alekseycalvin/neurealhistory
neurealhistory
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alekseycalvin/neurealhistory
SDXL fine-tuned on 100s of manually colorized historical photos
alekseycalvin/neurealistconjurerofzos
Preprompt thy SD spells with ‘zos’ to summon the visionary touch of generative art’s unsung arch-ancestor*, Austin Osman Spare* (1886-1956) *(more info below and in the README!)
alekseycalvin/soonrtarot
Neurealist Tarot (v_2) Replicate Version: a Tarot card generating model trained by A.C.T. SOON® over SD v1-5, from custom data and class sets, and via Dreambooth/Cog
alekseycalvin/neurealenin
NeureaLenin by soon®: a V. I. Lenin SDXL fine-tuning; use "LEN" token in prompts!
alekseycalvin/neurealhistory3
RealvisXL3 fine-tuned on 300+ colorized 1850s-1940s photos
alekseycalvin/acsoonr_flux
ACS token
alekseycalvin/anna_akhmatova_flux
Flux LoRa evoking the visage of poet Anna Akhmatova (1889-1966), whose voice emblematizes one self's capacity to universalize, harnessing, the pain of all, refracting it back dignified unto transcendence...
Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDa5an5c3brrvv3squnsjbnfvvwqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Rowland S Howard
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Rowland S Howard", "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, "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Rowland S Howard", 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, 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Rowland S Howard", "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, "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Rowland S Howard", "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, "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": "2023-08-12T05:37:43.640168Z", "created_at": "2023-08-12T05:36:38.566073Z", "data_removed": false, "error": null, "id": "a5an5c3brrvv3squnsjbnfvvwq", "input": { "width": 1024, "height": 1024, "prompt": "HST style Rowland S Howard", "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, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 35467\nPrompt: <s0><s1> style Rowland S Howard\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:38, 1.29it/s]\n 4%|▍ | 2/50 [00:01<00:23, 2.08it/s]\n 6%|▌ | 3/50 [00:01<00:18, 2.60it/s]\n 8%|▊ | 4/50 [00:01<00:15, 2.94it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.17it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.32it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.43it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.50it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.55it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.58it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.61it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.65it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:08<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:11<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.54it/s]", "metrics": { "predict_time": 16.836664, "total_time": 65.074095 }, "output": [ "https://replicate.delivery/pbxt/EGLn9pPrCz4OLxD5fAtERiFH4ro14m7IQWdAPVc4j0KTZmsIA/out-0.png" ], "started_at": "2023-08-12T05:37:26.803504Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a5an5c3brrvv3squnsjbnfvvwq", "cancel": "https://api.replicate.com/v1/predictions/a5an5c3brrvv3squnsjbnfvvwq/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 35467 Prompt: <s0><s1> style Rowland S Howard txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:38, 1.29it/s] 4%|▍ | 2/50 [00:01<00:23, 2.08it/s] 6%|▌ | 3/50 [00:01<00:18, 2.60it/s] 8%|▊ | 4/50 [00:01<00:15, 2.94it/s] 10%|█ | 5/50 [00:01<00:14, 3.17it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.32it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.43it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.50it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.55it/s] 20%|██ | 10/50 [00:03<00:11, 3.58it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.61it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.65it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.68it/s] 50%|█████ | 25/50 [00:07<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:08<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:10<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:11<00:02, 3.68it/s] 80%|████████ | 40/50 [00:11<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:14<00:00, 3.67it/s] 100%|██████████| 50/50 [00:14<00:00, 3.54it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDcuul4ntbb2sj73oyz6yv2emf44StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Syd Barrett
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.67
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Syd Barrett", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.67, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Syd Barrett", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.67, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Syd Barrett", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.67, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Syd Barrett", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.67, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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": "2023-08-12T05:39:18.428561Z", "created_at": "2023-08-12T05:39:02.526050Z", "data_removed": false, "error": null, "id": "cuul4ntbb2sj73oyz6yv2emf44", "input": { "width": 1024, "height": 1024, "prompt": "HST style Syd Barrett", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.67, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 14502\nPrompt: <s0><s1> style Syd Barrett\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, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/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 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/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, 3.67it/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<00:05, 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, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/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, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/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, 3.66it/s]", "metrics": { "predict_time": 15.905571, "total_time": 15.902511 }, "output": [ "https://replicate.delivery/pbxt/UZh2i6A1X2JUL9QP3TzSzBJ5seHGKcnasV1UjAKYHwtCamsIA/out-0.png" ], "started_at": "2023-08-12T05:39:02.522990Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cuul4ntbb2sj73oyz6yv2emf44", "cancel": "https://api.replicate.com/v1/predictions/cuul4ntbb2sj73oyz6yv2emf44/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 14502 Prompt: <s0><s1> style Syd Barrett 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, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/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 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.65it/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, 3.67it/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<00:05, 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, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/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, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/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, 3.66it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDntijrttba527pebosxbwlfhun4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style world of warcraft character, photo, film still, hst
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.82
- num_outputs
- 1
- guidance_scale
- 8.19
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 58
{ "width": 1024, "height": 1024, "prompt": "HST style world of warcraft character, photo, film still, hst", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 8.19, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 58 }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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style world of warcraft character, photo, film still, hst", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.82, num_outputs: 1, guidance_scale: 8.19, apply_watermark: false, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 58 } } ); // 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style world of warcraft character, photo, film still, hst", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 8.19, "apply_watermark": False, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 58 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style world of warcraft character, photo, film still, hst", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 8.19, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 58 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-12T06:20:29.295660Z", "created_at": "2023-08-12T06:20:11.632697Z", "data_removed": false, "error": null, "id": "ntijrttba527pebosxbwlfhun4", "input": { "width": 1024, "height": 1024, "prompt": "HST style world of warcraft character, photo, film still, hst", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 8.19, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 58 }, "logs": "Using seed: 8181\nPrompt: <s0><s1> style world of warcraft character, photo, film still, hst\ntxt2img mode\n 0%| | 0/58 [00:00<?, ?it/s]\n 2%|▏ | 1/58 [00:00<00:15, 3.68it/s]\n 3%|▎ | 2/58 [00:00<00:15, 3.67it/s]\n 5%|▌ | 3/58 [00:00<00:15, 3.66it/s]\n 7%|▋ | 4/58 [00:01<00:14, 3.66it/s]\n 9%|▊ | 5/58 [00:01<00:14, 3.66it/s]\n 10%|█ | 6/58 [00:01<00:14, 3.67it/s]\n 12%|█▏ | 7/58 [00:01<00:13, 3.67it/s]\n 14%|█▍ | 8/58 [00:02<00:13, 3.67it/s]\n 16%|█▌ | 9/58 [00:02<00:13, 3.67it/s]\n 17%|█▋ | 10/58 [00:02<00:13, 3.67it/s]\n 19%|█▉ | 11/58 [00:02<00:12, 3.67it/s]\n 21%|██ | 12/58 [00:03<00:12, 3.67it/s]\n 22%|██▏ | 13/58 [00:03<00:12, 3.67it/s]\n 24%|██▍ | 14/58 [00:03<00:11, 3.67it/s]\n 26%|██▌ | 15/58 [00:04<00:11, 3.67it/s]\n 28%|██▊ | 16/58 [00:04<00:11, 3.66it/s]\n 29%|██▉ | 17/58 [00:04<00:11, 3.66it/s]\n 31%|███ | 18/58 [00:04<00:10, 3.66it/s]\n 33%|███▎ | 19/58 [00:05<00:10, 3.66it/s]\n 34%|███▍ | 20/58 [00:05<00:10, 3.66it/s]\n 36%|███▌ | 21/58 [00:05<00:10, 3.66it/s]\n 38%|███▊ | 22/58 [00:06<00:09, 3.66it/s]\n 40%|███▉ | 23/58 [00:06<00:09, 3.66it/s]\n 41%|████▏ | 24/58 [00:06<00:09, 3.66it/s]\n 43%|████▎ | 25/58 [00:06<00:09, 3.66it/s]\n 45%|████▍ | 26/58 [00:07<00:08, 3.66it/s]\n 47%|████▋ | 27/58 [00:07<00:08, 3.66it/s]\n 48%|████▊ | 28/58 [00:07<00:08, 3.66it/s]\n 50%|█████ | 29/58 [00:07<00:07, 3.66it/s]\n 52%|█████▏ | 30/58 [00:08<00:07, 3.66it/s]\n 53%|█████▎ | 31/58 [00:08<00:07, 3.66it/s]\n 55%|█████▌ | 32/58 [00:08<00:07, 3.66it/s]\n 57%|█████▋ | 33/58 [00:09<00:06, 3.66it/s]\n 59%|█████▊ | 34/58 [00:09<00:06, 3.65it/s]\n 60%|██████ | 35/58 [00:09<00:06, 3.65it/s]\n 62%|██████▏ | 36/58 [00:09<00:06, 3.62it/s]\n 64%|██████▍ | 37/58 [00:10<00:05, 3.61it/s]\n 66%|██████▌ | 38/58 [00:10<00:05, 3.62it/s]\n 67%|██████▋ | 39/58 [00:10<00:05, 3.63it/s]\n 69%|██████▉ | 40/58 [00:10<00:04, 3.63it/s]\n 71%|███████ | 41/58 [00:11<00:04, 3.64it/s]\n 72%|███████▏ | 42/58 [00:11<00:04, 3.64it/s]\n 74%|███████▍ | 43/58 [00:11<00:04, 3.64it/s]\n 76%|███████▌ | 44/58 [00:12<00:03, 3.64it/s]\n 78%|███████▊ | 45/58 [00:12<00:03, 3.64it/s]\n 79%|███████▉ | 46/58 [00:12<00:03, 3.64it/s]\n 81%|████████ | 47/58 [00:12<00:03, 3.64it/s]\n 83%|████████▎ | 48/58 [00:13<00:02, 3.64it/s]\n 84%|████████▍ | 49/58 [00:13<00:02, 3.64it/s]\n 86%|████████▌ | 50/58 [00:13<00:02, 3.64it/s]\n 88%|████████▊ | 51/58 [00:13<00:01, 3.64it/s]\n 90%|████████▉ | 52/58 [00:14<00:01, 3.64it/s]\n 91%|█████████▏| 53/58 [00:14<00:01, 3.64it/s]\n 93%|█████████▎| 54/58 [00:14<00:01, 3.64it/s]\n 95%|█████████▍| 55/58 [00:15<00:00, 3.64it/s]\n 97%|█████████▋| 56/58 [00:15<00:00, 3.64it/s]\n 98%|█████████▊| 57/58 [00:15<00:00, 3.64it/s]\n100%|██████████| 58/58 [00:15<00:00, 3.64it/s]\n100%|██████████| 58/58 [00:15<00:00, 3.65it/s]", "metrics": { "predict_time": 17.655424, "total_time": 17.662963 }, "output": [ "https://replicate.delivery/pbxt/a9kWea8mtbVf7ky45neafcP5bgbYSmBv9AuzMh4bvWqxq1kFB/out-0.png" ], "started_at": "2023-08-12T06:20:11.640236Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ntijrttba527pebosxbwlfhun4", "cancel": "https://api.replicate.com/v1/predictions/ntijrttba527pebosxbwlfhun4/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 8181 Prompt: <s0><s1> style world of warcraft character, photo, film still, hst txt2img mode 0%| | 0/58 [00:00<?, ?it/s] 2%|▏ | 1/58 [00:00<00:15, 3.68it/s] 3%|▎ | 2/58 [00:00<00:15, 3.67it/s] 5%|▌ | 3/58 [00:00<00:15, 3.66it/s] 7%|▋ | 4/58 [00:01<00:14, 3.66it/s] 9%|▊ | 5/58 [00:01<00:14, 3.66it/s] 10%|█ | 6/58 [00:01<00:14, 3.67it/s] 12%|█▏ | 7/58 [00:01<00:13, 3.67it/s] 14%|█▍ | 8/58 [00:02<00:13, 3.67it/s] 16%|█▌ | 9/58 [00:02<00:13, 3.67it/s] 17%|█▋ | 10/58 [00:02<00:13, 3.67it/s] 19%|█▉ | 11/58 [00:02<00:12, 3.67it/s] 21%|██ | 12/58 [00:03<00:12, 3.67it/s] 22%|██▏ | 13/58 [00:03<00:12, 3.67it/s] 24%|██▍ | 14/58 [00:03<00:11, 3.67it/s] 26%|██▌ | 15/58 [00:04<00:11, 3.67it/s] 28%|██▊ | 16/58 [00:04<00:11, 3.66it/s] 29%|██▉ | 17/58 [00:04<00:11, 3.66it/s] 31%|███ | 18/58 [00:04<00:10, 3.66it/s] 33%|███▎ | 19/58 [00:05<00:10, 3.66it/s] 34%|███▍ | 20/58 [00:05<00:10, 3.66it/s] 36%|███▌ | 21/58 [00:05<00:10, 3.66it/s] 38%|███▊ | 22/58 [00:06<00:09, 3.66it/s] 40%|███▉ | 23/58 [00:06<00:09, 3.66it/s] 41%|████▏ | 24/58 [00:06<00:09, 3.66it/s] 43%|████▎ | 25/58 [00:06<00:09, 3.66it/s] 45%|████▍ | 26/58 [00:07<00:08, 3.66it/s] 47%|████▋ | 27/58 [00:07<00:08, 3.66it/s] 48%|████▊ | 28/58 [00:07<00:08, 3.66it/s] 50%|█████ | 29/58 [00:07<00:07, 3.66it/s] 52%|█████▏ | 30/58 [00:08<00:07, 3.66it/s] 53%|█████▎ | 31/58 [00:08<00:07, 3.66it/s] 55%|█████▌ | 32/58 [00:08<00:07, 3.66it/s] 57%|█████▋ | 33/58 [00:09<00:06, 3.66it/s] 59%|█████▊ | 34/58 [00:09<00:06, 3.65it/s] 60%|██████ | 35/58 [00:09<00:06, 3.65it/s] 62%|██████▏ | 36/58 [00:09<00:06, 3.62it/s] 64%|██████▍ | 37/58 [00:10<00:05, 3.61it/s] 66%|██████▌ | 38/58 [00:10<00:05, 3.62it/s] 67%|██████▋ | 39/58 [00:10<00:05, 3.63it/s] 69%|██████▉ | 40/58 [00:10<00:04, 3.63it/s] 71%|███████ | 41/58 [00:11<00:04, 3.64it/s] 72%|███████▏ | 42/58 [00:11<00:04, 3.64it/s] 74%|███████▍ | 43/58 [00:11<00:04, 3.64it/s] 76%|███████▌ | 44/58 [00:12<00:03, 3.64it/s] 78%|███████▊ | 45/58 [00:12<00:03, 3.64it/s] 79%|███████▉ | 46/58 [00:12<00:03, 3.64it/s] 81%|████████ | 47/58 [00:12<00:03, 3.64it/s] 83%|████████▎ | 48/58 [00:13<00:02, 3.64it/s] 84%|████████▍ | 49/58 [00:13<00:02, 3.64it/s] 86%|████████▌ | 50/58 [00:13<00:02, 3.64it/s] 88%|████████▊ | 51/58 [00:13<00:01, 3.64it/s] 90%|████████▉ | 52/58 [00:14<00:01, 3.64it/s] 91%|█████████▏| 53/58 [00:14<00:01, 3.64it/s] 93%|█████████▎| 54/58 [00:14<00:01, 3.64it/s] 95%|█████████▍| 55/58 [00:15<00:00, 3.64it/s] 97%|█████████▋| 56/58 [00:15<00:00, 3.64it/s] 98%|█████████▊| 57/58 [00:15<00:00, 3.64it/s] 100%|██████████| 58/58 [00:15<00:00, 3.64it/s] 100%|██████████| 58/58 [00:15<00:00, 3.65it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDwxhetdtbciq5km7zofqhqvkvd4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style photograph of Bob Dylan, award-winning cinematic photo, full height, top quality film still, clarity enhancement, realistic textures, pro 8k photography, realistically lined textured skin, lifelike eyes with irises and lashes, intricate details, extremely elaborate background, museum-quality
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- refine_steps
- 20
- guidance_scale
- 9.67
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- unrealistic anatomy, cgi, cartoon, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,
- prompt_strength
- 0.8
- num_inference_steps
- 42
{ "width": 1024, "height": 1024, "prompt": "HST style photograph of Bob Dylan, award-winning cinematic photo, full height, top quality film still, clarity enhancement, realistic textures, pro 8k photography, realistically lined textured skin, lifelike eyes with irises and lashes, intricate details, extremely elaborate background, museum-quality", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 9.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "unrealistic anatomy, cgi, cartoon, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.8, "num_inference_steps": 42 }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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style photograph of Bob Dylan, award-winning cinematic photo, full height, top quality film still, clarity enhancement, realistic textures, pro 8k photography, realistically lined textured skin, lifelike eyes with irises and lashes, intricate details, extremely elaborate background, museum-quality", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, refine_steps: 20, guidance_scale: 9.67, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "unrealistic anatomy, cgi, cartoon, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", prompt_strength: 0.8, num_inference_steps: 42 } } ); // 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style photograph of Bob Dylan, award-winning cinematic photo, full height, top quality film still, clarity enhancement, realistic textures, pro 8k photography, realistically lined textured skin, lifelike eyes with irises and lashes, intricate details, extremely elaborate background, museum-quality", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 9.67, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "unrealistic anatomy, cgi, cartoon, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.8, "num_inference_steps": 42 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style photograph of Bob Dylan, award-winning cinematic photo, full height, top quality film still, clarity enhancement, realistic textures, pro 8k photography, realistically lined textured skin, lifelike eyes with irises and lashes, intricate details, extremely elaborate background, museum-quality", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 9.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "unrealistic anatomy, cgi, cartoon, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.8, "num_inference_steps": 42 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-12T06:59:03.025928Z", "created_at": "2023-08-12T06:58:49.769350Z", "data_removed": false, "error": null, "id": "wxhetdtbciq5km7zofqhqvkvd4", "input": { "width": 1024, "height": 1024, "prompt": "HST style photograph of Bob Dylan, award-winning cinematic photo, full height, top quality film still, clarity enhancement, realistic textures, pro 8k photography, realistically lined textured skin, lifelike eyes with irises and lashes, intricate details, extremely elaborate background, museum-quality", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 9.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "unrealistic anatomy, cgi, cartoon, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.8, "num_inference_steps": 42 }, "logs": "Using seed: 15280\nPrompt: <s0><s1> style photograph of Bob Dylan, award-winning cinematic photo, full height, top quality film still, clarity enhancement, realistic textures, pro 8k photography, realistically lined textured skin, lifelike eyes with irises and lashes, intricate details, extremely elaborate background, museum-quality\ntxt2img mode\n 0%| | 0/42 [00:00<?, ?it/s]\n 2%|▏ | 1/42 [00:00<00:11, 3.70it/s]\n 5%|▍ | 2/42 [00:00<00:10, 3.67it/s]\n 7%|▋ | 3/42 [00:00<00:10, 3.67it/s]\n 10%|▉ | 4/42 [00:01<00:10, 3.66it/s]\n 12%|█▏ | 5/42 [00:01<00:10, 3.66it/s]\n 14%|█▍ | 6/42 [00:01<00:09, 3.66it/s]\n 17%|█▋ | 7/42 [00:01<00:09, 3.66it/s]\n 19%|█▉ | 8/42 [00:02<00:09, 3.67it/s]\n 21%|██▏ | 9/42 [00:02<00:08, 3.67it/s]\n 24%|██▍ | 10/42 [00:02<00:08, 3.68it/s]\n 26%|██▌ | 11/42 [00:02<00:08, 3.68it/s]\n 29%|██▊ | 12/42 [00:03<00:08, 3.68it/s]\n 31%|███ | 13/42 [00:03<00:07, 3.68it/s]\n 33%|███▎ | 14/42 [00:03<00:07, 3.68it/s]\n 36%|███▌ | 15/42 [00:04<00:07, 3.67it/s]\n 38%|███▊ | 16/42 [00:04<00:07, 3.67it/s]\n 40%|████ | 17/42 [00:04<00:06, 3.67it/s]\n 43%|████▎ | 18/42 [00:04<00:06, 3.68it/s]\n 45%|████▌ | 19/42 [00:05<00:06, 3.67it/s]\n 48%|████▊ | 20/42 [00:05<00:05, 3.67it/s]\n 50%|█████ | 21/42 [00:05<00:05, 3.67it/s]\n 52%|█████▏ | 22/42 [00:05<00:05, 3.67it/s]\n 55%|█████▍ | 23/42 [00:06<00:05, 3.67it/s]\n 57%|█████▋ | 24/42 [00:06<00:04, 3.67it/s]\n 60%|█████▉ | 25/42 [00:06<00:04, 3.67it/s]\n 62%|██████▏ | 26/42 [00:07<00:04, 3.67it/s]\n 64%|██████▍ | 27/42 [00:07<00:04, 3.67it/s]\n 67%|██████▋ | 28/42 [00:07<00:03, 3.67it/s]\n 69%|██████▉ | 29/42 [00:07<00:03, 3.67it/s]\n 71%|███████▏ | 30/42 [00:08<00:03, 3.67it/s]\n 74%|███████▍ | 31/42 [00:08<00:03, 3.66it/s]\n 76%|███████▌ | 32/42 [00:08<00:02, 3.67it/s]\n 79%|███████▊ | 33/42 [00:08<00:02, 3.66it/s]\n 81%|████████ | 34/42 [00:09<00:02, 3.67it/s]\n 83%|████████▎ | 35/42 [00:09<00:01, 3.66it/s]\n 86%|████████▌ | 36/42 [00:09<00:01, 3.66it/s]\n 88%|████████▊ | 37/42 [00:10<00:01, 3.66it/s]\n 90%|█████████ | 38/42 [00:10<00:01, 3.66it/s]\n 93%|█████████▎| 39/42 [00:10<00:00, 3.66it/s]\n 95%|█████████▌| 40/42 [00:10<00:00, 3.66it/s]\n 98%|█████████▊| 41/42 [00:11<00:00, 3.66it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.66it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.67it/s]", "metrics": { "predict_time": 13.300811, "total_time": 13.256578 }, "output": [ "https://replicate.delivery/pbxt/pes4sVgsGeqpHEoebdDCwcKWRpQCtmsjUaxl07vX4Yyt9byiA/out-0.png" ], "started_at": "2023-08-12T06:58:49.725117Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wxhetdtbciq5km7zofqhqvkvd4", "cancel": "https://api.replicate.com/v1/predictions/wxhetdtbciq5km7zofqhqvkvd4/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 15280 Prompt: <s0><s1> style photograph of Bob Dylan, award-winning cinematic photo, full height, top quality film still, clarity enhancement, realistic textures, pro 8k photography, realistically lined textured skin, lifelike eyes with irises and lashes, intricate details, extremely elaborate background, museum-quality txt2img mode 0%| | 0/42 [00:00<?, ?it/s] 2%|▏ | 1/42 [00:00<00:11, 3.70it/s] 5%|▍ | 2/42 [00:00<00:10, 3.67it/s] 7%|▋ | 3/42 [00:00<00:10, 3.67it/s] 10%|▉ | 4/42 [00:01<00:10, 3.66it/s] 12%|█▏ | 5/42 [00:01<00:10, 3.66it/s] 14%|█▍ | 6/42 [00:01<00:09, 3.66it/s] 17%|█▋ | 7/42 [00:01<00:09, 3.66it/s] 19%|█▉ | 8/42 [00:02<00:09, 3.67it/s] 21%|██▏ | 9/42 [00:02<00:08, 3.67it/s] 24%|██▍ | 10/42 [00:02<00:08, 3.68it/s] 26%|██▌ | 11/42 [00:02<00:08, 3.68it/s] 29%|██▊ | 12/42 [00:03<00:08, 3.68it/s] 31%|███ | 13/42 [00:03<00:07, 3.68it/s] 33%|███▎ | 14/42 [00:03<00:07, 3.68it/s] 36%|███▌ | 15/42 [00:04<00:07, 3.67it/s] 38%|███▊ | 16/42 [00:04<00:07, 3.67it/s] 40%|████ | 17/42 [00:04<00:06, 3.67it/s] 43%|████▎ | 18/42 [00:04<00:06, 3.68it/s] 45%|████▌ | 19/42 [00:05<00:06, 3.67it/s] 48%|████▊ | 20/42 [00:05<00:05, 3.67it/s] 50%|█████ | 21/42 [00:05<00:05, 3.67it/s] 52%|█████▏ | 22/42 [00:05<00:05, 3.67it/s] 55%|█████▍ | 23/42 [00:06<00:05, 3.67it/s] 57%|█████▋ | 24/42 [00:06<00:04, 3.67it/s] 60%|█████▉ | 25/42 [00:06<00:04, 3.67it/s] 62%|██████▏ | 26/42 [00:07<00:04, 3.67it/s] 64%|██████▍ | 27/42 [00:07<00:04, 3.67it/s] 67%|██████▋ | 28/42 [00:07<00:03, 3.67it/s] 69%|██████▉ | 29/42 [00:07<00:03, 3.67it/s] 71%|███████▏ | 30/42 [00:08<00:03, 3.67it/s] 74%|███████▍ | 31/42 [00:08<00:03, 3.66it/s] 76%|███████▌ | 32/42 [00:08<00:02, 3.67it/s] 79%|███████▊ | 33/42 [00:08<00:02, 3.66it/s] 81%|████████ | 34/42 [00:09<00:02, 3.67it/s] 83%|████████▎ | 35/42 [00:09<00:01, 3.66it/s] 86%|████████▌ | 36/42 [00:09<00:01, 3.66it/s] 88%|████████▊ | 37/42 [00:10<00:01, 3.66it/s] 90%|█████████ | 38/42 [00:10<00:01, 3.66it/s] 93%|█████████▎| 39/42 [00:10<00:00, 3.66it/s] 95%|█████████▌| 40/42 [00:10<00:00, 3.66it/s] 98%|█████████▊| 41/42 [00:11<00:00, 3.66it/s] 100%|██████████| 42/42 [00:11<00:00, 3.66it/s] 100%|██████████| 42/42 [00:11<00:00, 3.67it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDu7xkcl3byawqorhn3zpbqtjdsiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style photo of Janelle Monáe in Leningrad, street level, 8k, uhd, realistic, film still, cinematic
- refine
- no_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.8
- num_outputs
- 1
- refine_steps
- 20
- guidance_scale
- 7.64
- apply_watermark
- high_noise_frac
- 0.73
- negative_prompt
- two subjects, duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,
- prompt_strength
- 0.95
- num_inference_steps
- 52
{ "width": 1024, "height": 1024, "prompt": "HST style photo of Janelle Monáe in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 }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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style photo of Janelle Monáe in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", refine: "no_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.8, num_outputs: 1, refine_steps: 20, guidance_scale: 7.64, apply_watermark: false, high_noise_frac: 0.73, negative_prompt: "two subjects, duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", prompt_strength: 0.95, num_inference_steps: 52 } } ); // 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style photo of Janelle Monáe in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": False, "high_noise_frac": 0.73, "negative_prompt": "two subjects, duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style photo of Janelle Monáe in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-17T04:32:23.334251Z", "created_at": "2023-08-17T04:30:48.322427Z", "data_removed": false, "error": null, "id": "u7xkcl3byawqorhn3zpbqtjdsi", "input": { "width": 1024, "height": 1024, "prompt": "HST style photo of Janelle Monáe in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 }, "logs": "Using seed: 9453\nPrompt: <s0><s1> style photo of Janelle Monáe in Leningrad, street level, 8k, uhd, realistic, film still, cinematic\ntxt2img mode\n 0%| | 0/52 [00:00<?, ?it/s]\n 2%|▏ | 1/52 [00:00<00:41, 1.22it/s]\n 4%|▍ | 2/52 [00:01<00:24, 2.01it/s]\n 6%|▌ | 3/52 [00:01<00:19, 2.54it/s]\n 8%|▊ | 4/52 [00:01<00:16, 2.89it/s]\n 10%|▉ | 5/52 [00:01<00:14, 3.14it/s]\n 12%|█▏ | 6/52 [00:02<00:13, 3.30it/s]\n 13%|█▎ | 7/52 [00:02<00:13, 3.42it/s]\n 15%|█▌ | 8/52 [00:02<00:12, 3.50it/s]\n 17%|█▋ | 9/52 [00:02<00:12, 3.55it/s]\n 19%|█▉ | 10/52 [00:03<00:11, 3.59it/s]\n 21%|██ | 11/52 [00:03<00:11, 3.61it/s]\n 23%|██▎ | 12/52 [00:03<00:11, 3.63it/s]\n 25%|██▌ | 13/52 [00:04<00:10, 3.64it/s]\n 27%|██▋ | 14/52 [00:04<00:10, 3.65it/s]\n 29%|██▉ | 15/52 [00:04<00:10, 3.66it/s]\n 31%|███ | 16/52 [00:04<00:09, 3.66it/s]\n 33%|███▎ | 17/52 [00:05<00:09, 3.67it/s]\n 35%|███▍ | 18/52 [00:05<00:09, 3.67it/s]\n 37%|███▋ | 19/52 [00:05<00:08, 3.67it/s]\n 38%|███▊ | 20/52 [00:05<00:08, 3.67it/s]\n 40%|████ | 21/52 [00:06<00:08, 3.67it/s]\n 42%|████▏ | 22/52 [00:06<00:08, 3.67it/s]\n 44%|████▍ | 23/52 [00:06<00:07, 3.67it/s]\n 46%|████▌ | 24/52 [00:07<00:07, 3.67it/s]\n 48%|████▊ | 25/52 [00:07<00:07, 3.66it/s]\n 50%|█████ | 26/52 [00:07<00:07, 3.66it/s]\n 52%|█████▏ | 27/52 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 28/52 [00:08<00:06, 3.66it/s]\n 56%|█████▌ | 29/52 [00:08<00:06, 3.66it/s]\n 58%|█████▊ | 30/52 [00:08<00:06, 3.66it/s]\n 60%|█████▉ | 31/52 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 32/52 [00:09<00:05, 3.66it/s]\n 63%|██████▎ | 33/52 [00:09<00:05, 3.66it/s]\n 65%|██████▌ | 34/52 [00:09<00:04, 3.66it/s]\n 67%|██████▋ | 35/52 [00:10<00:04, 3.67it/s]\n 69%|██████▉ | 36/52 [00:10<00:04, 3.67it/s]\n 71%|███████ | 37/52 [00:10<00:04, 3.68it/s]\n 73%|███████▎ | 38/52 [00:10<00:03, 3.68it/s]\n 75%|███████▌ | 39/52 [00:11<00:03, 3.68it/s]\n 77%|███████▋ | 40/52 [00:11<00:03, 3.68it/s]\n 79%|███████▉ | 41/52 [00:11<00:02, 3.68it/s]\n 81%|████████ | 42/52 [00:11<00:02, 3.68it/s]\n 83%|████████▎ | 43/52 [00:12<00:02, 3.68it/s]\n 85%|████████▍ | 44/52 [00:12<00:02, 3.68it/s]\n 87%|████████▋ | 45/52 [00:12<00:01, 3.68it/s]\n 88%|████████▊ | 46/52 [00:13<00:01, 3.68it/s]\n 90%|█████████ | 47/52 [00:13<00:01, 3.68it/s]\n 92%|█████████▏| 48/52 [00:13<00:01, 3.68it/s]\n 94%|█████████▍| 49/52 [00:13<00:00, 3.67it/s]\n 96%|█████████▌| 50/52 [00:14<00:00, 3.67it/s]\n 98%|█████████▊| 51/52 [00:14<00:00, 3.67it/s]\n100%|██████████| 52/52 [00:14<00:00, 3.67it/s]\n100%|██████████| 52/52 [00:14<00:00, 3.54it/s]", "metrics": { "predict_time": 16.702568, "total_time": 95.011824 }, "output": [ "https://replicate.delivery/pbxt/W17aY1aMS4pPItQpRasrjSvO2nS66ndrgItvT9NnfOYrpatIA/out-0.png" ], "started_at": "2023-08-17T04:32:06.631683Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/u7xkcl3byawqorhn3zpbqtjdsi", "cancel": "https://api.replicate.com/v1/predictions/u7xkcl3byawqorhn3zpbqtjdsi/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 9453 Prompt: <s0><s1> style photo of Janelle Monáe in Leningrad, street level, 8k, uhd, realistic, film still, cinematic txt2img mode 0%| | 0/52 [00:00<?, ?it/s] 2%|▏ | 1/52 [00:00<00:41, 1.22it/s] 4%|▍ | 2/52 [00:01<00:24, 2.01it/s] 6%|▌ | 3/52 [00:01<00:19, 2.54it/s] 8%|▊ | 4/52 [00:01<00:16, 2.89it/s] 10%|▉ | 5/52 [00:01<00:14, 3.14it/s] 12%|█▏ | 6/52 [00:02<00:13, 3.30it/s] 13%|█▎ | 7/52 [00:02<00:13, 3.42it/s] 15%|█▌ | 8/52 [00:02<00:12, 3.50it/s] 17%|█▋ | 9/52 [00:02<00:12, 3.55it/s] 19%|█▉ | 10/52 [00:03<00:11, 3.59it/s] 21%|██ | 11/52 [00:03<00:11, 3.61it/s] 23%|██▎ | 12/52 [00:03<00:11, 3.63it/s] 25%|██▌ | 13/52 [00:04<00:10, 3.64it/s] 27%|██▋ | 14/52 [00:04<00:10, 3.65it/s] 29%|██▉ | 15/52 [00:04<00:10, 3.66it/s] 31%|███ | 16/52 [00:04<00:09, 3.66it/s] 33%|███▎ | 17/52 [00:05<00:09, 3.67it/s] 35%|███▍ | 18/52 [00:05<00:09, 3.67it/s] 37%|███▋ | 19/52 [00:05<00:08, 3.67it/s] 38%|███▊ | 20/52 [00:05<00:08, 3.67it/s] 40%|████ | 21/52 [00:06<00:08, 3.67it/s] 42%|████▏ | 22/52 [00:06<00:08, 3.67it/s] 44%|████▍ | 23/52 [00:06<00:07, 3.67it/s] 46%|████▌ | 24/52 [00:07<00:07, 3.67it/s] 48%|████▊ | 25/52 [00:07<00:07, 3.66it/s] 50%|█████ | 26/52 [00:07<00:07, 3.66it/s] 52%|█████▏ | 27/52 [00:07<00:06, 3.66it/s] 54%|█████▍ | 28/52 [00:08<00:06, 3.66it/s] 56%|█████▌ | 29/52 [00:08<00:06, 3.66it/s] 58%|█████▊ | 30/52 [00:08<00:06, 3.66it/s] 60%|█████▉ | 31/52 [00:08<00:05, 3.66it/s] 62%|██████▏ | 32/52 [00:09<00:05, 3.66it/s] 63%|██████▎ | 33/52 [00:09<00:05, 3.66it/s] 65%|██████▌ | 34/52 [00:09<00:04, 3.66it/s] 67%|██████▋ | 35/52 [00:10<00:04, 3.67it/s] 69%|██████▉ | 36/52 [00:10<00:04, 3.67it/s] 71%|███████ | 37/52 [00:10<00:04, 3.68it/s] 73%|███████▎ | 38/52 [00:10<00:03, 3.68it/s] 75%|███████▌ | 39/52 [00:11<00:03, 3.68it/s] 77%|███████▋ | 40/52 [00:11<00:03, 3.68it/s] 79%|███████▉ | 41/52 [00:11<00:02, 3.68it/s] 81%|████████ | 42/52 [00:11<00:02, 3.68it/s] 83%|████████▎ | 43/52 [00:12<00:02, 3.68it/s] 85%|████████▍ | 44/52 [00:12<00:02, 3.68it/s] 87%|████████▋ | 45/52 [00:12<00:01, 3.68it/s] 88%|████████▊ | 46/52 [00:13<00:01, 3.68it/s] 90%|█████████ | 47/52 [00:13<00:01, 3.68it/s] 92%|█████████▏| 48/52 [00:13<00:01, 3.68it/s] 94%|█████████▍| 49/52 [00:13<00:00, 3.67it/s] 96%|█████████▌| 50/52 [00:14<00:00, 3.67it/s] 98%|█████████▊| 51/52 [00:14<00:00, 3.67it/s] 100%|██████████| 52/52 [00:14<00:00, 3.67it/s] 100%|██████████| 52/52 [00:14<00:00, 3.54it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDfonqm7tbhzk4d32phpygarnm5mStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Marlene Dietrich
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.73
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cross-eyed, wrong eyes, wrong hands, deformed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Marlene Dietrich", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Marlene Dietrich", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.73, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "cross-eyed, wrong eyes, wrong hands, deformed", 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Marlene Dietrich", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Marlene Dietrich", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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": "2023-08-18T09:38:32.053012Z", "created_at": "2023-08-18T09:38:16.531870Z", "data_removed": false, "error": null, "id": "fonqm7tbhzk4d32phpygarnm5m", "input": { "width": 1024, "height": 1024, "prompt": "HST style Marlene Dietrich", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 42063\nPrompt: <s0><s1> style Marlene Dietrich\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, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/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, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/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, 3.63it/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, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/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, 3.66it/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, 3.65it/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, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 15.51387, "total_time": 15.521142 }, "output": [ "https://replicate.delivery/pbxt/VFAuXNfX5oyzSCMxXHQgNiiUpjYK5wtbofe7QCeBg1zfC3ZLC/out-0.png" ], "started_at": "2023-08-18T09:38:16.539142Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fonqm7tbhzk4d32phpygarnm5m", "cancel": "https://api.replicate.com/v1/predictions/fonqm7tbhzk4d32phpygarnm5m/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 42063 Prompt: <s0><s1> style Marlene Dietrich 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, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/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, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/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, 3.63it/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, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/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, 3.66it/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, 3.65it/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, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDr6msgc3b6wnp4zajvaz4rk3akqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Christopher Marlowe, analog color photo
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.73
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cross-eyed, wrong eyes, wrong hands, deformed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Christopher Marlowe, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Christopher Marlowe, analog color photo", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.73, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "cross-eyed, wrong eyes, wrong hands, deformed", 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Christopher Marlowe, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Christopher Marlowe, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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": "2023-08-18T09:42:47.517432Z", "created_at": "2023-08-18T09:42:31.885391Z", "data_removed": false, "error": null, "id": "r6msgc3b6wnp4zajvaz4rk3akq", "input": { "width": 1024, "height": 1024, "prompt": "HST style Christopher Marlowe, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 38520\nPrompt: <s0><s1> style Christopher Marlowe, analog color photo\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, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/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, 3.67it/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, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/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, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/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, 3.65it/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, 3.65it/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, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 15.659068, "total_time": 15.632041 }, "output": [ "https://replicate.delivery/pbxt/fQifJA65rYqnXEcwmU03LCGOPb8eHAMCQJHD3d7eiOYbx7sFB/out-0.png" ], "started_at": "2023-08-18T09:42:31.858364Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/r6msgc3b6wnp4zajvaz4rk3akq", "cancel": "https://api.replicate.com/v1/predictions/r6msgc3b6wnp4zajvaz4rk3akq/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 38520 Prompt: <s0><s1> style Christopher Marlowe, analog color photo 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, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/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, 3.67it/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, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/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, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s] 40%|████ | 20/50 [00:05<00:08, 3.63it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/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, 3.65it/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, 3.65it/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, 3.64it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDuuajrmtbqyclibep45q6enlreiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style aztec emperor Montezuma, analog color photo
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.73
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cross-eyed, wrong eyes, wrong hands, deformed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style aztec emperor Montezuma, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style aztec emperor Montezuma, analog color photo", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.73, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "cross-eyed, wrong eyes, wrong hands, deformed", 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style aztec emperor Montezuma, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style aztec emperor Montezuma, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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": "2023-08-18T09:45:16.050285Z", "created_at": "2023-08-18T09:45:00.535469Z", "data_removed": false, "error": null, "id": "uuajrmtbqyclibep45q6enlrei", "input": { "width": 1024, "height": 1024, "prompt": "HST style aztec emperor Montezuma, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 7746\nPrompt: <s0><s1> style aztec emperor Montezuma, analog color photo\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, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/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, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/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, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/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, 3.64it/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 [00:10<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 15.541087, "total_time": 15.514816 }, "output": [ "https://replicate.delivery/pbxt/Pfc62F9OS3X9A6bpgeTwniX3KtefIGSqhLcrDNfSUrfyqvzWE/out-0.png" ], "started_at": "2023-08-18T09:45:00.509198Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uuajrmtbqyclibep45q6enlrei", "cancel": "https://api.replicate.com/v1/predictions/uuajrmtbqyclibep45q6enlrei/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 7746 Prompt: <s0><s1> style aztec emperor Montezuma, analog color photo 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, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/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, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/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, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/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, 3.64it/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 [00:10<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDoeuth63b3q3ybn62zanpwkzsgqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style analog photo of Shakespeare in Leningrad, street level, 8k, uhd, realistic, film still, cinematic
- refine
- no_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.8
- num_outputs
- 1
- refine_steps
- 20
- guidance_scale
- 7.64
- apply_watermark
- high_noise_frac
- 0.73
- negative_prompt
- two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,
- prompt_strength
- 0.95
- num_inference_steps
- 52
{ "width": 1024, "height": 1024, "prompt": "HST style analog photo of Shakespeare in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 }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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style analog photo of Shakespeare in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", refine: "no_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.8, num_outputs: 1, refine_steps: 20, guidance_scale: 7.64, apply_watermark: false, high_noise_frac: 0.73, negative_prompt: "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", prompt_strength: 0.95, num_inference_steps: 52 } } ); // 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style analog photo of Shakespeare in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": False, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style analog photo of Shakespeare in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-18T09:45:45.370256Z", "created_at": "2023-08-18T09:45:29.158818Z", "data_removed": false, "error": null, "id": "oeuth63b3q3ybn62zanpwkzsgq", "input": { "width": 1024, "height": 1024, "prompt": "HST style analog photo of Shakespeare in Leningrad, street level, 8k, uhd, realistic, film still, cinematic", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 }, "logs": "Using seed: 4817\nPrompt: <s0><s1> style analog photo of Shakespeare in Leningrad, street level, 8k, uhd, realistic, film still, cinematic\ntxt2img mode\n 0%| | 0/52 [00:00<?, ?it/s]\n 2%|▏ | 1/52 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/52 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/52 [00:00<00:13, 3.68it/s]\n 8%|▊ | 4/52 [00:01<00:13, 3.67it/s]\n 10%|▉ | 5/52 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/52 [00:01<00:12, 3.67it/s]\n 13%|█▎ | 7/52 [00:01<00:12, 3.66it/s]\n 15%|█▌ | 8/52 [00:02<00:12, 3.65it/s]\n 17%|█▋ | 9/52 [00:02<00:11, 3.65it/s]\n 19%|█▉ | 10/52 [00:02<00:11, 3.65it/s]\n 21%|██ | 11/52 [00:03<00:11, 3.65it/s]\n 23%|██▎ | 12/52 [00:03<00:10, 3.65it/s]\n 25%|██▌ | 13/52 [00:03<00:10, 3.65it/s]\n 27%|██▋ | 14/52 [00:03<00:10, 3.65it/s]\n 29%|██▉ | 15/52 [00:04<00:10, 3.65it/s]\n 31%|███ | 16/52 [00:04<00:09, 3.65it/s]\n 33%|███▎ | 17/52 [00:04<00:09, 3.65it/s]\n 35%|███▍ | 18/52 [00:04<00:09, 3.65it/s]\n 37%|███▋ | 19/52 [00:05<00:09, 3.65it/s]\n 38%|███▊ | 20/52 [00:05<00:08, 3.65it/s]\n 40%|████ | 21/52 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 22/52 [00:06<00:08, 3.65it/s]\n 44%|████▍ | 23/52 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 24/52 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 25/52 [00:06<00:07, 3.64it/s]\n 50%|█████ | 26/52 [00:07<00:07, 3.64it/s]\n 52%|█████▏ | 27/52 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 28/52 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 29/52 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 30/52 [00:08<00:06, 3.64it/s]\n 60%|█████▉ | 31/52 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 32/52 [00:08<00:05, 3.64it/s]\n 63%|██████▎ | 33/52 [00:09<00:05, 3.64it/s]\n 65%|██████▌ | 34/52 [00:09<00:04, 3.64it/s]\n 67%|██████▋ | 35/52 [00:09<00:04, 3.64it/s]\n 69%|██████▉ | 36/52 [00:09<00:04, 3.64it/s]\n 71%|███████ | 37/52 [00:10<00:04, 3.64it/s]\n 73%|███████▎ | 38/52 [00:10<00:03, 3.64it/s]\n 75%|███████▌ | 39/52 [00:10<00:03, 3.63it/s]\n 77%|███████▋ | 40/52 [00:10<00:03, 3.63it/s]\n 79%|███████▉ | 41/52 [00:11<00:03, 3.64it/s]\n 81%|████████ | 42/52 [00:11<00:02, 3.64it/s]\n 83%|████████▎ | 43/52 [00:11<00:02, 3.64it/s]\n 85%|████████▍ | 44/52 [00:12<00:02, 3.64it/s]\n 87%|████████▋ | 45/52 [00:12<00:01, 3.64it/s]\n 88%|████████▊ | 46/52 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 47/52 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 48/52 [00:13<00:01, 3.64it/s]\n 94%|█████████▍| 49/52 [00:13<00:00, 3.64it/s]\n 96%|█████████▌| 50/52 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 51/52 [00:13<00:00, 3.64it/s]\n100%|██████████| 52/52 [00:14<00:00, 3.63it/s]\n100%|██████████| 52/52 [00:14<00:00, 3.65it/s]", "metrics": { "predict_time": 16.218982, "total_time": 16.211438 }, "output": [ "https://replicate.delivery/pbxt/GBxCo7v5gaZzO93pqSMm88H2NuVdyE0KTUYiy8myZdLyvzWE/out-0.png" ], "started_at": "2023-08-18T09:45:29.151274Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/oeuth63b3q3ybn62zanpwkzsgq", "cancel": "https://api.replicate.com/v1/predictions/oeuth63b3q3ybn62zanpwkzsgq/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 4817 Prompt: <s0><s1> style analog photo of Shakespeare in Leningrad, street level, 8k, uhd, realistic, film still, cinematic txt2img mode 0%| | 0/52 [00:00<?, ?it/s] 2%|▏ | 1/52 [00:00<00:13, 3.70it/s] 4%|▍ | 2/52 [00:00<00:13, 3.68it/s] 6%|▌ | 3/52 [00:00<00:13, 3.68it/s] 8%|▊ | 4/52 [00:01<00:13, 3.67it/s] 10%|▉ | 5/52 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/52 [00:01<00:12, 3.67it/s] 13%|█▎ | 7/52 [00:01<00:12, 3.66it/s] 15%|█▌ | 8/52 [00:02<00:12, 3.65it/s] 17%|█▋ | 9/52 [00:02<00:11, 3.65it/s] 19%|█▉ | 10/52 [00:02<00:11, 3.65it/s] 21%|██ | 11/52 [00:03<00:11, 3.65it/s] 23%|██▎ | 12/52 [00:03<00:10, 3.65it/s] 25%|██▌ | 13/52 [00:03<00:10, 3.65it/s] 27%|██▋ | 14/52 [00:03<00:10, 3.65it/s] 29%|██▉ | 15/52 [00:04<00:10, 3.65it/s] 31%|███ | 16/52 [00:04<00:09, 3.65it/s] 33%|███▎ | 17/52 [00:04<00:09, 3.65it/s] 35%|███▍ | 18/52 [00:04<00:09, 3.65it/s] 37%|███▋ | 19/52 [00:05<00:09, 3.65it/s] 38%|███▊ | 20/52 [00:05<00:08, 3.65it/s] 40%|████ | 21/52 [00:05<00:08, 3.65it/s] 42%|████▏ | 22/52 [00:06<00:08, 3.65it/s] 44%|████▍ | 23/52 [00:06<00:07, 3.64it/s] 46%|████▌ | 24/52 [00:06<00:07, 3.64it/s] 48%|████▊ | 25/52 [00:06<00:07, 3.64it/s] 50%|█████ | 26/52 [00:07<00:07, 3.64it/s] 52%|█████▏ | 27/52 [00:07<00:06, 3.64it/s] 54%|█████▍ | 28/52 [00:07<00:06, 3.64it/s] 56%|█████▌ | 29/52 [00:07<00:06, 3.64it/s] 58%|█████▊ | 30/52 [00:08<00:06, 3.64it/s] 60%|█████▉ | 31/52 [00:08<00:05, 3.64it/s] 62%|██████▏ | 32/52 [00:08<00:05, 3.64it/s] 63%|██████▎ | 33/52 [00:09<00:05, 3.64it/s] 65%|██████▌ | 34/52 [00:09<00:04, 3.64it/s] 67%|██████▋ | 35/52 [00:09<00:04, 3.64it/s] 69%|██████▉ | 36/52 [00:09<00:04, 3.64it/s] 71%|███████ | 37/52 [00:10<00:04, 3.64it/s] 73%|███████▎ | 38/52 [00:10<00:03, 3.64it/s] 75%|███████▌ | 39/52 [00:10<00:03, 3.63it/s] 77%|███████▋ | 40/52 [00:10<00:03, 3.63it/s] 79%|███████▉ | 41/52 [00:11<00:03, 3.64it/s] 81%|████████ | 42/52 [00:11<00:02, 3.64it/s] 83%|████████▎ | 43/52 [00:11<00:02, 3.64it/s] 85%|████████▍ | 44/52 [00:12<00:02, 3.64it/s] 87%|████████▋ | 45/52 [00:12<00:01, 3.64it/s] 88%|████████▊ | 46/52 [00:12<00:01, 3.64it/s] 90%|█████████ | 47/52 [00:12<00:01, 3.64it/s] 92%|█████████▏| 48/52 [00:13<00:01, 3.64it/s] 94%|█████████▍| 49/52 [00:13<00:00, 3.64it/s] 96%|█████████▌| 50/52 [00:13<00:00, 3.64it/s] 98%|█████████▊| 51/52 [00:13<00:00, 3.64it/s] 100%|██████████| 52/52 [00:14<00:00, 3.63it/s] 100%|██████████| 52/52 [00:14<00:00, 3.65it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDm6eywjlbf7ixsmstlgs7tswa5uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Erich Fromm, analog color photo
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.73
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cross-eyed, wrong eyes, wrong hands, deformed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Erich Fromm, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Erich Fromm, analog color photo", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.73, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "cross-eyed, wrong eyes, wrong hands, deformed", 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Erich Fromm, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Erich Fromm, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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": "2023-08-18T09:50:25.787671Z", "created_at": "2023-08-18T09:50:10.166587Z", "data_removed": false, "error": null, "id": "m6eywjlbf7ixsmstlgs7tswa5u", "input": { "width": 1024, "height": 1024, "prompt": "HST style Erich Fromm, analog color photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 26428\nPrompt: <s0><s1> style Erich Fromm, analog color photo\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, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/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, 3.66it/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, 3.67it/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<00:05, 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, 3.66it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/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, 3.66it/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": 15.615569, "total_time": 15.621084 }, "output": [ "https://replicate.delivery/pbxt/AOaV2TWWUHY0FxIpSJZRz3ySCgLZVU0jItQejeb98RxgDPbRA/out-0.png" ], "started_at": "2023-08-18T09:50:10.172102Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m6eywjlbf7ixsmstlgs7tswa5u", "cancel": "https://api.replicate.com/v1/predictions/m6eywjlbf7ixsmstlgs7tswa5u/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 26428 Prompt: <s0><s1> style Erich Fromm, analog color photo 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, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/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, 3.66it/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, 3.67it/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<00:05, 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, 3.66it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/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, 3.66it/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
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDhs47j2dbanmsuj6zzqqj2usn34StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Leonora Carrington, analog color photo
- refine
- no_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.73
- num_outputs
- 1
- guidance_scale
- 8.67
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cross-eyed, wrong eyes, wrong hands, deformed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Leonora Carrington, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Leonora Carrington, analog color photo", refine: "no_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.73, num_outputs: 1, guidance_scale: 8.67, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "cross-eyed, wrong eyes, wrong hands, deformed", 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Leonora Carrington, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Leonora Carrington, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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": "2023-08-18T09:56:20.363028Z", "created_at": "2023-08-18T09:56:04.715479Z", "data_removed": false, "error": null, "id": "hs47j2dbanmsuj6zzqqj2usn34", "input": { "width": 1024, "height": 1024, "prompt": "HST style Leonora Carrington, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 18389\nPrompt: <s0><s1> style Leonora Carrington, analog color photo\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, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/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, 3.64it/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, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/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, 3.63it/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, 3.63it/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, 3.63it/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, 3.63it/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, 3.63it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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, 3.64it/s]", "metrics": { "predict_time": 15.635371, "total_time": 15.647549 }, "output": [ "https://replicate.delivery/pbxt/3Q4w5Bif0C3ek0hwfF8LUOv3rgWFqzZrCm2lNDVekZzNk8sFB/out-0.png" ], "started_at": "2023-08-18T09:56:04.727657Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hs47j2dbanmsuj6zzqqj2usn34", "cancel": "https://api.replicate.com/v1/predictions/hs47j2dbanmsuj6zzqqj2usn34/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 18389 Prompt: <s0><s1> style Leonora Carrington, analog color photo 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, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/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, 3.64it/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, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.63it/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, 3.63it/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, 3.63it/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, 3.63it/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, 3.63it/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, 3.63it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:10<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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, 3.64it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDcz53yztbs4uox4ischitox2dwqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Emily Dickinson, analog photo, 8k, uhd, realistic,
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.8
- num_outputs
- 1
- refine_steps
- 20
- guidance_scale
- 7.64
- apply_watermark
- high_noise_frac
- 0.73
- negative_prompt
- two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,
- prompt_strength
- 0.95
- num_inference_steps
- 52
{ "width": 1024, "height": 1024, "prompt": "HST style Emily Dickinson, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 }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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Emily Dickinson, analog photo, 8k, uhd, realistic, ", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.8, num_outputs: 1, refine_steps: 20, guidance_scale: 7.64, apply_watermark: false, high_noise_frac: 0.73, negative_prompt: "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", prompt_strength: 0.95, num_inference_steps: 52 } } ); // 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Emily Dickinson, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": False, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Emily Dickinson, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-18T09:56:37.843802Z", "created_at": "2023-08-18T09:56:21.588520Z", "data_removed": false, "error": null, "id": "cz53yztbs4uox4ischitox2dwq", "input": { "width": 1024, "height": 1024, "prompt": "HST style Emily Dickinson, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 7.64, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 }, "logs": "Using seed: 64801\nPrompt: <s0><s1> style Emily Dickinson, analog photo, 8k, uhd, realistic,\ntxt2img mode\n 0%| | 0/52 [00:00<?, ?it/s]\n 2%|▏ | 1/52 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/52 [00:00<00:13, 3.66it/s]\n 6%|▌ | 3/52 [00:00<00:13, 3.65it/s]\n 8%|▊ | 4/52 [00:01<00:13, 3.64it/s]\n 10%|▉ | 5/52 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/52 [00:01<00:12, 3.64it/s]\n 13%|█▎ | 7/52 [00:01<00:12, 3.63it/s]\n 15%|█▌ | 8/52 [00:02<00:12, 3.63it/s]\n 17%|█▋ | 9/52 [00:02<00:11, 3.63it/s]\n 19%|█▉ | 10/52 [00:02<00:11, 3.63it/s]\n 21%|██ | 11/52 [00:03<00:11, 3.63it/s]\n 23%|██▎ | 12/52 [00:03<00:11, 3.63it/s]\n 25%|██▌ | 13/52 [00:03<00:10, 3.63it/s]\n 27%|██▋ | 14/52 [00:03<00:10, 3.63it/s]\n 29%|██▉ | 15/52 [00:04<00:10, 3.63it/s]\n 31%|███ | 16/52 [00:04<00:09, 3.63it/s]\n 33%|███▎ | 17/52 [00:04<00:09, 3.63it/s]\n 35%|███▍ | 18/52 [00:04<00:09, 3.63it/s]\n 37%|███▋ | 19/52 [00:05<00:09, 3.63it/s]\n 38%|███▊ | 20/52 [00:05<00:08, 3.63it/s]\n 40%|████ | 21/52 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 22/52 [00:06<00:08, 3.63it/s]\n 44%|████▍ | 23/52 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 24/52 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 25/52 [00:06<00:07, 3.63it/s]\n 50%|█████ | 26/52 [00:07<00:07, 3.62it/s]\n 52%|█████▏ | 27/52 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 28/52 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 29/52 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 30/52 [00:08<00:06, 3.63it/s]\n 60%|█████▉ | 31/52 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 32/52 [00:08<00:05, 3.63it/s]\n 63%|██████▎ | 33/52 [00:09<00:05, 3.63it/s]\n 65%|██████▌ | 34/52 [00:09<00:04, 3.63it/s]\n 67%|██████▋ | 35/52 [00:09<00:04, 3.63it/s]\n 69%|██████▉ | 36/52 [00:09<00:04, 3.62it/s]\n 71%|███████ | 37/52 [00:10<00:04, 3.62it/s]\n 73%|███████▎ | 38/52 [00:10<00:03, 3.63it/s]\n 75%|███████▌ | 39/52 [00:10<00:03, 3.63it/s]\n 77%|███████▋ | 40/52 [00:11<00:03, 3.63it/s]\n 79%|███████▉ | 41/52 [00:11<00:03, 3.62it/s]\n 81%|████████ | 42/52 [00:11<00:02, 3.63it/s]\n 83%|████████▎ | 43/52 [00:11<00:02, 3.63it/s]\n 85%|████████▍ | 44/52 [00:12<00:02, 3.63it/s]\n 87%|████████▋ | 45/52 [00:12<00:01, 3.62it/s]\n 88%|████████▊ | 46/52 [00:12<00:01, 3.62it/s]\n 90%|█████████ | 47/52 [00:12<00:01, 3.62it/s]\n 92%|█████████▏| 48/52 [00:13<00:01, 3.62it/s]\n 94%|█████████▍| 49/52 [00:13<00:00, 3.62it/s]\n 96%|█████████▌| 50/52 [00:13<00:00, 3.62it/s]\n 98%|█████████▊| 51/52 [00:14<00:00, 3.62it/s]\n100%|██████████| 52/52 [00:14<00:00, 3.62it/s]\n100%|██████████| 52/52 [00:14<00:00, 3.63it/s]", "metrics": { "predict_time": 16.292511, "total_time": 16.255282 }, "output": [ "https://replicate.delivery/pbxt/sf7IO39qCmynbqIzO4B3CqhfmBSTPGfFPcaU5GHEdQJpSesFB/out-0.png" ], "started_at": "2023-08-18T09:56:21.551291Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cz53yztbs4uox4ischitox2dwq", "cancel": "https://api.replicate.com/v1/predictions/cz53yztbs4uox4ischitox2dwq/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 64801 Prompt: <s0><s1> style Emily Dickinson, analog photo, 8k, uhd, realistic, txt2img mode 0%| | 0/52 [00:00<?, ?it/s] 2%|▏ | 1/52 [00:00<00:13, 3.67it/s] 4%|▍ | 2/52 [00:00<00:13, 3.66it/s] 6%|▌ | 3/52 [00:00<00:13, 3.65it/s] 8%|▊ | 4/52 [00:01<00:13, 3.64it/s] 10%|▉ | 5/52 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/52 [00:01<00:12, 3.64it/s] 13%|█▎ | 7/52 [00:01<00:12, 3.63it/s] 15%|█▌ | 8/52 [00:02<00:12, 3.63it/s] 17%|█▋ | 9/52 [00:02<00:11, 3.63it/s] 19%|█▉ | 10/52 [00:02<00:11, 3.63it/s] 21%|██ | 11/52 [00:03<00:11, 3.63it/s] 23%|██▎ | 12/52 [00:03<00:11, 3.63it/s] 25%|██▌ | 13/52 [00:03<00:10, 3.63it/s] 27%|██▋ | 14/52 [00:03<00:10, 3.63it/s] 29%|██▉ | 15/52 [00:04<00:10, 3.63it/s] 31%|███ | 16/52 [00:04<00:09, 3.63it/s] 33%|███▎ | 17/52 [00:04<00:09, 3.63it/s] 35%|███▍ | 18/52 [00:04<00:09, 3.63it/s] 37%|███▋ | 19/52 [00:05<00:09, 3.63it/s] 38%|███▊ | 20/52 [00:05<00:08, 3.63it/s] 40%|████ | 21/52 [00:05<00:08, 3.63it/s] 42%|████▏ | 22/52 [00:06<00:08, 3.63it/s] 44%|████▍ | 23/52 [00:06<00:07, 3.63it/s] 46%|████▌ | 24/52 [00:06<00:07, 3.63it/s] 48%|████▊ | 25/52 [00:06<00:07, 3.63it/s] 50%|█████ | 26/52 [00:07<00:07, 3.62it/s] 52%|█████▏ | 27/52 [00:07<00:06, 3.63it/s] 54%|█████▍ | 28/52 [00:07<00:06, 3.63it/s] 56%|█████▌ | 29/52 [00:07<00:06, 3.63it/s] 58%|█████▊ | 30/52 [00:08<00:06, 3.63it/s] 60%|█████▉ | 31/52 [00:08<00:05, 3.63it/s] 62%|██████▏ | 32/52 [00:08<00:05, 3.63it/s] 63%|██████▎ | 33/52 [00:09<00:05, 3.63it/s] 65%|██████▌ | 34/52 [00:09<00:04, 3.63it/s] 67%|██████▋ | 35/52 [00:09<00:04, 3.63it/s] 69%|██████▉ | 36/52 [00:09<00:04, 3.62it/s] 71%|███████ | 37/52 [00:10<00:04, 3.62it/s] 73%|███████▎ | 38/52 [00:10<00:03, 3.63it/s] 75%|███████▌ | 39/52 [00:10<00:03, 3.63it/s] 77%|███████▋ | 40/52 [00:11<00:03, 3.63it/s] 79%|███████▉ | 41/52 [00:11<00:03, 3.62it/s] 81%|████████ | 42/52 [00:11<00:02, 3.63it/s] 83%|████████▎ | 43/52 [00:11<00:02, 3.63it/s] 85%|████████▍ | 44/52 [00:12<00:02, 3.63it/s] 87%|████████▋ | 45/52 [00:12<00:01, 3.62it/s] 88%|████████▊ | 46/52 [00:12<00:01, 3.62it/s] 90%|█████████ | 47/52 [00:12<00:01, 3.62it/s] 92%|█████████▏| 48/52 [00:13<00:01, 3.62it/s] 94%|█████████▍| 49/52 [00:13<00:00, 3.62it/s] 96%|█████████▌| 50/52 [00:13<00:00, 3.62it/s] 98%|█████████▊| 51/52 [00:14<00:00, 3.62it/s] 100%|██████████| 52/52 [00:14<00:00, 3.62it/s] 100%|██████████| 52/52 [00:14<00:00, 3.63it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDfbtxnttbezhgvre7b6enfzb35mStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Guy Debord, analog color photo
- refine
- no_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.73
- num_outputs
- 1
- guidance_scale
- 8.67
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cross-eyed, wrong eyes, wrong hands, deformed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Guy Debord, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Guy Debord, analog color photo", refine: "no_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.73, num_outputs: 1, guidance_scale: 8.67, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "cross-eyed, wrong eyes, wrong hands, deformed", 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Guy Debord, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Guy Debord, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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": "2023-08-18T09:57:12.673855Z", "created_at": "2023-08-18T09:56:56.997458Z", "data_removed": false, "error": null, "id": "fbtxnttbezhgvre7b6enfzb35m", "input": { "width": 1024, "height": 1024, "prompt": "HST style Guy Debord, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 51254\nPrompt: <s0><s1> style Guy Debord, analog color photo\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, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.66it/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, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.65it/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, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/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, 3.63it/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, 3.63it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.64it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/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, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 15.677513, "total_time": 15.676397 }, "output": [ "https://replicate.delivery/pbxt/vlDQUyvu9FZgJxHCE8rPqNAhBSUr9Gda1OmI2djPUk9dyzWE/out-0.png" ], "started_at": "2023-08-18T09:56:56.996342Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fbtxnttbezhgvre7b6enfzb35m", "cancel": "https://api.replicate.com/v1/predictions/fbtxnttbezhgvre7b6enfzb35m/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 51254 Prompt: <s0><s1> style Guy Debord, analog color photo 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, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 3.66it/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, 3.65it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.65it/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, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/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, 3.63it/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, 3.63it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:10<00:02, 3.64it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/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, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDjsu6uilbbjwfgkvezlklhatjkiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Aleister Crowley, analog color photo
- refine
- no_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.73
- num_outputs
- 1
- guidance_scale
- 8.67
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cross-eyed, wrong eyes, wrong hands, deformed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Aleister Crowley, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Aleister Crowley, analog color photo", refine: "no_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.73, num_outputs: 1, guidance_scale: 8.67, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "cross-eyed, wrong eyes, wrong hands, deformed", 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Aleister Crowley, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Aleister Crowley, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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": "2023-08-18T10:00:01.298561Z", "created_at": "2023-08-18T09:59:40.108833Z", "data_removed": false, "error": null, "id": "jsu6uilbbjwfgkvezlklhatjki", "input": { "width": 1024, "height": 1024, "prompt": "HST style Aleister Crowley, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 61940\nPrompt: <s0><s1> style Aleister Crowley, analog color photo\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, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/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, 3.63it/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, 3.64it/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, 3.63it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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": 21.227908, "total_time": 21.189728 }, "output": [ "https://replicate.delivery/pbxt/Fe910oTmUV1LF66Ewh6b7Pu0XLj8FZFJljSnMozVbJfaMPbRA/out-0.png" ], "started_at": "2023-08-18T09:59:40.070653Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jsu6uilbbjwfgkvezlklhatjki", "cancel": "https://api.replicate.com/v1/predictions/jsu6uilbbjwfgkvezlklhatjki/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 61940 Prompt: <s0><s1> style Aleister Crowley, analog color photo 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, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s] 20%|██ | 10/50 [00:02<00:10, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/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, 3.63it/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, 3.64it/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, 3.63it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:11<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDfslive3bz53jkg2i3yjo34okf4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Voltaire, analog color photo
- refine
- no_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.73
- num_outputs
- 1
- guidance_scale
- 8.67
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cross-eyed, wrong eyes, wrong hands, deformed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Voltaire, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Voltaire, analog color photo", refine: "no_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.73, num_outputs: 1, guidance_scale: 8.67, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "cross-eyed, wrong eyes, wrong hands, deformed", 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Voltaire, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Voltaire, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "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": "2023-08-18T10:01:34.988594Z", "created_at": "2023-08-18T10:01:19.173647Z", "data_removed": false, "error": null, "id": "fslive3bz53jkg2i3yjo34okf4", "input": { "width": 1024, "height": 1024, "prompt": "HST style Voltaire, analog color photo", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 63491\nPrompt: <s0><s1> style Voltaire, analog color photo\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, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 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, 3.64it/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, 3.65it/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, 3.66it/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, 3.65it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/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, 3.65it/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, 3.65it/s]", "metrics": { "predict_time": 15.829486, "total_time": 15.814947 }, "output": [ "https://replicate.delivery/pbxt/FGj9Eff3C0nxfIYduTuCnG0uw3z4TB7XdB4Pap5VfYQ238sFB/out-0.png" ], "started_at": "2023-08-18T10:01:19.159108Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fslive3bz53jkg2i3yjo34okf4", "cancel": "https://api.replicate.com/v1/predictions/fslive3bz53jkg2i3yjo34okf4/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 63491 Prompt: <s0><s1> style Voltaire, analog color photo 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, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s] 20%|██ | 10/50 [00:02<00:10, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 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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, 3.65it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92ID6bcfa5dbas3dapnen6dkj5kw2eStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Paul Verlaine, analog photo, 8k, uhd, realistic,
- refine
- no_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.8
- num_outputs
- 1
- refine_steps
- 20
- guidance_scale
- 8.43
- apply_watermark
- high_noise_frac
- 0.73
- negative_prompt
- two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,
- prompt_strength
- 0.95
- num_inference_steps
- 52
{ "width": 1024, "height": 1024, "prompt": "HST style Paul Verlaine, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8.43, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 }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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Paul Verlaine, analog photo, 8k, uhd, realistic, ", refine: "no_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.8, num_outputs: 1, refine_steps: 20, guidance_scale: 8.43, apply_watermark: false, high_noise_frac: 0.73, negative_prompt: "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", prompt_strength: 0.95, num_inference_steps: 52 } } ); // 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Paul Verlaine, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8.43, "apply_watermark": False, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Paul Verlaine, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8.43, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-18T10:05:19.145945Z", "created_at": "2023-08-18T10:05:02.661631Z", "data_removed": false, "error": null, "id": "6bcfa5dbas3dapnen6dkj5kw2e", "input": { "width": 1024, "height": 1024, "prompt": "HST style Paul Verlaine, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8.43, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 52 }, "logs": "Using seed: 38910\nPrompt: <s0><s1> style Paul Verlaine, analog photo, 8k, uhd, realistic,\ntxt2img mode\n 0%| | 0/52 [00:00<?, ?it/s]\n 2%|▏ | 1/52 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/52 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/52 [00:00<00:13, 3.66it/s]\n 8%|▊ | 4/52 [00:01<00:13, 3.65it/s]\n 10%|▉ | 5/52 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/52 [00:01<00:12, 3.65it/s]\n 13%|█▎ | 7/52 [00:01<00:12, 3.65it/s]\n 15%|█▌ | 8/52 [00:02<00:12, 3.64it/s]\n 17%|█▋ | 9/52 [00:02<00:11, 3.64it/s]\n 19%|█▉ | 10/52 [00:02<00:11, 3.64it/s]\n 21%|██ | 11/52 [00:03<00:11, 3.64it/s]\n 23%|██▎ | 12/52 [00:03<00:10, 3.64it/s]\n 25%|██▌ | 13/52 [00:03<00:10, 3.64it/s]\n 27%|██▋ | 14/52 [00:03<00:10, 3.64it/s]\n 29%|██▉ | 15/52 [00:04<00:10, 3.64it/s]\n 31%|███ | 16/52 [00:04<00:09, 3.64it/s]\n 33%|███▎ | 17/52 [00:04<00:09, 3.64it/s]\n 35%|███▍ | 18/52 [00:04<00:09, 3.64it/s]\n 37%|███▋ | 19/52 [00:05<00:09, 3.64it/s]\n 38%|███▊ | 20/52 [00:05<00:08, 3.64it/s]\n 40%|████ | 21/52 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 22/52 [00:06<00:08, 3.64it/s]\n 44%|████▍ | 23/52 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 24/52 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 25/52 [00:06<00:07, 3.64it/s]\n 50%|█████ | 26/52 [00:07<00:07, 3.64it/s]\n 52%|█████▏ | 27/52 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 28/52 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 29/52 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 30/52 [00:08<00:06, 3.64it/s]\n 60%|█████▉ | 31/52 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 32/52 [00:08<00:05, 3.64it/s]\n 63%|██████▎ | 33/52 [00:09<00:05, 3.64it/s]\n 65%|██████▌ | 34/52 [00:09<00:04, 3.64it/s]\n 67%|██████▋ | 35/52 [00:09<00:04, 3.64it/s]\n 69%|██████▉ | 36/52 [00:09<00:04, 3.64it/s]\n 71%|███████ | 37/52 [00:10<00:04, 3.64it/s]\n 73%|███████▎ | 38/52 [00:10<00:03, 3.64it/s]\n 75%|███████▌ | 39/52 [00:10<00:03, 3.64it/s]\n 77%|███████▋ | 40/52 [00:10<00:03, 3.64it/s]\n 79%|███████▉ | 41/52 [00:11<00:03, 3.64it/s]\n 81%|████████ | 42/52 [00:11<00:02, 3.64it/s]\n 83%|████████▎ | 43/52 [00:11<00:02, 3.64it/s]\n 85%|████████▍ | 44/52 [00:12<00:02, 3.64it/s]\n 87%|████████▋ | 45/52 [00:12<00:01, 3.64it/s]\n 88%|████████▊ | 46/52 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 47/52 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 48/52 [00:13<00:01, 3.64it/s]\n 94%|█████████▍| 49/52 [00:13<00:00, 3.63it/s]\n 96%|█████████▌| 50/52 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 51/52 [00:14<00:00, 3.64it/s]\n100%|██████████| 52/52 [00:14<00:00, 3.63it/s]\n100%|██████████| 52/52 [00:14<00:00, 3.64it/s]", "metrics": { "predict_time": 16.491379, "total_time": 16.484314 }, "output": [ "https://replicate.delivery/pbxt/5RU9FZfzf1t8kUbaPplYANeVlnt292dQNNu1cLumrqK9iesFB/out-0.png" ], "started_at": "2023-08-18T10:05:02.654566Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6bcfa5dbas3dapnen6dkj5kw2e", "cancel": "https://api.replicate.com/v1/predictions/6bcfa5dbas3dapnen6dkj5kw2e/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 38910 Prompt: <s0><s1> style Paul Verlaine, analog photo, 8k, uhd, realistic, txt2img mode 0%| | 0/52 [00:00<?, ?it/s] 2%|▏ | 1/52 [00:00<00:13, 3.68it/s] 4%|▍ | 2/52 [00:00<00:13, 3.67it/s] 6%|▌ | 3/52 [00:00<00:13, 3.66it/s] 8%|▊ | 4/52 [00:01<00:13, 3.65it/s] 10%|▉ | 5/52 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/52 [00:01<00:12, 3.65it/s] 13%|█▎ | 7/52 [00:01<00:12, 3.65it/s] 15%|█▌ | 8/52 [00:02<00:12, 3.64it/s] 17%|█▋ | 9/52 [00:02<00:11, 3.64it/s] 19%|█▉ | 10/52 [00:02<00:11, 3.64it/s] 21%|██ | 11/52 [00:03<00:11, 3.64it/s] 23%|██▎ | 12/52 [00:03<00:10, 3.64it/s] 25%|██▌ | 13/52 [00:03<00:10, 3.64it/s] 27%|██▋ | 14/52 [00:03<00:10, 3.64it/s] 29%|██▉ | 15/52 [00:04<00:10, 3.64it/s] 31%|███ | 16/52 [00:04<00:09, 3.64it/s] 33%|███▎ | 17/52 [00:04<00:09, 3.64it/s] 35%|███▍ | 18/52 [00:04<00:09, 3.64it/s] 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81%|████████ | 42/52 [00:11<00:02, 3.64it/s] 83%|████████▎ | 43/52 [00:11<00:02, 3.64it/s] 85%|████████▍ | 44/52 [00:12<00:02, 3.64it/s] 87%|████████▋ | 45/52 [00:12<00:01, 3.64it/s] 88%|████████▊ | 46/52 [00:12<00:01, 3.64it/s] 90%|█████████ | 47/52 [00:12<00:01, 3.64it/s] 92%|█████████▏| 48/52 [00:13<00:01, 3.64it/s] 94%|█████████▍| 49/52 [00:13<00:00, 3.63it/s] 96%|█████████▌| 50/52 [00:13<00:00, 3.64it/s] 98%|█████████▊| 51/52 [00:14<00:00, 3.64it/s] 100%|██████████| 52/52 [00:14<00:00, 3.63it/s] 100%|██████████| 52/52 [00:14<00:00, 3.64it/s]Prediction
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92ID7phwfydbvdatkxsaxklcawsxayStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style Napoleon Bonaparte, analog color photo, front-facing
- refine
- no_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.73
- num_outputs
- 1
- guidance_scale
- 8.67
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cross-eyed, wrong eyes, wrong hands, deformed, elongated limbs, stretched arms
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "HST style Napoleon Bonaparte, analog color photo, front-facing", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed, elongated limbs, stretched arms", "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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style Napoleon Bonaparte, analog color photo, front-facing", refine: "no_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.73, num_outputs: 1, guidance_scale: 8.67, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "cross-eyed, wrong eyes, wrong hands, deformed, elongated limbs, stretched arms", 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style Napoleon Bonaparte, analog color photo, front-facing", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed, elongated limbs, stretched arms", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style Napoleon Bonaparte, analog color photo, front-facing", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed, elongated limbs, stretched arms", "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": "2023-08-18T10:10:26.436453Z", "created_at": "2023-08-18T10:10:10.564727Z", "data_removed": false, "error": null, "id": "7phwfydbvdatkxsaxklcawsxay", "input": { "width": 1024, "height": 1024, "prompt": "HST style Napoleon Bonaparte, analog color photo, front-facing", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.73, "num_outputs": 1, "guidance_scale": 8.67, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "cross-eyed, wrong eyes, wrong hands, deformed, elongated limbs, stretched arms", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 36728\nPrompt: <s0><s1> style Napoleon Bonaparte, analog color photo, front-facing\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, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/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, 3.67it/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, 3.66it/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, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/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, 3.65it/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, 3.66it/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": 15.869801, "total_time": 15.871726 }, "output": [ "https://replicate.delivery/pbxt/rC3P7hyqjwKQC5oe3VvdUxLBvvEeIuJTbFjfHEdHbUKjsesFB/out-0.png" ], "started_at": "2023-08-18T10:10:10.566652Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7phwfydbvdatkxsaxklcawsxay", "cancel": "https://api.replicate.com/v1/predictions/7phwfydbvdatkxsaxklcawsxay/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 36728 Prompt: <s0><s1> style Napoleon Bonaparte, analog color photo, front-facing 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, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.66it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.66it/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, 3.67it/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, 3.66it/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, 3.66it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/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, 3.65it/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, 3.66it/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
alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92IDmolzowlbsk74ywdoc2wc7n67haStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- HST style poet Hart Crane, analog photo, 8k, uhd, realistic,
- refine
- no_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.8
- num_outputs
- 1
- refine_steps
- 20
- guidance_scale
- 8.43
- apply_watermark
- high_noise_frac
- 0.73
- negative_prompt
- facial hair, two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,
- prompt_strength
- 0.95
- num_inference_steps
- 41
{ "width": 1024, "height": 1024, "prompt": "HST style poet Hart Crane, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8.43, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "facial hair, two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 41 }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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", { input: { width: 1024, height: 1024, prompt: "HST style poet Hart Crane, analog photo, 8k, uhd, realistic, ", refine: "no_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.8, num_outputs: 1, refine_steps: 20, guidance_scale: 8.43, apply_watermark: false, high_noise_frac: 0.73, negative_prompt: "facial hair, two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", prompt_strength: 0.95, num_inference_steps: 41 } } ); // 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 alekseycalvin/neurealhistory using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", input={ "width": 1024, "height": 1024, "prompt": "HST style poet Hart Crane, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8.43, "apply_watermark": False, "high_noise_frac": 0.73, "negative_prompt": "facial hair, two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 41 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run alekseycalvin/neurealhistory 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": "alekseycalvin/neurealhistory:0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92", "input": { "width": 1024, "height": 1024, "prompt": "HST style poet Hart Crane, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8.43, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "facial hair, two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 41 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-18T10:10:40.426582Z", "created_at": "2023-08-18T10:10:24.078851Z", "data_removed": false, "error": null, "id": "molzowlbsk74ywdoc2wc7n67ha", "input": { "width": 1024, "height": 1024, "prompt": "HST style poet Hart Crane, analog photo, 8k, uhd, realistic, ", "refine": "no_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8.43, "apply_watermark": false, "high_noise_frac": 0.73, "negative_prompt": "facial hair, two subjects, (elongated legs), (elongated arms), duplicates, stretched body, unrealistic anatomy, blurred faces, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.9), distorted, cgi, cartoon, (deformed, distorted, disfigured:1.6), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, amputation, deformed faces, warped, unrealistic, bad hands, wrong proportions, blur, oversaturated, overbloomed, painting, drawing, illustration,", "prompt_strength": 0.95, "num_inference_steps": 41 }, "logs": "Using seed: 62575\nPrompt: <s0><s1> style poet Hart Crane, analog photo, 8k, uhd, realistic,\ntxt2img mode\n 0%| | 0/41 [00:00<?, ?it/s]\n 2%|▏ | 1/41 [00:00<00:10, 3.66it/s]\n 5%|▍ | 2/41 [00:00<00:10, 3.66it/s]\n 7%|▋ | 3/41 [00:00<00:10, 3.67it/s]\n 10%|▉ | 4/41 [00:01<00:10, 3.66it/s]\n 12%|█▏ | 5/41 [00:01<00:09, 3.66it/s]\n 15%|█▍ | 6/41 [00:01<00:09, 3.66it/s]\n 17%|█▋ | 7/41 [00:01<00:09, 3.66it/s]\n 20%|█▉ | 8/41 [00:02<00:09, 3.66it/s]\n 22%|██▏ | 9/41 [00:02<00:08, 3.65it/s]\n 24%|██▍ | 10/41 [00:02<00:08, 3.66it/s]\n 27%|██▋ | 11/41 [00:03<00:08, 3.66it/s]\n 29%|██▉ | 12/41 [00:03<00:07, 3.65it/s]\n 32%|███▏ | 13/41 [00:03<00:07, 3.64it/s]\n 34%|███▍ | 14/41 [00:03<00:07, 3.65it/s]\n 37%|███▋ | 15/41 [00:04<00:07, 3.65it/s]\n 39%|███▉ | 16/41 [00:04<00:06, 3.64it/s]\n 41%|████▏ | 17/41 [00:04<00:06, 3.64it/s]\n 44%|████▍ | 18/41 [00:04<00:06, 3.64it/s]\n 46%|████▋ | 19/41 [00:05<00:06, 3.65it/s]\n 49%|████▉ | 20/41 [00:05<00:05, 3.64it/s]\n 51%|█████ | 21/41 [00:05<00:05, 3.64it/s]\n 54%|█████▎ | 22/41 [00:06<00:05, 3.64it/s]\n 56%|█████▌ | 23/41 [00:06<00:04, 3.64it/s]\n 59%|█████▊ | 24/41 [00:06<00:04, 3.64it/s]\n 61%|██████ | 25/41 [00:06<00:04, 3.64it/s]\n 63%|██████▎ | 26/41 [00:07<00:04, 3.65it/s]\n 66%|██████▌ | 27/41 [00:07<00:03, 3.64it/s]\n 68%|██████▊ | 28/41 [00:07<00:03, 3.64it/s]\n 71%|███████ | 29/41 [00:07<00:03, 3.64it/s]\n 73%|███████▎ | 30/41 [00:08<00:03, 3.64it/s]\n 76%|███████▌ | 31/41 [00:08<00:02, 3.64it/s]\n 78%|███████▊ | 32/41 [00:08<00:02, 3.64it/s]\n 80%|████████ | 33/41 [00:09<00:02, 3.64it/s]\n 83%|████████▎ | 34/41 [00:09<00:01, 3.64it/s]\n 85%|████████▌ | 35/41 [00:09<00:01, 3.64it/s]\n 88%|████████▊ | 36/41 [00:09<00:01, 3.63it/s]\n 90%|█████████ | 37/41 [00:10<00:01, 3.64it/s]\n 93%|█████████▎| 38/41 [00:10<00:00, 3.64it/s]\n 95%|█████████▌| 39/41 [00:10<00:00, 3.63it/s]\n 98%|█████████▊| 40/41 [00:10<00:00, 3.63it/s]\n100%|██████████| 41/41 [00:11<00:00, 3.64it/s]\n100%|██████████| 41/41 [00:11<00:00, 3.64it/s]", "metrics": { "predict_time": 13.448501, "total_time": 16.347731 }, "output": [ "https://replicate.delivery/pbxt/kYdwGnRgxnptA5QKwtjZDtPPhQAFCdnCeQtwbrvUF71PrntIA/out-0.png" ], "started_at": "2023-08-18T10:10:26.978081Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/molzowlbsk74ywdoc2wc7n67ha", "cancel": "https://api.replicate.com/v1/predictions/molzowlbsk74ywdoc2wc7n67ha/cancel" }, "version": "0d659f5880c7818592754c2853906fd549cfb20041ca949c932ba0d211644d92" }Generated inUsing seed: 62575 Prompt: <s0><s1> style poet Hart Crane, analog photo, 8k, uhd, realistic, txt2img mode 0%| | 0/41 [00:00<?, ?it/s] 2%|▏ | 1/41 [00:00<00:10, 3.66it/s] 5%|▍ | 2/41 [00:00<00:10, 3.66it/s] 7%|▋ | 3/41 [00:00<00:10, 3.67it/s] 10%|▉ | 4/41 [00:01<00:10, 3.66it/s] 12%|█▏ | 5/41 [00:01<00:09, 3.66it/s] 15%|█▍ | 6/41 [00:01<00:09, 3.66it/s] 17%|█▋ | 7/41 [00:01<00:09, 3.66it/s] 20%|█▉ | 8/41 [00:02<00:09, 3.66it/s] 22%|██▏ | 9/41 [00:02<00:08, 3.65it/s] 24%|██▍ | 10/41 [00:02<00:08, 3.66it/s] 27%|██▋ | 11/41 [00:03<00:08, 3.66it/s] 29%|██▉ | 12/41 [00:03<00:07, 3.65it/s] 32%|███▏ | 13/41 [00:03<00:07, 3.64it/s] 34%|███▍ | 14/41 [00:03<00:07, 3.65it/s] 37%|███▋ | 15/41 [00:04<00:07, 3.65it/s] 39%|███▉ | 16/41 [00:04<00:06, 3.64it/s] 41%|████▏ | 17/41 [00:04<00:06, 3.64it/s] 44%|████▍ | 18/41 [00:04<00:06, 3.64it/s] 46%|████▋ | 19/41 [00:05<00:06, 3.65it/s] 49%|████▉ | 20/41 [00:05<00:05, 3.64it/s] 51%|█████ | 21/41 [00:05<00:05, 3.64it/s] 54%|█████▎ | 22/41 [00:06<00:05, 3.64it/s] 56%|█████▌ | 23/41 [00:06<00:04, 3.64it/s] 59%|█████▊ | 24/41 [00:06<00:04, 3.64it/s] 61%|██████ | 25/41 [00:06<00:04, 3.64it/s] 63%|██████▎ | 26/41 [00:07<00:04, 3.65it/s] 66%|██████▌ | 27/41 [00:07<00:03, 3.64it/s] 68%|██████▊ | 28/41 [00:07<00:03, 3.64it/s] 71%|███████ | 29/41 [00:07<00:03, 3.64it/s] 73%|███████▎ | 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