ugleh
/
flux-dev-lora-dixit
Flux LoRA, use 'DIXIT' to trigger generation, creates images with the art style of the board game DIXIT.
- Public
- 199 runs
-
H100
Prediction
ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92IDw5rc5rkvb9rm40chjqmam3q9e4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A DIXIT image of toys in a toy store
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A DIXIT image of toys in a toy store", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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 ugleh/flux-dev-lora-dixit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92", { input: { model: "dev", prompt: "A DIXIT image of toys in a toy store", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 ugleh/flux-dev-lora-dixit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92", input={ "model": "dev", "prompt": "A DIXIT image of toys in a toy store", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run ugleh/flux-dev-lora-dixit 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": "fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92", "input": { "model": "dev", "prompt": "A DIXIT image of toys in a toy store", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-08-27T22:16:01.145977Z", "created_at": "2024-08-27T22:15:26.810000Z", "data_removed": false, "error": null, "id": "w5rc5rkvb9rm40chjqmam3q9e4", "input": { "model": "dev", "prompt": "A DIXIT image of toys in a toy store", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 60021\nPrompt: A DIXIT image of toys in a toy store\ntxt2img mode\nUsing dev model\nfree=9507482099712\nDownloading weights\n2024-08-27T22:15:26Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpsv3u1d4f/weights url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar\n2024-08-27T22:15:28Z | INFO | [ Complete ] dest=/tmp/tmpsv3u1d4f/weights size=\"172 MB\" total_elapsed=1.669s url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar\nDownloaded weights in 1.70s\nLoaded LoRAs in 26.16s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.67it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.23it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.94it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.81it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.76it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.73it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.70it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.68it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.67it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.67it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.66it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.65it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.66it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.66it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.66it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.65it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.66it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.66it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.66it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.66it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.66it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.66it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.66it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.66it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]", "metrics": { "predict_time": 34.32776936, "total_time": 34.335977 }, "output": [ "https://replicate.delivery/yhqm/GLu9EK8hZp4YHZ9yDnfC4BKrR5v8f0fSetioTeU0ZwkGEB4aC/out-0.webp" ], "started_at": "2024-08-27T22:15:26.818208Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w5rc5rkvb9rm40chjqmam3q9e4", "cancel": "https://api.replicate.com/v1/predictions/w5rc5rkvb9rm40chjqmam3q9e4/cancel" }, "version": "fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92" }Generated inUsing seed: 60021 Prompt: A DIXIT image of toys in a toy store txt2img mode Using dev model free=9507482099712 Downloading weights 2024-08-27T22:15:26Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpsv3u1d4f/weights url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar 2024-08-27T22:15:28Z | INFO | [ Complete ] dest=/tmp/tmpsv3u1d4f/weights size="172 MB" total_elapsed=1.669s url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar Downloaded weights in 1.70s Loaded LoRAs in 26.16s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.67it/s] 7%|▋ | 2/28 [00:00<00:06, 4.23it/s] 11%|█ | 3/28 [00:00<00:06, 3.94it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.81it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.76it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.73it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.70it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.68it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.67it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.67it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.66it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.65it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.66it/s] 50%|█████ | 14/28 [00:03<00:03, 3.66it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.66it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.65it/s] 61%|██████ | 17/28 [00:04<00:03, 3.66it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.66it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.66it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.66it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.66it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.66it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.66it/s] 100%|██████████| 28/28 [00:07<00:00, 3.66it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s]Prediction
ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92ID2yezv86hm5rm60chk8ctf0fxcmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A DIXIT image of a green dragon with a boxy head, large white eyes with black pupils, and a friendly expression. The dragon has two sharp white teeth visible in its open mouth, a pink tongue, and white horns on top of its head, all outlined in thick black against a solid black background, giving it a playful and cheerful appearance.
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A DIXIT image of a green dragon with a boxy head, large white eyes with black pupils, and a friendly expression. The dragon has two sharp white teeth visible in its open mouth, a pink tongue, and white horns on top of its head, all outlined in thick black against a solid black background, giving it a playful and cheerful appearance.", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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 ugleh/flux-dev-lora-dixit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92", { input: { model: "dev", prompt: "A DIXIT image of a green dragon with a boxy head, large white eyes with black pupils, and a friendly expression. The dragon has two sharp white teeth visible in its open mouth, a pink tongue, and white horns on top of its head, all outlined in thick black against a solid black background, giving it a playful and cheerful appearance.", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 ugleh/flux-dev-lora-dixit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92", input={ "model": "dev", "prompt": "A DIXIT image of a green dragon with a boxy head, large white eyes with black pupils, and a friendly expression. The dragon has two sharp white teeth visible in its open mouth, a pink tongue, and white horns on top of its head, all outlined in thick black against a solid black background, giving it a playful and cheerful appearance.", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run ugleh/flux-dev-lora-dixit 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": "fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92", "input": { "model": "dev", "prompt": "A DIXIT image of a green dragon with a boxy head, large white eyes with black pupils, and a friendly expression. The dragon has two sharp white teeth visible in its open mouth, a pink tongue, and white horns on top of its head, all outlined in thick black against a solid black background, giving it a playful and cheerful appearance.", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-08-28T17:48:07.785922Z", "created_at": "2024-08-28T17:47:49.025000Z", "data_removed": false, "error": null, "id": "2yezv86hm5rm60chk8ctf0fxcm", "input": { "model": "dev", "prompt": "A DIXIT image of a green dragon with a boxy head, large white eyes with black pupils, and a friendly expression. The dragon has two sharp white teeth visible in its open mouth, a pink tongue, and white horns on top of its head, all outlined in thick black against a solid black background, giving it a playful and cheerful appearance.", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 1870\nPrompt: A DIXIT image of a green dragon with a boxy head, large white eyes with black pupils, and a friendly expression. The dragon has two sharp white teeth visible in its open mouth, a pink tongue, and white horns on top of its head, all outlined in thick black against a solid black background, giving it a playful and cheerful appearance.\ntxt2img mode\nUsing dev model\nfree=9196017799168\nDownloading weights\n2024-08-28T17:47:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpz8zuztyz/weights url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar\n2024-08-28T17:47:51Z | INFO | [ Complete ] dest=/tmp/tmpz8zuztyz/weights size=\"172 MB\" total_elapsed=2.015s url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar\nDownloaded weights in 2.05s\nLoaded LoRAs in 10.70s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.70it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.25it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.69it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.69it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]", "metrics": { "predict_time": 18.751792915, "total_time": 18.760922 }, "output": [ "https://replicate.delivery/yhqm/gM9xzE4dPQp7E1VxbGeZaiviJBsDFxcsANKIIdL4olxrporJA/out-0.webp" ], "started_at": "2024-08-28T17:47:49.034129Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2yezv86hm5rm60chk8ctf0fxcm", "cancel": "https://api.replicate.com/v1/predictions/2yezv86hm5rm60chk8ctf0fxcm/cancel" }, "version": "fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92" }Generated inUsing seed: 1870 Prompt: A DIXIT image of a green dragon with a boxy head, large white eyes with black pupils, and a friendly expression. The dragon has two sharp white teeth visible in its open mouth, a pink tongue, and white horns on top of its head, all outlined in thick black against a solid black background, giving it a playful and cheerful appearance. txt2img mode Using dev model free=9196017799168 Downloading weights 2024-08-28T17:47:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpz8zuztyz/weights url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar 2024-08-28T17:47:51Z | INFO | [ Complete ] dest=/tmp/tmpz8zuztyz/weights size="172 MB" total_elapsed=2.015s url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar Downloaded weights in 2.05s Loaded LoRAs in 10.70s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.70it/s] 7%|▋ | 2/28 [00:00<00:06, 4.25it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s] 50%|█████ | 14/28 [00:03<00:03, 3.69it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s] 61%|██████ | 17/28 [00:04<00:02, 3.69it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s]Prediction
ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92ID6z2wna4m95rm00chkn3bqnwnpmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A DIXIT image of balls in a ball pit. The balls are alive and have eyes and mouths. The ball pit of filled with multicolored balls me
- lora_scale
- 1
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A DIXIT image of balls in a ball pit. The balls are alive and have eyes and mouths. The ball pit of filled with multicolored balls me", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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 ugleh/flux-dev-lora-dixit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92", { input: { model: "dev", prompt: "A DIXIT image of balls in a ball pit. The balls are alive and have eyes and mouths. The ball pit of filled with multicolored balls me", lora_scale: 1, num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 ugleh/flux-dev-lora-dixit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92", input={ "model": "dev", "prompt": "A DIXIT image of balls in a ball pit. The balls are alive and have eyes and mouths. The ball pit of filled with multicolored balls me", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run ugleh/flux-dev-lora-dixit 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": "fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92", "input": { "model": "dev", "prompt": "A DIXIT image of balls in a ball pit. The balls are alive and have eyes and mouths. The ball pit of filled with multicolored balls me", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-08-29T08:36:14.453277Z", "created_at": "2024-08-29T08:35:34.089000Z", "data_removed": false, "error": null, "id": "6z2wna4m95rm00chkn3bqnwnpm", "input": { "model": "dev", "prompt": "A DIXIT image of balls in a ball pit. The balls are alive and have eyes and mouths. The ball pit of filled with multicolored balls me", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 20850\nPrompt: A DIXIT image of balls in a ball pit. The balls are alive and have eyes and mouths. The ball pit of filled with multicolored balls me\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.54s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:26, 1.01it/s]\n 7%|▋ | 2/28 [00:01<00:22, 1.15it/s]\n 11%|█ | 3/28 [00:02<00:23, 1.07it/s]\n 14%|█▍ | 4/28 [00:03<00:23, 1.04it/s]\n 18%|█▊ | 5/28 [00:04<00:22, 1.03it/s]\n 21%|██▏ | 6/28 [00:05<00:21, 1.02it/s]\n 25%|██▌ | 7/28 [00:06<00:20, 1.01it/s]\n 29%|██▊ | 8/28 [00:07<00:19, 1.01it/s]\n 32%|███▏ | 9/28 [00:08<00:18, 1.01it/s]\n 36%|███▌ | 10/28 [00:09<00:17, 1.01it/s]\n 39%|███▉ | 11/28 [00:10<00:16, 1.00it/s]\n 43%|████▎ | 12/28 [00:11<00:15, 1.00it/s]\n 46%|████▋ | 13/28 [00:12<00:14, 1.00it/s]\n 50%|█████ | 14/28 [00:13<00:13, 1.00it/s]\n 54%|█████▎ | 15/28 [00:14<00:13, 1.00s/it]\n 57%|█████▋ | 16/28 [00:15<00:11, 1.00it/s]\n 61%|██████ | 17/28 [00:16<00:10, 1.00it/s]\n 64%|██████▍ | 18/28 [00:17<00:09, 1.00it/s]\n 68%|██████▊ | 19/28 [00:18<00:08, 1.00it/s]\n 71%|███████▏ | 20/28 [00:19<00:07, 1.00it/s]\n 75%|███████▌ | 21/28 [00:20<00:06, 1.00it/s]\n 79%|███████▊ | 22/28 [00:21<00:05, 1.00it/s]\n 82%|████████▏ | 23/28 [00:22<00:04, 1.00it/s]\n 86%|████████▌ | 24/28 [00:23<00:03, 1.00it/s]\n 89%|████████▉ | 25/28 [00:24<00:02, 1.00it/s]\n 93%|█████████▎| 26/28 [00:25<00:02, 1.00s/it]\n 96%|█████████▋| 27/28 [00:26<00:01, 1.00s/it]\n100%|██████████| 28/28 [00:27<00:00, 1.00it/s]\n100%|██████████| 28/28 [00:27<00:00, 1.01it/s]", "metrics": { "predict_time": 37.934697009, "total_time": 40.364277 }, "output": [ "https://replicate.delivery/yhqm/dtPfUrp92enz7kpJniTasxejqwjqk4RkzRntDBfPN1u4P5dNB/out-0.webp", "https://replicate.delivery/yhqm/6ZH55qgzaD6iAJJKLxaNdM9bI2TLhgf0e2U08e8ke8b6P5dNB/out-1.webp", "https://replicate.delivery/yhqm/hLLrUa4beRUCayPkJymflggpUB2HG1i6pFDAVS1SNyYen8umA/out-2.webp", "https://replicate.delivery/yhqm/zDHiYLW26DLQMBrA2W9jnMsyh8NicloZMvO0XwfRbhQfTeumA/out-3.webp" ], "started_at": "2024-08-29T08:35:36.518580Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6z2wna4m95rm00chkn3bqnwnpm", "cancel": "https://api.replicate.com/v1/predictions/6z2wna4m95rm00chkn3bqnwnpm/cancel" }, "version": "fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92" }Generated inUsing seed: 20850 Prompt: A DIXIT image of balls in a ball pit. The balls are alive and have eyes and mouths. The ball pit of filled with multicolored balls me txt2img mode Using dev model Loaded LoRAs in 8.54s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:26, 1.01it/s] 7%|▋ | 2/28 [00:01<00:22, 1.15it/s] 11%|█ | 3/28 [00:02<00:23, 1.07it/s] 14%|█▍ | 4/28 [00:03<00:23, 1.04it/s] 18%|█▊ | 5/28 [00:04<00:22, 1.03it/s] 21%|██▏ | 6/28 [00:05<00:21, 1.02it/s] 25%|██▌ | 7/28 [00:06<00:20, 1.01it/s] 29%|██▊ | 8/28 [00:07<00:19, 1.01it/s] 32%|███▏ | 9/28 [00:08<00:18, 1.01it/s] 36%|███▌ | 10/28 [00:09<00:17, 1.01it/s] 39%|███▉ | 11/28 [00:10<00:16, 1.00it/s] 43%|████▎ | 12/28 [00:11<00:15, 1.00it/s] 46%|████▋ | 13/28 [00:12<00:14, 1.00it/s] 50%|█████ | 14/28 [00:13<00:13, 1.00it/s] 54%|█████▎ | 15/28 [00:14<00:13, 1.00s/it] 57%|█████▋ | 16/28 [00:15<00:11, 1.00it/s] 61%|██████ | 17/28 [00:16<00:10, 1.00it/s] 64%|██████▍ | 18/28 [00:17<00:09, 1.00it/s] 68%|██████▊ | 19/28 [00:18<00:08, 1.00it/s] 71%|███████▏ | 20/28 [00:19<00:07, 1.00it/s] 75%|███████▌ | 21/28 [00:20<00:06, 1.00it/s] 79%|███████▊ | 22/28 [00:21<00:05, 1.00it/s] 82%|████████▏ | 23/28 [00:22<00:04, 1.00it/s] 86%|████████▌ | 24/28 [00:23<00:03, 1.00it/s] 89%|████████▉ | 25/28 [00:24<00:02, 1.00it/s] 93%|█████████▎| 26/28 [00:25<00:02, 1.00s/it] 96%|█████████▋| 27/28 [00:26<00:01, 1.00s/it] 100%|██████████| 28/28 [00:27<00:00, 1.00it/s] 100%|██████████| 28/28 [00:27<00:00, 1.01it/s]
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