paulbasic
/
flux_paulh
a private model to play around and create fun
- Public
- 168 runs
-
H100
Prediction
paulbasic/flux_paulh:cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301IDyd8h52q1x5rm00chrrabpnybjcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- flux_paulh in a glitter outfit sitting on a motorbike driving on route 66
- 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": "flux_paulh in a glitter outfit sitting on a motorbike driving on route 66", "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 paulbasic/flux_paulh using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "paulbasic/flux_paulh:cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301", { input: { model: "dev", prompt: "flux_paulh in a glitter outfit sitting on a motorbike driving on route 66", 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 paulbasic/flux_paulh using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "paulbasic/flux_paulh:cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301", input={ "model": "dev", "prompt": "flux_paulh in a glitter outfit sitting on a motorbike driving on route 66", "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 paulbasic/flux_paulh 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": "cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301", "input": { "model": "dev", "prompt": "flux_paulh in a glitter outfit sitting on a motorbike driving on route 66", "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": "2024-09-06T06:46:14.012051Z", "created_at": "2024-09-06T06:45:43.017000Z", "data_removed": false, "error": null, "id": "yd8h52q1x5rm00chrrabpnybjc", "input": { "model": "dev", "prompt": "flux_paulh in a glitter outfit sitting on a motorbike driving on route 66", "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: 29231\nPrompt: flux_paulh in a glitter outfit sitting on a motorbike driving on route 66\n[!] txt2img mode\nUsing dev model\nfree=8714768650240\nDownloading weights\n2024-09-06T06:45:52Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp_3kv66zd/weights url=https://replicate.delivery/yhqm/iOaPnbaMFJLfD6VLfyNyoNyff6f0eWHBHVSA5m98ll1481R2E/trained_model.tar\n2024-09-06T06:45:56Z | INFO | [ Complete ] dest=/tmp/tmp_3kv66zd/weights size=\"172 MB\" total_elapsed=4.537s url=https://replicate.delivery/yhqm/iOaPnbaMFJLfD6VLfyNyoNyff6f0eWHBHVSA5m98ll1481R2E/trained_model.tar\nDownloaded weights in 4.57s\nLoaded LoRAs in 13.21s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.49it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.93it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.71it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.56it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.52it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.50it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.49it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.48it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.48it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.47it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.46it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.46it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.47it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.46it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.46it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.49it/s]", "metrics": { "predict_time": 21.773297099, "total_time": 30.995051 }, "output": [ "https://replicate.delivery/yhqm/OPYRVET2efjprURdnXaOP57NGNt58UOiYj7nv1ooMxp1cFaTA/out-0.webp" ], "started_at": "2024-09-06T06:45:52.238754Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yd8h52q1x5rm00chrrabpnybjc", "cancel": "https://api.replicate.com/v1/predictions/yd8h52q1x5rm00chrrabpnybjc/cancel" }, "version": "cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301" }Generated inUsing seed: 29231 Prompt: flux_paulh in a glitter outfit sitting on a motorbike driving on route 66 [!] txt2img mode Using dev model free=8714768650240 Downloading weights 2024-09-06T06:45:52Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp_3kv66zd/weights url=https://replicate.delivery/yhqm/iOaPnbaMFJLfD6VLfyNyoNyff6f0eWHBHVSA5m98ll1481R2E/trained_model.tar 2024-09-06T06:45:56Z | INFO | [ Complete ] dest=/tmp/tmp_3kv66zd/weights size="172 MB" total_elapsed=4.537s url=https://replicate.delivery/yhqm/iOaPnbaMFJLfD6VLfyNyoNyff6f0eWHBHVSA5m98ll1481R2E/trained_model.tar Downloaded weights in 4.57s Loaded LoRAs in 13.21s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.49it/s] 7%|▋ | 2/28 [00:00<00:06, 3.93it/s] 11%|█ | 3/28 [00:00<00:06, 3.71it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.56it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.52it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.50it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.49it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.48it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.48it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s] 50%|█████ | 14/28 [00:03<00:04, 3.47it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s] 61%|██████ | 17/28 [00:04<00:03, 3.46it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.46it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.47it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.46it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.46it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.49it/s]Prediction
paulbasic/flux_paulh:cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301IDdcdmq3semnrm40chrrg9p9aj2gStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- flux_paulh a close up shot in a black suit with an afro standing in front of a club in a 70s style
- 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": "flux_paulh a close up shot in a black suit with an afro standing in front of a club in a 70s style", "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 paulbasic/flux_paulh using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "paulbasic/flux_paulh:cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301", { input: { model: "dev", prompt: "flux_paulh a close up shot in a black suit with an afro standing in front of a club in a 70s style", 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 paulbasic/flux_paulh using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "paulbasic/flux_paulh:cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301", input={ "model": "dev", "prompt": "flux_paulh a close up shot in a black suit with an afro standing in front of a club in a 70s style", "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 paulbasic/flux_paulh 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": "cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301", "input": { "model": "dev", "prompt": "flux_paulh a close up shot in a black suit with an afro standing in front of a club in a 70s style", "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": "2024-09-06T06:58:41.501082Z", "created_at": "2024-09-06T06:58:03.557000Z", "data_removed": false, "error": null, "id": "dcdmq3semnrm40chrrg9p9aj2g", "input": { "model": "dev", "prompt": "flux_paulh a close up shot in a black suit with an afro standing in front of a club in a 70s style", "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: 31181\nPrompt: flux_paulh a close up shot in a black suit with an afro standing in front of a club in a 70s style\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 6.88s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:01<00:28, 1.05s/it]\n 7%|▋ | 2/28 [00:01<00:24, 1.07it/s]\n 11%|█ | 3/28 [00:02<00:24, 1.01it/s]\n 14%|█▍ | 4/28 [00:04<00:24, 1.02s/it]\n 18%|█▊ | 5/28 [00:05<00:23, 1.03s/it]\n 21%|██▏ | 6/28 [00:06<00:22, 1.04s/it]\n 25%|██▌ | 7/28 [00:07<00:21, 1.05s/it]\n 29%|██▊ | 8/28 [00:08<00:20, 1.05s/it]\n 32%|███▏ | 9/28 [00:09<00:19, 1.05s/it]\n 36%|███▌ | 10/28 [00:10<00:18, 1.05s/it]\n 39%|███▉ | 11/28 [00:11<00:17, 1.05s/it]\n 43%|████▎ | 12/28 [00:12<00:16, 1.06s/it]\n 46%|████▋ | 13/28 [00:13<00:15, 1.06s/it]\n 50%|█████ | 14/28 [00:14<00:14, 1.06s/it]\n 54%|█████▎ | 15/28 [00:15<00:13, 1.06s/it]\n 57%|█████▋ | 16/28 [00:16<00:12, 1.06s/it]\n 61%|██████ | 17/28 [00:17<00:11, 1.06s/it]\n 64%|██████▍ | 18/28 [00:18<00:10, 1.06s/it]\n 68%|██████▊ | 19/28 [00:19<00:09, 1.06s/it]\n 71%|███████▏ | 20/28 [00:20<00:08, 1.06s/it]\n 75%|███████▌ | 21/28 [00:22<00:07, 1.06s/it]\n 79%|███████▊ | 22/28 [00:23<00:06, 1.06s/it]\n 82%|████████▏ | 23/28 [00:24<00:05, 1.06s/it]\n 86%|████████▌ | 24/28 [00:25<00:04, 1.06s/it]\n 89%|████████▉ | 25/28 [00:26<00:03, 1.06s/it]\n 93%|█████████▎| 26/28 [00:27<00:02, 1.06s/it]\n 96%|█████████▋| 27/28 [00:28<00:01, 1.06s/it]\n100%|██████████| 28/28 [00:29<00:00, 1.06s/it]\n100%|██████████| 28/28 [00:29<00:00, 1.05s/it]", "metrics": { "predict_time": 37.933744089, "total_time": 37.944082 }, "output": [ "https://replicate.delivery/yhqm/ttMeKuAGJQ1QDyfxQoBredsVb9Hv47DvQBxSjTEInzEDRL0mA/out-0.webp", "https://replicate.delivery/yhqm/JB0yzcGzdCpHHZF3j8iPRHwW7MOYMXWE7t4qeoCbVtehoFaTA/out-1.webp", "https://replicate.delivery/yhqm/fYN96sCcefZDwpJFjCVWj8PBvwUZQowJtOSYRBO6rrvCRL0mA/out-2.webp", "https://replicate.delivery/yhqm/Lp7CqevDZRRqfUkNhssAeV6ZHmFLoO9QdCNmIuXSkHuDRL0mA/out-3.webp" ], "started_at": "2024-09-06T06:58:03.567338Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dcdmq3semnrm40chrrg9p9aj2g", "cancel": "https://api.replicate.com/v1/predictions/dcdmq3semnrm40chrrg9p9aj2g/cancel" }, "version": "cbd3f40b7861ea82048cbae7f8e81e7b267f07b8f63d3d381fae57311da7b301" }Generated inUsing seed: 31181 Prompt: flux_paulh a close up shot in a black suit with an afro standing in front of a club in a 70s style [!] txt2img mode Using dev model Loaded LoRAs in 6.88s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:01<00:28, 1.05s/it] 7%|▋ | 2/28 [00:01<00:24, 1.07it/s] 11%|█ | 3/28 [00:02<00:24, 1.01it/s] 14%|█▍ | 4/28 [00:04<00:24, 1.02s/it] 18%|█▊ | 5/28 [00:05<00:23, 1.03s/it] 21%|██▏ | 6/28 [00:06<00:22, 1.04s/it] 25%|██▌ | 7/28 [00:07<00:21, 1.05s/it] 29%|██▊ | 8/28 [00:08<00:20, 1.05s/it] 32%|███▏ | 9/28 [00:09<00:19, 1.05s/it] 36%|███▌ | 10/28 [00:10<00:18, 1.05s/it] 39%|███▉ | 11/28 [00:11<00:17, 1.05s/it] 43%|████▎ | 12/28 [00:12<00:16, 1.06s/it] 46%|████▋ | 13/28 [00:13<00:15, 1.06s/it] 50%|█████ | 14/28 [00:14<00:14, 1.06s/it] 54%|█████▎ | 15/28 [00:15<00:13, 1.06s/it] 57%|█████▋ | 16/28 [00:16<00:12, 1.06s/it] 61%|██████ | 17/28 [00:17<00:11, 1.06s/it] 64%|██████▍ | 18/28 [00:18<00:10, 1.06s/it] 68%|██████▊ | 19/28 [00:19<00:09, 1.06s/it] 71%|███████▏ | 20/28 [00:20<00:08, 1.06s/it] 75%|███████▌ | 21/28 [00:22<00:07, 1.06s/it] 79%|███████▊ | 22/28 [00:23<00:06, 1.06s/it] 82%|████████▏ | 23/28 [00:24<00:05, 1.06s/it] 86%|████████▌ | 24/28 [00:25<00:04, 1.06s/it] 89%|████████▉ | 25/28 [00:26<00:03, 1.06s/it] 93%|█████████▎| 26/28 [00:27<00:02, 1.06s/it] 96%|█████████▋| 27/28 [00:28<00:01, 1.06s/it] 100%|██████████| 28/28 [00:29<00:00, 1.06s/it] 100%|██████████| 28/28 [00:29<00:00, 1.05s/it]
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