colinmcdonnell22 / yap-man
- Public
- 69 runs
-
H100
Prediction
colinmcdonnell22/yap-man:ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01fIDnjfnvep325rm80ckg35bfk1tncStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- schnell
- prompt
- three people hanging out in the cafe YAP
- go_fast
- lora_scale
- 1.3
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 4
{ "model": "schnell", "prompt": "three people hanging out in the cafe YAP", "go_fast": true, "lora_scale": 1.3, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 4 }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 colinmcdonnell22/yap-man using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "colinmcdonnell22/yap-man:ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01f", { input: { model: "schnell", prompt: "three people hanging out in the cafe YAP", go_fast: true, lora_scale: 1.3, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "png", guidance_scale: 3.5, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 4 } } ); // 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 colinmcdonnell22/yap-man using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "colinmcdonnell22/yap-man:ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01f", input={ "model": "schnell", "prompt": "three people hanging out in the cafe YAP", "go_fast": True, "lora_scale": 1.3, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 4 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run colinmcdonnell22/yap-man 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": "colinmcdonnell22/yap-man:ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01f", "input": { "model": "schnell", "prompt": "three people hanging out in the cafe YAP", "go_fast": true, "lora_scale": 1.3, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-01T05:56:33.766415Z", "created_at": "2024-12-01T05:56:32.145000Z", "data_removed": false, "error": null, "id": "njfnvep325rm80ckg35bfk1tnc", "input": { "model": "schnell", "prompt": "three people hanging out in the cafe YAP", "go_fast": true, "lora_scale": 1.3, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 4 }, "logs": "Lora https://replicate.delivery/xezq/cAOTQkvOA4Z7EJmvygxcOCSdpsOCUprcPHqfCOqlBTGETN7JA/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 2633343159\n 0%| | 0/4 [00:00<?, ?it/s]\n 50%|█████ | 2/4 [00:00<00:00, 18.77it/s]\n100%|██████████| 4/4 [00:00<00:00, 13.86it/s]\n100%|██████████| 4/4 [00:00<00:00, 14.42it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 0.834542508, "total_time": 1.621415 }, "output": [ "https://replicate.delivery/xezq/86GqQoMDKY6VERrFJQNRStke74AXvmerPKYBZe1WWfyGJrZPB/out-0.png" ], "started_at": "2024-12-01T05:56:32.931872Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-bqpun7qlook2wb5lsdaacprgntep2xav6nziu5cl2eoy5h5a632a", "get": "https://api.replicate.com/v1/predictions/njfnvep325rm80ckg35bfk1tnc", "cancel": "https://api.replicate.com/v1/predictions/njfnvep325rm80ckg35bfk1tnc/cancel" }, "version": "ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01f" }Generated inLora https://replicate.delivery/xezq/cAOTQkvOA4Z7EJmvygxcOCSdpsOCUprcPHqfCOqlBTGETN7JA/trained_model.tar already loaded running quantized prediction Using seed: 2633343159 0%| | 0/4 [00:00<?, ?it/s] 50%|█████ | 2/4 [00:00<00:00, 18.77it/s] 100%|██████████| 4/4 [00:00<00:00, 13.86it/s] 100%|██████████| 4/4 [00:00<00:00, 14.42it/s] Total safe images: 1 out of 1Prediction
colinmcdonnell22/yap-man:ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01fID593c09kmd1rmc0ckg36t5bvmdrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- schnell
- prompt
- three people hanging out in the cafe YAP
- go_fast
- lora_scale
- 1.4
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 20
{ "model": "schnell", "prompt": "three people hanging out in the cafe YAP", "go_fast": true, "lora_scale": 1.4, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 20 }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 colinmcdonnell22/yap-man using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "colinmcdonnell22/yap-man:ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01f", { input: { model: "schnell", prompt: "three people hanging out in the cafe YAP", go_fast: true, lora_scale: 1.4, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "png", guidance_scale: 3.5, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 20 } } ); // 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 colinmcdonnell22/yap-man using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "colinmcdonnell22/yap-man:ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01f", input={ "model": "schnell", "prompt": "three people hanging out in the cafe YAP", "go_fast": True, "lora_scale": 1.4, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 20 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run colinmcdonnell22/yap-man 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": "colinmcdonnell22/yap-man:ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01f", "input": { "model": "schnell", "prompt": "three people hanging out in the cafe YAP", "go_fast": true, "lora_scale": 1.4, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 20 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-01T05:59:44.557444Z", "created_at": "2024-12-01T05:59:28.616000Z", "data_removed": false, "error": null, "id": "593c09kmd1rmc0ckg36t5bvmdr", "input": { "model": "schnell", "prompt": "three people hanging out in the cafe YAP", "go_fast": true, "lora_scale": 1.4, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 20 }, "logs": "2024-12-01 05:59:41.871 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys\n2024-12-01 05:59:41.871 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 13408.79it/s]\n2024-12-01 05:59:41.894 | SUCCESS | fp8.lora_loading:unload_loras:559 - LoRAs unloaded in 0.024s\n2024-12-01 05:59:41.896 | INFO | fp8.lora_loading:convert_lora_weights:493 - Loading LoRA weights for /src/weights-cache/a6d4801f8529a70d\n2024-12-01 05:59:41.964 | INFO | fp8.lora_loading:convert_lora_weights:514 - LoRA weights loaded\n2024-12-01 05:59:41.964 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys\n2024-12-01 05:59:41.964 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 40%|████ | 122/304 [00:00<00:00, 1211.04it/s]\nApplying LoRA: 80%|████████ | 244/304 [00:00<00:00, 944.66it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 959.64it/s]\n2024-12-01 05:59:42.281 | SUCCESS | fp8.lora_loading:load_lora:534 - LoRA applied in 0.39s\nrunning quantized prediction\nUsing seed: 523984155\n 0%| | 0/20 [00:00<?, ?it/s]\n 10%|█ | 2/20 [00:00<00:00, 18.64it/s]\n 20%|██ | 4/20 [00:00<00:01, 13.69it/s]\n 30%|███ | 6/20 [00:00<00:01, 12.61it/s]\n 40%|████ | 8/20 [00:00<00:00, 12.21it/s]\n 50%|█████ | 10/20 [00:00<00:00, 11.93it/s]\n 60%|██████ | 12/20 [00:00<00:00, 11.46it/s]\n 70%|███████ | 14/20 [00:01<00:00, 11.47it/s]\n 80%|████████ | 16/20 [00:01<00:00, 11.50it/s]\n 90%|█████████ | 18/20 [00:01<00:00, 11.51it/s]\n100%|██████████| 20/20 [00:01<00:00, 11.53it/s]\n100%|██████████| 20/20 [00:01<00:00, 11.89it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 2.684982845, "total_time": 15.941444 }, "output": [ "https://replicate.delivery/xezq/nYceFGEVC7wMJydvtuRLmuTs1Khzq7iC9iZlH9aOX0GoaN7JA/out-0.png" ], "started_at": "2024-12-01T05:59:41.872461Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-oryvr2xj55obmro7nagaq2ibwkfpozc5koqf4bn2xlrt4eu5esmq", "get": "https://api.replicate.com/v1/predictions/593c09kmd1rmc0ckg36t5bvmdr", "cancel": "https://api.replicate.com/v1/predictions/593c09kmd1rmc0ckg36t5bvmdr/cancel" }, "version": "ab0e0fc932f74ebf4a66e0e92551f2c7141adaa67573ead1fc3a035daf42a01f" }Generated in2024-12-01 05:59:41.871 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys 2024-12-01 05:59:41.871 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 13408.79it/s] 2024-12-01 05:59:41.894 | SUCCESS | fp8.lora_loading:unload_loras:559 - LoRAs unloaded in 0.024s 2024-12-01 05:59:41.896 | INFO | fp8.lora_loading:convert_lora_weights:493 - Loading LoRA weights for /src/weights-cache/a6d4801f8529a70d 2024-12-01 05:59:41.964 | INFO | fp8.lora_loading:convert_lora_weights:514 - LoRA weights loaded 2024-12-01 05:59:41.964 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys 2024-12-01 05:59:41.964 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 40%|████ | 122/304 [00:00<00:00, 1211.04it/s] Applying LoRA: 80%|████████ | 244/304 [00:00<00:00, 944.66it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 959.64it/s] 2024-12-01 05:59:42.281 | SUCCESS | fp8.lora_loading:load_lora:534 - LoRA applied in 0.39s running quantized prediction Using seed: 523984155 0%| | 0/20 [00:00<?, ?it/s] 10%|█ | 2/20 [00:00<00:00, 18.64it/s] 20%|██ | 4/20 [00:00<00:01, 13.69it/s] 30%|███ | 6/20 [00:00<00:01, 12.61it/s] 40%|████ | 8/20 [00:00<00:00, 12.21it/s] 50%|█████ | 10/20 [00:00<00:00, 11.93it/s] 60%|██████ | 12/20 [00:00<00:00, 11.46it/s] 70%|███████ | 14/20 [00:01<00:00, 11.47it/s] 80%|████████ | 16/20 [00:01<00:00, 11.50it/s] 90%|█████████ | 18/20 [00:01<00:00, 11.51it/s] 100%|██████████| 20/20 [00:01<00:00, 11.53it/s] 100%|██████████| 20/20 [00:01<00:00, 11.89it/s] Total safe images: 1 out of 1
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