aramintak
/
japanese-poster-prints
A mid-century Japanese block print style
- Public
- 578 runs
-
L40S
- Paper
Prediction
aramintak/japanese-poster-prints:674cf07bIDq9gyc724tsrgg0cfz6y9qyewcrStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- bubble tea, minimalist, daiton style, block print
- sampler
- Default
- scheduler
- Default
- lora_strength
- 1
- output_format
- webp
- output_quality
- 80
- negative_prompt
- signature, watermark, kanji
- number_of_images
- 1
{ "width": 1024, "height": 1024, "prompt": "bubble tea, minimalist, daiton style, block print", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "signature, watermark, kanji", "number_of_images": 1 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run aramintak/japanese-poster-prints using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "aramintak/japanese-poster-prints:674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a", { input: { width: 1024, height: 1024, prompt: "bubble tea, minimalist, daiton style, block print", sampler: "Default", scheduler: "Default", lora_strength: 1, output_format: "webp", output_quality: 80, negative_prompt: "signature, watermark, kanji", number_of_images: 1 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun aramintak/japanese-poster-prints using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "aramintak/japanese-poster-prints:674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a", input={ "width": 1024, "height": 1024, "prompt": "bubble tea, minimalist, daiton style, block print", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "signature, watermark, kanji", "number_of_images": 1 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run aramintak/japanese-poster-prints 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": "674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a", "input": { "width": 1024, "height": 1024, "prompt": "bubble tea, minimalist, daiton style, block print", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "signature, watermark, kanji", "number_of_images": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-08T21:23:43.449555Z", "created_at": "2024-06-08T21:23:36.278000Z", "data_removed": false, "error": null, "id": "q9gyc724tsrgg0cfz6y9qyewcr", "input": { "width": 1024, "height": 1024, "prompt": "bubble tea, minimalist, daiton style, block print", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "signature, watermark, kanji", "number_of_images": 1 }, "logs": "Random seed set to: 974293991\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 6.5\nLORA: Using a lora. Lora strength: 1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 10, title: Load LoRA, class type: LoraLoader\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nRequested to load SDXLClipModel\nLoading 1 new model\nExecuting node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\nRequested to load SDXL\nLoading 1 new model\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:03, 5.34it/s]\n 10%|█ | 2/20 [00:00<00:03, 5.30it/s]\n 15%|█▌ | 3/20 [00:00<00:03, 5.28it/s]\n 20%|██ | 4/20 [00:00<00:03, 5.30it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 5.29it/s]\n 30%|███ | 6/20 [00:01<00:02, 5.28it/s]\n 35%|███▌ | 7/20 [00:01<00:02, 5.28it/s]\n 40%|████ | 8/20 [00:01<00:02, 5.29it/s]\n 45%|████▌ | 9/20 [00:01<00:02, 5.28it/s]\n 50%|█████ | 10/20 [00:01<00:01, 5.29it/s]\n 55%|█████▌ | 11/20 [00:02<00:01, 5.29it/s]\n 60%|██████ | 12/20 [00:02<00:01, 5.30it/s]\n 65%|██████▌ | 13/20 [00:02<00:01, 5.29it/s]\n 70%|███████ | 14/20 [00:02<00:01, 5.30it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 5.30it/s]\n 80%|████████ | 16/20 [00:03<00:00, 5.30it/s]\n 85%|████████▌ | 17/20 [00:03<00:00, 5.31it/s]\n 90%|█████████ | 18/20 [00:03<00:00, 5.31it/s]\n 95%|█████████▌| 19/20 [00:03<00:00, 5.33it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.34it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.30it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 5.61 seconds\noutputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png", "metrics": { "predict_time": 7.12209, "total_time": 7.171555 }, "output": [ "https://replicate.delivery/pbxt/jBHCweOsXC0KfEgVFMwqtMsBrMml5B8xHjoY5w3BAD7euP5lA/R8__00001_.webp" ], "started_at": "2024-06-08T21:23:36.327465Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/q9gyc724tsrgg0cfz6y9qyewcr", "cancel": "https://api.replicate.com/v1/predictions/q9gyc724tsrgg0cfz6y9qyewcr/cancel" }, "version": "674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a" }Generated inRandom seed set to: 974293991 ============================== Generation settings Sampler: dpmpp_2m_sde_gpu Scheduler: karras Steps: 20 CFG: 6.5 LORA: Using a lora. Lora strength: 1.0 ============================== Running workflow got prompt Executing node 10, title: Load LoRA, class type: LoraLoader Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Requested to load SDXLClipModel Loading 1 new model Executing node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 3, title: KSampler, class type: KSampler Requested to load SDXL Loading 1 new model 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:03, 5.34it/s] 10%|█ | 2/20 [00:00<00:03, 5.30it/s] 15%|█▌ | 3/20 [00:00<00:03, 5.28it/s] 20%|██ | 4/20 [00:00<00:03, 5.30it/s] 25%|██▌ | 5/20 [00:00<00:02, 5.29it/s] 30%|███ | 6/20 [00:01<00:02, 5.28it/s] 35%|███▌ | 7/20 [00:01<00:02, 5.28it/s] 40%|████ | 8/20 [00:01<00:02, 5.29it/s] 45%|████▌ | 9/20 [00:01<00:02, 5.28it/s] 50%|█████ | 10/20 [00:01<00:01, 5.29it/s] 55%|█████▌ | 11/20 [00:02<00:01, 5.29it/s] 60%|██████ | 12/20 [00:02<00:01, 5.30it/s] 65%|██████▌ | 13/20 [00:02<00:01, 5.29it/s] 70%|███████ | 14/20 [00:02<00:01, 5.30it/s] 75%|███████▌ | 15/20 [00:02<00:00, 5.30it/s] 80%|████████ | 16/20 [00:03<00:00, 5.30it/s] 85%|████████▌ | 17/20 [00:03<00:00, 5.31it/s] 90%|█████████ | 18/20 [00:03<00:00, 5.31it/s] 95%|█████████▌| 19/20 [00:03<00:00, 5.33it/s] 100%|██████████| 20/20 [00:03<00:00, 5.34it/s] 100%|██████████| 20/20 [00:03<00:00, 5.30it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 5.61 seconds outputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== R8__00001_.pngPrediction
aramintak/japanese-poster-prints:674cf07bIDkb4h1fp4tnrgm0cfz6y9v0dh1gStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a bird, daiton style
- sampler
- Default
- scheduler
- Default
- lora_strength
- 1
- output_format
- webp
- output_quality
- 80
- number_of_images
- 1
{ "width": 1024, "height": 1024, "prompt": "a bird, daiton style", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run aramintak/japanese-poster-prints using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "aramintak/japanese-poster-prints:674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a", { input: { width: 1024, height: 1024, prompt: "a bird, daiton style", sampler: "Default", scheduler: "Default", lora_strength: 1, output_format: "webp", output_quality: 80, number_of_images: 1 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun aramintak/japanese-poster-prints using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "aramintak/japanese-poster-prints:674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a", input={ "width": 1024, "height": 1024, "prompt": "a bird, daiton style", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run aramintak/japanese-poster-prints 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": "674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a", "input": { "width": 1024, "height": 1024, "prompt": "a bird, daiton style", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-08T21:24:14.719390Z", "created_at": "2024-06-08T21:24:09.045000Z", "data_removed": false, "error": null, "id": "kb4h1fp4tnrgm0cfz6y9v0dh1g", "input": { "width": 1024, "height": 1024, "prompt": "a bird, daiton style", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }, "logs": "Random seed set to: 2715952489\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 6.5\nLORA: Using a lora. Lora strength: 1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:03, 5.34it/s]\n 10%|█ | 2/20 [00:00<00:03, 5.28it/s]\n 15%|█▌ | 3/20 [00:00<00:03, 5.27it/s]\n 20%|██ | 4/20 [00:00<00:03, 5.26it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 5.28it/s]\n 30%|███ | 6/20 [00:01<00:02, 5.27it/s]\n 35%|███▌ | 7/20 [00:01<00:02, 5.28it/s]\n 40%|████ | 8/20 [00:01<00:02, 5.27it/s]\n 45%|████▌ | 9/20 [00:01<00:02, 5.27it/s]\n 50%|█████ | 10/20 [00:01<00:01, 5.27it/s]\n 55%|█████▌ | 11/20 [00:02<00:01, 5.27it/s]\n 60%|██████ | 12/20 [00:02<00:01, 5.28it/s]\n 65%|██████▌ | 13/20 [00:02<00:01, 5.28it/s]\n 70%|███████ | 14/20 [00:02<00:01, 5.28it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 5.27it/s]\n 80%|████████ | 16/20 [00:03<00:00, 5.27it/s]\n 85%|████████▌ | 17/20 [00:03<00:00, 5.28it/s]\n 90%|█████████ | 18/20 [00:03<00:00, 5.28it/s]\n 95%|█████████▌| 19/20 [00:03<00:00, 5.31it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.34it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.29it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.26 seconds\noutputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png", "metrics": { "predict_time": 5.624495, "total_time": 5.67439 }, "output": [ "https://replicate.delivery/pbxt/jBSCv0L4KV7rEtAdkii8Nio9PwW3o6TtyixbEHfckpje3n8SA/R8__00001_.webp" ], "started_at": "2024-06-08T21:24:09.094895Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kb4h1fp4tnrgm0cfz6y9v0dh1g", "cancel": "https://api.replicate.com/v1/predictions/kb4h1fp4tnrgm0cfz6y9v0dh1g/cancel" }, "version": "674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a" }Generated inRandom seed set to: 2715952489 ============================== Generation settings Sampler: dpmpp_2m_sde_gpu Scheduler: karras Steps: 20 CFG: 6.5 LORA: Using a lora. Lora strength: 1.0 ============================== Running workflow got prompt Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 3, title: KSampler, class type: KSampler 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:03, 5.34it/s] 10%|█ | 2/20 [00:00<00:03, 5.28it/s] 15%|█▌ | 3/20 [00:00<00:03, 5.27it/s] 20%|██ | 4/20 [00:00<00:03, 5.26it/s] 25%|██▌ | 5/20 [00:00<00:02, 5.28it/s] 30%|███ | 6/20 [00:01<00:02, 5.27it/s] 35%|███▌ | 7/20 [00:01<00:02, 5.28it/s] 40%|████ | 8/20 [00:01<00:02, 5.27it/s] 45%|████▌ | 9/20 [00:01<00:02, 5.27it/s] 50%|█████ | 10/20 [00:01<00:01, 5.27it/s] 55%|█████▌ | 11/20 [00:02<00:01, 5.27it/s] 60%|██████ | 12/20 [00:02<00:01, 5.28it/s] 65%|██████▌ | 13/20 [00:02<00:01, 5.28it/s] 70%|███████ | 14/20 [00:02<00:01, 5.28it/s] 75%|███████▌ | 15/20 [00:02<00:00, 5.27it/s] 80%|████████ | 16/20 [00:03<00:00, 5.27it/s] 85%|████████▌ | 17/20 [00:03<00:00, 5.28it/s] 90%|█████████ | 18/20 [00:03<00:00, 5.28it/s] 95%|█████████▌| 19/20 [00:03<00:00, 5.31it/s] 100%|██████████| 20/20 [00:03<00:00, 5.34it/s] 100%|██████████| 20/20 [00:03<00:00, 5.29it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 4.26 seconds outputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== R8__00001_.pngPrediction
aramintak/japanese-poster-prints:674cf07bID0mzvgxszthrgp0cfz6yr6s5b0gStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a happy kid, minimalist, daiton style, block print
- sampler
- Default
- scheduler
- Default
- lora_strength
- 1
- output_format
- webp
- output_quality
- 80
- negative_prompt
- signature, watermark, kanji
- number_of_images
- 1
{ "width": 1024, "height": 1024, "prompt": "a happy kid, minimalist, daiton style, block print", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "signature, watermark, kanji", "number_of_images": 1 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run aramintak/japanese-poster-prints using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "aramintak/japanese-poster-prints:674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a", { input: { width: 1024, height: 1024, prompt: "a happy kid, minimalist, daiton style, block print", sampler: "Default", scheduler: "Default", lora_strength: 1, output_format: "webp", output_quality: 80, negative_prompt: "signature, watermark, kanji", number_of_images: 1 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun aramintak/japanese-poster-prints using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "aramintak/japanese-poster-prints:674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a", input={ "width": 1024, "height": 1024, "prompt": "a happy kid, minimalist, daiton style, block print", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "signature, watermark, kanji", "number_of_images": 1 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run aramintak/japanese-poster-prints 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": "674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a", "input": { "width": 1024, "height": 1024, "prompt": "a happy kid, minimalist, daiton style, block print", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "signature, watermark, kanji", "number_of_images": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-08T21:24:46.349383Z", "created_at": "2024-06-08T21:24:40.532000Z", "data_removed": false, "error": null, "id": "0mzvgxszthrgp0cfz6yr6s5b0g", "input": { "width": 1024, "height": 1024, "prompt": "a happy kid, minimalist, daiton style, block print", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "signature, watermark, kanji", "number_of_images": 1 }, "logs": "Random seed set to: 3582104029\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 6.5\nLORA: Using a lora. Lora strength: 1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 3, title: KSampler, class type: KSampler\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:03, 5.35it/s]\n 10%|█ | 2/20 [00:00<00:03, 5.28it/s]\n 15%|█▌ | 3/20 [00:00<00:03, 5.26it/s]\n 20%|██ | 4/20 [00:00<00:03, 5.26it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 5.25it/s]\n 30%|███ | 6/20 [00:01<00:02, 5.24it/s]\n 35%|███▌ | 7/20 [00:01<00:02, 5.24it/s]\n 40%|████ | 8/20 [00:01<00:02, 5.24it/s]\n 45%|████▌ | 9/20 [00:01<00:02, 5.24it/s]\n 50%|█████ | 10/20 [00:01<00:01, 5.24it/s]\n 55%|█████▌ | 11/20 [00:02<00:01, 5.24it/s]\n 60%|██████ | 12/20 [00:02<00:01, 5.24it/s]\n 65%|██████▌ | 13/20 [00:02<00:01, 5.23it/s]\n 70%|███████ | 14/20 [00:02<00:01, 5.24it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 5.24it/s]\n 80%|████████ | 16/20 [00:03<00:00, 5.23it/s]\n 85%|████████▌ | 17/20 [00:03<00:00, 5.24it/s]\n 90%|█████████ | 18/20 [00:03<00:00, 5.24it/s]\n 95%|█████████▌| 19/20 [00:03<00:00, 5.27it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.30it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.25it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.20 seconds\noutputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png", "metrics": { "predict_time": 5.704962, "total_time": 5.817383 }, "output": [ "https://replicate.delivery/pbxt/7QigxGXZPBqnCtz6fS53AznqZIaRHmbDRnbeGkenzcG7wP5lA/R8__00001_.webp" ], "started_at": "2024-06-08T21:24:40.644421Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/0mzvgxszthrgp0cfz6yr6s5b0g", "cancel": "https://api.replicate.com/v1/predictions/0mzvgxszthrgp0cfz6yr6s5b0g/cancel" }, "version": "674cf07b1ec4aa426f72c6953b92fe8db34c67b03fc1963343fdd18157ee257a" }Generated inRandom seed set to: 3582104029 ============================== Generation settings Sampler: dpmpp_2m_sde_gpu Scheduler: karras Steps: 20 CFG: 6.5 LORA: Using a lora. Lora strength: 1.0 ============================== Running workflow got prompt Executing node 3, title: KSampler, class type: KSampler 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:03, 5.35it/s] 10%|█ | 2/20 [00:00<00:03, 5.28it/s] 15%|█▌ | 3/20 [00:00<00:03, 5.26it/s] 20%|██ | 4/20 [00:00<00:03, 5.26it/s] 25%|██▌ | 5/20 [00:00<00:02, 5.25it/s] 30%|███ | 6/20 [00:01<00:02, 5.24it/s] 35%|███▌ | 7/20 [00:01<00:02, 5.24it/s] 40%|████ | 8/20 [00:01<00:02, 5.24it/s] 45%|████▌ | 9/20 [00:01<00:02, 5.24it/s] 50%|█████ | 10/20 [00:01<00:01, 5.24it/s] 55%|█████▌ | 11/20 [00:02<00:01, 5.24it/s] 60%|██████ | 12/20 [00:02<00:01, 5.24it/s] 65%|██████▌ | 13/20 [00:02<00:01, 5.23it/s] 70%|███████ | 14/20 [00:02<00:01, 5.24it/s] 75%|███████▌ | 15/20 [00:02<00:00, 5.24it/s] 80%|████████ | 16/20 [00:03<00:00, 5.23it/s] 85%|████████▌ | 17/20 [00:03<00:00, 5.24it/s] 90%|█████████ | 18/20 [00:03<00:00, 5.24it/s] 95%|█████████▌| 19/20 [00:03<00:00, 5.27it/s] 100%|██████████| 20/20 [00:03<00:00, 5.30it/s] 100%|██████████| 20/20 [00:03<00:00, 5.25it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 4.20 seconds outputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== R8__00001_.png
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