jfals82 / apple_hq_blur
(Updated 9 months, 1 week ago)
- Public
- 50 runs
- SDXL fine-tune
Prediction
jfals82/apple_hq_blur:f4780fba64e34ca99d0a1f4d26757499dd630a22e41c635d5901ff5908913645IDxk0cv3jg2drgm0chqcp85h5rawStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @jfals82Input
- width
- 1024
- height
- 1024
- prompt
- high-rise foyer, bright furniture, plants, sunlight, modern minimalism
- 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": "high-rise foyer, bright furniture, plants, sunlight, modern minimalism", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jfals82/apple_hq_blur using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jfals82/apple_hq_blur:f4780fba64e34ca99d0a1f4d26757499dd630a22e41c635d5901ff5908913645", { input: { width: 1024, height: 1024, prompt: "high-rise foyer, bright furniture, plants, sunlight, modern minimalism", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, 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 jfals82/apple_hq_blur using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jfals82/apple_hq_blur:f4780fba64e34ca99d0a1f4d26757499dd630a22e41c635d5901ff5908913645", input={ "width": 1024, "height": 1024, "prompt": "high-rise foyer, bright furniture, plants, sunlight, modern minimalism", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "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 jfals82/apple_hq_blur 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": "jfals82/apple_hq_blur:f4780fba64e34ca99d0a1f4d26757499dd630a22e41c635d5901ff5908913645", "input": { "width": 1024, "height": 1024, "prompt": "high-rise foyer, bright furniture, plants, sunlight, modern minimalism", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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": "2024-09-04T03:55:54.800752Z", "created_at": "2024-09-04T03:55:29.171000Z", "data_removed": false, "error": null, "id": "xk0cv3jg2drgm0chqcp85h5raw", "input": { "width": 1024, "height": 1024, "prompt": "high-rise foyer, bright furniture, plants, sunlight, modern minimalism", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 62832\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: high-rise foyer, bright furniture, plants, sunlight, modern minimalism\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.29it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.28it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.27it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.28it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.28it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.27it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.27it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.27it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.27it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.27it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.27it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.27it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.27it/s]\n 30%|███ | 15/50 [00:03<00:08, 4.27it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.26it/s]\n 34%|███▍ | 17/50 [00:03<00:07, 4.26it/s]\n 36%|███▌ | 18/50 [00:04<00:07, 4.26it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 4.26it/s]\n 40%|████ | 20/50 [00:04<00:07, 4.26it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.26it/s]\n 44%|████▍ | 22/50 [00:05<00:06, 4.26it/s]\n 46%|████▌ | 23/50 [00:05<00:06, 4.26it/s]\n 48%|████▊ | 24/50 [00:05<00:06, 4.25it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.26it/s]\n 52%|█████▏ | 26/50 [00:06<00:05, 4.26it/s]\n 54%|█████▍ | 27/50 [00:06<00:05, 4.26it/s]\n 56%|█████▌ | 28/50 [00:06<00:05, 4.25it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.25it/s]\n 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4.25it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.26it/s]", "metrics": { "predict_time": 16.028295584, "total_time": 25.629752 }, "output": [ "https://replicate.delivery/pbxt/Ok7t38nffUgkR0clw4eswyw5pbc0eftaggTRH0SEfRZhSMW2E/out-0.png" ], "started_at": "2024-09-04T03:55:38.772456Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xk0cv3jg2drgm0chqcp85h5raw", "cancel": "https://api.replicate.com/v1/predictions/xk0cv3jg2drgm0chqcp85h5raw/cancel" }, "version": "f4780fba64e34ca99d0a1f4d26757499dd630a22e41c635d5901ff5908913645" }Generated inUsing seed: 62832 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: high-rise foyer, bright furniture, plants, sunlight, modern minimalism txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.29it/s] 4%|▍ | 2/50 [00:00<00:11, 4.28it/s] 6%|▌ | 3/50 [00:00<00:10, 4.27it/s] 8%|▊ | 4/50 [00:00<00:10, 4.28it/s] 10%|█ | 5/50 [00:01<00:10, 4.28it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.27it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.27it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.27it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.27it/s] 20%|██ | 10/50 [00:02<00:09, 4.27it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.27it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.27it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.27it/s] 30%|███ | 15/50 [00:03<00:08, 4.27it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.26it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.26it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.26it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.26it/s] 40%|████ | 20/50 [00:04<00:07, 4.26it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.26it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.26it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.26it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.25it/s] 50%|█████ | 25/50 [00:05<00:05, 4.26it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.26it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.26it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.25it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.25it/s] 60%|██████ | 30/50 [00:07<00:04, 4.25it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.25it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.25it/s] 66%|██████▌ | 33/50 [00:07<00:04, 4.24it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.25it/s] 70%|███████ | 35/50 [00:08<00:03, 4.25it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.25it/s] 74%|███████▍ | 37/50 [00:08<00:03, 4.25it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.25it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.25it/s] 80%|████████ | 40/50 [00:09<00:02, 4.24it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.25it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.25it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.24it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.24it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.24it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.25it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.24it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.25it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.25it/s] 100%|██████████| 50/50 [00:11<00:00, 4.25it/s] 100%|██████████| 50/50 [00:11<00:00, 4.26it/s]Prediction
jfals82/apple_hq_blur:d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1IDnt5fsd0hthrgm0chvjsr5w6vf0StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, a portrait of jared, huge muscles, strength, football player, at the football stadium, dusk, Friday night lights, quarterback
- 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
- 0.5
- num_inference_steps
- 100
{ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a portrait of jared, huge muscles, strength, football player, at the football stadium, dusk, Friday night lights, quarterback", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.5, "num_inference_steps": 100 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jfals82/apple_hq_blur using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jfals82/apple_hq_blur:d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, a portrait of jared, huge muscles, strength, football player, at the football stadium, dusk, Friday night lights, quarterback", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.5, num_inference_steps: 100 } } ); // 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 jfals82/apple_hq_blur using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jfals82/apple_hq_blur:d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a portrait of jared, huge muscles, strength, football player, at the football stadium, dusk, Friday night lights, quarterback", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.5, "num_inference_steps": 100 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run jfals82/apple_hq_blur 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": "jfals82/apple_hq_blur:d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a portrait of jared, huge muscles, strength, football player, at the football stadium, dusk, Friday night lights, quarterback", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.5, "num_inference_steps": 100 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-10T16:10:39.059381Z", "created_at": "2024-09-10T16:10:08.724000Z", "data_removed": false, "error": null, "id": "nt5fsd0hthrgm0chvjsr5w6vf0", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a portrait of jared, huge muscles, strength, football player, at the football stadium, dusk, Friday night lights, quarterback", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.5, "num_inference_steps": 100 }, "logs": "Using seed: 6815\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of <s0><s1>, a portrait of jared, huge muscles, strength, football player, at the football stadium, dusk, Friday night lights, quarterback\ntxt2img mode\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:23, 4.30it/s]\n 2%|▏ | 2/100 [00:00<00:22, 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[00:21<00:02, 4.22it/s]\n 92%|█████████▏| 92/100 [00:21<00:01, 4.22it/s]\n 93%|█████████▎| 93/100 [00:21<00:01, 4.22it/s]\n 94%|█████████▍| 94/100 [00:22<00:01, 4.21it/s]\n 95%|█████████▌| 95/100 [00:22<00:01, 4.22it/s]\n 96%|█████████▌| 96/100 [00:22<00:00, 4.22it/s]\n 97%|█████████▋| 97/100 [00:22<00:00, 4.22it/s]\n 98%|█████████▊| 98/100 [00:23<00:00, 4.22it/s]\n 99%|█████████▉| 99/100 [00:23<00:00, 4.23it/s]\n100%|██████████| 100/100 [00:23<00:00, 4.23it/s]\n100%|██████████| 100/100 [00:23<00:00, 4.24it/s]", "metrics": { "predict_time": 28.007878125, "total_time": 30.335381 }, "output": [ "https://replicate.delivery/pbxt/aURFXYG0N9JwP9TssS1VvlacI9pnMn6MK4gUe7IYHfOfLE3mA/out-0.png" ], "started_at": "2024-09-10T16:10:11.051503Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nt5fsd0hthrgm0chvjsr5w6vf0", "cancel": "https://api.replicate.com/v1/predictions/nt5fsd0hthrgm0chvjsr5w6vf0/cancel" }, "version": "d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1" }Generated inUsing seed: 6815 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of <s0><s1>, a portrait of jared, huge muscles, strength, football player, at the football stadium, dusk, Friday night lights, quarterback txt2img mode 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:23, 4.30it/s] 2%|▏ | 2/100 [00:00<00:22, 4.27it/s] 3%|▎ | 3/100 [00:00<00:22, 4.26it/s] 4%|▍ | 4/100 [00:00<00:22, 4.25it/s] 5%|▌ | 5/100 [00:01<00:22, 4.25it/s] 6%|▌ | 6/100 [00:01<00:22, 4.26it/s] 7%|▋ | 7/100 [00:01<00:21, 4.26it/s] 8%|▊ | 8/100 [00:01<00:21, 4.27it/s] 9%|▉ | 9/100 [00:02<00:21, 4.27it/s] 10%|█ | 10/100 [00:02<00:21, 4.27it/s] 11%|█ | 11/100 [00:02<00:20, 4.26it/s] 12%|█▏ | 12/100 [00:02<00:20, 4.27it/s] 13%|█▎ | 13/100 [00:03<00:20, 4.27it/s] 14%|█▍ | 14/100 [00:03<00:20, 4.27it/s] 15%|█▌ | 15/100 [00:03<00:19, 4.26it/s] 16%|█▌ | 16/100 [00:03<00:19, 4.26it/s] 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jfals82/apple_hq_blur:d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1ID55tjvdzk1srgp0chvkmvw2hty8StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, a portrait of jared, bodybuilding, in a church, stained glass
- 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
- 0.5
- num_inference_steps
- 100
{ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a portrait of jared, bodybuilding, in a church, stained glass", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.5, "num_inference_steps": 100 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jfals82/apple_hq_blur using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jfals82/apple_hq_blur:d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, a portrait of jared, bodybuilding, in a church, stained glass", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.5, num_inference_steps: 100 } } ); // 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 jfals82/apple_hq_blur using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jfals82/apple_hq_blur:d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a portrait of jared, bodybuilding, in a church, stained glass", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.5, "num_inference_steps": 100 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run jfals82/apple_hq_blur 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": "jfals82/apple_hq_blur:d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a portrait of jared, bodybuilding, in a church, stained glass", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.5, "num_inference_steps": 100 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-10T17:11:28.310651Z", "created_at": "2024-09-10T17:10:05.326000Z", "data_removed": false, "error": null, "id": "55tjvdzk1srgp0chvkmvw2hty8", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a portrait of jared, bodybuilding, in a church, stained glass", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.5, "num_inference_steps": 100 }, "logs": "Using seed: 14199\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of <s0><s1>, a portrait of jared, bodybuilding, in a church, stained glass\ntxt2img mode\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:23, 4.27it/s]\n 2%|▏ | 2/100 [00:00<00:23, 4.25it/s]\n 3%|▎ | 3/100 [00:00<00:22, 4.24it/s]\n 4%|▍ | 4/100 [00:00<00:22, 4.23it/s]\n 5%|▌ | 5/100 [00:01<00:22, 4.22it/s]\n 6%|▌ 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94/100 [00:22<00:01, 4.21it/s]\n 95%|█████████▌| 95/100 [00:22<00:01, 4.21it/s]\n 96%|█████████▌| 96/100 [00:22<00:00, 4.21it/s]\n 97%|█████████▋| 97/100 [00:23<00:00, 4.21it/s]\n 98%|█████████▊| 98/100 [00:23<00:00, 4.20it/s]\n 99%|█████████▉| 99/100 [00:23<00:00, 4.21it/s]\n100%|██████████| 100/100 [00:23<00:00, 4.21it/s]\n100%|██████████| 100/100 [00:23<00:00, 4.21it/s]", "metrics": { "predict_time": 28.649573757, "total_time": 82.984651 }, "output": [ "https://replicate.delivery/pbxt/MGkmJE5sbzqZMtP4tsF1DUc6gzusWA5bkZSaCAl1llPwv42E/out-0.png" ], "started_at": "2024-09-10T17:10:59.661078Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/55tjvdzk1srgp0chvkmvw2hty8", "cancel": "https://api.replicate.com/v1/predictions/55tjvdzk1srgp0chvkmvw2hty8/cancel" }, "version": "d86787cca2a6970e4330dc2da2e76ec223486bc6c457c186ba5a43f9d2b707d1" }Generated inUsing seed: 14199 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of <s0><s1>, a portrait of jared, bodybuilding, in a church, stained glass txt2img mode 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:23, 4.27it/s] 2%|▏ | 2/100 [00:00<00:23, 4.25it/s] 3%|▎ | 3/100 [00:00<00:22, 4.24it/s] 4%|▍ | 4/100 [00:00<00:22, 4.23it/s] 5%|▌ | 5/100 [00:01<00:22, 4.22it/s] 6%|▌ | 6/100 [00:01<00:22, 4.22it/s] 7%|▋ | 7/100 [00:01<00:22, 4.22it/s] 8%|▊ | 8/100 [00:01<00:21, 4.22it/s] 9%|▉ | 9/100 [00:02<00:21, 4.22it/s] 10%|█ | 10/100 [00:02<00:21, 4.22it/s] 11%|█ | 11/100 [00:02<00:21, 4.22it/s] 12%|█▏ | 12/100 [00:02<00:20, 4.22it/s] 13%|█▎ | 13/100 [00:03<00:20, 4.22it/s] 14%|█▍ | 14/100 [00:03<00:20, 4.22it/s] 15%|█▌ | 15/100 [00:03<00:20, 4.22it/s] 16%|█▌ | 16/100 [00:03<00:19, 4.21it/s] 17%|█▋ | 17/100 [00:04<00:19, 4.21it/s] 18%|█▊ | 18/100 [00:04<00:19, 4.21it/s] 19%|█▉ | 19/100 [00:04<00:19, 4.21it/s] 20%|██ | 20/100 [00:04<00:18, 4.22it/s] 21%|██ | 21/100 [00:04<00:18, 4.21it/s] 22%|██▏ | 22/100 [00:05<00:18, 4.21it/s] 23%|██▎ | 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