adirik
/
leditsplusplus
LEdits++ for image editing
Prediction
adirik/leditsplusplus:ea5b4a96IDhw6si63bv7m3amueh2qhvfcsouStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- skip
- 0.2
- source_prompt
- edit_threshold
- 0.9, 0.85
- editing_prompts
- tennis ball, tomato
- edit_warmup_steps
- 0
- edit_guidance_scale
- 5.0, 10.0
- num_inversion_steps
- 50
- source_guidance_scale
- 3.5
- reverse_editing_directions
- True, False
{ "skip": 0.2, "image": "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", "source_prompt": "", "edit_threshold": "0.9, 0.85", "editing_prompts": "tennis ball, tomato", "edit_warmup_steps": 0, "edit_guidance_scale": "5.0, 10.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "True, False" }
Install Replicateβs Node.js client library:npm install replicate
Set theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run adirik/leditsplusplus using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/leditsplusplus:ea5b4a96d43c51d9b5a579177b9044e631fbb4dbfaae51367a226a65663dfebe", { input: { skip: 0.2, image: "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", source_prompt: "", edit_threshold: "0.9, 0.85", editing_prompts: "tennis ball, tomato", edit_warmup_steps: 0, edit_guidance_scale: "5.0, 10.0", num_inversion_steps: 50, source_guidance_scale: 3.5, reverse_editing_directions: "True, False" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicateβs Python client library:pip install replicate
Set theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the client:import replicate
Run adirik/leditsplusplus using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/leditsplusplus:ea5b4a96d43c51d9b5a579177b9044e631fbb4dbfaae51367a226a65663dfebe", input={ "skip": 0.2, "image": "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", "source_prompt": "", "edit_threshold": "0.9, 0.85", "editing_prompts": "tennis ball, tomato", "edit_warmup_steps": 0, "edit_guidance_scale": "5.0, 10.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "True, False" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run adirik/leditsplusplus using Replicateβs 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": "ea5b4a96d43c51d9b5a579177b9044e631fbb4dbfaae51367a226a65663dfebe", "input": { "skip": 0.2, "image": "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", "source_prompt": "", "edit_threshold": "0.9, 0.85", "editing_prompts": "tennis ball, tomato", "edit_warmup_steps": 0, "edit_guidance_scale": "5.0, 10.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "True, False" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicateβs HTTP API reference docs.
Output
{ "completed_at": "2024-03-27T09:47:28.583402Z", "created_at": "2024-03-27T09:46:08.567289Z", "data_removed": false, "error": null, "id": "hw6si63bv7m3amueh2qhvfcsou", "input": { "skip": 0.2, "image": "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", "source_prompt": "", "edit_threshold": "0.9, 0.85", "editing_prompts": "tennis ball, tomato", "edit_warmup_steps": 0, "edit_guidance_scale": "5.0, 10.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "True, False" }, "logs": "Your input images far exceed the default resolution of the underlying diffusion model. The output images may contain severe artifacts! Consider down-sampling the input using the `height` and `width` parameters\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|β | 1/50 [00:00<00:08, 5.82it/s]\n 6%|β | 3/50 [00:00<00:04, 9.75it/s]\n 10%|β | 5/50 [00:00<00:04, 10.71it/s]\n 14%|ββ | 7/50 [00:00<00:03, 11.19it/s]\n 18%|ββ | 9/50 [00:00<00:03, 11.51it/s]\n 22%|βββ | 11/50 [00:00<00:03, 11.67it/s]\n 26%|βββ | 13/50 [00:01<00:03, 11.75it/s]\n 30%|βββ | 15/50 [00:01<00:02, 11.73it/s]\n 34%|ββββ | 17/50 [00:01<00:02, 11.86it/s]\n 38%|ββββ | 19/50 [00:01<00:02, 11.91it/s]\n 42%|βββββ | 21/50 [00:01<00:02, 11.90it/s]\n 46%|βββββ | 23/50 [00:02<00:02, 11.92it/s]\n 50%|βββββ | 25/50 [00:02<00:02, 11.94it/s]\n 54%|ββββββ | 27/50 [00:02<00:01, 11.95it/s]\n 58%|ββββββ | 29/50 [00:02<00:01, 11.89it/s]\n 62%|βββββββ | 31/50 [00:02<00:01, 11.91it/s]\n 66%|βββββββ | 33/50 [00:02<00:01, 11.94it/s]\n 70%|βββββββ | 35/50 [00:03<00:01, 11.96it/s]\n 74%|ββββββββ | 37/50 [00:03<00:01, 11.96it/s]\n 78%|ββββββββ | 39/50 [00:03<00:00, 11.96it/s]\n 82%|βββββββββ | 41/50 [00:03<00:00, 11.95it/s]\n 86%|βββββββββ | 43/50 [00:03<00:00, 11.93it/s]\n 90%|βββββββββ | 45/50 [00:03<00:00, 11.92it/s]\n 94%|ββββββββββ| 47/50 [00:04<00:00, 11.95it/s]\n 98%|ββββββββββ| 49/50 [00:04<00:00, 11.96it/s]\n100%|ββββββββββ| 50/50 [00:04<00:00, 11.68it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|β | 1/50 [00:00<00:13, 3.65it/s]\n 4%|β | 2/50 [00:00<00:11, 4.24it/s]\n 6%|β | 3/50 [00:00<00:10, 4.47it/s]\n 8%|β | 4/50 [00:00<00:10, 4.58it/s]\n 10%|β | 5/50 [00:01<00:09, 4.65it/s]\n 12%|ββ | 6/50 [00:01<00:09, 4.69it/s]\n 14%|ββ | 7/50 [00:01<00:09, 4.71it/s]\n 16%|ββ | 8/50 [00:01<00:08, 4.73it/s]\n 18%|ββ | 9/50 [00:01<00:08, 4.74it/s]\n 20%|ββ | 10/50 [00:02<00:08, 4.74it/s]\n 22%|βββ | 11/50 [00:02<00:08, 4.74it/s]\n 24%|βββ | 12/50 [00:02<00:08, 4.75it/s]\n 26%|βββ | 13/50 [00:02<00:07, 4.75it/s]\n 28%|βββ | 14/50 [00:03<00:07, 4.75it/s]\n 30%|βββ | 15/50 [00:03<00:07, 4.75it/s]\n 32%|ββββ | 16/50 [00:03<00:07, 4.75it/s]\n 34%|ββββ | 17/50 [00:03<00:06, 4.76it/s]\n 36%|ββββ | 18/50 [00:03<00:06, 4.76it/s]\n 38%|ββββ | 19/50 [00:04<00:06, 4.76it/s]\n 40%|ββββ | 20/50 [00:04<00:06, 4.76it/s]\n 42%|βββββ | 21/50 [00:04<00:06, 4.75it/s]\n 44%|βββββ | 22/50 [00:04<00:05, 4.76it/s]\n 46%|βββββ | 23/50 [00:04<00:05, 4.76it/s]\n 48%|βββββ | 24/50 [00:05<00:05, 4.76it/s]\n 50%|βββββ | 25/50 [00:05<00:05, 4.75it/s]\n 52%|ββββββ | 26/50 [00:05<00:05, 4.75it/s]\n 54%|ββββββ | 27/50 [00:05<00:04, 4.75it/s]\n 56%|ββββββ | 28/50 [00:05<00:04, 4.75it/s]\n 58%|ββββββ | 29/50 [00:06<00:04, 4.75it/s]\n 60%|ββββββ | 30/50 [00:06<00:04, 4.75it/s]\n 62%|βββββββ | 31/50 [00:06<00:03, 4.75it/s]\n 64%|βββββββ | 32/50 [00:06<00:03, 4.75it/s]\n 66%|βββββββ | 33/50 [00:07<00:03, 4.75it/s]\n 68%|βββββββ | 34/50 [00:07<00:03, 4.75it/s]\n 70%|βββββββ | 35/50 [00:07<00:03, 4.69it/s]\n 72%|ββββββββ | 36/50 [00:07<00:02, 4.71it/s]\n 74%|ββββββββ | 37/50 [00:07<00:02, 4.72it/s]\n 76%|ββββββββ | 38/50 [00:08<00:02, 4.73it/s]\n 78%|ββββββββ | 39/50 [00:08<00:02, 4.73it/s]\n 80%|ββββββββ | 40/50 [00:08<00:02, 4.74it/s]\n 82%|βββββββββ | 41/50 [00:08<00:01, 4.74it/s]\n 84%|βββββββββ | 42/50 [00:08<00:01, 4.71it/s]\n 86%|βββββββββ | 43/50 [00:09<00:01, 4.72it/s]\n 88%|βββββββββ | 44/50 [00:09<00:01, 4.72it/s]\n 90%|βββββββββ | 45/50 [00:09<00:01, 4.73it/s]\n 92%|ββββββββββ| 46/50 [00:09<00:00, 4.73it/s]\n 94%|ββββββββββ| 47/50 [00:09<00:00, 4.73it/s]\n 96%|ββββββββββ| 48/50 [00:10<00:00, 4.73it/s]\n 98%|ββββββββββ| 49/50 [00:10<00:00, 4.73it/s]\n100%|ββββββββββ| 50/50 [00:10<00:00, 4.73it/s]\n100%|ββββββββββ| 50/50 [00:10<00:00, 4.72it/s]", "metrics": { "predict_time": 19.052099, "total_time": 80.016113 }, "output": "https://replicate.delivery/pbxt/zwHbyMZQby5TDhyewKnOcIoVg6ZisivUxvJiL6DlMXrX6MSJA/output.png", "started_at": "2024-03-27T09:47:09.531303Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hw6si63bv7m3amueh2qhvfcsou", "cancel": "https://api.replicate.com/v1/predictions/hw6si63bv7m3amueh2qhvfcsou/cancel" }, "version": "18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2" }
Generated inYour input images far exceed the default resolution of the underlying diffusion model. The output images may contain severe artifacts! Consider down-sampling the input using the `height` and `width` parameters 0%| | 0/50 [00:00<?, ?it/s] 2%|β | 1/50 [00:00<00:08, 5.82it/s] 6%|β | 3/50 [00:00<00:04, 9.75it/s] 10%|β | 5/50 [00:00<00:04, 10.71it/s] 14%|ββ | 7/50 [00:00<00:03, 11.19it/s] 18%|ββ | 9/50 [00:00<00:03, 11.51it/s] 22%|βββ | 11/50 [00:00<00:03, 11.67it/s] 26%|βββ | 13/50 [00:01<00:03, 11.75it/s] 30%|βββ | 15/50 [00:01<00:02, 11.73it/s] 34%|ββββ | 17/50 [00:01<00:02, 11.86it/s] 38%|ββββ | 19/50 [00:01<00:02, 11.91it/s] 42%|βββββ | 21/50 [00:01<00:02, 11.90it/s] 46%|βββββ | 23/50 [00:02<00:02, 11.92it/s] 50%|βββββ | 25/50 [00:02<00:02, 11.94it/s] 54%|ββββββ | 27/50 [00:02<00:01, 11.95it/s] 58%|ββββββ | 29/50 [00:02<00:01, 11.89it/s] 62%|βββββββ | 31/50 [00:02<00:01, 11.91it/s] 66%|βββββββ | 33/50 [00:02<00:01, 11.94it/s] 70%|βββββββ | 35/50 [00:03<00:01, 11.96it/s] 74%|ββββββββ | 37/50 [00:03<00:01, 11.96it/s] 78%|ββββββββ | 39/50 [00:03<00:00, 11.96it/s] 82%|βββββββββ | 41/50 [00:03<00:00, 11.95it/s] 86%|βββββββββ | 43/50 [00:03<00:00, 11.93it/s] 90%|βββββββββ | 45/50 [00:03<00:00, 11.92it/s] 94%|ββββββββββ| 47/50 [00:04<00:00, 11.95it/s] 98%|ββββββββββ| 49/50 [00:04<00:00, 11.96it/s] 100%|ββββββββββ| 50/50 [00:04<00:00, 11.68it/s] 0%| | 0/50 [00:00<?, ?it/s] 2%|β | 1/50 [00:00<00:13, 3.65it/s] 4%|β | 2/50 [00:00<00:11, 4.24it/s] 6%|β | 3/50 [00:00<00:10, 4.47it/s] 8%|β | 4/50 [00:00<00:10, 4.58it/s] 10%|β | 5/50 [00:01<00:09, 4.65it/s] 12%|ββ | 6/50 [00:01<00:09, 4.69it/s] 14%|ββ | 7/50 [00:01<00:09, 4.71it/s] 16%|ββ | 8/50 [00:01<00:08, 4.73it/s] 18%|ββ | 9/50 [00:01<00:08, 4.74it/s] 20%|ββ | 10/50 [00:02<00:08, 4.74it/s] 22%|βββ | 11/50 [00:02<00:08, 4.74it/s] 24%|βββ | 12/50 [00:02<00:08, 4.75it/s] 26%|βββ | 13/50 [00:02<00:07, 4.75it/s] 28%|βββ | 14/50 [00:03<00:07, 4.75it/s] 30%|βββ | 15/50 [00:03<00:07, 4.75it/s] 32%|ββββ | 16/50 [00:03<00:07, 4.75it/s] 34%|ββββ | 17/50 [00:03<00:06, 4.76it/s] 36%|ββββ | 18/50 [00:03<00:06, 4.76it/s] 38%|ββββ | 19/50 [00:04<00:06, 4.76it/s] 40%|ββββ | 20/50 [00:04<00:06, 4.76it/s] 42%|βββββ | 21/50 [00:04<00:06, 4.75it/s] 44%|βββββ | 22/50 [00:04<00:05, 4.76it/s] 46%|βββββ | 23/50 [00:04<00:05, 4.76it/s] 48%|βββββ | 24/50 [00:05<00:05, 4.76it/s] 50%|βββββ | 25/50 [00:05<00:05, 4.75it/s] 52%|ββββββ | 26/50 [00:05<00:05, 4.75it/s] 54%|ββββββ | 27/50 [00:05<00:04, 4.75it/s] 56%|ββββββ | 28/50 [00:05<00:04, 4.75it/s] 58%|ββββββ | 29/50 [00:06<00:04, 4.75it/s] 60%|ββββββ | 30/50 [00:06<00:04, 4.75it/s] 62%|βββββββ | 31/50 [00:06<00:03, 4.75it/s] 64%|βββββββ | 32/50 [00:06<00:03, 4.75it/s] 66%|βββββββ | 33/50 [00:07<00:03, 4.75it/s] 68%|βββββββ | 34/50 [00:07<00:03, 4.75it/s] 70%|βββββββ | 35/50 [00:07<00:03, 4.69it/s] 72%|ββββββββ | 36/50 [00:07<00:02, 4.71it/s] 74%|ββββββββ | 37/50 [00:07<00:02, 4.72it/s] 76%|ββββββββ | 38/50 [00:08<00:02, 4.73it/s] 78%|ββββββββ | 39/50 [00:08<00:02, 4.73it/s] 80%|ββββββββ | 40/50 [00:08<00:02, 4.74it/s] 82%|βββββββββ | 41/50 [00:08<00:01, 4.74it/s] 84%|βββββββββ | 42/50 [00:08<00:01, 4.71it/s] 86%|βββββββββ | 43/50 [00:09<00:01, 4.72it/s] 88%|βββββββββ | 44/50 [00:09<00:01, 4.72it/s] 90%|βββββββββ | 45/50 [00:09<00:01, 4.73it/s] 92%|ββββββββββ| 46/50 [00:09<00:00, 4.73it/s] 94%|ββββββββββ| 47/50 [00:09<00:00, 4.73it/s] 96%|ββββββββββ| 48/50 [00:10<00:00, 4.73it/s] 98%|ββββββββββ| 49/50 [00:10<00:00, 4.73it/s] 100%|ββββββββββ| 50/50 [00:10<00:00, 4.73it/s] 100%|ββββββββββ| 50/50 [00:10<00:00, 4.72it/s]
Prediction
adirik/leditsplusplus:ea5b4a96IDiots6ptb7k45tak4fwusfgatlaStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- skip
- 0.3
- edit_threshold
- 0.75
- editing_prompts
- glasses
- edit_warmup_steps
- 8
- edit_guidance_scale
- 3.0
- num_inversion_steps
- 50
- source_guidance_scale
- 3.5
- reverse_editing_directions
- False
{ "skip": 0.3, "image": "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", "edit_threshold": "0.75", "editing_prompts": "glasses", "edit_warmup_steps": 8, "edit_guidance_scale": "3.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "False" }
Install Replicateβs Node.js client library:npm install replicate
Set theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run adirik/leditsplusplus using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/leditsplusplus:ea5b4a96d43c51d9b5a579177b9044e631fbb4dbfaae51367a226a65663dfebe", { input: { skip: 0.3, image: "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", edit_threshold: "0.75", editing_prompts: "glasses", edit_warmup_steps: 8, edit_guidance_scale: "3.0", num_inversion_steps: 50, source_guidance_scale: 3.5, reverse_editing_directions: "False" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicateβs Python client library:pip install replicate
Set theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the client:import replicate
Run adirik/leditsplusplus using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/leditsplusplus:ea5b4a96d43c51d9b5a579177b9044e631fbb4dbfaae51367a226a65663dfebe", input={ "skip": 0.3, "image": "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", "edit_threshold": "0.75", "editing_prompts": "glasses", "edit_warmup_steps": 8, "edit_guidance_scale": "3.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "False" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run adirik/leditsplusplus using Replicateβs 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": "ea5b4a96d43c51d9b5a579177b9044e631fbb4dbfaae51367a226a65663dfebe", "input": { "skip": 0.3, "image": "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", "edit_threshold": "0.75", "editing_prompts": "glasses", "edit_warmup_steps": 8, "edit_guidance_scale": "3.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "False" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicateβs HTTP API reference docs.
Output
{ "completed_at": "2024-03-27T09:58:44.144141Z", "created_at": "2024-03-27T09:58:32.337830Z", "data_removed": false, "error": null, "id": "iots6ptb7k45tak4fwusfgatla", "input": { "skip": 0.3, "image": "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", "edit_threshold": "0.75", "editing_prompts": "glasses", "edit_warmup_steps": 8, "edit_guidance_scale": "3.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "False" }, "logs": "Your input images far exceed the default resolution of the underlying diffusion model. The output images may contain severe artifacts! Consider down-sampling the input using the `height` and `width` parameters\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|β | 2/50 [00:00<00:03, 13.15it/s]\n 8%|β | 4/50 [00:00<00:03, 12.97it/s]\n 12%|ββ | 6/50 [00:00<00:03, 12.77it/s]\n 16%|ββ | 8/50 [00:00<00:03, 12.73it/s]\n 20%|ββ | 10/50 [00:00<00:03, 12.76it/s]\n 24%|βββ | 12/50 [00:00<00:02, 12.75it/s]\n 28%|βββ | 14/50 [00:01<00:02, 12.78it/s]\n 32%|ββββ | 16/50 [00:01<00:02, 12.85it/s]\n 36%|ββββ | 18/50 [00:01<00:02, 12.82it/s]\n 40%|ββββ | 20/50 [00:01<00:02, 12.79it/s]\n 44%|βββββ | 22/50 [00:01<00:02, 12.79it/s]\n 48%|βββββ | 24/50 [00:01<00:02, 12.70it/s]\n 52%|ββββββ | 26/50 [00:02<00:01, 12.67it/s]\n 56%|ββββββ | 28/50 [00:02<00:01, 12.70it/s]\n 60%|ββββββ | 30/50 [00:02<00:01, 12.70it/s]\n 64%|βββββββ | 32/50 [00:02<00:01, 12.63it/s]\n 68%|βββββββ | 34/50 [00:02<00:01, 12.60it/s]\n 72%|ββββββββ | 36/50 [00:02<00:01, 12.62it/s]\n 76%|ββββββββ | 38/50 [00:02<00:00, 12.60it/s]\n 80%|ββββββββ | 40/50 [00:03<00:00, 12.63it/s]\n 84%|βββββββββ | 42/50 [00:03<00:00, 12.67it/s]\n 88%|βββββββββ | 44/50 [00:03<00:00, 12.63it/s]\n 92%|ββββββββββ| 46/50 [00:03<00:00, 12.60it/s]\n 96%|ββββββββββ| 48/50 [00:03<00:00, 12.56it/s]\n100%|ββββββββββ| 50/50 [00:03<00:00, 12.36it/s]\n100%|ββββββββββ| 50/50 [00:03<00:00, 12.66it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|β | 1/50 [00:00<00:06, 7.98it/s]\n 4%|β | 2/50 [00:00<00:06, 7.96it/s]\n 6%|β | 3/50 [00:00<00:05, 7.95it/s]\n 8%|β | 4/50 [00:00<00:05, 7.95it/s]\n 10%|β | 5/50 [00:00<00:05, 7.95it/s]\n 12%|ββ | 6/50 [00:00<00:05, 7.93it/s]\n 14%|ββ | 7/50 [00:00<00:05, 7.94it/s]\n 16%|ββ | 8/50 [00:01<00:05, 7.94it/s]\n 18%|ββ | 9/50 [00:01<00:05, 7.93it/s]\n 20%|ββ | 10/50 [00:01<00:05, 7.93it/s]\n 22%|βββ | 11/50 [00:01<00:04, 7.93it/s]\n 24%|βββ | 12/50 [00:01<00:04, 7.93it/s]\n 26%|βββ | 13/50 [00:01<00:04, 7.93it/s]\n 28%|βββ | 14/50 [00:01<00:04, 7.74it/s]\n 30%|βββ | 15/50 [00:01<00:04, 7.80it/s]\n 32%|ββββ | 16/50 [00:02<00:04, 7.84it/s]\n 34%|ββββ | 17/50 [00:02<00:04, 7.87it/s]\n 36%|ββββ | 18/50 [00:02<00:04, 7.89it/s]\n 38%|ββββ | 19/50 [00:02<00:03, 7.90it/s]\n 40%|ββββ | 20/50 [00:02<00:03, 7.91it/s]\n 42%|βββββ | 21/50 [00:02<00:03, 7.92it/s]\n 44%|βββββ | 22/50 [00:02<00:03, 7.92it/s]\n 46%|βββββ | 23/50 [00:02<00:03, 7.92it/s]\n 48%|βββββ | 24/50 [00:03<00:03, 7.93it/s]\n 50%|βββββ | 25/50 [00:03<00:03, 7.93it/s]\n 52%|ββββββ | 26/50 [00:03<00:03, 7.93it/s]\n 54%|ββββββ | 27/50 [00:03<00:02, 7.92it/s]\n 56%|ββββββ | 28/50 [00:03<00:02, 7.93it/s]\n 58%|ββββββ | 29/50 [00:03<00:02, 7.91it/s]\n 60%|ββββββ | 30/50 [00:03<00:02, 7.91it/s]\n 62%|βββββββ | 31/50 [00:03<00:02, 7.91it/s]\n 64%|βββββββ | 32/50 [00:04<00:02, 7.92it/s]\n 66%|βββββββ | 33/50 [00:04<00:02, 7.92it/s]\n 68%|βββββββ | 34/50 [00:04<00:02, 7.92it/s]\n 70%|βββββββ | 35/50 [00:04<00:01, 7.92it/s]\n 72%|ββββββββ | 36/50 [00:04<00:01, 7.92it/s]\n 74%|ββββββββ | 37/50 [00:04<00:01, 7.76it/s]\n 76%|ββββββββ | 38/50 [00:04<00:01, 7.80it/s]\n 78%|ββββββββ | 39/50 [00:04<00:01, 7.84it/s]\n 80%|ββββββββ | 40/50 [00:05<00:01, 7.87it/s]\n 82%|βββββββββ | 41/50 [00:05<00:01, 7.87it/s]\n 84%|βββββββββ | 42/50 [00:05<00:01, 7.89it/s]\n 86%|βββββββββ | 43/50 [00:05<00:00, 7.90it/s]\n 88%|βββββββββ | 44/50 [00:05<00:00, 7.92it/s]\n 90%|βββββββββ | 45/50 [00:05<00:00, 7.92it/s]\n 92%|ββββββββββ| 46/50 [00:05<00:00, 7.92it/s]\n 94%|ββββββββββ| 47/50 [00:05<00:00, 7.92it/s]\n 96%|ββββββββββ| 48/50 [00:06<00:00, 7.92it/s]\n 98%|ββββββββββ| 49/50 [00:06<00:00, 7.93it/s]\n100%|ββββββββββ| 50/50 [00:06<00:00, 7.93it/s]\n100%|ββββββββββ| 50/50 [00:06<00:00, 7.90it/s]", "metrics": { "predict_time": 11.797109, "total_time": 11.806311 }, "output": "https://replicate.delivery/pbxt/cyvUwrQY1KLzMJEODz13q4fcg0NpAcqAbeRGrPStsvPTfzIlA/output.png", "started_at": "2024-03-27T09:58:32.347032Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iots6ptb7k45tak4fwusfgatla", "cancel": "https://api.replicate.com/v1/predictions/iots6ptb7k45tak4fwusfgatla/cancel" }, "version": "18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2" }
Generated inYour input images far exceed the default resolution of the underlying diffusion model. The output images may contain severe artifacts! Consider down-sampling the input using the `height` and `width` parameters 0%| | 0/50 [00:00<?, ?it/s] 4%|β | 2/50 [00:00<00:03, 13.15it/s] 8%|β | 4/50 [00:00<00:03, 12.97it/s] 12%|ββ | 6/50 [00:00<00:03, 12.77it/s] 16%|ββ | 8/50 [00:00<00:03, 12.73it/s] 20%|ββ | 10/50 [00:00<00:03, 12.76it/s] 24%|βββ | 12/50 [00:00<00:02, 12.75it/s] 28%|βββ | 14/50 [00:01<00:02, 12.78it/s] 32%|ββββ | 16/50 [00:01<00:02, 12.85it/s] 36%|ββββ | 18/50 [00:01<00:02, 12.82it/s] 40%|ββββ | 20/50 [00:01<00:02, 12.79it/s] 44%|βββββ | 22/50 [00:01<00:02, 12.79it/s] 48%|βββββ | 24/50 [00:01<00:02, 12.70it/s] 52%|ββββββ | 26/50 [00:02<00:01, 12.67it/s] 56%|ββββββ | 28/50 [00:02<00:01, 12.70it/s] 60%|ββββββ | 30/50 [00:02<00:01, 12.70it/s] 64%|βββββββ | 32/50 [00:02<00:01, 12.63it/s] 68%|βββββββ | 34/50 [00:02<00:01, 12.60it/s] 72%|ββββββββ | 36/50 [00:02<00:01, 12.62it/s] 76%|ββββββββ | 38/50 [00:02<00:00, 12.60it/s] 80%|ββββββββ | 40/50 [00:03<00:00, 12.63it/s] 84%|βββββββββ | 42/50 [00:03<00:00, 12.67it/s] 88%|βββββββββ | 44/50 [00:03<00:00, 12.63it/s] 92%|ββββββββββ| 46/50 [00:03<00:00, 12.60it/s] 96%|ββββββββββ| 48/50 [00:03<00:00, 12.56it/s] 100%|ββββββββββ| 50/50 [00:03<00:00, 12.36it/s] 100%|ββββββββββ| 50/50 [00:03<00:00, 12.66it/s] 0%| | 0/50 [00:00<?, ?it/s] 2%|β | 1/50 [00:00<00:06, 7.98it/s] 4%|β | 2/50 [00:00<00:06, 7.96it/s] 6%|β | 3/50 [00:00<00:05, 7.95it/s] 8%|β | 4/50 [00:00<00:05, 7.95it/s] 10%|β | 5/50 [00:00<00:05, 7.95it/s] 12%|ββ | 6/50 [00:00<00:05, 7.93it/s] 14%|ββ | 7/50 [00:00<00:05, 7.94it/s] 16%|ββ | 8/50 [00:01<00:05, 7.94it/s] 18%|ββ | 9/50 [00:01<00:05, 7.93it/s] 20%|ββ | 10/50 [00:01<00:05, 7.93it/s] 22%|βββ | 11/50 [00:01<00:04, 7.93it/s] 24%|βββ | 12/50 [00:01<00:04, 7.93it/s] 26%|βββ | 13/50 [00:01<00:04, 7.93it/s] 28%|βββ | 14/50 [00:01<00:04, 7.74it/s] 30%|βββ | 15/50 [00:01<00:04, 7.80it/s] 32%|ββββ | 16/50 [00:02<00:04, 7.84it/s] 34%|ββββ | 17/50 [00:02<00:04, 7.87it/s] 36%|ββββ | 18/50 [00:02<00:04, 7.89it/s] 38%|ββββ | 19/50 [00:02<00:03, 7.90it/s] 40%|ββββ | 20/50 [00:02<00:03, 7.91it/s] 42%|βββββ | 21/50 [00:02<00:03, 7.92it/s] 44%|βββββ | 22/50 [00:02<00:03, 7.92it/s] 46%|βββββ | 23/50 [00:02<00:03, 7.92it/s] 48%|βββββ | 24/50 [00:03<00:03, 7.93it/s] 50%|βββββ | 25/50 [00:03<00:03, 7.93it/s] 52%|ββββββ | 26/50 [00:03<00:03, 7.93it/s] 54%|ββββββ | 27/50 [00:03<00:02, 7.92it/s] 56%|ββββββ | 28/50 [00:03<00:02, 7.93it/s] 58%|ββββββ | 29/50 [00:03<00:02, 7.91it/s] 60%|ββββββ | 30/50 [00:03<00:02, 7.91it/s] 62%|βββββββ | 31/50 [00:03<00:02, 7.91it/s] 64%|βββββββ | 32/50 [00:04<00:02, 7.92it/s] 66%|βββββββ | 33/50 [00:04<00:02, 7.92it/s] 68%|βββββββ | 34/50 [00:04<00:02, 7.92it/s] 70%|βββββββ | 35/50 [00:04<00:01, 7.92it/s] 72%|ββββββββ | 36/50 [00:04<00:01, 7.92it/s] 74%|ββββββββ | 37/50 [00:04<00:01, 7.76it/s] 76%|ββββββββ | 38/50 [00:04<00:01, 7.80it/s] 78%|ββββββββ | 39/50 [00:04<00:01, 7.84it/s] 80%|ββββββββ | 40/50 [00:05<00:01, 7.87it/s] 82%|βββββββββ | 41/50 [00:05<00:01, 7.87it/s] 84%|βββββββββ | 42/50 [00:05<00:01, 7.89it/s] 86%|βββββββββ | 43/50 [00:05<00:00, 7.90it/s] 88%|βββββββββ | 44/50 [00:05<00:00, 7.92it/s] 90%|βββββββββ | 45/50 [00:05<00:00, 7.92it/s] 92%|ββββββββββ| 46/50 [00:05<00:00, 7.92it/s] 94%|ββββββββββ| 47/50 [00:05<00:00, 7.92it/s] 96%|ββββββββββ| 48/50 [00:06<00:00, 7.92it/s] 98%|ββββββββββ| 49/50 [00:06<00:00, 7.93it/s] 100%|ββββββββββ| 50/50 [00:06<00:00, 7.93it/s] 100%|ββββββββββ| 50/50 [00:06<00:00, 7.90it/s]
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