danjimenezm
/
food-gen-v1
- Public
- 314 runs
-
T4
Prediction
danjimenezm/food-gen-v1:235647b5ID7pibjm2cc5g6vbapbbi3c6gwjeStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- A professional food photo of a burger on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.9
- num_inference_steps
- 100
{ "image": "https://replicate.delivery/pbxt/IdRYiWvuJURc1mFMyoIZ9uoR0SLkApped1uRHSLuxOUTYg9s/burgertest2.jpeg", "width": 512, "height": 512, "prompt": "A professional food photo of a burger on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.9, "num_inference_steps": 100 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run danjimenezm/food-gen-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "danjimenezm/food-gen-v1:235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443", { input: { image: "https://replicate.delivery/pbxt/IdRYiWvuJURc1mFMyoIZ9uoR0SLkApped1uRHSLuxOUTYg9s/burgertest2.jpeg", width: 512, height: 512, prompt: "A professional food photo of a burger on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.9, num_inference_steps: 100 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run danjimenezm/food-gen-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "danjimenezm/food-gen-v1:235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443", input={ "image": "https://replicate.delivery/pbxt/IdRYiWvuJURc1mFMyoIZ9uoR0SLkApped1uRHSLuxOUTYg9s/burgertest2.jpeg", "width": 512, "height": 512, "prompt": "A professional food photo of a burger on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.9, "num_inference_steps": 100 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run danjimenezm/food-gen-v1 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": "235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443", "input": { "image": "https://replicate.delivery/pbxt/IdRYiWvuJURc1mFMyoIZ9uoR0SLkApped1uRHSLuxOUTYg9s/burgertest2.jpeg", "width": 512, "height": 512, "prompt": "A professional food photo of a burger on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.9, "num_inference_steps": 100 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-11T21:14:26.175184Z", "created_at": "2023-04-11T21:03:37.241885Z", "data_removed": false, "error": null, "id": "7pibjm2cc5g6vbapbbi3c6gwje", "input": { "image": "https://replicate.delivery/pbxt/IdRYiWvuJURc1mFMyoIZ9uoR0SLkApped1uRHSLuxOUTYg9s/burgertest2.jpeg", "width": 512, "height": 512, "prompt": "A professional food photo of a burger on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.9, "num_inference_steps": 100 }, "logs": "Using seed: 47569\n 0%| | 0/90 [00:00<?, ?it/s]\n 1%| | 1/90 [00:00<00:21, 4.22it/s]\n 2%|▏ | 2/90 [00:00<00:19, 4.54it/s]\n 3%|▎ | 3/90 [00:00<00:18, 4.65it/s]\n 4%|▍ | 4/90 [00:00<00:18, 4.71it/s]\n 6%|▌ | 5/90 [00:01<00:18, 4.71it/s]\n 7%|▋ | 6/90 [00:01<00:17, 4.71it/s]\n 8%|▊ | 7/90 [00:01<00:17, 4.73it/s]\n 9%|▉ | 8/90 [00:01<00:17, 4.75it/s]\n 10%|█ | 9/90 [00:01<00:17, 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"https://api.replicate.com/v1/predictions/7pibjm2cc5g6vbapbbi3c6gwje", "cancel": "https://api.replicate.com/v1/predictions/7pibjm2cc5g6vbapbbi3c6gwje/cancel" }, "version": "08bc07087eaabf26835dbb72e076fe1711fea2f451547f4ec0969473090831e9" }
Generated inUsing seed: 47569 0%| | 0/90 [00:00<?, ?it/s] 1%| | 1/90 [00:00<00:21, 4.22it/s] 2%|▏ | 2/90 [00:00<00:19, 4.54it/s] 3%|▎ | 3/90 [00:00<00:18, 4.65it/s] 4%|▍ | 4/90 [00:00<00:18, 4.71it/s] 6%|▌ | 5/90 [00:01<00:18, 4.71it/s] 7%|▋ | 6/90 [00:01<00:17, 4.71it/s] 8%|▊ | 7/90 [00:01<00:17, 4.73it/s] 9%|▉ | 8/90 [00:01<00:17, 4.75it/s] 10%|█ | 9/90 [00:01<00:17, 4.76it/s] 11%|█ | 10/90 [00:02<00:16, 4.75it/s] 12%|█▏ | 11/90 [00:02<00:16, 4.74it/s] 13%|█▎ | 12/90 [00:02<00:16, 4.75it/s] 14%|█▍ | 13/90 [00:02<00:16, 4.76it/s] 16%|█▌ | 14/90 [00:02<00:15, 4.75it/s] 17%|█▋ | 15/90 [00:03<00:15, 4.75it/s] 18%|█▊ | 16/90 [00:03<00:15, 4.74it/s] 19%|█▉ | 17/90 [00:03<00:15, 4.75it/s] 20%|██ | 18/90 [00:03<00:15, 4.77it/s] 21%|██ | 19/90 [00:04<00:14, 4.76it/s] 22%|██▏ | 20/90 [00:04<00:14, 4.76it/s] 23%|██▎ | 21/90 [00:04<00:14, 4.75it/s] 24%|██▍ | 22/90 [00:04<00:14, 4.75it/s] 26%|██▌ | 23/90 [00:04<00:14, 4.77it/s] 27%|██▋ | 24/90 [00:05<00:13, 4.76it/s] 28%|██▊ | 25/90 [00:05<00:13, 4.75it/s] 29%|██▉ | 26/90 [00:05<00:13, 4.75it/s] 30%|███ | 27/90 [00:05<00:13, 4.75it/s] 31%|███ | 28/90 [00:05<00:13, 4.76it/s] 32%|███▏ | 29/90 [00:06<00:12, 4.75it/s] 33%|███▎ | 30/90 [00:06<00:12, 4.75it/s] 34%|███▍ | 31/90 [00:06<00:12, 4.74it/s] 36%|███▌ | 32/90 [00:06<00:12, 4.74it/s] 37%|███▋ | 33/90 [00:06<00:11, 4.75it/s] 38%|███▊ | 34/90 [00:07<00:11, 4.75it/s] 39%|███▉ | 35/90 [00:07<00:11, 4.74it/s] 40%|████ | 36/90 [00:07<00:11, 4.74it/s] 41%|████ | 37/90 [00:07<00:11, 4.73it/s] 42%|████▏ | 38/90 [00:08<00:10, 4.73it/s] 43%|████▎ | 39/90 [00:08<00:10, 4.73it/s] 44%|████▍ | 40/90 [00:08<00:10, 4.74it/s] 46%|████▌ | 41/90 [00:08<00:10, 4.73it/s] 47%|████▋ | 42/90 [00:08<00:10, 4.72it/s] 48%|████▊ | 43/90 [00:09<00:09, 4.72it/s] 49%|████▉ | 44/90 [00:09<00:09, 4.71it/s] 50%|█████ | 45/90 [00:09<00:09, 4.72it/s] 51%|█████ | 46/90 [00:09<00:09, 4.72it/s] 52%|█████▏ | 47/90 [00:09<00:09, 4.73it/s] 53%|█████▎ | 48/90 [00:10<00:08, 4.73it/s] 54%|█████▍ | 49/90 [00:10<00:08, 4.73it/s] 56%|█████▌ | 50/90 [00:10<00:08, 4.73it/s] 57%|█████▋ | 51/90 [00:10<00:08, 4.73it/s] 58%|█████▊ | 52/90 [00:10<00:08, 4.72it/s] 59%|█████▉ | 53/90 [00:11<00:07, 4.73it/s] 60%|██████ | 54/90 [00:11<00:07, 4.74it/s] 61%|██████ | 55/90 [00:11<00:07, 4.74it/s] 62%|██████▏ | 56/90 [00:11<00:07, 4.75it/s] 63%|██████▎ | 57/90 [00:12<00:06, 4.75it/s] 64%|██████▍ | 58/90 [00:12<00:06, 4.75it/s] 66%|██████▌ | 59/90 [00:12<00:06, 4.74it/s] 67%|██████▋ | 60/90 [00:12<00:06, 4.73it/s] 68%|██████▊ | 61/90 [00:12<00:06, 4.73it/s] 69%|██████▉ | 62/90 [00:13<00:05, 4.72it/s] 70%|███████ | 63/90 [00:13<00:05, 4.73it/s] 71%|███████ | 64/90 [00:13<00:05, 4.72it/s] 72%|███████▏ | 65/90 [00:13<00:05, 4.73it/s] 73%|███████▎ | 66/90 [00:13<00:05, 4.72it/s] 74%|███████▍ | 67/90 [00:14<00:04, 4.72it/s] 76%|███████▌ | 68/90 [00:14<00:04, 4.72it/s] 77%|███████▋ | 69/90 [00:14<00:04, 4.72it/s] 78%|███████▊ | 70/90 [00:14<00:04, 4.72it/s] 79%|███████▉ | 71/90 [00:15<00:04, 4.72it/s] 80%|████████ | 72/90 [00:15<00:03, 4.71it/s] 81%|████████ | 73/90 [00:15<00:03, 4.71it/s] 82%|████████▏ | 74/90 [00:15<00:03, 4.72it/s] 83%|████████▎ | 75/90 [00:15<00:03, 4.71it/s] 84%|████████▍ | 76/90 [00:16<00:02, 4.71it/s] 86%|████████▌ | 77/90 [00:16<00:02, 4.71it/s] 87%|████████▋ | 78/90 [00:16<00:02, 4.70it/s] 88%|████████▊ | 79/90 [00:16<00:02, 4.71it/s] 89%|████████▉ | 80/90 [00:16<00:02, 4.70it/s] 90%|█████████ | 81/90 [00:17<00:01, 4.71it/s] 91%|█████████ | 82/90 [00:17<00:01, 4.70it/s] 92%|█████████▏| 83/90 [00:17<00:01, 4.71it/s] 93%|█████████▎| 84/90 [00:17<00:01, 4.71it/s] 94%|█████████▍| 85/90 [00:17<00:01, 4.71it/s] 96%|█████████▌| 86/90 [00:18<00:00, 4.70it/s] 97%|█████████▋| 87/90 [00:18<00:00, 4.69it/s] 98%|█████████▊| 88/90 [00:18<00:00, 4.71it/s] 99%|█████████▉| 89/90 [00:18<00:00, 4.71it/s] 100%|██████████| 90/90 [00:19<00:00, 4.71it/s] 100%|██████████| 90/90 [00:19<00:00, 4.73it/s]
Prediction
danjimenezm/food-gen-v1:235647b5ID6pk7uzqpnbecnd55ntnw5xwkvqStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.9
- num_inference_steps
- 100
{ "image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", "width": 512, "height": 512, "prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.9, "num_inference_steps": 100 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run danjimenezm/food-gen-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "danjimenezm/food-gen-v1:235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443", { input: { image: "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", width: 512, height: 512, prompt: "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.9, num_inference_steps: 100 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run danjimenezm/food-gen-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "danjimenezm/food-gen-v1:235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443", input={ "image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", "width": 512, "height": 512, "prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.9, "num_inference_steps": 100 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run danjimenezm/food-gen-v1 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": "235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443", "input": { "image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", "width": 512, "height": 512, "prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.9, "num_inference_steps": 100 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-10T23:35:48.340181Z", "created_at": "2023-04-10T23:35:29.942182Z", "data_removed": false, "error": null, "id": "6pk7uzqpnbecnd55ntnw5xwkvq", "input": { "image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", "width": 512, "height": 512, "prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.9, "num_inference_steps": 100 }, "logs": "Using seed: 38955\n 0%| | 0/90 [00:00<?, ?it/s]\n 1%| | 1/90 [00:00<00:19, 4.56it/s]\n 2%|▏ | 2/90 [00:00<00:17, 5.01it/s]\n 3%|▎ | 3/90 [00:00<00:16, 5.18it/s]\n 4%|▍ | 4/90 [00:00<00:16, 5.28it/s]\n 6%|▌ | 5/90 [00:00<00:15, 5.34it/s]\n 7%|▋ | 6/90 [00:01<00:15, 5.32it/s]\n 8%|▊ | 7/90 [00:01<00:15, 5.31it/s]\n 9%|▉ | 8/90 [00:01<00:15, 5.33it/s]\n 10%|█ | 9/90 [00:01<00:15, 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"https://api.replicate.com/v1/predictions/6pk7uzqpnbecnd55ntnw5xwkvq", "cancel": "https://api.replicate.com/v1/predictions/6pk7uzqpnbecnd55ntnw5xwkvq/cancel" }, "version": "08bc07087eaabf26835dbb72e076fe1711fea2f451547f4ec0969473090831e9" }
Generated inUsing seed: 38955 0%| | 0/90 [00:00<?, ?it/s] 1%| | 1/90 [00:00<00:19, 4.56it/s] 2%|▏ | 2/90 [00:00<00:17, 5.01it/s] 3%|▎ | 3/90 [00:00<00:16, 5.18it/s] 4%|▍ | 4/90 [00:00<00:16, 5.28it/s] 6%|▌ | 5/90 [00:00<00:15, 5.34it/s] 7%|▋ | 6/90 [00:01<00:15, 5.32it/s] 8%|▊ | 7/90 [00:01<00:15, 5.31it/s] 9%|▉ | 8/90 [00:01<00:15, 5.33it/s] 10%|█ | 9/90 [00:01<00:15, 5.38it/s] 11%|█ | 10/90 [00:01<00:14, 5.41it/s] 12%|█▏ | 11/90 [00:02<00:14, 5.40it/s] 13%|█▎ | 12/90 [00:02<00:14, 5.39it/s] 14%|█▍ | 13/90 [00:02<00:14, 5.37it/s] 16%|█▌ | 14/90 [00:02<00:14, 5.36it/s] 17%|█▋ | 15/90 [00:02<00:13, 5.39it/s] 18%|█▊ | 16/90 [00:03<00:13, 5.40it/s] 19%|█▉ | 17/90 [00:03<00:13, 5.39it/s] 20%|██ | 18/90 [00:03<00:13, 5.38it/s] 21%|██ | 19/90 [00:03<00:13, 5.36it/s] 22%|██▏ | 20/90 [00:03<00:13, 5.37it/s] 23%|██▎ | 21/90 [00:03<00:12, 5.40it/s] 24%|██▍ | 22/90 [00:04<00:12, 5.40it/s] 26%|██▌ | 23/90 [00:04<00:12, 5.40it/s] 27%|██▋ | 24/90 [00:04<00:12, 5.37it/s] 28%|██▊ | 25/90 [00:04<00:12, 5.36it/s] 29%|██▉ | 26/90 [00:04<00:11, 5.38it/s] 30%|███ | 27/90 [00:05<00:11, 5.39it/s] 31%|███ | 28/90 [00:05<00:11, 5.41it/s] 32%|███▏ | 29/90 [00:05<00:11, 5.40it/s] 33%|███▎ | 30/90 [00:05<00:11, 5.36it/s] 34%|███▍ | 31/90 [00:05<00:11, 5.36it/s] 36%|███▌ | 32/90 [00:05<00:10, 5.37it/s] 37%|███▋ | 33/90 [00:06<00:10, 5.38it/s] 38%|███▊ | 34/90 [00:06<00:10, 5.38it/s] 39%|███▉ | 35/90 [00:06<00:10, 5.34it/s] 40%|████ | 36/90 [00:06<00:10, 5.36it/s] 41%|████ | 37/90 [00:06<00:09, 5.38it/s] 42%|████▏ | 38/90 [00:07<00:09, 5.38it/s] 43%|████▎ | 39/90 [00:07<00:09, 5.36it/s] 44%|████▍ | 40/90 [00:07<00:09, 5.33it/s] 46%|████▌ | 41/90 [00:07<00:09, 5.34it/s] 47%|████▋ | 42/90 [00:07<00:08, 5.33it/s] 48%|████▊ | 43/90 [00:08<00:08, 5.33it/s] 49%|████▉ | 44/90 [00:08<00:08, 5.36it/s] 50%|█████ | 45/90 [00:08<00:08, 5.36it/s] 51%|█████ | 46/90 [00:08<00:08, 5.36it/s] 52%|█████▏ | 47/90 [00:08<00:08, 5.34it/s] 53%|█████▎ | 48/90 [00:08<00:07, 5.35it/s] 54%|█████▍ | 49/90 [00:09<00:07, 5.38it/s] 56%|█████▌ | 50/90 [00:09<00:07, 5.39it/s] 57%|█████▋ | 51/90 [00:09<00:07, 5.38it/s] 58%|█████▊ | 52/90 [00:09<00:07, 5.34it/s] 59%|█████▉ | 53/90 [00:09<00:06, 5.33it/s] 60%|██████ | 54/90 [00:10<00:06, 5.35it/s] 61%|██████ | 55/90 [00:10<00:06, 5.34it/s] 62%|██████▏ | 56/90 [00:10<00:06, 5.32it/s] 63%|██████▎ | 57/90 [00:10<00:06, 5.33it/s] 64%|██████▍ | 58/90 [00:10<00:06, 5.32it/s] 66%|██████▌ | 59/90 [00:11<00:05, 5.31it/s] 67%|██████▋ | 60/90 [00:11<00:05, 5.31it/s] 68%|██████▊ | 61/90 [00:11<00:05, 5.32it/s] 69%|██████▉ | 62/90 [00:11<00:05, 5.30it/s] 70%|███████ | 63/90 [00:11<00:05, 5.32it/s] 71%|███████ | 64/90 [00:11<00:04, 5.32it/s] 72%|███████▏ | 65/90 [00:12<00:04, 5.30it/s] 73%|███████▎ | 66/90 [00:12<00:04, 5.31it/s] 74%|███████▍ | 67/90 [00:12<00:04, 5.33it/s] 76%|███████▌ | 68/90 [00:12<00:04, 5.33it/s] 77%|███████▋ | 69/90 [00:12<00:03, 5.31it/s] 78%|███████▊ | 70/90 [00:13<00:03, 5.32it/s] 79%|███████▉ | 71/90 [00:13<00:03, 5.31it/s] 80%|████████ | 72/90 [00:13<00:03, 5.30it/s] 81%|████████ | 73/90 [00:13<00:03, 5.32it/s] 82%|████████▏ | 74/90 [00:13<00:03, 5.30it/s] 83%|████████▎ | 75/90 [00:14<00:02, 5.28it/s] 84%|████████▍ | 76/90 [00:14<00:02, 5.30it/s] 86%|████████▌ | 77/90 [00:14<00:02, 5.31it/s] 87%|████████▋ | 78/90 [00:14<00:02, 5.28it/s] 88%|████████▊ | 79/90 [00:14<00:02, 5.32it/s] 89%|████████▉ | 80/90 [00:14<00:01, 5.30it/s] 90%|█████████ | 81/90 [00:15<00:01, 5.27it/s] 91%|█████████ | 82/90 [00:15<00:01, 5.29it/s] 92%|█████████▏| 83/90 [00:15<00:01, 5.27it/s] 93%|█████████▎| 84/90 [00:15<00:01, 5.29it/s] 94%|█████████▍| 85/90 [00:15<00:00, 5.31it/s] 96%|█████████▌| 86/90 [00:16<00:00, 5.28it/s] 97%|█████████▋| 87/90 [00:16<00:00, 5.30it/s] 98%|█████████▊| 88/90 [00:16<00:00, 5.31it/s] 99%|█████████▉| 89/90 [00:16<00:00, 5.29it/s] 100%|██████████| 90/90 [00:16<00:00, 5.30it/s] 100%|██████████| 90/90 [00:16<00:00, 5.33it/s]
Prediction
danjimenezm/food-gen-v1:235647b5IDknimaawk65ehnkp2ws7ajkvr3uStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 7.5
- negative_prompt
- olives
- prompt_strength
- 0.95
- num_inference_steps
- 100
{ "image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", "width": 512, "height": 512, "prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "olives", "prompt_strength": 0.95, "num_inference_steps": 100 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run danjimenezm/food-gen-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "danjimenezm/food-gen-v1:235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443", { input: { image: "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", width: 512, height: 512, prompt: "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 7.5, negative_prompt: "olives", prompt_strength: 0.95, num_inference_steps: 100 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run danjimenezm/food-gen-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "danjimenezm/food-gen-v1:235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443", input={ "image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", "width": 512, "height": 512, "prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "olives", "prompt_strength": 0.95, "num_inference_steps": 100 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run danjimenezm/food-gen-v1 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": "235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443", "input": { "image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", "width": 512, "height": 512, "prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "olives", "prompt_strength": 0.95, "num_inference_steps": 100 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-10T23:37:48.748001Z", "created_at": "2023-04-10T23:37:29.207395Z", "data_removed": false, "error": null, "id": "knimaawk65ehnkp2ws7ajkvr3u", "input": { "image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png", "width": 512, "height": 512, "prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "olives", "prompt_strength": 0.95, "num_inference_steps": 100 }, "logs": "Using seed: 13512\n 0%| | 0/95 [00:00<?, ?it/s]\n 1%| | 1/95 [00:00<00:18, 5.02it/s]\n 2%|▏ | 2/95 [00:00<00:17, 5.20it/s]\n 3%|▎ | 3/95 [00:00<00:17, 5.25it/s]\n 4%|▍ | 4/95 [00:00<00:17, 5.31it/s]\n 5%|▌ | 5/95 [00:00<00:16, 5.31it/s]\n 6%|▋ | 6/95 [00:01<00:16, 5.26it/s]\n 7%|▋ | 7/95 [00:01<00:16, 5.29it/s]\n 8%|▊ | 8/95 [00:01<00:16, 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81%|████████ | 77/95 [00:14<00:03, 5.22it/s]\n 82%|████████▏ | 78/95 [00:14<00:03, 5.21it/s]\n 83%|████████▎ | 79/95 [00:15<00:03, 5.21it/s]\n 84%|████████▍ | 80/95 [00:15<00:02, 5.21it/s]\n 85%|████████▌ | 81/95 [00:15<00:02, 5.22it/s]\n 86%|████████▋ | 82/95 [00:15<00:02, 5.23it/s]\n 87%|████████▋ | 83/95 [00:15<00:02, 5.22it/s]\n 88%|████████▊ | 84/95 [00:16<00:02, 5.21it/s]\n 89%|████████▉ | 85/95 [00:16<00:01, 5.22it/s]\n 91%|█████████ | 86/95 [00:16<00:01, 5.21it/s]\n 92%|█████████▏| 87/95 [00:16<00:01, 5.21it/s]\n 93%|█████████▎| 88/95 [00:16<00:01, 5.22it/s]\n 94%|█████████▎| 89/95 [00:16<00:01, 5.21it/s]\n 95%|█████████▍| 90/95 [00:17<00:00, 5.21it/s]\n 96%|█████████▌| 91/95 [00:17<00:00, 5.21it/s]\n 97%|█████████▋| 92/95 [00:17<00:00, 5.24it/s]\n 98%|█████████▊| 93/95 [00:17<00:00, 5.23it/s]\n 99%|█████████▉| 94/95 [00:17<00:00, 5.23it/s]\n100%|██████████| 95/95 [00:18<00:00, 5.23it/s]\n100%|██████████| 95/95 [00:18<00:00, 5.24it/s]", "metrics": { "predict_time": 19.447508, "total_time": 19.540606 }, "output": [ "https://replicate.delivery/pbxt/rxReynRdEa1fuUhh9oyCG0gR7L0udQk3uMr0HJzLoHJMfJhhA/out-0.png" ], "started_at": "2023-04-10T23:37:29.300493Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/knimaawk65ehnkp2ws7ajkvr3u", "cancel": "https://api.replicate.com/v1/predictions/knimaawk65ehnkp2ws7ajkvr3u/cancel" }, "version": "08bc07087eaabf26835dbb72e076fe1711fea2f451547f4ec0969473090831e9" }
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