omerbt
/
multidiffusion
Fusing Diffusion Paths for Controlled Image Generation
Prediction
omerbt/multidiffusion:fd8e12cbIDiwwaat27dnam3jag2kq4ofao6iStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 4096
- height
- 512
- prompt
- a photo of the dolomites
- scheduler
- K_EULER
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- 20
{ "width": 4096, "height": 512, "prompt": "a photo of the dolomites", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 }
Install Replicate’s 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 omerbt/multidiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "omerbt/multidiffusion:fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387", { input: { width: 4096, height: 512, prompt: "a photo of the dolomites", scheduler: "K_EULER", num_outputs: 1, guidance_scale: 7.5, num_inference_steps: 20 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s 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 omerbt/multidiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "omerbt/multidiffusion:fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387", input={ "width": 4096, "height": 512, "prompt": "a photo of the dolomites", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 } ) 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 omerbt/multidiffusion 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": "fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387", "input": { "width": 4096, "height": 512, "prompt": "a photo of the dolomites", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/omerbt/multidiffusion@sha256:fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387 \ -i 'width=4096' \ -i 'height=512' \ -i 'prompt="a photo of the dolomites"' \ -i 'scheduler="K_EULER"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'num_inference_steps=20'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/omerbt/multidiffusion@sha256:fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 4096, "height": 512, "prompt": "a photo of the dolomites", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-24T22:54:34.259451Z", "created_at": "2023-02-24T22:49:16.619509Z", "data_removed": false, "error": null, "id": "iwwaat27dnam3jag2kq4ofao6i", "input": { "width": 4096, "height": 512, "prompt": "a photo of the dolomites", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 }, "logs": "Using seed: 11915\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:10<03:21, 10.59s/it]\n 10%|█ | 2/20 [00:19<02:51, 9.54s/it]\n 15%|█▌ | 3/20 [00:28<02:37, 9.24s/it]\n 20%|██ | 4/20 [00:37<02:26, 9.13s/it]\n 25%|██▌ | 5/20 [00:46<02:16, 9.09s/it]\n 30%|███ | 6/20 [00:55<02:07, 9.09s/it]\n 35%|███▌ | 7/20 [01:04<01:58, 9.12s/it]\n 40%|████ | 8/20 [01:13<01:49, 9.16s/it]\n 45%|████▌ | 9/20 [01:23<01:41, 9.19s/it]\n 50%|█████ | 10/20 [01:32<01:32, 9.23s/it]\n 55%|█████▌ | 11/20 [01:41<01:23, 9.29s/it]\n 60%|██████ | 12/20 [01:51<01:14, 9.36s/it]\n 65%|██████▌ | 13/20 [02:00<01:05, 9.39s/it]\n 70%|███████ | 14/20 [02:10<00:56, 9.39s/it]\n 75%|███████▌ | 15/20 [02:19<00:46, 9.38s/it]\n 80%|████████ | 16/20 [02:28<00:37, 9.36s/it]\n 85%|████████▌ | 17/20 [02:38<00:28, 9.34s/it]\n 90%|█████████ | 18/20 [02:47<00:18, 9.33s/it]\n 95%|█████████▌| 19/20 [02:56<00:09, 9.34s/it]\n100%|██████████| 20/20 [03:06<00:00, 9.35s/it]\n100%|██████████| 20/20 [03:06<00:00, 9.31s/it]", "metrics": { "predict_time": 191.736824, "total_time": 317.639942 }, "output": [ "https://replicate.delivery/pbxt/5yWjQbNu3paaFZEUdR9eBvJCg3gtWgDheTjSNvM9HYmpIvhQA/out-0.png" ], "started_at": "2023-02-24T22:51:22.522627Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iwwaat27dnam3jag2kq4ofao6i", "cancel": "https://api.replicate.com/v1/predictions/iwwaat27dnam3jag2kq4ofao6i/cancel" }, "version": "963ffeaa7d992479bbbf6ad5b07f2ebbd51453f5214de98c87b32b97b44f5863" }
Generated inUsing seed: 11915 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:10<03:21, 10.59s/it] 10%|█ | 2/20 [00:19<02:51, 9.54s/it] 15%|█▌ | 3/20 [00:28<02:37, 9.24s/it] 20%|██ | 4/20 [00:37<02:26, 9.13s/it] 25%|██▌ | 5/20 [00:46<02:16, 9.09s/it] 30%|███ | 6/20 [00:55<02:07, 9.09s/it] 35%|███▌ | 7/20 [01:04<01:58, 9.12s/it] 40%|████ | 8/20 [01:13<01:49, 9.16s/it] 45%|████▌ | 9/20 [01:23<01:41, 9.19s/it] 50%|█████ | 10/20 [01:32<01:32, 9.23s/it] 55%|█████▌ | 11/20 [01:41<01:23, 9.29s/it] 60%|██████ | 12/20 [01:51<01:14, 9.36s/it] 65%|██████▌ | 13/20 [02:00<01:05, 9.39s/it] 70%|███████ | 14/20 [02:10<00:56, 9.39s/it] 75%|███████▌ | 15/20 [02:19<00:46, 9.38s/it] 80%|████████ | 16/20 [02:28<00:37, 9.36s/it] 85%|████████▌ | 17/20 [02:38<00:28, 9.34s/it] 90%|█████████ | 18/20 [02:47<00:18, 9.33s/it] 95%|█████████▌| 19/20 [02:56<00:09, 9.34s/it] 100%|██████████| 20/20 [03:06<00:00, 9.35s/it] 100%|██████████| 20/20 [03:06<00:00, 9.31s/it]
Prediction
omerbt/multidiffusion:fd8e12cbIDqyiwfho7n5aafjr7oni3zgalwiStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 4096
- height
- 512
- prompt
- a photo of a city skyline at night
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- 20
{ "width": 4096, "height": 512, "prompt": "a photo of a city skyline at night", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 }
Install Replicate’s 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 omerbt/multidiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "omerbt/multidiffusion:fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387", { input: { width: 4096, height: 512, prompt: "a photo of a city skyline at night", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, num_inference_steps: 20 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s 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 omerbt/multidiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "omerbt/multidiffusion:fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387", input={ "width": 4096, "height": 512, "prompt": "a photo of a city skyline at night", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 } ) 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 omerbt/multidiffusion 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": "fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387", "input": { "width": 4096, "height": 512, "prompt": "a photo of a city skyline at night", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/omerbt/multidiffusion@sha256:fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387 \ -i 'width=4096' \ -i 'height=512' \ -i 'prompt="a photo of a city skyline at night"' \ -i 'scheduler="DDIM"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'num_inference_steps=20'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/omerbt/multidiffusion@sha256:fd8e12cbfb7cf355d9d80afef487a34bf58b916bedc5bff5f7a8273fe72c1387
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 4096, "height": 512, "prompt": "a photo of a city skyline at night", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-24T23:06:53.309387Z", "created_at": "2023-02-24T23:05:44.172018Z", "data_removed": false, "error": null, "id": "qyiwfho7n5aafjr7oni3zgalwi", "input": { "width": 4096, "height": 512, "prompt": "a photo of a city skyline at night", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 20 }, "logs": "Using seed: 57162\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:03<01:03, 3.33s/it]\n 10%|█ | 2/20 [00:06<00:59, 3.30s/it]\n 15%|█▌ | 3/20 [00:09<00:55, 3.28s/it]\n 20%|██ | 4/20 [00:13<00:53, 3.35s/it]\n 25%|██▌ | 5/20 [00:16<00:49, 3.30s/it]\n 30%|███ | 6/20 [00:19<00:44, 3.21s/it]\n 35%|███▌ | 7/20 [00:22<00:41, 3.17s/it]\n 40%|████ | 8/20 [00:25<00:37, 3.14s/it]\n 45%|████▌ | 9/20 [00:28<00:34, 3.14s/it]\n 50%|█████ | 10/20 [00:32<00:32, 3.27s/it]\n 55%|█████▌ | 11/20 [00:35<00:30, 3.35s/it]\n 60%|██████ | 12/20 [00:39<00:27, 3.41s/it]\n 65%|██████▌ | 13/20 [00:43<00:24, 3.44s/it]\n 70%|███████ | 14/20 [00:46<00:20, 3.47s/it]\n 75%|███████▌ | 15/20 [00:50<00:17, 3.48s/it]\n 80%|████████ | 16/20 [00:53<00:13, 3.43s/it]\n 85%|████████▌ | 17/20 [00:56<00:10, 3.43s/it]\n 90%|█████████ | 18/20 [01:00<00:06, 3.42s/it]\n 95%|█████████▌| 19/20 [01:03<00:03, 3.43s/it]\n100%|██████████| 20/20 [01:07<00:00, 3.41s/it]\n100%|██████████| 20/20 [01:07<00:00, 3.35s/it]", "metrics": { "predict_time": 69.066816, "total_time": 69.137369 }, "output": [ "https://replicate.delivery/pbxt/BmhVHMk1Lx5iDh7Ak0Y3EjmP6OeFotuDtOwbFw8pjf0MUvhQA/out-0.png" ], "started_at": "2023-02-24T23:05:44.242571Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qyiwfho7n5aafjr7oni3zgalwi", "cancel": "https://api.replicate.com/v1/predictions/qyiwfho7n5aafjr7oni3zgalwi/cancel" }, "version": "24dd8961771359f16dfd1238566483fbfa2a90092ed9eeac0337041f45387aca" }
Generated inUsing seed: 57162 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:03<01:03, 3.33s/it] 10%|█ | 2/20 [00:06<00:59, 3.30s/it] 15%|█▌ | 3/20 [00:09<00:55, 3.28s/it] 20%|██ | 4/20 [00:13<00:53, 3.35s/it] 25%|██▌ | 5/20 [00:16<00:49, 3.30s/it] 30%|███ | 6/20 [00:19<00:44, 3.21s/it] 35%|███▌ | 7/20 [00:22<00:41, 3.17s/it] 40%|████ | 8/20 [00:25<00:37, 3.14s/it] 45%|████▌ | 9/20 [00:28<00:34, 3.14s/it] 50%|█████ | 10/20 [00:32<00:32, 3.27s/it] 55%|█████▌ | 11/20 [00:35<00:30, 3.35s/it] 60%|██████ | 12/20 [00:39<00:27, 3.41s/it] 65%|██████▌ | 13/20 [00:43<00:24, 3.44s/it] 70%|███████ | 14/20 [00:46<00:20, 3.47s/it] 75%|███████▌ | 15/20 [00:50<00:17, 3.48s/it] 80%|████████ | 16/20 [00:53<00:13, 3.43s/it] 85%|████████▌ | 17/20 [00:56<00:10, 3.43s/it] 90%|█████████ | 18/20 [01:00<00:06, 3.42s/it] 95%|█████████▌| 19/20 [01:03<00:03, 3.43s/it] 100%|██████████| 20/20 [01:07<00:00, 3.41s/it] 100%|██████████| 20/20 [01:07<00:00, 3.35s/it]
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