aicapcut / anima-pencil-v310-with-layer-diffuse
Generate image with transparent background
- Public
- 634 runs
-
L40S
- GitHub
Prediction
aicapcut/anima-pencil-v310-with-layer-diffuse:b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31IDrj9py4mvdxrgp0cfdhs8jfa9a8StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- cfg
- 6
- width
- 768
- height
- 1280
- prompt
- 1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored
- scheduler
- ddim_uniform
- num_outputs
- 1
- sampler_name
- dpmpp_sde
- negative_prompt
- watermark, text
- num_inference_steps
- 25
{ "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 }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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31", { input: { cfg: 6, width: 768, height: 1280, prompt: "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", scheduler: "ddim_uniform", num_outputs: 1, sampler_name: "dpmpp_sde", negative_prompt: "watermark, text", num_inference_steps: 25 } } ); // 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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31", input={ "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 } ) # 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 aicapcut/anima-pencil-v310-with-layer-diffuse 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": "aicapcut/anima-pencil-v310-with-layer-diffuse:b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31", "input": { "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-12T11:00:15.451245Z", "created_at": "2024-05-12T10:56:41.327000Z", "data_removed": false, "error": null, "id": "rj9py4mvdxrgp0cfdhs8jfa9a8", "input": { "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 }, "logs": "Requested to load SDXLClipModel\nLoading 1 new model\nRequested to load SDXL\nLoading 1 new model\n 0%| | 0/25 [00:00<?, ?it/s]/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=11.676048278808594 and t1=11.676046.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n 4%|▍ | 1/25 [00:00<00:15, 1.57it/s]\n 8%|▊ | 2/25 [00:01<00:11, 2.02it/s]\n 12%|█▏ | 3/25 [00:01<00:09, 2.22it/s]\n 16%|█▌ | 4/25 [00:01<00:09, 2.29it/s]\n 20%|██ | 5/25 [00:02<00:08, 2.39it/s]\n 24%|██▍ | 6/25 [00:02<00:07, 2.42it/s]\n 28%|██▊ | 7/25 [00:03<00:07, 2.45it/s]\n 32%|███▏ | 8/25 [00:03<00:06, 2.46it/s]\n 36%|███▌ | 9/25 [00:03<00:06, 2.44it/s]\n 40%|████ | 10/25 [00:04<00:06, 2.46it/s]\n 44%|████▍ | 11/25 [00:04<00:05, 2.47it/s]\n 48%|████▊ | 12/25 [00:05<00:05, 2.48it/s]\n 52%|█████▏ | 13/25 [00:05<00:04, 2.49it/s]\n 56%|█████▌ | 14/25 [00:05<00:04, 2.49it/s]\n 60%|██████ | 15/25 [00:06<00:03, 2.50it/s]\n 64%|██████▍ | 16/25 [00:06<00:03, 2.51it/s]\n 68%|██████▊ | 17/25 [00:07<00:03, 2.51it/s]\n 72%|███████▏ | 18/25 [00:07<00:02, 2.51it/s]\n 76%|███████▌ | 19/25 [00:07<00:02, 2.51it/s]\n 80%|████████ | 20/25 [00:08<00:01, 2.51it/s]\n 84%|████████▍ | 21/25 [00:08<00:01, 2.51it/s]\n 88%|████████▊ | 22/25 [00:09<00:01, 2.51it/s]\n 92%|█████████▏| 23/25 [00:09<00:00, 2.50it/s]\n 96%|█████████▌| 24/25 [00:09<00:00, 2.54it/s]\n100%|██████████| 25/25 [00:10<00:00, 3.00it/s]\n100%|██████████| 25/25 [00:10<00:00, 2.50it/s]\nRequested to load AutoencoderKL\nLoading 1 new model\n/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/diffusers/models/unet_2d_blocks.py:76: FutureWarning: `get_down_block` is deprecated and will be removed in version 0.29. Importing `get_down_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_down_block`, instead.\ndeprecate(\"get_down_block\", \"0.29\", deprecation_message)\n/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/diffusers/models/unet_2d_blocks.py:213: FutureWarning: `get_up_block` is deprecated and will be removed in version 0.29. Importing `get_up_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_up_block`, instead.\ndeprecate(\"get_up_block\", \"0.29\", deprecation_message)\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:00<00:02, 3.40it/s]\n 25%|██▌ | 2/8 [00:00<00:01, 4.59it/s]\n 38%|███▊ | 3/8 [00:00<00:00, 5.91it/s]\n 50%|█████ | 4/8 [00:00<00:00, 6.01it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 6.07it/s]\n 75%|███████▌ | 6/8 [00:01<00:00, 6.11it/s]\n 88%|████████▊ | 7/8 [00:01<00:00, 6.13it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.14it/s]\n100%|██████████| 8/8 [00:01<00:00, 5.80it/s]\n[Path('output/output_00001_.png')]", "metrics": { "predict_time": 15.767516, "total_time": 214.124245 }, "output": [ "https://replicate.delivery/pbxt/aRQXOjCO08L0NNSjS8f5zpifJbjsgeRdarRXiex8YKe0npcWC/output_00001_.png" ], "started_at": "2024-05-12T10:59:59.683729Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rj9py4mvdxrgp0cfdhs8jfa9a8", "cancel": "https://api.replicate.com/v1/predictions/rj9py4mvdxrgp0cfdhs8jfa9a8/cancel" }, "version": "b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31" }Generated inRequested to load SDXLClipModel Loading 1 new model Requested to load SDXL Loading 1 new model 0%| | 0/25 [00:00<?, ?it/s]/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=11.676048278808594 and t1=11.676046. warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.") 4%|▍ | 1/25 [00:00<00:15, 1.57it/s] 8%|▊ | 2/25 [00:01<00:11, 2.02it/s] 12%|█▏ | 3/25 [00:01<00:09, 2.22it/s] 16%|█▌ | 4/25 [00:01<00:09, 2.29it/s] 20%|██ | 5/25 [00:02<00:08, 2.39it/s] 24%|██▍ | 6/25 [00:02<00:07, 2.42it/s] 28%|██▊ | 7/25 [00:03<00:07, 2.45it/s] 32%|███▏ | 8/25 [00:03<00:06, 2.46it/s] 36%|███▌ | 9/25 [00:03<00:06, 2.44it/s] 40%|████ | 10/25 [00:04<00:06, 2.46it/s] 44%|████▍ | 11/25 [00:04<00:05, 2.47it/s] 48%|████▊ | 12/25 [00:05<00:05, 2.48it/s] 52%|█████▏ | 13/25 [00:05<00:04, 2.49it/s] 56%|█████▌ | 14/25 [00:05<00:04, 2.49it/s] 60%|██████ | 15/25 [00:06<00:03, 2.50it/s] 64%|██████▍ | 16/25 [00:06<00:03, 2.51it/s] 68%|██████▊ | 17/25 [00:07<00:03, 2.51it/s] 72%|███████▏ | 18/25 [00:07<00:02, 2.51it/s] 76%|███████▌ | 19/25 [00:07<00:02, 2.51it/s] 80%|████████ | 20/25 [00:08<00:01, 2.51it/s] 84%|████████▍ | 21/25 [00:08<00:01, 2.51it/s] 88%|████████▊ | 22/25 [00:09<00:01, 2.51it/s] 92%|█████████▏| 23/25 [00:09<00:00, 2.50it/s] 96%|█████████▌| 24/25 [00:09<00:00, 2.54it/s] 100%|██████████| 25/25 [00:10<00:00, 3.00it/s] 100%|██████████| 25/25 [00:10<00:00, 2.50it/s] Requested to load AutoencoderKL Loading 1 new model /root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/diffusers/models/unet_2d_blocks.py:76: FutureWarning: `get_down_block` is deprecated and will be removed in version 0.29. Importing `get_down_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_down_block`, instead. deprecate("get_down_block", "0.29", deprecation_message) /root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/diffusers/models/unet_2d_blocks.py:213: FutureWarning: `get_up_block` is deprecated and will be removed in version 0.29. Importing `get_up_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_up_block`, instead. deprecate("get_up_block", "0.29", deprecation_message) 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:00<00:02, 3.40it/s] 25%|██▌ | 2/8 [00:00<00:01, 4.59it/s] 38%|███▊ | 3/8 [00:00<00:00, 5.91it/s] 50%|█████ | 4/8 [00:00<00:00, 6.01it/s] 62%|██████▎ | 5/8 [00:00<00:00, 6.07it/s] 75%|███████▌ | 6/8 [00:01<00:00, 6.11it/s] 88%|████████▊ | 7/8 [00:01<00:00, 6.13it/s] 100%|██████████| 8/8 [00:01<00:00, 6.14it/s] 100%|██████████| 8/8 [00:01<00:00, 5.80it/s] [Path('output/output_00001_.png')]Prediction
aicapcut/anima-pencil-v310-with-layer-diffuse:765354a0c739ef8951e50f9ff8aaa839f97627a3fa8a1b0b08941a9e79d800e0IDqma6psezm5rgj0cfe5hspd2v6cStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- cfg
- 6
- width
- 767
- height
- 1280
- prompt
- 1girl, solo, cute, white short hair, oversized T shirt
- scheduler
- ddim_uniform
- num_outputs
- 1
- sampler_name
- dpmpp_sde
- negative_prompt
- watermark, text
- num_inference_steps
- 25
{ "cfg": 6, "width": 767, "height": 1280, "prompt": "1girl, solo, cute, white short hair, oversized T shirt", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 }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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:765354a0c739ef8951e50f9ff8aaa839f97627a3fa8a1b0b08941a9e79d800e0", { input: { cfg: 6, width: 767, height: 1280, prompt: "1girl, solo, cute, white short hair, oversized T shirt", scheduler: "ddim_uniform", num_outputs: 1, sampler_name: "dpmpp_sde", negative_prompt: "watermark, text", num_inference_steps: 25 } } ); // 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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:765354a0c739ef8951e50f9ff8aaa839f97627a3fa8a1b0b08941a9e79d800e0", input={ "cfg": 6, "width": 767, "height": 1280, "prompt": "1girl, solo, cute, white short hair, oversized T shirt", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 } ) # 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 aicapcut/anima-pencil-v310-with-layer-diffuse 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": "aicapcut/anima-pencil-v310-with-layer-diffuse:765354a0c739ef8951e50f9ff8aaa839f97627a3fa8a1b0b08941a9e79d800e0", "input": { "cfg": 6, "width": 767, "height": 1280, "prompt": "1girl, solo, cute, white short hair, oversized T shirt", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-13T09:58:55.618774Z", "created_at": "2024-05-13T09:58:41.825000Z", "data_removed": false, "error": null, "id": "qma6psezm5rgj0cfe5hspd2v6c", "input": { "cfg": 6, "width": 767, "height": 1280, "prompt": "1girl, solo, cute, white short hair, oversized T shirt", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 }, "logs": "[Info] Output witdh has been resized to 768\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:09, 2.64it/s]\n 8%|▊ | 2/25 [00:00<00:09, 2.54it/s]\n 12%|█▏ | 3/25 [00:01<00:08, 2.53it/s]\n 16%|█▌ | 4/25 [00:01<00:08, 2.48it/s]\n 20%|██ | 5/25 [00:01<00:07, 2.51it/s]\n 24%|██▍ | 6/25 [00:02<00:07, 2.50it/s]\n 28%|██▊ | 7/25 [00:02<00:07, 2.49it/s]\n 32%|███▏ | 8/25 [00:03<00:06, 2.48it/s]\n 36%|███▌ | 9/25 [00:03<00:06, 2.47it/s]\n 40%|████ | 10/25 [00:04<00:06, 2.48it/s]\n 44%|████▍ | 11/25 [00:04<00:05, 2.48it/s]\n 48%|████▊ | 12/25 [00:04<00:05, 2.48it/s]\n 52%|█████▏ | 13/25 [00:05<00:04, 2.49it/s]\n 56%|█████▌ | 14/25 [00:05<00:04, 2.48it/s]\n 60%|██████ | 15/25 [00:06<00:04, 2.49it/s]\n 64%|██████▍ | 16/25 [00:06<00:03, 2.50it/s]\n 68%|██████▊ | 17/25 [00:06<00:03, 2.51it/s]\n 72%|███████▏ | 18/25 [00:07<00:02, 2.50it/s]\n 76%|███████▌ | 19/25 [00:07<00:02, 2.50it/s]\n 80%|████████ | 20/25 [00:08<00:02, 2.50it/s]\n 84%|████████▍ | 21/25 [00:08<00:01, 2.48it/s]\n 88%|████████▊ | 22/25 [00:08<00:01, 2.49it/s]\n 92%|█████████▏| 23/25 [00:09<00:00, 2.49it/s]\n 96%|█████████▌| 24/25 [00:09<00:00, 2.53it/s]\n100%|██████████| 25/25 [00:09<00:00, 3.03it/s]\n100%|██████████| 25/25 [00:09<00:00, 2.56it/s]\n 0%| | 0/8 [00:00<?, ?it/s]\n 25%|██▌ | 2/8 [00:00<00:00, 9.86it/s]\n 38%|███▊ | 3/8 [00:00<00:00, 7.90it/s]\n 50%|█████ | 4/8 [00:00<00:00, 7.16it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 6.78it/s]\n 75%|███████▌ | 6/8 [00:00<00:00, 6.56it/s]\n 88%|████████▊ | 7/8 [00:01<00:00, 6.43it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.35it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.81it/s]\n[Path('utils/output/output_00008_.png')]", "metrics": { "predict_time": 13.747735, "total_time": 13.793774 }, "output": [ "https://replicate.delivery/pbxt/m5MBziweH1T9Saj365FCfS83LLrQE84ikmO2yO6ZXGLeyynlA/output_00008_.png" ], "started_at": "2024-05-13T09:58:41.871039Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qma6psezm5rgj0cfe5hspd2v6c", "cancel": "https://api.replicate.com/v1/predictions/qma6psezm5rgj0cfe5hspd2v6c/cancel" }, "version": "765354a0c739ef8951e50f9ff8aaa839f97627a3fa8a1b0b08941a9e79d800e0" }Generated in[Info] Output witdh has been resized to 768 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:09, 2.64it/s] 8%|▊ | 2/25 [00:00<00:09, 2.54it/s] 12%|█▏ | 3/25 [00:01<00:08, 2.53it/s] 16%|█▌ | 4/25 [00:01<00:08, 2.48it/s] 20%|██ | 5/25 [00:01<00:07, 2.51it/s] 24%|██▍ | 6/25 [00:02<00:07, 2.50it/s] 28%|██▊ | 7/25 [00:02<00:07, 2.49it/s] 32%|███▏ | 8/25 [00:03<00:06, 2.48it/s] 36%|███▌ | 9/25 [00:03<00:06, 2.47it/s] 40%|████ | 10/25 [00:04<00:06, 2.48it/s] 44%|████▍ | 11/25 [00:04<00:05, 2.48it/s] 48%|████▊ | 12/25 [00:04<00:05, 2.48it/s] 52%|█████▏ | 13/25 [00:05<00:04, 2.49it/s] 56%|█████▌ | 14/25 [00:05<00:04, 2.48it/s] 60%|██████ | 15/25 [00:06<00:04, 2.49it/s] 64%|██████▍ | 16/25 [00:06<00:03, 2.50it/s] 68%|██████▊ | 17/25 [00:06<00:03, 2.51it/s] 72%|███████▏ | 18/25 [00:07<00:02, 2.50it/s] 76%|███████▌ | 19/25 [00:07<00:02, 2.50it/s] 80%|████████ | 20/25 [00:08<00:02, 2.50it/s] 84%|████████▍ | 21/25 [00:08<00:01, 2.48it/s] 88%|████████▊ | 22/25 [00:08<00:01, 2.49it/s] 92%|█████████▏| 23/25 [00:09<00:00, 2.49it/s] 96%|█████████▌| 24/25 [00:09<00:00, 2.53it/s] 100%|██████████| 25/25 [00:09<00:00, 3.03it/s] 100%|██████████| 25/25 [00:09<00:00, 2.56it/s] 0%| | 0/8 [00:00<?, ?it/s] 25%|██▌ | 2/8 [00:00<00:00, 9.86it/s] 38%|███▊ | 3/8 [00:00<00:00, 7.90it/s] 50%|█████ | 4/8 [00:00<00:00, 7.16it/s] 62%|██████▎ | 5/8 [00:00<00:00, 6.78it/s] 75%|███████▌ | 6/8 [00:00<00:00, 6.56it/s] 88%|████████▊ | 7/8 [00:01<00:00, 6.43it/s] 100%|██████████| 8/8 [00:01<00:00, 6.35it/s] 100%|██████████| 8/8 [00:01<00:00, 6.81it/s] [Path('utils/output/output_00008_.png')]Prediction
aicapcut/anima-pencil-v310-with-layer-diffuse:b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31IDyjhspr292srgm0cfd5d8xy6v8gStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- cfg
- 6
- width
- 768
- height
- 1280
- prompt
- 1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored
- scheduler
- ddim_uniform
- num_outputs
- 1
- sampler_name
- dpmpp_sde
- negative_prompt
- watermark, text
- num_inference_steps
- 25
{ "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 }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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31", { input: { cfg: 6, width: 768, height: 1280, prompt: "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", scheduler: "ddim_uniform", num_outputs: 1, sampler_name: "dpmpp_sde", negative_prompt: "watermark, text", num_inference_steps: 25 } } ); // 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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31", input={ "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 } ) # 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 aicapcut/anima-pencil-v310-with-layer-diffuse 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": "aicapcut/anima-pencil-v310-with-layer-diffuse:b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31", "input": { "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-11T20:31:31.294594Z", "created_at": "2024-05-11T20:31:15.734000Z", "data_removed": false, "error": null, "id": "yjhspr292srgm0cfd5d8xy6v8g", "input": { "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, cute, white short hair, wizard hat, close-up, oversized shirt, shorts, black thighhighs, lazy, bored", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 }, "logs": "Requested to load SDXL\nLoading 1 new model\n 0%| | 0/25 [00:00<?, ?it/s]/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=11.676048278808594 and t1=11.676046.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n 4%|▍ | 1/25 [00:00<00:09, 2.62it/s]\n 8%|▊ | 2/25 [00:00<00:09, 2.53it/s]\n 12%|█▏ | 3/25 [00:01<00:08, 2.52it/s]\n 16%|█▌ | 4/25 [00:01<00:08, 2.48it/s]\n 20%|██ | 5/25 [00:01<00:07, 2.50it/s]\n 24%|██▍ | 6/25 [00:02<00:07, 2.49it/s]\n 28%|██▊ | 7/25 [00:02<00:07, 2.49it/s]\n 32%|███▏ | 8/25 [00:03<00:06, 2.48it/s]\n 36%|███▌ | 9/25 [00:03<00:06, 2.47it/s]\n 40%|████ | 10/25 [00:04<00:06, 2.47it/s]\n 44%|████▍ | 11/25 [00:04<00:05, 2.48it/s]\n 48%|████▊ | 12/25 [00:04<00:05, 2.48it/s]\n 52%|█████▏ | 13/25 [00:05<00:04, 2.47it/s]\n 56%|█████▌ | 14/25 [00:05<00:04, 2.48it/s]\n 60%|██████ | 15/25 [00:06<00:04, 2.49it/s]\n 64%|██████▍ | 16/25 [00:06<00:03, 2.49it/s]\n 68%|██████▊ | 17/25 [00:06<00:03, 2.49it/s]\n 72%|███████▏ | 18/25 [00:07<00:02, 2.47it/s]\n 76%|███████▌ | 19/25 [00:07<00:02, 2.48it/s]\n 80%|████████ | 20/25 [00:08<00:02, 2.48it/s]\n 84%|████████▍ | 21/25 [00:08<00:01, 2.48it/s]\n 88%|████████▊ | 22/25 [00:08<00:01, 2.47it/s]\n 92%|█████████▏| 23/25 [00:09<00:00, 2.47it/s]\n 96%|█████████▌| 24/25 [00:09<00:00, 2.51it/s]\n100%|██████████| 25/25 [00:09<00:00, 3.01it/s]\n100%|██████████| 25/25 [00:09<00:00, 2.54it/s]\n 0%| | 0/8 [00:00<?, ?it/s]\n 25%|██▌ | 2/8 [00:00<00:00, 9.86it/s]\n 38%|███▊ | 3/8 [00:00<00:00, 7.90it/s]\n 50%|█████ | 4/8 [00:00<00:00, 7.15it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 6.78it/s]\n 75%|███████▌ | 6/8 [00:00<00:00, 6.56it/s]\n 88%|████████▊ | 7/8 [00:01<00:00, 6.43it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.34it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.80it/s]\n[Path('output/output_00006_.png')]", "metrics": { "predict_time": 15.51078, "total_time": 15.560594 }, "output": [ "https://replicate.delivery/pbxt/yhSJgs93qzabJZCyOGBorVC2LAfHcc6fgT6ZGi4LgfkC9wmlA/output_00006_.png" ], "started_at": "2024-05-11T20:31:15.783814Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yjhspr292srgm0cfd5d8xy6v8g", "cancel": "https://api.replicate.com/v1/predictions/yjhspr292srgm0cfd5d8xy6v8g/cancel" }, "version": "b7abfd25b512e4d0cfb390c9904f1b6c6f37d8e97edb2bbe420191a97f845c31" }Generated inRequested to load SDXL Loading 1 new model 0%| | 0/25 [00:00<?, ?it/s]/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=11.676048278808594 and t1=11.676046. warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.") 4%|▍ | 1/25 [00:00<00:09, 2.62it/s] 8%|▊ | 2/25 [00:00<00:09, 2.53it/s] 12%|█▏ | 3/25 [00:01<00:08, 2.52it/s] 16%|█▌ | 4/25 [00:01<00:08, 2.48it/s] 20%|██ | 5/25 [00:01<00:07, 2.50it/s] 24%|██▍ | 6/25 [00:02<00:07, 2.49it/s] 28%|██▊ | 7/25 [00:02<00:07, 2.49it/s] 32%|███▏ | 8/25 [00:03<00:06, 2.48it/s] 36%|███▌ | 9/25 [00:03<00:06, 2.47it/s] 40%|████ | 10/25 [00:04<00:06, 2.47it/s] 44%|████▍ | 11/25 [00:04<00:05, 2.48it/s] 48%|████▊ | 12/25 [00:04<00:05, 2.48it/s] 52%|█████▏ | 13/25 [00:05<00:04, 2.47it/s] 56%|█████▌ | 14/25 [00:05<00:04, 2.48it/s] 60%|██████ | 15/25 [00:06<00:04, 2.49it/s] 64%|██████▍ | 16/25 [00:06<00:03, 2.49it/s] 68%|██████▊ | 17/25 [00:06<00:03, 2.49it/s] 72%|███████▏ | 18/25 [00:07<00:02, 2.47it/s] 76%|███████▌ | 19/25 [00:07<00:02, 2.48it/s] 80%|████████ | 20/25 [00:08<00:02, 2.48it/s] 84%|████████▍ | 21/25 [00:08<00:01, 2.48it/s] 88%|████████▊ | 22/25 [00:08<00:01, 2.47it/s] 92%|█████████▏| 23/25 [00:09<00:00, 2.47it/s] 96%|█████████▌| 24/25 [00:09<00:00, 2.51it/s] 100%|██████████| 25/25 [00:09<00:00, 3.01it/s] 100%|██████████| 25/25 [00:09<00:00, 2.54it/s] 0%| | 0/8 [00:00<?, ?it/s] 25%|██▌ | 2/8 [00:00<00:00, 9.86it/s] 38%|███▊ | 3/8 [00:00<00:00, 7.90it/s] 50%|█████ | 4/8 [00:00<00:00, 7.15it/s] 62%|██████▎ | 5/8 [00:00<00:00, 6.78it/s] 75%|███████▌ | 6/8 [00:00<00:00, 6.56it/s] 88%|████████▊ | 7/8 [00:01<00:00, 6.43it/s] 100%|██████████| 8/8 [00:01<00:00, 6.34it/s] 100%|██████████| 8/8 [00:01<00:00, 6.80it/s] [Path('output/output_00006_.png')]Prediction
aicapcut/anima-pencil-v310-with-layer-diffuse:bb3ea7280ad16e541590c7e577c52e0e306f077fb79bbf54137323b855886721IDe0qp3drxr1rgm0cfd5dtg12btcStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- cfg
- 6
- width
- 768
- height
- 1280
- prompt
- 1girl, solo, black coat, bow, chibi, coat, hair ornament, long hair, looking at viewer, pleated skirt, red background, red eyes, scarf, skirt, white dress, white hair, masterpiece, best quality
- scheduler
- normal
- num_outputs
- 1
- sampler_name
- dpmpp_sde
- negative_prompt
- watermark, text
- num_inference_steps
- 20
{ "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, black coat, bow, chibi, coat, hair ornament, long hair, looking at viewer, pleated skirt, red background, red eyes, scarf, skirt, white dress, white hair, masterpiece, best quality", "scheduler": "normal", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 20 }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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:bb3ea7280ad16e541590c7e577c52e0e306f077fb79bbf54137323b855886721", { input: { cfg: 6, width: 768, height: 1280, prompt: "1girl, solo, black coat, bow, chibi, coat, hair ornament, long hair, looking at viewer, pleated skirt, red background, red eyes, scarf, skirt, white dress, white hair, masterpiece, best quality", scheduler: "normal", num_outputs: 1, sampler_name: "dpmpp_sde", negative_prompt: "watermark, text", num_inference_steps: 20 } } ); // 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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:bb3ea7280ad16e541590c7e577c52e0e306f077fb79bbf54137323b855886721", input={ "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, black coat, bow, chibi, coat, hair ornament, long hair, looking at viewer, pleated skirt, red background, red eyes, scarf, skirt, white dress, white hair, masterpiece, best quality", "scheduler": "normal", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 20 } ) # 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 aicapcut/anima-pencil-v310-with-layer-diffuse 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": "aicapcut/anima-pencil-v310-with-layer-diffuse:bb3ea7280ad16e541590c7e577c52e0e306f077fb79bbf54137323b855886721", "input": { "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, black coat, bow, chibi, coat, hair ornament, long hair, looking at viewer, pleated skirt, red background, red eyes, scarf, skirt, white dress, white hair, masterpiece, best quality", "scheduler": "normal", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 20 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-11T20:39:15.396534Z", "created_at": "2024-05-11T20:32:10.176000Z", "data_removed": false, "error": null, "id": "e0qp3drxr1rgm0cfd5dtg12btc", "input": { "cfg": 6, "width": 768, "height": 1280, "prompt": "1girl, solo, black coat, bow, chibi, coat, hair ornament, long hair, looking at viewer, pleated skirt, red background, red eyes, scarf, skirt, white dress, white hair, masterpiece, best quality", "scheduler": "normal", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 20 }, "logs": "Requested to load SDXLClipModel\nLoading 1 new model\nRequested to load SDXL\nLoading 1 new model\n 0%| | 0/20 [00:00<?, ?it/s]/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=14.614640235900879 and t1=14.61464.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n 5%|▌ | 1/20 [00:00<00:11, 1.64it/s]\n 10%|█ | 2/20 [00:01<00:08, 2.05it/s]\n 15%|█▌ | 3/20 [00:01<00:07, 2.20it/s]\n 20%|██ | 4/20 [00:01<00:06, 2.30it/s]\n 25%|██▌ | 5/20 [00:02<00:06, 2.36it/s]\n 30%|███ | 6/20 [00:02<00:05, 2.40it/s]\n 35%|███▌ | 7/20 [00:03<00:05, 2.44it/s]\n 40%|████ | 8/20 [00:03<00:04, 2.44it/s]\n 45%|████▌ | 9/20 [00:03<00:04, 2.45it/s]\n 50%|█████ | 10/20 [00:04<00:04, 2.46it/s]\n 55%|█████▌ | 11/20 [00:04<00:03, 2.47it/s]\n 60%|██████ | 12/20 [00:05<00:03, 2.46it/s]\n 65%|██████▌ | 13/20 [00:05<00:02, 2.47it/s]\n 70%|███████ | 14/20 [00:05<00:02, 2.46it/s]\n 75%|███████▌ | 15/20 [00:06<00:02, 2.45it/s]\n 80%|████████ | 16/20 [00:06<00:01, 2.46it/s]\n 85%|████████▌ | 17/20 [00:07<00:01, 2.46it/s]\n 90%|█████████ | 18/20 [00:07<00:00, 2.45it/s]\n 95%|█████████▌| 19/20 [00:07<00:00, 2.51it/s]\n100%|██████████| 20/20 [00:08<00:00, 3.01it/s]\n100%|██████████| 20/20 [00:08<00:00, 2.48it/s]\nRequested to load AutoencoderKL\nLoading 1 new model\n/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/diffusers/models/unet_2d_blocks.py:76: FutureWarning: `get_down_block` is deprecated and will be removed in version 0.29. Importing `get_down_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_down_block`, instead.\ndeprecate(\"get_down_block\", \"0.29\", deprecation_message)\n/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/diffusers/models/unet_2d_blocks.py:213: FutureWarning: `get_up_block` is deprecated and will be removed in version 0.29. Importing `get_up_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_up_block`, instead.\ndeprecate(\"get_up_block\", \"0.29\", deprecation_message)\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]\n 25%|██▌ | 2/8 [00:00<00:01, 4.53it/s]\n 38%|███▊ | 3/8 [00:00<00:00, 5.89it/s]\n 50%|█████ | 4/8 [00:00<00:00, 6.00it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 6.06it/s]\n 75%|███████▌ | 6/8 [00:01<00:00, 6.10it/s]\n 88%|████████▊ | 7/8 [00:01<00:00, 6.12it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.14it/s]\n100%|██████████| 8/8 [00:01<00:00, 5.79it/s]\n[Path('output/output_00001_.png')]", "metrics": { "predict_time": 13.562735, "total_time": 425.220534 }, "output": [ "https://replicate.delivery/pbxt/c4WXhehAQfhvxkupyhrR4tpbLyJ3kCKeVzdzNwjlWr0lLxmlA/output_00001_.png" ], "started_at": "2024-05-11T20:39:01.833799Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/e0qp3drxr1rgm0cfd5dtg12btc", "cancel": "https://api.replicate.com/v1/predictions/e0qp3drxr1rgm0cfd5dtg12btc/cancel" }, "version": "bb3ea7280ad16e541590c7e577c52e0e306f077fb79bbf54137323b855886721" }Generated inRequested to load SDXLClipModel Loading 1 new model Requested to load SDXL Loading 1 new model 0%| | 0/20 [00:00<?, ?it/s]/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=14.614640235900879 and t1=14.61464. warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.") 5%|▌ | 1/20 [00:00<00:11, 1.64it/s] 10%|█ | 2/20 [00:01<00:08, 2.05it/s] 15%|█▌ | 3/20 [00:01<00:07, 2.20it/s] 20%|██ | 4/20 [00:01<00:06, 2.30it/s] 25%|██▌ | 5/20 [00:02<00:06, 2.36it/s] 30%|███ | 6/20 [00:02<00:05, 2.40it/s] 35%|███▌ | 7/20 [00:03<00:05, 2.44it/s] 40%|████ | 8/20 [00:03<00:04, 2.44it/s] 45%|████▌ | 9/20 [00:03<00:04, 2.45it/s] 50%|█████ | 10/20 [00:04<00:04, 2.46it/s] 55%|█████▌ | 11/20 [00:04<00:03, 2.47it/s] 60%|██████ | 12/20 [00:05<00:03, 2.46it/s] 65%|██████▌ | 13/20 [00:05<00:02, 2.47it/s] 70%|███████ | 14/20 [00:05<00:02, 2.46it/s] 75%|███████▌ | 15/20 [00:06<00:02, 2.45it/s] 80%|████████ | 16/20 [00:06<00:01, 2.46it/s] 85%|████████▌ | 17/20 [00:07<00:01, 2.46it/s] 90%|█████████ | 18/20 [00:07<00:00, 2.45it/s] 95%|█████████▌| 19/20 [00:07<00:00, 2.51it/s] 100%|██████████| 20/20 [00:08<00:00, 3.01it/s] 100%|██████████| 20/20 [00:08<00:00, 2.48it/s] Requested to load AutoencoderKL Loading 1 new model /root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/diffusers/models/unet_2d_blocks.py:76: FutureWarning: `get_down_block` is deprecated and will be removed in version 0.29. Importing `get_down_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_down_block`, instead. deprecate("get_down_block", "0.29", deprecation_message) /root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/diffusers/models/unet_2d_blocks.py:213: FutureWarning: `get_up_block` is deprecated and will be removed in version 0.29. Importing `get_up_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_up_block`, instead. deprecate("get_up_block", "0.29", deprecation_message) 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s] 25%|██▌ | 2/8 [00:00<00:01, 4.53it/s] 38%|███▊ | 3/8 [00:00<00:00, 5.89it/s] 50%|█████ | 4/8 [00:00<00:00, 6.00it/s] 62%|██████▎ | 5/8 [00:00<00:00, 6.06it/s] 75%|███████▌ | 6/8 [00:01<00:00, 6.10it/s] 88%|████████▊ | 7/8 [00:01<00:00, 6.12it/s] 100%|██████████| 8/8 [00:01<00:00, 6.14it/s] 100%|██████████| 8/8 [00:01<00:00, 5.79it/s] [Path('output/output_00001_.png')]Prediction
aicapcut/anima-pencil-v310-with-layer-diffuse:86dd1ea8a023054c6eade0be7b7ccdabdd8e19f67973aef87a52587f57aa4775ID2jjtzatvqxrgm0cfeznsj2nahgStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- cfg
- 6
- width
- 1280
- height
- 720
- prompt
- scientist anime girl, white lab coat, glasses, holding banana, smiling, pure white background
- scheduler
- ddim_uniform
- num_outputs
- 1
- sampler_name
- dpmpp_sde
- negative_prompt
- watermark, text
- num_inference_steps
- 25
{ "cfg": 6, "width": 1280, "height": 720, "prompt": "scientist anime girl, white lab coat, glasses, holding banana, smiling, pure white background", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 }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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:86dd1ea8a023054c6eade0be7b7ccdabdd8e19f67973aef87a52587f57aa4775", { input: { cfg: 6, width: 1280, height: 720, prompt: "scientist anime girl, white lab coat, glasses, holding banana, smiling, pure white background", scheduler: "ddim_uniform", num_outputs: 1, sampler_name: "dpmpp_sde", negative_prompt: "watermark, text", num_inference_steps: 25 } } ); // 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 aicapcut/anima-pencil-v310-with-layer-diffuse using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "aicapcut/anima-pencil-v310-with-layer-diffuse:86dd1ea8a023054c6eade0be7b7ccdabdd8e19f67973aef87a52587f57aa4775", input={ "cfg": 6, "width": 1280, "height": 720, "prompt": "scientist anime girl, white lab coat, glasses, holding banana, smiling, pure white background", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 } ) # 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 aicapcut/anima-pencil-v310-with-layer-diffuse 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": "aicapcut/anima-pencil-v310-with-layer-diffuse:86dd1ea8a023054c6eade0be7b7ccdabdd8e19f67973aef87a52587f57aa4775", "input": { "cfg": 6, "width": 1280, "height": 720, "prompt": "scientist anime girl, white lab coat, glasses, holding banana, smiling, pure white background", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-14T16:27:37.905830Z", "created_at": "2024-05-14T16:24:24.255000Z", "data_removed": false, "error": null, "id": "2jjtzatvqxrgm0cfeznsj2nahg", "input": { "cfg": 6, "width": 1280, "height": 720, "prompt": "scientist anime girl, white lab coat, glasses, holding banana, smiling, pure white background", "scheduler": "ddim_uniform", "num_outputs": 1, "sampler_name": "dpmpp_sde", "negative_prompt": "watermark, text", "num_inference_steps": 25 }, "logs": "[Info] Output height has been resized to 768\nRequested to load SDXLClipModel\nLoading 1 new model\nRequested to load SDXL\nLoading 1 new model\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:14, 1.62it/s]\n 8%|▊ | 2/25 [00:01<00:11, 2.05it/s]\n 12%|█▏ | 3/25 [00:01<00:09, 2.25it/s]\n 16%|█▌ | 4/25 [00:01<00:09, 2.32it/s]\n 20%|██ | 5/25 [00:02<00:08, 2.41it/s]\n 24%|██▍ | 6/25 [00:02<00:07, 2.44it/s]\n 28%|██▊ | 7/25 [00:03<00:07, 2.46it/s]\n 32%|███▏ | 8/25 [00:03<00:06, 2.47it/s]\n 36%|███▌ | 9/25 [00:03<00:06, 2.46it/s]\n 40%|████ | 10/25 [00:04<00:06, 2.46it/s]\n 44%|████▍ | 11/25 [00:04<00:05, 2.47it/s]\n 48%|████▊ | 12/25 [00:05<00:05, 2.47it/s]\n 52%|█████▏ | 13/25 [00:05<00:04, 2.48it/s]\n 56%|█████▌ | 14/25 [00:05<00:04, 2.48it/s]\n 60%|██████ | 15/25 [00:06<00:04, 2.50it/s]\n 64%|██████▍ | 16/25 [00:06<00:03, 2.50it/s]\n 68%|██████▊ | 17/25 [00:07<00:03, 2.50it/s]\n 72%|███████▏ | 18/25 [00:07<00:02, 2.49it/s]\n 76%|███████▌ | 19/25 [00:07<00:02, 2.50it/s]\n 80%|████████ | 20/25 [00:08<00:01, 2.50it/s]\n 84%|████████▍ | 21/25 [00:08<00:01, 2.50it/s]\n 88%|████████▊ | 22/25 [00:09<00:01, 2.50it/s]\n 92%|█████████▏| 23/25 [00:09<00:00, 2.30it/s]\n 96%|█████████▌| 24/25 [00:09<00:00, 2.39it/s]\n100%|██████████| 25/25 [00:10<00:00, 2.90it/s]\n100%|██████████| 25/25 [00:10<00:00, 2.48it/s]\nRequested to load AutoencoderKL\nLoading 1 new model\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:00<00:02, 3.48it/s]\n 25%|██▌ | 2/8 [00:00<00:01, 4.38it/s]\n 38%|███▊ | 3/8 [00:00<00:00, 5.73it/s]\n 50%|█████ | 4/8 [00:00<00:00, 5.90it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 6.00it/s]\n 75%|███████▌ | 6/8 [00:01<00:00, 6.06it/s]\n 88%|████████▊ | 7/8 [00:01<00:00, 6.10it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.12it/s]\n100%|██████████| 8/8 [00:01<00:00, 5.74it/s]\n[Path('utils/output/output_00001_.png')]", "metrics": { "predict_time": 16.116459, "total_time": 193.65083 }, "output": [ "https://replicate.delivery/pbxt/3dSNbKuwLkoaIFmoVj51enOqeL3fCglQfjZe9BzAdszGfCFtE/output_00001_.png" ], "started_at": "2024-05-14T16:27:21.789371Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2jjtzatvqxrgm0cfeznsj2nahg", "cancel": "https://api.replicate.com/v1/predictions/2jjtzatvqxrgm0cfeznsj2nahg/cancel" }, "version": "86dd1ea8a023054c6eade0be7b7ccdabdd8e19f67973aef87a52587f57aa4775" }Generated in[Info] Output height has been resized to 768 Requested to load SDXLClipModel Loading 1 new model Requested to load SDXL Loading 1 new model 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:14, 1.62it/s] 8%|▊ | 2/25 [00:01<00:11, 2.05it/s] 12%|█▏ | 3/25 [00:01<00:09, 2.25it/s] 16%|█▌ | 4/25 [00:01<00:09, 2.32it/s] 20%|██ | 5/25 [00:02<00:08, 2.41it/s] 24%|██▍ | 6/25 [00:02<00:07, 2.44it/s] 28%|██▊ | 7/25 [00:03<00:07, 2.46it/s] 32%|███▏ | 8/25 [00:03<00:06, 2.47it/s] 36%|███▌ | 9/25 [00:03<00:06, 2.46it/s] 40%|████ | 10/25 [00:04<00:06, 2.46it/s] 44%|████▍ | 11/25 [00:04<00:05, 2.47it/s] 48%|████▊ | 12/25 [00:05<00:05, 2.47it/s] 52%|█████▏ | 13/25 [00:05<00:04, 2.48it/s] 56%|█████▌ | 14/25 [00:05<00:04, 2.48it/s] 60%|██████ | 15/25 [00:06<00:04, 2.50it/s] 64%|██████▍ | 16/25 [00:06<00:03, 2.50it/s] 68%|██████▊ | 17/25 [00:07<00:03, 2.50it/s] 72%|███████▏ | 18/25 [00:07<00:02, 2.49it/s] 76%|███████▌ | 19/25 [00:07<00:02, 2.50it/s] 80%|████████ | 20/25 [00:08<00:01, 2.50it/s] 84%|████████▍ | 21/25 [00:08<00:01, 2.50it/s] 88%|████████▊ | 22/25 [00:09<00:01, 2.50it/s] 92%|█████████▏| 23/25 [00:09<00:00, 2.30it/s] 96%|█████████▌| 24/25 [00:09<00:00, 2.39it/s] 100%|██████████| 25/25 [00:10<00:00, 2.90it/s] 100%|██████████| 25/25 [00:10<00:00, 2.48it/s] Requested to load AutoencoderKL Loading 1 new model 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:00<00:02, 3.48it/s] 25%|██▌ | 2/8 [00:00<00:01, 4.38it/s] 38%|███▊ | 3/8 [00:00<00:00, 5.73it/s] 50%|█████ | 4/8 [00:00<00:00, 5.90it/s] 62%|██████▎ | 5/8 [00:00<00:00, 6.00it/s] 75%|███████▌ | 6/8 [00:01<00:00, 6.06it/s] 88%|████████▊ | 7/8 [00:01<00:00, 6.10it/s] 100%|██████████| 8/8 [00:01<00:00, 6.12it/s] 100%|██████████| 8/8 [00:01<00:00, 5.74it/s] [Path('utils/output/output_00001_.png')]
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