22-hours
/
vintedois-diffusion
Generate beautiful images with simple prompts
- Public
- 247.2K runs
-
T4
Prediction
22-hours/vintedois-diffusion:28cea91bInput
- seed
- 44
- width
- 640
- height
- 640
- prompt
- victorian city landscape
- scheduler
- K_EULER_ANCESTRAL
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 1
- num_inference_steps
- "50"
{ "seed": 44, "width": 640, "height": 640, "prompt": "victorian city landscape", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "50" }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 22-hours/vintedois-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "22-hours/vintedois-diffusion:28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd", { input: { seed: 44, width: 640, height: 640, prompt: "victorian city landscape", scheduler: "K_EULER_ANCESTRAL", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 1, num_inference_steps: "50" } } ); 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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun 22-hours/vintedois-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "22-hours/vintedois-diffusion:28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd", input={ "seed": 44, "width": 640, "height": 640, "prompt": "victorian city landscape", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "50" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run 22-hours/vintedois-diffusion 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": "28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd", "input": { "seed": 44, "width": 640, "height": 640, "prompt": "victorian city landscape", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "50" } }' \ https://api.replicate.com/v1/predictionsTo 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 cogIf 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/22-hours/vintedois-diffusion@sha256:28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd \ -i 'seed=44' \ -i 'width=640' \ -i 'height=640' \ -i 'prompt="victorian city landscape"' \ -i 'scheduler="K_EULER_ANCESTRAL"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=1' \ -i 'num_inference_steps="50"'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/22-hours/vintedois-diffusion@sha256:28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 44, "width": 640, "height": 640, "prompt": "victorian city landscape", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "50" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-08T16:52:47.560905Z", "created_at": "2023-02-08T16:51:57.049366Z", "data_removed": false, "error": null, "id": "7zm7kijennfzvjyqisdiag5x4m", "input": { "seed": 44, "width": 640, "height": 640, "prompt": "victorian city landscape", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "50" }, "logs": "Using seed: 44\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:48, 1.01it/s]\n 4%|▍ | 2/50 [00:01<00:46, 1.03it/s]\n 6%|▌ | 3/50 [00:02<00:45, 1.03it/s]\n 8%|▊ | 4/50 [00:03<00:44, 1.04it/s]\n 10%|█ | 5/50 [00:04<00:43, 1.04it/s]\n 12%|█▏ | 6/50 [00:05<00:42, 1.04it/s]\n 14%|█▍ | 7/50 [00:06<00:41, 1.04it/s]\n 16%|█▌ | 8/50 [00:07<00:40, 1.04it/s]\n 18%|█▊ | 9/50 [00:08<00:39, 1.04it/s]\n 20%|██ | 10/50 [00:09<00:38, 1.04it/s]\n 22%|██▏ | 11/50 [00:10<00:37, 1.04it/s]\n 24%|██▍ | 12/50 [00:11<00:36, 1.04it/s]\n 26%|██▌ | 13/50 [00:12<00:35, 1.04it/s]\n 28%|██▊ | 14/50 [00:13<00:34, 1.04it/s]\n 30%|███ | 15/50 [00:14<00:33, 1.03it/s]\n 32%|███▏ | 16/50 [00:15<00:32, 1.03it/s]\n 34%|███▍ | 17/50 [00:16<00:31, 1.03it/s]\n 36%|███▌ | 18/50 [00:17<00:31, 1.03it/s]\n 38%|███▊ | 19/50 [00:18<00:30, 1.03it/s]\n 40%|████ | 20/50 [00:19<00:29, 1.03it/s]\n 42%|████▏ | 21/50 [00:20<00:28, 1.03it/s]\n 44%|████▍ | 22/50 [00:21<00:27, 1.03it/s]\n 46%|████▌ | 23/50 [00:22<00:26, 1.03it/s]\n 48%|████▊ | 24/50 [00:23<00:25, 1.03it/s]\n 50%|█████ | 25/50 [00:24<00:24, 1.03it/s]\n 52%|█████▏ | 26/50 [00:25<00:23, 1.03it/s]\n 54%|█████▍ | 27/50 [00:26<00:22, 1.03it/s]\n 56%|█████▌ | 28/50 [00:27<00:21, 1.03it/s]\n 58%|█████▊ | 29/50 [00:28<00:20, 1.03it/s]\n 60%|██████ | 30/50 [00:29<00:19, 1.03it/s]\n 62%|██████▏ | 31/50 [00:30<00:18, 1.03it/s]\n 64%|██████▍ | 32/50 [00:31<00:17, 1.03it/s]\n 66%|██████▌ | 33/50 [00:31<00:16, 1.03it/s]\n 68%|██████▊ | 34/50 [00:32<00:15, 1.03it/s]\n 70%|███████ | 35/50 [00:33<00:14, 1.03it/s]\n 72%|███████▏ | 36/50 [00:34<00:13, 1.03it/s]\n 74%|███████▍ | 37/50 [00:35<00:12, 1.03it/s]\n 76%|███████▌ | 38/50 [00:36<00:11, 1.03it/s]\n 78%|███████▊ | 39/50 [00:37<00:10, 1.02it/s]\n 80%|████████ | 40/50 [00:38<00:09, 1.02it/s]\n 82%|████████▏ | 41/50 [00:39<00:08, 1.02it/s]\n 84%|████████▍ | 42/50 [00:40<00:07, 1.02it/s]\n 86%|████████▌ | 43/50 [00:41<00:06, 1.02it/s]\n 88%|████████▊ | 44/50 [00:42<00:05, 1.02it/s]\n 90%|█████████ | 45/50 [00:43<00:04, 1.02it/s]\n 92%|█████████▏| 46/50 [00:44<00:03, 1.02it/s]\n 94%|█████████▍| 47/50 [00:45<00:02, 1.02it/s]\n 96%|█████████▌| 48/50 [00:46<00:01, 1.02it/s]\n 98%|█████████▊| 49/50 [00:47<00:00, 1.02it/s]\n100%|██████████| 50/50 [00:48<00:00, 1.02it/s]\n100%|██████████| 50/50 [00:48<00:00, 1.03it/s]", "metrics": { "predict_time": 50.451546, "total_time": 50.511539 }, "output": [ "https://replicate.delivery/pbxt/j0ALfNdwwfvXG0wLdr69LlpW8TnbuH9M11xZMMWu4GGeqw4gA/out-0.png" ], "started_at": "2023-02-08T16:51:57.109359Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7zm7kijennfzvjyqisdiag5x4m", "cancel": "https://api.replicate.com/v1/predictions/7zm7kijennfzvjyqisdiag5x4m/cancel" }, "version": "28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd" }Generated inUsing seed: 44 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:48, 1.01it/s] 4%|▍ | 2/50 [00:01<00:46, 1.03it/s] 6%|▌ | 3/50 [00:02<00:45, 1.03it/s] 8%|▊ | 4/50 [00:03<00:44, 1.04it/s] 10%|█ | 5/50 [00:04<00:43, 1.04it/s] 12%|█▏ | 6/50 [00:05<00:42, 1.04it/s] 14%|█▍ | 7/50 [00:06<00:41, 1.04it/s] 16%|█▌ | 8/50 [00:07<00:40, 1.04it/s] 18%|█▊ | 9/50 [00:08<00:39, 1.04it/s] 20%|██ | 10/50 [00:09<00:38, 1.04it/s] 22%|██▏ | 11/50 [00:10<00:37, 1.04it/s] 24%|██▍ | 12/50 [00:11<00:36, 1.04it/s] 26%|██▌ | 13/50 [00:12<00:35, 1.04it/s] 28%|██▊ | 14/50 [00:13<00:34, 1.04it/s] 30%|███ | 15/50 [00:14<00:33, 1.03it/s] 32%|███▏ | 16/50 [00:15<00:32, 1.03it/s] 34%|███▍ | 17/50 [00:16<00:31, 1.03it/s] 36%|███▌ | 18/50 [00:17<00:31, 1.03it/s] 38%|███▊ | 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22-hours/vintedois-diffusion:28cea91bInput
- seed
- 44
- width
- 640
- height
- 640
- prompt
- photo of an old man in a jungle, looking at the camera
- scheduler
- K_EULER_ANCESTRAL
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 1
- num_inference_steps
- "30"
{ "seed": 44, "width": 640, "height": 640, "prompt": "photo of an old man in a jungle, looking at the camera", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "30" }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 22-hours/vintedois-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "22-hours/vintedois-diffusion:28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd", { input: { seed: 44, width: 640, height: 640, prompt: "photo of an old man in a jungle, looking at the camera", scheduler: "K_EULER_ANCESTRAL", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 1, num_inference_steps: "30" } } ); 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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun 22-hours/vintedois-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "22-hours/vintedois-diffusion:28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd", input={ "seed": 44, "width": 640, "height": 640, "prompt": "photo of an old man in a jungle, looking at the camera", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "30" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run 22-hours/vintedois-diffusion 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": "28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd", "input": { "seed": 44, "width": 640, "height": 640, "prompt": "photo of an old man in a jungle, looking at the camera", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "30" } }' \ https://api.replicate.com/v1/predictionsTo 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 cogIf 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/22-hours/vintedois-diffusion@sha256:28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd \ -i 'seed=44' \ -i 'width=640' \ -i 'height=640' \ -i 'prompt="photo of an old man in a jungle, looking at the camera"' \ -i 'scheduler="K_EULER_ANCESTRAL"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=1' \ -i 'num_inference_steps="30"'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/22-hours/vintedois-diffusion@sha256:28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 44, "width": 640, "height": 640, "prompt": "photo of an old man in a jungle, looking at the camera", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "30" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-08T20:38:36.199682Z", "created_at": "2023-02-08T20:35:18.006974Z", "data_removed": false, "error": null, "id": "p4cfg5sk4re4fc7nzxy4d46duq", "input": { "seed": 44, "width": 640, "height": 640, "prompt": "photo of an old man in a jungle, looking at the camera", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 1, "num_inference_steps": "30" }, "logs": "Using seed: 44\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:02<01:05, 2.25s/it]\n 7%|▋ | 2/30 [00:03<00:41, 1.47s/it]\n 10%|█ | 3/30 [00:04<00:33, 1.22s/it]\n 13%|█▎ | 4/30 [00:05<00:28, 1.11s/it]\n 17%|█▋ | 5/30 [00:05<00:26, 1.04s/it]\n 20%|██ | 6/30 [00:06<00:24, 1.00s/it]\n 23%|██▎ | 7/30 [00:07<00:22, 1.02it/s]\n 27%|██▋ | 8/30 [00:08<00:21, 1.04it/s]\n 30%|███ | 9/30 [00:09<00:20, 1.05it/s]\n 33%|███▎ | 10/30 [00:10<00:18, 1.06it/s]\n 37%|███▋ | 11/30 [00:11<00:17, 1.06it/s]\n 40%|████ | 12/30 [00:12<00:16, 1.06it/s]\n 43%|████▎ | 13/30 [00:13<00:15, 1.07it/s]\n 47%|████▋ | 14/30 [00:14<00:14, 1.07it/s]\n 50%|█████ | 15/30 [00:15<00:14, 1.07it/s]\n 53%|█████▎ | 16/30 [00:16<00:13, 1.06it/s]\n 57%|█████▋ | 17/30 [00:17<00:12, 1.06it/s]\n 60%|██████ | 18/30 [00:18<00:11, 1.06it/s]\n 63%|██████▎ | 19/30 [00:19<00:10, 1.06it/s]\n 67%|██████▋ | 20/30 [00:19<00:09, 1.06it/s]\n 70%|███████ | 21/30 [00:20<00:08, 1.06it/s]\n 73%|███████▎ | 22/30 [00:21<00:07, 1.06it/s]\n 77%|███████▋ | 23/30 [00:22<00:06, 1.06it/s]\n 80%|████████ | 24/30 [00:23<00:05, 1.06it/s]\n 83%|████████▎ | 25/30 [00:24<00:04, 1.06it/s]\n 87%|████████▋ | 26/30 [00:25<00:03, 1.06it/s]\n 90%|█████████ | 27/30 [00:26<00:02, 1.06it/s]\n 93%|█████████▎| 28/30 [00:27<00:01, 1.06it/s]\n 97%|█████████▋| 29/30 [00:28<00:00, 1.06it/s]\n100%|██████████| 30/30 [00:29<00:00, 1.06it/s]\n100%|██████████| 30/30 [00:29<00:00, 1.02it/s]", "metrics": { "predict_time": 33.138677, "total_time": 198.192708 }, "output": [ "https://replicate.delivery/pbxt/VocmoncW6vafIqTpQnvGJfatWKRUrUggOEdujApt6JpLpbcQA/out-0.png" ], "started_at": "2023-02-08T20:38:03.061005Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/p4cfg5sk4re4fc7nzxy4d46duq", "cancel": "https://api.replicate.com/v1/predictions/p4cfg5sk4re4fc7nzxy4d46duq/cancel" }, "version": "28cea91bdfced0e2dc7fda466cc0a46501c0edc84905b2120ea02e0707b967fd" }Generated inUsing seed: 44 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:02<01:05, 2.25s/it] 7%|▋ | 2/30 [00:03<00:41, 1.47s/it] 10%|█ | 3/30 [00:04<00:33, 1.22s/it] 13%|█▎ | 4/30 [00:05<00:28, 1.11s/it] 17%|█▋ | 5/30 [00:05<00:26, 1.04s/it] 20%|██ | 6/30 [00:06<00:24, 1.00s/it] 23%|██▎ | 7/30 [00:07<00:22, 1.02it/s] 27%|██▋ | 8/30 [00:08<00:21, 1.04it/s] 30%|███ | 9/30 [00:09<00:20, 1.05it/s] 33%|███▎ | 10/30 [00:10<00:18, 1.06it/s] 37%|███▋ | 11/30 [00:11<00:17, 1.06it/s] 40%|████ | 12/30 [00:12<00:16, 1.06it/s] 43%|████▎ | 13/30 [00:13<00:15, 1.07it/s] 47%|████▋ | 14/30 [00:14<00:14, 1.07it/s] 50%|█████ | 15/30 [00:15<00:14, 1.07it/s] 53%|█████▎ | 16/30 [00:16<00:13, 1.06it/s] 57%|█████▋ | 17/30 [00:17<00:12, 1.06it/s] 60%|██████ | 18/30 [00:18<00:11, 1.06it/s] 63%|██████▎ | 19/30 [00:19<00:10, 1.06it/s] 67%|██████▋ | 20/30 [00:19<00:09, 1.06it/s] 70%|███████ | 21/30 [00:20<00:08, 1.06it/s] 73%|███████▎ | 22/30 [00:21<00:07, 1.06it/s] 77%|███████▋ | 23/30 [00:22<00:06, 1.06it/s] 80%|████████ | 24/30 [00:23<00:05, 1.06it/s] 83%|████████▎ | 25/30 [00:24<00:04, 1.06it/s] 87%|████████▋ | 26/30 [00:25<00:03, 1.06it/s] 90%|█████████ | 27/30 [00:26<00:02, 1.06it/s] 93%|█████████▎| 28/30 [00:27<00:01, 1.06it/s] 97%|█████████▋| 29/30 [00:28<00:00, 1.06it/s] 100%|██████████| 30/30 [00:29<00:00, 1.06it/s] 100%|██████████| 30/30 [00:29<00:00, 1.02it/s]
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