marcodemutiis / photodaddy
Generates Photo Daddies
- Public
- 108 runs
-
L40S
- SDXL fine-tune
Prediction
marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00ID3wl2sbdbbvghkdcjurz4hvr4j4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a photographer PhotoDaddy underwater
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a photographer PhotoDaddy underwater", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run marcodemutiis/photodaddy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", { input: { width: 1024, height: 1024, prompt: "a photographer PhotoDaddy underwater", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun marcodemutiis/photodaddy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", input={ "width": 1024, "height": 1024, "prompt": "a photographer PhotoDaddy underwater", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run marcodemutiis/photodaddy 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": "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", "input": { "width": 1024, "height": 1024, "prompt": "a photographer PhotoDaddy underwater", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-10T21:41:02.393786Z", "created_at": "2024-03-10T21:39:35.936811Z", "data_removed": false, "error": null, "id": "3wl2sbdbbvghkdcjurz4hvr4j4", "input": { "width": 1024, "height": 1024, "prompt": "a photographer PhotoDaddy underwater", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 37911\nEnsuring enough disk space...\nFree disk space: 2687274070016\nDownloading weights: https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar\n2024-03-10T21:40:45Z | INFO | [ Initiating ] dest=/src/weights-cache/4b14a8fc3a629ca1 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar\n2024-03-10T21:40:46Z | INFO | [ Complete ] dest=/src/weights-cache/4b14a8fc3a629ca1 size=\"186 MB\" total_elapsed=0.567s url=https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar\nb''\nDownloaded weights in 0.7362329959869385 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a photographer <s0><s1> underwater\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/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 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/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, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<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 [00:07<00:05, 3.69it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.69it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<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 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 16.664661, "total_time": 86.456975 }, "output": [ "https://replicate.delivery/pbxt/O6Ie3fWEuVvZm0fborQ6hNA3QSZ64W3vyqKMhoM1gPtbX79kA/out-0.png" ], "started_at": "2024-03-10T21:40:45.729125Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3wl2sbdbbvghkdcjurz4hvr4j4", "cancel": "https://api.replicate.com/v1/predictions/3wl2sbdbbvghkdcjurz4hvr4j4/cancel" }, "version": "f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00" }Generated inUsing seed: 37911 Ensuring enough disk space... Free disk space: 2687274070016 Downloading weights: https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar 2024-03-10T21:40:45Z | INFO | [ Initiating ] dest=/src/weights-cache/4b14a8fc3a629ca1 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar 2024-03-10T21:40:46Z | INFO | [ Complete ] dest=/src/weights-cache/4b14a8fc3a629ca1 size="186 MB" total_elapsed=0.567s url=https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar b'' Downloaded weights in 0.7362329959869385 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a photographer <s0><s1> underwater txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:12, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/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 [00:02<00:10, 3.67it/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, 3.67it/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, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<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 [00:07<00:05, 3.69it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s] 60%|██████ | 30/50 [00:08<00:05, 3.69it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<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 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00IDiyougwtb2zgnp2m7nay75dnvzyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a human photographer PhotoDaddy going on a slide
- 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": "a human photographer PhotoDaddy going on a slide", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run marcodemutiis/photodaddy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", { input: { width: 1024, height: 1024, prompt: "a human photographer PhotoDaddy going on a slide", 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 marcodemutiis/photodaddy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", input={ "width": 1024, "height": 1024, "prompt": "a human photographer PhotoDaddy going on a slide", "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 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run marcodemutiis/photodaddy 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": "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", "input": { "width": 1024, "height": 1024, "prompt": "a human photographer PhotoDaddy going on a slide", "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": "2024-03-10T21:51:29.804272Z", "created_at": "2024-03-10T21:51:11.747943Z", "data_removed": false, "error": null, "id": "iyougwtb2zgnp2m7nay75dnvzy", "input": { "width": 1024, "height": 1024, "prompt": "a human photographer PhotoDaddy going on a slide", "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: 11073\nEnsuring enough disk space...\nFree disk space: 2754534952960\nDownloading weights: https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar\n2024-03-10T21:51:12Z | INFO | [ Initiating ] dest=/src/weights-cache/4b14a8fc3a629ca1 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar\n2024-03-10T21:51:13Z | INFO | [ Complete ] dest=/src/weights-cache/4b14a8fc3a629ca1 size=\"186 MB\" total_elapsed=0.918s url=https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar\nb''\nDownloaded weights in 1.0192365646362305 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a human photographer <s0><s1> going on a slide\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.64it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.64it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.64it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.62it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.62it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.62it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.62it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.61it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]", "metrics": { "predict_time": 16.923503, "total_time": 18.056329 }, "output": [ "https://replicate.delivery/pbxt/O3oDGoUTYdqlDZmPCcMYyBj8eUffvFvYQhxeFLU0qSsHW37JB/out-0.png" ], "started_at": "2024-03-10T21:51:12.880769Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iyougwtb2zgnp2m7nay75dnvzy", "cancel": "https://api.replicate.com/v1/predictions/iyougwtb2zgnp2m7nay75dnvzy/cancel" }, "version": "f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00" }Generated inUsing seed: 11073 Ensuring enough disk space... Free disk space: 2754534952960 Downloading weights: https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar 2024-03-10T21:51:12Z | INFO | [ Initiating ] dest=/src/weights-cache/4b14a8fc3a629ca1 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar 2024-03-10T21:51:13Z | INFO | [ Complete ] dest=/src/weights-cache/4b14a8fc3a629ca1 size="186 MB" total_elapsed=0.918s url=https://replicate.delivery/pbxt/FpffVM7yeqW9Aopekzxf0FipTWVEe35gxUc0yFGp7gBQGTvnE/trained_model.tar b'' Downloaded weights in 1.0192365646362305 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a human photographer <s0><s1> going on a slide txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.64it/s] 4%|▍ | 2/50 [00:00<00:13, 3.64it/s] 6%|▌ | 3/50 [00:00<00:12, 3.64it/s] 8%|▊ | 4/50 [00:01<00:12, 3.64it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s] 50%|█████ | 25/50 [00:06<00:06, 3.62it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s] 60%|██████ | 30/50 [00:08<00:05, 3.62it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s] 70%|███████ | 35/50 [00:09<00:04, 3.62it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s] 80%|████████ | 40/50 [00:11<00:02, 3.62it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.61it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s]Prediction
marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00ID7wyv62tbuiscy7zq322usiwjhuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a bunch of human photographers PhotoDaddy swimming in a ball pit taking pictures of each others
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- deformed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a bunch of human photographers PhotoDaddy swimming in a ball pit taking pictures of each others", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run marcodemutiis/photodaddy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", { input: { width: 1024, height: 1024, prompt: "a bunch of human photographers PhotoDaddy swimming in a ball pit taking pictures of each others", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "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 marcodemutiis/photodaddy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", input={ "width": 1024, "height": 1024, "prompt": "a bunch of human photographers PhotoDaddy swimming in a ball pit taking pictures of each others", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "deformed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run marcodemutiis/photodaddy 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": "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", "input": { "width": 1024, "height": 1024, "prompt": "a bunch of human photographers PhotoDaddy swimming in a ball pit taking pictures of each others", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "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": "2024-03-10T21:57:14.068955Z", "created_at": "2024-03-10T21:56:52.054117Z", "data_removed": false, "error": null, "id": "7wyv62tbuiscy7zq322usiwjhu", "input": { "width": 1024, "height": 1024, "prompt": "a bunch of human photographers PhotoDaddy swimming in a ball pit taking pictures of each others", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "deformed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 16160\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a bunch of human photographers <s0><s1> swimming in a ball pit taking pictures of each others\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/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 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<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 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/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, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.903186, "total_time": 22.014838 }, "output": [ "https://replicate.delivery/pbxt/RIe0efW0XJdfnQuVOTrvmhLSSN7qP7Y8jiuwTesdrKTJXv3TC/out-0.png" ], "started_at": "2024-03-10T21:56:58.165769Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7wyv62tbuiscy7zq322usiwjhu", "cancel": "https://api.replicate.com/v1/predictions/7wyv62tbuiscy7zq322usiwjhu/cancel" }, "version": "f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00" }Generated inUsing seed: 16160 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a bunch of human photographers <s0><s1> swimming in a ball pit taking pictures of each others txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 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100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00IDuu625j3bmrydx5vj3huwaujqfqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a photographer PhotoDaddy fighting a war
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a photographer PhotoDaddy fighting a war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run marcodemutiis/photodaddy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", { input: { width: 1024, height: 1024, prompt: "a photographer PhotoDaddy fighting a war", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun marcodemutiis/photodaddy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", input={ "width": 1024, "height": 1024, "prompt": "a photographer PhotoDaddy fighting a war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run marcodemutiis/photodaddy 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": "marcodemutiis/photodaddy:f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00", "input": { "width": 1024, "height": 1024, "prompt": "a photographer PhotoDaddy fighting a war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-10T22:21:57.075810Z", "created_at": "2024-03-10T22:21:35.070888Z", "data_removed": false, "error": null, "id": "uu625j3bmrydx5vj3huwaujqfq", "input": { "width": 1024, "height": 1024, "prompt": "a photographer PhotoDaddy fighting a war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 53765\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a photographer <s0><s1> fighting a war\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/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, 3.67it/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, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/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, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/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, 3.66it/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, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 17.327047, "total_time": 22.004922 }, "output": [ "https://replicate.delivery/pbxt/6AVQ1ddswe1AD6bYJuQ8YboarDtONiXugCdgBJAuuAiBJfekA/out-0.png" ], "started_at": "2024-03-10T22:21:39.748763Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uu625j3bmrydx5vj3huwaujqfq", "cancel": "https://api.replicate.com/v1/predictions/uu625j3bmrydx5vj3huwaujqfq/cancel" }, "version": "f2ac635ab6b9ec026e6b9931a3692c1c28d5614c470808beeb3d0ff7efca5e00" }Generated inUsing seed: 53765 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a photographer <s0><s1> fighting a war txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:12, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/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, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 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