thijssdaniels / room-gpt
- Public
- 3.8K runs
-
L40S
- SDXL fine-tune
Prediction
thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1IDmctcre3bvdy5fknna6bayftmjiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- mask
- null
- seed
- null
- image
- null
- width
- 1024
- height
- 1024
- prompt
- a close up corner room white walls with big windows
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- refine_steps
- null
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a close up corner room white walls with big windows", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "refine_steps": null, "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 replicate
Import 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 thijssdaniels/room-gpt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", { input: { width: 1024, height: 1024, prompt: "a close up corner room white walls with big windows", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, 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 replicate
Import the client:import replicate
Run thijssdaniels/room-gpt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", input={ "width": 1024, "height": 1024, "prompt": "a close up corner room white walls with big windows", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run thijssdaniels/room-gpt 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": "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", "input": { "width": 1024, "height": 1024, "prompt": "a close up corner room white walls with big windows", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-30T15:24:38.561858Z", "created_at": "2023-10-30T15:23:31.454878Z", "data_removed": false, "error": null, "id": "mctcre3bvdy5fknna6bayftmji", "input": { "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a close up corner room white walls with big windows", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "refine_steps": null, "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: 1251\nskipping loading .. weights already loaded\nPrompt: a close up corner room white walls with big windows\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:56, 1.16s/it]\n 4%|▍ | 2/50 [00:02<00:55, 1.15s/it]\n 6%|▌ | 3/50 [00:03<00:54, 1.15s/it]\n 8%|▊ | 4/50 [00:04<00:53, 1.16s/it]\n 10%|█ | 5/50 [00:05<00:51, 1.15s/it]\n 12%|█▏ | 6/50 [00:06<00:50, 1.15s/it]\n 14%|█▍ | 7/50 [00:08<00:49, 1.16s/it]\n 16%|█▌ | 8/50 [00:09<00:48, 1.16s/it]\n 18%|█▊ | 9/50 [00:10<00:47, 1.16s/it]\n 20%|██ | 10/50 [00:11<00:46, 1.16s/it]\n 22%|██▏ | 11/50 [00:12<00:45, 1.16s/it]\n 24%|██▍ | 12/50 [00:13<00:44, 1.16s/it]\n 26%|██▌ | 13/50 [00:15<00:42, 1.16s/it]\n 28%|██▊ | 14/50 [00:16<00:41, 1.16s/it]\n 30%|███ | 15/50 [00:17<00:40, 1.16s/it]\n 32%|███▏ | 16/50 [00:18<00:39, 1.16s/it]\n 34%|███▍ | 17/50 [00:19<00:38, 1.16s/it]\n 36%|███▌ | 18/50 [00:20<00:37, 1.17s/it]\n 38%|███▊ | 19/50 [00:22<00:36, 1.16s/it]\n 40%|████ | 20/50 [00:23<00:34, 1.16s/it]\n 42%|████▏ | 21/50 [00:24<00:33, 1.16s/it]\n 44%|████▍ | 22/50 [00:25<00:32, 1.16s/it]\n 46%|████▌ | 23/50 [00:26<00:31, 1.16s/it]\n 48%|████▊ | 24/50 [00:27<00:30, 1.16s/it]\n 50%|█████ | 25/50 [00:28<00:29, 1.16s/it]\n 52%|█████▏ | 26/50 [00:30<00:27, 1.16s/it]\n 54%|█████▍ | 27/50 [00:31<00:26, 1.16s/it]\n 56%|█████▌ | 28/50 [00:32<00:25, 1.16s/it]\n 58%|█████▊ | 29/50 [00:33<00:24, 1.16s/it]\n 60%|██████ | 30/50 [00:34<00:23, 1.16s/it]\n 62%|██████▏ | 31/50 [00:35<00:22, 1.16s/it]\n 64%|██████▍ | 32/50 [00:37<00:20, 1.16s/it]\n 66%|██████▌ | 33/50 [00:38<00:19, 1.16s/it]\n 68%|██████▊ | 34/50 [00:39<00:18, 1.16s/it]\n 70%|███████ | 35/50 [00:40<00:17, 1.16s/it]\n 72%|███████▏ | 36/50 [00:41<00:16, 1.16s/it]\n 74%|███████▍ | 37/50 [00:42<00:15, 1.16s/it]\n 76%|███████▌ | 38/50 [00:44<00:13, 1.17s/it]\n 78%|███████▊ | 39/50 [00:45<00:12, 1.16s/it]\n 80%|████████ | 40/50 [00:46<00:11, 1.16s/it]\n 82%|████████▏ | 41/50 [00:47<00:10, 1.16s/it]\n 84%|████████▍ | 42/50 [00:48<00:09, 1.16s/it]\n 86%|████████▌ | 43/50 [00:49<00:08, 1.17s/it]\n 88%|████████▊ | 44/50 [00:51<00:07, 1.17s/it]\n 90%|█████████ | 45/50 [00:52<00:05, 1.17s/it]\n 92%|█████████▏| 46/50 [00:53<00:04, 1.17s/it]\n 94%|█████████▍| 47/50 [00:54<00:03, 1.17s/it]\n 96%|█████████▌| 48/50 [00:55<00:02, 1.17s/it]\n 98%|█████████▊| 49/50 [00:56<00:01, 1.17s/it]\n100%|██████████| 50/50 [00:58<00:00, 1.16s/it]\n100%|██████████| 50/50 [00:58<00:00, 1.16s/it]", "metrics": { "predict_time": 64.415894, "total_time": 67.10698 }, "output": [ "https://pbxt.replicate.delivery/F2EcSgm4sF4aMF9YOQJHvRK73fpyBfaafTADuxCTuSHolvmjA/out-0.png", "https://pbxt.replicate.delivery/req3zPRWWS14AiCfPAxDH1870lb5oJYeSqlYybZudUXqlvmjA/out-1.png", "https://pbxt.replicate.delivery/8y7et4fxf0PJWoQK0higorf7T5JhlK7WkADSofGWfIaat81cE/out-2.png", "https://pbxt.replicate.delivery/XiBGzd1PSvrBEFI3tqOTcqj15kGsalSCBHYqfX7E28Cb5r5IA/out-3.png" ], "started_at": "2023-10-30T15:23:34.145964Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mctcre3bvdy5fknna6bayftmji", "cancel": "https://api.replicate.com/v1/predictions/mctcre3bvdy5fknna6bayftmji/cancel" }, "version": "bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1" }
Generated inUsing seed: 1251 skipping loading .. weights already loaded Prompt: a close up corner room white walls with big windows txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:56, 1.16s/it] 4%|▍ | 2/50 [00:02<00:55, 1.15s/it] 6%|▌ | 3/50 [00:03<00:54, 1.15s/it] 8%|▊ | 4/50 [00:04<00:53, 1.16s/it] 10%|█ | 5/50 [00:05<00:51, 1.15s/it] 12%|█▏ | 6/50 [00:06<00:50, 1.15s/it] 14%|█▍ | 7/50 [00:08<00:49, 1.16s/it] 16%|█▌ | 8/50 [00:09<00:48, 1.16s/it] 18%|█▊ | 9/50 [00:10<00:47, 1.16s/it] 20%|██ | 10/50 [00:11<00:46, 1.16s/it] 22%|██▏ | 11/50 [00:12<00:45, 1.16s/it] 24%|██▍ | 12/50 [00:13<00:44, 1.16s/it] 26%|██▌ | 13/50 [00:15<00:42, 1.16s/it] 28%|██▊ | 14/50 [00:16<00:41, 1.16s/it] 30%|███ | 15/50 [00:17<00:40, 1.16s/it] 32%|███▏ | 16/50 [00:18<00:39, 1.16s/it] 34%|███▍ | 17/50 [00:19<00:38, 1.16s/it] 36%|███▌ | 18/50 [00:20<00:37, 1.17s/it] 38%|███▊ | 19/50 [00:22<00:36, 1.16s/it] 40%|████ | 20/50 [00:23<00:34, 1.16s/it] 42%|████▏ | 21/50 [00:24<00:33, 1.16s/it] 44%|████▍ | 22/50 [00:25<00:32, 1.16s/it] 46%|████▌ | 23/50 [00:26<00:31, 1.16s/it] 48%|████▊ | 24/50 [00:27<00:30, 1.16s/it] 50%|█████ | 25/50 [00:28<00:29, 1.16s/it] 52%|█████▏ | 26/50 [00:30<00:27, 1.16s/it] 54%|█████▍ | 27/50 [00:31<00:26, 1.16s/it] 56%|█████▌ | 28/50 [00:32<00:25, 1.16s/it] 58%|█████▊ | 29/50 [00:33<00:24, 1.16s/it] 60%|██████ | 30/50 [00:34<00:23, 1.16s/it] 62%|██████▏ | 31/50 [00:35<00:22, 1.16s/it] 64%|██████▍ | 32/50 [00:37<00:20, 1.16s/it] 66%|██████▌ | 33/50 [00:38<00:19, 1.16s/it] 68%|██████▊ | 34/50 [00:39<00:18, 1.16s/it] 70%|███████ | 35/50 [00:40<00:17, 1.16s/it] 72%|███████▏ | 36/50 [00:41<00:16, 1.16s/it] 74%|███████▍ | 37/50 [00:42<00:15, 1.16s/it] 76%|███████▌ | 38/50 [00:44<00:13, 1.17s/it] 78%|███████▊ | 39/50 [00:45<00:12, 1.16s/it] 80%|████████ | 40/50 [00:46<00:11, 1.16s/it] 82%|████████▏ | 41/50 [00:47<00:10, 1.16s/it] 84%|████████▍ | 42/50 [00:48<00:09, 1.16s/it] 86%|████████▌ | 43/50 [00:49<00:08, 1.17s/it] 88%|████████▊ | 44/50 [00:51<00:07, 1.17s/it] 90%|█████████ | 45/50 [00:52<00:05, 1.17s/it] 92%|█████████▏| 46/50 [00:53<00:04, 1.17s/it] 94%|█████████▍| 47/50 [00:54<00:03, 1.17s/it] 96%|█████████▌| 48/50 [00:55<00:02, 1.17s/it] 98%|█████████▊| 49/50 [00:56<00:01, 1.17s/it] 100%|██████████| 50/50 [00:58<00:00, 1.16s/it] 100%|██████████| 50/50 [00:58<00:00, 1.16s/it]
Prediction
thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1IDnjrrzidbajt7fxwjpjflkapcpiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- width
- 1024
- height
- 1024
- prompt
- a minimal room with a wooden floor, white walls and big windows coming from the right
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- refine_steps
- null
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": null, "width": 1024, "height": 1024, "prompt": "a minimal room with a wooden floor, white walls and big windows coming from the right", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "refine_steps": null, "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 replicate
Import 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 thijssdaniels/room-gpt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", { input: { width: 1024, height: 1024, prompt: "a minimal room with a wooden floor, white walls and big windows coming from the right", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, 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 replicate
Import the client:import replicate
Run thijssdaniels/room-gpt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", input={ "width": 1024, "height": 1024, "prompt": "a minimal room with a wooden floor, white walls and big windows coming from the right", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run thijssdaniels/room-gpt 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": "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", "input": { "width": 1024, "height": 1024, "prompt": "a minimal room with a wooden floor, white walls and big windows coming from the right", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-01T11:45:20.351240Z", "created_at": "2023-11-01T11:44:21.213471Z", "data_removed": false, "error": null, "id": "njrrzidbajt7fxwjpjflkapcpi", "input": { "seed": null, "width": 1024, "height": 1024, "prompt": "a minimal room with a wooden floor, white walls and big windows coming from the right", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "refine_steps": null, "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: 1636\nskipping loading .. weights already loaded\nPrompt: a minimal room with a wooden floor, white walls and big windows coming from the right\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.05s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.05s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.05s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.05s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.05s/it]\n 22%|██▏ | 11/50 [00:11<00:40, 1.05s/it]\n 24%|██▍ | 12/50 [00:12<00:39, 1.05s/it]\n 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it]\n 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it]\n 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.05s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.05s/it]\n 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it]\n 66%|██████▌ | 33/50 [00:34<00:17, 1.06s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it]\n 70%|███████ | 35/50 [00:36<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:37<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]", "metrics": { "predict_time": 57.732686, "total_time": 59.137769 }, "output": [ "https://pbxt.replicate.delivery/jEcTT5P9H7pLBp0L6MWdOxJ8ELeQ79i2eUtYY1fQc5Pdi9njA/out-0.png", "https://pbxt.replicate.delivery/BA9yfEBfjgsjuU7ww1mafH43q62xEi32I2Pfq9W3PBleJ2fcE/out-1.png", "https://pbxt.replicate.delivery/kZSfpoNef5Ob6p8UW8KibelfA8rV2jeo0ey5WsXozRxwnYfzRA/out-2.png", "https://pbxt.replicate.delivery/0VubxNvx5RaXFJNrqzI9F0vGuyNmiy8qTPZVPLbhQCAUsf5IA/out-3.png" ], "started_at": "2023-11-01T11:44:22.618554Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/njrrzidbajt7fxwjpjflkapcpi", "cancel": "https://api.replicate.com/v1/predictions/njrrzidbajt7fxwjpjflkapcpi/cancel" }, "version": "bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1" }
Generated inUsing seed: 1636 skipping loading .. weights already loaded Prompt: a minimal room with a wooden floor, white walls and big windows coming from the right txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.05s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it] 20%|██ | 10/50 [00:10<00:42, 1.05s/it] 22%|██▏ | 11/50 [00:11<00:40, 1.05s/it] 24%|██▍ | 12/50 [00:12<00:39, 1.05s/it] 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it] 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it] 30%|███ | 15/50 [00:15<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it] 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it] 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it] 40%|████ | 20/50 [00:21<00:31, 1.05s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it] 50%|█████ | 25/50 [00:26<00:26, 1.06s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it] 60%|██████ | 30/50 [00:31<00:21, 1.05s/it] 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it] 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it] 66%|██████▌ | 33/50 [00:34<00:17, 1.06s/it] 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it] 70%|███████ | 35/50 [00:36<00:15, 1.06s/it] 72%|███████▏ | 36/50 [00:37<00:14, 1.06s/it] 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it] 80%|████████ | 40/50 [00:42<00:10, 1.06s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it] 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it]
Prediction
thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1ID7p6fkptb7rjvw2y33vn7e4542qStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- width
- 1024
- height
- 1024
- prompt
- a minimal empty room with a wooden floor, white walls, a plant and big windows coming from the right
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- refine_steps
- null
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": null, "width": 1024, "height": 1024, "prompt": "a minimal empty room with a wooden floor, white walls, a plant and big windows coming from the right", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "refine_steps": null, "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 replicate
Import 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 thijssdaniels/room-gpt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", { input: { width: 1024, height: 1024, prompt: "a minimal empty room with a wooden floor, white walls, a plant and big windows coming from the right", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, 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 replicate
Import the client:import replicate
Run thijssdaniels/room-gpt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", input={ "width": 1024, "height": 1024, "prompt": "a minimal empty room with a wooden floor, white walls, a plant and big windows coming from the right", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run thijssdaniels/room-gpt 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": "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", "input": { "width": 1024, "height": 1024, "prompt": "a minimal empty room with a wooden floor, white walls, a plant and big windows coming from the right", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-01T11:54:24.080330Z", "created_at": "2023-11-01T11:52:37.812743Z", "data_removed": false, "error": null, "id": "7p6fkptb7rjvw2y33vn7e4542q", "input": { "seed": null, "width": 1024, "height": 1024, "prompt": "a minimal empty room with a wooden floor, white walls, a plant and big windows coming from the right", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "refine_steps": null, "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: 51789\nEnsuring enough disk space...\nFree disk space: 1892225826816\nDownloading weights: https://pbxt.replicate.delivery/2EEztsoT4TbSNhqWtTOtXijFFYcodo1vnXvC2ub2bafq3r5IA/trained_model.tar\nb'Downloaded 186 MB bytes in 3.116s (60 MB/s)\\nExtracted 186 MB in 0.056s (3.3 GB/s)\\n'\nDownloaded weights in 3.803703784942627 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a minimal empty room with a wooden floor, white walls, a plant and big windows coming from the right\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:01<01:18, 1.59s/it]\n 4%|▍ | 2/50 [00:02<01:01, 1.28s/it]\n 6%|▌ | 3/50 [00:03<00:55, 1.18s/it]\n 8%|▊ | 4/50 [00:04<00:52, 1.13s/it]\n 10%|█ | 5/50 [00:05<00:49, 1.11s/it]\n 12%|█▏ | 6/50 [00:06<00:48, 1.09s/it]\n 14%|█▍ | 7/50 [00:07<00:46, 1.08s/it]\n 16%|█▌ | 8/50 [00:09<00:45, 1.08s/it]\n 18%|█▊ | 9/50 [00:10<00:44, 1.07s/it]\n 20%|██ | 10/50 [00:11<00:42, 1.07s/it]\n 22%|██▏ | 11/50 [00:12<00:41, 1.07s/it]\n 24%|██▍ | 12/50 [00:13<00:40, 1.07s/it]\n 26%|██▌ | 13/50 [00:14<00:39, 1.07s/it]\n 28%|██▊ | 14/50 [00:15<00:38, 1.06s/it]\n 30%|███ | 15/50 [00:16<00:37, 1.06s/it]\n 32%|███▏ | 16/50 [00:17<00:36, 1.06s/it]\n 34%|███▍ | 17/50 [00:18<00:35, 1.06s/it]\n 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.06s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:26<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:27<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:28<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:29<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:30<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:31<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:32<00:21, 1.07s/it]\n 62%|██████▏ | 31/50 [00:33<00:20, 1.07s/it]\n 64%|██████▍ | 32/50 [00:34<00:19, 1.07s/it]\n 66%|██████▌ | 33/50 [00:35<00:18, 1.07s/it]\n 68%|██████▊ | 34/50 [00:36<00:17, 1.07s/it]\n 70%|███████ | 35/50 [00:37<00:15, 1.07s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.07s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.07s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.07s/it]\n 78%|███████▊ | 39/50 [00:42<00:11, 1.07s/it]\n 80%|████████ | 40/50 [00:43<00:10, 1.07s/it]\n 82%|████████▏ | 41/50 [00:44<00:09, 1.07s/it]\n 84%|████████▍ | 42/50 [00:45<00:08, 1.07s/it]\n 86%|████████▌ | 43/50 [00:46<00:07, 1.07s/it]\n 88%|████████▊ | 44/50 [00:47<00:06, 1.07s/it]\n 90%|█████████ | 45/50 [00:48<00:05, 1.07s/it]\n 92%|█████████▏| 46/50 [00:49<00:04, 1.07s/it]\n 94%|█████████▍| 47/50 [00:50<00:03, 1.07s/it]\n 96%|█████████▌| 48/50 [00:51<00:02, 1.07s/it]\n 98%|█████████▊| 49/50 [00:52<00:01, 1.07s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.07s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.08s/it]", "metrics": { "predict_time": 65.552725, "total_time": 106.267587 }, "output": [ "https://pbxt.replicate.delivery/Sfpd7cjtjlwMWqhvl4X5PE5t7QsSdLClFEJsKkbm3Vx2cfzRA/out-0.png", "https://pbxt.replicate.delivery/mDUareoqg93e70abY8VcArZarakvd3I0BNCpe68Su6Pcz9njA/out-1.png", "https://pbxt.replicate.delivery/y7ezUeSvrLqgtU9bZhPoN4r8xuAJw0yETfm9ZdK3Om7fm7PHB/out-2.png", "https://pbxt.replicate.delivery/S4pCsVZMpxYZHJMCPjMeN1qr1BkWfseWkbOylk96zTyfm7PHB/out-3.png" ], "started_at": "2023-11-01T11:53:18.527605Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7p6fkptb7rjvw2y33vn7e4542q", "cancel": "https://api.replicate.com/v1/predictions/7p6fkptb7rjvw2y33vn7e4542q/cancel" }, "version": "bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1" }
Generated inUsing seed: 51789 Ensuring enough disk space... Free disk space: 1892225826816 Downloading weights: https://pbxt.replicate.delivery/2EEztsoT4TbSNhqWtTOtXijFFYcodo1vnXvC2ub2bafq3r5IA/trained_model.tar b'Downloaded 186 MB bytes in 3.116s (60 MB/s)\nExtracted 186 MB in 0.056s (3.3 GB/s)\n' Downloaded weights in 3.803703784942627 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a minimal empty room with a wooden floor, white walls, a plant and big windows coming from the right txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:01<01:18, 1.59s/it] 4%|▍ | 2/50 [00:02<01:01, 1.28s/it] 6%|▌ | 3/50 [00:03<00:55, 1.18s/it] 8%|▊ | 4/50 [00:04<00:52, 1.13s/it] 10%|█ | 5/50 [00:05<00:49, 1.11s/it] 12%|█▏ | 6/50 [00:06<00:48, 1.09s/it] 14%|█▍ | 7/50 [00:07<00:46, 1.08s/it] 16%|█▌ | 8/50 [00:09<00:45, 1.08s/it] 18%|█▊ | 9/50 [00:10<00:44, 1.07s/it] 20%|██ | 10/50 [00:11<00:42, 1.07s/it] 22%|██▏ | 11/50 [00:12<00:41, 1.07s/it] 24%|██▍ | 12/50 [00:13<00:40, 1.07s/it] 26%|██▌ | 13/50 [00:14<00:39, 1.07s/it] 28%|██▊ | 14/50 [00:15<00:38, 1.06s/it] 30%|███ | 15/50 [00:16<00:37, 1.06s/it] 32%|███▏ | 16/50 [00:17<00:36, 1.06s/it] 34%|███▍ | 17/50 [00:18<00:35, 1.06s/it] 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it] 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it] 40%|████ | 20/50 [00:21<00:31, 1.06s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it] 48%|████▊ | 24/50 [00:26<00:27, 1.06s/it] 50%|█████ | 25/50 [00:27<00:26, 1.06s/it] 52%|█████▏ | 26/50 [00:28<00:25, 1.06s/it] 54%|█████▍ | 27/50 [00:29<00:24, 1.06s/it] 56%|█████▌ | 28/50 [00:30<00:23, 1.06s/it] 58%|█████▊ | 29/50 [00:31<00:22, 1.06s/it] 60%|██████ | 30/50 [00:32<00:21, 1.07s/it] 62%|██████▏ | 31/50 [00:33<00:20, 1.07s/it] 64%|██████▍ | 32/50 [00:34<00:19, 1.07s/it] 66%|██████▌ | 33/50 [00:35<00:18, 1.07s/it] 68%|██████▊ | 34/50 [00:36<00:17, 1.07s/it] 70%|███████ | 35/50 [00:37<00:15, 1.07s/it] 72%|███████▏ | 36/50 [00:38<00:14, 1.07s/it] 74%|███████▍ | 37/50 [00:39<00:13, 1.07s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.07s/it] 78%|███████▊ | 39/50 [00:42<00:11, 1.07s/it] 80%|████████ | 40/50 [00:43<00:10, 1.07s/it] 82%|████████▏ | 41/50 [00:44<00:09, 1.07s/it] 84%|████████▍ | 42/50 [00:45<00:08, 1.07s/it] 86%|████████▌ | 43/50 [00:46<00:07, 1.07s/it] 88%|████████▊ | 44/50 [00:47<00:06, 1.07s/it] 90%|█████████ | 45/50 [00:48<00:05, 1.07s/it] 92%|█████████▏| 46/50 [00:49<00:04, 1.07s/it] 94%|█████████▍| 47/50 [00:50<00:03, 1.07s/it] 96%|█████████▌| 48/50 [00:51<00:02, 1.07s/it] 98%|█████████▊| 49/50 [00:52<00:01, 1.07s/it] 100%|██████████| 50/50 [00:53<00:00, 1.07s/it] 100%|██████████| 50/50 [00:53<00:00, 1.08s/it]
Prediction
thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1IDgiar7hdbpexibtmcey22nt3crmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- width
- 1024
- height
- 1024
- prompt
- a minimalist room with big windows coming from the left, wooden fish bone floor and new york view
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- refine_steps
- null
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": null, "width": 1024, "height": 1024, "prompt": "a minimalist room with big windows coming from the left, wooden fish bone floor and new york view", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "refine_steps": null, "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 replicate
Import 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 thijssdaniels/room-gpt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", { input: { width: 1024, height: 1024, prompt: "a minimalist room with big windows coming from the left, wooden fish bone floor and new york view", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, 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 replicate
Import the client:import replicate
Run thijssdaniels/room-gpt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", input={ "width": 1024, "height": 1024, "prompt": "a minimalist room with big windows coming from the left, wooden fish bone floor and new york view", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run thijssdaniels/room-gpt 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": "thijssdaniels/room-gpt:bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1", "input": { "width": 1024, "height": 1024, "prompt": "a minimalist room with big windows coming from the left, wooden fish bone floor and new york view", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-01T12:24:39.139312Z", "created_at": "2023-11-01T12:22:56.701858Z", "data_removed": false, "error": null, "id": "giar7hdbpexibtmcey22nt3crm", "input": { "seed": null, "width": 1024, "height": 1024, "prompt": "a minimalist room with big windows coming from the left, wooden fish bone floor and new york view", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "refine_steps": null, "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: 30254\nEnsuring enough disk space...\nFree disk space: 2613738053632\nDownloading weights: https://pbxt.replicate.delivery/2EEztsoT4TbSNhqWtTOtXijFFYcodo1vnXvC2ub2bafq3r5IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.206s (902 MB/s)\\nExtracted 186 MB in 0.052s (3.6 GB/s)\\n'\nDownloaded weights in 0.32448792457580566 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a minimalist room with big windows coming from the left, wooden fish bone floor and new york view\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:01<01:17, 1.58s/it]\n 4%|▍ | 2/50 [00:02<01:01, 1.27s/it]\n 6%|▌ | 3/50 [00:03<00:55, 1.17s/it]\n 8%|▊ | 4/50 [00:04<00:51, 1.13s/it]\n 10%|█ | 5/50 [00:05<00:49, 1.10s/it]\n 12%|█▏ | 6/50 [00:06<00:47, 1.08s/it]\n 14%|█▍ | 7/50 [00:07<00:46, 1.08s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.07s/it]\n 18%|█▊ | 9/50 [00:10<00:43, 1.07s/it]\n 20%|██ | 10/50 [00:11<00:42, 1.06s/it]\n 22%|██▏ | 11/50 [00:12<00:41, 1.06s/it]\n 24%|██▍ | 12/50 [00:13<00:40, 1.06s/it]\n 26%|██▌ | 13/50 [00:14<00:39, 1.06s/it]\n 28%|██▊ | 14/50 [00:15<00:38, 1.06s/it]\n 30%|███ | 15/50 [00:16<00:37, 1.06s/it]\n 32%|███▏ | 16/50 [00:17<00:35, 1.06s/it]\n 34%|███▍ | 17/50 [00:18<00:34, 1.06s/it]\n 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.06s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:28<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:29<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:30<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:31<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:32<00:21, 1.06s/it]\n 62%|██████▏ | 31/50 [00:33<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:34<00:19, 1.06s/it]\n 66%|██████▌ | 33/50 [00:35<00:18, 1.06s/it]\n 68%|██████▊ | 34/50 [00:36<00:17, 1.06s/it]\n 70%|███████ | 35/50 [00:37<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:45<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:46<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:47<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:48<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:49<00:04, 1.07s/it]\n 94%|█████████▍| 47/50 [00:50<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:51<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:52<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.07s/it]", "metrics": { "predict_time": 61.861286, "total_time": 102.437454 }, "output": [ "https://pbxt.replicate.delivery/QqEgwBLAJrJkK94BeCIC6tKLqm0u7QHiR0rtCyUIkzLCrfzRA/out-0.png", "https://pbxt.replicate.delivery/yE1xegftCBg6wUAo1G2jci6OOfQrqKuPBYepmkGzPeKow6fcE/out-1.png", "https://pbxt.replicate.delivery/J0mS4X6gtH4PCtCRNg2NuGPxf1JQsBLOZebGllVzkE9GWfnjA/out-2.png", "https://pbxt.replicate.delivery/IATY7aLzxToNO1amefUujxIAlk000VsH0Wi0u4Elnu8GWfnjA/out-3.png" ], "started_at": "2023-11-01T12:23:37.278026Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/giar7hdbpexibtmcey22nt3crm", "cancel": "https://api.replicate.com/v1/predictions/giar7hdbpexibtmcey22nt3crm/cancel" }, "version": "bfcb42751f8f702e4661daa3e592c960cdec178831df79d361c54a78e8ec87e1" }
Generated inUsing seed: 30254 Ensuring enough disk space... Free disk space: 2613738053632 Downloading weights: https://pbxt.replicate.delivery/2EEztsoT4TbSNhqWtTOtXijFFYcodo1vnXvC2ub2bafq3r5IA/trained_model.tar b'Downloaded 186 MB bytes in 0.206s (902 MB/s)\nExtracted 186 MB in 0.052s (3.6 GB/s)\n' Downloaded weights in 0.32448792457580566 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a minimalist room with big windows coming from the left, wooden fish bone floor and new york view txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:01<01:17, 1.58s/it] 4%|▍ | 2/50 [00:02<01:01, 1.27s/it] 6%|▌ | 3/50 [00:03<00:55, 1.17s/it] 8%|▊ | 4/50 [00:04<00:51, 1.13s/it] 10%|█ | 5/50 [00:05<00:49, 1.10s/it] 12%|█▏ | 6/50 [00:06<00:47, 1.08s/it] 14%|█▍ | 7/50 [00:07<00:46, 1.08s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.07s/it] 18%|█▊ | 9/50 [00:10<00:43, 1.07s/it] 20%|██ | 10/50 [00:11<00:42, 1.06s/it] 22%|██▏ | 11/50 [00:12<00:41, 1.06s/it] 24%|██▍ | 12/50 [00:13<00:40, 1.06s/it] 26%|██▌ | 13/50 [00:14<00:39, 1.06s/it] 28%|██▊ | 14/50 [00:15<00:38, 1.06s/it] 30%|███ | 15/50 [00:16<00:37, 1.06s/it] 32%|███▏ | 16/50 [00:17<00:35, 1.06s/it] 34%|███▍ | 17/50 [00:18<00:34, 1.06s/it] 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it] 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it] 40%|████ | 20/50 [00:21<00:31, 1.06s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it] 50%|█████ | 25/50 [00:26<00:26, 1.06s/it] 52%|█████▏ | 26/50 [00:28<00:25, 1.06s/it] 54%|█████▍ | 27/50 [00:29<00:24, 1.06s/it] 56%|█████▌ | 28/50 [00:30<00:23, 1.06s/it] 58%|█████▊ | 29/50 [00:31<00:22, 1.06s/it] 60%|██████ | 30/50 [00:32<00:21, 1.06s/it] 62%|██████▏ | 31/50 [00:33<00:20, 1.06s/it] 64%|██████▍ | 32/50 [00:34<00:19, 1.06s/it] 66%|██████▌ | 33/50 [00:35<00:18, 1.06s/it] 68%|██████▊ | 34/50 [00:36<00:17, 1.06s/it] 70%|███████ | 35/50 [00:37<00:15, 1.06s/it] 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it] 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it] 80%|████████ | 40/50 [00:42<00:10, 1.06s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it] 84%|████████▍ | 42/50 [00:45<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:46<00:07, 1.06s/it] 88%|████████▊ | 44/50 [00:47<00:06, 1.06s/it] 90%|█████████ | 45/50 [00:48<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:49<00:04, 1.07s/it] 94%|█████████▍| 47/50 [00:50<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:51<00:02, 1.06s/it] 98%|█████████▊| 49/50 [00:52<00:01, 1.06s/it] 100%|██████████| 50/50 [00:53<00:00, 1.06s/it] 100%|██████████| 50/50 [00:53<00:00, 1.07s/it]
Want to make some of these yourself?
Run this model