fofr
/
sdxl-emoji
An SDXL fine-tune based on Apple Emojis
- Public
- 9.3M runs
-
L40S
- SDXL fine-tune
Prediction
fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31ID25w6pkdbzqbfnvj35qzkyljmceStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1024
- height
- 1024
- prompt
- A TOK emoji of a man
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A TOK emoji of a man", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }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/sdxl-emoji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", { input: { width: 1024, height: 1024, prompt: "A TOK emoji of a man", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, 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 fofr/sdxl-emoji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", input={ "width": 1024, "height": 1024, "prompt": "A TOK emoji of a man", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-emoji 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": "e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", "input": { "width": 1024, "height": 1024, "prompt": "A TOK emoji of a man", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "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": "2023-09-04T09:35:43.334363Z", "created_at": "2023-09-04T09:35:28.157906Z", "data_removed": false, "error": null, "id": "25w6pkdbzqbfnvj35qzkyljmce", "input": { "width": 1024, "height": 1024, "prompt": "A TOK emoji of a man", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 57727\nPrompt: A <s0><s1> emoji of a man\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 15.177713, "total_time": 15.176457 }, "output": [ "https://replicate.delivery/pbxt/a3z81v5vwlKfLq1H5uBqpVmkHalOVup0jSLma9E2UaF3tawIA/out-0.png" ], "started_at": "2023-09-04T09:35:28.156650Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/25w6pkdbzqbfnvj35qzkyljmce", "cancel": "https://api.replicate.com/v1/predictions/25w6pkdbzqbfnvj35qzkyljmce/cancel" }, "version": "e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31" }Generated inUsing seed: 57727 Prompt: A <s0><s1> emoji of a man txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 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74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]Prediction
fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31IDv3vwup3bommw264amkfoxhpvieStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1024
- height
- 1024
- prompt
- A TOK emoji of a tiger face, white background
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.99
- negative_prompt
- soft
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A TOK emoji of a tiger face, white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.99, "negative_prompt": "soft", "prompt_strength": 0.8, "num_inference_steps": 50 }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/sdxl-emoji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", { input: { width: 1024, height: 1024, prompt: "A TOK emoji of a tiger face, white background", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.99, negative_prompt: "soft", prompt_strength: 0.8, 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 fofr/sdxl-emoji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", input={ "width": 1024, "height": 1024, "prompt": "A TOK emoji of a tiger face, white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.99, "negative_prompt": "soft", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-emoji 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": "e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", "input": { "width": 1024, "height": 1024, "prompt": "A TOK emoji of a tiger face, white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.99, "negative_prompt": "soft", "prompt_strength": 0.8, "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": "2023-09-04T09:36:34.471003Z", "created_at": "2023-09-04T09:36:19.219972Z", "data_removed": false, "error": null, "id": "v3vwup3bommw264amkfoxhpvie", "input": { "width": 1024, "height": 1024, "prompt": "A TOK emoji of a tiger face, white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.99, "negative_prompt": "soft", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 25529\nPrompt: A <s0><s1> emoji of a tiger face, white background\ntxt2img mode\n 0%| | 0/49 [00:00<?, ?it/s]\n 2%|▏ | 1/49 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/49 [00:00<00:12, 3.68it/s]\n 6%|▌ | 3/49 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/49 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/49 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/49 [00:01<00:11, 3.66it/s]\n 14%|█▍ | 7/49 [00:01<00:11, 3.66it/s]\n 16%|█▋ | 8/49 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/49 [00:02<00:10, 3.66it/s]\n 20%|██ | 10/49 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/49 [00:03<00:10, 3.66it/s]\n 24%|██▍ | 12/49 [00:03<00:10, 3.65it/s]\n 27%|██▋ | 13/49 [00:03<00:09, 3.65it/s]\n 29%|██▊ | 14/49 [00:03<00:09, 3.66it/s]\n 31%|███ | 15/49 [00:04<00:09, 3.66it/s]\n 33%|███▎ | 16/49 [00:04<00:09, 3.67it/s]\n 35%|███▍ | 17/49 [00:04<00:08, 3.67it/s]\n 37%|███▋ | 18/49 [00:04<00:08, 3.67it/s]\n 39%|███▉ | 19/49 [00:05<00:08, 3.67it/s]\n 41%|████ | 20/49 [00:05<00:07, 3.68it/s]\n 43%|████▎ | 21/49 [00:05<00:07, 3.68it/s]\n 45%|████▍ | 22/49 [00:06<00:07, 3.68it/s]\n 47%|████▋ | 23/49 [00:06<00:07, 3.68it/s]\n 49%|████▉ | 24/49 [00:06<00:06, 3.67it/s]\n 51%|█████ | 25/49 [00:06<00:06, 3.67it/s]\n 53%|█████▎ | 26/49 [00:07<00:06, 3.67it/s]\n 55%|█████▌ | 27/49 [00:07<00:05, 3.67it/s]\n 57%|█████▋ | 28/49 [00:07<00:05, 3.67it/s]\n 59%|█████▉ | 29/49 [00:07<00:05, 3.67it/s]\n 61%|██████ | 30/49 [00:08<00:05, 3.67it/s]\n 63%|██████▎ | 31/49 [00:08<00:04, 3.67it/s]\n 65%|██████▌ | 32/49 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4.18it/s]", "metrics": { "predict_time": 15.261507, "total_time": 15.251031 }, "output": [ "https://replicate.delivery/pbxt/J6vOuC0Yj647JRa9YAUMq1vbGKFAiOreQcKuJmHLI0wQuawIA/out-0.png" ], "started_at": "2023-09-04T09:36:19.209496Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/v3vwup3bommw264amkfoxhpvie", "cancel": "https://api.replicate.com/v1/predictions/v3vwup3bommw264amkfoxhpvie/cancel" }, "version": "e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31" }Generated inUsing seed: 25529 Prompt: A <s0><s1> emoji of a tiger face, white background txt2img mode 0%| | 0/49 [00:00<?, ?it/s] 2%|▏ | 1/49 [00:00<00:13, 3.69it/s] 4%|▍ | 2/49 [00:00<00:12, 3.68it/s] 6%|▌ | 3/49 [00:00<00:12, 3.67it/s] 8%|▊ | 4/49 [00:01<00:12, 3.67it/s] 10%|█ | 5/49 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/49 [00:01<00:11, 3.66it/s] 14%|█▍ | 7/49 [00:01<00:11, 3.66it/s] 16%|█▋ | 8/49 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/49 [00:02<00:10, 3.66it/s] 20%|██ | 10/49 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fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31IDtujb733b6z5wdippv6r3pt3dliStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1024
- height
- 1024
- prompt
- A TOK emoji of a llama face, white background
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.99
- negative_prompt
- soft, underexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A TOK emoji of a llama face, white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.99, "negative_prompt": "soft, underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }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/sdxl-emoji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", { input: { width: 1024, height: 1024, prompt: "A TOK emoji of a llama face, white background", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.99, negative_prompt: "soft, underexposed", prompt_strength: 0.8, 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 fofr/sdxl-emoji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", input={ "width": 1024, "height": 1024, "prompt": "A TOK emoji of a llama face, white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.99, "negative_prompt": "soft, underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-emoji 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": "e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", "input": { "width": 1024, "height": 1024, "prompt": "A TOK emoji of a llama face, white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.99, "negative_prompt": "soft, underexposed", "prompt_strength": 0.8, "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": "2023-09-04T09:39:54.990951Z", "created_at": "2023-09-04T09:39:39.744312Z", "data_removed": false, "error": null, "id": "tujb733b6z5wdippv6r3pt3dli", "input": { "width": 1024, "height": 1024, "prompt": "A TOK emoji of a llama face, white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.99, "negative_prompt": "soft, underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 11550\nPrompt: A <s0><s1> emoji of a llama face, white background\ntxt2img mode\n 0%| | 0/49 [00:00<?, ?it/s]\n 2%|▏ | 1/49 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/49 [00:00<00:12, 3.67it/s]\n 6%|▌ | 3/49 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/49 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/49 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/49 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/49 [00:01<00:11, 3.67it/s]\n 16%|█▋ | 8/49 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/49 [00:02<00:10, 3.66it/s]\n 20%|██ | 10/49 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/49 [00:03<00:10, 3.66it/s]\n 24%|██▍ | 12/49 [00:03<00:10, 3.66it/s]\n 27%|██▋ | 13/49 [00:03<00:09, 3.66it/s]\n 29%|██▊ | 14/49 [00:03<00:09, 3.66it/s]\n 31%|███ | 15/49 [00:04<00:09, 3.66it/s]\n 33%|███▎ | 16/49 [00:04<00:09, 3.66it/s]\n 35%|███▍ | 17/49 [00:04<00:08, 3.66it/s]\n 37%|███▋ | 18/49 [00:04<00:08, 3.66it/s]\n 39%|███▉ | 19/49 [00:05<00:08, 3.66it/s]\n 41%|████ | 20/49 [00:05<00:07, 3.66it/s]\n 43%|████▎ | 21/49 [00:05<00:07, 3.65it/s]\n 45%|████▍ | 22/49 [00:06<00:07, 3.65it/s]\n 47%|████▋ | 23/49 [00:06<00:07, 3.65it/s]\n 49%|████▉ | 24/49 [00:06<00:06, 3.65it/s]\n 51%|█████ | 25/49 [00:06<00:06, 3.65it/s]\n 53%|█████▎ | 26/49 [00:07<00:06, 3.65it/s]\n 55%|█████▌ | 27/49 [00:07<00:06, 3.65it/s]\n 57%|█████▋ | 28/49 [00:07<00:05, 3.65it/s]\n 59%|█████▉ | 29/49 [00:07<00:05, 3.65it/s]\n 61%|██████ | 30/49 [00:08<00:05, 3.65it/s]\n 63%|██████▎ | 31/49 [00:08<00:04, 3.65it/s]\n 65%|██████▌ | 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4.30it/s]", "metrics": { "predict_time": 15.237764, "total_time": 15.246639 }, "output": [ "https://replicate.delivery/pbxt/cNFerMxyBD1UfERJB29hHCGJujf0DShhcDWcaqxlX9aUfVDGB/out-0.png" ], "started_at": "2023-09-04T09:39:39.753187Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tujb733b6z5wdippv6r3pt3dli", "cancel": "https://api.replicate.com/v1/predictions/tujb733b6z5wdippv6r3pt3dli/cancel" }, "version": "e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31" }Generated inUsing seed: 11550 Prompt: A <s0><s1> emoji of a llama face, white background txt2img mode 0%| | 0/49 [00:00<?, ?it/s] 2%|▏ | 1/49 [00:00<00:13, 3.67it/s] 4%|▍ | 2/49 [00:00<00:12, 3.67it/s] 6%|▌ | 3/49 [00:00<00:12, 3.66it/s] 8%|▊ | 4/49 [00:01<00:12, 3.66it/s] 10%|█ | 5/49 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/49 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/49 [00:01<00:11, 3.67it/s] 16%|█▋ | 8/49 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/49 [00:02<00:10, 3.66it/s] 20%|██ | 10/49 [00:02<00:10, 3.66it/s] 22%|██▏ | 11/49 [00:03<00:10, 3.66it/s] 24%|██▍ | 12/49 [00:03<00:10, 3.66it/s] 27%|██▋ | 13/49 [00:03<00:09, 3.66it/s] 29%|██▊ | 14/49 [00:03<00:09, 3.66it/s] 31%|███ | 15/49 [00:04<00:09, 3.66it/s] 33%|███▎ | 16/49 [00:04<00:09, 3.66it/s] 35%|███▍ | 17/49 [00:04<00:08, 3.66it/s] 37%|███▋ | 18/49 [00:04<00:08, 3.66it/s] 39%|███▉ | 19/49 [00:05<00:08, 3.66it/s] 41%|████ | 20/49 [00:05<00:07, 3.66it/s] 43%|████▎ | 21/49 [00:05<00:07, 3.65it/s] 45%|████▍ | 22/49 [00:06<00:07, 3.65it/s] 47%|████▋ | 23/49 [00:06<00:07, 3.65it/s] 49%|████▉ | 24/49 [00:06<00:06, 3.65it/s] 51%|█████ | 25/49 [00:06<00:06, 3.65it/s] 53%|█████▎ | 26/49 [00:07<00:06, 3.65it/s] 55%|█████▌ | 27/49 [00:07<00:06, 3.65it/s] 57%|█████▋ | 28/49 [00:07<00:05, 3.65it/s] 59%|█████▉ | 29/49 [00:07<00:05, 3.65it/s] 61%|██████ | 30/49 [00:08<00:05, 3.65it/s] 63%|██████▎ | 31/49 [00:08<00:04, 3.65it/s] 65%|██████▌ | 32/49 [00:08<00:04, 3.65it/s] 67%|██████▋ | 33/49 [00:09<00:04, 3.65it/s] 69%|██████▉ | 34/49 [00:09<00:04, 3.65it/s] 71%|███████▏ | 35/49 [00:09<00:03, 3.65it/s] 73%|███████▎ | 36/49 [00:09<00:03, 3.65it/s] 76%|███████▌ | 37/49 [00:10<00:03, 3.65it/s] 78%|███████▊ | 38/49 [00:10<00:03, 3.65it/s] 80%|███████▉ | 39/49 [00:10<00:02, 3.64it/s] 82%|████████▏ | 40/49 [00:10<00:02, 3.64it/s] 84%|████████▎ | 41/49 [00:11<00:02, 3.64it/s] 86%|████████▌ | 42/49 [00:11<00:01, 3.64it/s] 88%|████████▊ | 43/49 [00:11<00:01, 3.64it/s] 90%|████████▉ | 44/49 [00:12<00:01, 3.64it/s] 92%|█████████▏| 45/49 [00:12<00:01, 3.64it/s] 94%|█████████▍| 46/49 [00:12<00:00, 3.64it/s] 96%|█████████▌| 47/49 [00:12<00:00, 3.64it/s] 98%|█████████▊| 48/49 [00:13<00:00, 3.64it/s] 100%|██████████| 49/49 [00:13<00:00, 3.64it/s] 100%|██████████| 49/49 [00:13<00:00, 3.65it/s] 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:00<00:00, 4.30it/s] 100%|██████████| 1/1 [00:00<00:00, 4.30it/s]Prediction
fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31IDukstcgdb3rramwzb5vnqx57rf4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- mask
- image
- width
- 1024
- height
- 1024
- prompt
- A TOK emoji of a simple camera on a white background
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 10.06
- apply_watermark
- high_noise_frac
- 0.95
- negative_prompt
- soft, underexposed, cropped
- prompt_strength
- 1
- num_inference_steps
- 50
{ "mask": "https://replicate.delivery/pbxt/JT6K0z55b9kVVAvjzirEYBSMB3kGbRWdw6nbdxsvbIqY4K99/1024x1024-border-mask.png", "image": "https://replicate.delivery/pbxt/JT6K1cpUtBo05KuNUd44NI9ZYuOB3JvCaeHFuRNGpq3saPaV/1024x1024-white.png", "width": 1024, "height": 1024, "prompt": "A TOK emoji of a simple camera on a white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 10.06, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "soft, underexposed, cropped", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-emoji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", { input: { mask: "https://replicate.delivery/pbxt/JT6K0z55b9kVVAvjzirEYBSMB3kGbRWdw6nbdxsvbIqY4K99/1024x1024-border-mask.png", image: "https://replicate.delivery/pbxt/JT6K1cpUtBo05KuNUd44NI9ZYuOB3JvCaeHFuRNGpq3saPaV/1024x1024-white.png", width: 1024, height: 1024, prompt: "A TOK emoji of a simple camera on a white background", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 10.06, apply_watermark: false, high_noise_frac: 0.95, negative_prompt: "soft, underexposed, cropped", 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 fofr/sdxl-emoji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-emoji:e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", input={ "mask": "https://replicate.delivery/pbxt/JT6K0z55b9kVVAvjzirEYBSMB3kGbRWdw6nbdxsvbIqY4K99/1024x1024-border-mask.png", "image": "https://replicate.delivery/pbxt/JT6K1cpUtBo05KuNUd44NI9ZYuOB3JvCaeHFuRNGpq3saPaV/1024x1024-white.png", "width": 1024, "height": 1024, "prompt": "A TOK emoji of a simple camera on a white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 10.06, "apply_watermark": False, "high_noise_frac": 0.95, "negative_prompt": "soft, underexposed, cropped", "prompt_strength": 1, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-emoji 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": "e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31", "input": { "mask": "https://replicate.delivery/pbxt/JT6K0z55b9kVVAvjzirEYBSMB3kGbRWdw6nbdxsvbIqY4K99/1024x1024-border-mask.png", "image": "https://replicate.delivery/pbxt/JT6K1cpUtBo05KuNUd44NI9ZYuOB3JvCaeHFuRNGpq3saPaV/1024x1024-white.png", "width": 1024, "height": 1024, "prompt": "A TOK emoji of a simple camera on a white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 10.06, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "soft, underexposed, cropped", "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": "2023-09-04T09:49:49.467276Z", "created_at": "2023-09-04T09:49:32.773064Z", "data_removed": false, "error": null, "id": "ukstcgdb3rramwzb5vnqx57rf4", "input": { "mask": "https://replicate.delivery/pbxt/JT6K0z55b9kVVAvjzirEYBSMB3kGbRWdw6nbdxsvbIqY4K99/1024x1024-border-mask.png", "image": "https://replicate.delivery/pbxt/JT6K1cpUtBo05KuNUd44NI9ZYuOB3JvCaeHFuRNGpq3saPaV/1024x1024-white.png", "width": 1024, "height": 1024, "prompt": "A TOK emoji of a simple camera on a white background", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 10.06, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "soft, underexposed, cropped", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 57480\nPrompt: A <s0><s1> emoji of a simple camera on a white background\ninpainting mode\n 0%| | 0/47 [00:00<?, ?it/s]\n 2%|▏ | 1/47 [00:00<00:12, 3.67it/s]\n 4%|▍ | 2/47 [00:00<00:12, 3.66it/s]\n 6%|▋ | 3/47 [00:00<00:12, 3.66it/s]\n 9%|▊ | 4/47 [00:01<00:11, 3.67it/s]\n 11%|█ | 5/47 [00:01<00:11, 3.67it/s]\n 13%|█▎ | 6/47 [00:01<00:11, 3.67it/s]\n 15%|█▍ | 7/47 [00:01<00:10, 3.68it/s]\n 17%|█▋ | 8/47 [00:02<00:10, 3.68it/s]\n 19%|█▉ | 9/47 [00:02<00:10, 3.68it/s]\n 21%|██▏ | 10/47 [00:02<00:10, 3.68it/s]\n 23%|██▎ | 11/47 [00:02<00:09, 3.68it/s]\n 26%|██▌ | 12/47 [00:03<00:09, 3.67it/s]\n 28%|██▊ | 13/47 [00:03<00:09, 3.67it/s]\n 30%|██▉ | 14/47 [00:03<00:08, 3.67it/s]\n 32%|███▏ | 15/47 [00:04<00:08, 3.67it/s]\n 34%|███▍ | 16/47 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 17/47 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 18/47 [00:04<00:07, 3.67it/s]\n 40%|████ | 19/47 [00:05<00:07, 3.67it/s]\n 43%|████▎ | 20/47 [00:05<00:07, 3.67it/s]\n 45%|████▍ | 21/47 [00:05<00:07, 3.67it/s]\n 47%|████▋ | 22/47 [00:05<00:06, 3.67it/s]\n 49%|████▉ | 23/47 [00:06<00:06, 3.67it/s]\n 51%|█████ | 24/47 [00:06<00:06, 3.67it/s]\n 53%|█████▎ | 25/47 [00:06<00:06, 3.66it/s]\n 55%|█████▌ | 26/47 [00:07<00:05, 3.66it/s]\n 57%|█████▋ | 27/47 [00:07<00:05, 3.66it/s]\n 60%|█████▉ | 28/47 [00:07<00:05, 3.67it/s]\n 62%|██████▏ | 29/47 [00:07<00:04, 3.67it/s]\n 64%|██████▍ | 30/47 [00:08<00:04, 3.66it/s]\n 66%|██████▌ | 31/47 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 32/47 [00:08<00:04, 3.66it/s]\n 70%|███████ | 33/47 [00:08<00:03, 3.66it/s]\n 72%|███████▏ | 34/47 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 35/47 [00:09<00:03, 3.66it/s]\n 77%|███████▋ | 36/47 [00:09<00:03, 3.66it/s]\n 79%|███████▊ | 37/47 [00:10<00:02, 3.66it/s]\n 81%|████████ | 38/47 [00:10<00:02, 3.66it/s]\n 83%|████████▎ | 39/47 [00:10<00:02, 3.66it/s]\n 85%|████████▌ | 40/47 [00:10<00:01, 3.66it/s]\n 87%|████████▋ | 41/47 [00:11<00:01, 3.66it/s]\n 89%|████████▉ | 42/47 [00:11<00:01, 3.66it/s]\n 91%|█████████▏| 43/47 [00:11<00:01, 3.66it/s]\n 94%|█████████▎| 44/47 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 45/47 [00:12<00:00, 3.66it/s]\n 98%|█████████▊| 46/47 [00:12<00:00, 3.66it/s]\n100%|██████████| 47/47 [00:12<00:00, 3.66it/s]\n100%|██████████| 47/47 [00:12<00:00, 3.67it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.33it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.31it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.30it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.30it/s]", "metrics": { "predict_time": 16.685967, "total_time": 16.694212 }, "output": [ "https://replicate.delivery/pbxt/DKFghOgmkTKVCpwgfIeKkTqMemHQMKtW9yxLYqeLyeonHtGMC/out-0.png" ], "started_at": "2023-09-04T09:49:32.781309Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ukstcgdb3rramwzb5vnqx57rf4", "cancel": "https://api.replicate.com/v1/predictions/ukstcgdb3rramwzb5vnqx57rf4/cancel" }, "version": "e6484351b3c943cbd507d938df8abc598cb05c44f4e67ee82be0beea5f495f31" }Generated inUsing seed: 57480 Prompt: A <s0><s1> emoji of a simple camera on a white background inpainting mode 0%| | 0/47 [00:00<?, ?it/s] 2%|▏ | 1/47 [00:00<00:12, 3.67it/s] 4%|▍ | 2/47 [00:00<00:12, 3.66it/s] 6%|▋ | 3/47 [00:00<00:12, 3.66it/s] 9%|▊ | 4/47 [00:01<00:11, 3.67it/s] 11%|█ | 5/47 [00:01<00:11, 3.67it/s] 13%|█▎ | 6/47 [00:01<00:11, 3.67it/s] 15%|█▍ | 7/47 [00:01<00:10, 3.68it/s] 17%|█▋ | 8/47 [00:02<00:10, 3.68it/s] 19%|█▉ | 9/47 [00:02<00:10, 3.68it/s] 21%|██▏ | 10/47 [00:02<00:10, 3.68it/s] 23%|██▎ | 11/47 [00:02<00:09, 3.68it/s] 26%|██▌ | 12/47 [00:03<00:09, 3.67it/s] 28%|██▊ | 13/47 [00:03<00:09, 3.67it/s] 30%|██▉ | 14/47 [00:03<00:08, 3.67it/s] 32%|███▏ | 15/47 [00:04<00:08, 3.67it/s] 34%|███▍ | 16/47 [00:04<00:08, 3.67it/s] 36%|███▌ | 17/47 [00:04<00:08, 3.67it/s] 38%|███▊ | 18/47 [00:04<00:07, 3.67it/s] 40%|████ | 19/47 [00:05<00:07, 3.67it/s] 43%|████▎ | 20/47 [00:05<00:07, 3.67it/s] 45%|████▍ | 21/47 [00:05<00:07, 3.67it/s] 47%|████▋ | 22/47 [00:05<00:06, 3.67it/s] 49%|████▉ | 23/47 [00:06<00:06, 3.67it/s] 51%|█████ | 24/47 [00:06<00:06, 3.67it/s] 53%|█████▎ | 25/47 [00:06<00:06, 3.66it/s] 55%|█████▌ | 26/47 [00:07<00:05, 3.66it/s] 57%|█████▋ | 27/47 [00:07<00:05, 3.66it/s] 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