copilot-us
/
sdxl-tnmt
An attempt to render Teenage Mutant Ninja Turtles: Mutant Mayhem-like images
- Public
- 93 runs
-
L40S
- SDXL fine-tune
- Paper
Prediction
copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dcIDlzm4madb25rdqqcmiot7xuvuqaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A big panda standing on two legs holding a katana in NY
- 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
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A big panda standing on two legs holding a katana in NY", "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, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", { input: { width: 1024, height: 1024, prompt: "A big panda standing on two legs holding a katana in NY", 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, 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 copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", input={ "width": 1024, "height": 1024, "prompt": "A big panda standing on two legs holding a katana in NY", "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, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run copilot-us/sdxl-tnmt 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": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", "input": { "width": 1024, "height": 1024, "prompt": "A big panda standing on two legs holding a katana in NY", "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, "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": "2023-12-08T07:10:28.495098Z", "created_at": "2023-12-08T07:10:08.852211Z", "data_removed": false, "error": null, "id": "lzm4madb25rdqqcmiot7xuvuqa", "input": { "width": 1024, "height": 1024, "prompt": "A big panda standing on two legs holding a katana in NY", "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, "num_inference_steps": 50 }, "logs": "Using seed: 33423\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A big panda standing on two legs holding a katana in NY\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.66it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.65it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.63it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.62it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.62it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.62it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.62it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.62it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.62it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.62it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]", "metrics": { "predict_time": 16.968646, "total_time": 19.642887 }, "output": [ "https://replicate.delivery/pbxt/JxijZGlJyKrzJhlAH97wt5BEFLde5v3IafJLScNf6f7N2cAIB/out-0.png" ], "started_at": "2023-12-08T07:10:11.526452Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lzm4madb25rdqqcmiot7xuvuqa", "cancel": "https://api.replicate.com/v1/predictions/lzm4madb25rdqqcmiot7xuvuqa/cancel" }, "version": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc" }Generated inUsing seed: 33423 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A big panda standing on two legs holding a katana in NY txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.66it/s] 6%|▌ | 3/50 [00:00<00:12, 3.65it/s] 8%|▊ | 4/50 [00:01<00:12, 3.64it/s] 10%|█ | 5/50 [00:01<00:12, 3.63it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s] 50%|█████ | 25/50 [00:06<00:06, 3.62it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s] 60%|██████ | 30/50 [00:08<00:05, 3.62it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.62it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s] 80%|████████ | 40/50 [00:11<00:02, 3.62it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.62it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.62it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.62it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s]Prediction
copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dcID6zx2cjdb6ntq3mpspdmzidro4yStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A moose standing on two legs holding a fighting axe in Kyiv
- 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
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A moose standing on two legs holding a fighting axe in Kyiv", "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, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", { input: { width: 1024, height: 1024, prompt: "A moose standing on two legs holding a fighting axe in Kyiv", 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, 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 copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", input={ "width": 1024, "height": 1024, "prompt": "A moose standing on two legs holding a fighting axe in Kyiv", "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, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run copilot-us/sdxl-tnmt 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": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", "input": { "width": 1024, "height": 1024, "prompt": "A moose standing on two legs holding a fighting axe in Kyiv", "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, "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": "2023-12-08T07:21:02.604077Z", "created_at": "2023-12-08T07:20:42.961464Z", "data_removed": false, "error": null, "id": "6zx2cjdb6ntq3mpspdmzidro4y", "input": { "width": 1024, "height": 1024, "prompt": "A moose standing on two legs holding a fighting axe in Kyiv", "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, "num_inference_steps": 50 }, "logs": "Using seed: 22936\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A moose standing on two legs holding a fighting axe in Kyiv\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.65it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.65it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 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[00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 16.305936, "total_time": 19.642613 }, "output": [ "https://replicate.delivery/pbxt/J3l6l7mG6TaeVqyphKoZFBDCYQadt7I66r6CxjcT5pnurDAJA/out-0.png" ], "started_at": "2023-12-08T07:20:46.298141Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6zx2cjdb6ntq3mpspdmzidro4y", "cancel": "https://api.replicate.com/v1/predictions/6zx2cjdb6ntq3mpspdmzidro4y/cancel" }, "version": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc" }Generated inUsing seed: 22936 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A moose standing on two legs holding a fighting axe in Kyiv txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.65it/s] 6%|▌ | 3/50 [00:00<00:12, 3.65it/s] 8%|▊ | 4/50 [00:01<00:12, 3.64it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 3.66it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s] 50%|█████ | 25/50 [00:06<00:06, 3.65it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.60it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dcIDx6324udbqvunloonwjprppvxpiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A big panda standing on two legs holding a cossack saber in Kyiv
- 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
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A big panda standing on two legs holding a cossack saber in Kyiv", "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, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", { input: { width: 1024, height: 1024, prompt: "A big panda standing on two legs holding a cossack saber in Kyiv", 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, 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 copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", input={ "width": 1024, "height": 1024, "prompt": "A big panda standing on two legs holding a cossack saber in Kyiv", "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, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run copilot-us/sdxl-tnmt 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": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", "input": { "width": 1024, "height": 1024, "prompt": "A big panda standing on two legs holding a cossack saber in Kyiv", "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, "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": "2023-12-08T07:18:00.736443Z", "created_at": "2023-12-08T07:17:38.545443Z", "data_removed": false, "error": null, "id": "x6324udbqvunloonwjprppvxpi", "input": { "width": 1024, "height": 1024, "prompt": "A big panda standing on two legs holding a cossack saber in Kyiv", "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, "num_inference_steps": 50 }, "logs": "Using seed: 48887\nEnsuring enough disk space...\nFree disk space: 1754577903616\nDownloading weights: https://replicate.delivery/pbxt/eROVrsHHPm2EeU4eDL6EhKTLKlOjtQcT0vn51lMYKpVOpNAkA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.341s (546 MB/s)\\nExtracted 186 MB in 0.053s (3.5 GB/s)\\n'\nDownloaded weights in 0.5155699253082275 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A big panda standing on two legs holding a cossack saber in Kyiv\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.65it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.65it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.62it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.64it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 16.868565, "total_time": 22.191 }, "output": [ "https://replicate.delivery/pbxt/xI6a39ENYFKbGBd3yRVgoHbc4SubnZZksyWURCKA0lwJ1BgE/out-0.png" ], "started_at": "2023-12-08T07:17:43.867878Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/x6324udbqvunloonwjprppvxpi", "cancel": "https://api.replicate.com/v1/predictions/x6324udbqvunloonwjprppvxpi/cancel" }, "version": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc" }Generated inUsing seed: 48887 Ensuring enough disk space... Free disk space: 1754577903616 Downloading weights: https://replicate.delivery/pbxt/eROVrsHHPm2EeU4eDL6EhKTLKlOjtQcT0vn51lMYKpVOpNAkA/trained_model.tar b'Downloaded 186 MB bytes in 0.341s (546 MB/s)\nExtracted 186 MB in 0.053s (3.5 GB/s)\n' Downloaded weights in 0.5155699253082275 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A big panda standing on two legs holding a cossack saber in Kyiv txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.65it/s] 6%|▌ | 3/50 [00:00<00:12, 3.65it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s] 40%|████ | 20/50 [00:05<00:08, 3.63it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s] 50%|█████ | 25/50 [00:06<00:06, 3.62it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.64it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s] 80%|████████ | 40/50 [00:11<00:02, 3.64it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]Prediction
copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dcIDfrlkq5dbqkypulctfvmo5uaxeqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- Make him holding a sword
- 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
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/K0mooyBUVszG9pOZXXGk1Dx3ERlE96Exlp98G1oxcviPXLQM/IMG-20200603-WA0003.jpg", "width": 1024, "height": 1024, "prompt": "Make him holding a sword", "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, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", { input: { image: "https://replicate.delivery/pbxt/K0mooyBUVszG9pOZXXGk1Dx3ERlE96Exlp98G1oxcviPXLQM/IMG-20200603-WA0003.jpg", width: 1024, height: 1024, prompt: "Make him holding a sword", 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, 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 copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", input={ "image": "https://replicate.delivery/pbxt/K0mooyBUVszG9pOZXXGk1Dx3ERlE96Exlp98G1oxcviPXLQM/IMG-20200603-WA0003.jpg", "width": 1024, "height": 1024, "prompt": "Make him holding a sword", "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, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run copilot-us/sdxl-tnmt 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": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", "input": { "image": "https://replicate.delivery/pbxt/K0mooyBUVszG9pOZXXGk1Dx3ERlE96Exlp98G1oxcviPXLQM/IMG-20200603-WA0003.jpg", "width": 1024, "height": 1024, "prompt": "Make him holding a sword", "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, "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": "2023-12-08T07:26:55.350329Z", "created_at": "2023-12-08T07:25:51.631432Z", "data_removed": false, "error": null, "id": "frlkq5dbqkypulctfvmo5uaxeq", "input": { "image": "https://replicate.delivery/pbxt/K0mooyBUVszG9pOZXXGk1Dx3ERlE96Exlp98G1oxcviPXLQM/IMG-20200603-WA0003.jpg", "width": 1024, "height": 1024, "prompt": "Make him holding a sword", "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, "num_inference_steps": 50 }, "logs": "Using seed: 40841\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Make him holding a sword\nimg2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:13, 2.86it/s]\n 5%|▌ | 2/40 [00:00<00:13, 2.91it/s]\n 8%|▊ | 3/40 [00:01<00:12, 2.93it/s]\n 10%|█ | 4/40 [00:01<00:12, 2.93it/s]\n 12%|█▎ | 5/40 [00:01<00:11, 2.94it/s]\n 15%|█▌ | 6/40 [00:02<00:11, 2.94it/s]\n 18%|█▊ | 7/40 [00:02<00:11, 2.94it/s]\n 20%|██ | 8/40 [00:02<00:10, 2.94it/s]\n 22%|██▎ | 9/40 [00:03<00:10, 2.94it/s]\n 25%|██▌ | 10/40 [00:03<00:10, 2.94it/s]\n 28%|██▊ | 11/40 [00:03<00:09, 2.94it/s]\n 30%|███ | 12/40 [00:04<00:09, 2.94it/s]\n 32%|███▎ | 13/40 [00:04<00:09, 2.94it/s]\n 35%|███▌ | 14/40 [00:04<00:08, 2.94it/s]\n 38%|███▊ | 15/40 [00:05<00:08, 2.94it/s]\n 40%|████ | 16/40 [00:05<00:08, 2.94it/s]\n 42%|████▎ | 17/40 [00:05<00:07, 2.93it/s]\n 45%|████▌ | 18/40 [00:06<00:07, 2.93it/s]\n 48%|████▊ | 19/40 [00:06<00:07, 2.94it/s]\n 50%|█████ | 20/40 [00:06<00:06, 2.94it/s]\n 52%|█████▎ | 21/40 [00:07<00:06, 2.94it/s]\n 55%|█████▌ | 22/40 [00:07<00:06, 2.94it/s]\n 57%|█████▊ | 23/40 [00:07<00:05, 2.94it/s]\n 60%|██████ | 24/40 [00:08<00:05, 2.94it/s]\n 62%|██████▎ | 25/40 [00:08<00:05, 2.94it/s]\n 65%|██████▌ | 26/40 [00:08<00:04, 2.94it/s]\n 68%|██████▊ | 27/40 [00:09<00:04, 2.94it/s]\n 70%|███████ | 28/40 [00:09<00:04, 2.94it/s]\n 72%|███████▎ | 29/40 [00:09<00:03, 2.94it/s]\n 75%|███████▌ | 30/40 [00:10<00:03, 2.94it/s]\n 78%|███████▊ | 31/40 [00:10<00:03, 2.94it/s]\n 80%|████████ | 32/40 [00:10<00:02, 2.94it/s]\n 82%|████████▎ | 33/40 [00:11<00:02, 2.94it/s]\n 85%|████████▌ | 34/40 [00:11<00:02, 2.94it/s]\n 88%|████████▊ | 35/40 [00:11<00:01, 2.94it/s]\n 90%|█████████ | 36/40 [00:12<00:01, 2.94it/s]\n 92%|█████████▎| 37/40 [00:12<00:01, 2.94it/s]\n 95%|█████████▌| 38/40 [00:12<00:00, 2.94it/s]\n 98%|█████████▊| 39/40 [00:13<00:00, 2.94it/s]\n100%|██████████| 40/40 [00:13<00:00, 2.94it/s]\n100%|██████████| 40/40 [00:13<00:00, 2.94it/s]", "metrics": { "predict_time": 17.230588, "total_time": 63.718897 }, "output": [ "https://replicate.delivery/pbxt/5Hwk7xkxSQq5DBSBAvglzhDHILgERlTfjBpfFeJA8nZ85OAkA/out-0.png" ], "started_at": "2023-12-08T07:26:38.119741Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/frlkq5dbqkypulctfvmo5uaxeq", "cancel": "https://api.replicate.com/v1/predictions/frlkq5dbqkypulctfvmo5uaxeq/cancel" }, "version": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc" }Generated inUsing seed: 40841 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Make him holding a sword img2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:13, 2.86it/s] 5%|▌ | 2/40 [00:00<00:13, 2.91it/s] 8%|▊ | 3/40 [00:01<00:12, 2.93it/s] 10%|█ | 4/40 [00:01<00:12, 2.93it/s] 12%|█▎ | 5/40 [00:01<00:11, 2.94it/s] 15%|█▌ | 6/40 [00:02<00:11, 2.94it/s] 18%|█▊ | 7/40 [00:02<00:11, 2.94it/s] 20%|██ | 8/40 [00:02<00:10, 2.94it/s] 22%|██▎ | 9/40 [00:03<00:10, 2.94it/s] 25%|██▌ | 10/40 [00:03<00:10, 2.94it/s] 28%|██▊ | 11/40 [00:03<00:09, 2.94it/s] 30%|███ | 12/40 [00:04<00:09, 2.94it/s] 32%|███▎ | 13/40 [00:04<00:09, 2.94it/s] 35%|███▌ | 14/40 [00:04<00:08, 2.94it/s] 38%|███▊ | 15/40 [00:05<00:08, 2.94it/s] 40%|████ | 16/40 [00:05<00:08, 2.94it/s] 42%|████▎ | 17/40 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100%|██████████| 40/40 [00:13<00:00, 2.94it/s] 100%|██████████| 40/40 [00:13<00:00, 2.94it/s]Prediction
copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dcIDqk5hdvdb5jplbbd4g4hmwwjtlyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A cartoon human-like falcon holding a sword in midnight Kyiv
- 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
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A cartoon human-like falcon holding a sword in midnight Kyiv", "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, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", { input: { width: 1024, height: 1024, prompt: "A cartoon human-like falcon holding a sword in midnight Kyiv", 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, 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 copilot-us/sdxl-tnmt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "copilot-us/sdxl-tnmt:f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", input={ "width": 1024, "height": 1024, "prompt": "A cartoon human-like falcon holding a sword in midnight Kyiv", "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, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run copilot-us/sdxl-tnmt 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": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc", "input": { "width": 1024, "height": 1024, "prompt": "A cartoon human-like falcon holding a sword in midnight Kyiv", "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, "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": "2023-12-08T07:49:33.586387Z", "created_at": "2023-12-08T07:49:11.424743Z", "data_removed": false, "error": null, "id": "qk5hdvdb5jplbbd4g4hmwwjtly", "input": { "width": 1024, "height": 1024, "prompt": "A cartoon human-like falcon holding a sword in midnight Kyiv", "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, "num_inference_steps": 50 }, "logs": "Using seed: 41668\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A cartoon human-like falcon holding a sword in midnight Kyiv\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 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[00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 16.824641, "total_time": 22.161644 }, "output": [ "https://replicate.delivery/pbxt/qf3zfMKkAjmPp0CpVmC2QgRawc9xpiC8NL3tYbY4TeIYkPAkA/out-0.png" ], "started_at": "2023-12-08T07:49:16.761746Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qk5hdvdb5jplbbd4g4hmwwjtly", "cancel": "https://api.replicate.com/v1/predictions/qk5hdvdb5jplbbd4g4hmwwjtly/cancel" }, "version": "f4e6397f77cf17fc192509c1a2a12d675fa8cadfaf7bee05cea6ca883e8559dc" }Generated inUsing seed: 41668 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A cartoon human-like falcon holding a sword in midnight Kyiv txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 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