fofr / sdxl-2004
An SDXL fine-tune based on bad 2004 digital photography
- Public
- 13.8K runs
-
L40S
Prediction
fofr/sdxl-2004:54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714IDz5n2litbhbrnbymecjby3larbaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a monster from 2004 in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a monster from 2004 in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "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 fofr/sdxl-2004 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-2004:54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714", { input: { width: 1024, height: 1024, prompt: "A photo of a monster from 2004 in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, 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 fofr/sdxl-2004 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-2004:54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714", input={ "width": 1024, "height": 1024, "prompt": "A photo of a monster from 2004 in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "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 fofr/sdxl-2004 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": "fofr/sdxl-2004:54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a monster from 2004 in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "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": "2023-08-27T19:20:45.261577Z", "created_at": "2023-08-27T19:19:49.855236Z", "data_removed": false, "error": null, "id": "z5n2litbhbrnbymecjby3larba", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a monster from 2004 in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 20755\nPrompt: A photo of a monster from 2004 in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:48, 1.00it/s]\n 4%|▍ | 2/50 [00:01<00:47, 1.00it/s]\n 6%|▌ | 3/50 [00:02<00:46, 1.00it/s]\n 8%|▊ | 4/50 [00:03<00:45, 1.00it/s]\n 10%|█ | 5/50 [00:04<00:44, 1.00it/s]\n 12%|█▏ | 6/50 [00:05<00:43, 1.00it/s]\n 14%|█▍ | 7/50 [00:06<00:43, 1.00s/it]\n 16%|█▌ | 8/50 [00:07<00:42, 1.00s/it]\n 18%|█▊ | 9/50 [00:09<00:41, 1.00s/it]\n 20%|██ | 10/50 [00:10<00:40, 1.00s/it]\n 22%|██▏ | 11/50 [00:11<00:39, 1.00s/it]\n 24%|██▍ | 12/50 [00:12<00:38, 1.00s/it]\n 26%|██▌ | 13/50 [00:13<00:37, 1.00s/it]\n 28%|██▊ | 14/50 [00:14<00:36, 1.00s/it]\n 30%|███ | 15/50 [00:15<00:35, 1.00s/it]\n 32%|███▏ | 16/50 [00:16<00:34, 1.00s/it]\n 34%|███▍ | 17/50 [00:17<00:33, 1.00s/it]\n 36%|███▌ | 18/50 [00:18<00:32, 1.00s/it]\n 38%|███▊ | 19/50 [00:19<00:31, 1.00s/it]\n 40%|████ | 20/50 [00:20<00:30, 1.01s/it]\n 42%|████▏ | 21/50 [00:21<00:29, 1.01s/it]\n 44%|████▍ | 22/50 [00:22<00:28, 1.01s/it]\n 46%|████▌ | 23/50 [00:23<00:27, 1.01s/it]\n 48%|████▊ | 24/50 [00:24<00:26, 1.01s/it]\n 50%|█████ | 25/50 [00:25<00:25, 1.01s/it]\n 52%|█████▏ | 26/50 [00:26<00:24, 1.00s/it]\n 54%|█████▍ | 27/50 [00:27<00:23, 1.01s/it]\n 56%|█████▌ | 28/50 [00:28<00:22, 1.01s/it]\n 58%|█████▊ | 29/50 [00:29<00:21, 1.01s/it]\n 60%|██████ | 30/50 [00:30<00:20, 1.01s/it]\n 62%|██████▏ | 31/50 [00:31<00:19, 1.01s/it]\n 64%|██████▍ | 32/50 [00:32<00:18, 1.01s/it]\n 66%|██████▌ | 33/50 [00:33<00:17, 1.01s/it]\n 68%|██████▊ | 34/50 [00:34<00:16, 1.01s/it]\n 70%|███████ | 35/50 [00:35<00:15, 1.01s/it]\n 72%|███████▏ | 36/50 [00:36<00:14, 1.01s/it]\n 74%|███████▍ | 37/50 [00:37<00:13, 1.01s/it]\n 76%|███████▌ | 38/50 [00:38<00:12, 1.01s/it]\n 78%|███████▊ | 39/50 [00:39<00:11, 1.01s/it]\n 80%|████████ | 40/50 [00:40<00:10, 1.01s/it]\n 82%|████████▏ | 41/50 [00:41<00:09, 1.01s/it]\n 84%|████████▍ | 42/50 [00:42<00:08, 1.01s/it]\n 86%|████████▌ | 43/50 [00:43<00:07, 1.01s/it]\n 88%|████████▊ | 44/50 [00:44<00:06, 1.01s/it]\n 90%|█████████ | 45/50 [00:45<00:05, 1.01s/it]\n 92%|█████████▏| 46/50 [00:46<00:04, 1.01s/it]\n 94%|█████████▍| 47/50 [00:47<00:03, 1.01s/it]\n 96%|█████████▌| 48/50 [00:48<00:02, 1.01s/it]\n 98%|█████████▊| 49/50 [00:49<00:01, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.01s/it]", "metrics": { "predict_time": 55.427467, "total_time": 55.406341 }, "output": [ "https://pbxt.replicate.delivery/ObEwc3FlTAaXIZFuIaTksVtp5vWbFiNAESYoEgeweT6LQVeiA/out-0.png", "https://pbxt.replicate.delivery/xedpHPC4gDyyL6Pw3f1fICayjqNaUElCx6KTjbVyWrfxAV5FB/out-1.png", "https://pbxt.replicate.delivery/DKVEpyGDbIZ2OVZM6YMNS6UUpS2tUEvdfw4gHyg0LQLGoKvIA/out-2.png", "https://pbxt.replicate.delivery/36OsvZv94NIkNhRQntY7IBpoREL8NbGNOufuhozSbp2GoKvIA/out-3.png" ], "started_at": "2023-08-27T19:19:49.834110Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/z5n2litbhbrnbymecjby3larba", "cancel": "https://api.replicate.com/v1/predictions/z5n2litbhbrnbymecjby3larba/cancel" }, "version": "54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714" }Generated inUsing seed: 20755 Prompt: A photo of a monster from 2004 in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:48, 1.00it/s] 4%|▍ | 2/50 [00:01<00:47, 1.00it/s] 6%|▌ | 3/50 [00:02<00:46, 1.00it/s] 8%|▊ | 4/50 [00:03<00:45, 1.00it/s] 10%|█ | 5/50 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1.01s/it]Prediction
fofr/sdxl-2004:54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714IDl26veidbqdob46wxitlsxzsa6uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1152
- height
- 768
- prompt
- A photo of a cyberpunk in a living room from 2004 in the style of TOK
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.46
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.95
- negative_prompt
- distorted, ugly, disfigured, broken
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1152, "height": 768, "prompt": "A photo of a cyberpunk in a living room from 2004 in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.46, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "distorted, ugly, disfigured, broken", "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 fofr/sdxl-2004 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-2004:54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714", { input: { width: 1152, height: 768, prompt: "A photo of a cyberpunk in a living room from 2004 in the style of TOK", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.46, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.95, negative_prompt: "distorted, ugly, disfigured, broken", 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 fofr/sdxl-2004 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-2004:54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714", input={ "width": 1152, "height": 768, "prompt": "A photo of a cyberpunk in a living room from 2004 in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.46, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.95, "negative_prompt": "distorted, ugly, disfigured, broken", "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 fofr/sdxl-2004 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": "fofr/sdxl-2004:54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714", "input": { "width": 1152, "height": 768, "prompt": "A photo of a cyberpunk in a living room from 2004 in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.46, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "distorted, ugly, disfigured, broken", "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": "2023-08-28T14:31:20.871853Z", "created_at": "2023-08-28T14:31:07.609924Z", "data_removed": false, "error": null, "id": "l26veidbqdob46wxitlsxzsa6u", "input": { "width": 1152, "height": 768, "prompt": "A photo of a cyberpunk in a living room from 2004 in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.46, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "distorted, ugly, disfigured, broken", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 765\nPrompt: A photo of a cyberpunk in a living room from 2004 in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/47 [00:00<?, ?it/s]\n 2%|▏ | 1/47 [00:00<00:11, 4.17it/s]\n 4%|▍ | 2/47 [00:00<00:10, 4.26it/s]\n 6%|▋ | 3/47 [00:00<00:10, 4.28it/s]\n 9%|▊ | 4/47 [00:00<00:10, 4.30it/s]\n 11%|█ | 5/47 [00:01<00:09, 4.31it/s]\n 13%|█▎ | 6/47 [00:01<00:09, 4.31it/s]\n 15%|█▍ | 7/47 [00:01<00:09, 4.32it/s]\n 17%|█▋ | 8/47 [00:01<00:09, 4.32it/s]\n 19%|█▉ | 9/47 [00:02<00:08, 4.32it/s]\n 21%|██▏ | 10/47 [00:02<00:08, 4.32it/s]\n 23%|██▎ | 11/47 [00:02<00:08, 4.32it/s]\n 26%|██▌ | 12/47 [00:02<00:08, 4.31it/s]\n 28%|██▊ | 13/47 [00:03<00:07, 4.31it/s]\n 30%|██▉ | 14/47 [00:03<00:07, 4.31it/s]\n 32%|███▏ | 15/47 [00:03<00:07, 4.31it/s]\n 34%|███▍ | 16/47 [00:03<00:07, 4.31it/s]\n 36%|███▌ | 17/47 [00:03<00:06, 4.31it/s]\n 38%|███▊ | 18/47 [00:04<00:06, 4.30it/s]\n 40%|████ | 19/47 [00:04<00:06, 4.30it/s]\n 43%|████▎ | 20/47 [00:04<00:06, 4.31it/s]\n 45%|████▍ | 21/47 [00:04<00:06, 4.31it/s]\n 47%|████▋ | 22/47 [00:05<00:05, 4.31it/s]\n 49%|████▉ | 23/47 [00:05<00:05, 4.30it/s]\n 51%|█████ | 24/47 [00:05<00:05, 4.31it/s]\n 53%|█████▎ | 25/47 [00:05<00:05, 4.30it/s]\n 55%|█████▌ | 26/47 [00:06<00:04, 4.30it/s]\n 57%|█████▋ | 27/47 [00:06<00:04, 4.30it/s]\n 60%|█████▉ | 28/47 [00:06<00:04, 4.30it/s]\n 62%|██████▏ | 29/47 [00:06<00:04, 4.30it/s]\n 64%|██████▍ | 30/47 [00:06<00:03, 4.30it/s]\n 66%|██████▌ | 31/47 [00:07<00:03, 4.30it/s]\n 68%|██████▊ | 32/47 [00:07<00:03, 4.30it/s]\n 70%|███████ | 33/47 [00:07<00:03, 4.30it/s]\n 72%|███████▏ | 34/47 [00:07<00:03, 4.31it/s]\n 74%|███████▍ | 35/47 [00:08<00:02, 4.30it/s]\n 77%|███████▋ | 36/47 [00:08<00:02, 4.30it/s]\n 79%|███████▊ | 37/47 [00:08<00:02, 4.30it/s]\n 81%|████████ | 38/47 [00:08<00:02, 4.30it/s]\n 83%|████████▎ | 39/47 [00:09<00:01, 4.30it/s]\n 85%|████████▌ | 40/47 [00:09<00:01, 4.30it/s]\n 87%|████████▋ | 41/47 [00:09<00:01, 4.30it/s]\n 89%|████████▉ | 42/47 [00:09<00:01, 4.30it/s]\n 91%|█████████▏| 43/47 [00:09<00:00, 4.30it/s]\n 94%|█████████▎| 44/47 [00:10<00:00, 4.30it/s]\n 96%|█████████▌| 45/47 [00:10<00:00, 4.30it/s]\n 98%|█████████▊| 46/47 [00:10<00:00, 4.30it/s]\n100%|██████████| 47/47 [00:10<00:00, 4.30it/s]\n100%|██████████| 47/47 [00:10<00:00, 4.30it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 5.31it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.43it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.47it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.45it/s]", "metrics": { "predict_time": 13.261222, "total_time": 13.261929 }, "output": [ "https://pbxt.replicate.delivery/8LKCty2D5b5BBBjylErfI8Xqf4OTSsnA0TIJccnpPct3GmeiA/out-0.png" ], "started_at": "2023-08-28T14:31:07.610631Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/l26veidbqdob46wxitlsxzsa6u", "cancel": "https://api.replicate.com/v1/predictions/l26veidbqdob46wxitlsxzsa6u/cancel" }, "version": "54a4e82bf8357890caa42f088f64d556f21d553c98da81e59313054cd10ce714" }Generated inUsing seed: 765 Prompt: A photo of a cyberpunk in a living room from 2004 in the style of <s0><s1> txt2img mode 0%| | 0/47 [00:00<?, ?it/s] 2%|▏ | 1/47 [00:00<00:11, 4.17it/s] 4%|▍ | 2/47 [00:00<00:10, 4.26it/s] 6%|▋ | 3/47 [00:00<00:10, 4.28it/s] 9%|▊ | 4/47 [00:00<00:10, 4.30it/s] 11%|█ | 5/47 [00:01<00:09, 4.31it/s] 13%|█▎ | 6/47 [00:01<00:09, 4.31it/s] 15%|█▍ | 7/47 [00:01<00:09, 4.32it/s] 17%|█▋ | 8/47 [00:01<00:09, 4.32it/s] 19%|█▉ | 9/47 [00:02<00:08, 4.32it/s] 21%|██▏ | 10/47 [00:02<00:08, 4.32it/s] 23%|██▎ | 11/47 [00:02<00:08, 4.32it/s] 26%|██▌ | 12/47 [00:02<00:08, 4.31it/s] 28%|██▊ | 13/47 [00:03<00:07, 4.31it/s] 30%|██▉ | 14/47 [00:03<00:07, 4.31it/s] 32%|███▏ | 15/47 [00:03<00:07, 4.31it/s] 34%|███▍ | 16/47 [00:03<00:07, 4.31it/s] 36%|███▌ | 17/47 [00:03<00:06, 4.31it/s] 38%|███▊ | 18/47 [00:04<00:06, 4.30it/s] 40%|████ | 19/47 [00:04<00:06, 4.30it/s] 43%|████▎ | 20/47 [00:04<00:06, 4.31it/s] 45%|████▍ | 21/47 [00:04<00:06, 4.31it/s] 47%|████▋ | 22/47 [00:05<00:05, 4.31it/s] 49%|████▉ | 23/47 [00:05<00:05, 4.30it/s] 51%|█████ | 24/47 [00:05<00:05, 4.31it/s] 53%|█████▎ | 25/47 [00:05<00:05, 4.30it/s] 55%|█████▌ | 26/47 [00:06<00:04, 4.30it/s] 57%|█████▋ | 27/47 [00:06<00:04, 4.30it/s] 60%|█████▉ | 28/47 [00:06<00:04, 4.30it/s] 62%|██████▏ | 29/47 [00:06<00:04, 4.30it/s] 64%|██████▍ | 30/47 [00:06<00:03, 4.30it/s] 66%|██████▌ | 31/47 [00:07<00:03, 4.30it/s] 68%|██████▊ | 32/47 [00:07<00:03, 4.30it/s] 70%|███████ | 33/47 [00:07<00:03, 4.30it/s] 72%|███████▏ | 34/47 [00:07<00:03, 4.31it/s] 74%|███████▍ | 35/47 [00:08<00:02, 4.30it/s] 77%|███████▋ | 36/47 [00:08<00:02, 4.30it/s] 79%|███████▊ | 37/47 [00:08<00:02, 4.30it/s] 81%|████████ | 38/47 [00:08<00:02, 4.30it/s] 83%|████████▎ | 39/47 [00:09<00:01, 4.30it/s] 85%|████████▌ | 40/47 [00:09<00:01, 4.30it/s] 87%|████████▋ | 41/47 [00:09<00:01, 4.30it/s] 89%|████████▉ | 42/47 [00:09<00:01, 4.30it/s] 91%|█████████▏| 43/47 [00:09<00:00, 4.30it/s] 94%|█████████▎| 44/47 [00:10<00:00, 4.30it/s] 96%|█████████▌| 45/47 [00:10<00:00, 4.30it/s] 98%|█████████▊| 46/47 [00:10<00:00, 4.30it/s] 100%|██████████| 47/47 [00:10<00:00, 4.30it/s] 100%|██████████| 47/47 [00:10<00:00, 4.30it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 5.31it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.43it/s] 100%|██████████| 3/3 [00:00<00:00, 5.47it/s] 100%|██████████| 3/3 [00:00<00:00, 5.45it/s]
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