kazdatahelp / azhrq
- Public
- 40 runs
-
H100
Prediction
kazdatahelp/azhrq:20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964dIDr2aancbkg9rm20chsvb9sxyb68StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of AZHRQ as a woman doctor in central hospital
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 4:5
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of AZHRQ as a woman doctor in central hospital ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run kazdatahelp/azhrq using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kazdatahelp/azhrq:20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964d", { input: { model: "dev", prompt: "a photo of AZHRQ as a woman doctor in central hospital ", lora_scale: 1, num_outputs: 1, aspect_ratio: "4:5", output_format: "png", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 kazdatahelp/azhrq using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kazdatahelp/azhrq:20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964d", input={ "model": "dev", "prompt": "a photo of AZHRQ as a woman doctor in central hospital ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run kazdatahelp/azhrq 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": "kazdatahelp/azhrq:20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964d", "input": { "model": "dev", "prompt": "a photo of AZHRQ as a woman doctor in central hospital ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T23:34:30.557129Z", "created_at": "2024-09-07T23:34:06.466000Z", "data_removed": false, "error": null, "id": "r2aancbkg9rm20chsvb9sxyb68", "input": { "model": "dev", "prompt": "a photo of AZHRQ as a woman doctor in central hospital ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 10368\nPrompt: a photo of AZHRQ as a woman doctor in central hospital\n[!] txt2img mode\nUsing dev model\nfree=8735418744832\nDownloading weights\n2024-09-07T23:34:12Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpu83rc7co/weights url=https://replicate.delivery/yhqm/8pc8IZ90YRL9Kl5kIJOe6Xm2duGlzcGYaSZoAtAdeMSqsoaTA/trained_model.tar\n2024-09-07T23:34:13Z | INFO | [ Complete ] dest=/tmp/tmpu83rc7co/weights size=\"194 MB\" total_elapsed=1.686s url=https://replicate.delivery/yhqm/8pc8IZ90YRL9Kl5kIJOe6Xm2duGlzcGYaSZoAtAdeMSqsoaTA/trained_model.tar\nDownloaded weights in 1.73s\nLoaded LoRAs in 10.60s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 3.90it/s]\n 7%|▋ | 2/28 [00:00<00:05, 4.39it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.15it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.04it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.99it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.96it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.94it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.92it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.92it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.91it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.91it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.90it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.90it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.90it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.90it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.89it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.89it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.89it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.89it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.90it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.89it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.90it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 3.90it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.90it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.90it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.90it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 3.90it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.90it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.92it/s]", "metrics": { "predict_time": 18.603726143, "total_time": 24.091129 }, "output": [ "https://replicate.delivery/yhqm/LqLhjn8TkAJeLiVkhvD172RCUOS45PCKiG8viJkwJ1IDqUtJA/out-0.png" ], "started_at": "2024-09-07T23:34:11.953403Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/r2aancbkg9rm20chsvb9sxyb68", "cancel": "https://api.replicate.com/v1/predictions/r2aancbkg9rm20chsvb9sxyb68/cancel" }, "version": "20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964d" }Generated inUsing seed: 10368 Prompt: a photo of AZHRQ as a woman doctor in central hospital [!] txt2img mode Using dev model free=8735418744832 Downloading weights 2024-09-07T23:34:12Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpu83rc7co/weights url=https://replicate.delivery/yhqm/8pc8IZ90YRL9Kl5kIJOe6Xm2duGlzcGYaSZoAtAdeMSqsoaTA/trained_model.tar 2024-09-07T23:34:13Z | INFO | [ Complete ] dest=/tmp/tmpu83rc7co/weights size="194 MB" total_elapsed=1.686s url=https://replicate.delivery/yhqm/8pc8IZ90YRL9Kl5kIJOe6Xm2duGlzcGYaSZoAtAdeMSqsoaTA/trained_model.tar Downloaded weights in 1.73s Loaded LoRAs in 10.60s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 3.90it/s] 7%|▋ | 2/28 [00:00<00:05, 4.39it/s] 11%|█ | 3/28 [00:00<00:06, 4.15it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.04it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.99it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.96it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.94it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.92it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.92it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.91it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.91it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.90it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.90it/s] 50%|█████ | 14/28 [00:03<00:03, 3.90it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.90it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.89it/s] 61%|██████ | 17/28 [00:04<00:02, 3.89it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.89it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.89it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.90it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.89it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.90it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.90it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.90it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.90it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.90it/s] 96%|█████████▋| 27/28 [00:06<00:00, 3.90it/s] 100%|██████████| 28/28 [00:07<00:00, 3.90it/s] 100%|██████████| 28/28 [00:07<00:00, 3.92it/s]Prediction
kazdatahelp/azhrq:20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964dIDpfg0t5v0mxrm60chsveb8pabzgStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of AZHRQ as a famous woman actress receiving an OSCAR statue
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 4:5
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of AZHRQ as a famous woman actress receiving an OSCAR statue ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run kazdatahelp/azhrq using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kazdatahelp/azhrq:20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964d", { input: { model: "dev", prompt: "a photo of AZHRQ as a famous woman actress receiving an OSCAR statue ", lora_scale: 1, num_outputs: 1, aspect_ratio: "4:5", output_format: "png", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 kazdatahelp/azhrq using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kazdatahelp/azhrq:20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964d", input={ "model": "dev", "prompt": "a photo of AZHRQ as a famous woman actress receiving an OSCAR statue ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run kazdatahelp/azhrq 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": "kazdatahelp/azhrq:20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964d", "input": { "model": "dev", "prompt": "a photo of AZHRQ as a famous woman actress receiving an OSCAR statue ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T23:40:50.506097Z", "created_at": "2024-09-07T23:40:34.855000Z", "data_removed": false, "error": null, "id": "pfg0t5v0mxrm60chsveb8pabzg", "input": { "model": "dev", "prompt": "a photo of AZHRQ as a famous woman actress receiving an OSCAR statue ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 62016\nPrompt: a photo of AZHRQ as a famous woman actress receiving an OSCAR statue\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 7.69s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 3.90it/s]\n 7%|▋ | 2/28 [00:00<00:05, 4.39it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.15it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.05it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.99it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.96it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.94it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.92it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.92it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.91it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.91it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.90it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.90it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.90it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.90it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.90it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.89it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.90it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.90it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.89it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.89it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.89it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 3.89it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.89it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.89it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.89it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 3.90it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.90it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.92it/s]", "metrics": { "predict_time": 15.640647077, "total_time": 15.651097 }, "output": [ "https://replicate.delivery/yhqm/7LeZFR5Nejt6V0YXKKmzlJCndMiiNe4KdSxReUoimf7UQLVbC/out-0.png" ], "started_at": "2024-09-07T23:40:34.865449Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pfg0t5v0mxrm60chsveb8pabzg", "cancel": "https://api.replicate.com/v1/predictions/pfg0t5v0mxrm60chsveb8pabzg/cancel" }, "version": "20d52f6d9e83a15bbd6ce2c335e9fa4e5890027611f3c3f57568101f4c9c964d" }Generated inUsing seed: 62016 Prompt: a photo of AZHRQ as a famous woman actress receiving an OSCAR statue [!] txt2img mode Using dev model Loaded LoRAs in 7.69s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 3.90it/s] 7%|▋ | 2/28 [00:00<00:05, 4.39it/s] 11%|█ | 3/28 [00:00<00:06, 4.15it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.05it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.99it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.96it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.94it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.92it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.92it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.91it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.91it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.90it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.90it/s] 50%|█████ | 14/28 [00:03<00:03, 3.90it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.90it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.90it/s] 61%|██████ | 17/28 [00:04<00:02, 3.89it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.90it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.90it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.89it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.89it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.89it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.89it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.89it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.89it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.89it/s] 96%|█████████▋| 27/28 [00:06<00:00, 3.90it/s] 100%|██████████| 28/28 [00:07<00:00, 3.90it/s] 100%|██████████| 28/28 [00:07<00:00, 3.92it/s]
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