fofr / sdxl-energy-drink
SDXL fine-tuned on energy drink designs
- Public
- 1.6K runs
-
L40S
- SDXL fine-tune
Prediction
fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110ddID3h543clb2bqpsizphdsj6matnqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK energy drinks can, Super Mario themed, white background
- 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
- text, words,
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drinks can, Super Mario themed, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "text, words,", "prompt_strength": 0.8, "num_inference_steps": 25 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK energy drinks can, Super Mario themed, white background", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "text, words,", prompt_strength: 0.8, num_inference_steps: 25 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drinks can, Super Mario themed, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "text, words,", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-energy-drink 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-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drinks can, Super Mario themed, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "text, words,", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-10T10:01:52.927662Z", "created_at": "2024-01-10T10:01:30.754800Z", "data_removed": false, "error": null, "id": "3h543clb2bqpsizphdsj6matnq", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drinks can, Super Mario themed, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "text, words,", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 27407\nEnsuring enough disk space...\nFree disk space: 1945079513088\nDownloading weights: https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar\n2024-01-10T10:01:37Z | INFO | [ Initiating ] dest=/src/weights-cache/1075171f2b09d76f minimum_chunk_size=150M url=https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar\n2024-01-10T10:01:43Z | INFO | [ Complete ] dest=/src/weights-cache/1075171f2b09d76f size=\"186 MB\" total_elapsed=6.539s url=https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar\nb''\nDownloaded weights in 6.714761734008789 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of a <s0><s1> energy drinks can, Super Mario themed, white background\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:06, 3.68it/s]\n 8%|▊ | 2/25 [00:00<00:06, 3.68it/s]\n 12%|█▏ | 3/25 [00:00<00:05, 3.68it/s]\n 16%|█▌ | 4/25 [00:01<00:05, 3.68it/s]\n 20%|██ | 5/25 [00:01<00:05, 3.67it/s]\n 24%|██▍ | 6/25 [00:01<00:05, 3.68it/s]\n 28%|██▊ | 7/25 [00:01<00:04, 3.68it/s]\n 32%|███▏ | 8/25 [00:02<00:04, 3.68it/s]\n 36%|███▌ | 9/25 [00:02<00:04, 3.68it/s]\n 40%|████ | 10/25 [00:02<00:04, 3.68it/s]\n 44%|████▍ | 11/25 [00:02<00:03, 3.68it/s]\n 48%|████▊ | 12/25 [00:03<00:03, 3.67it/s]\n 52%|█████▏ | 13/25 [00:03<00:03, 3.68it/s]\n 56%|█████▌ | 14/25 [00:03<00:02, 3.67it/s]\n 60%|██████ | 15/25 [00:04<00:02, 3.67it/s]\n 64%|██████▍ | 16/25 [00:04<00:02, 3.67it/s]\n 68%|██████▊ | 17/25 [00:04<00:02, 3.67it/s]\n 72%|███████▏ | 18/25 [00:04<00:01, 3.66it/s]\n 76%|███████▌ | 19/25 [00:05<00:01, 3.66it/s]\n 80%|████████ | 20/25 [00:05<00:01, 3.66it/s]\n 84%|████████▍ | 21/25 [00:05<00:01, 3.66it/s]\n 88%|████████▊ | 22/25 [00:05<00:00, 3.66it/s]\n 92%|█████████▏| 23/25 [00:06<00:00, 3.66it/s]\n 96%|█████████▌| 24/25 [00:06<00:00, 3.66it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.66it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.67it/s]", "metrics": { "predict_time": 15.856876, "total_time": 22.172862 }, "output": [ "https://replicate.delivery/pbxt/fKZfLPfOhHPXIITzADOtlbolrBuZ6qckaVDsTvtuZzngoDWkA/out-0.png" ], "started_at": "2024-01-10T10:01:37.070786Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3h543clb2bqpsizphdsj6matnq", "cancel": "https://api.replicate.com/v1/predictions/3h543clb2bqpsizphdsj6matnq/cancel" }, "version": "4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd" }
Generated inUsing seed: 27407 Ensuring enough disk space... Free disk space: 1945079513088 Downloading weights: https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar 2024-01-10T10:01:37Z | INFO | [ Initiating ] dest=/src/weights-cache/1075171f2b09d76f minimum_chunk_size=150M url=https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar 2024-01-10T10:01:43Z | INFO | [ Complete ] dest=/src/weights-cache/1075171f2b09d76f size="186 MB" total_elapsed=6.539s url=https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar b'' Downloaded weights in 6.714761734008789 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of a <s0><s1> energy drinks can, Super Mario themed, white background txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:06, 3.68it/s] 8%|▊ | 2/25 [00:00<00:06, 3.68it/s] 12%|█▏ | 3/25 [00:00<00:05, 3.68it/s] 16%|█▌ | 4/25 [00:01<00:05, 3.68it/s] 20%|██ | 5/25 [00:01<00:05, 3.67it/s] 24%|██▍ | 6/25 [00:01<00:05, 3.68it/s] 28%|██▊ | 7/25 [00:01<00:04, 3.68it/s] 32%|███▏ | 8/25 [00:02<00:04, 3.68it/s] 36%|███▌ | 9/25 [00:02<00:04, 3.68it/s] 40%|████ | 10/25 [00:02<00:04, 3.68it/s] 44%|████▍ | 11/25 [00:02<00:03, 3.68it/s] 48%|████▊ | 12/25 [00:03<00:03, 3.67it/s] 52%|█████▏ | 13/25 [00:03<00:03, 3.68it/s] 56%|█████▌ | 14/25 [00:03<00:02, 3.67it/s] 60%|██████ | 15/25 [00:04<00:02, 3.67it/s] 64%|██████▍ | 16/25 [00:04<00:02, 3.67it/s] 68%|██████▊ | 17/25 [00:04<00:02, 3.67it/s] 72%|███████▏ | 18/25 [00:04<00:01, 3.66it/s] 76%|███████▌ | 19/25 [00:05<00:01, 3.66it/s] 80%|████████ | 20/25 [00:05<00:01, 3.66it/s] 84%|████████▍ | 21/25 [00:05<00:01, 3.66it/s] 88%|████████▊ | 22/25 [00:05<00:00, 3.66it/s] 92%|█████████▏| 23/25 [00:06<00:00, 3.66it/s] 96%|█████████▌| 24/25 [00:06<00:00, 3.66it/s] 100%|██████████| 25/25 [00:06<00:00, 3.66it/s] 100%|██████████| 25/25 [00:06<00:00, 3.67it/s]
Prediction
fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110ddIDapu2wb3bs2gwvbh3vfstyh27oaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK energy drink can, fox, white background
- 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
- ugly, broken, distorted, text, words
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, fox, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK energy drink can, fox, white background", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "ugly, broken, distorted, text, words", prompt_strength: 0.8, num_inference_steps: 25 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, fox, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-energy-drink 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-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, fox, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-10T10:03:10.218041Z", "created_at": "2024-01-10T10:03:01.171669Z", "data_removed": false, "error": null, "id": "apu2wb3bs2gwvbh3vfstyh27oa", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, fox, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 61545\nskipping loading .. weights already loaded\nPrompt: A photo of a <s0><s1> energy drink can, fox, white background\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:06, 3.71it/s]\n 8%|▊ | 2/25 [00:00<00:06, 3.71it/s]\n 12%|█▏ | 3/25 [00:00<00:05, 3.71it/s]\n 16%|█▌ | 4/25 [00:01<00:05, 3.70it/s]\n 20%|██ | 5/25 [00:01<00:05, 3.69it/s]\n 24%|██▍ | 6/25 [00:01<00:05, 3.69it/s]\n 28%|██▊ | 7/25 [00:01<00:04, 3.70it/s]\n 32%|███▏ | 8/25 [00:02<00:04, 3.69it/s]\n 36%|███▌ | 9/25 [00:02<00:04, 3.69it/s]\n 40%|████ | 10/25 [00:02<00:04, 3.69it/s]\n 44%|████▍ | 11/25 [00:02<00:03, 3.70it/s]\n 48%|████▊ | 12/25 [00:03<00:03, 3.69it/s]\n 52%|█████▏ | 13/25 [00:03<00:03, 3.69it/s]\n 56%|█████▌ | 14/25 [00:03<00:02, 3.69it/s]\n 60%|██████ | 15/25 [00:04<00:02, 3.69it/s]\n 64%|██████▍ | 16/25 [00:04<00:02, 3.69it/s]\n 68%|██████▊ | 17/25 [00:04<00:02, 3.69it/s]\n 72%|███████▏ | 18/25 [00:04<00:01, 3.69it/s]\n 76%|███████▌ | 19/25 [00:05<00:01, 3.69it/s]\n 80%|████████ | 20/25 [00:05<00:01, 3.69it/s]\n 84%|████████▍ | 21/25 [00:05<00:01, 3.69it/s]\n 88%|████████▊ | 22/25 [00:05<00:00, 3.68it/s]\n 92%|█████████▏| 23/25 [00:06<00:00, 3.68it/s]\n 96%|█████████▌| 24/25 [00:06<00:00, 3.68it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.68it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.69it/s]", "metrics": { "predict_time": 9.008888, "total_time": 9.046372 }, "output": [ "https://replicate.delivery/pbxt/ejHfFzRNSQvj9kEN5weXvxgsHLrysSV55MKQZQ77yGg7qDWkA/out-0.png" ], "started_at": "2024-01-10T10:03:01.209153Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/apu2wb3bs2gwvbh3vfstyh27oa", "cancel": "https://api.replicate.com/v1/predictions/apu2wb3bs2gwvbh3vfstyh27oa/cancel" }, "version": "4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd" }
Generated inUsing seed: 61545 skipping loading .. weights already loaded Prompt: A photo of a <s0><s1> energy drink can, fox, white background txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:06, 3.71it/s] 8%|▊ | 2/25 [00:00<00:06, 3.71it/s] 12%|█▏ | 3/25 [00:00<00:05, 3.71it/s] 16%|█▌ | 4/25 [00:01<00:05, 3.70it/s] 20%|██ | 5/25 [00:01<00:05, 3.69it/s] 24%|██▍ | 6/25 [00:01<00:05, 3.69it/s] 28%|██▊ | 7/25 [00:01<00:04, 3.70it/s] 32%|███▏ | 8/25 [00:02<00:04, 3.69it/s] 36%|███▌ | 9/25 [00:02<00:04, 3.69it/s] 40%|████ | 10/25 [00:02<00:04, 3.69it/s] 44%|████▍ | 11/25 [00:02<00:03, 3.70it/s] 48%|████▊ | 12/25 [00:03<00:03, 3.69it/s] 52%|█████▏ | 13/25 [00:03<00:03, 3.69it/s] 56%|█████▌ | 14/25 [00:03<00:02, 3.69it/s] 60%|██████ | 15/25 [00:04<00:02, 3.69it/s] 64%|██████▍ | 16/25 [00:04<00:02, 3.69it/s] 68%|██████▊ | 17/25 [00:04<00:02, 3.69it/s] 72%|███████▏ | 18/25 [00:04<00:01, 3.69it/s] 76%|███████▌ | 19/25 [00:05<00:01, 3.69it/s] 80%|████████ | 20/25 [00:05<00:01, 3.69it/s] 84%|████████▍ | 21/25 [00:05<00:01, 3.69it/s] 88%|████████▊ | 22/25 [00:05<00:00, 3.68it/s] 92%|█████████▏| 23/25 [00:06<00:00, 3.68it/s] 96%|█████████▌| 24/25 [00:06<00:00, 3.68it/s] 100%|██████████| 25/25 [00:06<00:00, 3.68it/s] 100%|██████████| 25/25 [00:06<00:00, 3.69it/s]
Prediction
fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110ddIDrg7c3gdb7wtwtute3lfpjj3qq4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK energy drink can, sloth, totally stressed, very tired, white background
- 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
- tall can, many cans, ugly, broken, distorted, text, words
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, sloth, totally stressed, very tired, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "tall can, many cans, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK energy drink can, sloth, totally stressed, very tired, white background", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "tall can, many cans, ugly, broken, distorted, text, words", prompt_strength: 0.8, num_inference_steps: 25 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, sloth, totally stressed, very tired, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "tall can, many cans, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-energy-drink 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-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, sloth, totally stressed, very tired, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "tall can, many cans, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-10T10:19:26.283750Z", "created_at": "2024-01-10T10:19:18.139156Z", "data_removed": false, "error": null, "id": "rg7c3gdb7wtwtute3lfpjj3qq4", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, sloth, totally stressed, very tired, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "tall can, many cans, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 56228\nskipping loading .. weights already loaded\nPrompt: A photo of a <s0><s1> energy drink can, sloth, totally stressed, very tired, white background\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:06, 3.72it/s]\n 8%|▊ | 2/25 [00:00<00:06, 3.70it/s]\n 12%|█▏ | 3/25 [00:00<00:05, 3.69it/s]\n 16%|█▌ | 4/25 [00:01<00:05, 3.67it/s]\n 20%|██ | 5/25 [00:01<00:05, 3.67it/s]\n 24%|██▍ | 6/25 [00:01<00:05, 3.68it/s]\n 28%|██▊ | 7/25 [00:01<00:04, 3.68it/s]\n 32%|███▏ | 8/25 [00:02<00:04, 3.67it/s]\n 36%|███▌ | 9/25 [00:02<00:04, 3.67it/s]\n 40%|████ | 10/25 [00:02<00:04, 3.68it/s]\n 44%|████▍ | 11/25 [00:02<00:03, 3.67it/s]\n 48%|████▊ | 12/25 [00:03<00:03, 3.67it/s]\n 52%|█████▏ | 13/25 [00:03<00:03, 3.67it/s]\n 56%|█████▌ | 14/25 [00:03<00:02, 3.67it/s]\n 60%|██████ | 15/25 [00:04<00:02, 3.67it/s]\n 64%|██████▍ | 16/25 [00:04<00:02, 3.67it/s]\n 68%|██████▊ | 17/25 [00:04<00:02, 3.67it/s]\n 72%|███████▏ | 18/25 [00:04<00:01, 3.67it/s]\n 76%|███████▌ | 19/25 [00:05<00:01, 3.67it/s]\n 80%|████████ | 20/25 [00:05<00:01, 3.67it/s]\n 84%|████████▍ | 21/25 [00:05<00:01, 3.67it/s]\n 88%|████████▊ | 22/25 [00:05<00:00, 3.67it/s]\n 92%|█████████▏| 23/25 [00:06<00:00, 3.66it/s]\n 96%|█████████▌| 24/25 [00:06<00:00, 3.67it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.67it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.67it/s]", "metrics": { "predict_time": 8.140399, "total_time": 8.144594 }, "output": [ "https://replicate.delivery/pbxt/uPqO41Xy2fUAB6mibzfEHalMQgRjg2lEVPnXtLa544stECLSA/out-0.png" ], "started_at": "2024-01-10T10:19:18.143351Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rg7c3gdb7wtwtute3lfpjj3qq4", "cancel": "https://api.replicate.com/v1/predictions/rg7c3gdb7wtwtute3lfpjj3qq4/cancel" }, "version": "4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd" }
Generated inUsing seed: 56228 skipping loading .. weights already loaded Prompt: A photo of a <s0><s1> energy drink can, sloth, totally stressed, very tired, white background txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:06, 3.72it/s] 8%|▊ | 2/25 [00:00<00:06, 3.70it/s] 12%|█▏ | 3/25 [00:00<00:05, 3.69it/s] 16%|█▌ | 4/25 [00:01<00:05, 3.67it/s] 20%|██ | 5/25 [00:01<00:05, 3.67it/s] 24%|██▍ | 6/25 [00:01<00:05, 3.68it/s] 28%|██▊ | 7/25 [00:01<00:04, 3.68it/s] 32%|███▏ | 8/25 [00:02<00:04, 3.67it/s] 36%|███▌ | 9/25 [00:02<00:04, 3.67it/s] 40%|████ | 10/25 [00:02<00:04, 3.68it/s] 44%|████▍ | 11/25 [00:02<00:03, 3.67it/s] 48%|████▊ | 12/25 [00:03<00:03, 3.67it/s] 52%|█████▏ | 13/25 [00:03<00:03, 3.67it/s] 56%|█████▌ | 14/25 [00:03<00:02, 3.67it/s] 60%|██████ | 15/25 [00:04<00:02, 3.67it/s] 64%|██████▍ | 16/25 [00:04<00:02, 3.67it/s] 68%|██████▊ | 17/25 [00:04<00:02, 3.67it/s] 72%|███████▏ | 18/25 [00:04<00:01, 3.67it/s] 76%|███████▌ | 19/25 [00:05<00:01, 3.67it/s] 80%|████████ | 20/25 [00:05<00:01, 3.67it/s] 84%|████████▍ | 21/25 [00:05<00:01, 3.67it/s] 88%|████████▊ | 22/25 [00:05<00:00, 3.67it/s] 92%|█████████▏| 23/25 [00:06<00:00, 3.66it/s] 96%|█████████▌| 24/25 [00:06<00:00, 3.67it/s] 100%|██████████| 25/25 [00:06<00:00, 3.67it/s] 100%|██████████| 25/25 [00:06<00:00, 3.67it/s]
Prediction
fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110ddIDutwfnvdbu74jbzprs3wykdkzxqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK energy drink can, ferrari, pop art, very tired, white background
- 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
- tall can, many cans, ugly, broken, distorted, text, words
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, ferrari, pop art, very tired, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "tall can, many cans, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK energy drink can, ferrari, pop art, very tired, white background", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "tall can, many cans, ugly, broken, distorted, text, words", prompt_strength: 0.8, num_inference_steps: 25 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, ferrari, pop art, very tired, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "tall can, many cans, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-energy-drink 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-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, ferrari, pop art, very tired, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "tall can, many cans, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-10T10:21:27.908785Z", "created_at": "2024-01-10T10:21:00.275567Z", "data_removed": false, "error": null, "id": "utwfnvdbu74jbzprs3wykdkzxq", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, ferrari, pop art, very tired, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "tall can, many cans, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 42913\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of a <s0><s1> energy drink can, ferrari, pop art, very tired, white background\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:06, 3.70it/s]\n 8%|▊ | 2/25 [00:00<00:06, 3.68it/s]\n 12%|█▏ | 3/25 [00:00<00:05, 3.68it/s]\n 16%|█▌ | 4/25 [00:01<00:05, 3.67it/s]\n 20%|██ | 5/25 [00:01<00:05, 3.67it/s]\n 24%|██▍ | 6/25 [00:01<00:05, 3.67it/s]\n 28%|██▊ | 7/25 [00:01<00:04, 3.67it/s]\n 32%|███▏ | 8/25 [00:02<00:04, 3.66it/s]\n 36%|███▌ | 9/25 [00:02<00:04, 3.66it/s]\n 40%|████ | 10/25 [00:02<00:04, 3.66it/s]\n 44%|████▍ | 11/25 [00:02<00:03, 3.66it/s]\n 48%|████▊ | 12/25 [00:03<00:03, 3.66it/s]\n 52%|█████▏ | 13/25 [00:03<00:03, 3.66it/s]\n 56%|█████▌ | 14/25 [00:03<00:03, 3.66it/s]\n 60%|██████ | 15/25 [00:04<00:02, 3.65it/s]\n 64%|██████▍ | 16/25 [00:04<00:02, 3.65it/s]\n 68%|██████▊ | 17/25 [00:04<00:02, 3.65it/s]\n 72%|███████▏ | 18/25 [00:04<00:01, 3.66it/s]\n 76%|███████▌ | 19/25 [00:05<00:01, 3.65it/s]\n 80%|████████ | 20/25 [00:05<00:01, 3.65it/s]\n 84%|████████▍ | 21/25 [00:05<00:01, 3.65it/s]\n 88%|████████▊ | 22/25 [00:06<00:00, 3.66it/s]\n 92%|█████████▏| 23/25 [00:06<00:00, 3.65it/s]\n 96%|█████████▌| 24/25 [00:06<00:00, 3.65it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.66it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.66it/s]", "metrics": { "predict_time": 9.337399, "total_time": 27.633218 }, "output": [ "https://replicate.delivery/pbxt/l9YbEYi0hmbLJhqd3P8x6bKuQVk5ifGBJjTHTrKCck2TDhFJA/out-0.png" ], "started_at": "2024-01-10T10:21:18.571386Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/utwfnvdbu74jbzprs3wykdkzxq", "cancel": "https://api.replicate.com/v1/predictions/utwfnvdbu74jbzprs3wykdkzxq/cancel" }, "version": "4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd" }
Generated inUsing seed: 42913 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of a <s0><s1> energy drink can, ferrari, pop art, very tired, white background txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:06, 3.70it/s] 8%|▊ | 2/25 [00:00<00:06, 3.68it/s] 12%|█▏ | 3/25 [00:00<00:05, 3.68it/s] 16%|█▌ | 4/25 [00:01<00:05, 3.67it/s] 20%|██ | 5/25 [00:01<00:05, 3.67it/s] 24%|██▍ | 6/25 [00:01<00:05, 3.67it/s] 28%|██▊ | 7/25 [00:01<00:04, 3.67it/s] 32%|███▏ | 8/25 [00:02<00:04, 3.66it/s] 36%|███▌ | 9/25 [00:02<00:04, 3.66it/s] 40%|████ | 10/25 [00:02<00:04, 3.66it/s] 44%|████▍ | 11/25 [00:02<00:03, 3.66it/s] 48%|████▊ | 12/25 [00:03<00:03, 3.66it/s] 52%|█████▏ | 13/25 [00:03<00:03, 3.66it/s] 56%|█████▌ | 14/25 [00:03<00:03, 3.66it/s] 60%|██████ | 15/25 [00:04<00:02, 3.65it/s] 64%|██████▍ | 16/25 [00:04<00:02, 3.65it/s] 68%|██████▊ | 17/25 [00:04<00:02, 3.65it/s] 72%|███████▏ | 18/25 [00:04<00:01, 3.66it/s] 76%|███████▌ | 19/25 [00:05<00:01, 3.65it/s] 80%|████████ | 20/25 [00:05<00:01, 3.65it/s] 84%|████████▍ | 21/25 [00:05<00:01, 3.65it/s] 88%|████████▊ | 22/25 [00:06<00:00, 3.66it/s] 92%|█████████▏| 23/25 [00:06<00:00, 3.65it/s] 96%|█████████▌| 24/25 [00:06<00:00, 3.65it/s] 100%|██████████| 25/25 [00:06<00:00, 3.66it/s] 100%|██████████| 25/25 [00:06<00:00, 3.66it/s]
Prediction
fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110ddIDy7sm37dbzhb4sdncoozwaohz2yStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK energy drink can, EPIC, fire, epic landscape, white background
- 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
- many cans, tall can, ugly, broken, distorted, text, words
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, EPIC, fire, epic landscape, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "many cans, tall can, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK energy drink can, EPIC, fire, epic landscape, white background", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "many cans, tall can, ugly, broken, distorted, text, words", prompt_strength: 0.8, num_inference_steps: 25 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-energy-drink using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, EPIC, fire, epic landscape, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "many cans, tall can, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-energy-drink 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-energy-drink:4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, EPIC, fire, epic landscape, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "many cans, tall can, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-10T10:28:50.105582Z", "created_at": "2024-01-10T10:28:21.795469Z", "data_removed": false, "error": null, "id": "y7sm37dbzhb4sdncoozwaohz2y", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK energy drink can, EPIC, fire, epic landscape, white background", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "many cans, tall can, ugly, broken, distorted, text, words", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 20773\nEnsuring enough disk space...\nFree disk space: 2007921754112\nDownloading weights: https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar\n2024-01-10T10:28:40Z | INFO | [ Initiating ] dest=/src/weights-cache/1075171f2b09d76f minimum_chunk_size=150M url=https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar\n2024-01-10T10:28:40Z | INFO | [ Complete ] dest=/src/weights-cache/1075171f2b09d76f size=\"186 MB\" total_elapsed=0.478s url=https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar\nb''\nDownloaded weights in 0.6441628932952881 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of a <s0><s1> energy drink can, EPIC, fire, epic landscape, white background\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:06, 3.66it/s]\n 8%|▊ | 2/25 [00:00<00:06, 3.66it/s]\n 12%|█▏ | 3/25 [00:00<00:06, 3.66it/s]\n 16%|█▌ | 4/25 [00:01<00:05, 3.65it/s]\n 20%|██ | 5/25 [00:01<00:05, 3.65it/s]\n 24%|██▍ | 6/25 [00:01<00:05, 3.65it/s]\n 28%|██▊ | 7/25 [00:01<00:04, 3.65it/s]\n 32%|███▏ | 8/25 [00:02<00:04, 3.65it/s]\n 36%|███▌ | 9/25 [00:02<00:04, 3.64it/s]\n 40%|████ | 10/25 [00:02<00:04, 3.65it/s]\n 44%|████▍ | 11/25 [00:03<00:03, 3.65it/s]\n 48%|████▊ | 12/25 [00:03<00:03, 3.64it/s]\n 52%|█████▏ | 13/25 [00:03<00:03, 3.64it/s]\n 56%|█████▌ | 14/25 [00:03<00:03, 3.64it/s]\n 60%|██████ | 15/25 [00:04<00:02, 3.64it/s]\n 64%|██████▍ | 16/25 [00:04<00:02, 3.64it/s]\n 68%|██████▊ | 17/25 [00:04<00:02, 3.64it/s]\n 72%|███████▏ | 18/25 [00:04<00:01, 3.64it/s]\n 76%|███████▌ | 19/25 [00:05<00:01, 3.64it/s]\n 80%|████████ | 20/25 [00:05<00:01, 3.64it/s]\n 84%|████████▍ | 21/25 [00:05<00:01, 3.64it/s]\n 88%|████████▊ | 22/25 [00:06<00:00, 3.64it/s]\n 92%|█████████▏| 23/25 [00:06<00:00, 3.63it/s]\n 96%|█████████▌| 24/25 [00:06<00:00, 3.64it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.64it/s]\n100%|██████████| 25/25 [00:06<00:00, 3.64it/s]", "metrics": { "predict_time": 9.907591, "total_time": 28.310113 }, "output": [ "https://replicate.delivery/pbxt/5yMXMLvPPE5oLZbu5mqCY0UXIFAGnBbooSLs5l4iWZbYjwiE/out-0.png" ], "started_at": "2024-01-10T10:28:40.197991Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/y7sm37dbzhb4sdncoozwaohz2y", "cancel": "https://api.replicate.com/v1/predictions/y7sm37dbzhb4sdncoozwaohz2y/cancel" }, "version": "4bf3a3032664de2ba70247b3858f6a31569edec83dfabf6cf60cb98224a110dd" }
Generated inUsing seed: 20773 Ensuring enough disk space... Free disk space: 2007921754112 Downloading weights: https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar 2024-01-10T10:28:40Z | INFO | [ Initiating ] dest=/src/weights-cache/1075171f2b09d76f minimum_chunk_size=150M url=https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar 2024-01-10T10:28:40Z | INFO | [ Complete ] dest=/src/weights-cache/1075171f2b09d76f size="186 MB" total_elapsed=0.478s url=https://replicate.delivery/pbxt/1YQb0H6kYCqKD9XwE9CEZj4KxuUh0epmZg1r20AeaiXTyBLSA/trained_model.tar b'' Downloaded weights in 0.6441628932952881 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of a <s0><s1> energy drink can, EPIC, fire, epic landscape, white background txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:06, 3.66it/s] 8%|▊ | 2/25 [00:00<00:06, 3.66it/s] 12%|█▏ | 3/25 [00:00<00:06, 3.66it/s] 16%|█▌ | 4/25 [00:01<00:05, 3.65it/s] 20%|██ | 5/25 [00:01<00:05, 3.65it/s] 24%|██▍ | 6/25 [00:01<00:05, 3.65it/s] 28%|██▊ | 7/25 [00:01<00:04, 3.65it/s] 32%|███▏ | 8/25 [00:02<00:04, 3.65it/s] 36%|███▌ | 9/25 [00:02<00:04, 3.64it/s] 40%|████ | 10/25 [00:02<00:04, 3.65it/s] 44%|████▍ | 11/25 [00:03<00:03, 3.65it/s] 48%|████▊ | 12/25 [00:03<00:03, 3.64it/s] 52%|█████▏ | 13/25 [00:03<00:03, 3.64it/s] 56%|█████▌ | 14/25 [00:03<00:03, 3.64it/s] 60%|██████ | 15/25 [00:04<00:02, 3.64it/s] 64%|██████▍ | 16/25 [00:04<00:02, 3.64it/s] 68%|██████▊ | 17/25 [00:04<00:02, 3.64it/s] 72%|███████▏ | 18/25 [00:04<00:01, 3.64it/s] 76%|███████▌ | 19/25 [00:05<00:01, 3.64it/s] 80%|████████ | 20/25 [00:05<00:01, 3.64it/s] 84%|████████▍ | 21/25 [00:05<00:01, 3.64it/s] 88%|████████▊ | 22/25 [00:06<00:00, 3.64it/s] 92%|█████████▏| 23/25 [00:06<00:00, 3.63it/s] 96%|█████████▌| 24/25 [00:06<00:00, 3.64it/s] 100%|██████████| 25/25 [00:06<00:00, 3.64it/s] 100%|██████████| 25/25 [00:06<00:00, 3.64it/s]
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