adirik/syncdiffusion
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syncdiffusion
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adirik/syncdiffusion
Generate panoramic images with text prompts
adirik/stylemc-old
Text-Guided Image Generation and Manipulation
adirik/lightweight-openpose
PyTorch version of Lightweight OpenPose as introduced in "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose"
adirik/t2i-adapter-sdxl-lineart
Modify images using line art
adirik/t2i-adapter-sdxl-canny
Modify images using canny edges
adirik/t2i-adapter-sdxl-sketch
Modify images using sketches
adirik/t2i-adapter-sdxl-openpose
Modify images using human pose
adirik/t2i-adapter-sdxl-depth-midas
Modify images using depth maps
adirik/inst-inpaint
Inst-Inpaint: Instructing to Remove Objects with Diffusion Models
adirik/owlvit-base-patch32
Zero-shot / open vocabulary object detection
adirik/deforum-kandinsky-2-2
Generate videos from text prompts with Kandinsky-2.2
adirik/grounding-dino
Detect everything with language!
adirik/wonder3d
Generates 3D assets from images
adirik/mvdream
Generate 3D assets using text descriptions
adirik/codet
Detects objects in an image
adirik/titanet-large
Performs speaker identity verification
adirik/styletts2
Generates speech from text
adirik/texture
Generate texture for your mesh with text prompts
adirik/kosmos-g
Kosmos-G: Generating Images in Context with Multimodal Large Language Models
adirik/masactrl-stable-diffusion-v1-4
Edit real or generated images
adirik/masactrl-anything-v4-0
Edit real or generated images
Prediction
adirik/syncdiffusion:a2c97d1c34b88c075e38899a38a371e2016917ed358f4b8618b32101f4897a1dIDzpkqvwtbaiz7imbdnasc43uodaStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 2
- width
- 2048
- height
- 512
- prompt
- natural landscape in anime style illustration
- stride
- 16
- sync_freq
- 1
- sync_weight
- 20
- loop_closure
- guidance_scale
- 7.5
- sync_threshold
- 5
- negative_prompt
- sync_decay_rate
- 0.99
- num_inference_steps
- 50
{ "seed": 2, "width": 2048, "height": 512, "prompt": "natural landscape in anime style illustration", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/syncdiffusion:a2c97d1c34b88c075e38899a38a371e2016917ed358f4b8618b32101f4897a1d", { input: { seed: 2, width: 2048, height: 512, prompt: "natural landscape in anime style illustration", stride: 16, sync_freq: 1, sync_weight: 20, loop_closure: false, guidance_scale: 7.5, sync_threshold: 5, negative_prompt: "", sync_decay_rate: 0.99, 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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/syncdiffusion:a2c97d1c34b88c075e38899a38a371e2016917ed358f4b8618b32101f4897a1d", input={ "seed": 2, "width": 2048, "height": 512, "prompt": "natural landscape in anime style illustration", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": False, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion 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": "adirik/syncdiffusion:a2c97d1c34b88c075e38899a38a371e2016917ed358f4b8618b32101f4897a1d", "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "natural landscape in anime style illustration", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/adirik/syncdiffusion@sha256:a2c97d1c34b88c075e38899a38a371e2016917ed358f4b8618b32101f4897a1d \ -i 'seed=2' \ -i 'width=2048' \ -i 'height=512' \ -i 'prompt="natural landscape in anime style illustration"' \ -i 'stride=16' \ -i 'sync_freq=1' \ -i 'sync_weight=20' \ -i 'loop_closure=false' \ -i 'guidance_scale=7.5' \ -i 'sync_threshold=5' \ -i 'negative_prompt=""' \ -i 'sync_decay_rate=0.99' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/adirik/syncdiffusion@sha256:a2c97d1c34b88c075e38899a38a371e2016917ed358f4b8618b32101f4897a1dcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "natural landscape in anime style illustration", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-01-29T10:12:49.546770Z", "created_at": "2024-01-29T10:09:30.073441Z", "data_removed": false, "error": null, "id": "zpkqvwtbaiz7imbdnasc43uoda", "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "natural landscape in anime style illustration", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 }, "logs": "[INFO] number of views to process: 13\n/src/syncdiffusion/syncdiffusion_model.py:150: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\nlatent = torch.randn((1, self.unet.in_channels, height // 8, width // 8))\n[INFO] using exponential decay scheduler with decay rate 0.99\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:06<05:04, 6.21s/it]\n 4%|▍ | 2/50 [00:11<04:34, 5.72s/it]\n 6%|▌ | 3/50 [00:16<04:21, 5.56s/it]\n 8%|▊ | 4/50 [00:22<04:12, 5.48s/it]\n 10%|█ | 5/50 [00:27<04:04, 5.44s/it]\n 12%|█▏ | 6/50 [00:28<02:52, 3.91s/it]\n 14%|█▍ | 7/50 [00:29<02:06, 2.94s/it]\n 16%|█▌ | 8/50 [00:30<01:36, 2.30s/it]\n 18%|█▊ | 9/50 [00:31<01:16, 1.88s/it]\n 20%|██ | 10/50 [00:32<01:03, 1.59s/it]\n 22%|██▏ | 11/50 [00:33<00:54, 1.39s/it]\n 24%|██▍ | 12/50 [00:34<00:47, 1.25s/it]\n 26%|██▌ | 13/50 [00:35<00:42, 1.16s/it]\n 28%|██▊ | 14/50 [00:36<00:39, 1.09s/it]\n 30%|███ | 15/50 [00:37<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:38<00:34, 1.02s/it]\n 34%|███▍ | 17/50 [00:38<00:32, 1.01it/s]\n 36%|███▌ | 18/50 [00:39<00:31, 1.02it/s]\n 38%|███▊ | 19/50 [00:40<00:29, 1.04it/s]\n 40%|████ | 20/50 [00:41<00:28, 1.04it/s]\n 42%|████▏ | 21/50 [00:42<00:27, 1.05it/s]\n 44%|████▍ | 22/50 [00:43<00:26, 1.05it/s]\n 46%|████▌ | 23/50 [00:44<00:25, 1.05it/s]\n 48%|████▊ | 24/50 [00:45<00:24, 1.06it/s]\n 50%|█████ | 25/50 [00:46<00:23, 1.06it/s]\n 52%|█████▏ | 26/50 [00:47<00:22, 1.06it/s]\n 54%|█████▍ | 27/50 [00:48<00:21, 1.06it/s]\n 56%|█████▌ | 28/50 [00:49<00:20, 1.06it/s]\n 58%|█████▊ | 29/50 [00:50<00:19, 1.06it/s]\n 60%|██████ | 30/50 [00:51<00:18, 1.06it/s]\n 62%|██████▏ | 31/50 [00:52<00:17, 1.06it/s]\n 64%|██████▍ | 32/50 [00:53<00:16, 1.06it/s]\n 66%|██████▌ | 33/50 [00:54<00:16, 1.06it/s]\n 68%|██████▊ | 34/50 [00:54<00:15, 1.06it/s]\n 70%|███████ | 35/50 [00:55<00:14, 1.06it/s]\n 72%|███████▏ | 36/50 [00:56<00:13, 1.06it/s]\n 74%|███████▍ | 37/50 [00:57<00:12, 1.06it/s]\n 76%|███████▌ | 38/50 [00:58<00:11, 1.06it/s]\n 78%|███████▊ | 39/50 [00:59<00:10, 1.06it/s]\n 80%|████████ | 40/50 [01:00<00:09, 1.06it/s]\n 82%|████████▏ | 41/50 [01:01<00:08, 1.06it/s]\n 84%|████████▍ | 42/50 [01:02<00:07, 1.06it/s]\n 86%|████████▌ | 43/50 [01:03<00:06, 1.06it/s]\n 88%|████████▊ | 44/50 [01:04<00:05, 1.06it/s]\n 90%|█████████ | 45/50 [01:05<00:04, 1.06it/s]\n 92%|█████████▏| 46/50 [01:06<00:03, 1.06it/s]\n 94%|█████████▍| 47/50 [01:07<00:02, 1.06it/s]\n 96%|█████████▌| 48/50 [01:08<00:01, 1.06it/s]\n 98%|█████████▊| 49/50 [01:09<00:00, 1.06it/s]\n100%|██████████| 50/50 [01:10<00:00, 1.06it/s]\n100%|██████████| 50/50 [01:10<00:00, 1.40s/it]\n[INFO] Done!", "metrics": { "predict_time": 72.613723, "total_time": 199.473329 }, "output": "https://replicate.delivery/pbxt/EArYhWvUBobJPtU3oHeRkx92iiSvH4iejFvZ1Nq55e5BhlikA/output.png", "started_at": "2024-01-29T10:11:36.933047Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zpkqvwtbaiz7imbdnasc43uoda", "cancel": "https://api.replicate.com/v1/predictions/zpkqvwtbaiz7imbdnasc43uoda/cancel" }, "version": "a2c97d1c34b88c075e38899a38a371e2016917ed358f4b8618b32101f4897a1d" }Generated in[INFO] number of views to process: 13 /src/syncdiffusion/syncdiffusion_model.py:150: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'. latent = torch.randn((1, self.unet.in_channels, height // 8, width // 8)) [INFO] using exponential decay scheduler with decay rate 0.99 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:06<05:04, 6.21s/it] 4%|▍ | 2/50 [00:11<04:34, 5.72s/it] 6%|▌ | 3/50 [00:16<04:21, 5.56s/it] 8%|▊ | 4/50 [00:22<04:12, 5.48s/it] 10%|█ | 5/50 [00:27<04:04, 5.44s/it] 12%|█▏ | 6/50 [00:28<02:52, 3.91s/it] 14%|█▍ | 7/50 [00:29<02:06, 2.94s/it] 16%|█▌ | 8/50 [00:30<01:36, 2.30s/it] 18%|█▊ | 9/50 [00:31<01:16, 1.88s/it] 20%|██ | 10/50 [00:32<01:03, 1.59s/it] 22%|██▏ | 11/50 [00:33<00:54, 1.39s/it] 24%|██▍ | 12/50 [00:34<00:47, 1.25s/it] 26%|██▌ | 13/50 [00:35<00:42, 1.16s/it] 28%|██▊ | 14/50 [00:36<00:39, 1.09s/it] 30%|███ | 15/50 [00:37<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:38<00:34, 1.02s/it] 34%|███▍ | 17/50 [00:38<00:32, 1.01it/s] 36%|███▌ | 18/50 [00:39<00:31, 1.02it/s] 38%|███▊ | 19/50 [00:40<00:29, 1.04it/s] 40%|████ | 20/50 [00:41<00:28, 1.04it/s] 42%|████▏ | 21/50 [00:42<00:27, 1.05it/s] 44%|████▍ | 22/50 [00:43<00:26, 1.05it/s] 46%|████▌ | 23/50 [00:44<00:25, 1.05it/s] 48%|████▊ | 24/50 [00:45<00:24, 1.06it/s] 50%|█████ | 25/50 [00:46<00:23, 1.06it/s] 52%|█████▏ | 26/50 [00:47<00:22, 1.06it/s] 54%|█████▍ | 27/50 [00:48<00:21, 1.06it/s] 56%|█████▌ | 28/50 [00:49<00:20, 1.06it/s] 58%|█████▊ | 29/50 [00:50<00:19, 1.06it/s] 60%|██████ | 30/50 [00:51<00:18, 1.06it/s] 62%|██████▏ | 31/50 [00:52<00:17, 1.06it/s] 64%|██████▍ | 32/50 [00:53<00:16, 1.06it/s] 66%|██████▌ | 33/50 [00:54<00:16, 1.06it/s] 68%|██████▊ | 34/50 [00:54<00:15, 1.06it/s] 70%|███████ | 35/50 [00:55<00:14, 1.06it/s] 72%|███████▏ | 36/50 [00:56<00:13, 1.06it/s] 74%|███████▍ | 37/50 [00:57<00:12, 1.06it/s] 76%|███████▌ | 38/50 [00:58<00:11, 1.06it/s] 78%|███████▊ | 39/50 [00:59<00:10, 1.06it/s] 80%|████████ | 40/50 [01:00<00:09, 1.06it/s] 82%|████████▏ | 41/50 [01:01<00:08, 1.06it/s] 84%|████████▍ | 42/50 [01:02<00:07, 1.06it/s] 86%|████████▌ | 43/50 [01:03<00:06, 1.06it/s] 88%|████████▊ | 44/50 [01:04<00:05, 1.06it/s] 90%|█████████ | 45/50 [01:05<00:04, 1.06it/s] 92%|█████████▏| 46/50 [01:06<00:03, 1.06it/s] 94%|█████████▍| 47/50 [01:07<00:02, 1.06it/s] 96%|█████████▌| 48/50 [01:08<00:01, 1.06it/s] 98%|█████████▊| 49/50 [01:09<00:00, 1.06it/s] 100%|██████████| 50/50 [01:10<00:00, 1.06it/s] 100%|██████████| 50/50 [01:10<00:00, 1.40s/it] [INFO] Done!Prediction
adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933IDmoytvpdbssmlz7yhymr5cvvhyaStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 2
- width
- 2048
- height
- 512
- prompt
- A photo of a rock concert
- stride
- 16
- sync_freq
- 1
- sync_weight
- 20
- loop_closure
- guidance_scale
- 7.5
- sync_threshold
- 5
- negative_prompt
- sync_decay_rate
- 0.99
- num_inference_steps
- 50
{ "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a rock concert", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", { input: { seed: 2, width: 2048, height: 512, prompt: "A photo of a rock concert", stride: 16, sync_freq: 1, sync_weight: 20, loop_closure: false, guidance_scale: 7.5, sync_threshold: 5, negative_prompt: "", sync_decay_rate: 0.99, 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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", input={ "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a rock concert", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": False, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion 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": "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a rock concert", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/adirik/syncdiffusion@sha256:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933 \ -i 'seed=2' \ -i 'width=2048' \ -i 'height=512' \ -i 'prompt="A photo of a rock concert"' \ -i 'stride=16' \ -i 'sync_freq=1' \ -i 'sync_weight=20' \ -i 'loop_closure=false' \ -i 'guidance_scale=7.5' \ -i 'sync_threshold=5' \ -i 'negative_prompt=""' \ -i 'sync_decay_rate=0.99' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/adirik/syncdiffusion@sha256:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a rock concert", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-01-29T11:41:40.792455Z", "created_at": "2024-01-29T11:38:51.553891Z", "data_removed": false, "error": null, "id": "moytvpdbssmlz7yhymr5cvvhya", "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a rock concert", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 }, "logs": "[INFO] number of views to process: 13\n/src/syncdiffusion/syncdiffusion_model.py:150: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\nlatent = torch.randn((1, self.unet.in_channels, height // 8, width // 8))\n[INFO] using exponential decay scheduler with decay rate 0.99\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:06<05:03, 6.20s/it]\n 4%|▍ | 2/50 [00:11<04:34, 5.72s/it]\n 6%|▌ | 3/50 [00:16<04:21, 5.57s/it]\n 8%|▊ | 4/50 [00:22<04:12, 5.49s/it]\n 10%|█ | 5/50 [00:27<04:05, 5.46s/it]\n 12%|█▏ | 6/50 [00:28<02:52, 3.92s/it]\n 14%|█▍ | 7/50 [00:29<02:06, 2.95s/it]\n 16%|█▌ | 8/50 [00:30<01:36, 2.31s/it]\n 18%|█▊ | 9/50 [00:31<01:17, 1.88s/it]\n 20%|██ | 10/50 [00:32<01:03, 1.59s/it]\n 22%|██▏ | 11/50 [00:33<00:54, 1.39s/it]\n 24%|██▍ | 12/50 [00:34<00:47, 1.26s/it]\n 26%|██▌ | 13/50 [00:35<00:42, 1.16s/it]\n 28%|██▊ | 14/50 [00:36<00:39, 1.10s/it]\n 30%|███ | 15/50 [00:37<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:38<00:34, 1.02s/it]\n 34%|███▍ | 17/50 [00:39<00:32, 1.00it/s]\n 36%|███▌ | 18/50 [00:39<00:31, 1.02it/s]\n 38%|███▊ | 19/50 [00:40<00:30, 1.03it/s]\n 40%|████ | 20/50 [00:41<00:28, 1.04it/s]\n 42%|████▏ | 21/50 [00:42<00:27, 1.04it/s]\n 44%|████▍ | 22/50 [00:43<00:26, 1.05it/s]\n 46%|████▌ | 23/50 [00:44<00:25, 1.05it/s]\n 48%|████▊ | 24/50 [00:45<00:24, 1.05it/s]\n 50%|█████ | 25/50 [00:46<00:23, 1.05it/s]\n 52%|█████▏ | 26/50 [00:47<00:22, 1.06it/s]\n 54%|█████▍ | 27/50 [00:48<00:21, 1.06it/s]\n 56%|█████▌ | 28/50 [00:49<00:20, 1.06it/s]\n 58%|█████▊ | 29/50 [00:50<00:19, 1.06it/s]\n 60%|██████ | 30/50 [00:51<00:18, 1.06it/s]\n 62%|██████▏ | 31/50 [00:52<00:17, 1.06it/s]\n 64%|██████▍ | 32/50 [00:53<00:16, 1.06it/s]\n 66%|██████▌ | 33/50 [00:54<00:16, 1.06it/s]\n 68%|██████▊ | 34/50 [00:55<00:15, 1.06it/s]\n 70%|███████ | 35/50 [00:56<00:14, 1.06it/s]\n 72%|███████▏ | 36/50 [00:57<00:13, 1.06it/s]\n 74%|███████▍ | 37/50 [00:57<00:12, 1.06it/s]\n 76%|███████▌ | 38/50 [00:58<00:11, 1.06it/s]\n 78%|███████▊ | 39/50 [00:59<00:10, 1.06it/s]\n 80%|████████ | 40/50 [01:00<00:09, 1.06it/s]\n 82%|████████▏ | 41/50 [01:01<00:08, 1.06it/s]\n 84%|████████▍ | 42/50 [01:02<00:07, 1.06it/s]\n 86%|████████▌ | 43/50 [01:03<00:06, 1.06it/s]\n 88%|████████▊ | 44/50 [01:04<00:05, 1.06it/s]\n 90%|█████████ | 45/50 [01:05<00:04, 1.06it/s]\n 92%|█████████▏| 46/50 [01:06<00:03, 1.06it/s]\n 94%|█████████▍| 47/50 [01:07<00:02, 1.06it/s]\n 96%|█████████▌| 48/50 [01:08<00:01, 1.06it/s]\n 98%|█████████▊| 49/50 [01:09<00:00, 1.06it/s]\n100%|██████████| 50/50 [01:10<00:00, 1.06it/s]\n100%|██████████| 50/50 [01:10<00:00, 1.40s/it]\n[INFO] Done!", "metrics": { "predict_time": 72.801733, "total_time": 169.238564 }, "output": "https://replicate.delivery/pbxt/P91aAIXKfNVpcCgfoEtuotmcOZRAegzDGTHM5BVP296mHoikA/output.png", "started_at": "2024-01-29T11:40:27.990722Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/moytvpdbssmlz7yhymr5cvvhya", "cancel": "https://api.replicate.com/v1/predictions/moytvpdbssmlz7yhymr5cvvhya/cancel" }, "version": "5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933" }Generated in[INFO] number of views to process: 13 /src/syncdiffusion/syncdiffusion_model.py:150: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'. latent = torch.randn((1, self.unet.in_channels, height // 8, width // 8)) [INFO] using exponential decay scheduler with decay rate 0.99 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:06<05:03, 6.20s/it] 4%|▍ | 2/50 [00:11<04:34, 5.72s/it] 6%|▌ | 3/50 [00:16<04:21, 5.57s/it] 8%|▊ | 4/50 [00:22<04:12, 5.49s/it] 10%|█ | 5/50 [00:27<04:05, 5.46s/it] 12%|█▏ | 6/50 [00:28<02:52, 3.92s/it] 14%|█▍ | 7/50 [00:29<02:06, 2.95s/it] 16%|█▌ | 8/50 [00:30<01:36, 2.31s/it] 18%|█▊ | 9/50 [00:31<01:17, 1.88s/it] 20%|██ | 10/50 [00:32<01:03, 1.59s/it] 22%|██▏ | 11/50 [00:33<00:54, 1.39s/it] 24%|██▍ | 12/50 [00:34<00:47, 1.26s/it] 26%|██▌ | 13/50 [00:35<00:42, 1.16s/it] 28%|██▊ | 14/50 [00:36<00:39, 1.10s/it] 30%|███ | 15/50 [00:37<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:38<00:34, 1.02s/it] 34%|███▍ | 17/50 [00:39<00:32, 1.00it/s] 36%|███▌ | 18/50 [00:39<00:31, 1.02it/s] 38%|███▊ | 19/50 [00:40<00:30, 1.03it/s] 40%|████ | 20/50 [00:41<00:28, 1.04it/s] 42%|████▏ | 21/50 [00:42<00:27, 1.04it/s] 44%|████▍ | 22/50 [00:43<00:26, 1.05it/s] 46%|████▌ | 23/50 [00:44<00:25, 1.05it/s] 48%|████▊ | 24/50 [00:45<00:24, 1.05it/s] 50%|█████ | 25/50 [00:46<00:23, 1.05it/s] 52%|█████▏ | 26/50 [00:47<00:22, 1.06it/s] 54%|█████▍ | 27/50 [00:48<00:21, 1.06it/s] 56%|█████▌ | 28/50 [00:49<00:20, 1.06it/s] 58%|█████▊ | 29/50 [00:50<00:19, 1.06it/s] 60%|██████ | 30/50 [00:51<00:18, 1.06it/s] 62%|██████▏ | 31/50 [00:52<00:17, 1.06it/s] 64%|██████▍ | 32/50 [00:53<00:16, 1.06it/s] 66%|██████▌ | 33/50 [00:54<00:16, 1.06it/s] 68%|██████▊ | 34/50 [00:55<00:15, 1.06it/s] 70%|███████ | 35/50 [00:56<00:14, 1.06it/s] 72%|███████▏ | 36/50 [00:57<00:13, 1.06it/s] 74%|███████▍ | 37/50 [00:57<00:12, 1.06it/s] 76%|███████▌ | 38/50 [00:58<00:11, 1.06it/s] 78%|███████▊ | 39/50 [00:59<00:10, 1.06it/s] 80%|████████ | 40/50 [01:00<00:09, 1.06it/s] 82%|████████▏ | 41/50 [01:01<00:08, 1.06it/s] 84%|████████▍ | 42/50 [01:02<00:07, 1.06it/s] 86%|████████▌ | 43/50 [01:03<00:06, 1.06it/s] 88%|████████▊ | 44/50 [01:04<00:05, 1.06it/s] 90%|█████████ | 45/50 [01:05<00:04, 1.06it/s] 92%|█████████▏| 46/50 [01:06<00:03, 1.06it/s] 94%|█████████▍| 47/50 [01:07<00:02, 1.06it/s] 96%|█████████▌| 48/50 [01:08<00:01, 1.06it/s] 98%|█████████▊| 49/50 [01:09<00:00, 1.06it/s] 100%|██████████| 50/50 [01:10<00:00, 1.06it/s] 100%|██████████| 50/50 [01:10<00:00, 1.40s/it] [INFO] Done!Prediction
adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933IDiaaxyx3barbejqm3slkiqiuby4StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 2
- width
- 2048
- height
- 512
- prompt
- A photo of a grassland with animals
- stride
- 16
- sync_freq
- 1
- sync_weight
- 20
- loop_closure
- guidance_scale
- 7.5
- sync_threshold
- 5
- negative_prompt
- sync_decay_rate
- 0.99
- num_inference_steps
- 50
{ "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a grassland with animals", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", { input: { seed: 2, width: 2048, height: 512, prompt: "A photo of a grassland with animals", stride: 16, sync_freq: 1, sync_weight: 20, loop_closure: false, guidance_scale: 7.5, sync_threshold: 5, negative_prompt: "", sync_decay_rate: 0.99, 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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", input={ "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a grassland with animals", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": False, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion 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": "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a grassland with animals", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/adirik/syncdiffusion@sha256:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933 \ -i 'seed=2' \ -i 'width=2048' \ -i 'height=512' \ -i 'prompt="A photo of a grassland with animals"' \ -i 'stride=16' \ -i 'sync_freq=1' \ -i 'sync_weight=20' \ -i 'loop_closure=false' \ -i 'guidance_scale=7.5' \ -i 'sync_threshold=5' \ -i 'negative_prompt=""' \ -i 'sync_decay_rate=0.99' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/adirik/syncdiffusion@sha256:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a grassland with animals", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-01-29T11:42:52.731320Z", "created_at": "2024-01-29T11:39:09.915346Z", "data_removed": false, "error": null, "id": "iaaxyx3barbejqm3slkiqiuby4", "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "A photo of a grassland with animals", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 }, "logs": "[INFO] number of views to process: 13\n[INFO] using exponential decay scheduler with decay rate 0.99\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:05<04:23, 5.38s/it]\n 4%|▍ | 2/50 [00:10<04:18, 5.39s/it]\n 6%|▌ | 3/50 [00:16<04:13, 5.40s/it]\n 8%|▊ | 4/50 [00:21<04:08, 5.40s/it]\n 10%|█ | 5/50 [00:27<04:03, 5.40s/it]\n 12%|█▏ | 6/50 [00:27<02:51, 3.89s/it]\n 14%|█▍ | 7/50 [00:28<02:05, 2.92s/it]\n 16%|█▌ | 8/50 [00:29<01:36, 2.29s/it]\n 18%|█▊ | 9/50 [00:30<01:16, 1.87s/it]\n 20%|██ | 10/50 [00:31<01:03, 1.59s/it]\n 22%|██▏ | 11/50 [00:32<00:54, 1.39s/it]\n 24%|██▍ | 12/50 [00:33<00:47, 1.26s/it]\n 26%|██▌ | 13/50 [00:34<00:42, 1.16s/it]\n 28%|██▊ | 14/50 [00:35<00:39, 1.10s/it]\n 30%|███ | 15/50 [00:36<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:37<00:34, 1.02s/it]\n 34%|███▍ | 17/50 [00:38<00:32, 1.00it/s]\n 36%|███▌ | 18/50 [00:39<00:31, 1.02it/s]\n 38%|███▊ | 19/50 [00:40<00:30, 1.03it/s]\n 40%|████ | 20/50 [00:41<00:28, 1.04it/s]\n 42%|████▏ | 21/50 [00:42<00:27, 1.04it/s]\n 44%|████▍ | 22/50 [00:43<00:26, 1.05it/s]\n 46%|████▌ | 23/50 [00:44<00:25, 1.05it/s]\n 48%|████▊ | 24/50 [00:44<00:24, 1.05it/s]\n 50%|█████ | 25/50 [00:45<00:23, 1.05it/s]\n 52%|█████▏ | 26/50 [00:46<00:22, 1.06it/s]\n 54%|█████▍ | 27/50 [00:47<00:21, 1.06it/s]\n 56%|█████▌ | 28/50 [00:48<00:20, 1.06it/s]\n 58%|█████▊ | 29/50 [00:49<00:19, 1.06it/s]\n 60%|██████ | 30/50 [00:50<00:18, 1.06it/s]\n 62%|██████▏ | 31/50 [00:51<00:17, 1.06it/s]\n 64%|██████▍ | 32/50 [00:52<00:17, 1.06it/s]\n 66%|██████▌ | 33/50 [00:53<00:16, 1.06it/s]\n 68%|██████▊ | 34/50 [00:54<00:15, 1.06it/s]\n 70%|███████ | 35/50 [00:55<00:14, 1.06it/s]\n 72%|███████▏ | 36/50 [00:56<00:13, 1.06it/s]\n 74%|███████▍ | 37/50 [00:57<00:12, 1.06it/s]\n 76%|███████▌ | 38/50 [00:58<00:11, 1.06it/s]\n 78%|███████▊ | 39/50 [00:59<00:10, 1.06it/s]\n 80%|████████ | 40/50 [01:00<00:09, 1.06it/s]\n 82%|████████▏ | 41/50 [01:01<00:08, 1.06it/s]\n 84%|████████▍ | 42/50 [01:01<00:07, 1.06it/s]\n 86%|████████▌ | 43/50 [01:02<00:06, 1.05it/s]\n 88%|████████▊ | 44/50 [01:03<00:05, 1.06it/s]\n 90%|█████████ | 45/50 [01:04<00:04, 1.06it/s]\n 92%|█████████▏| 46/50 [01:05<00:03, 1.06it/s]\n 94%|█████████▍| 47/50 [01:06<00:02, 1.06it/s]\n 96%|█████████▌| 48/50 [01:07<00:01, 1.06it/s]\n 98%|█████████▊| 49/50 [01:08<00:00, 1.05it/s]\n100%|██████████| 50/50 [01:09<00:00, 1.05it/s]\n100%|██████████| 50/50 [01:09<00:00, 1.39s/it]\n[INFO] Done!", "metrics": { "predict_time": 71.464258, "total_time": 222.815974 }, "output": "https://replicate.delivery/pbxt/ag6X207wWm73CZ0wX3eWhqymBjotqUTcMfpXec1xwga2JoikA/output.png", "started_at": "2024-01-29T11:41:41.267062Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iaaxyx3barbejqm3slkiqiuby4", "cancel": "https://api.replicate.com/v1/predictions/iaaxyx3barbejqm3slkiqiuby4/cancel" }, "version": "5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933" }Generated in[INFO] number of views to process: 13 [INFO] using exponential decay scheduler with decay rate 0.99 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:05<04:23, 5.38s/it] 4%|▍ | 2/50 [00:10<04:18, 5.39s/it] 6%|▌ | 3/50 [00:16<04:13, 5.40s/it] 8%|▊ | 4/50 [00:21<04:08, 5.40s/it] 10%|█ | 5/50 [00:27<04:03, 5.40s/it] 12%|█▏ | 6/50 [00:27<02:51, 3.89s/it] 14%|█▍ | 7/50 [00:28<02:05, 2.92s/it] 16%|█▌ | 8/50 [00:29<01:36, 2.29s/it] 18%|█▊ | 9/50 [00:30<01:16, 1.87s/it] 20%|██ | 10/50 [00:31<01:03, 1.59s/it] 22%|██▏ | 11/50 [00:32<00:54, 1.39s/it] 24%|██▍ | 12/50 [00:33<00:47, 1.26s/it] 26%|██▌ | 13/50 [00:34<00:42, 1.16s/it] 28%|██▊ | 14/50 [00:35<00:39, 1.10s/it] 30%|███ | 15/50 [00:36<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:37<00:34, 1.02s/it] 34%|███▍ | 17/50 [00:38<00:32, 1.00it/s] 36%|███▌ | 18/50 [00:39<00:31, 1.02it/s] 38%|███▊ | 19/50 [00:40<00:30, 1.03it/s] 40%|████ | 20/50 [00:41<00:28, 1.04it/s] 42%|████▏ | 21/50 [00:42<00:27, 1.04it/s] 44%|████▍ | 22/50 [00:43<00:26, 1.05it/s] 46%|████▌ | 23/50 [00:44<00:25, 1.05it/s] 48%|████▊ | 24/50 [00:44<00:24, 1.05it/s] 50%|█████ | 25/50 [00:45<00:23, 1.05it/s] 52%|█████▏ | 26/50 [00:46<00:22, 1.06it/s] 54%|█████▍ | 27/50 [00:47<00:21, 1.06it/s] 56%|█████▌ | 28/50 [00:48<00:20, 1.06it/s] 58%|█████▊ | 29/50 [00:49<00:19, 1.06it/s] 60%|██████ | 30/50 [00:50<00:18, 1.06it/s] 62%|██████▏ | 31/50 [00:51<00:17, 1.06it/s] 64%|██████▍ | 32/50 [00:52<00:17, 1.06it/s] 66%|██████▌ | 33/50 [00:53<00:16, 1.06it/s] 68%|██████▊ | 34/50 [00:54<00:15, 1.06it/s] 70%|███████ | 35/50 [00:55<00:14, 1.06it/s] 72%|███████▏ | 36/50 [00:56<00:13, 1.06it/s] 74%|███████▍ | 37/50 [00:57<00:12, 1.06it/s] 76%|███████▌ | 38/50 [00:58<00:11, 1.06it/s] 78%|███████▊ | 39/50 [00:59<00:10, 1.06it/s] 80%|████████ | 40/50 [01:00<00:09, 1.06it/s] 82%|████████▏ | 41/50 [01:01<00:08, 1.06it/s] 84%|████████▍ | 42/50 [01:01<00:07, 1.06it/s] 86%|████████▌ | 43/50 [01:02<00:06, 1.05it/s] 88%|████████▊ | 44/50 [01:03<00:05, 1.06it/s] 90%|█████████ | 45/50 [01:04<00:04, 1.06it/s] 92%|█████████▏| 46/50 [01:05<00:03, 1.06it/s] 94%|█████████▍| 47/50 [01:06<00:02, 1.06it/s] 96%|█████████▌| 48/50 [01:07<00:01, 1.06it/s] 98%|█████████▊| 49/50 [01:08<00:00, 1.05it/s] 100%|██████████| 50/50 [01:09<00:00, 1.05it/s] 100%|██████████| 50/50 [01:09<00:00, 1.39s/it] [INFO] Done!Prediction
adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933IDu3yhuw3buzta577olxzep3ucr4StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 2
- width
- 2048
- height
- 512
- prompt
- A cinematic view of a castle in the sunset
- stride
- 16
- sync_freq
- 1
- sync_weight
- 20
- loop_closure
- guidance_scale
- 7.5
- sync_threshold
- 5
- negative_prompt
- sync_decay_rate
- 0.99
- num_inference_steps
- 50
{ "seed": 2, "width": 2048, "height": 512, "prompt": "A cinematic view of a castle in the sunset", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", { input: { seed: 2, width: 2048, height: 512, prompt: "A cinematic view of a castle in the sunset", stride: 16, sync_freq: 1, sync_weight: 20, loop_closure: false, guidance_scale: 7.5, sync_threshold: 5, negative_prompt: "", sync_decay_rate: 0.99, 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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", input={ "seed": 2, "width": 2048, "height": 512, "prompt": "A cinematic view of a castle in the sunset", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": False, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion 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": "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "A cinematic view of a castle in the sunset", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/adirik/syncdiffusion@sha256:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933 \ -i 'seed=2' \ -i 'width=2048' \ -i 'height=512' \ -i 'prompt="A cinematic view of a castle in the sunset"' \ -i 'stride=16' \ -i 'sync_freq=1' \ -i 'sync_weight=20' \ -i 'loop_closure=false' \ -i 'guidance_scale=7.5' \ -i 'sync_threshold=5' \ -i 'negative_prompt=""' \ -i 'sync_decay_rate=0.99' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/adirik/syncdiffusion@sha256:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "A cinematic view of a castle in the sunset", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-01-29T11:43:01.058226Z", "created_at": "2024-01-29T11:39:30.559106Z", "data_removed": false, "error": null, "id": "u3yhuw3buzta577olxzep3ucr4", "input": { "seed": 2, "width": 2048, "height": 512, "prompt": "A cinematic view of a castle in the sunset", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 }, "logs": "[INFO] number of views to process: 13\n[INFO] using exponential decay scheduler with decay rate 0.99\n/src/syncdiffusion/syncdiffusion_model.py:150: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\nlatent = torch.randn((1, self.unet.in_channels, height // 8, width // 8))\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:07<06:11, 7.58s/it]\n 4%|▍ | 2/50 [00:12<05:01, 6.28s/it]\n 6%|▌ | 3/50 [00:18<04:35, 5.86s/it]\n 8%|▊ | 4/50 [00:23<04:20, 5.67s/it]\n 10%|█ | 5/50 [00:29<04:10, 5.56s/it]\n 12%|█▏ | 6/50 [00:29<02:55, 3.99s/it]\n 14%|█▍ | 7/50 [00:30<02:08, 2.99s/it]\n 16%|█▌ | 8/50 [00:31<01:38, 2.34s/it]\n 18%|█▊ | 9/50 [00:32<01:17, 1.90s/it]\n 20%|██ | 10/50 [00:33<01:04, 1.60s/it]\n 22%|██▏ | 11/50 [00:34<00:54, 1.40s/it]\n 24%|██▍ | 12/50 [00:35<00:47, 1.26s/it]\n 26%|██▌ | 13/50 [00:36<00:43, 1.16s/it]\n 28%|██▊ | 14/50 [00:37<00:39, 1.10s/it]\n 30%|███ | 15/50 [00:38<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:39<00:34, 1.02s/it]\n 34%|███▍ | 17/50 [00:40<00:32, 1.01it/s]\n 36%|███▌ | 18/50 [00:41<00:31, 1.02it/s]\n 38%|███▊ | 19/50 [00:42<00:29, 1.04it/s]\n 40%|████ | 20/50 [00:43<00:28, 1.04it/s]\n 42%|████▏ | 21/50 [00:44<00:27, 1.05it/s]\n 44%|████▍ | 22/50 [00:45<00:26, 1.05it/s]\n 46%|████▌ | 23/50 [00:45<00:25, 1.06it/s]\n 48%|████▊ | 24/50 [00:46<00:24, 1.06it/s]\n 50%|█████ | 25/50 [00:47<00:23, 1.06it/s]\n 52%|█████▏ | 26/50 [00:48<00:22, 1.06it/s]\n 54%|█████▍ | 27/50 [00:49<00:21, 1.06it/s]\n 56%|█████▌ | 28/50 [00:50<00:20, 1.06it/s]\n 58%|█████▊ | 29/50 [00:51<00:19, 1.06it/s]\n 60%|██████ | 30/50 [00:52<00:18, 1.06it/s]\n 62%|██████▏ | 31/50 [00:53<00:17, 1.06it/s]\n 64%|██████▍ | 32/50 [00:54<00:17, 1.06it/s]\n 66%|██████▌ | 33/50 [00:55<00:16, 1.06it/s]\n 68%|██████▊ | 34/50 [00:56<00:15, 1.06it/s]\n 70%|███████ | 35/50 [00:57<00:14, 1.06it/s]\n 72%|███████▏ | 36/50 [00:58<00:13, 1.06it/s]\n 74%|███████▍ | 37/50 [00:59<00:12, 1.06it/s]\n 76%|███████▌ | 38/50 [01:00<00:11, 1.06it/s]\n 78%|███████▊ | 39/50 [01:01<00:10, 1.06it/s]\n 80%|████████ | 40/50 [01:01<00:09, 1.06it/s]\n 82%|████████▏ | 41/50 [01:02<00:08, 1.06it/s]\n 84%|████████▍ | 42/50 [01:03<00:07, 1.06it/s]\n 86%|████████▌ | 43/50 [01:04<00:06, 1.06it/s]\n 88%|████████▊ | 44/50 [01:05<00:05, 1.06it/s]\n 90%|█████████ | 45/50 [01:06<00:04, 1.06it/s]\n 92%|█████████▏| 46/50 [01:07<00:03, 1.06it/s]\n 94%|█████████▍| 47/50 [01:08<00:02, 1.06it/s]\n 96%|█████████▌| 48/50 [01:09<00:01, 1.06it/s]\n 98%|█████████▊| 49/50 [01:10<00:00, 1.06it/s]\n100%|██████████| 50/50 [01:11<00:00, 1.06it/s]\n100%|██████████| 50/50 [01:11<00:00, 1.43s/it]\n[INFO] Done!", "metrics": { "predict_time": 73.898416, "total_time": 210.49912 }, "output": "https://replicate.delivery/pbxt/iIuWg5KXaAZiEd3loftSODcpPcqvYeFKaUSeJzCC9QAIKoikA/output.png", "started_at": "2024-01-29T11:41:47.159810Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/u3yhuw3buzta577olxzep3ucr4", "cancel": "https://api.replicate.com/v1/predictions/u3yhuw3buzta577olxzep3ucr4/cancel" }, "version": "5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933" }Generated in[INFO] number of views to process: 13 [INFO] using exponential decay scheduler with decay rate 0.99 /src/syncdiffusion/syncdiffusion_model.py:150: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'. latent = torch.randn((1, self.unet.in_channels, height // 8, width // 8)) 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:07<06:11, 7.58s/it] 4%|▍ | 2/50 [00:12<05:01, 6.28s/it] 6%|▌ | 3/50 [00:18<04:35, 5.86s/it] 8%|▊ | 4/50 [00:23<04:20, 5.67s/it] 10%|█ | 5/50 [00:29<04:10, 5.56s/it] 12%|█▏ | 6/50 [00:29<02:55, 3.99s/it] 14%|█▍ | 7/50 [00:30<02:08, 2.99s/it] 16%|█▌ | 8/50 [00:31<01:38, 2.34s/it] 18%|█▊ | 9/50 [00:32<01:17, 1.90s/it] 20%|██ | 10/50 [00:33<01:04, 1.60s/it] 22%|██▏ | 11/50 [00:34<00:54, 1.40s/it] 24%|██▍ | 12/50 [00:35<00:47, 1.26s/it] 26%|██▌ | 13/50 [00:36<00:43, 1.16s/it] 28%|██▊ | 14/50 [00:37<00:39, 1.10s/it] 30%|███ | 15/50 [00:38<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:39<00:34, 1.02s/it] 34%|███▍ | 17/50 [00:40<00:32, 1.01it/s] 36%|███▌ | 18/50 [00:41<00:31, 1.02it/s] 38%|███▊ | 19/50 [00:42<00:29, 1.04it/s] 40%|████ | 20/50 [00:43<00:28, 1.04it/s] 42%|████▏ | 21/50 [00:44<00:27, 1.05it/s] 44%|████▍ | 22/50 [00:45<00:26, 1.05it/s] 46%|████▌ | 23/50 [00:45<00:25, 1.06it/s] 48%|████▊ | 24/50 [00:46<00:24, 1.06it/s] 50%|█████ | 25/50 [00:47<00:23, 1.06it/s] 52%|█████▏ | 26/50 [00:48<00:22, 1.06it/s] 54%|█████▍ | 27/50 [00:49<00:21, 1.06it/s] 56%|█████▌ | 28/50 [00:50<00:20, 1.06it/s] 58%|█████▊ | 29/50 [00:51<00:19, 1.06it/s] 60%|██████ | 30/50 [00:52<00:18, 1.06it/s] 62%|██████▏ | 31/50 [00:53<00:17, 1.06it/s] 64%|██████▍ | 32/50 [00:54<00:17, 1.06it/s] 66%|██████▌ | 33/50 [00:55<00:16, 1.06it/s] 68%|██████▊ | 34/50 [00:56<00:15, 1.06it/s] 70%|███████ | 35/50 [00:57<00:14, 1.06it/s] 72%|███████▏ | 36/50 [00:58<00:13, 1.06it/s] 74%|███████▍ | 37/50 [00:59<00:12, 1.06it/s] 76%|███████▌ | 38/50 [01:00<00:11, 1.06it/s] 78%|███████▊ | 39/50 [01:01<00:10, 1.06it/s] 80%|████████ | 40/50 [01:01<00:09, 1.06it/s] 82%|████████▏ | 41/50 [01:02<00:08, 1.06it/s] 84%|████████▍ | 42/50 [01:03<00:07, 1.06it/s] 86%|████████▌ | 43/50 [01:04<00:06, 1.06it/s] 88%|████████▊ | 44/50 [01:05<00:05, 1.06it/s] 90%|█████████ | 45/50 [01:06<00:04, 1.06it/s] 92%|█████████▏| 46/50 [01:07<00:03, 1.06it/s] 94%|█████████▍| 47/50 [01:08<00:02, 1.06it/s] 96%|█████████▌| 48/50 [01:09<00:01, 1.06it/s] 98%|█████████▊| 49/50 [01:10<00:00, 1.06it/s] 100%|██████████| 50/50 [01:11<00:00, 1.06it/s] 100%|██████████| 50/50 [01:11<00:00, 1.43s/it] [INFO] Done!Prediction
adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933IDdqfts6dbieut5axrxnfuqdmbo4StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 2
- width
- 512
- height
- 2048
- prompt
- A bird's eye view of an alley with shops
- stride
- 16
- sync_freq
- 1
- sync_weight
- 20
- loop_closure
- guidance_scale
- 7.5
- sync_threshold
- 5
- negative_prompt
- sync_decay_rate
- 0.99
- num_inference_steps
- 50
{ "seed": 2, "width": 512, "height": 2048, "prompt": "A bird's eye view of an alley with shops", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", { input: { seed: 2, width: 512, height: 2048, prompt: "A bird's eye view of an alley with shops", stride: 16, sync_freq: 1, sync_weight: 20, loop_closure: false, guidance_scale: 7.5, sync_threshold: 5, negative_prompt: "", sync_decay_rate: 0.99, 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 adirik/syncdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", input={ "seed": 2, "width": 512, "height": 2048, "prompt": "A bird's eye view of an alley with shops", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": False, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "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 adirik/syncdiffusion 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": "adirik/syncdiffusion:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933", "input": { "seed": 2, "width": 512, "height": 2048, "prompt": "A bird\'s eye view of an alley with shops", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/adirik/syncdiffusion@sha256:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933 \ -i 'seed=2' \ -i 'width=512' \ -i 'height=2048' \ -i $'prompt="A bird\'s eye view of an alley with shops"' \ -i 'stride=16' \ -i 'sync_freq=1' \ -i 'sync_weight=20' \ -i 'loop_closure=false' \ -i 'guidance_scale=7.5' \ -i 'sync_threshold=5' \ -i 'negative_prompt=""' \ -i 'sync_decay_rate=0.99' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/adirik/syncdiffusion@sha256:5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 2, "width": 512, "height": 2048, "prompt": "A bird\'s eye view of an alley with shops", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-01-29T11:44:04.588744Z", "created_at": "2024-01-29T11:40:08.173621Z", "data_removed": false, "error": null, "id": "dqfts6dbieut5axrxnfuqdmbo4", "input": { "seed": 2, "width": 512, "height": 2048, "prompt": "A bird's eye view of an alley with shops", "stride": 16, "sync_freq": 1, "sync_weight": 20, "loop_closure": false, "guidance_scale": 7.5, "sync_threshold": 5, "negative_prompt": "", "sync_decay_rate": 0.99, "num_inference_steps": 50 }, "logs": "[INFO] number of views to process: 13\n[INFO] using exponential decay scheduler with decay rate 0.99\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:05<04:24, 5.39s/it]\n 4%|▍ | 2/50 [00:10<04:19, 5.40s/it]\n 6%|▌ | 3/50 [00:16<04:14, 5.41s/it]\n 8%|▊ | 4/50 [00:21<04:08, 5.41s/it]\n 10%|█ | 5/50 [00:27<04:03, 5.41s/it]\n 12%|█▏ | 6/50 [00:27<02:51, 3.89s/it]\n 14%|█▍ | 7/50 [00:28<02:05, 2.93s/it]\n 16%|█▌ | 8/50 [00:29<01:36, 2.30s/it]\n 18%|█▊ | 9/50 [00:30<01:16, 1.88s/it]\n 20%|██ | 10/50 [00:31<01:03, 1.59s/it]\n 22%|██▏ | 11/50 [00:32<00:54, 1.39s/it]\n 24%|██▍ | 12/50 [00:33<00:47, 1.26s/it]\n 26%|██▌ | 13/50 [00:34<00:43, 1.16s/it]\n 28%|██▊ | 14/50 [00:35<00:39, 1.10s/it]\n 30%|███ | 15/50 [00:36<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:37<00:34, 1.02s/it]\n 34%|███▍ | 17/50 [00:38<00:32, 1.00it/s]\n 36%|███▌ | 18/50 [00:39<00:31, 1.02it/s]\n 38%|███▊ | 19/50 [00:40<00:30, 1.03it/s]\n 40%|████ | 20/50 [00:41<00:29, 1.03it/s]\n 42%|████▏ | 21/50 [00:42<00:27, 1.04it/s]\n 44%|████▍ | 22/50 [00:43<00:26, 1.05it/s]\n 46%|████▌ | 23/50 [00:44<00:25, 1.05it/s]\n 48%|████▊ | 24/50 [00:45<00:24, 1.05it/s]\n 50%|█████ | 25/50 [00:45<00:23, 1.05it/s]\n 52%|█████▏ | 26/50 [00:46<00:22, 1.05it/s]\n 54%|█████▍ | 27/50 [00:47<00:21, 1.05it/s]\n 56%|█████▌ | 28/50 [00:48<00:20, 1.05it/s]\n 58%|█████▊ | 29/50 [00:49<00:19, 1.05it/s]\n 60%|██████ | 30/50 [00:50<00:18, 1.05it/s]\n 62%|██████▏ | 31/50 [00:51<00:18, 1.05it/s]\n 64%|██████▍ | 32/50 [00:52<00:17, 1.05it/s]\n 66%|██████▌ | 33/50 [00:53<00:16, 1.05it/s]\n 68%|██████▊ | 34/50 [00:54<00:15, 1.05it/s]\n 70%|███████ | 35/50 [00:55<00:14, 1.05it/s]\n 72%|███████▏ | 36/50 [00:56<00:13, 1.05it/s]\n 74%|███████▍ | 37/50 [00:57<00:12, 1.05it/s]\n 76%|███████▌ | 38/50 [00:58<00:11, 1.05it/s]\n 78%|███████▊ | 39/50 [00:59<00:10, 1.05it/s]\n 80%|████████ | 40/50 [01:00<00:09, 1.05it/s]\n 82%|████████▏ | 41/50 [01:01<00:08, 1.05it/s]\n 84%|████████▍ | 42/50 [01:02<00:07, 1.05it/s]\n 86%|████████▌ | 43/50 [01:03<00:06, 1.05it/s]\n 88%|████████▊ | 44/50 [01:04<00:05, 1.06it/s]\n 90%|█████████ | 45/50 [01:04<00:04, 1.05it/s]\n 92%|█████████▏| 46/50 [01:05<00:03, 1.06it/s]\n 94%|█████████▍| 47/50 [01:06<00:02, 1.05it/s]\n 96%|█████████▌| 48/50 [01:07<00:01, 1.06it/s]\n 98%|█████████▊| 49/50 [01:08<00:00, 1.05it/s]\n100%|██████████| 50/50 [01:09<00:00, 1.05it/s]\n100%|██████████| 50/50 [01:09<00:00, 1.39s/it]\n[INFO] Done!", "metrics": { "predict_time": 71.581753, "total_time": 236.415123 }, "output": "https://replicate.delivery/pbxt/GU0UiJFzYKY3Ht3Wotv9HGqb9ypjKa7nv80tnewXDJ0BDqIJA/output.png", "started_at": "2024-01-29T11:42:53.006991Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dqfts6dbieut5axrxnfuqdmbo4", "cancel": "https://api.replicate.com/v1/predictions/dqfts6dbieut5axrxnfuqdmbo4/cancel" }, "version": "5ea1d5f9fa17059c162571d5934ea8dcb754f5fcff6dd0db5533a7f314a8f933" }Generated in[INFO] number of views to process: 13 [INFO] using exponential decay scheduler with decay rate 0.99 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:05<04:24, 5.39s/it] 4%|▍ | 2/50 [00:10<04:19, 5.40s/it] 6%|▌ | 3/50 [00:16<04:14, 5.41s/it] 8%|▊ | 4/50 [00:21<04:08, 5.41s/it] 10%|█ | 5/50 [00:27<04:03, 5.41s/it] 12%|█▏ | 6/50 [00:27<02:51, 3.89s/it] 14%|█▍ | 7/50 [00:28<02:05, 2.93s/it] 16%|█▌ | 8/50 [00:29<01:36, 2.30s/it] 18%|█▊ | 9/50 [00:30<01:16, 1.88s/it] 20%|██ | 10/50 [00:31<01:03, 1.59s/it] 22%|██▏ | 11/50 [00:32<00:54, 1.39s/it] 24%|██▍ | 12/50 [00:33<00:47, 1.26s/it] 26%|██▌ | 13/50 [00:34<00:43, 1.16s/it] 28%|██▊ | 14/50 [00:35<00:39, 1.10s/it] 30%|███ | 15/50 [00:36<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:37<00:34, 1.02s/it] 34%|███▍ | 17/50 [00:38<00:32, 1.00it/s] 36%|███▌ | 18/50 [00:39<00:31, 1.02it/s] 38%|███▊ | 19/50 [00:40<00:30, 1.03it/s] 40%|████ | 20/50 [00:41<00:29, 1.03it/s] 42%|████▏ | 21/50 [00:42<00:27, 1.04it/s] 44%|████▍ | 22/50 [00:43<00:26, 1.05it/s] 46%|████▌ | 23/50 [00:44<00:25, 1.05it/s] 48%|████▊ | 24/50 [00:45<00:24, 1.05it/s] 50%|█████ | 25/50 [00:45<00:23, 1.05it/s] 52%|█████▏ | 26/50 [00:46<00:22, 1.05it/s] 54%|█████▍ | 27/50 [00:47<00:21, 1.05it/s] 56%|█████▌ | 28/50 [00:48<00:20, 1.05it/s] 58%|█████▊ | 29/50 [00:49<00:19, 1.05it/s] 60%|██████ | 30/50 [00:50<00:18, 1.05it/s] 62%|██████▏ | 31/50 [00:51<00:18, 1.05it/s] 64%|██████▍ | 32/50 [00:52<00:17, 1.05it/s] 66%|██████▌ | 33/50 [00:53<00:16, 1.05it/s] 68%|██████▊ | 34/50 [00:54<00:15, 1.05it/s] 70%|███████ | 35/50 [00:55<00:14, 1.05it/s] 72%|███████▏ | 36/50 [00:56<00:13, 1.05it/s] 74%|███████▍ | 37/50 [00:57<00:12, 1.05it/s] 76%|███████▌ | 38/50 [00:58<00:11, 1.05it/s] 78%|███████▊ | 39/50 [00:59<00:10, 1.05it/s] 80%|████████ | 40/50 [01:00<00:09, 1.05it/s] 82%|████████▏ | 41/50 [01:01<00:08, 1.05it/s] 84%|████████▍ | 42/50 [01:02<00:07, 1.05it/s] 86%|████████▌ | 43/50 [01:03<00:06, 1.05it/s] 88%|████████▊ | 44/50 [01:04<00:05, 1.06it/s] 90%|█████████ | 45/50 [01:04<00:04, 1.05it/s] 92%|█████████▏| 46/50 [01:05<00:03, 1.06it/s] 94%|█████████▍| 47/50 [01:06<00:02, 1.05it/s] 96%|█████████▌| 48/50 [01:07<00:01, 1.06it/s] 98%|█████████▊| 49/50 [01:08<00:00, 1.05it/s] 100%|██████████| 50/50 [01:09<00:00, 1.05it/s] 100%|██████████| 50/50 [01:09<00:00, 1.39s/it] [INFO] Done!
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