fofr / flux-nobel-prize-2024-sketch
- Public
- 2.3K runs
-
H100
Prediction
fofr/flux-nobel-prize-2024-sketch:59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310IDk1xaxyc87xrm60cje4sb3dykn8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A NOBEL24 gold and black pen sketch illustration of Ronald McDonald clown on textured paper
- lora_scale
- 1
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A NOBEL24 gold and black pen sketch illustration of Ronald McDonald clown on textured paper", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-nobel-prize-2024-sketch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-nobel-prize-2024-sketch:59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310", { input: { model: "dev", prompt: "A NOBEL24 gold and black pen sketch illustration of Ronald McDonald clown on textured paper", lora_scale: 1, num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/flux-nobel-prize-2024-sketch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-nobel-prize-2024-sketch:59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310", input={ "model": "dev", "prompt": "A NOBEL24 gold and black pen sketch illustration of Ronald McDonald clown on textured paper", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-nobel-prize-2024-sketch 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/flux-nobel-prize-2024-sketch:59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310", "input": { "model": "dev", "prompt": "A NOBEL24 gold and black pen sketch illustration of Ronald McDonald clown on textured paper", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-09T12:15:05.067145Z", "created_at": "2024-10-09T12:13:10.079000Z", "data_removed": false, "error": null, "id": "k1xaxyc87xrm60cje4sb3dykn8", "input": { "model": "dev", "prompt": "A NOBEL24 gold and black pen sketch illustration of Ronald McDonald clown on textured paper", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 54843\nPrompt: A NOBEL24 gold and black pen sketch illustration of Ronald McDonald clown on textured paper\n[!] txt2img mode\nUsing dev model\nfree=7679981101056\nDownloading weights\n2024-10-09T12:14:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxd04fq3t/weights url=https://replicate.delivery/yhqm/ipGLsptzlT5ZB1QOyfHpaf9L63RHw22JNfZRTjniBtcYeHUOB/trained_model.tar\n2024-10-09T12:14:27Z | INFO | [ Complete ] dest=/tmp/tmpxd04fq3t/weights size=\"172 MB\" total_elapsed=2.777s url=https://replicate.delivery/yhqm/ipGLsptzlT5ZB1QOyfHpaf9L63RHw22JNfZRTjniBtcYeHUOB/trained_model.tar\nDownloaded weights in 2.81s\nLoaded LoRAs in 3.40s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:01<00:34, 1.29s/it]\n 7%|▋ | 2/28 [00:02<00:30, 1.16s/it]\n 11%|█ | 3/28 [00:03<00:30, 1.22s/it]\n 14%|█▍ | 4/28 [00:04<00:29, 1.25s/it]\n 18%|█▊ | 5/28 [00:06<00:28, 1.26s/it]\n 21%|██▏ | 6/28 [00:07<00:27, 1.27s/it]\n 25%|██▌ | 7/28 [00:08<00:26, 1.28s/it]\n 29%|██▊ | 8/28 [00:10<00:25, 1.28s/it]\n 32%|███▏ | 9/28 [00:11<00:24, 1.28s/it]\n 36%|███▌ | 10/28 [00:12<00:23, 1.28s/it]\n 39%|███▉ | 11/28 [00:13<00:21, 1.29s/it]\n 43%|████▎ | 12/28 [00:15<00:20, 1.29s/it]\n 46%|████▋ | 13/28 [00:16<00:19, 1.29s/it]\n 50%|█████ | 14/28 [00:17<00:18, 1.29s/it]\n 54%|█████▎ | 15/28 [00:19<00:16, 1.29s/it]\n 57%|█████▋ | 16/28 [00:20<00:15, 1.29s/it]\n 61%|██████ | 17/28 [00:21<00:14, 1.29s/it]\n 64%|██████▍ | 18/28 [00:22<00:12, 1.29s/it]\n 68%|██████▊ | 19/28 [00:24<00:11, 1.29s/it]\n 71%|███████▏ | 20/28 [00:25<00:10, 1.29s/it]\n 75%|███████▌ | 21/28 [00:26<00:09, 1.29s/it]\n 79%|███████▊ | 22/28 [00:28<00:07, 1.29s/it]\n 82%|████████▏ | 23/28 [00:29<00:06, 1.29s/it]\n 86%|████████▌ | 24/28 [00:30<00:05, 1.29s/it]\n 89%|████████▉ | 25/28 [00:31<00:03, 1.29s/it]\n 93%|█████████▎| 26/28 [00:33<00:02, 1.29s/it]\n 96%|█████████▋| 27/28 [00:34<00:01, 1.29s/it]\n100%|██████████| 28/28 [00:35<00:00, 1.29s/it]\n100%|██████████| 28/28 [00:35<00:00, 1.28s/it]", "metrics": { "predict_time": 40.611535032, "total_time": 114.988145 }, "output": [ "https://replicate.delivery/yhqm/CiLwemRWB3Q4VqmBNshOfta9zHGmReopBvEBJNwK1aySuEKnA/out-0.webp", "https://replicate.delivery/yhqm/hSzYLqccBaIxEhIjAeqUPkzXiISTxNo23t6VXsCaqYykLhyJA/out-1.webp", "https://replicate.delivery/yhqm/SDxAQNOVSlK2C1y4o3wGYILX48aovVY5qhmpkI8a7JZylQ5E/out-2.webp", "https://replicate.delivery/yhqm/wNowO0FeVGXqaSo1m0kBIKRKhb3utIb8ncDrT6hUEuqkLhyJA/out-3.webp" ], "started_at": "2024-10-09T12:14:24.455610Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/k1xaxyc87xrm60cje4sb3dykn8", "cancel": "https://api.replicate.com/v1/predictions/k1xaxyc87xrm60cje4sb3dykn8/cancel" }, "version": "59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310" }Generated inUsing seed: 54843 Prompt: A NOBEL24 gold and black pen sketch illustration of Ronald McDonald clown on textured paper [!] txt2img mode Using dev model free=7679981101056 Downloading weights 2024-10-09T12:14:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxd04fq3t/weights url=https://replicate.delivery/yhqm/ipGLsptzlT5ZB1QOyfHpaf9L63RHw22JNfZRTjniBtcYeHUOB/trained_model.tar 2024-10-09T12:14:27Z | INFO | [ Complete ] dest=/tmp/tmpxd04fq3t/weights size="172 MB" total_elapsed=2.777s url=https://replicate.delivery/yhqm/ipGLsptzlT5ZB1QOyfHpaf9L63RHw22JNfZRTjniBtcYeHUOB/trained_model.tar Downloaded weights in 2.81s Loaded LoRAs in 3.40s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:01<00:34, 1.29s/it] 7%|▋ | 2/28 [00:02<00:30, 1.16s/it] 11%|█ | 3/28 [00:03<00:30, 1.22s/it] 14%|█▍ | 4/28 [00:04<00:29, 1.25s/it] 18%|█▊ | 5/28 [00:06<00:28, 1.26s/it] 21%|██▏ | 6/28 [00:07<00:27, 1.27s/it] 25%|██▌ | 7/28 [00:08<00:26, 1.28s/it] 29%|██▊ | 8/28 [00:10<00:25, 1.28s/it] 32%|███▏ | 9/28 [00:11<00:24, 1.28s/it] 36%|███▌ | 10/28 [00:12<00:23, 1.28s/it] 39%|███▉ | 11/28 [00:13<00:21, 1.29s/it] 43%|████▎ | 12/28 [00:15<00:20, 1.29s/it] 46%|████▋ | 13/28 [00:16<00:19, 1.29s/it] 50%|█████ | 14/28 [00:17<00:18, 1.29s/it] 54%|█████▎ | 15/28 [00:19<00:16, 1.29s/it] 57%|█████▋ | 16/28 [00:20<00:15, 1.29s/it] 61%|██████ | 17/28 [00:21<00:14, 1.29s/it] 64%|██████▍ | 18/28 [00:22<00:12, 1.29s/it] 68%|██████▊ | 19/28 [00:24<00:11, 1.29s/it] 71%|███████▏ | 20/28 [00:25<00:10, 1.29s/it] 75%|███████▌ | 21/28 [00:26<00:09, 1.29s/it] 79%|███████▊ | 22/28 [00:28<00:07, 1.29s/it] 82%|████████▏ | 23/28 [00:29<00:06, 1.29s/it] 86%|████████▌ | 24/28 [00:30<00:05, 1.29s/it] 89%|████████▉ | 25/28 [00:31<00:03, 1.29s/it] 93%|█████████▎| 26/28 [00:33<00:02, 1.29s/it] 96%|█████████▋| 27/28 [00:34<00:01, 1.29s/it] 100%|██████████| 28/28 [00:35<00:00, 1.29s/it] 100%|██████████| 28/28 [00:35<00:00, 1.28s/it]Prediction
fofr/flux-nobel-prize-2024-sketch:59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310IDtraa104gzdrm60cje4qtxztsxwStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A NOBEL24 gold and black pen sketch illustration of a cat on textured paper
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A NOBEL24 gold and black pen sketch illustration of a cat on textured paper", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-nobel-prize-2024-sketch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-nobel-prize-2024-sketch:59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310", { input: { model: "dev", prompt: "A NOBEL24 gold and black pen sketch illustration of a cat on textured paper", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/flux-nobel-prize-2024-sketch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-nobel-prize-2024-sketch:59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310", input={ "model": "dev", "prompt": "A NOBEL24 gold and black pen sketch illustration of a cat on textured paper", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-nobel-prize-2024-sketch 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/flux-nobel-prize-2024-sketch:59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310", "input": { "model": "dev", "prompt": "A NOBEL24 gold and black pen sketch illustration of a cat on textured paper", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-09T12:10:08.093121Z", "created_at": "2024-10-09T12:09:55.707000Z", "data_removed": false, "error": null, "id": "traa104gzdrm60cje4qtxztsxw", "input": { "model": "dev", "prompt": "A NOBEL24 gold and black pen sketch illustration of a cat on textured paper", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 12948\nPrompt: A NOBEL24 gold and black pen sketch illustration of a cat on textured paper\n[!] txt2img mode\nUsing dev model\nfree=8727123849216\nDownloading weights\n2024-10-09T12:09:55Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpjodarlt8/weights url=https://replicate.delivery/yhqm/ipGLsptzlT5ZB1QOyfHpaf9L63RHw22JNfZRTjniBtcYeHUOB/trained_model.tar\n2024-10-09T12:09:57Z | INFO | [ Complete ] dest=/tmp/tmpjodarlt8/weights size=\"172 MB\" total_elapsed=1.545s url=https://replicate.delivery/yhqm/ipGLsptzlT5ZB1QOyfHpaf9L63RHw22JNfZRTjniBtcYeHUOB/trained_model.tar\nDownloaded weights in 1.57s\nLoaded LoRAs in 2.35s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:09, 2.88it/s]\n 7%|▋ | 2/28 [00:00<00:08, 3.21it/s]\n 11%|█ | 3/28 [00:00<00:08, 3.06it/s]\n 14%|█▍ | 4/28 [00:01<00:08, 2.99it/s]\n 18%|█▊ | 5/28 [00:01<00:07, 2.95it/s]\n 21%|██▏ | 6/28 [00:02<00:07, 2.93it/s]\n 25%|██▌ | 7/28 [00:02<00:07, 2.91it/s]\n 29%|██▊ | 8/28 [00:02<00:06, 2.90it/s]\n 32%|███▏ | 9/28 [00:03<00:06, 2.90it/s]\n 36%|███▌ | 10/28 [00:03<00:06, 2.89it/s]\n 39%|███▉ | 11/28 [00:03<00:05, 2.89it/s]\n 43%|████▎ | 12/28 [00:04<00:05, 2.89it/s]\n 46%|████▋ | 13/28 [00:04<00:05, 2.89it/s]\n 50%|█████ | 14/28 [00:04<00:04, 2.89it/s]\n 54%|█████▎ | 15/28 [00:05<00:04, 2.89it/s]\n 57%|█████▋ | 16/28 [00:05<00:04, 2.89it/s]\n 61%|██████ | 17/28 [00:05<00:03, 2.89it/s]\n 64%|██████▍ | 18/28 [00:06<00:03, 2.88it/s]\n 68%|██████▊ | 19/28 [00:06<00:03, 2.88it/s]\n 71%|███████▏ | 20/28 [00:06<00:02, 2.88it/s]\n 75%|███████▌ | 21/28 [00:07<00:02, 2.88it/s]\n 79%|███████▊ | 22/28 [00:07<00:02, 2.88it/s]\n 82%|████████▏ | 23/28 [00:07<00:01, 2.88it/s]\n 86%|████████▌ | 24/28 [00:08<00:01, 2.88it/s]\n 89%|████████▉ | 25/28 [00:08<00:01, 2.88it/s]\n 93%|█████████▎| 26/28 [00:08<00:00, 2.88it/s]\n 96%|█████████▋| 27/28 [00:09<00:00, 2.88it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.88it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.90it/s]", "metrics": { "predict_time": 12.375425965, "total_time": 12.386121 }, "output": [ "https://replicate.delivery/yhqm/oKziA7quc5KMHVpr1z2hKc4LuNzf9fe80h8b3OdUFQfCKJUOB/out-0.webp" ], "started_at": "2024-10-09T12:09:55.717695Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/traa104gzdrm60cje4qtxztsxw", "cancel": "https://api.replicate.com/v1/predictions/traa104gzdrm60cje4qtxztsxw/cancel" }, "version": "59bb08ad52de964441a75b8c6e36955cde3d00e4789dc131b2cb5c357239e310" }Generated inUsing seed: 12948 Prompt: A NOBEL24 gold and black pen sketch illustration of a cat on textured paper [!] txt2img mode Using dev model free=8727123849216 Downloading weights 2024-10-09T12:09:55Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpjodarlt8/weights url=https://replicate.delivery/yhqm/ipGLsptzlT5ZB1QOyfHpaf9L63RHw22JNfZRTjniBtcYeHUOB/trained_model.tar 2024-10-09T12:09:57Z | INFO | [ Complete ] dest=/tmp/tmpjodarlt8/weights size="172 MB" total_elapsed=1.545s url=https://replicate.delivery/yhqm/ipGLsptzlT5ZB1QOyfHpaf9L63RHw22JNfZRTjniBtcYeHUOB/trained_model.tar Downloaded weights in 1.57s Loaded LoRAs in 2.35s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.88it/s] 7%|▋ | 2/28 [00:00<00:08, 3.21it/s] 11%|█ | 3/28 [00:00<00:08, 3.06it/s] 14%|█▍ | 4/28 [00:01<00:08, 2.99it/s] 18%|█▊ | 5/28 [00:01<00:07, 2.95it/s] 21%|██▏ | 6/28 [00:02<00:07, 2.93it/s] 25%|██▌ | 7/28 [00:02<00:07, 2.91it/s] 29%|██▊ | 8/28 [00:02<00:06, 2.90it/s] 32%|███▏ | 9/28 [00:03<00:06, 2.90it/s] 36%|███▌ | 10/28 [00:03<00:06, 2.89it/s] 39%|███▉ | 11/28 [00:03<00:05, 2.89it/s] 43%|████▎ | 12/28 [00:04<00:05, 2.89it/s] 46%|████▋ | 13/28 [00:04<00:05, 2.89it/s] 50%|█████ | 14/28 [00:04<00:04, 2.89it/s] 54%|█████▎ | 15/28 [00:05<00:04, 2.89it/s] 57%|█████▋ | 16/28 [00:05<00:04, 2.89it/s] 61%|██████ | 17/28 [00:05<00:03, 2.89it/s] 64%|██████▍ | 18/28 [00:06<00:03, 2.88it/s] 68%|██████▊ | 19/28 [00:06<00:03, 2.88it/s] 71%|███████▏ | 20/28 [00:06<00:02, 2.88it/s] 75%|███████▌ | 21/28 [00:07<00:02, 2.88it/s] 79%|███████▊ | 22/28 [00:07<00:02, 2.88it/s] 82%|████████▏ | 23/28 [00:07<00:01, 2.88it/s] 86%|████████▌ | 24/28 [00:08<00:01, 2.88it/s] 89%|████████▉ | 25/28 [00:08<00:01, 2.88it/s] 93%|█████████▎| 26/28 [00:08<00:00, 2.88it/s] 96%|█████████▋| 27/28 [00:09<00:00, 2.88it/s] 100%|██████████| 28/28 [00:09<00:00, 2.88it/s] 100%|██████████| 28/28 [00:09<00:00, 2.90it/s]
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