adirik / local-prompt-mixing
Generating object-level shape variations with Stable Diffusion
Prediction
adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6fID5wehl3lbgvxb576vythrymlisaStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 10
- image
- steps
- 50
- prompt
- A decorated lamp in the livingroom
- proxy_words
- guidance_scale
- 7.5
- end_prompt_range
- 14
- object_of_interest
- lamp
- start_prompt_range
- 4
- objects_to_preserve
- livingroom
- number_of_variations
- 4
{ "seed": 10, "image": "https://replicate.delivery/pbxt/K2C4XSQCGBXZGGiL4XYHH7EUdRjI1QJgzm5Fa4zCDREmURNt/lamp_simple.png", "steps": 50, "prompt": "A decorated lamp in the livingroom", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 14, "object_of_interest": "lamp", "start_prompt_range": 4, "objects_to_preserve": "livingroom", "number_of_variations": 4 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", { input: { seed: 10, image: "https://replicate.delivery/pbxt/K2C4XSQCGBXZGGiL4XYHH7EUdRjI1QJgzm5Fa4zCDREmURNt/lamp_simple.png", steps: 50, prompt: "A decorated lamp in the livingroom", proxy_words: "", guidance_scale: 7.5, end_prompt_range: 14, object_of_interest: "lamp", start_prompt_range: 4, objects_to_preserve: "livingroom", number_of_variations: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", input={ "seed": 10, "image": "https://replicate.delivery/pbxt/K2C4XSQCGBXZGGiL4XYHH7EUdRjI1QJgzm5Fa4zCDREmURNt/lamp_simple.png", "steps": 50, "prompt": "A decorated lamp in the livingroom", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 14, "object_of_interest": "lamp", "start_prompt_range": 4, "objects_to_preserve": "livingroom", "number_of_variations": 4 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run adirik/local-prompt-mixing 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": "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", "input": { "seed": 10, "image": "https://replicate.delivery/pbxt/K2C4XSQCGBXZGGiL4XYHH7EUdRjI1QJgzm5Fa4zCDREmURNt/lamp_simple.png", "steps": 50, "prompt": "A decorated lamp in the livingroom", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 14, "object_of_interest": "lamp", "start_prompt_range": 4, "objects_to_preserve": "livingroom", "number_of_variations": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-12T06:40:52.154702Z", "created_at": "2023-12-12T06:35:06.741435Z", "data_removed": false, "error": null, "id": "5wehl3lbgvxb576vythrymlisa", "input": { "seed": 10, "image": "https://replicate.delivery/pbxt/K2C4XSQCGBXZGGiL4XYHH7EUdRjI1QJgzm5Fa4zCDREmURNt/lamp_simple.png", "steps": 50, "prompt": "A decorated lamp in the livingroom", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 14, "object_of_interest": "lamp", "start_prompt_range": 4, "objects_to_preserve": "livingroom", "number_of_variations": 4 }, "logs": "0%| | 0/15 [00:00<?, ?it/s]\n 7%|▋ | 1/15 [00:00<00:02, 6.10it/s]\n 13%|█▎ | 2/15 [00:01<00:08, 1.62it/s]\n 20%|██ | 3/15 [00:02<00:09, 1.31it/s]\n 27%|██▋ | 4/15 [00:02<00:09, 1.20it/s]\n 33%|███▎ | 5/15 [00:03<00:08, 1.14it/s]\n 40%|████ | 6/15 [00:04<00:08, 1.11it/s]\n 47%|████▋ | 7/15 [00:05<00:07, 1.09it/s]\n 53%|█████▎ | 8/15 [00:06<00:06, 1.08it/s]\n 60%|██████ | 9/15 [00:07<00:05, 1.07it/s]\n 67%|██████▋ | 10/15 [00:08<00:04, 1.07it/s]\n 73%|███████▎ | 11/15 [00:09<00:03, 1.06it/s]\n 80%|████████ | 12/15 [00:10<00:02, 1.06it/s]\n 87%|████████▋ | 13/15 [00:11<00:01, 1.06it/s]\n 93%|█████████▎| 14/15 [00:12<00:00, 1.06it/s]\n100%|██████████| 15/15 [00:13<00:00, 1.06it/s]\n100%|██████████| 15/15 [00:13<00:00, 1.12it/s]\nprompt: a photo of a {word}\ninitial word: lamp\nTOP 50 SIMILAR WORDS:\n['lamp', 'light', 'torch', 'ligh', 'candle', 'led', 'pole', 'moth', 'cfl', 'bell', 'cup', 'cone', 'jar', 'lite', 'flame', 'bucket', 'globe', 'corner', 'pot', 'mug', 'stool', 'plant', 'decor', 'pillow', 'chair', 'brush', 'speaker', 'drum', 'bin', 'enligh', 'glass', 'trophy', 'horn', 'ight', 'bottle', 'hid', 'acup', 'bone', 'mic', 'paint', 'clock', 'illumin', 'fork', 'something', 'spoon', 'tv', 'lit', 'instrument', 'broom', 'table']\n 0%| | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:00<00:00, 60.48it/s]\nprompt: a decorated {word} in the livingroom\ninitial word: lamp\nTOP 5 SIMILAR WORDS:\n['lamp', 'light', 'lite', 'ligh', 'candle']\nThe config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.\n/root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler {\n\"_class_name\": \"DDIMScheduler\",\n\"_diffusers_version\": \"0.10.2\",\n\"beta_end\": 0.012,\n\"beta_schedule\": \"scaled_linear\",\n\"beta_start\": 0.00085,\n\"clip_sample\": false,\n\"num_train_timesteps\": 1000,\n\"prediction_type\": \"epsilon\",\n\"set_alpha_to_one\": false,\n\"steps_offset\": 0,\n\"trained_betas\": null\n}\nis outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\ndeprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\nStart null text inversion\nDDIM inversion...\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:02, 16.20it/s]\n 8%|▊ | 4/50 [00:00<00:03, 12.51it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 11.72it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 11.35it/s]\n 20%|██ | 10/50 [00:00<00:03, 11.16it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 11.05it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 10.98it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 10.92it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 10.91it/s]\n 40%|████ | 20/50 [00:01<00:02, 10.88it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 10.81it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 10.86it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 10.79it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 10.91it/s]\n 60%|██████ | 30/50 [00:02<00:01, 10.91it/s]\n 64%|██████▍ | 32/50 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5.16it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.07it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.02it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 4.98it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.96it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.95it/s]\n 20%|██ | 10/50 [00:01<00:08, 4.94it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 4.94it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.92it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.92it/s]\n 28%|██▊ | 14/50 [00:02<00:07, 4.92it/s]\n 30%|███ | 15/50 [00:02<00:07, 4.92it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 4.93it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.92it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.92it/s]\n 38%|███▊ | 19/50 [00:03<00:06, 4.92it/s]\n 40%|████ | 20/50 [00:03<00:06, 4.91it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.90it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.91it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 4.91it/s]\n 48%|████▊ | 24/50 [00:04<00:05, 4.90it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.91it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.91it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.91it/s]\n 56%|█████▌ | 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98%|█████████▊| 49/50 [00:17<00:00, 3.04it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 3.05it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 2.85it/s]\n100%|██████████| 4/4 [01:12<00:00, 18.08s/it]\n100%|██████████| 4/4 [01:12<00:00, 18.11s/it]", "metrics": { "predict_time": 208.200383, "total_time": 345.413267 }, "output": [ "https://replicate.delivery/pbxt/bcocee9tNDo5jUc13On1IevSTilOPLPz26nv9sUuq1gkT2CkA/light.jpg", "https://replicate.delivery/pbxt/GGvHqnBeBtyNLC2vBuPy15rabSfl7OSR2XeFz3eoX8BInsFIB/lite.jpg", "https://replicate.delivery/pbxt/T29CSx1gG34HHpJ7dFOguU17l73PpykpvDjF9yFv8fi5ktAJA/ligh.jpg", "https://replicate.delivery/pbxt/74G9xCqe6rXwTKwcmTrUqudfnmphYq1CHXRKtzcrf4OmT2CkA/candle.jpg" ], "started_at": "2023-12-12T06:37:23.954319Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5wehl3lbgvxb576vythrymlisa", "cancel": "https://api.replicate.com/v1/predictions/5wehl3lbgvxb576vythrymlisa/cancel" }, "version": "66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f" }Generated in0%| | 0/15 [00:00<?, ?it/s] 7%|▋ | 1/15 [00:00<00:02, 6.10it/s] 13%|█▎ | 2/15 [00:01<00:08, 1.62it/s] 20%|██ | 3/15 [00:02<00:09, 1.31it/s] 27%|██▋ | 4/15 [00:02<00:09, 1.20it/s] 33%|███▎ | 5/15 [00:03<00:08, 1.14it/s] 40%|████ | 6/15 [00:04<00:08, 1.11it/s] 47%|████▋ | 7/15 [00:05<00:07, 1.09it/s] 53%|█████▎ | 8/15 [00:06<00:06, 1.08it/s] 60%|██████ | 9/15 [00:07<00:05, 1.07it/s] 67%|██████▋ | 10/15 [00:08<00:04, 1.07it/s] 73%|███████▎ | 11/15 [00:09<00:03, 1.06it/s] 80%|████████ | 12/15 [00:10<00:02, 1.06it/s] 87%|████████▋ | 13/15 [00:11<00:01, 1.06it/s] 93%|█████████▎| 14/15 [00:12<00:00, 1.06it/s] 100%|██████████| 15/15 [00:13<00:00, 1.06it/s] 100%|██████████| 15/15 [00:13<00:00, 1.12it/s] prompt: a photo of a {word} initial word: lamp TOP 50 SIMILAR WORDS: ['lamp', 'light', 'torch', 'ligh', 'candle', 'led', 'pole', 'moth', 'cfl', 'bell', 'cup', 'cone', 'jar', 'lite', 'flame', 'bucket', 'globe', 'corner', 'pot', 'mug', 'stool', 'plant', 'decor', 'pillow', 'chair', 'brush', 'speaker', 'drum', 'bin', 'enligh', 'glass', 'trophy', 'horn', 'ight', 'bottle', 'hid', 'acup', 'bone', 'mic', 'paint', 'clock', 'illumin', 'fork', 'something', 'spoon', 'tv', 'lit', 'instrument', 'broom', 'table'] 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:00<00:00, 60.48it/s] prompt: a decorated {word} in the livingroom initial word: lamp TOP 5 SIMILAR WORDS: ['lamp', 'light', 'lite', 'ligh', 'candle'] The config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file. /root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler { "_class_name": "DDIMScheduler", "_diffusers_version": "0.10.2", "beta_end": 0.012, "beta_schedule": "scaled_linear", "beta_start": 0.00085, "clip_sample": false, "num_train_timesteps": 1000, "prediction_type": "epsilon", "set_alpha_to_one": false, "steps_offset": 0, "trained_betas": null } is outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) Start null text inversion DDIM inversion... 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:02, 16.20it/s] 8%|▊ | 4/50 [00:00<00:03, 12.51it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.72it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.35it/s] 20%|██ | 10/50 [00:00<00:03, 11.16it/s] 24%|██▍ | 12/50 [00:01<00:03, 11.05it/s] 28%|██▊ | 14/50 [00:01<00:03, 10.98it/s] 32%|███▏ | 16/50 [00:01<00:03, 10.92it/s] 36%|███▌ | 18/50 [00:01<00:02, 10.91it/s] 40%|████ | 20/50 [00:01<00:02, 10.88it/s] 44%|████▍ | 22/50 [00:01<00:02, 10.81it/s] 48%|████▊ | 24/50 [00:02<00:02, 10.86it/s] 52%|█████▏ | 26/50 [00:02<00:02, 10.79it/s] 56%|█████▌ | 28/50 [00:02<00:02, 10.91it/s] 60%|██████ | 30/50 [00:02<00:01, 10.91it/s] 64%|██████▍ | 32/50 [00:02<00:01, 10.91it/s] 68%|██████▊ | 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adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6fID56rlpclbjidwzjxvspojsdye2qStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 10
- image
- steps
- 50
- prompt
- A bottle on the table
- guidance_scale
- 7.5
- end_prompt_range
- 17
- object_of_interest
- bottle
- start_prompt_range
- 7
- objects_to_preserve
- table
- number_of_variations
- 1
{ "seed": 10, "image": "https://replicate.delivery/pbxt/K2C5U6NHwH3Ux41sDUqbIVKYzv1NwcA6KXKNQk6SUoHbm0Ni/real.jpg", "steps": 50, "prompt": "A bottle on the table", "guidance_scale": 7.5, "end_prompt_range": 17, "object_of_interest": "bottle", "start_prompt_range": 7, "objects_to_preserve": "table", "number_of_variations": 1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", { input: { seed: 10, image: "https://replicate.delivery/pbxt/K2C5U6NHwH3Ux41sDUqbIVKYzv1NwcA6KXKNQk6SUoHbm0Ni/real.jpg", steps: 50, prompt: "A bottle on the table", guidance_scale: 7.5, end_prompt_range: 17, object_of_interest: "bottle", start_prompt_range: 7, objects_to_preserve: "table", number_of_variations: 1 } } ); // 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 adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", input={ "seed": 10, "image": "https://replicate.delivery/pbxt/K2C5U6NHwH3Ux41sDUqbIVKYzv1NwcA6KXKNQk6SUoHbm0Ni/real.jpg", "steps": 50, "prompt": "A bottle on the table", "guidance_scale": 7.5, "end_prompt_range": 17, "object_of_interest": "bottle", "start_prompt_range": 7, "objects_to_preserve": "table", "number_of_variations": 1 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run adirik/local-prompt-mixing 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": "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", "input": { "seed": 10, "image": "https://replicate.delivery/pbxt/K2C5U6NHwH3Ux41sDUqbIVKYzv1NwcA6KXKNQk6SUoHbm0Ni/real.jpg", "steps": 50, "prompt": "A bottle on the table", "guidance_scale": 7.5, "end_prompt_range": 17, "object_of_interest": "bottle", "start_prompt_range": 7, "objects_to_preserve": "table", "number_of_variations": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-12T06:41:27.251737Z", "created_at": "2023-12-12T06:36:05.360062Z", "data_removed": false, "error": null, "id": "56rlpclbjidwzjxvspojsdye2q", "input": { "seed": 10, "image": "https://replicate.delivery/pbxt/K2C5U6NHwH3Ux41sDUqbIVKYzv1NwcA6KXKNQk6SUoHbm0Ni/real.jpg", "steps": 50, "prompt": "A bottle on the table", "guidance_scale": 7.5, "end_prompt_range": 17, "object_of_interest": "bottle", "start_prompt_range": 7, "objects_to_preserve": "table", "number_of_variations": 1 }, "logs": "0%| | 0/15 [00:00<?, ?it/s]\n 7%|▋ | 1/15 [00:00<00:02, 6.12it/s]\n 13%|█▎ | 2/15 [00:01<00:08, 1.60it/s]\n 20%|██ | 3/15 [00:02<00:09, 1.29it/s]\n 27%|██▋ | 4/15 [00:03<00:09, 1.18it/s]\n 33%|███▎ | 5/15 [00:03<00:08, 1.13it/s]\n 40%|████ | 6/15 [00:04<00:08, 1.10it/s]\n 47%|████▋ | 7/15 [00:05<00:07, 1.08it/s]\n 53%|█████▎ | 8/15 [00:06<00:06, 1.07it/s]\n 60%|██████ | 9/15 [00:07<00:05, 1.06it/s]\n 67%|██████▋ | 10/15 [00:08<00:04, 1.06it/s]\n 73%|███████▎ | 11/15 [00:09<00:03, 1.05it/s]\n 80%|████████ | 12/15 [00:10<00:02, 1.05it/s]\n 87%|████████▋ | 13/15 [00:11<00:01, 1.05it/s]\n 93%|█████████▎| 14/15 [00:12<00:00, 1.05it/s]\n100%|██████████| 15/15 [00:13<00:00, 1.05it/s]\n100%|██████████| 15/15 [00:13<00:00, 1.11it/s]\nprompt: a photo of a {word}\ninitial word: bottle\nTOP 50 SIMILAR WORDS:\n['bottle', 'drink', 'wine', 'beer', 'jar', 'water', 'can', 'cup', 'bottlen', 'glass', 'schol', 'milk', 'whiskey', 'phone', 'whisky', 'gin', 'liquid', 'coke', 'something', 'product', 'shoe', 'ale', 'liter', 'rum', 'one', 'food', 'photom', 'foodand', 'rock', 'paint', 'chemical', 'gun', 'toy', 'things', 'shot', 'colour', 'pill', 'juice', 'present', 'head', 'shake', 'light', 'fruit', 'shoes', 'lager', 'stone', 'bier', 'color', 'candle', 'view']\n 0%| | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:00<00:00, 71.98it/s]\nprompt: a {word} on the table\ninitial word: bottle\nTOP 2 SIMILAR WORDS:\n['bottle', 'glass']\nThe config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.\n/root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler {\n\"_class_name\": \"DDIMScheduler\",\n\"_diffusers_version\": \"0.10.2\",\n\"beta_end\": 0.012,\n\"beta_schedule\": \"scaled_linear\",\n\"beta_start\": 0.00085,\n\"clip_sample\": false,\n\"num_train_timesteps\": 1000,\n\"prediction_type\": \"epsilon\",\n\"set_alpha_to_one\": false,\n\"steps_offset\": 0,\n\"trained_betas\": null\n}\nis outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\ndeprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\nStart null text inversion\nDDIM inversion...\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 15.90it/s]\n 8%|▊ | 4/50 [00:00<00:03, 12.49it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 11.60it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 11.28it/s]\n 20%|██ | 10/50 [00:00<00:03, 11.10it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 10.99it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 10.92it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 10.81it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 10.86it/s]\n 40%|████ | 20/50 [00:01<00:02, 10.83it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 10.75it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 10.79it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 10.78it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 10.78it/s]\n 60%|██████ | 30/50 [00:02<00:01, 10.77it/s]\n 64%|██████▍ | 32/50 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500/500 [01:42<00:00, 3.65it/s]\n100%|██████████| 500/500 [01:42<00:00, 4.87it/s]\nEnd null text inversion\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.41it/s]\n 4%|▍ | 2/50 [00:00<00:08, 5.68it/s]\n 6%|▌ | 3/50 [00:00<00:08, 5.26it/s]\n 8%|▊ | 4/50 [00:00<00:09, 5.10it/s]\n 10%|█ | 5/50 [00:00<00:09, 4.98it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 4.94it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 4.92it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.89it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.87it/s]\n 20%|██ | 10/50 [00:01<00:08, 4.87it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.86it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.85it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.85it/s]\n 28%|██▊ | 14/50 [00:02<00:07, 4.85it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.85it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.85it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.84it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.84it/s]\n 38%|███▊ | 19/50 [00:03<00:06, 4.85it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.84it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.84it/s]\n 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4.85it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.83it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.84it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.85it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.84it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 4.84it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.84it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.84it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.87it/s]\nDo self attn other obj masking\n 0%| | 0/1 [00:00<?, ?it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\u001b[A\n 2%|▏ | 1/50 [00:00<00:13, 3.53it/s]\u001b[A\n 4%|▍ | 2/50 [00:00<00:15, 3.17it/s]\u001b[A\n 6%|▌ | 3/50 [00:00<00:15, 3.06it/s]\u001b[A\n 8%|▊ | 4/50 [00:01<00:15, 3.02it/s]\u001b[A\n 10%|█ | 5/50 [00:01<00:15, 2.99it/s]\u001b[A\n 12%|█▏ | 6/50 [00:01<00:14, 2.97it/s]\u001b[A\n 14%|█▍ | 7/50 [00:02<00:14, 2.96it/s]\u001b[A\n 16%|█▌ | 8/50 [00:02<00:14, 2.96it/s]\u001b[A\n 18%|█▊ | 9/50 [00:02<00:13, 2.95it/s]\u001b[A\n 20%|██ | 10/50 [00:03<00:13, 2.95it/s]\u001b[A\n 22%|██▏ | 11/50 [00:03<00:13, 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62%|██████▏ | 31/50 [00:10<00:06, 2.94it/s]\u001b[A\n 64%|██████▍ | 32/50 [00:10<00:06, 2.94it/s]\u001b[A\n 66%|██████▌ | 33/50 [00:11<00:05, 2.94it/s]\u001b[A\n 68%|██████▊ | 34/50 [00:11<00:05, 2.93it/s]\u001b[A\n 70%|███████ | 35/50 [00:11<00:05, 2.94it/s]\u001b[A\n 72%|███████▏ | 36/50 [00:13<00:10, 1.37it/s]\u001b[A\n 74%|███████▍ | 37/50 [00:13<00:07, 1.67it/s]\u001b[A\n 76%|███████▌ | 38/50 [00:14<00:06, 1.92it/s]\u001b[A\n 78%|███████▊ | 39/50 [00:14<00:05, 2.15it/s]\u001b[A\n 80%|████████ | 40/50 [00:14<00:04, 2.34it/s]\u001b[A\n 82%|████████▏ | 41/50 [00:15<00:03, 2.63it/s]\u001b[A\n 84%|████████▍ | 42/50 [00:15<00:02, 2.73it/s]\u001b[A\n 86%|████████▌ | 43/50 [00:15<00:02, 2.81it/s]\u001b[A\n 88%|████████▊ | 44/50 [00:16<00:02, 2.87it/s]\u001b[A\n 90%|█████████ | 45/50 [00:16<00:01, 2.91it/s]\u001b[A\n 92%|█████████▏| 46/50 [00:16<00:01, 2.94it/s]\u001b[A\n 94%|█████████▍| 47/50 [00:17<00:01, 2.96it/s]\u001b[A\n 96%|█████████▌| 48/50 [00:17<00:00, 2.98it/s]\u001b[A\n 98%|█████████▊| 49/50 [00:17<00:00, 2.99it/s]\u001b[A\n100%|██████████| 50/50 [00:18<00:00, 3.00it/s]\u001b[A\n100%|██████████| 50/50 [00:18<00:00, 2.77it/s]\n100%|██████████| 1/1 [00:18<00:00, 18.43s/it]\n100%|██████████| 1/1 [00:18<00:00, 18.43s/it]", "metrics": { "predict_time": 157.637535, "total_time": 321.891675 }, "output": [ "https://replicate.delivery/pbxt/vib0pdzIxSKMP52WufAifOHVggUfejgoWR4jH6SSfUJ3SZLQC/glass.jpg" ], "started_at": "2023-12-12T06:38:49.614202Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/56rlpclbjidwzjxvspojsdye2q", "cancel": "https://api.replicate.com/v1/predictions/56rlpclbjidwzjxvspojsdye2q/cancel" }, "version": "66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f" }Generated in0%| | 0/15 [00:00<?, ?it/s] 7%|▋ | 1/15 [00:00<00:02, 6.12it/s] 13%|█▎ | 2/15 [00:01<00:08, 1.60it/s] 20%|██ | 3/15 [00:02<00:09, 1.29it/s] 27%|██▋ | 4/15 [00:03<00:09, 1.18it/s] 33%|███▎ | 5/15 [00:03<00:08, 1.13it/s] 40%|████ | 6/15 [00:04<00:08, 1.10it/s] 47%|████▋ | 7/15 [00:05<00:07, 1.08it/s] 53%|█████▎ | 8/15 [00:06<00:06, 1.07it/s] 60%|██████ | 9/15 [00:07<00:05, 1.06it/s] 67%|██████▋ | 10/15 [00:08<00:04, 1.06it/s] 73%|███████▎ | 11/15 [00:09<00:03, 1.05it/s] 80%|████████ | 12/15 [00:10<00:02, 1.05it/s] 87%|████████▋ | 13/15 [00:11<00:01, 1.05it/s] 93%|█████████▎| 14/15 [00:12<00:00, 1.05it/s] 100%|██████████| 15/15 [00:13<00:00, 1.05it/s] 100%|██████████| 15/15 [00:13<00:00, 1.11it/s] prompt: a photo of a {word} initial word: bottle TOP 50 SIMILAR WORDS: ['bottle', 'drink', 'wine', 'beer', 'jar', 'water', 'can', 'cup', 'bottlen', 'glass', 'schol', 'milk', 'whiskey', 'phone', 'whisky', 'gin', 'liquid', 'coke', 'something', 'product', 'shoe', 'ale', 'liter', 'rum', 'one', 'food', 'photom', 'foodand', 'rock', 'paint', 'chemical', 'gun', 'toy', 'things', 'shot', 'colour', 'pill', 'juice', 'present', 'head', 'shake', 'light', 'fruit', 'shoes', 'lager', 'stone', 'bier', 'color', 'candle', 'view'] 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:00<00:00, 71.98it/s] prompt: a {word} on the table initial word: bottle TOP 2 SIMILAR WORDS: ['bottle', 'glass'] The config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file. /root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler { "_class_name": "DDIMScheduler", "_diffusers_version": "0.10.2", "beta_end": 0.012, "beta_schedule": "scaled_linear", "beta_start": 0.00085, "clip_sample": false, "num_train_timesteps": 1000, "prediction_type": "epsilon", "set_alpha_to_one": false, "steps_offset": 0, "trained_betas": null } is outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) Start null text inversion DDIM inversion... 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 15.90it/s] 8%|▊ | 4/50 [00:00<00:03, 12.49it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.60it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.28it/s] 20%|██ | 10/50 [00:00<00:03, 11.10it/s] 24%|██▍ | 12/50 [00:01<00:03, 10.99it/s] 28%|██▊ | 14/50 [00:01<00:03, 10.92it/s] 32%|███▏ | 16/50 [00:01<00:03, 10.81it/s] 36%|███▌ | 18/50 [00:01<00:02, 10.86it/s] 40%|████ | 20/50 [00:01<00:02, 10.83it/s] 44%|████▍ | 22/50 [00:01<00:02, 10.75it/s] 48%|████▊ | 24/50 [00:02<00:02, 10.79it/s] 52%|█████▏ | 26/50 [00:02<00:02, 10.78it/s] 56%|█████▌ | 28/50 [00:02<00:02, 10.78it/s] 60%|██████ | 30/50 [00:02<00:01, 10.77it/s] 64%|██████▍ | 32/50 [00:02<00:01, 10.76it/s] 68%|██████▊ | 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adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6fID56rfuylbpukbee4wmu5oi5j3yeStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 10
- image
- steps
- 50
- prompt
- A vase with flowers on the table
- proxy_words
- guidance_scale
- 7.5
- end_prompt_range
- 17
- object_of_interest
- vase
- start_prompt_range
- 7
- objects_to_preserve
- flowers
- number_of_variations
- 2
{ "seed": 10, "image": "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg", "steps": 50, "prompt": "A vase with flowers on the table", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 17, "object_of_interest": "vase", "start_prompt_range": 7, "objects_to_preserve": "flowers", "number_of_variations": 2 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", { input: { seed: 10, image: "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg", steps: 50, prompt: "A vase with flowers on the table", proxy_words: "", guidance_scale: 7.5, end_prompt_range: 17, object_of_interest: "vase", start_prompt_range: 7, objects_to_preserve: "flowers", number_of_variations: 2 } } ); // 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 adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", input={ "seed": 10, "image": "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg", "steps": 50, "prompt": "A vase with flowers on the table", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 17, "object_of_interest": "vase", "start_prompt_range": 7, "objects_to_preserve": "flowers", "number_of_variations": 2 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run adirik/local-prompt-mixing 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": "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", "input": { "seed": 10, "image": "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg", "steps": 50, "prompt": "A vase with flowers on the table", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 17, "object_of_interest": "vase", "start_prompt_range": 7, "objects_to_preserve": "flowers", "number_of_variations": 2 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-12T06:46:42.089061Z", "created_at": "2023-12-12T06:43:44.461587Z", "data_removed": false, "error": null, "id": "56rfuylbpukbee4wmu5oi5j3ye", "input": { "seed": 10, "image": "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg", "steps": 50, "prompt": "A vase with flowers on the table", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 17, "object_of_interest": "vase", "start_prompt_range": 7, "objects_to_preserve": "flowers", "number_of_variations": 2 }, "logs": "0%| | 0/15 [00:00<?, ?it/s]\n 7%|▋ | 1/15 [00:00<00:01, 7.23it/s]\n 13%|█▎ | 2/15 [00:01<00:08, 1.62it/s]\n 20%|██ | 3/15 [00:02<00:09, 1.31it/s]\n 27%|██▋ | 4/15 [00:02<00:09, 1.20it/s]\n 33%|███▎ | 5/15 [00:03<00:08, 1.14it/s]\n 40%|████ | 6/15 [00:04<00:08, 1.11it/s]\n 47%|████▋ | 7/15 [00:05<00:07, 1.09it/s]\n 53%|█████▎ | 8/15 [00:06<00:06, 1.08it/s]\n 60%|██████ | 9/15 [00:07<00:05, 1.07it/s]\n 67%|██████▋ | 10/15 [00:08<00:04, 1.07it/s]\n 73%|███████▎ | 11/15 [00:09<00:03, 1.06it/s]\n 80%|████████ | 12/15 [00:10<00:02, 1.06it/s]\n 87%|████████▋ | 13/15 [00:11<00:01, 1.05it/s]\n 93%|█████████▎| 14/15 [00:12<00:00, 1.05it/s]\n100%|██████████| 15/15 [00:13<00:00, 1.05it/s]\n100%|██████████| 15/15 [00:13<00:00, 1.12it/s]\nprompt: a photo of a {word}\ninitial word: vase\nTOP 50 SIMILAR WORDS:\n['jar', 'pot', 'plant', 'cup', 'flower', 'bottle', 'bowl', 'lamp', 'glass', 'mug', 'rose', 'trophy', 'decor', 'candle', 'acup', 'bucket', 'something', 'water', 'drink', 'table', 'grass', 'pipe', 'one', 'bloom', 'gift', 'scent', 'bone', 'globe', 'bell', 'stone', 'abstract', 'clock', 'rock', 'drum', 'present', 'ÅŁ', 'ÄŁ', 'âł', 'Ãł', 'phone', 've', 'milk', 'vege', 'wine', 'landscape', 'head', 'can', 'garden', 'grave', 'ðŁ']\n 0%| | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:00<00:00, 62.06it/s]\nprompt: a {word} with flowers on the table\ninitial word: vase\nTOP 3 SIMILAR WORDS:\n['pot', 'jar', 'cup']\nThe config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.\n/root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler {\n\"_class_name\": \"DDIMScheduler\",\n\"_diffusers_version\": \"0.10.2\",\n\"beta_end\": 0.012,\n\"beta_schedule\": \"scaled_linear\",\n\"beta_start\": 0.00085,\n\"clip_sample\": false,\n\"num_train_timesteps\": 1000,\n\"prediction_type\": \"epsilon\",\n\"set_alpha_to_one\": false,\n\"steps_offset\": 0,\n\"trained_betas\": null\n}\nis outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\ndeprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\nStart null text inversion\nDDIM inversion...\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:02, 16.13it/s]\n 8%|▊ | 4/50 [00:00<00:03, 12.51it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 11.72it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 11.37it/s]\n 20%|██ | 10/50 [00:00<00:03, 11.14it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 11.04it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 10.97it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 10.92it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 10.88it/s]\n 40%|████ | 20/50 [00:01<00:02, 10.88it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 10.88it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 10.87it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 10.85it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 10.85it/s]\n 60%|██████ | 30/50 [00:02<00:01, 10.82it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 10.73it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 10.85it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 10.84it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 10.84it/s]\n 80%|████████ | 40/50 [00:03<00:00, 10.84it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 10.83it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 10.82it/s]\n 92%|█████████▏| 46/50 [00:04<00:00, 10.83it/s]\n 96%|█████████▌| 48/50 [00:04<00:00, 10.83it/s]\n100%|██████████| 50/50 [00:04<00:00, 10.83it/s]\n100%|██████████| 50/50 [00:04<00:00, 10.97it/s]\nNull-text optimization...\n 0%| | 0/500 [00:00<?, ?it/s]\n 0%| | 1/500 [00:00<03:22, 2.47it/s]\n 0%| | 2/500 [00:00<02:40, 3.09it/s]\n 1%| | 3/500 [00:00<02:27, 3.37it/s]\n 1%| | 4/500 [00:01<02:21, 3.51it/s]\n 1%| | 5/500 [00:01<02:17, 3.60it/s]\n 1%| | 6/500 [00:01<02:15, 3.65it/s]\n 1%|▏ | 7/500 [00:02<02:13, 3.69it/s]\n 2%|▏ | 8/500 [00:02<02:12, 3.71it/s]\n 2%|▏ | 9/500 [00:02<02:11, 3.72it/s]\n 2%|▏ | 10/500 [00:02<02:11, 3.73it/s]\n 2%|▏ | 11/500 [00:03<02:46, 2.94it/s]\n 2%|▏ 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12/50 [00:02<00:07, 4.93it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.90it/s]\n 28%|██▊ | 14/50 [00:02<00:07, 4.91it/s]\n 30%|███ | 15/50 [00:02<00:07, 4.90it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 4.93it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.92it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.91it/s]\n 38%|███▊ | 19/50 [00:03<00:06, 4.91it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.90it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.90it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.90it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 4.90it/s]\n 48%|████▊ | 24/50 [00:04<00:05, 4.90it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.89it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.89it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.91it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 4.91it/s]\n 58%|█████▊ | 29/50 [00:05<00:04, 4.91it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.90it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.90it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 4.91it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 4.91it/s]\n 68%|██████▊ | 34/50 [00:06<00:03, 4.90it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.90it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.91it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 4.90it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 4.91it/s]\n 78%|███████▊ | 39/50 [00:07<00:02, 4.91it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.89it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.90it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 4.90it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 4.91it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 4.91it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.91it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.91it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.91it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 4.91it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 4.90it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.91it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.94it/s]\nDo self attn other obj masking\n 0%| | 0/3 [00:00<?, ?it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\u001b[A\n 2%|▏ | 1/50 [00:00<00:13, 3.57it/s]\u001b[A\n 4%|▍ | 2/50 [00:00<00:14, 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| 41/50 [00:14<00:03, 2.67it/s]\u001b[A\n 84%|████████▍ | 42/50 [00:15<00:02, 2.78it/s]\u001b[A\n 86%|████████▌ | 43/50 [00:15<00:02, 2.86it/s]\u001b[A\n 88%|████████▊ | 44/50 [00:15<00:02, 2.92it/s]\u001b[A\n 90%|█████████ | 45/50 [00:16<00:01, 2.96it/s]\u001b[A\n 92%|█████████▏| 46/50 [00:16<00:01, 2.99it/s]\u001b[A\n 94%|█████████▍| 47/50 [00:16<00:00, 3.01it/s]\u001b[A\n 96%|█████████▌| 48/50 [00:17<00:00, 3.03it/s]\u001b[A\n 98%|█████████▊| 49/50 [00:17<00:00, 3.04it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 3.05it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 2.81it/s]\n 33%|███▎ | 1/3 [00:18<00:36, 18.18s/it]\n 0%| | 0/50 [00:00<?, ?it/s]\u001b[A\n 2%|▏ | 1/50 [00:00<00:13, 3.56it/s]\u001b[A\n 4%|▍ | 2/50 [00:00<00:14, 3.20it/s]\u001b[A\n 6%|▌ | 3/50 [00:00<00:15, 3.11it/s]\u001b[A\n 8%|▊ | 4/50 [00:01<00:15, 3.05it/s]\u001b[A\n 10%|█ | 5/50 [00:01<00:14, 3.03it/s]\u001b[A\n 12%|█▏ | 6/50 [00:01<00:14, 3.01it/s]\u001b[A\n 14%|█▍ | 7/50 [00:02<00:14, 3.01it/s]\u001b[A\n 16%|█▌ | 8/50 [00:02<00:13, 3.00it/s]\u001b[A\n 18%|█▊ | 9/50 [00:02<00:13, 2.99it/s]\u001b[A\n 20%|██ | 10/50 [00:03<00:13, 2.99it/s]\u001b[A\n 22%|██▏ | 11/50 [00:03<00:13, 2.98it/s]\u001b[A\n 24%|██▍ | 12/50 [00:03<00:12, 2.98it/s]\u001b[A\n 26%|██▌ | 13/50 [00:04<00:12, 2.98it/s]\u001b[A\n 28%|██▊ | 14/50 [00:04<00:12, 2.98it/s]\u001b[A\n 30%|███ | 15/50 [00:04<00:11, 2.98it/s]\u001b[A\n 32%|███▏ | 16/50 [00:05<00:11, 2.98it/s]\u001b[A\n 34%|███▍ | 17/50 [00:05<00:11, 2.98it/s]\u001b[A\n 36%|███▌ | 18/50 [00:05<00:10, 2.98it/s]\u001b[A\n 38%|███▊ | 19/50 [00:06<00:10, 2.98it/s]\u001b[A\n 40%|████ | 20/50 [00:06<00:10, 2.98it/s]\u001b[A\n 42%|████▏ | 21/50 [00:06<00:09, 2.98it/s]\u001b[A\n 44%|████▍ | 22/50 [00:07<00:09, 2.98it/s]\u001b[A\n 46%|████▌ | 23/50 [00:07<00:09, 2.98it/s]\u001b[A\n 48%|████▊ | 24/50 [00:07<00:08, 2.98it/s]\u001b[A\n 50%|█████ | 25/50 [00:08<00:08, 2.98it/s]\u001b[A\n 52%|█████▏ | 26/50 [00:08<00:08, 2.98it/s]\u001b[A\n 54%|█████▍ | 27/50 [00:09<00:07, 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3.03it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 3.04it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 2.81it/s]\n100%|██████████| 3/3 [00:54<00:00, 18.19s/it]\n100%|██████████| 3/3 [00:54<00:00, 18.19s/it]", "metrics": { "predict_time": 177.593375, "total_time": 177.627474 }, "output": [ "https://replicate.delivery/pbxt/tsoTLnv5VkZULFXeiP8fUXdaKfC6y3nRqb1D36cRjNThesFIB/pot.jpg", "https://replicate.delivery/pbxt/1yefI0hblIqbZE03OZUmVbM9N8j983qXeV4JLPUeelfS0zWgE/jar.jpg", "https://replicate.delivery/pbxt/tBAx4boguErsO5TqcpjipCUA8I6iwMVTaDIB13VaG2S0zWgE/cup.jpg" ], "started_at": "2023-12-12T06:43:44.495686Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/56rfuylbpukbee4wmu5oi5j3ye", "cancel": "https://api.replicate.com/v1/predictions/56rfuylbpukbee4wmu5oi5j3ye/cancel" }, "version": "66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f" }Generated in0%| | 0/15 [00:00<?, ?it/s] 7%|▋ | 1/15 [00:00<00:01, 7.23it/s] 13%|█▎ | 2/15 [00:01<00:08, 1.62it/s] 20%|██ | 3/15 [00:02<00:09, 1.31it/s] 27%|██▋ | 4/15 [00:02<00:09, 1.20it/s] 33%|███▎ | 5/15 [00:03<00:08, 1.14it/s] 40%|████ | 6/15 [00:04<00:08, 1.11it/s] 47%|████▋ | 7/15 [00:05<00:07, 1.09it/s] 53%|█████▎ | 8/15 [00:06<00:06, 1.08it/s] 60%|██████ | 9/15 [00:07<00:05, 1.07it/s] 67%|██████▋ | 10/15 [00:08<00:04, 1.07it/s] 73%|███████▎ | 11/15 [00:09<00:03, 1.06it/s] 80%|████████ | 12/15 [00:10<00:02, 1.06it/s] 87%|████████▋ | 13/15 [00:11<00:01, 1.05it/s] 93%|█████████▎| 14/15 [00:12<00:00, 1.05it/s] 100%|██████████| 15/15 [00:13<00:00, 1.05it/s] 100%|██████████| 15/15 [00:13<00:00, 1.12it/s] prompt: a photo of a {word} initial word: vase TOP 50 SIMILAR WORDS: ['jar', 'pot', 'plant', 'cup', 'flower', 'bottle', 'bowl', 'lamp', 'glass', 'mug', 'rose', 'trophy', 'decor', 'candle', 'acup', 'bucket', 'something', 'water', 'drink', 'table', 'grass', 'pipe', 'one', 'bloom', 'gift', 'scent', 'bone', 'globe', 'bell', 'stone', 'abstract', 'clock', 'rock', 'drum', 'present', 'ÅŁ', 'ÄŁ', 'âł', 'Ãł', 'phone', 've', 'milk', 'vege', 'wine', 'landscape', 'head', 'can', 'garden', 'grave', 'ðŁ'] 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:00<00:00, 62.06it/s] prompt: a {word} with flowers on the table initial word: vase TOP 3 SIMILAR WORDS: ['pot', 'jar', 'cup'] The config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file. /root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler { "_class_name": "DDIMScheduler", "_diffusers_version": "0.10.2", "beta_end": 0.012, "beta_schedule": "scaled_linear", "beta_start": 0.00085, "clip_sample": false, "num_train_timesteps": 1000, "prediction_type": "epsilon", "set_alpha_to_one": false, "steps_offset": 0, "trained_betas": null } is outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) Start null text inversion DDIM inversion... 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:02, 16.13it/s] 8%|▊ | 4/50 [00:00<00:03, 12.51it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.72it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.37it/s] 20%|██ | 10/50 [00:00<00:03, 11.14it/s] 24%|██▍ | 12/50 [00:01<00:03, 11.04it/s] 28%|██▊ | 14/50 [00:01<00:03, 10.97it/s] 32%|███▏ | 16/50 [00:01<00:03, 10.92it/s] 36%|███▌ | 18/50 [00:01<00:02, 10.88it/s] 40%|████ | 20/50 [00:01<00:02, 10.88it/s] 44%|████▍ | 22/50 [00:01<00:02, 10.88it/s] 48%|████▊ | 24/50 [00:02<00:02, 10.87it/s] 52%|█████▏ | 26/50 [00:02<00:02, 10.85it/s] 56%|█████▌ | 28/50 [00:02<00:02, 10.85it/s] 60%|██████ | 30/50 [00:02<00:01, 10.82it/s] 64%|██████▍ | 32/50 [00:02<00:01, 10.73it/s] 68%|██████▊ | 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adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6fIDyn6g7ftb74oyd3r4sg4sjwic64StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 10
- image
- steps
- 50
- prompt
- A bowl of rice on the table
- proxy_words
- guidance_scale
- 7.5
- end_prompt_range
- 15
- object_of_interest
- bowl
- start_prompt_range
- 4
- objects_to_preserve
- table
- number_of_variations
- 4
{ "seed": 10, "image": "https://replicate.delivery/pbxt/K2C6Q0YS5HONiijUqfqhjH22LXZy7wEEqfrthA738bbDvz3f/real.jpg", "steps": 50, "prompt": "A bowl of rice on the table", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 15, "object_of_interest": "bowl", "start_prompt_range": 4, "objects_to_preserve": "table", "number_of_variations": 4 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", { input: { seed: 10, image: "https://replicate.delivery/pbxt/K2C6Q0YS5HONiijUqfqhjH22LXZy7wEEqfrthA738bbDvz3f/real.jpg", steps: 50, prompt: "A bowl of rice on the table", proxy_words: "", guidance_scale: 7.5, end_prompt_range: 15, object_of_interest: "bowl", start_prompt_range: 4, objects_to_preserve: "table", number_of_variations: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", input={ "seed": 10, "image": "https://replicate.delivery/pbxt/K2C6Q0YS5HONiijUqfqhjH22LXZy7wEEqfrthA738bbDvz3f/real.jpg", "steps": 50, "prompt": "A bowl of rice on the table", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 15, "object_of_interest": "bowl", "start_prompt_range": 4, "objects_to_preserve": "table", "number_of_variations": 4 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run adirik/local-prompt-mixing 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": "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f", "input": { "seed": 10, "image": "https://replicate.delivery/pbxt/K2C6Q0YS5HONiijUqfqhjH22LXZy7wEEqfrthA738bbDvz3f/real.jpg", "steps": 50, "prompt": "A bowl of rice on the table", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 15, "object_of_interest": "bowl", "start_prompt_range": 4, "objects_to_preserve": "table", "number_of_variations": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-12T06:50:00.319236Z", "created_at": "2023-12-12T06:44:26.003718Z", "data_removed": false, "error": null, "id": "yn6g7ftb74oyd3r4sg4sjwic64", "input": { "seed": 10, "image": "https://replicate.delivery/pbxt/K2C6Q0YS5HONiijUqfqhjH22LXZy7wEEqfrthA738bbDvz3f/real.jpg", "steps": 50, "prompt": "A bowl of rice on the table", "proxy_words": "", "guidance_scale": 7.5, "end_prompt_range": 15, "object_of_interest": "bowl", "start_prompt_range": 4, "objects_to_preserve": "table", "number_of_variations": 4 }, "logs": "0%| | 0/15 [00:00<?, ?it/s]\n 7%|▋ | 1/15 [00:00<00:01, 7.27it/s]\n 13%|█▎ | 2/15 [00:01<00:08, 1.61it/s]\n 20%|██ | 3/15 [00:02<00:09, 1.29it/s]\n 27%|██▋ | 4/15 [00:03<00:09, 1.18it/s]\n 33%|███▎ | 5/15 [00:03<00:08, 1.12it/s]\n 40%|████ | 6/15 [00:04<00:08, 1.09it/s]\n 47%|████▋ | 7/15 [00:05<00:07, 1.08it/s]\n 53%|█████▎ | 8/15 [00:06<00:06, 1.07it/s]\n 60%|██████ | 9/15 [00:07<00:05, 1.06it/s]\n 67%|██████▋ | 10/15 [00:08<00:04, 1.05it/s]\n 73%|███████▎ | 11/15 [00:09<00:03, 1.05it/s]\n 80%|████████ | 12/15 [00:10<00:02, 1.05it/s]\n 87%|████████▋ | 13/15 [00:11<00:01, 1.04it/s]\n 93%|█████████▎| 14/15 [00:12<00:00, 1.04it/s]\n100%|██████████| 15/15 [00:13<00:00, 1.04it/s]\n100%|██████████| 15/15 [00:13<00:00, 1.10it/s]\nprompt: a photo of a {word}\ninitial word: bowl\nTOP 50 SIMILAR WORDS:\n['bowl', 'pot', 'cup', 'dish', 'bucket', 'acup', 'plate', 'tub', 'jar', 'circle', 'spoon', 'globe', 'mug', 'ring', 'round', 'ball', 'plant', 'pipe', 'boat', 'something', 'drum', 'basketball', 'lid', 'bone', 'pott', 'fruit', 'table', 'water', 'glass', 'food', 'phone', 'pie', 'tray', 'âĿ', 'âŀ', 'colour', 'color', 'than', 'can', 'weight', 'brush', 'bell', '羣', 'watch', 'âł', 'ÅŁ', 'ÄŁ', 'Ãł', 'dial', 'one']\n 0%| | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:00<00:00, 47.64it/s]\nprompt: a {word} of rice on the table\ninitial word: bowl\nTOP 5 SIMILAR WORDS:\n['bowl', 'dish', 'cup', 'pot', 'plate']\nThe config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.\n/root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler {\n\"_class_name\": \"DDIMScheduler\",\n\"_diffusers_version\": \"0.10.2\",\n\"beta_end\": 0.012,\n\"beta_schedule\": \"scaled_linear\",\n\"beta_start\": 0.00085,\n\"clip_sample\": false,\n\"num_train_timesteps\": 1000,\n\"prediction_type\": \"epsilon\",\n\"set_alpha_to_one\": false,\n\"steps_offset\": 0,\n\"trained_betas\": null\n}\nis outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\ndeprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\nStart null text inversion\nDDIM inversion...\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:02, 16.32it/s]\n 8%|▊ | 4/50 [00:00<00:03, 12.51it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 11.67it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 11.27it/s]\n 20%|██ | 10/50 [00:00<00:03, 11.06it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 10.97it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 10.91it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 10.88it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 10.85it/s]\n 40%|████ | 20/50 [00:01<00:02, 10.78it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 10.69it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 10.81it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 10.78it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 10.79it/s]\n 60%|██████ | 30/50 [00:02<00:01, 10.79it/s]\n 64%|██████▍ | 32/50 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497/500 [01:29<00:00, 3.62it/s]\n100%|█████████▉| 498/500 [01:29<00:00, 3.65it/s]\n100%|█████████▉| 499/500 [01:30<00:00, 3.68it/s]\n100%|██████████| 500/500 [01:30<00:00, 3.66it/s]\n100%|██████████| 500/500 [01:30<00:00, 5.53it/s]\nEnd null text inversion\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.49it/s]\n 4%|▍ | 2/50 [00:00<00:08, 5.65it/s]\n 6%|▌ | 3/50 [00:00<00:08, 5.34it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.15it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.03it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.01it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 4.98it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.96it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.94it/s]\n 20%|██ | 10/50 [00:01<00:08, 4.92it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 4.92it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.92it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.92it/s]\n 28%|██▊ | 14/50 [00:02<00:07, 4.91it/s]\n 30%|███ | 15/50 [00:02<00:07, 4.91it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 4.91it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.92it/s]\n 36%|███▌ | 18/50 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[00:08<00:02, 4.90it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.90it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 4.90it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 4.90it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 4.91it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.91it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.90it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.91it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 4.91it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 4.90it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.91it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.94it/s]\nDo self attn other obj masking\n 0%| | 0/4 [00:00<?, ?it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\u001b[A\n 2%|▏ | 1/50 [00:00<00:13, 3.56it/s]\u001b[A\n 4%|▍ | 2/50 [00:00<00:15, 3.20it/s]\u001b[A\n 6%|▌ | 3/50 [00:00<00:15, 3.10it/s]\u001b[A\n 8%|▊ | 4/50 [00:01<00:15, 3.05it/s]\u001b[A\n 10%|█ | 5/50 [00:01<00:14, 3.02it/s]\u001b[A\n 12%|█▏ | 6/50 [00:01<00:14, 3.01it/s]\u001b[A\n 14%|█▍ | 7/50 [00:02<00:14, 3.00it/s]\u001b[A\n 16%|█▌ | 8/50 [00:02<00:14, 2.99it/s]\u001b[A\n 18%|█▊ | 9/50 [00:02<00:13, 2.99it/s]\u001b[A\n 20%|██ | 10/50 [00:03<00:13, 2.99it/s]\u001b[A\n 22%|██▏ | 11/50 [00:03<00:13, 2.98it/s]\u001b[A\n 24%|██▍ | 12/50 [00:03<00:12, 2.98it/s]\u001b[A\n 26%|██▌ | 13/50 [00:04<00:12, 2.98it/s]\u001b[A\n 28%|██▊ | 14/50 [00:04<00:12, 2.98it/s]\u001b[A\n 30%|███ | 15/50 [00:04<00:11, 2.98it/s]\u001b[A\n 32%|███▏ | 16/50 [00:05<00:11, 2.98it/s]\u001b[A\n 34%|███▍ | 17/50 [00:05<00:11, 2.98it/s]\u001b[A\n 36%|███▌ | 18/50 [00:05<00:10, 2.98it/s]\u001b[A\n 38%|███▊ | 19/50 [00:06<00:10, 2.98it/s]\u001b[A\n 40%|████ | 20/50 [00:06<00:10, 2.97it/s]\u001b[A\n 42%|████▏ | 21/50 [00:06<00:09, 2.98it/s]\u001b[A\n 44%|████▍ | 22/50 [00:07<00:09, 2.98it/s]\u001b[A\n 46%|████▌ | 23/50 [00:07<00:09, 2.99it/s]\u001b[A\n 48%|████▊ | 24/50 [00:07<00:08, 2.98it/s]\u001b[A\n 50%|█████ | 25/50 [00:08<00:08, 2.98it/s]\u001b[A\n 52%|█████▏ | 26/50 [00:08<00:08, 2.98it/s]\u001b[A\n 54%|█████▍ | 27/50 [00:09<00:07, 2.98it/s]\u001b[A\n 56%|█████▌ | 28/50 [00:09<00:07, 2.98it/s]\u001b[A\n 58%|█████▊ | 29/50 [00:09<00:07, 2.98it/s]\u001b[A\n 60%|██████ | 30/50 [00:10<00:06, 2.98it/s]\u001b[A\n 62%|██████▏ | 31/50 [00:10<00:06, 2.98it/s]\u001b[A\n 64%|██████▍ | 32/50 [00:10<00:06, 2.98it/s]\u001b[A\n 66%|██████▌ | 33/50 [00:11<00:05, 2.98it/s]\u001b[A\n 68%|██████▊ | 34/50 [00:11<00:05, 2.98it/s]\u001b[A\n 70%|███████ | 35/50 [00:11<00:05, 2.97it/s]\u001b[A\n 72%|███████▏ | 36/50 [00:13<00:10, 1.36it/s]\u001b[A\n 74%|███████▍ | 37/50 [00:13<00:07, 1.67it/s]\u001b[A\n 76%|███████▌ | 38/50 [00:13<00:06, 1.93it/s]\u001b[A\n 78%|███████▊ | 39/50 [00:14<00:05, 2.16it/s]\u001b[A\n 80%|████████ | 40/50 [00:14<00:04, 2.35it/s]\u001b[A\n 82%|████████▏ | 41/50 [00:14<00:03, 2.65it/s]\u001b[A\n 84%|████████▍ | 42/50 [00:15<00:02, 2.76it/s]\u001b[A\n 86%|████████▌ | 43/50 [00:15<00:02, 2.85it/s]\u001b[A\n 88%|████████▊ | 44/50 [00:15<00:02, 2.91it/s]\u001b[A\n 90%|█████████ | 45/50 [00:16<00:01, 2.95it/s]\u001b[A\n 92%|█████████▏| 46/50 [00:16<00:01, 2.98it/s]\u001b[A\n 94%|█████████▍| 47/50 [00:16<00:00, 3.01it/s]\u001b[A\n 96%|█████████▌| 48/50 [00:17<00:00, 3.02it/s]\u001b[A\n 98%|█████████▊| 49/50 [00:17<00:00, 3.04it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 3.04it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 2.80it/s]\n 25%|██▌ | 1/4 [00:18<00:54, 18.22s/it]\n 0%| | 0/50 [00:00<?, ?it/s]\u001b[A\n 2%|▏ | 1/50 [00:00<00:13, 3.56it/s]\u001b[A\n 4%|▍ | 2/50 [00:00<00:14, 3.21it/s]\u001b[A\n 6%|▌ | 3/50 [00:00<00:15, 3.10it/s]\u001b[A\n 8%|▊ | 4/50 [00:01<00:15, 3.05it/s]\u001b[A\n 10%|█ | 5/50 [00:01<00:14, 3.03it/s]\u001b[A\n 12%|█▏ | 6/50 [00:01<00:14, 3.01it/s]\u001b[A\n 14%|█▍ | 7/50 [00:02<00:14, 3.00it/s]\u001b[A\n 16%|█▌ | 8/50 [00:02<00:14, 2.99it/s]\u001b[A\n 18%|█▊ | 9/50 [00:02<00:13, 2.99it/s]\u001b[A\n 20%|██ | 10/50 [00:03<00:13, 2.99it/s]\u001b[A\n 22%|██▏ | 11/50 [00:03<00:13, 2.98it/s]\u001b[A\n 24%|██▍ | 12/50 [00:03<00:12, 2.98it/s]\u001b[A\n 26%|██▌ | 13/50 [00:04<00:12, 2.98it/s]\u001b[A\n 28%|██▊ | 14/50 [00:04<00:12, 2.98it/s]\u001b[A\n 30%|███ | 15/50 [00:04<00:11, 2.98it/s]\u001b[A\n 32%|███▏ | 16/50 [00:05<00:11, 2.98it/s]\u001b[A\n 34%|███▍ | 17/50 [00:05<00:11, 2.98it/s]\u001b[A\n 36%|███▌ | 18/50 [00:05<00:10, 2.98it/s]\u001b[A\n 38%|███▊ | 19/50 [00:06<00:10, 2.98it/s]\u001b[A\n 40%|████ | 20/50 [00:06<00:10, 2.97it/s]\u001b[A\n 42%|████▏ | 21/50 [00:06<00:09, 2.98it/s]\u001b[A\n 44%|████▍ | 22/50 [00:07<00:09, 2.98it/s]\u001b[A\n 46%|████▌ | 23/50 [00:07<00:09, 2.98it/s]\u001b[A\n 48%|████▊ | 24/50 [00:08<00:08, 2.98it/s]\u001b[A\n 50%|█████ | 25/50 [00:08<00:08, 2.98it/s]\u001b[A\n 52%|█████▏ | 26/50 [00:08<00:08, 2.98it/s]\u001b[A\n 54%|█████▍ | 27/50 [00:09<00:07, 2.98it/s]\u001b[A\n 56%|█████▌ | 28/50 [00:09<00:07, 2.98it/s]\u001b[A\n 58%|█████▊ | 29/50 [00:09<00:07, 2.98it/s]\u001b[A\n 60%|██████ | 30/50 [00:10<00:06, 2.98it/s]\u001b[A\n 62%|██████▏ | 31/50 [00:10<00:06, 2.98it/s]\u001b[A\n 64%|██████▍ | 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82%|████████▏ | 41/50 [00:14<00:03, 2.66it/s]\u001b[A\n 84%|████████▍ | 42/50 [00:15<00:02, 2.77it/s]\u001b[A\n 86%|████████▌ | 43/50 [00:15<00:02, 2.85it/s]\u001b[A\n 88%|████████▊ | 44/50 [00:15<00:02, 2.91it/s]\u001b[A\n 90%|█████████ | 45/50 [00:16<00:01, 2.96it/s]\u001b[A\n 92%|█████████▏| 46/50 [00:16<00:01, 2.98it/s]\u001b[A\n 94%|█████████▍| 47/50 [00:16<00:00, 3.01it/s]\u001b[A\n 96%|█████████▌| 48/50 [00:17<00:00, 3.02it/s]\u001b[A\n 98%|█████████▊| 49/50 [00:17<00:00, 3.03it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 3.04it/s]\u001b[A\n100%|██████████| 50/50 [00:17<00:00, 2.80it/s]\n100%|██████████| 4/4 [01:12<00:00, 18.19s/it]\n100%|██████████| 4/4 [01:12<00:00, 18.20s/it]", "metrics": { "predict_time": 197.941265, "total_time": 334.315518 }, "output": [ "https://replicate.delivery/pbxt/4nSwSRzrjo57E91aNFwCpZLhOWyQscR0eQPC1Dq9ICFLptAJA/dish.jpg", "https://replicate.delivery/pbxt/fiZ2XteGJsh5MEziM3bkNfRfHE1sKJt2NX9Z1WeFjLafl0WgE/cup.jpg", "https://replicate.delivery/pbxt/ZIflqw5S3mSLSqplv0YiPezG5L0yKSG7AIHgyQy4zBXXSbBSA/pot.jpg", "https://replicate.delivery/pbxt/1DgQ9WAFlmZDLNeB3LO9o0m0eBQeRvxQ4MqEf2KXFkyjJtFIB/plate.jpg" ], "started_at": "2023-12-12T06:46:42.377971Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yn6g7ftb74oyd3r4sg4sjwic64", "cancel": "https://api.replicate.com/v1/predictions/yn6g7ftb74oyd3r4sg4sjwic64/cancel" }, "version": "66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f" }Generated in0%| | 0/15 [00:00<?, ?it/s] 7%|▋ | 1/15 [00:00<00:01, 7.27it/s] 13%|█▎ | 2/15 [00:01<00:08, 1.61it/s] 20%|██ | 3/15 [00:02<00:09, 1.29it/s] 27%|██▋ | 4/15 [00:03<00:09, 1.18it/s] 33%|███▎ | 5/15 [00:03<00:08, 1.12it/s] 40%|████ | 6/15 [00:04<00:08, 1.09it/s] 47%|████▋ | 7/15 [00:05<00:07, 1.08it/s] 53%|█████▎ | 8/15 [00:06<00:06, 1.07it/s] 60%|██████ | 9/15 [00:07<00:05, 1.06it/s] 67%|██████▋ | 10/15 [00:08<00:04, 1.05it/s] 73%|███████▎ | 11/15 [00:09<00:03, 1.05it/s] 80%|████████ | 12/15 [00:10<00:02, 1.05it/s] 87%|████████▋ | 13/15 [00:11<00:01, 1.04it/s] 93%|█████████▎| 14/15 [00:12<00:00, 1.04it/s] 100%|██████████| 15/15 [00:13<00:00, 1.04it/s] 100%|██████████| 15/15 [00:13<00:00, 1.10it/s] prompt: a photo of a {word} initial word: bowl TOP 50 SIMILAR WORDS: ['bowl', 'pot', 'cup', 'dish', 'bucket', 'acup', 'plate', 'tub', 'jar', 'circle', 'spoon', 'globe', 'mug', 'ring', 'round', 'ball', 'plant', 'pipe', 'boat', 'something', 'drum', 'basketball', 'lid', 'bone', 'pott', 'fruit', 'table', 'water', 'glass', 'food', 'phone', 'pie', 'tray', 'âĿ', 'âŀ', 'colour', 'color', 'than', 'can', 'weight', 'brush', 'bell', '羣', 'watch', 'âł', 'ÅŁ', 'ÄŁ', 'Ãł', 'dial', 'one'] 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:00<00:00, 47.64it/s] prompt: a {word} of rice on the table initial word: bowl TOP 5 SIMILAR WORDS: ['bowl', 'dish', 'cup', 'pot', 'plate'] The config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file. /root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler { "_class_name": "DDIMScheduler", "_diffusers_version": "0.10.2", "beta_end": 0.012, "beta_schedule": "scaled_linear", "beta_start": 0.00085, "clip_sample": false, "num_train_timesteps": 1000, "prediction_type": "epsilon", "set_alpha_to_one": false, "steps_offset": 0, "trained_betas": null } is outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) Start null text inversion DDIM inversion... 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:02, 16.32it/s] 8%|▊ | 4/50 [00:00<00:03, 12.51it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.67it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.27it/s] 20%|██ | 10/50 [00:00<00:03, 11.06it/s] 24%|██▍ | 12/50 [00:01<00:03, 10.97it/s] 28%|██▊ | 14/50 [00:01<00:03, 10.91it/s] 32%|███▏ | 16/50 [00:01<00:03, 10.88it/s] 36%|███▌ | 18/50 [00:01<00:02, 10.85it/s] 40%|████ | 20/50 [00:01<00:02, 10.78it/s] 44%|████▍ | 22/50 [00:01<00:02, 10.69it/s] 48%|████▊ | 24/50 [00:02<00:02, 10.81it/s] 52%|█████▏ | 26/50 [00:02<00:02, 10.78it/s] 56%|█████▌ | 28/50 [00:02<00:02, 10.79it/s] 60%|██████ | 30/50 [00:02<00:01, 10.79it/s] 64%|██████▍ | 32/50 [00:02<00:01, 10.78it/s] 68%|██████▊ | 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70%|███████ | 35/50 [00:11<00:05, 2.98it/s] 72%|███████▏ | 36/50 [00:13<00:10, 1.37it/s] 74%|███████▍ | 37/50 [00:13<00:07, 1.68it/s] 76%|███████▌ | 38/50 [00:13<00:06, 1.94it/s] 78%|███████▊ | 39/50 [00:14<00:05, 2.17it/s] 80%|████████ | 40/50 [00:14<00:04, 2.36it/s] 82%|████████▏ | 41/50 [00:14<00:03, 2.66it/s] 84%|████████▍ | 42/50 [00:15<00:02, 2.77it/s] 86%|████████▌ | 43/50 [00:15<00:02, 2.85it/s] 88%|████████▊ | 44/50 [00:15<00:02, 2.91it/s] 90%|█████████ | 45/50 [00:16<00:01, 2.96it/s] 92%|█████████▏| 46/50 [00:16<00:01, 2.98it/s] 94%|█████████▍| 47/50 [00:16<00:00, 3.01it/s] 96%|█████████▌| 48/50 [00:17<00:00, 3.02it/s] 98%|█████████▊| 49/50 [00:17<00:00, 3.03it/s] 100%|██████████| 50/50 [00:17<00:00, 3.04it/s] 100%|██████████| 50/50 [00:17<00:00, 2.80it/s] 100%|██████████| 4/4 [01:12<00:00, 18.19s/it] 100%|██████████| 4/4 [01:12<00:00, 18.20s/it]
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