erbridge / dbag
Look! A Dylan!
- Public
- 286 runs
Prediction
erbridge/dbag:1f8987d797c99d0f721fc7283f25c023b3328e7a7eb9e861b3494c8962d392d8Input
- model
- dev
- prompt
- TOK, A vibrant watercolor painting of TOK lounging on a sun-drenched beach. Soft, translucent washes of turquoise and azure blend seamlessly for the ocean, while golden sand is rendered with loose, expressive brushstrokes. TOK's form is captured with fluid lines and gentle color gradients, surrounded by splashes of coral and lavender from nearby seashells. Wispy clouds drift across a warm, pastel sky, creating a dreamy atmosphere. The composition balances TOK's relaxed posture against the vast, serene seascape.
- lora_scale
- 1.2
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 7.5
- output_quality
- 80
- num_inference_steps
- 50
{ "model": "dev", "prompt": "TOK, A vibrant watercolor painting of TOK lounging on a sun-drenched beach. Soft, translucent washes of turquoise and azure blend seamlessly for the ocean, while golden sand is rendered with loose, expressive brushstrokes. TOK's form is captured with fluid lines and gentle color gradients, surrounded by splashes of coral and lavender from nearby seashells. Wispy clouds drift across a warm, pastel sky, creating a dreamy atmosphere. The composition balances TOK's relaxed posture against the vast, serene seascape.\n", "lora_scale": 1.2, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 7.5, "output_quality": 80, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 erbridge/dbag using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "erbridge/dbag:1f8987d797c99d0f721fc7283f25c023b3328e7a7eb9e861b3494c8962d392d8", { input: { model: "dev", prompt: "TOK, A vibrant watercolor painting of TOK lounging on a sun-drenched beach. Soft, translucent washes of turquoise and azure blend seamlessly for the ocean, while golden sand is rendered with loose, expressive brushstrokes. TOK's form is captured with fluid lines and gentle color gradients, surrounded by splashes of coral and lavender from nearby seashells. Wispy clouds drift across a warm, pastel sky, creating a dreamy atmosphere. The composition balances TOK's relaxed posture against the vast, serene seascape.\n", lora_scale: 1.2, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 7.5, output_quality: 80, 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 replicate
Import the client:import replicate
Run erbridge/dbag using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "erbridge/dbag:1f8987d797c99d0f721fc7283f25c023b3328e7a7eb9e861b3494c8962d392d8", input={ "model": "dev", "prompt": "TOK, A vibrant watercolor painting of TOK lounging on a sun-drenched beach. Soft, translucent washes of turquoise and azure blend seamlessly for the ocean, while golden sand is rendered with loose, expressive brushstrokes. TOK's form is captured with fluid lines and gentle color gradients, surrounded by splashes of coral and lavender from nearby seashells. Wispy clouds drift across a warm, pastel sky, creating a dreamy atmosphere. The composition balances TOK's relaxed posture against the vast, serene seascape.\n", "lora_scale": 1.2, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 7.5, "output_quality": 80, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run erbridge/dbag 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": "erbridge/dbag:1f8987d797c99d0f721fc7283f25c023b3328e7a7eb9e861b3494c8962d392d8", "input": { "model": "dev", "prompt": "TOK, A vibrant watercolor painting of TOK lounging on a sun-drenched beach. Soft, translucent washes of turquoise and azure blend seamlessly for the ocean, while golden sand is rendered with loose, expressive brushstrokes. TOK\'s form is captured with fluid lines and gentle color gradients, surrounded by splashes of coral and lavender from nearby seashells. Wispy clouds drift across a warm, pastel sky, creating a dreamy atmosphere. The composition balances TOK\'s relaxed posture against the vast, serene seascape.\\n", "lora_scale": 1.2, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 7.5, "output_quality": 80, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-15T20:08:09.679565Z", "created_at": "2024-08-15T20:07:45.802000Z", "data_removed": false, "error": null, "id": "5vm3h825h9rm00chaykv49v8mg", "input": { "model": "dev", "prompt": "TOK, A vibrant watercolor painting of TOK lounging on a sun-drenched beach. Soft, translucent washes of turquoise and azure blend seamlessly for the ocean, while golden sand is rendered with loose, expressive brushstrokes. TOK's form is captured with fluid lines and gentle color gradients, surrounded by splashes of coral and lavender from nearby seashells. Wispy clouds drift across a warm, pastel sky, creating a dreamy atmosphere. The composition balances TOK's relaxed posture against the vast, serene seascape.\n", "lora_scale": 1.2, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 7.5, "output_quality": 80, "num_inference_steps": 50 }, "logs": "Using seed: 21114\nPrompt: TOK, A vibrant watercolor painting of TOK lounging on a sun-drenched beach. Soft, translucent washes of turquoise and azure blend seamlessly for the ocean, while golden sand is rendered with loose, expressive brushstrokes. TOK's form is captured with fluid lines and gentle color gradients, surrounded by splashes of coral and lavender from nearby seashells. Wispy clouds drift across a warm, pastel sky, creating a dreamy atmosphere. The composition balances TOK's relaxed posture against the vast, serene seascape.\ntxt2img mode\nUsing dev model\nLoading LoRA weights from https://replicate.delivery/yhqm/F5TvdEQN1IKnDhZrhI35rIaH7ZlX0P1KovXioVNWfhn7XgpJA/trained_model.tar\nLoRA weights loaded successfully\nThe following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: [\"drift across a warm, pastel sky, creating a dreamy atmosphere. the composition balances tok's relaxed posture against the vast, serene seascape.\"]\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.50it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.93it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.74it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.63it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.59it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.57it/s]\n 14%|█▍ | 7/50 [00:01<00:12, 3.55it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.55it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.54it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.54it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.52it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.53it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.52it/s]\n 28%|██▊ | 14/50 [00:03<00:10, 3.53it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.51it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.53it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.53it/s]\n 36%|███▌ | 18/50 [00:05<00:09, 3.52it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.52it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.51it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.50it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.50it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.50it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.49it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.50it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.50it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.51it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.52it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.52it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.52it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.52it/s]\n 64%|██████▍ | 32/50 [00:09<00:05, 3.52it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.52it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.51it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.52it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.52it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.49it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.51it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.50it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.50it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.50it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.50it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.49it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.50it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.49it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.49it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.50it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.48it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.47it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.48it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.52it/s]", "metrics": { "predict_time": 23.808553032, "total_time": 23.877565 }, "output": [ "https://replicate.delivery/yhqm/PVHtll0JbuKlEBLEYeTFdrHuBKLmGdjHx0WkBodysmlUkgpJA/out-0.webp" ], "started_at": "2024-08-15T20:07:45.871012Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5vm3h825h9rm00chaykv49v8mg", "cancel": "https://api.replicate.com/v1/predictions/5vm3h825h9rm00chaykv49v8mg/cancel" }, "version": "1f8987d797c99d0f721fc7283f25c023b3328e7a7eb9e861b3494c8962d392d8" }
Generated inUsing seed: 21114 Prompt: TOK, A vibrant watercolor painting of TOK lounging on a sun-drenched beach. Soft, translucent washes of turquoise and azure blend seamlessly for the ocean, while golden sand is rendered with loose, expressive brushstrokes. TOK's form is captured with fluid lines and gentle color gradients, surrounded by splashes of coral and lavender from nearby seashells. Wispy clouds drift across a warm, pastel sky, creating a dreamy atmosphere. The composition balances TOK's relaxed posture against the vast, serene seascape. txt2img mode Using dev model Loading LoRA weights from https://replicate.delivery/yhqm/F5TvdEQN1IKnDhZrhI35rIaH7ZlX0P1KovXioVNWfhn7XgpJA/trained_model.tar LoRA weights loaded successfully The following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ["drift across a warm, pastel sky, creating a dreamy atmosphere. the composition balances tok's relaxed posture against the vast, serene seascape."] 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.50it/s] 4%|▍ | 2/50 [00:00<00:12, 3.93it/s] 6%|▌ | 3/50 [00:00<00:12, 3.74it/s] 8%|▊ | 4/50 [00:01<00:12, 3.63it/s] 10%|█ | 5/50 [00:01<00:12, 3.59it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.57it/s] 14%|█▍ | 7/50 [00:01<00:12, 3.55it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.55it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.54it/s] 20%|██ | 10/50 [00:02<00:11, 3.54it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.52it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.53it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.52it/s] 28%|██▊ | 14/50 [00:03<00:10, 3.53it/s] 30%|███ | 15/50 [00:04<00:09, 3.51it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.53it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.53it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.52it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.52it/s] 40%|████ | 20/50 [00:05<00:08, 3.51it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.50it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.50it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.50it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.49it/s] 50%|█████ | 25/50 [00:07<00:07, 3.50it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.50it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.51it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.52it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.52it/s] 60%|██████ | 30/50 [00:08<00:05, 3.52it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.52it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.52it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.52it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.51it/s] 70%|███████ | 35/50 [00:09<00:04, 3.52it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.52it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.49it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.51it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.50it/s] 80%|████████ | 40/50 [00:11<00:02, 3.50it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.50it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.50it/s] 86%|████████▌ | 43/50 [00:12<00:02, 3.49it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.50it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.49it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.49it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.50it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.48it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.47it/s] 100%|██████████| 50/50 [00:14<00:00, 3.48it/s] 100%|██████████| 50/50 [00:14<00:00, 3.52it/s]
Prediction
erbridge/dbag:4eb213c9dd29c15ff04be0c5dbc314820ffce2d6fb80c61272bfc9d2850f1a71IDbch6kr3b6bjb2o363uxb5qaooyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @erbridgeInput
- width
- 1024
- height
- 1024
- prompt
- A watercolor painting of TOK on the beach
- 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 watercolor painting of TOK on the beach", "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 replicate
Import 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 erbridge/dbag using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "erbridge/dbag:4eb213c9dd29c15ff04be0c5dbc314820ffce2d6fb80c61272bfc9d2850f1a71", { input: { width: 1024, height: 1024, prompt: "A watercolor painting of TOK on the beach", 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 replicate
Import the client:import replicate
Run erbridge/dbag using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "erbridge/dbag:4eb213c9dd29c15ff04be0c5dbc314820ffce2d6fb80c61272bfc9d2850f1a71", input={ "width": 1024, "height": 1024, "prompt": "A watercolor painting of TOK on the beach", "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 erbridge/dbag 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": "erbridge/dbag:4eb213c9dd29c15ff04be0c5dbc314820ffce2d6fb80c61272bfc9d2850f1a71", "input": { "width": 1024, "height": 1024, "prompt": "A watercolor painting of TOK on the beach", "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-25T13:36:59.553809Z", "created_at": "2023-08-25T13:36:02.915675Z", "data_removed": false, "error": null, "id": "bch6kr3b6bjb2o363uxb5qaooy", "input": { "width": 1024, "height": 1024, "prompt": "A watercolor painting of TOK on the beach", "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: 53154\nPrompt: A watercolor painting of <s0><s1> on the beach\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:42, 1.16it/s]\n 4%|▍ | 2/50 [00:01<00:24, 1.94it/s]\n 6%|▌ | 3/50 [00:01<00:19, 2.47it/s]\n 8%|▊ | 4/50 [00:01<00:16, 2.84it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.09it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.26it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.38it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.47it/s]\n 18%|█▊ | 9/50 [00:03<00:11, 3.52it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.56it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.59it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.61it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.62it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:06<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.65it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.64it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.64it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.64it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.64it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.63it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.50it/s]", "metrics": { "predict_time": 16.492833, "total_time": 56.638134 }, "output": [ "https://replicate.delivery/pbxt/gQ0NFsHn2eWgHyD1D33PfzhXQS3fTTdJ9cBXca0GEFw3DM7iA/out-0.png" ], "started_at": "2023-08-25T13:36:43.060976Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bch6kr3b6bjb2o363uxb5qaooy", "cancel": "https://api.replicate.com/v1/predictions/bch6kr3b6bjb2o363uxb5qaooy/cancel" }, "version": "4eb213c9dd29c15ff04be0c5dbc314820ffce2d6fb80c61272bfc9d2850f1a71" }
Generated inUsing seed: 53154 Prompt: A watercolor painting of <s0><s1> on the beach txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:42, 1.16it/s] 4%|▍ | 2/50 [00:01<00:24, 1.94it/s] 6%|▌ | 3/50 [00:01<00:19, 2.47it/s] 8%|▊ | 4/50 [00:01<00:16, 2.84it/s] 10%|█ | 5/50 [00:01<00:14, 3.09it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.26it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.38it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.47it/s] 18%|█▊ | 9/50 [00:03<00:11, 3.52it/s] 20%|██ | 10/50 [00:03<00:11, 3.56it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.59it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.61it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:06<00:08, 3.64it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.64it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.65it/s] 50%|█████ | 25/50 [00:07<00:06, 3.65it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.64it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s] 70%|███████ | 35/50 [00:10<00:04, 3.64it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.64it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.64it/s] 80%|████████ | 40/50 [00:11<00:02, 3.64it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.64it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.63it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.64it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.63it/s] 100%|██████████| 50/50 [00:14<00:00, 3.63it/s] 100%|██████████| 50/50 [00:14<00:00, 3.50it/s]
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