faraz2023/andreas-feininger2
andreas-feininger2
Prediction
faraz2023/andreas-feininger2:c281f81eIDgokuok3bcjagn6us2r2vf4452yStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 340234
- image
- null
- width
- 2048
- height
- 2048
- prompt
- In style of andreas feininger, a large blooming red rose in the middle of gloomy city. The flower is one connected plant.
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- refine_steps
- null
- guidance_scale
- 9.47
- apply_watermark
- high_noise_frac
- 0.77
- negative_prompt
- disconnected iterm; ugly; low-quality
- prompt_strength
- 0.8
- num_inference_steps
- 114
{ "seed": 340234, "image": null, "width": 2048, "height": 2048, "prompt": "In style of andreas feininger, a large blooming red rose in the middle of gloomy city. The flower is one connected plant. \n", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "guidance_scale": 9.47, "apply_watermark": false, "high_noise_frac": 0.77, "negative_prompt": "disconnected iterm; ugly; low-quality", "prompt_strength": 0.8, "num_inference_steps": 114 }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 faraz2023/andreas-feininger2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "faraz2023/andreas-feininger2:c281f81e1840b5f84f30dcfd471a9df88855fe86d66a606ce6917963e6cd6bd4", { input: { seed: 340234, width: 2048, height: 2048, prompt: "In style of andreas feininger, a large blooming red rose in the middle of gloomy city. The flower is one connected plant. \n", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 9.47, apply_watermark: false, high_noise_frac: 0.77, negative_prompt: "disconnected iterm; ugly; low-quality", prompt_strength: 0.8, num_inference_steps: 114 } } ); // 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 faraz2023/andreas-feininger2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "faraz2023/andreas-feininger2:c281f81e1840b5f84f30dcfd471a9df88855fe86d66a606ce6917963e6cd6bd4", input={ "seed": 340234, "width": 2048, "height": 2048, "prompt": "In style of andreas feininger, a large blooming red rose in the middle of gloomy city. The flower is one connected plant. \n", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 9.47, "apply_watermark": False, "high_noise_frac": 0.77, "negative_prompt": "disconnected iterm; ugly; low-quality", "prompt_strength": 0.8, "num_inference_steps": 114 } ) # 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 faraz2023/andreas-feininger2 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": "faraz2023/andreas-feininger2:c281f81e1840b5f84f30dcfd471a9df88855fe86d66a606ce6917963e6cd6bd4", "input": { "seed": 340234, "width": 2048, "height": 2048, "prompt": "In style of andreas feininger, a large blooming red rose in the middle of gloomy city. The flower is one connected plant. \\n", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 9.47, "apply_watermark": false, "high_noise_frac": 0.77, "negative_prompt": "disconnected iterm; ugly; low-quality", "prompt_strength": 0.8, "num_inference_steps": 114 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-10T21:53:04.546496Z", "created_at": "2023-10-10T21:50:46.431928Z", "data_removed": false, "error": null, "id": "gokuok3bcjagn6us2r2vf4452y", "input": { "seed": 340234, "image": null, "width": 2048, "height": 2048, "prompt": "In style of andreas feininger, a large blooming red rose in the middle of gloomy city. The flower is one connected plant. \n", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "guidance_scale": 9.47, "apply_watermark": false, "high_noise_frac": 0.77, "negative_prompt": "disconnected iterm; ugly; low-quality", "prompt_strength": 0.8, "num_inference_steps": 114 }, "logs": "Using seed: 340234\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In style of andreas feininger, a large blooming red rose in the middle of gloomy city. The flower is one connected plant.\ntxt2img mode\n 0%| | 0/66 [00:00<?, ?it/s]\n 2%|▏ | 1/66 [00:01<01:29, 1.38s/it]\n 3%|▎ | 2/66 [00:02<01:28, 1.38s/it]\n 5%|▍ | 3/66 [00:04<01:27, 1.38s/it]\n 6%|▌ | 4/66 [00:05<01:25, 1.38s/it]\n 8%|▊ | 5/66 [00:06<01:24, 1.39s/it]\n 9%|▉ | 6/66 [00:08<01:23, 1.39s/it]\n 11%|█ | 7/66 [00:09<01:21, 1.39s/it]\n 12%|█▏ | 8/66 [00:11<01:20, 1.39s/it]\n 14%|█▎ | 9/66 [00:12<01:18, 1.38s/it]\n 15%|█▌ | 10/66 [00:13<01:17, 1.38s/it]\n 17%|█▋ | 11/66 [00:15<01:16, 1.39s/it]\n 18%|█▊ | 12/66 [00:16<01:14, 1.38s/it]\n 20%|█▉ | 13/66 [00:18<01:13, 1.39s/it]\n 21%|██ | 14/66 [00:19<01:12, 1.39s/it]\n 23%|██▎ | 15/66 [00:20<01:10, 1.39s/it]\n 24%|██▍ | 16/66 [00:22<01:09, 1.39s/it]\n 26%|██▌ | 17/66 [00:23<01:08, 1.39s/it]\n 27%|██▋ | 18/66 [00:24<01:06, 1.39s/it]\n 29%|██▉ | 19/66 [00:26<01:05, 1.39s/it]\n 30%|███ | 20/66 [00:27<01:03, 1.39s/it]\n 32%|███▏ | 21/66 [00:29<01:02, 1.39s/it]\n 33%|███▎ | 22/66 [00:30<01:01, 1.39s/it]\n 35%|███▍ | 23/66 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22/29 [00:31<00:09, 1.42s/it]\n 79%|███████▉ | 23/29 [00:32<00:08, 1.42s/it]\n 83%|████████▎ | 24/29 [00:33<00:07, 1.42s/it]\n 86%|████████▌ | 25/29 [00:35<00:05, 1.42s/it]\n 90%|████████▉ | 26/29 [00:36<00:04, 1.42s/it]\n 93%|█████████▎| 27/29 [00:38<00:02, 1.42s/it]\n 97%|█████████▋| 28/29 [00:39<00:01, 1.42s/it]\n100%|██████████| 29/29 [00:41<00:00, 1.42s/it]\n100%|██████████| 29/29 [00:41<00:00, 1.42s/it]", "metrics": { "predict_time": 137.478891, "total_time": 138.114568 }, "output": [ "https://pbxt.replicate.delivery/dOOeuOKt0kVwUiaEPNcxu0dRJV4gJOxJv2JS08mpoKofm3sRA/out-0.png" ], "started_at": "2023-10-10T21:50:47.067605Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gokuok3bcjagn6us2r2vf4452y", "cancel": "https://api.replicate.com/v1/predictions/gokuok3bcjagn6us2r2vf4452y/cancel" }, "version": "c281f81e1840b5f84f30dcfd471a9df88855fe86d66a606ce6917963e6cd6bd4" }Generated inUsing seed: 340234 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In style of andreas feininger, a large blooming red rose in the middle of gloomy city. The flower is one connected plant. txt2img mode 0%| | 0/66 [00:00<?, ?it/s] 2%|▏ | 1/66 [00:01<01:29, 1.38s/it] 3%|▎ | 2/66 [00:02<01:28, 1.38s/it] 5%|▍ | 3/66 [00:04<01:27, 1.38s/it] 6%|▌ | 4/66 [00:05<01:25, 1.38s/it] 8%|▊ | 5/66 [00:06<01:24, 1.39s/it] 9%|▉ | 6/66 [00:08<01:23, 1.39s/it] 11%|█ | 7/66 [00:09<01:21, 1.39s/it] 12%|█▏ | 8/66 [00:11<01:20, 1.39s/it] 14%|█▎ | 9/66 [00:12<01:18, 1.38s/it] 15%|█▌ | 10/66 [00:13<01:17, 1.38s/it] 17%|█▋ | 11/66 [00:15<01:16, 1.39s/it] 18%|█▊ | 12/66 [00:16<01:14, 1.38s/it] 20%|█▉ | 13/66 [00:18<01:13, 1.39s/it] 21%|██ | 14/66 [00:19<01:12, 1.39s/it] 23%|██▎ | 15/66 [00:20<01:10, 1.39s/it] 24%|██▍ | 16/66 [00:22<01:09, 1.39s/it] 26%|██▌ | 17/66 [00:23<01:08, 1.39s/it] 27%|██▋ | 18/66 [00:24<01:06, 1.39s/it] 29%|██▉ | 19/66 [00:26<01:05, 1.39s/it] 30%|███ | 20/66 [00:27<01:03, 1.39s/it] 32%|███▏ | 21/66 [00:29<01:02, 1.39s/it] 33%|███▎ | 22/66 [00:30<01:01, 1.39s/it] 35%|███▍ | 23/66 [00:31<00:59, 1.39s/it] 36%|███▋ | 24/66 [00:33<00:58, 1.39s/it] 38%|███▊ | 25/66 [00:34<00:56, 1.39s/it] 39%|███▉ | 26/66 [00:36<00:55, 1.39s/it] 41%|████ | 27/66 [00:37<00:54, 1.39s/it] 42%|████▏ | 28/66 [00:38<00:52, 1.39s/it] 44%|████▍ | 29/66 [00:40<00:51, 1.39s/it] 45%|████▌ | 30/66 [00:41<00:50, 1.39s/it] 47%|████▋ | 31/66 [00:43<00:48, 1.39s/it] 48%|████▊ | 32/66 [00:44<00:47, 1.39s/it] 50%|█████ | 33/66 [00:45<00:45, 1.39s/it] 52%|█████▏ | 34/66 [00:47<00:44, 1.39s/it] 53%|█████▎ | 35/66 [00:48<00:43, 1.39s/it] 55%|█████▍ | 36/66 [00:49<00:41, 1.39s/it] 56%|█████▌ | 37/66 [00:51<00:40, 1.39s/it] 58%|█████▊ | 38/66 [00:52<00:38, 1.39s/it] 59%|█████▉ | 39/66 [00:54<00:37, 1.39s/it] 61%|██████ | 40/66 [00:55<00:36, 1.39s/it] 62%|██████▏ | 41/66 [00:56<00:34, 1.39s/it] 64%|██████▎ | 42/66 [00:58<00:33, 1.39s/it] 65%|██████▌ | 43/66 [00:59<00:32, 1.39s/it] 67%|██████▋ | 44/66 [01:01<00:30, 1.39s/it] 68%|██████▊ | 45/66 [01:02<00:29, 1.39s/it] 70%|██████▉ | 46/66 [01:03<00:27, 1.39s/it] 71%|███████ | 47/66 [01:05<00:26, 1.39s/it] 73%|███████▎ | 48/66 [01:06<00:25, 1.39s/it] 74%|███████▍ | 49/66 [01:08<00:23, 1.39s/it] 76%|███████▌ | 50/66 [01:09<00:22, 1.39s/it] 77%|███████▋ | 51/66 [01:10<00:20, 1.39s/it] 79%|███████▉ | 52/66 [01:12<00:19, 1.39s/it] 80%|████████ | 53/66 [01:13<00:18, 1.39s/it] 82%|████████▏ | 54/66 [01:15<00:16, 1.39s/it] 83%|████████▎ | 55/66 [01:16<00:15, 1.39s/it] 85%|████████▍ | 56/66 [01:17<00:13, 1.39s/it] 86%|████████▋ | 57/66 [01:19<00:12, 1.39s/it] 88%|████████▊ | 58/66 [01:20<00:11, 1.40s/it] 89%|████████▉ | 59/66 [01:22<00:09, 1.39s/it] 91%|█████████ | 60/66 [01:23<00:08, 1.40s/it] 92%|█████████▏| 61/66 [01:24<00:06, 1.39s/it] 94%|█████████▍| 62/66 [01:26<00:05, 1.40s/it] 95%|█████████▌| 63/66 [01:27<00:04, 1.39s/it] 97%|█████████▋| 64/66 [01:29<00:02, 1.39s/it] 98%|█████████▊| 65/66 [01:30<00:01, 1.39s/it] 100%|██████████| 66/66 [01:31<00:00, 1.40s/it] 100%|██████████| 66/66 [01:31<00:00, 1.39s/it] 0%| | 0/29 [00:00<?, ?it/s] 3%|▎ | 1/29 [00:01<00:39, 1.41s/it] 7%|▋ | 2/29 [00:02<00:38, 1.42s/it] 10%|█ | 3/29 [00:04<00:36, 1.42s/it] 14%|█▍ | 4/29 [00:05<00:35, 1.41s/it] 17%|█▋ | 5/29 [00:07<00:33, 1.42s/it] 21%|██ | 6/29 [00:08<00:32, 1.42s/it] 24%|██▍ | 7/29 [00:09<00:31, 1.41s/it] 28%|██▊ | 8/29 [00:11<00:29, 1.41s/it] 31%|███ | 9/29 [00:12<00:28, 1.41s/it] 34%|███▍ | 10/29 [00:14<00:26, 1.41s/it] 38%|███▊ | 11/29 [00:15<00:25, 1.41s/it] 41%|████▏ | 12/29 [00:16<00:24, 1.41s/it] 45%|████▍ | 13/29 [00:18<00:22, 1.41s/it] 48%|████▊ | 14/29 [00:19<00:21, 1.41s/it] 52%|█████▏ | 15/29 [00:21<00:19, 1.42s/it] 55%|█████▌ | 16/29 [00:22<00:18, 1.42s/it] 59%|█████▊ | 17/29 [00:24<00:17, 1.42s/it] 62%|██████▏ | 18/29 [00:25<00:15, 1.42s/it] 66%|██████▌ | 19/29 [00:26<00:14, 1.42s/it] 69%|██████▉ | 20/29 [00:28<00:12, 1.42s/it] 72%|███████▏ | 21/29 [00:29<00:11, 1.42s/it] 76%|███████▌ | 22/29 [00:31<00:09, 1.42s/it] 79%|███████▉ | 23/29 [00:32<00:08, 1.42s/it] 83%|████████▎ | 24/29 [00:33<00:07, 1.42s/it] 86%|████████▌ | 25/29 [00:35<00:05, 1.42s/it] 90%|████████▉ | 26/29 [00:36<00:04, 1.42s/it] 93%|█████████▎| 27/29 [00:38<00:02, 1.42s/it] 97%|█████████▋| 28/29 [00:39<00:01, 1.42s/it] 100%|██████████| 29/29 [00:41<00:00, 1.42s/it] 100%|██████████| 29/29 [00:41<00:00, 1.42s/it]
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