hunterkamerman
/
sdxl-hope
A fine-tuned SDXL lora based on the Obama hope poster
- Public
- 306 runs
-
L40S
- SDXL fine-tune
- GitHub
Prediction
hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524eIDtawny3tbsalanfmqifgetymuimStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK hope style images red white and blue, kim kardashian portriat
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "TOK hope style images red white and blue, kim kardashian portriat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", { input: { width: 1024, height: 1024, prompt: "TOK hope style images red white and blue, kim kardashian portriat", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, 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 hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", input={ "width": 1024, "height": 1024, "prompt": "TOK hope style images red white and blue, kim kardashian portriat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "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 hunterkamerman/sdxl-hope 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": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style images red white and blue, kim kardashian portriat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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": "2023-11-08T19:41:12.967542Z", "created_at": "2023-11-08T19:40:53.702138Z", "data_removed": false, "error": null, "id": "tawny3tbsalanfmqifgetymuim", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style images red white and blue, kim kardashian portriat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 4752\nEnsuring enough disk space...\nFree disk space: 1807458734080\nDownloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.115s (1.6 GB/s)\\nExtracted 186 MB in 0.061s (3.1 GB/s)\\n'\nDownloaded weights in 0.326418399810791 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> hope style images red white and blue, kim kardashian portriat\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/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 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/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 [00:05<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/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, 3.66it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/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, 3.65it/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, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/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, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 16.585255, "total_time": 19.265404 }, "output": [ "https://replicate.delivery/pbxt/Lx1fQNkmWAW1PqBpps8EP89HRAWrUdhEXgin0d23CJOssM7IA/out-0.png" ], "started_at": "2023-11-08T19:40:56.382287Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tawny3tbsalanfmqifgetymuim", "cancel": "https://api.replicate.com/v1/predictions/tawny3tbsalanfmqifgetymuim/cancel" }, "version": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e" }Generated inUsing seed: 4752 Ensuring enough disk space... Free disk space: 1807458734080 Downloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar b'Downloaded 186 MB bytes in 0.115s (1.6 GB/s)\nExtracted 186 MB in 0.061s (3.1 GB/s)\n' Downloaded weights in 0.326418399810791 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1> hope style images red white and blue, kim kardashian portriat txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:12, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/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 [00:05<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/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, 3.66it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/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, 3.65it/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, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/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, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]Prediction
hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524eIDkcfhi6tb6kc33ogu5omgpe5kw4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK hope style poster red white and blue, ryan gosling
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "TOK hope style poster red white and blue, ryan gosling ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", { input: { width: 1024, height: 1024, prompt: "TOK hope style poster red white and blue, ryan gosling ", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, 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 hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", input={ "width": 1024, "height": 1024, "prompt": "TOK hope style poster red white and blue, ryan gosling ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "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 hunterkamerman/sdxl-hope 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": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style poster red white and blue, ryan gosling ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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": "2023-11-08T19:42:34.442646Z", "created_at": "2023-11-08T19:42:09.877770Z", "data_removed": false, "error": null, "id": "kcfhi6tb6kc33ogu5omgpe5kw4", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style poster red white and blue, ryan gosling ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 58864\nEnsuring enough disk space...\nFree disk space: 1910368194560\nDownloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar\nb'Downloaded 186 MB bytes in 4.294s (43 MB/s)\\nExtracted 186 MB in 0.063s (2.9 GB/s)\\n'\nDownloaded weights in 4.686601877212524 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> hope style poster red white and blue, ryan gosling\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%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 20.154017, "total_time": 24.564876 }, "output": [ "https://replicate.delivery/pbxt/Jds7VNuHXy4sB5ipOa57Dwg0FPc6aX8yTAZ1cUsGYkdqWmdE/out-0.png" ], "started_at": "2023-11-08T19:42:14.288629Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kcfhi6tb6kc33ogu5omgpe5kw4", "cancel": "https://api.replicate.com/v1/predictions/kcfhi6tb6kc33ogu5omgpe5kw4/cancel" }, "version": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e" }Generated inUsing seed: 58864 Ensuring enough disk space... Free disk space: 1910368194560 Downloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar b'Downloaded 186 MB bytes in 4.294s (43 MB/s)\nExtracted 186 MB in 0.063s (2.9 GB/s)\n' Downloaded weights in 4.686601877212524 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1> hope style poster red white and blue, ryan gosling 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, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]Prediction
hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524eIDb62ljklbzmxsk5hxqltiznrfjaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK hope style poster red white and blue, batman portrait
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "TOK hope style poster red white and blue, batman portrait ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", { input: { width: 1024, height: 1024, prompt: "TOK hope style poster red white and blue, batman portrait ", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, 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 hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", input={ "width": 1024, "height": 1024, "prompt": "TOK hope style poster red white and blue, batman portrait ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "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 hunterkamerman/sdxl-hope 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": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style poster red white and blue, batman portrait ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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": "2023-11-08T20:05:30.269159Z", "created_at": "2023-11-08T20:05:12.263322Z", "data_removed": false, "error": null, "id": "b62ljklbzmxsk5hxqltiznrfja", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style poster red white and blue, batman portrait ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 4535\nEnsuring enough disk space...\nFree disk space: 1521804214272\nDownloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.263s (706 MB/s)\\nExtracted 186 MB in 0.054s (3.4 GB/s)\\n'\nDownloaded weights in 0.44001221656799316 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> hope style poster red white and blue, batman portrait\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.66it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.65it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.64it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.64it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 16.634088, "total_time": 18.005837 }, "output": [ "https://replicate.delivery/pbxt/ps5jJDSaK76MDJMbpFgdXkNYKpB1eY17Y2rXDxi5eLlJwZ2RA/out-0.png" ], "started_at": "2023-11-08T20:05:13.635071Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/b62ljklbzmxsk5hxqltiznrfja", "cancel": "https://api.replicate.com/v1/predictions/b62ljklbzmxsk5hxqltiznrfja/cancel" }, "version": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e" }Generated inUsing seed: 4535 Ensuring enough disk space... Free disk space: 1521804214272 Downloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar b'Downloaded 186 MB bytes in 0.263s (706 MB/s)\nExtracted 186 MB in 0.054s (3.4 GB/s)\n' Downloaded weights in 0.44001221656799316 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1> hope style poster red white and blue, batman portrait txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.66it/s] 4%|▍ | 2/50 [00:00<00:13, 3.65it/s] 6%|▌ | 3/50 [00:00<00:12, 3.64it/s] 8%|▊ | 4/50 [00:01<00:12, 3.64it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s] 50%|█████ | 25/50 [00:06<00:06, 3.63it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s] 70%|███████ | 35/50 [00:09<00:04, 3.64it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s] 80%|████████ | 40/50 [00:11<00:02, 3.64it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]Prediction
hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524eIDxwajqdtbbyzpyr6zjoadagkcuaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK hope style images red white and blue, kanye west with sunglasses
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "TOK hope style images red white and blue, kanye west with sunglasses", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", { input: { width: 1024, height: 1024, prompt: "TOK hope style images red white and blue, kanye west with sunglasses", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, 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 hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", input={ "width": 1024, "height": 1024, "prompt": "TOK hope style images red white and blue, kanye west with sunglasses", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "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 hunterkamerman/sdxl-hope 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": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style images red white and blue, kanye west with sunglasses", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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": "2023-11-08T20:12:19.660988Z", "created_at": "2023-11-08T20:12:00.143888Z", "data_removed": false, "error": null, "id": "xwajqdtbbyzpyr6zjoadagkcua", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style images red white and blue, kanye west with sunglasses", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 63121\nEnsuring enough disk space...\nFree disk space: 1920395046912\nDownloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.237s (784 MB/s)\\nExtracted 186 MB in 0.054s (3.5 GB/s)\\n'\nDownloaded weights in 0.3710744380950928 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> hope style images red white and blue, kanye west with sunglasses\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/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, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/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, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/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, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 16.588761, "total_time": 19.5171 }, "output": [ "https://replicate.delivery/pbxt/33pvYqGfPKWTF6JevaxOCcqb8Myxvbv2IPVYn6y9de4EtzsjA/out-0.png" ], "started_at": "2023-11-08T20:12:03.072227Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xwajqdtbbyzpyr6zjoadagkcua", "cancel": "https://api.replicate.com/v1/predictions/xwajqdtbbyzpyr6zjoadagkcua/cancel" }, "version": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e" }Generated inUsing seed: 63121 Ensuring enough disk space... Free disk space: 1920395046912 Downloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar b'Downloaded 186 MB bytes in 0.237s (784 MB/s)\nExtracted 186 MB in 0.054s (3.5 GB/s)\n' Downloaded weights in 0.3710744380950928 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1> hope style images red white and blue, kanye west with sunglasses txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/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, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.64it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s] 50%|█████ | 25/50 [00:06<00:06, 3.64it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.64it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/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, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/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, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524eIDaolq7adbg4dwivyzvd2cy3kpi4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK hope style poster red white and blue, obama and kim kardashian with hope in painted
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "TOK hope style poster red white and blue, obama and kim kardashian with hope in painted", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", { input: { width: 1024, height: 1024, prompt: "TOK hope style poster red white and blue, obama and kim kardashian with hope in painted", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, 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 hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", input={ "width": 1024, "height": 1024, "prompt": "TOK hope style poster red white and blue, obama and kim kardashian with hope in painted", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "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 hunterkamerman/sdxl-hope 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": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style poster red white and blue, obama and kim kardashian with hope in painted", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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": "2023-11-09T00:02:42.511944Z", "created_at": "2023-11-09T00:02:22.132551Z", "data_removed": false, "error": null, "id": "aolq7adbg4dwivyzvd2cy3kpi4", "input": { "width": 1024, "height": 1024, "prompt": "TOK hope style poster red white and blue, obama and kim kardashian with hope in painted", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 46572\nEnsuring enough disk space...\nFree disk space: 1445390569472\nDownloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.269s (691 MB/s)\\nExtracted 186 MB in 0.055s (3.4 GB/s)\\n'\nDownloaded weights in 0.43337011337280273 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> hope style poster red white and blue, obama and kim kardashian with hope in painted\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.62it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.60it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.61it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.62it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.62it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.61it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.61it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.61it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.60it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.60it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.60it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.60it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.60it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.60it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.60it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.60it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.60it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.60it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.60it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.60it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 17.26123, "total_time": 20.379393 }, "output": [ "https://replicate.delivery/pbxt/WtF9gaFqSqptBxz66S0AUAA4CTeieszSkSAr1QrdYh7hOd2RA/out-0.png" ], "started_at": "2023-11-09T00:02:25.250714Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/aolq7adbg4dwivyzvd2cy3kpi4", "cancel": "https://api.replicate.com/v1/predictions/aolq7adbg4dwivyzvd2cy3kpi4/cancel" }, "version": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e" }Generated inUsing seed: 46572 Ensuring enough disk space... Free disk space: 1445390569472 Downloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar b'Downloaded 186 MB bytes in 0.269s (691 MB/s)\nExtracted 186 MB in 0.055s (3.4 GB/s)\n' Downloaded weights in 0.43337011337280273 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1> hope style poster red white and blue, obama and kim kardashian with hope in painted txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.62it/s] 4%|▍ | 2/50 [00:00<00:13, 3.60it/s] 6%|▌ | 3/50 [00:00<00:13, 3.61it/s] 8%|▊ | 4/50 [00:01<00:12, 3.62it/s] 10%|█ | 5/50 [00:01<00:12, 3.62it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.61it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s] 40%|████ | 20/50 [00:05<00:08, 3.61it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s] 50%|█████ | 25/50 [00:06<00:06, 3.61it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.60it/s] 60%|██████ | 30/50 [00:08<00:05, 3.60it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.60it/s] 70%|███████ | 35/50 [00:09<00:04, 3.60it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.60it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.60it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.60it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.60it/s] 80%|████████ | 40/50 [00:11<00:02, 3.60it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.60it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.60it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.60it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s]Prediction
hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524eIDlm2xnz3b5fjak3pjug3ovd2zxqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK poster style images red white and blue, kim kardashian nicki minaj for president
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "TOK poster style images red white and blue, kim kardashian nicki minaj for president", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", { input: { width: 1024, height: 1024, prompt: "TOK poster style images red white and blue, kim kardashian nicki minaj for president", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, 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 hunterkamerman/sdxl-hope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-hope:fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", input={ "width": 1024, "height": 1024, "prompt": "TOK poster style images red white and blue, kim kardashian nicki minaj for president", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "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 hunterkamerman/sdxl-hope 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": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e", "input": { "width": 1024, "height": 1024, "prompt": "TOK poster style images red white and blue, kim kardashian nicki minaj for president", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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": "2023-11-10T02:20:21.606104Z", "created_at": "2023-11-10T02:19:31.556417Z", "data_removed": false, "error": null, "id": "lm2xnz3b5fjak3pjug3ovd2zxq", "input": { "width": 1024, "height": 1024, "prompt": "TOK poster style images red white and blue, kim kardashian nicki minaj for president", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 24040\nEnsuring enough disk space...\nFree disk space: 1842962419712\nDownloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar\nb'Downloaded 186 MB bytes in 3.369s (55 MB/s)\\nExtracted 186 MB in 0.052s (3.6 GB/s)\\n'\nDownloaded weights in 3.8281373977661133 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> poster style images red white and blue, kim kardashian nicki minaj for president\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.63it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.63it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.63it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.63it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.62it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.62it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.61it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]", "metrics": { "predict_time": 20.699025, "total_time": 50.049687 }, "output": [ "https://replicate.delivery/pbxt/LAD1F9xMtCruO5vGvf3VHokfR85SbFhucRZTKuBbuBqkV02RA/out-0.png" ], "started_at": "2023-11-10T02:20:00.907079Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lm2xnz3b5fjak3pjug3ovd2zxq", "cancel": "https://api.replicate.com/v1/predictions/lm2xnz3b5fjak3pjug3ovd2zxq/cancel" }, "version": "fddbefd953964708f15b02ffd7fc64240e9158d809ccfbee554eaf610ddc524e" }Generated inUsing seed: 24040 Ensuring enough disk space... Free disk space: 1842962419712 Downloading weights: https://replicate.delivery/pbxt/iAidWyoTBjL3M1mw5Ng31y8UOgiNw8role3L9bBf06uLuY2RA/trained_model.tar b'Downloaded 186 MB bytes in 3.369s (55 MB/s)\nExtracted 186 MB in 0.052s (3.6 GB/s)\n' Downloaded weights in 3.8281373977661133 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1> poster style images red white and blue, kim kardashian nicki minaj for president txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.63it/s] 4%|▍ | 2/50 [00:00<00:13, 3.63it/s] 6%|▌ | 3/50 [00:00<00:12, 3.63it/s] 8%|▊ | 4/50 [00:01<00:12, 3.63it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.63it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.63it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s] 50%|█████ | 25/50 [00:06<00:06, 3.63it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s] 70%|███████ | 35/50 [00:09<00:04, 3.62it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s] 80%|████████ | 40/50 [00:11<00:02, 3.62it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.61it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s]
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