visoar / cat-xiaobai
My Cat Xiaobai
- Public
- 468 runs
-
L40S
- SDXL fine-tune
Prediction
visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655aIDf3645bdbdbfe3ey3yppn3iwbmmStatusSucceededSourceAPIHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- photo of TOK,photo of TOK
- scheduler
- K_EULER
- lora_scale
- 0.95
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- negative_prompt
- ,NSFW
{ "width": 1024, "height": 1024, "prompt": " photo of TOK,photo of TOK", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": ",NSFW" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run visoar/cat-xiaobai using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a", { input: { width: 1024, height: 1024, prompt: " photo of TOK,photo of TOK", scheduler: "K_EULER", lora_scale: 0.95, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, negative_prompt: ",NSFW" } } ); // 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 visoar/cat-xiaobai using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a", input={ "width": 1024, "height": 1024, "prompt": " photo of TOK,photo of TOK", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "negative_prompt": ",NSFW" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run visoar/cat-xiaobai 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": "visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a", "input": { "width": 1024, "height": 1024, "prompt": " photo of TOK,photo of TOK", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": ",NSFW" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-08T02:42:42.949682Z", "created_at": "2024-01-08T02:42:21.009069Z", "data_removed": false, "error": null, "id": "f3645bdbdbfe3ey3yppn3iwbmm", "input": { "width": 1024, "height": 1024, "prompt": " photo of TOK,photo of TOK", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": ",NSFW" }, "logs": "Using seed: 60124\nEnsuring enough disk space...\nFree disk space: 2216797736960\nDownloading weights: https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar\n2024-01-08T02:42:24Z | INFO | [ Initiating ] dest=/src/weights-cache/1879b646fb71c7ef minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar\n2024-01-08T02:42:27Z | INFO | [ Complete ] dest=/src/weights-cache/1879b646fb71c7ef size=\"186 MB\" total_elapsed=3.336s url=https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar\nb''\nDownloaded weights in 3.4535627365112305 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: photo of <s0><s1>,photo of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 18.862969, "total_time": 21.940613 }, "output": [ "https://replicate.delivery/pbxt/24tbWz8NMfxOLKfAcyS95DEbqNYuvAzL1NK6nDzT6F7iMRKSA/out-0.png" ], "started_at": "2024-01-08T02:42:24.086713Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f3645bdbdbfe3ey3yppn3iwbmm", "cancel": "https://api.replicate.com/v1/predictions/f3645bdbdbfe3ey3yppn3iwbmm/cancel" }, "version": "d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a" }Generated inUsing seed: 60124 Ensuring enough disk space... Free disk space: 2216797736960 Downloading weights: https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar 2024-01-08T02:42:24Z | INFO | [ Initiating ] dest=/src/weights-cache/1879b646fb71c7ef minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar 2024-01-08T02:42:27Z | INFO | [ Complete ] dest=/src/weights-cache/1879b646fb71c7ef size="186 MB" total_elapsed=3.336s url=https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar b'' Downloaded weights in 3.4535627365112305 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: photo of <s0><s1>,photo of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s] 50%|█████ | 25/50 [00:06<00:06, 3.65it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655aIDfym6gx3bnlzohrj5jjh5xdmzv4StatusSucceededSourceAPIHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- anime artwork photo of TOK,a CAT,photo of TOK . anime style, key visual, vibrant, studio anime, highly detailed
- scheduler
- K_EULER
- lora_scale
- 0.95
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- negative_prompt
- photo, deformed, black and white, realism, disfigured, low contrast,NSFW
{ "width": 1024, "height": 1024, "prompt": "anime artwork photo of TOK,a CAT,photo of TOK . anime style, key visual, vibrant, studio anime, highly detailed", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast,NSFW" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run visoar/cat-xiaobai using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a", { input: { width: 1024, height: 1024, prompt: "anime artwork photo of TOK,a CAT,photo of TOK . anime style, key visual, vibrant, studio anime, highly detailed", scheduler: "K_EULER", lora_scale: 0.95, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, negative_prompt: "photo, deformed, black and white, realism, disfigured, low contrast,NSFW" } } ); // 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 visoar/cat-xiaobai using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a", input={ "width": 1024, "height": 1024, "prompt": "anime artwork photo of TOK,a CAT,photo of TOK . anime style, key visual, vibrant, studio anime, highly detailed", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast,NSFW" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run visoar/cat-xiaobai 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": "visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a", "input": { "width": 1024, "height": 1024, "prompt": "anime artwork photo of TOK,a CAT,photo of TOK . anime style, key visual, vibrant, studio anime, highly detailed", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast,NSFW" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-08T02:46:14.523150Z", "created_at": "2024-01-08T02:45:51.673378Z", "data_removed": false, "error": null, "id": "fym6gx3bnlzohrj5jjh5xdmzv4", "input": { "width": 1024, "height": 1024, "prompt": "anime artwork photo of TOK,a CAT,photo of TOK . anime style, key visual, vibrant, studio anime, highly detailed", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast,NSFW" }, "logs": "Using seed: 5457\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: anime artwork photo of <s0><s1>,a CAT,photo of <s0><s1> . anime style, key visual, vibrant, studio anime, highly detailed\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 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3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 15.243236, "total_time": 22.849772 }, "output": [ "https://replicate.delivery/pbxt/gWh0k4PUSDakNB484dc9nUFbA0RZJloZI4BHIQBC4Sq9TkiE/out-0.png" ], "started_at": "2024-01-08T02:45:59.279914Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fym6gx3bnlzohrj5jjh5xdmzv4", "cancel": "https://api.replicate.com/v1/predictions/fym6gx3bnlzohrj5jjh5xdmzv4/cancel" }, "version": "d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a" }Generated inUsing seed: 5457 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: anime artwork photo of <s0><s1>,a CAT,photo of <s0><s1> . anime style, key visual, vibrant, studio anime, highly detailed txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 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visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655aID7t2hhttbouutp5bxpbypzvxxvyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Photo of TOK,a cute cat
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.95
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Photo of TOK,a cute cat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run visoar/cat-xiaobai using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a", { input: { width: 1024, height: 1024, prompt: "Photo of TOK,a cute cat", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.95, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 visoar/cat-xiaobai using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a", input={ "width": 1024, "height": 1024, "prompt": "Photo of TOK,a cute cat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run visoar/cat-xiaobai 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": "visoar/cat-xiaobai:d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a", "input": { "width": 1024, "height": 1024, "prompt": "Photo of TOK,a cute cat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-08T02:48:05.969494Z", "created_at": "2024-01-08T02:47:07.143402Z", "data_removed": false, "error": null, "id": "7t2hhttbouutp5bxpbypzvxxvy", "input": { "width": 1024, "height": 1024, "prompt": "Photo of TOK,a cute cat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 28927\nEnsuring enough disk space...\nFree disk space: 1712736944128\nDownloading weights: https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar\n2024-01-08T02:47:07Z | INFO | [ Initiating ] dest=/src/weights-cache/1879b646fb71c7ef minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar\n2024-01-08T02:47:07Z | INFO | [ Complete ] dest=/src/weights-cache/1879b646fb71c7ef size=\"186 MB\" total_elapsed=0.529s url=https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar\nb''\nDownloaded weights in 0.6370673179626465 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Photo of <s0><s1>,a cute cat\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.05s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.05s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.05s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.05s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.05s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.05s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.05s/it]\n 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it]\n 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it]\n 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it]\n 38%|███▊ | 19/50 [00:19<00:32, 1.05s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.05s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.05s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.05s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.05s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.05s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.05s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.05s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.05s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.05s/it]\n 62%|██████▏ | 31/50 [00:32<00:19, 1.05s/it]\n 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it]\n 66%|██████▌ | 33/50 [00:34<00:17, 1.05s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.05s/it]\n 70%|███████ | 35/50 [00:36<00:15, 1.05s/it]\n 72%|███████▏ | 36/50 [00:37<00:14, 1.05s/it]\n 74%|███████▍ | 37/50 [00:38<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.05s/it]", "metrics": { "predict_time": 58.818447, "total_time": 58.826092 }, "output": [ "https://replicate.delivery/pbxt/6ikjPoMgrc4fAyvkmnASCLMiXwzMZgBFihWi8kf5zLqkRRKSA/out-0.png", "https://replicate.delivery/pbxt/4OBjavJHicbICx71MAksL2jyf6K980RquByoFEOXqxjyoIFJA/out-1.png", "https://replicate.delivery/pbxt/FJtJKxk2sGrvORo8iz9Pq3Kq4ZsNayKN1e0FXaVCex2lRRKSA/out-2.png", "https://replicate.delivery/pbxt/Z4rwY42cs5otK9kMRZdPkY8n7XCsiS2is4wnLMe7mY6yoIFJA/out-3.png" ], "started_at": "2024-01-08T02:47:07.151047Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7t2hhttbouutp5bxpbypzvxxvy", "cancel": "https://api.replicate.com/v1/predictions/7t2hhttbouutp5bxpbypzvxxvy/cancel" }, "version": "d77dbdd56022ea3033d0320f267b182abbefc6a090022a0f26798bfc9dec655a" }Generated inUsing seed: 28927 Ensuring enough disk space... Free disk space: 1712736944128 Downloading weights: https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar 2024-01-08T02:47:07Z | INFO | [ Initiating ] dest=/src/weights-cache/1879b646fb71c7ef minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar 2024-01-08T02:47:07Z | INFO | [ Complete ] dest=/src/weights-cache/1879b646fb71c7ef size="186 MB" total_elapsed=0.529s url=https://replicate.delivery/pbxt/nEWo2dtOGg7zCJ5TgxN23SPAfxvh0qpBFyE17NeCT9GFIRKSA/trained_model.tar b'' Downloaded weights in 0.6370673179626465 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Photo of <s0><s1>,a cute cat txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.05s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it] 20%|██ | 10/50 [00:10<00:42, 1.05s/it] 22%|██▏ | 11/50 [00:11<00:41, 1.05s/it] 24%|██▍ | 12/50 [00:12<00:40, 1.05s/it] 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it] 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it] 30%|███ | 15/50 [00:15<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it] 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it] 38%|███▊ | 19/50 [00:19<00:32, 1.05s/it] 40%|████ | 20/50 [00:21<00:31, 1.05s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.05s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.05s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it] 50%|█████ | 25/50 [00:26<00:26, 1.05s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.05s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.05s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.05s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.05s/it] 60%|██████ | 30/50 [00:31<00:21, 1.05s/it] 62%|██████▏ | 31/50 [00:32<00:19, 1.05s/it] 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it] 66%|██████▌ | 33/50 [00:34<00:17, 1.05s/it] 68%|██████▊ | 34/50 [00:35<00:16, 1.05s/it] 70%|███████ | 35/50 [00:36<00:15, 1.05s/it] 72%|███████▏ | 36/50 [00:37<00:14, 1.05s/it] 74%|███████▍ | 37/50 [00:38<00:13, 1.06s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it] 80%|████████ | 40/50 [00:42<00:10, 1.06s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it] 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.05s/it]
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