profdl / logos
Trained on logo designs, black logos on white backgrounds
- Public
- 4.9K runs
-
L40S
- SDXL fine-tune
Prediction
profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595IDo2imwq3bsqqgbmsbo5kzrpt5xqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- abstract logo in the style of logo, centered, icon, flat, graphic, simple, bold, iconic, minimal, black and white shapes
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail,
- prompt_strength
- 0.9
- num_inference_steps
- 27
{ "width": 1024, "height": 1024, "prompt": "abstract logo in the style of logo, centered, icon, flat, graphic, simple, bold, iconic, minimal, black and white shapes", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", { input: { width: 1024, height: 1024, prompt: "abstract logo in the style of logo, centered, icon, flat, graphic, simple, bold, iconic, minimal, black and white shapes", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", prompt_strength: 0.9, num_inference_steps: 27 } } ); // 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 profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", input={ "width": 1024, "height": 1024, "prompt": "abstract logo in the style of logo, centered, icon, flat, graphic, simple, bold, iconic, minimal, black and white shapes", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run profdl/logos 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": "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", "input": { "width": 1024, "height": 1024, "prompt": "abstract logo in the style of logo, centered, icon, flat, graphic, simple, bold, iconic, minimal, black and white shapes", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-12T20:04:03.787904Z", "created_at": "2023-12-12T20:03:55.121906Z", "data_removed": false, "error": null, "id": "o2imwq3bsqqgbmsbo5kzrpt5xq", "input": { "width": 1024, "height": 1024, "prompt": "abstract logo in the style of logo, centered, icon, flat, graphic, simple, bold, iconic, minimal, black and white shapes", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 }, "logs": "Using seed: 4110\nskipping loading .. weights already loaded\nPrompt: abstract logo in the style of logo, centered, icon, flat, graphic, simple, bold, iconic, minimal, black and white shapes\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:07, 3.66it/s]\n 7%|▋ | 2/27 [00:00<00:06, 3.65it/s]\n 11%|█ | 3/27 [00:00<00:06, 3.66it/s]\n 15%|█▍ | 4/27 [00:01<00:06, 3.66it/s]\n 19%|█▊ | 5/27 [00:01<00:06, 3.66it/s]\n 22%|██▏ | 6/27 [00:01<00:05, 3.66it/s]\n 26%|██▌ | 7/27 [00:01<00:05, 3.66it/s]\n 30%|██▉ | 8/27 [00:02<00:05, 3.65it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 3.66it/s]\n 37%|███▋ | 10/27 [00:02<00:04, 3.66it/s]\n 41%|████ | 11/27 [00:03<00:04, 3.65it/s]\n 44%|████▍ | 12/27 [00:03<00:04, 3.65it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 3.66it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 3.65it/s]\n 56%|█████▌ | 15/27 [00:04<00:03, 3.65it/s]\n 59%|█████▉ | 16/27 [00:04<00:03, 3.65it/s]\n 63%|██████▎ | 17/27 [00:04<00:02, 3.65it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 3.65it/s]\n 70%|███████ | 19/27 [00:05<00:02, 3.65it/s]\n 74%|███████▍ | 20/27 [00:05<00:01, 3.65it/s]\n 78%|███████▊ | 21/27 [00:05<00:01, 3.65it/s]\n 81%|████████▏ | 22/27 [00:06<00:01, 3.65it/s]\n 85%|████████▌ | 23/27 [00:06<00:01, 3.65it/s]\n 89%|████████▉ | 24/27 [00:06<00:00, 3.65it/s]\n 93%|█████████▎| 25/27 [00:06<00:00, 3.65it/s]\n 96%|█████████▋| 26/27 [00:07<00:00, 3.65it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.65it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.65it/s]", "metrics": { "predict_time": 8.648127, "total_time": 8.665998 }, "output": [ "https://replicate.delivery/pbxt/HD5rT3BQYHpdHBLEogFSxKGql1PXvqVJ3XRghibEND6suZgE/out-0.png" ], "started_at": "2023-12-12T20:03:55.139777Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/o2imwq3bsqqgbmsbo5kzrpt5xq", "cancel": "https://api.replicate.com/v1/predictions/o2imwq3bsqqgbmsbo5kzrpt5xq/cancel" }, "version": "3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595" }Generated inUsing seed: 4110 skipping loading .. weights already loaded Prompt: abstract logo in the style of logo, centered, icon, flat, graphic, simple, bold, iconic, minimal, black and white shapes txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:07, 3.66it/s] 7%|▋ | 2/27 [00:00<00:06, 3.65it/s] 11%|█ | 3/27 [00:00<00:06, 3.66it/s] 15%|█▍ | 4/27 [00:01<00:06, 3.66it/s] 19%|█▊ | 5/27 [00:01<00:06, 3.66it/s] 22%|██▏ | 6/27 [00:01<00:05, 3.66it/s] 26%|██▌ | 7/27 [00:01<00:05, 3.66it/s] 30%|██▉ | 8/27 [00:02<00:05, 3.65it/s] 33%|███▎ | 9/27 [00:02<00:04, 3.66it/s] 37%|███▋ | 10/27 [00:02<00:04, 3.66it/s] 41%|████ | 11/27 [00:03<00:04, 3.65it/s] 44%|████▍ | 12/27 [00:03<00:04, 3.65it/s] 48%|████▊ | 13/27 [00:03<00:03, 3.66it/s] 52%|█████▏ | 14/27 [00:03<00:03, 3.65it/s] 56%|█████▌ | 15/27 [00:04<00:03, 3.65it/s] 59%|█████▉ | 16/27 [00:04<00:03, 3.65it/s] 63%|██████▎ | 17/27 [00:04<00:02, 3.65it/s] 67%|██████▋ | 18/27 [00:04<00:02, 3.65it/s] 70%|███████ | 19/27 [00:05<00:02, 3.65it/s] 74%|███████▍ | 20/27 [00:05<00:01, 3.65it/s] 78%|███████▊ | 21/27 [00:05<00:01, 3.65it/s] 81%|████████▏ | 22/27 [00:06<00:01, 3.65it/s] 85%|████████▌ | 23/27 [00:06<00:01, 3.65it/s] 89%|████████▉ | 24/27 [00:06<00:00, 3.65it/s] 93%|█████████▎| 25/27 [00:06<00:00, 3.65it/s] 96%|█████████▋| 26/27 [00:07<00:00, 3.65it/s] 100%|██████████| 27/27 [00:07<00:00, 3.65it/s] 100%|██████████| 27/27 [00:07<00:00, 3.65it/s]Prediction
profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595IDhbir4p3b4jogyhdmmao2k5fnziStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- abstract logo with two simple black shapes in the style of logo, centered, icon, flat, graphic, simple, bold, minimal, geometric, well proportioned,
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail,
- prompt_strength
- 0.9
- num_inference_steps
- 27
{ "width": 1024, "height": 1024, "prompt": "abstract logo with two simple black shapes in the style of logo, centered, icon, flat, graphic, simple, bold, minimal, geometric, well proportioned, ", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", { input: { width: 1024, height: 1024, prompt: "abstract logo with two simple black shapes in the style of logo, centered, icon, flat, graphic, simple, bold, minimal, geometric, well proportioned, ", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", prompt_strength: 0.9, num_inference_steps: 27 } } ); // 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 profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", input={ "width": 1024, "height": 1024, "prompt": "abstract logo with two simple black shapes in the style of logo, centered, icon, flat, graphic, simple, bold, minimal, geometric, well proportioned, ", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run profdl/logos 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": "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", "input": { "width": 1024, "height": 1024, "prompt": "abstract logo with two simple black shapes in the style of logo, centered, icon, flat, graphic, simple, bold, minimal, geometric, well proportioned, ", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-12T20:06:44.939721Z", "created_at": "2023-12-12T20:06:35.443981Z", "data_removed": false, "error": null, "id": "hbir4p3b4jogyhdmmao2k5fnzi", "input": { "width": 1024, "height": 1024, "prompt": "abstract logo with two simple black shapes in the style of logo, centered, icon, flat, graphic, simple, bold, minimal, geometric, well proportioned, ", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 }, "logs": "Using seed: 35790\nskipping loading .. weights already loaded\nPrompt: abstract logo with two simple black shapes in the style of logo, centered, icon, flat, graphic, simple, bold, minimal, geometric, well proportioned,\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:07, 3.69it/s]\n 7%|▋ | 2/27 [00:00<00:06, 3.68it/s]\n 11%|█ | 3/27 [00:00<00:06, 3.68it/s]\n 15%|█▍ | 4/27 [00:01<00:06, 3.68it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 3.68it/s]\n 22%|██▏ | 6/27 [00:01<00:05, 3.68it/s]\n 26%|██▌ | 7/27 [00:01<00:05, 3.68it/s]\n 30%|██▉ | 8/27 [00:02<00:05, 3.68it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 3.68it/s]\n 37%|███▋ | 10/27 [00:02<00:04, 3.68it/s]\n 41%|████ | 11/27 [00:02<00:04, 3.68it/s]\n 44%|████▍ | 12/27 [00:03<00:04, 3.68it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 3.68it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 3.67it/s]\n 56%|█████▌ | 15/27 [00:04<00:03, 3.67it/s]\n 59%|█████▉ | 16/27 [00:04<00:02, 3.68it/s]\n 63%|██████▎ | 17/27 [00:04<00:02, 3.67it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 3.67it/s]\n 70%|███████ | 19/27 [00:05<00:02, 3.67it/s]\n 74%|███████▍ | 20/27 [00:05<00:01, 3.67it/s]\n 78%|███████▊ | 21/27 [00:05<00:01, 3.67it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 3.67it/s]\n 85%|████████▌ | 23/27 [00:06<00:01, 3.67it/s]\n 89%|████████▉ | 24/27 [00:06<00:00, 3.67it/s]\n 93%|█████████▎| 25/27 [00:06<00:00, 3.67it/s]\n 96%|█████████▋| 26/27 [00:07<00:00, 3.67it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.67it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.67it/s]", "metrics": { "predict_time": 9.461837, "total_time": 9.49574 }, "output": [ "https://replicate.delivery/pbxt/qKnohG8IHLKPJh9hjTUEM69yEGm0qCrCHC8Oy55arfZqemBSA/out-0.png" ], "started_at": "2023-12-12T20:06:35.477884Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hbir4p3b4jogyhdmmao2k5fnzi", "cancel": "https://api.replicate.com/v1/predictions/hbir4p3b4jogyhdmmao2k5fnzi/cancel" }, "version": "3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595" }Generated inUsing seed: 35790 skipping loading .. weights already loaded Prompt: abstract logo with two simple black shapes in the style of logo, centered, icon, flat, graphic, simple, bold, minimal, geometric, well proportioned, txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:07, 3.69it/s] 7%|▋ | 2/27 [00:00<00:06, 3.68it/s] 11%|█ | 3/27 [00:00<00:06, 3.68it/s] 15%|█▍ | 4/27 [00:01<00:06, 3.68it/s] 19%|█▊ | 5/27 [00:01<00:05, 3.68it/s] 22%|██▏ | 6/27 [00:01<00:05, 3.68it/s] 26%|██▌ | 7/27 [00:01<00:05, 3.68it/s] 30%|██▉ | 8/27 [00:02<00:05, 3.68it/s] 33%|███▎ | 9/27 [00:02<00:04, 3.68it/s] 37%|███▋ | 10/27 [00:02<00:04, 3.68it/s] 41%|████ | 11/27 [00:02<00:04, 3.68it/s] 44%|████▍ | 12/27 [00:03<00:04, 3.68it/s] 48%|████▊ | 13/27 [00:03<00:03, 3.68it/s] 52%|█████▏ | 14/27 [00:03<00:03, 3.67it/s] 56%|█████▌ | 15/27 [00:04<00:03, 3.67it/s] 59%|█████▉ | 16/27 [00:04<00:02, 3.68it/s] 63%|██████▎ | 17/27 [00:04<00:02, 3.67it/s] 67%|██████▋ | 18/27 [00:04<00:02, 3.67it/s] 70%|███████ | 19/27 [00:05<00:02, 3.67it/s] 74%|███████▍ | 20/27 [00:05<00:01, 3.67it/s] 78%|███████▊ | 21/27 [00:05<00:01, 3.67it/s] 81%|████████▏ | 22/27 [00:05<00:01, 3.67it/s] 85%|████████▌ | 23/27 [00:06<00:01, 3.67it/s] 89%|████████▉ | 24/27 [00:06<00:00, 3.67it/s] 93%|█████████▎| 25/27 [00:06<00:00, 3.67it/s] 96%|█████████▋| 26/27 [00:07<00:00, 3.67it/s] 100%|██████████| 27/27 [00:07<00:00, 3.67it/s] 100%|██████████| 27/27 [00:07<00:00, 3.67it/s]Prediction
profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595ID5ndwgndbos4rvyszrpyinipoxaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- iconic logo in the style of logo
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 1
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- cropped, lines, linear, off center, circles, curves, color, texture, light, shading, complex, maximal, too much detail,
- prompt_strength
- 0.9
- num_inference_steps
- 27
{ "width": 1024, "height": 1024, "prompt": "iconic logo in the style of logo", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, circles, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", { input: { width: 1024, height: 1024, prompt: "iconic logo in the style of logo", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 1, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "cropped, lines, linear, off center, circles, curves, color, texture, light, shading, complex, maximal, too much detail, ", prompt_strength: 0.9, num_inference_steps: 27 } } ); // 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 profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", input={ "width": 1024, "height": 1024, "prompt": "iconic logo in the style of logo", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, circles, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run profdl/logos 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": "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", "input": { "width": 1024, "height": 1024, "prompt": "iconic logo in the style of logo", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, circles, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-12T20:09:34.657992Z", "created_at": "2023-12-12T20:09:22.419984Z", "data_removed": false, "error": null, "id": "5ndwgndbos4rvyszrpyinipoxa", "input": { "width": 1024, "height": 1024, "prompt": "iconic logo in the style of logo", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "cropped, lines, linear, off center, circles, curves, color, texture, light, shading, complex, maximal, too much detail, ", "prompt_strength": 0.9, "num_inference_steps": 27 }, "logs": "Using seed: 26276\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: iconic logo in the style of logo\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:07, 3.66it/s]\n 7%|▋ | 2/27 [00:00<00:06, 3.66it/s]\n 11%|█ | 3/27 [00:00<00:06, 3.67it/s]\n 15%|█▍ | 4/27 [00:01<00:06, 3.66it/s]\n 19%|█▊ | 5/27 [00:01<00:06, 3.66it/s]\n 22%|██▏ | 6/27 [00:01<00:05, 3.66it/s]\n 26%|██▌ | 7/27 [00:01<00:05, 3.66it/s]\n 30%|██▉ | 8/27 [00:02<00:05, 3.66it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 3.66it/s]\n 37%|███▋ | 10/27 [00:02<00:04, 3.66it/s]\n 41%|████ | 11/27 [00:03<00:04, 3.65it/s]\n 44%|████▍ | 12/27 [00:03<00:04, 3.65it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 3.65it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 3.65it/s]\n 56%|█████▌ | 15/27 [00:04<00:03, 3.65it/s]\n 59%|█████▉ | 16/27 [00:04<00:03, 3.65it/s]\n 63%|██████▎ | 17/27 [00:04<00:02, 3.65it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 3.65it/s]\n 70%|███████ | 19/27 [00:05<00:02, 3.65it/s]\n 74%|███████▍ | 20/27 [00:05<00:01, 3.65it/s]\n 78%|███████▊ | 21/27 [00:05<00:01, 3.65it/s]\n 81%|████████▏ | 22/27 [00:06<00:01, 3.65it/s]\n 85%|████████▌ | 23/27 [00:06<00:01, 3.65it/s]\n 89%|████████▉ | 24/27 [00:06<00:00, 3.65it/s]\n 93%|█████████▎| 25/27 [00:06<00:00, 3.65it/s]\n 96%|█████████▋| 26/27 [00:07<00:00, 3.65it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.65it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.65it/s]", "metrics": { "predict_time": 9.190973, "total_time": 12.238008 }, "output": [ "https://replicate.delivery/pbxt/Ww68QVdVa86aMZZk2by0txiYE1GPJ2kMFpfhsaI1GGGffNDkA/out-0.png" ], "started_at": "2023-12-12T20:09:25.467019Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5ndwgndbos4rvyszrpyinipoxa", "cancel": "https://api.replicate.com/v1/predictions/5ndwgndbos4rvyszrpyinipoxa/cancel" }, "version": "3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595" }Generated inUsing seed: 26276 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: iconic logo in the style of logo txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:07, 3.66it/s] 7%|▋ | 2/27 [00:00<00:06, 3.66it/s] 11%|█ | 3/27 [00:00<00:06, 3.67it/s] 15%|█▍ | 4/27 [00:01<00:06, 3.66it/s] 19%|█▊ | 5/27 [00:01<00:06, 3.66it/s] 22%|██▏ | 6/27 [00:01<00:05, 3.66it/s] 26%|██▌ | 7/27 [00:01<00:05, 3.66it/s] 30%|██▉ | 8/27 [00:02<00:05, 3.66it/s] 33%|███▎ | 9/27 [00:02<00:04, 3.66it/s] 37%|███▋ | 10/27 [00:02<00:04, 3.66it/s] 41%|████ | 11/27 [00:03<00:04, 3.65it/s] 44%|████▍ | 12/27 [00:03<00:04, 3.65it/s] 48%|████▊ | 13/27 [00:03<00:03, 3.65it/s] 52%|█████▏ | 14/27 [00:03<00:03, 3.65it/s] 56%|█████▌ | 15/27 [00:04<00:03, 3.65it/s] 59%|█████▉ | 16/27 [00:04<00:03, 3.65it/s] 63%|██████▎ | 17/27 [00:04<00:02, 3.65it/s] 67%|██████▋ | 18/27 [00:04<00:02, 3.65it/s] 70%|███████ | 19/27 [00:05<00:02, 3.65it/s] 74%|███████▍ | 20/27 [00:05<00:01, 3.65it/s] 78%|███████▊ | 21/27 [00:05<00:01, 3.65it/s] 81%|████████▏ | 22/27 [00:06<00:01, 3.65it/s] 85%|████████▌ | 23/27 [00:06<00:01, 3.65it/s] 89%|████████▉ | 24/27 [00:06<00:00, 3.65it/s] 93%|█████████▎| 25/27 [00:06<00:00, 3.65it/s] 96%|█████████▋| 26/27 [00:07<00:00, 3.65it/s] 100%|██████████| 27/27 [00:07<00:00, 3.65it/s] 100%|██████████| 27/27 [00:07<00:00, 3.65it/s]Prediction
profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595IDkt4kbj3boshjwhdtmykur4giiaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a rectangle in the style of logo
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 1
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 1
- negative_prompt
- off center, filled page, pattern, color, texture, detailed, lines, curves, circles
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a rectangle in the style of logo", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 1, "negative_prompt": "off center, filled page, pattern, color, texture, detailed, lines, curves, circles", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", { input: { width: 1024, height: 1024, prompt: "a rectangle in the style of logo", refine: "no_refiner", scheduler: "DDIM", lora_scale: 1, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 1, negative_prompt: "off center, filled page, pattern, color, texture, detailed, lines, curves, circles", prompt_strength: 1, 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 profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", input={ "width": 1024, "height": 1024, "prompt": "a rectangle in the style of logo", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 1, "negative_prompt": "off center, filled page, pattern, color, texture, detailed, lines, curves, circles", "prompt_strength": 1, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run profdl/logos 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": "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", "input": { "width": 1024, "height": 1024, "prompt": "a rectangle in the style of logo", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 1, "negative_prompt": "off center, filled page, pattern, color, texture, detailed, lines, curves, circles", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-12T20:16:04.642182Z", "created_at": "2023-12-12T20:15:39.110615Z", "data_removed": false, "error": null, "id": "kt4kbj3boshjwhdtmykur4giia", "input": { "width": 1024, "height": 1024, "prompt": "a rectangle in the style of logo", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 1, "negative_prompt": "off center, filled page, pattern, color, texture, detailed, lines, curves, circles", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 40825\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a rectangle in the style of logo\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<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.69it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.69it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.69it/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, 3.69it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/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, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<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, 3.69it/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": 15.823435, "total_time": 25.531567 }, "output": [ "https://replicate.delivery/pbxt/Irf1c96vtL39fElGMuQzFKbzZqqf9s94KkchyH53OLBIMODkA/out-0.png" ], "started_at": "2023-12-12T20:15:48.818747Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kt4kbj3boshjwhdtmykur4giia", "cancel": "https://api.replicate.com/v1/predictions/kt4kbj3boshjwhdtmykur4giia/cancel" }, "version": "3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595" }Generated inUsing seed: 40825 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a rectangle in the style of logo txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.69it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s] 40%|████ | 20/50 [00:05<00:08, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/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, 3.69it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/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, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<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, 3.69it/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
profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595IDa5xmeqlbg5avebjsaev6ehmo7uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a rectangle in the style of logo
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 1
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 1
- negative_prompt
- off center, filled page, pattern, color, texture, detailed, lines, curves, circles, wallpaper, maze
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a rectangle in the style of logo", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 1, "negative_prompt": "off center, filled page, pattern, color, texture, detailed, lines, curves, circles, wallpaper, maze", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", { input: { width: 1024, height: 1024, prompt: "a rectangle in the style of logo", refine: "no_refiner", scheduler: "DDIM", lora_scale: 1, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 1, negative_prompt: "off center, filled page, pattern, color, texture, detailed, lines, curves, circles, wallpaper, maze", prompt_strength: 1, 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 profdl/logos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", input={ "width": 1024, "height": 1024, "prompt": "a rectangle in the style of logo", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 1, "negative_prompt": "off center, filled page, pattern, color, texture, detailed, lines, curves, circles, wallpaper, maze", "prompt_strength": 1, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run profdl/logos 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": "profdl/logos:3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595", "input": { "width": 1024, "height": 1024, "prompt": "a rectangle in the style of logo", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 1, "negative_prompt": "off center, filled page, pattern, color, texture, detailed, lines, curves, circles, wallpaper, maze", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-12T20:16:49.983770Z", "created_at": "2023-12-12T20:16:29.199032Z", "data_removed": false, "error": null, "id": "a5xmeqlbg5avebjsaev6ehmo7u", "input": { "width": 1024, "height": 1024, "prompt": "a rectangle in the style of logo", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 1, "negative_prompt": "off center, filled page, pattern, color, texture, detailed, lines, curves, circles, wallpaper, maze", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 17582\nEnsuring enough disk space...\nFree disk space: 1382909263872\nDownloading weights: https://replicate.delivery/pbxt/n4SamkH28ALxKtSXBYMf8cJgeV1GaSzynIzpo0QyYq7yqmBSA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.093s (2.0 GB/s)\\nExtracted 186 MB in 0.057s (3.3 GB/s)\\n'\nDownloaded weights in 0.23775649070739746 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a rectangle in the style of logo\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.63it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.61it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.61it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.60it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.60it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.60it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.60it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.61it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.61it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.61it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.61it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.61it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.61it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.61it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.61it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.61it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.61it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.61it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.61it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.61it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.61it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.61it/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, 3.60it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 16.860669, "total_time": 20.784738 }, "output": [ "https://replicate.delivery/pbxt/Em7bd5HYaQ6tF5QMDTTD5EUmWyyUp7Ywwqqf6RXi3UnYjzAJA/out-0.png" ], "started_at": "2023-12-12T20:16:33.123101Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a5xmeqlbg5avebjsaev6ehmo7u", "cancel": "https://api.replicate.com/v1/predictions/a5xmeqlbg5avebjsaev6ehmo7u/cancel" }, "version": "3dc046194d013cb37ad388eb72f2dd4dff2dcde318b5317d342367f802843595" }Generated inUsing seed: 17582 Ensuring enough disk space... Free disk space: 1382909263872 Downloading weights: https://replicate.delivery/pbxt/n4SamkH28ALxKtSXBYMf8cJgeV1GaSzynIzpo0QyYq7yqmBSA/trained_model.tar b'Downloaded 186 MB bytes in 0.093s (2.0 GB/s)\nExtracted 186 MB in 0.057s (3.3 GB/s)\n' Downloaded weights in 0.23775649070739746 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a rectangle in the style of logo txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.63it/s] 4%|▍ | 2/50 [00:00<00:13, 3.61it/s] 6%|▌ | 3/50 [00:00<00:13, 3.61it/s] 8%|▊ | 4/50 [00:01<00:12, 3.60it/s] 10%|█ | 5/50 [00:01<00:12, 3.60it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.60it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.60it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.61it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.61it/s] 20%|██ | 10/50 [00:02<00:11, 3.61it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.61it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.61it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.61it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.61it/s] 30%|███ | 15/50 [00:04<00:09, 3.61it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.61it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s] 40%|████ | 20/50 [00:05<00:08, 3.61it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s] 50%|█████ | 25/50 [00:06<00:06, 3.61it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s] 60%|██████ | 30/50 [00:08<00:05, 3.61it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.61it/s] 70%|███████ | 35/50 [00:09<00:04, 3.61it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s] 80%|████████ | 40/50 [00:11<00:02, 3.61it/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, 3.60it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s]
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