flair for the colder months.\n\nHer hand lightly rests on the back of the chair, while the other holds a steaming latte, exuding warmth and sophistication. Her face features soft, striking Latina features with long wavy hair cascading over her shoulders. The table beside her is decorated with a croissant on a plate, an open journal, and a branded coffee cup, completing the Starbucks-inspired ambiance. Warm caramel, cream, and forest green tones dominate the scene, with fairy lights and candles subtly glowing in the background. The beige tero leggings, designed for ultimate winter comfort, are the centerpiece, with lighting emphasizing their sleek fit and cozy fleece texture, perfectly balancing style and warm", "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 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run dgtlcorp/tero using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dgtlcorp/tero:cc45fa51d9570923a2466eecb8544b44bc20a81f40a79f5d14e4de87cff0d897", "input": { "model": "dev", "prompt": "A cozy winter coffee shop setting with warm, inviting lighting and rustic wooden interiors. Large windows frame a snowy outdoor scene, with frosted glass and soft natural light streaming in. The Blonde model, with a curvy, hourglass figure enhanced by a BBL, stands confidently near a wooden table. She is wearing beige tero leggings with fleece lining, paired with a long, tailored wool coat that drapes elegantly to mid-thigh. Beneath the coat, she wears a fitted turtleneck top, adding a sophisticated and polished touch to her winter ensemble. Stylish knee-high boots complement the look, ensuring practicality and flair for the colder months.\\n\\nHer hand lightly rests on the back of the chair, while the other holds a steaming latte, exuding warmth and sophistication. Her face features soft, striking Latina features with long wavy hair cascading over her shoulders. The table beside her is decorated with a croissant on a plate, an open journal, and a branded coffee cup, completing the Starbucks-inspired ambiance. Warm caramel, cream, and forest green tones dominate the scene, with fairy lights and candles subtly glowing in the background. The beige tero leggings, designed for ultimate winter comfort, are the centerpiece, with lighting emphasizing their sleek fit and cozy fleece texture, perfectly balancing style and warm", "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 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-01-08T21:35:42.782533Z", "created_at": "2025-01-08T21:35:18.014000Z", "data_removed": false, "error": null, "id": "h5ba46z1zsrm80cm8zbtekbyz0", "input": { "model": "dev", "prompt": "A cozy winter coffee shop setting with warm, inviting lighting and rustic wooden interiors. Large windows frame a snowy outdoor scene, with frosted glass and soft natural light streaming in. The Blonde model, with a curvy, hourglass figure enhanced by a BBL, stands confidently near a wooden table. She is wearing beige tero leggings with fleece lining, paired with a long, tailored wool coat that drapes elegantly to mid-thigh. Beneath the coat, she wears a fitted turtleneck top, adding a sophisticated and polished touch to her winter ensemble. Stylish knee-high boots complement the look, ensuring practicality and flair for the colder months.\n\nHer hand lightly rests on the back of the chair, while the other holds a steaming latte, exuding warmth and sophistication. Her face features soft, striking Latina features with long wavy hair cascading over her shoulders. The table beside her is decorated with a croissant on a plate, an open journal, and a branded coffee cup, completing the Starbucks-inspired ambiance. Warm caramel, cream, and forest green tones dominate the scene, with fairy lights and candles subtly glowing in the background. The beige tero leggings, designed for ultimate winter comfort, are the centerpiece, with lighting emphasizing their sleek fit and cozy fleece texture, perfectly balancing style and warm", "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 }, "logs": "2025-01-08 21:35:18.118 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-08 21:35:18.118 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 91%|█████████▏| 278/304 [00:00<00:00, 2773.11it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2662.86it/s]\n2025-01-08 21:35:18.233 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s\n2025-01-08 21:35:18.234 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/e3ec5ed2de2cd034\n2025-01-08 21:35:18.349 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2025-01-08 21:35:18.349 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-08 21:35:18.349 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 91%|█████████▏| 278/304 [00:00<00:00, 2777.57it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2666.95it/s]\n2025-01-08 21:35:18.463 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.23s\nUsing seed: 61022\n0it [00:00, ?it/s]\n1it [00:00, 8.34it/s]\n2it [00:00, 5.84it/s]\n3it [00:00, 5.33it/s]\n4it [00:00, 5.12it/s]\n5it [00:00, 5.00it/s]\n6it [00:01, 4.92it/s]\n7it [00:01, 4.87it/s]\n8it [00:01, 4.85it/s]\n9it [00:01, 4.85it/s]\n10it [00:01, 4.83it/s]\n11it [00:02, 4.81it/s]\n12it [00:02, 4.80it/s]\n13it [00:02, 4.80it/s]\n14it [00:02, 4.80it/s]\n15it [00:03, 4.80it/s]\n16it [00:03, 4.80it/s]\n17it [00:03, 4.80it/s]\n18it [00:03, 4.79it/s]\n19it [00:03, 4.80it/s]\n20it [00:04, 4.80it/s]\n21it [00:04, 4.80it/s]\n22it [00:04, 4.79it/s]\n23it [00:04, 4.80it/s]\n24it [00:04, 4.79it/s]\n25it [00:05, 4.79it/s]\n26it [00:05, 4.79it/s]\n27it [00:05, 4.80it/s]\n28it [00:05, 4.80it/s]\n28it [00:05, 4.87it/s]\n0it [00:00, ?it/s]\n1it [00:00, 4.83it/s]\n2it [00:00, 4.79it/s]\n3it [00:00, 4.80it/s]\n4it [00:00, 4.79it/s]\n5it [00:01, 4.79it/s]\n6it [00:01, 4.79it/s]\n7it [00:01, 4.78it/s]\n8it [00:01, 4.78it/s]\n9it [00:01, 4.79it/s]\n10it [00:02, 4.78it/s]\n11it [00:02, 4.78it/s]\n12it [00:02, 4.77it/s]\n13it [00:02, 4.77it/s]\n14it [00:02, 4.77it/s]\n15it [00:03, 4.78it/s]\n16it [00:03, 4.78it/s]\n17it [00:03, 4.77it/s]\n18it [00:03, 4.77it/s]\n19it [00:03, 4.77it/s]\n20it [00:04, 4.77it/s]\n21it [00:04, 4.77it/s]\n22it [00:04, 4.77it/s]\n23it [00:04, 4.76it/s]\n24it [00:05, 4.76it/s]\n25it [00:05, 4.76it/s]\n26it [00:05, 4.77it/s]\n27it [00:05, 4.76it/s]\n28it [00:05, 4.76it/s]\n28it [00:05, 4.77it/s]\n0it [00:00, ?it/s]\n1it [00:00, 4.82it/s]\n2it [00:00, 4.78it/s]\n3it [00:00, 4.78it/s]\n4it [00:00, 4.78it/s]\n5it [00:01, 4.77it/s]\n6it [00:01, 4.76it/s]\n7it [00:01, 4.76it/s]\n8it [00:01, 4.76it/s]\n9it [00:01, 4.76it/s]\n10it [00:02, 4.76it/s]\n11it [00:02, 4.76it/s]\n12it [00:02, 4.76it/s]\n13it [00:02, 4.77it/s]\n14it [00:02, 4.77it/s]\n15it [00:03, 4.76it/s]\n16it [00:03, 4.76it/s]\n17it [00:03, 4.76it/s]\n18it [00:03, 4.76it/s]\n19it [00:03, 4.76it/s]\n20it [00:04, 4.76it/s]\n21it [00:04, 4.76it/s]\n22it [00:04, 4.76it/s]\n23it [00:04, 4.76it/s]\n24it [00:05, 4.77it/s]\n25it [00:05, 4.77it/s]\n26it [00:05, 4.76it/s]\n27it [00:05, 4.76it/s]\n28it [00:05, 4.76it/s]\n28it [00:05, 4.76it/s]\n0it [00:00, ?it/s]\n1it [00:00, 4.79it/s]\n2it [00:00, 4.77it/s]\n3it [00:00, 4.76it/s]\n4it [00:00, 4.76it/s]\n5it [00:01, 4.76it/s]\n6it [00:01, 4.75it/s]\n7it [00:01, 4.75it/s]\n8it [00:01, 4.75it/s]\n9it [00:01, 4.75it/s]\n10it [00:02, 4.76it/s]\n11it [00:02, 4.77it/s]\n12it [00:02, 4.77it/s]\n13it [00:02, 4.76it/s]\n14it [00:02, 4.76it/s]\n15it [00:03, 4.76it/s]\n16it [00:03, 4.77it/s]\n17it [00:03, 4.77it/s]\n18it [00:03, 4.76it/s]\n19it [00:03, 4.76it/s]\n20it [00:04, 4.77it/s]\n21it [00:04, 4.77it/s]\n22it [00:04, 4.77it/s]\n23it [00:04, 4.76it/s]\n24it [00:05, 4.76it/s]\n25it [00:05, 4.77it/s]\n26it [00:05, 4.76it/s]\n27it [00:05, 4.77it/s]\n28it [00:05, 4.76it/s]\n28it [00:05, 4.76it/s]\nTotal safe images: 4 out of 4", "metrics": { "predict_time": 24.663316508, "total_time": 24.768533 }, "output": [ "https://replicate.delivery/xezq/nt2lA7NnvF5XMpwoOeVz004HSsxeyWMzLVQrLRRZ29MuGKDUA/out-0.webp", "https://replicate.delivery/xezq/yTtONX3CBVIeEa7ZJffFNBtcqDCu30eq1AflbgfizshmrhyAF/out-1.webp", "https://replicate.delivery/xezq/9JLBlFYeiCwDcK9uBQ0FM3hnPvbTG47qLUUpTPWpC1KXDlBKA/out-2.webp", "https://replicate.delivery/xezq/RbV56B7o92IkEFlV8BGVwdeBrkwM8nNCjAbIaU7OHYKXDlBKA/out-3.webp" ], "started_at": "2025-01-08T21:35:18.119216Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-onnk6igbiixpmeqqcy44jfmq67y5kwmvb2ry6u5ztnhwtzabzgia", "get": "https://api.replicate.com/v1/predictions/h5ba46z1zsrm80cm8zbtekbyz0", "cancel": "https://api.replicate.com/v1/predictions/h5ba46z1zsrm80cm8zbtekbyz0/cancel" }, "version": "cc45fa51d9570923a2466eecb8544b44bc20a81f40a79f5d14e4de87cff0d897" }Generated in2025-01-08 21:35:18.118 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-08 21:35:18.118 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 91%|█████████▏| 278/304 [00:00<00:00, 2773.11it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2662.86it/s] 2025-01-08 21:35:18.233 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s 2025-01-08 21:35:18.234 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/e3ec5ed2de2cd034 2025-01-08 21:35:18.349 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2025-01-08 21:35:18.349 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-08 21:35:18.349 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 91%|█████████▏| 278/304 [00:00<00:00, 2777.57it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2666.95it/s] 2025-01-08 21:35:18.463 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.23s Using seed: 61022 0it [00:00, ?it/s] 1it [00:00, 8.34it/s] 2it [00:00, 5.84it/s] 3it [00:00, 5.33it/s] 4it [00:00, 5.12it/s] 5it [00:00, 5.00it/s] 6it [00:01, 4.92it/s] 7it [00:01, 4.87it/s] 8it [00:01, 4.85it/s] 9it [00:01, 4.85it/s] 10it [00:01, 4.83it/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, 4.79it/s] 19it [00:03, 4.80it/s] 20it [00:04, 4.80it/s] 21it [00:04, 4.80it/s] 22it [00:04, 4.79it/s] 23it [00:04, 4.80it/s] 24it [00:04, 4.79it/s] 25it [00:05, 4.79it/s] 26it [00:05, 4.79it/s] 27it [00:05, 4.80it/s] 28it [00:05, 4.80it/s] 28it [00:05, 4.87it/s] 0it [00:00, ?it/s] 1it [00:00, 4.83it/s] 2it [00:00, 4.79it/s] 3it [00:00, 4.80it/s] 4it [00:00, 4.79it/s] 5it [00:01, 4.79it/s] 6it [00:01, 4.79it/s] 7it [00:01, 4.78it/s] 8it [00:01, 4.78it/s] 9it [00:01, 4.79it/s] 10it [00:02, 4.78it/s] 11it [00:02, 4.78it/s] 12it [00:02, 4.77it/s] 13it [00:02, 4.77it/s] 14it [00:02, 4.77it/s] 15it [00:03, 4.78it/s] 16it [00:03, 4.78it/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, 4.76it/s] 24it [00:05, 4.76it/s] 25it [00:05, 4.76it/s] 26it [00:05, 4.77it/s] 27it [00:05, 4.76it/s] 28it [00:05, 4.76it/s] 28it [00:05, 4.77it/s] 0it [00:00, ?it/s] 1it [00:00, 4.82it/s] 2it [00:00, 4.78it/s] 3it [00:00, 4.78it/s] 4it [00:00, 4.78it/s] 5it [00:01, 4.77it/s] 6it [00:01, 4.76it/s] 7it [00:01, 4.76it/s] 8it [00:01, 4.76it/s] 9it [00:01, 4.76it/s] 10it [00:02, 4.76it/s] 11it [00:02, 4.76it/s] 12it [00:02, 4.76it/s] 13it [00:02, 4.77it/s] 14it [00:02, 4.77it/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, 4.76it/s] 20it [00:04, 4.76it/s] 21it [00:04, 4.76it/s] 22it [00:04, 4.76it/s] 23it [00:04, 4.76it/s] 24it [00:05, 4.77it/s] 25it [00:05, 4.77it/s] 26it [00:05, 4.76it/s] 27it [00:05, 4.76it/s] 28it [00:05, 4.76it/s] 28it [00:05, 4.76it/s] 0it [00:00, ?it/s] 1it [00:00, 4.79it/s] 2it [00:00, 4.77it/s] 3it [00:00, 4.76it/s] 4it [00:00, 4.76it/s] 5it [00:01, 4.76it/s] 6it [00:01, 4.75it/s] 7it [00:01, 4.75it/s] 8it [00:01, 4.75it/s] 9it [00:01, 4.75it/s] 10it [00:02, 4.76it/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, 4.77it/s] 17it [00:03, 4.77it/s] 18it [00:03, 4.76it/s] 19it [00:03, 4.76it/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, 4.76it/s] Total safe images: 4 out of 4
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