zeke
/
ziki-2024-08-30
Use trigger word ziki-2024-08-30 to activate the trained LoRA style.
- Public
- 20 runs
-
H100
Prediction
zeke/ziki-2024-08-30:c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0IDvr4xst5nk5rm40chmrzbzjj5c8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- ziki-2024-08-30 sitting on a couch in 1960s technicolor living room scene
- 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": "ziki-2024-08-30 sitting on a couch in 1960s technicolor living room scene", "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 zeke/ziki-2024-08-30 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/ziki-2024-08-30:c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0", { input: { model: "dev", prompt: "ziki-2024-08-30 sitting on a couch in 1960s technicolor living room scene", 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 zeke/ziki-2024-08-30 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/ziki-2024-08-30:c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0", input={ "model": "dev", "prompt": "ziki-2024-08-30 sitting on a couch in 1960s technicolor living room scene", "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 zeke/ziki-2024-08-30 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": "c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0", "input": { "model": "dev", "prompt": "ziki-2024-08-30 sitting on a couch in 1960s technicolor living room scene", "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": "2024-08-31T02:23:51.213650Z", "created_at": "2024-08-31T02:23:33.273000Z", "data_removed": false, "error": null, "id": "vr4xst5nk5rm40chmrzbzjj5c8", "input": { "model": "dev", "prompt": "ziki-2024-08-30 sitting on a couch in 1960s technicolor living room scene", "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: 13836\nPrompt: ziki-2024-08-30 sitting on a couch in 1960s technicolor living room scene\ntxt2img mode\nUsing dev model\nfree=9534417743872\nDownloading weights\n2024-08-31T02:23:33Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmph32vk0hc/weights url=https://replicate.delivery/yhqm/xCNpYxTgvAYLHd4IpwE9zjNUlI0eJOABlJf4gWdNOjWSoCYTA/trained_model.tar\n2024-08-31T02:23:35Z | INFO | [ Complete ] dest=/tmp/tmph32vk0hc/weights size=\"172 MB\" total_elapsed=1.794s url=https://replicate.delivery/yhqm/xCNpYxTgvAYLHd4IpwE9zjNUlI0eJOABlJf4gWdNOjWSoCYTA/trained_model.tar\nDownloaded weights in 1.82s\nLoaded LoRAs in 9.78s\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, 4.22it/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, 3.68it/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, 3.66it/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, 3.65it/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, 3.65it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.65it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.65it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.65it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.64it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.65it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.65it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.65it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.65it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]", "metrics": { "predict_time": 17.932919042, "total_time": 17.94065 }, "output": [ "https://replicate.delivery/yhqm/ipGbsfJNMnQpIKQeWMpNcBMiXKeaMQRfOBuorUq6pqdeWYAbC/out-0.webp" ], "started_at": "2024-08-31T02:23:33.280731Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vr4xst5nk5rm40chmrzbzjj5c8", "cancel": "https://api.replicate.com/v1/predictions/vr4xst5nk5rm40chmrzbzjj5c8/cancel" }, "version": "c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0" }Generated inUsing seed: 13836 Prompt: ziki-2024-08-30 sitting on a couch in 1960s technicolor living room scene txt2img mode Using dev model free=9534417743872 Downloading weights 2024-08-31T02:23:33Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmph32vk0hc/weights url=https://replicate.delivery/yhqm/xCNpYxTgvAYLHd4IpwE9zjNUlI0eJOABlJf4gWdNOjWSoCYTA/trained_model.tar 2024-08-31T02:23:35Z | INFO | [ Complete ] dest=/tmp/tmph32vk0hc/weights size="172 MB" total_elapsed=1.794s url=https://replicate.delivery/yhqm/xCNpYxTgvAYLHd4IpwE9zjNUlI0eJOABlJf4gWdNOjWSoCYTA/trained_model.tar Downloaded weights in 1.82s Loaded LoRAs in 9.78s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.67it/s] 7%|▋ | 2/28 [00:00<00:06, 4.22it/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, 3.68it/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, 3.66it/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, 3.65it/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, 3.65it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.65it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.65it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.65it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.64it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.65it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.65it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.65it/s] 100%|██████████| 28/28 [00:07<00:00, 3.65it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s]Prediction
zeke/ziki-2024-08-30:c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0Input
- model
- dev
- prompt
- antique mug shots of ziki-2024-08-30, black and white
- 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": "antique mug shots of ziki-2024-08-30, black and white", "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 zeke/ziki-2024-08-30 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/ziki-2024-08-30:c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0", { input: { model: "dev", prompt: "antique mug shots of ziki-2024-08-30, black and white", 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 zeke/ziki-2024-08-30 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/ziki-2024-08-30:c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0", input={ "model": "dev", "prompt": "antique mug shots of ziki-2024-08-30, black and white", "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 zeke/ziki-2024-08-30 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": "c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0", "input": { "model": "dev", "prompt": "antique mug shots of ziki-2024-08-30, black and white", "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": "2024-08-31T02:28:07.083459Z", "created_at": "2024-08-31T02:27:48.023000Z", "data_removed": false, "error": null, "id": "6cev5hcrpxrm40chms183hm6gm", "input": { "model": "dev", "prompt": "antique mug shots of ziki-2024-08-30, black and white", "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: 732\nPrompt: antique mug shots of ziki-2024-08-30, black and white\ntxt2img mode\nUsing dev model\nfree=9338751873024\nDownloading weights\n2024-08-31T02:27:48Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpk_ng1ez5/weights url=https://replicate.delivery/yhqm/xCNpYxTgvAYLHd4IpwE9zjNUlI0eJOABlJf4gWdNOjWSoCYTA/trained_model.tar\n2024-08-31T02:27:49Z | INFO | [ Complete ] dest=/tmp/tmpk_ng1ez5/weights size=\"172 MB\" total_elapsed=1.338s url=https://replicate.delivery/yhqm/xCNpYxTgvAYLHd4IpwE9zjNUlI0eJOABlJf4gWdNOjWSoCYTA/trained_model.tar\nDownloaded weights in 1.37s\nLoaded LoRAs in 10.93s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.66it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.22it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.95it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.73it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.71it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.68it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.68it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.67it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.67it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.67it/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, 3.67it/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, 3.66it/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": 19.052136182, "total_time": 19.060459 }, "output": [ "https://replicate.delivery/yhqm/PXsF4ehUdIz8FCKb772N3lB92EtQFtEYk72iyqFc1VbbjBsJA/out-0.webp" ], "started_at": "2024-08-31T02:27:48.031322Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6cev5hcrpxrm40chms183hm6gm", "cancel": "https://api.replicate.com/v1/predictions/6cev5hcrpxrm40chms183hm6gm/cancel" }, "version": "c6af5b98a1285f10f569ba72842f44676551a4195bd42dc0710b0bc42c0d38d0" }Generated inUsing seed: 732 Prompt: antique mug shots of ziki-2024-08-30, black and white txt2img mode Using dev model free=9338751873024 Downloading weights 2024-08-31T02:27:48Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpk_ng1ez5/weights url=https://replicate.delivery/yhqm/xCNpYxTgvAYLHd4IpwE9zjNUlI0eJOABlJf4gWdNOjWSoCYTA/trained_model.tar 2024-08-31T02:27:49Z | INFO | [ Complete ] dest=/tmp/tmpk_ng1ez5/weights size="172 MB" total_elapsed=1.338s url=https://replicate.delivery/yhqm/xCNpYxTgvAYLHd4IpwE9zjNUlI0eJOABlJf4gWdNOjWSoCYTA/trained_model.tar Downloaded weights in 1.37s Loaded LoRAs in 10.93s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.66it/s] 7%|▋ | 2/28 [00:00<00:06, 4.22it/s] 11%|█ | 3/28 [00:00<00:06, 3.95it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.73it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.71it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.68it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.68it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.67it/s] 50%|█████ | 14/28 [00:03<00:03, 3.67it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.67it/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, 3.67it/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, 3.66it/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]
Want to make some of these yourself?
Run this model