technillogue / sdxl-nyacomp
- Public
- 422 runs
-
L40S
Prediction
technillogue/sdxl-nyacomp:97e3581fa2bda3363cf85dce159b205bbad73210c0b9cfdc67122ef49650a526IDywby4ptbqls3ila3w7lfbuqfcaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- nyancat nvidia nyacomp gpu weight compression
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- load_settings_json
- {"RUN": "0"}
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\"RUN\": \"0\"}", "num_inference_steps": 50 }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 technillogue/sdxl-nyacomp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "technillogue/sdxl-nyacomp:97e3581fa2bda3363cf85dce159b205bbad73210c0b9cfdc67122ef49650a526", { input: { width: 1024, height: 1024, prompt: "nyancat nvidia nyacomp gpu weight compression", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, load_settings_json: "{\"RUN\": \"0\"}", num_inference_steps: 50 } } ); // 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 technillogue/sdxl-nyacomp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "technillogue/sdxl-nyacomp:97e3581fa2bda3363cf85dce159b205bbad73210c0b9cfdc67122ef49650a526", input={ "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\"RUN\": \"0\"}", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run technillogue/sdxl-nyacomp 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": "technillogue/sdxl-nyacomp:97e3581fa2bda3363cf85dce159b205bbad73210c0b9cfdc67122ef49650a526", "input": { "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\\"RUN\\": \\"0\\"}", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-22T08:22:12.831520Z", "created_at": "2024-01-22T08:20:32.311097Z", "data_removed": false, "error": null, "id": "ywby4ptbqls3ila3w7lfbuqfca", "input": { "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\"RUN\": \"0\"}", "num_inference_steps": 50 }, "logs": "Using seed: 17866\nPrompt: nyancat nvidia nyacomp gpu weight compression\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.11/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:01<01:03, 1.30s/it]\n 4%|▍ | 2/50 [00:01<00:31, 1.53it/s]\n 6%|▌ | 3/50 [00:01<00:21, 2.23it/s]\n 8%|▊ | 4/50 [00:01<00:16, 2.84it/s]\n 10%|█ | 5/50 [00:02<00:13, 3.35it/s]\n 12%|█▏ | 6/50 [00:02<00:11, 3.75it/s]\n 14%|█▍ | 7/50 [00:02<00:10, 4.06it/s]\n 16%|█▌ | 8/50 [00:02<00:09, 4.29it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.46it/s]\n 20%|██ | 10/50 [00:03<00:08, 4.59it/s]\n 22%|██▏ | 11/50 [00:03<00:08, 4.68it/s]\n 24%|██▍ | 12/50 [00:03<00:08, 4.74it/s]\n 26%|██▌ | 13/50 [00:03<00:07, 4.80it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.82it/s]\n 30%|███ | 15/50 [00:04<00:07, 4.84it/s]\n 32%|███▏ | 16/50 [00:04<00:07, 4.85it/s]\n 34%|███▍ | 17/50 [00:04<00:06, 4.87it/s]\n 36%|███▌ | 18/50 [00:04<00:06, 4.87it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.88it/s]\n 40%|████ | 20/50 [00:05<00:06, 4.88it/s]\n 42%|████▏ | 21/50 [00:05<00:05, 4.88it/s]\n 44%|████▍ | 22/50 [00:05<00:05, 4.89it/s]\n 46%|████▌ | 23/50 [00:05<00:05, 4.89it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.88it/s]\n 50%|█████ | 25/50 [00:06<00:05, 4.88it/s]\n 52%|█████▏ | 26/50 [00:06<00:04, 4.89it/s]\n 54%|█████▍ | 27/50 [00:06<00:04, 4.88it/s]\n 56%|█████▌ | 28/50 [00:06<00:04, 4.88it/s]\n 58%|█████▊ | 29/50 [00:07<00:04, 4.88it/s]\n 60%|██████ | 30/50 [00:07<00:04, 4.88it/s]\n 62%|██████▏ | 31/50 [00:07<00:03, 4.88it/s]\n 64%|██████▍ | 32/50 [00:07<00:03, 4.88it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.88it/s]\n 68%|██████▊ | 34/50 [00:08<00:03, 4.88it/s]\n 70%|███████ | 35/50 [00:08<00:03, 4.87it/s]\n 72%|███████▏ | 36/50 [00:08<00:02, 4.87it/s]\n 74%|███████▍ | 37/50 [00:08<00:02, 4.87it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.88it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.87it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.87it/s]\n 82%|████████▏ | 41/50 [00:09<00:01, 4.88it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.87it/s]\n 86%|████████▌ | 43/50 [00:09<00:01, 4.87it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.87it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.87it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.87it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.86it/s]\n 96%|█████████▌| 48/50 [00:10<00:00, 4.86it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.87it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.86it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.41it/s]", "metrics": { "predict_time": 16.30476, "total_time": 100.520423 }, "output": [ "https://replicate.delivery/pbxt/LHdB5QwP8e2sHiy4EJb6wZEvnEYlQQYmsNs5eO4t1sbze6dkA/out-0.png" ], "started_at": "2024-01-22T08:21:56.526760Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ywby4ptbqls3ila3w7lfbuqfca", "cancel": "https://api.replicate.com/v1/predictions/ywby4ptbqls3ila3w7lfbuqfca/cancel" }, "version": "97e3581fa2bda3363cf85dce159b205bbad73210c0b9cfdc67122ef49650a526" }Generated inUsing seed: 17866 Prompt: nyancat nvidia nyacomp gpu weight compression txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.11/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:01<01:03, 1.30s/it] 4%|▍ | 2/50 [00:01<00:31, 1.53it/s] 6%|▌ | 3/50 [00:01<00:21, 2.23it/s] 8%|▊ | 4/50 [00:01<00:16, 2.84it/s] 10%|█ | 5/50 [00:02<00:13, 3.35it/s] 12%|█▏ | 6/50 [00:02<00:11, 3.75it/s] 14%|█▍ | 7/50 [00:02<00:10, 4.06it/s] 16%|█▌ | 8/50 [00:02<00:09, 4.29it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.46it/s] 20%|██ | 10/50 [00:03<00:08, 4.59it/s] 22%|██▏ | 11/50 [00:03<00:08, 4.68it/s] 24%|██▍ | 12/50 [00:03<00:08, 4.74it/s] 26%|██▌ | 13/50 [00:03<00:07, 4.80it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.82it/s] 30%|███ | 15/50 [00:04<00:07, 4.84it/s] 32%|███▏ | 16/50 [00:04<00:07, 4.85it/s] 34%|███▍ | 17/50 [00:04<00:06, 4.87it/s] 36%|███▌ | 18/50 [00:04<00:06, 4.87it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.88it/s] 40%|████ | 20/50 [00:05<00:06, 4.88it/s] 42%|████▏ | 21/50 [00:05<00:05, 4.88it/s] 44%|████▍ | 22/50 [00:05<00:05, 4.89it/s] 46%|████▌ | 23/50 [00:05<00:05, 4.89it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.88it/s] 50%|█████ | 25/50 [00:06<00:05, 4.88it/s] 52%|█████▏ | 26/50 [00:06<00:04, 4.89it/s] 54%|█████▍ | 27/50 [00:06<00:04, 4.88it/s] 56%|█████▌ | 28/50 [00:06<00:04, 4.88it/s] 58%|█████▊ | 29/50 [00:07<00:04, 4.88it/s] 60%|██████ | 30/50 [00:07<00:04, 4.88it/s] 62%|██████▏ | 31/50 [00:07<00:03, 4.88it/s] 64%|██████▍ | 32/50 [00:07<00:03, 4.88it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.88it/s] 68%|██████▊ | 34/50 [00:08<00:03, 4.88it/s] 70%|███████ | 35/50 [00:08<00:03, 4.87it/s] 72%|███████▏ | 36/50 [00:08<00:02, 4.87it/s] 74%|███████▍ | 37/50 [00:08<00:02, 4.87it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.88it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.87it/s] 80%|████████ | 40/50 [00:09<00:02, 4.87it/s] 82%|████████▏ | 41/50 [00:09<00:01, 4.88it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.87it/s] 86%|████████▌ | 43/50 [00:09<00:01, 4.87it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.87it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.87it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.87it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.86it/s] 96%|█████████▌| 48/50 [00:10<00:00, 4.86it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.87it/s] 100%|██████████| 50/50 [00:11<00:00, 4.86it/s] 100%|██████████| 50/50 [00:11<00:00, 4.41it/s]Prediction
technillogue/sdxl-nyacomp:6d9ed5fc0cb7c205a94a91bfa80d1fc3ed3f906768ae25193d7d1d43624c11cfIDepex45tbip3yxtx5eltsb32dqaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- nyancat nvidia nyacomp gpu weight compression, colorful digital cat with compression artifacts
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- load_settings_json
- {"RUN": "0"}
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression, colorful digital cat with compression artifacts", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\"RUN\": \"0\"}", "num_inference_steps": 50 }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 technillogue/sdxl-nyacomp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "technillogue/sdxl-nyacomp:6d9ed5fc0cb7c205a94a91bfa80d1fc3ed3f906768ae25193d7d1d43624c11cf", { input: { width: 1024, height: 1024, prompt: "nyancat nvidia nyacomp gpu weight compression, colorful digital cat with compression artifacts", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, load_settings_json: "{\"RUN\": \"0\"}", num_inference_steps: 50 } } ); // 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 technillogue/sdxl-nyacomp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "technillogue/sdxl-nyacomp:6d9ed5fc0cb7c205a94a91bfa80d1fc3ed3f906768ae25193d7d1d43624c11cf", input={ "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression, colorful digital cat with compression artifacts", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\"RUN\": \"0\"}", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run technillogue/sdxl-nyacomp 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": "technillogue/sdxl-nyacomp:6d9ed5fc0cb7c205a94a91bfa80d1fc3ed3f906768ae25193d7d1d43624c11cf", "input": { "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression, colorful digital cat with compression artifacts", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\\"RUN\\": \\"0\\"}", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-22T10:15:20.276479Z", "created_at": "2024-01-22T10:12:15.828165Z", "data_removed": false, "error": null, "id": "epex45tbip3yxtx5eltsb32dqa", "input": { "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression, colorful digital cat with compression artifacts", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\"RUN\": \"0\"}", "num_inference_steps": 50 }, "logs": "Using seed: 62450\nPrompt: nyancat nvidia nyacomp gpu weight compression, colorful digital cat with compression artifacts\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.11/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:00<00:38, 1.26it/s]\n 4%|▍ | 2/50 [00:00<00:21, 2.24it/s]\n 6%|▌ | 3/50 [00:01<00:15, 2.97it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.50it/s]\n 10%|█ | 5/50 [00:01<00:11, 3.88it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.16it/s]\n 14%|█▍ | 7/50 [00:02<00:09, 4.36it/s]\n 16%|█▌ | 8/50 [00:02<00:09, 4.51it/s]\n 18%|█▊ | 9/50 [00:02<00:08, 4.61it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.69it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.74it/s]\n 24%|██▍ | 12/50 [00:03<00:07, 4.78it/s]\n 26%|██▌ | 13/50 [00:03<00:07, 4.81it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.83it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.83it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.85it/s]\n 34%|███▍ | 17/50 [00:04<00:06, 4.85it/s]\n 36%|███▌ | 18/50 [00:04<00:06, 4.86it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.86it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.85it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.86it/s]\n 44%|████▍ | 22/50 [00:05<00:05, 4.86it/s]\n 46%|████▌ | 23/50 [00:05<00:05, 4.86it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.87it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.86it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.86it/s]\n 54%|█████▍ | 27/50 [00:06<00:04, 4.86it/s]\n 56%|█████▌ | 28/50 [00:06<00:04, 4.86it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.86it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.85it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.85it/s]\n 64%|██████▍ | 32/50 [00:07<00:03, 4.85it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.85it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.86it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.85it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.85it/s]\n 74%|███████▍ | 37/50 [00:08<00:02, 4.85it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.85it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.85it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.85it/s]\n 82%|████████▏ | 41/50 [00:09<00:01, 4.84it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.85it/s]\n 86%|████████▌ | 43/50 [00:09<00:01, 4.85it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.85it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.85it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.84it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.85it/s]\n 96%|█████████▌| 48/50 [00:10<00:00, 4.84it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.85it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.84it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.59it/s]", "metrics": { "predict_time": 13.690281, "total_time": 184.448314 }, "output": [ "https://replicate.delivery/pbxt/AisVdwC22e08Si4ovO8cOOBpFjYyozD9uD0oCBkzi1ibkfOSA/out-0.png" ], "started_at": "2024-01-22T10:15:06.586198Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/epex45tbip3yxtx5eltsb32dqa", "cancel": "https://api.replicate.com/v1/predictions/epex45tbip3yxtx5eltsb32dqa/cancel" }, "version": "6d9ed5fc0cb7c205a94a91bfa80d1fc3ed3f906768ae25193d7d1d43624c11cf" }Generated inUsing seed: 62450 Prompt: nyancat nvidia nyacomp gpu weight compression, colorful digital cat with compression artifacts txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.11/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:00<00:38, 1.26it/s] 4%|▍ | 2/50 [00:00<00:21, 2.24it/s] 6%|▌ | 3/50 [00:01<00:15, 2.97it/s] 8%|▊ | 4/50 [00:01<00:13, 3.50it/s] 10%|█ | 5/50 [00:01<00:11, 3.88it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.16it/s] 14%|█▍ | 7/50 [00:02<00:09, 4.36it/s] 16%|█▌ | 8/50 [00:02<00:09, 4.51it/s] 18%|█▊ | 9/50 [00:02<00:08, 4.61it/s] 20%|██ | 10/50 [00:02<00:08, 4.69it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.74it/s] 24%|██▍ | 12/50 [00:03<00:07, 4.78it/s] 26%|██▌ | 13/50 [00:03<00:07, 4.81it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.83it/s] 30%|███ | 15/50 [00:03<00:07, 4.83it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.85it/s] 34%|███▍ | 17/50 [00:04<00:06, 4.85it/s] 36%|███▌ | 18/50 [00:04<00:06, 4.86it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.86it/s] 40%|████ | 20/50 [00:04<00:06, 4.85it/s] 42%|████▏ | 21/50 [00:04<00:05, 4.86it/s] 44%|████▍ | 22/50 [00:05<00:05, 4.86it/s] 46%|████▌ | 23/50 [00:05<00:05, 4.86it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.87it/s] 50%|█████ | 25/50 [00:05<00:05, 4.86it/s] 52%|█████▏ | 26/50 [00:05<00:04, 4.86it/s] 54%|█████▍ | 27/50 [00:06<00:04, 4.86it/s] 56%|█████▌ | 28/50 [00:06<00:04, 4.86it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.86it/s] 60%|██████ | 30/50 [00:06<00:04, 4.85it/s] 62%|██████▏ | 31/50 [00:06<00:03, 4.85it/s] 64%|██████▍ | 32/50 [00:07<00:03, 4.85it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.85it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.86it/s] 70%|███████ | 35/50 [00:07<00:03, 4.85it/s] 72%|███████▏ | 36/50 [00:07<00:02, 4.85it/s] 74%|███████▍ | 37/50 [00:08<00:02, 4.85it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.85it/s] 78%|███████▊ | 39/50 [00:08<00:02, 4.85it/s] 80%|████████ | 40/50 [00:08<00:02, 4.85it/s] 82%|████████▏ | 41/50 [00:09<00:01, 4.84it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.85it/s] 86%|████████▌ | 43/50 [00:09<00:01, 4.85it/s] 88%|████████▊ | 44/50 [00:09<00:01, 4.85it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.85it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.84it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.85it/s] 96%|█████████▌| 48/50 [00:10<00:00, 4.84it/s] 98%|█████████▊| 49/50 [00:10<00:00, 4.85it/s] 100%|██████████| 50/50 [00:10<00:00, 4.84it/s] 100%|██████████| 50/50 [00:10<00:00, 4.59it/s]Prediction
technillogue/sdxl-nyacomp:5f8125c11ca2286c2536e2a98f59c9988e65f916904986a133ccc5ce49d961a1IDysyrghdbfbpktgliyliwdl2ehyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- nyancat nvidia nyacomp gpu weight compression with compression artifacts jpeg jpg sleek kitty cat
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- load_settings_json
- {"RUN": "0"}
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression with compression artifacts jpeg jpg sleek kitty cat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\"RUN\": \"0\"}", "num_inference_steps": 50 }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 technillogue/sdxl-nyacomp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "technillogue/sdxl-nyacomp:5f8125c11ca2286c2536e2a98f59c9988e65f916904986a133ccc5ce49d961a1", { input: { width: 1024, height: 1024, prompt: "nyancat nvidia nyacomp gpu weight compression with compression artifacts jpeg jpg sleek kitty cat", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, load_settings_json: "{\"RUN\": \"0\"}", num_inference_steps: 50 } } ); // 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 technillogue/sdxl-nyacomp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "technillogue/sdxl-nyacomp:5f8125c11ca2286c2536e2a98f59c9988e65f916904986a133ccc5ce49d961a1", input={ "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression with compression artifacts jpeg jpg sleek kitty cat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\"RUN\": \"0\"}", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run technillogue/sdxl-nyacomp 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": "technillogue/sdxl-nyacomp:5f8125c11ca2286c2536e2a98f59c9988e65f916904986a133ccc5ce49d961a1", "input": { "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression with compression artifacts jpeg jpg sleek kitty cat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\\"RUN\\": \\"0\\"}", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-22T23:12:36.736429Z", "created_at": "2024-01-22T23:12:24.265039Z", "data_removed": false, "error": null, "id": "ysyrghdbfbpktgliyliwdl2ehy", "input": { "width": 1024, "height": 1024, "prompt": "nyancat nvidia nyacomp gpu weight compression with compression artifacts jpeg jpg sleek kitty cat", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "load_settings_json": "{\"RUN\": \"0\"}", "num_inference_steps": 50 }, "logs": "Using seed: 21913\nPrompt: nyancat nvidia nyacomp gpu weight compression with compression artifacts jpeg jpg sleek kitty cat\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:09, 4.90it/s]\n 4%|▍ | 2/50 [00:00<00:09, 4.89it/s]\n 6%|▌ | 3/50 [00:00<00:09, 4.87it/s]\n 8%|▊ | 4/50 [00:00<00:09, 4.86it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.84it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.84it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 4.84it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.84it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.83it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.82it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.82it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.82it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.83it/s]\n 28%|██▊ | 14/50 [00:02<00:07, 4.83it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.82it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.82it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.81it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.81it/s]\n 38%|███▊ | 19/50 [00:03<00:06, 4.82it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.82it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.82it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.82it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 4.82it/s]\n 48%|████▊ | 24/50 [00:04<00:05, 4.83it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.82it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.82it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.82it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 4.82it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.82it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.82it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.82it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 4.82it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 4.82it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.82it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.82it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.82it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 4.82it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 4.82it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.81it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.82it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.81it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 4.81it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 4.81it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.81it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.81it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.81it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.81it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 4.81it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.81it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.82it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.82it/s]", "metrics": { "predict_time": 12.437426, "total_time": 12.47139 }, "output": [ "https://replicate.delivery/pbxt/LLdbOvNtuXpCJJPGM5JSUIMA3Ih5gdG9Ml4QJo36qdyYoyjE/out-0.png" ], "started_at": "2024-01-22T23:12:24.299003Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ysyrghdbfbpktgliyliwdl2ehy", "cancel": "https://api.replicate.com/v1/predictions/ysyrghdbfbpktgliyliwdl2ehy/cancel" }, "version": "5f8125c11ca2286c2536e2a98f59c9988e65f916904986a133ccc5ce49d961a1" }Generated inUsing seed: 21913 Prompt: nyancat nvidia nyacomp gpu weight compression with compression artifacts jpeg jpg sleek kitty cat txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:09, 4.90it/s] 4%|▍ | 2/50 [00:00<00:09, 4.89it/s] 6%|▌ | 3/50 [00:00<00:09, 4.87it/s] 8%|▊ | 4/50 [00:00<00:09, 4.86it/s] 10%|█ | 5/50 [00:01<00:09, 4.84it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.84it/s] 14%|█▍ | 7/50 [00:01<00:08, 4.84it/s] 16%|█▌ | 8/50 [00:01<00:08, 4.84it/s] 18%|█▊ | 9/50 [00:01<00:08, 4.83it/s] 20%|██ | 10/50 [00:02<00:08, 4.82it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.82it/s] 24%|██▍ | 12/50 [00:02<00:07, 4.82it/s] 26%|██▌ | 13/50 [00:02<00:07, 4.83it/s] 28%|██▊ | 14/50 [00:02<00:07, 4.83it/s] 30%|███ | 15/50 [00:03<00:07, 4.82it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.82it/s] 34%|███▍ | 17/50 [00:03<00:06, 4.81it/s] 36%|███▌ | 18/50 [00:03<00:06, 4.81it/s] 38%|███▊ | 19/50 [00:03<00:06, 4.82it/s] 40%|████ | 20/50 [00:04<00:06, 4.82it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.82it/s] 44%|████▍ | 22/50 [00:04<00:05, 4.82it/s] 46%|████▌ | 23/50 [00:04<00:05, 4.82it/s] 48%|████▊ | 24/50 [00:04<00:05, 4.83it/s] 50%|█████ | 25/50 [00:05<00:05, 4.82it/s] 52%|█████▏ | 26/50 [00:05<00:04, 4.82it/s] 54%|█████▍ | 27/50 [00:05<00:04, 4.82it/s] 56%|█████▌ | 28/50 [00:05<00:04, 4.82it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.82it/s] 60%|██████ | 30/50 [00:06<00:04, 4.82it/s] 62%|██████▏ | 31/50 [00:06<00:03, 4.82it/s] 64%|██████▍ | 32/50 [00:06<00:03, 4.82it/s] 66%|██████▌ | 33/50 [00:06<00:03, 4.82it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.82it/s] 70%|███████ | 35/50 [00:07<00:03, 4.82it/s] 72%|███████▏ | 36/50 [00:07<00:02, 4.82it/s] 74%|███████▍ | 37/50 [00:07<00:02, 4.82it/s] 76%|███████▌ | 38/50 [00:07<00:02, 4.82it/s] 78%|███████▊ | 39/50 [00:08<00:02, 4.81it/s] 80%|████████ | 40/50 [00:08<00:02, 4.82it/s] 82%|████████▏ | 41/50 [00:08<00:01, 4.81it/s] 84%|████████▍ | 42/50 [00:08<00:01, 4.81it/s] 86%|████████▌ | 43/50 [00:08<00:01, 4.81it/s] 88%|████████▊ | 44/50 [00:09<00:01, 4.81it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.81it/s] 92%|█████████▏| 46/50 [00:09<00:00, 4.81it/s] 94%|█████████▍| 47/50 [00:09<00:00, 4.81it/s] 96%|█████████▌| 48/50 [00:09<00:00, 4.81it/s] 98%|█████████▊| 49/50 [00:10<00:00, 4.81it/s] 100%|██████████| 50/50 [00:10<00:00, 4.82it/s] 100%|██████████| 50/50 [00:10<00:00, 4.82it/s]
Want to make some of these yourself?
Run this model