chenxwh/ominicontrol-subject
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ominicontrol-subject
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chenxwh/ominicontrol-subject
Minimal and Universal Control for Diffusion Transformer - demo for Subject-driven generation
chenxwh/video-retalking
Audio-based Lip Synchronization for Talking Head Video
chenxwh/openvoice
Updated to OpenVoice v2: Versatile Instant Voice Cloning
chenxwh/hunyuandit
A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
chenxwh/sdxl-flash
Fast sdxl with higher quality
chenxwh/omost
Convert LLM's coding to image generation
chenxwh/depth-anything-v2
Depth estimation with faster inference speed, fewer parameters, and higher depth accuracy.
chenxwh/diffsynth-exvideo
Extended video synthesis model that generates 128 frames
chenxwh/cogvlm2
CogVLM2: Visual Language Models for Image and Video Understanding
chenxwh/cogvlm2-video
CogVLM2: Visual Language Models for Image and Video Understanding
chenxwh/depthcrafter
Generating Consistent Long Depth Sequences for Open-world Videos
chenxwh/cogview3
Finer and Faster Text-to-Image Generation via Relay Diffusion
chenxwh/lotus
Diffusion-based Visual Foundation Model for High-quality Dense Prediction
chenxwh/ml-depth-pro
Sharp Monocular Metric Depth in Less Than a Second
chenxwh/hart
Efficient Visual Generation with Hybrid Autoregressive Transformer
chenxwh/meissonic
Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis
chenxwh/depth-any-video
Depth Any Video with Scalable Synthetic Data
chenxwh/ltx-video
DiT-based video generation model for generating high-quality videos in real-time
chenxwh/nitrofusion
High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training
chenxwh/nova-t2v
Autoregressive Video Generation without Vector Quantization
chenxwh/nova-t2i
Autoregressive Image Generation without Vector Quantization
Minimal and Universal Control for Diffusion Transformer - demo for Subject-driven generation
Prediction
chenxwh/ominicontrol-subject:65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdbIDr4xzj98g9drm80cm3vq91ztmk0StatusSucceededSourceWebHardwareL40STotal durationCreatedInput
- image
- model
- subject
- prompt
- On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/MF5rBXkFkj5E0LhAU7kT6ADRBtTwQYouMqPenUpTZocf8BuB/penguin.jpg", "model": "subject", "prompt": "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.", "guidance_scale": 7.5, "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 chenxwh/ominicontrol-subject using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chenxwh/ominicontrol-subject:65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdb", { input: { image: "https://replicate.delivery/pbxt/MF5rBXkFkj5E0LhAU7kT6ADRBtTwQYouMqPenUpTZocf8BuB/penguin.jpg", model: "subject", prompt: "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.", guidance_scale: 7.5, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);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 chenxwh/ominicontrol-subject using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chenxwh/ominicontrol-subject:65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdb", input={ "image": "https://replicate.delivery/pbxt/MF5rBXkFkj5E0LhAU7kT6ADRBtTwQYouMqPenUpTZocf8BuB/penguin.jpg", "model": "subject", "prompt": "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.", "guidance_scale": 7.5, "num_inference_steps": 50 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chenxwh/ominicontrol-subject 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": "chenxwh/ominicontrol-subject:65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdb", "input": { "image": "https://replicate.delivery/pbxt/MF5rBXkFkj5E0LhAU7kT6ADRBtTwQYouMqPenUpTZocf8BuB/penguin.jpg", "model": "subject", "prompt": "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.", "guidance_scale": 7.5, "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": "2024-12-31T22:57:10.260392Z", "created_at": "2024-12-31T22:55:05.803000Z", "data_removed": false, "error": null, "id": "r4xzj98g9drm80cm3vq91ztmk0", "input": { "image": "https://replicate.delivery/pbxt/MF5rBXkFkj5E0LhAU7kT6ADRBtTwQYouMqPenUpTZocf8BuB/penguin.jpg", "model": "subject", "prompt": "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 43522\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:15, 3.06it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.51it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.45it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.42it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.41it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.40it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.39it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.39it/s]\n 18%|█▊ | 9/50 [00:02<00:12, 3.39it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.39it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.39it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 3.38it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.38it/s]\n 28%|██▊ | 14/50 [00:04<00:10, 3.38it/s]\n 30%|███ | 15/50 [00:04<00:10, 3.38it/s]\n 32%|███▏ | 16/50 [00:04<00:10, 3.38it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.38it/s]\n 36%|███▌ | 18/50 [00:05<00:09, 3.38it/s]\n 38%|███▊ | 19/50 [00:05<00:09, 3.38it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.38it/s]\n 42%|████▏ | 21/50 [00:06<00:08, 3.38it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.38it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.38it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.38it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.38it/s]\n 52%|█████▏ | 26/50 [00:07<00:07, 3.38it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.37it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.37it/s]\n 58%|█████▊ | 29/50 [00:08<00:06, 3.38it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.38it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.37it/s]\n 64%|██████▍ | 32/50 [00:09<00:05, 3.37it/s]\n 66%|██████▌ | 33/50 [00:09<00:05, 3.38it/s]\n 68%|██████▊ | 34/50 [00:10<00:04, 3.38it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.37it/s]\n 72%|███████▏ | 36/50 [00:10<00:04, 3.37it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.37it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.37it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.37it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.37it/s]\n 82%|████████▏ | 41/50 [00:12<00:02, 3.37it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.37it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.37it/s]\n 88%|████████▊ | 44/50 [00:13<00:01, 3.37it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.37it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.37it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.37it/s]\n 96%|█████████▌| 48/50 [00:14<00:00, 3.37it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.37it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.37it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.38it/s]", "metrics": { "predict_time": 15.584385529, "total_time": 124.457392 }, "output": "https://replicate.delivery/xezq/m8UefzxNznpvP0aqvZccfwpoJETfz2IefGSZwljMx2vvxoIAF/out.png", "started_at": "2024-12-31T22:56:54.676007Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-px7b2xvyzsxeakxkjaeijhs5foxiwzue5uxugyb5su4fbenfrjhq", "get": "https://api.replicate.com/v1/predictions/r4xzj98g9drm80cm3vq91ztmk0", "cancel": "https://api.replicate.com/v1/predictions/r4xzj98g9drm80cm3vq91ztmk0/cancel" }, "version": "65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdb" }Generated inUsing seed: 43522 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:15, 3.06it/s] 4%|▍ | 2/50 [00:00<00:13, 3.51it/s] 6%|▌ | 3/50 [00:00<00:13, 3.45it/s] 8%|▊ | 4/50 [00:01<00:13, 3.42it/s] 10%|█ | 5/50 [00:01<00:13, 3.41it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.40it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.39it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.39it/s] 18%|█▊ | 9/50 [00:02<00:12, 3.39it/s] 20%|██ | 10/50 [00:02<00:11, 3.39it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.39it/s] 24%|██▍ | 12/50 [00:03<00:11, 3.38it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.38it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.38it/s] 30%|███ | 15/50 [00:04<00:10, 3.38it/s] 32%|███▏ | 16/50 [00:04<00:10, 3.38it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.38it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.38it/s] 38%|███▊ | 19/50 [00:05<00:09, 3.38it/s] 40%|████ | 20/50 [00:05<00:08, 3.38it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.38it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.38it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.38it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.38it/s] 50%|█████ | 25/50 [00:07<00:07, 3.38it/s] 52%|█████▏ | 26/50 [00:07<00:07, 3.38it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.37it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.37it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.38it/s] 60%|██████ | 30/50 [00:08<00:05, 3.38it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.37it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.37it/s] 66%|██████▌ | 33/50 [00:09<00:05, 3.38it/s] 68%|██████▊ | 34/50 [00:10<00:04, 3.38it/s] 70%|███████ | 35/50 [00:10<00:04, 3.37it/s] 72%|███████▏ | 36/50 [00:10<00:04, 3.37it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.37it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.37it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.37it/s] 80%|████████ | 40/50 [00:11<00:02, 3.37it/s] 82%|████████▏ | 41/50 [00:12<00:02, 3.37it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.37it/s] 86%|████████▌ | 43/50 [00:12<00:02, 3.37it/s] 88%|████████▊ | 44/50 [00:13<00:01, 3.37it/s] 90%|█████████ | 45/50 [00:13<00:01, 3.37it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.37it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.37it/s] 96%|█████████▌| 48/50 [00:14<00:00, 3.37it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.37it/s] 100%|██████████| 50/50 [00:14<00:00, 3.37it/s] 100%|██████████| 50/50 [00:14<00:00, 3.38it/s]Prediction
chenxwh/ominicontrol-subject:65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdbIDsp96c39vghrmc0cm3vtv396qnwStatusSucceededSourceWebHardwareL40STotal durationCreatedInput
- image
- model
- subject_1024
- prompt
- A film style shot. On the moon, this item drives across the moon surface. The background is that Earth looms large in the foreground.
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "image": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/rc_car.jpg", "model": "subject_1024", "prompt": "A film style shot. On the moon, this item drives across the moon surface. The background is that Earth looms large in the foreground.", "guidance_scale": 7.5, "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 chenxwh/ominicontrol-subject using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chenxwh/ominicontrol-subject:65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdb", { input: { image: "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/rc_car.jpg", model: "subject_1024", prompt: "A film style shot. On the moon, this item drives across the moon surface. The background is that Earth looms large in the foreground.", guidance_scale: 7.5, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);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 chenxwh/ominicontrol-subject using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chenxwh/ominicontrol-subject:65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdb", input={ "image": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/rc_car.jpg", "model": "subject_1024", "prompt": "A film style shot. On the moon, this item drives across the moon surface. The background is that Earth looms large in the foreground.", "guidance_scale": 7.5, "num_inference_steps": 50 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chenxwh/ominicontrol-subject 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": "chenxwh/ominicontrol-subject:65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdb", "input": { "image": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/rc_car.jpg", "model": "subject_1024", "prompt": "A film style shot. On the moon, this item drives across the moon surface. The background is that Earth looms large in the foreground.", "guidance_scale": 7.5, "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": "2024-12-31T23:05:10.578645Z", "created_at": "2024-12-31T23:02:55.620000Z", "data_removed": false, "error": null, "id": "sp96c39vghrmc0cm3vtv396qnw", "input": { "image": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/rc_car.jpg", "model": "subject_1024", "prompt": "A film style shot. On the moon, this item drives across the moon surface. The background is that Earth looms large in the foreground.", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 39800\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<01:00, 1.23s/it]\n 4%|▍ | 2/50 [00:02<00:53, 1.12s/it]\n 6%|▌ | 3/50 [00:03<00:54, 1.16s/it]\n 8%|▊ | 4/50 [00:04<00:54, 1.18s/it]\n 10%|█ | 5/50 [00:05<00:53, 1.19s/it]\n 12%|█▏ | 6/50 [00:07<00:52, 1.20s/it]\n 14%|█▍ | 7/50 [00:08<00:51, 1.20s/it]\n 16%|█▌ | 8/50 [00:09<00:50, 1.20s/it]\n 18%|█▊ | 9/50 [00:10<00:49, 1.21s/it]\n 20%|██ | 10/50 [00:11<00:48, 1.21s/it]\n 22%|██▏ | 11/50 [00:13<00:47, 1.21s/it]\n 24%|██▍ | 12/50 [00:14<00:45, 1.21s/it]\n 26%|██▌ | 13/50 [00:15<00:44, 1.21s/it]\n 28%|██▊ | 14/50 [00:16<00:43, 1.21s/it]\n 30%|███ | 15/50 [00:18<00:42, 1.21s/it]\n 32%|███▏ | 16/50 [00:19<00:41, 1.21s/it]\n 34%|███▍ | 17/50 [00:20<00:40, 1.22s/it]\n 36%|███▌ | 18/50 [00:21<00:38, 1.22s/it]\n 38%|███▊ | 19/50 [00:22<00:37, 1.22s/it]\n 40%|████ | 20/50 [00:24<00:36, 1.22s/it]\n 42%|████▏ | 21/50 [00:25<00:35, 1.22s/it]\n 44%|████▍ | 22/50 [00:26<00:34, 1.22s/it]\n 46%|████▌ | 23/50 [00:27<00:32, 1.22s/it]\n 48%|████▊ | 24/50 [00:28<00:31, 1.22s/it]\n 50%|█████ | 25/50 [00:30<00:30, 1.22s/it]\n 52%|█████▏ | 26/50 [00:31<00:29, 1.22s/it]\n 54%|█████▍ | 27/50 [00:32<00:28, 1.22s/it]\n 56%|█████▌ | 28/50 [00:33<00:26, 1.22s/it]\n 58%|█████▊ | 29/50 [00:35<00:25, 1.22s/it]\n 60%|██████ | 30/50 [00:36<00:24, 1.23s/it]\n 62%|██████▏ | 31/50 [00:37<00:23, 1.23s/it]\n 64%|██████▍ | 32/50 [00:38<00:22, 1.23s/it]\n 66%|██████▌ | 33/50 [00:40<00:20, 1.23s/it]\n 68%|██████▊ | 34/50 [00:41<00:19, 1.23s/it]\n 70%|███████ | 35/50 [00:42<00:18, 1.23s/it]\n 72%|███████▏ | 36/50 [00:43<00:17, 1.23s/it]\n 74%|███████▍ | 37/50 [00:44<00:15, 1.23s/it]\n 76%|███████▌ | 38/50 [00:46<00:14, 1.23s/it]\n 78%|███████▊ | 39/50 [00:47<00:13, 1.23s/it]\n 80%|████████ | 40/50 [00:48<00:12, 1.23s/it]\n 82%|████████▏ | 41/50 [00:49<00:11, 1.23s/it]\n 84%|████████▍ | 42/50 [00:51<00:09, 1.23s/it]\n 86%|████████▌ | 43/50 [00:52<00:08, 1.23s/it]\n 88%|████████▊ | 44/50 [00:53<00:07, 1.23s/it]\n 90%|█████████ | 45/50 [00:54<00:06, 1.23s/it]\n 92%|█████████▏| 46/50 [00:56<00:04, 1.23s/it]\n 94%|█████████▍| 47/50 [00:57<00:03, 1.23s/it]\n 96%|█████████▌| 48/50 [00:58<00:02, 1.23s/it]\n 98%|█████████▊| 49/50 [00:59<00:01, 1.23s/it]\n100%|██████████| 50/50 [01:00<00:00, 1.23s/it]\n100%|██████████| 50/50 [01:00<00:00, 1.22s/it]", "metrics": { "predict_time": 62.402736252, "total_time": 134.958645 }, "output": "https://replicate.delivery/xezq/7f5elACmA2mju0ZmqTn5ovXwavL4jkO632wHaMSymHRmqiAUA/out.png", "started_at": "2024-12-31T23:04:08.175908Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-ogr7hottiq3m7s357gty5m3tvyd4u42v7f5tvz2whkyafdodszja", "get": "https://api.replicate.com/v1/predictions/sp96c39vghrmc0cm3vtv396qnw", "cancel": "https://api.replicate.com/v1/predictions/sp96c39vghrmc0cm3vtv396qnw/cancel" }, "version": "65f9489081d0f94bb3085b5a5df2758501c8620d6cb5e0b5874e8f48ba5e9cdb" }Generated inUsing seed: 39800 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<01:00, 1.23s/it] 4%|▍ | 2/50 [00:02<00:53, 1.12s/it] 6%|▌ | 3/50 [00:03<00:54, 1.16s/it] 8%|▊ | 4/50 [00:04<00:54, 1.18s/it] 10%|█ | 5/50 [00:05<00:53, 1.19s/it] 12%|█▏ | 6/50 [00:07<00:52, 1.20s/it] 14%|█▍ | 7/50 [00:08<00:51, 1.20s/it] 16%|█▌ | 8/50 [00:09<00:50, 1.20s/it] 18%|█▊ | 9/50 [00:10<00:49, 1.21s/it] 20%|██ | 10/50 [00:11<00:48, 1.21s/it] 22%|██▏ | 11/50 [00:13<00:47, 1.21s/it] 24%|██▍ | 12/50 [00:14<00:45, 1.21s/it] 26%|██▌ | 13/50 [00:15<00:44, 1.21s/it] 28%|██▊ | 14/50 [00:16<00:43, 1.21s/it] 30%|███ | 15/50 [00:18<00:42, 1.21s/it] 32%|███▏ | 16/50 [00:19<00:41, 1.21s/it] 34%|███▍ | 17/50 [00:20<00:40, 1.22s/it] 36%|███▌ | 18/50 [00:21<00:38, 1.22s/it] 38%|███▊ | 19/50 [00:22<00:37, 1.22s/it] 40%|████ | 20/50 [00:24<00:36, 1.22s/it] 42%|████▏ | 21/50 [00:25<00:35, 1.22s/it] 44%|████▍ | 22/50 [00:26<00:34, 1.22s/it] 46%|████▌ | 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1.23s/it] 92%|█████████▏| 46/50 [00:56<00:04, 1.23s/it] 94%|█████████▍| 47/50 [00:57<00:03, 1.23s/it] 96%|█████████▌| 48/50 [00:58<00:02, 1.23s/it] 98%|█████████▊| 49/50 [00:59<00:01, 1.23s/it] 100%|██████████| 50/50 [01:00<00:00, 1.23s/it] 100%|██████████| 50/50 [01:00<00:00, 1.22s/it]
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