adirik / masactrl-anything-v4-0
Edit real or generated images
Prediction
adirik/masactrl-anything-v4-0:9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800IDpuk7yk3b7aqhegw24tbbb7i6ryStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- source_prompt
- 1boy, casual, outdoors, sitting
- target_prompt
- 1boy, casual, outdoors, standing
- guidance_scale
- 7.5
- masactrl_start_step
- 4
- num_inference_steps
- 50
- masactrl_start_layer
- 10
{ "source_prompt": "1boy, casual, outdoors, sitting", "target_prompt": "1boy, casual, outdoors, standing", "guidance_scale": 7.5, "masactrl_start_step": 4, "num_inference_steps": 50, "masactrl_start_layer": 10 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/masactrl-anything-v4-0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/masactrl-anything-v4-0:9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800", { input: { source_prompt: "1boy, casual, outdoors, sitting", target_prompt: "1boy, casual, outdoors, standing", guidance_scale: 7.5, masactrl_start_step: 4, num_inference_steps: 50, masactrl_start_layer: 10 } } ); // 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 adirik/masactrl-anything-v4-0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/masactrl-anything-v4-0:9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800", input={ "source_prompt": "1boy, casual, outdoors, sitting", "target_prompt": "1boy, casual, outdoors, standing", "guidance_scale": 7.5, "masactrl_start_step": 4, "num_inference_steps": 50, "masactrl_start_layer": 10 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run adirik/masactrl-anything-v4-0 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/masactrl-anything-v4-0:9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800", "input": { "source_prompt": "1boy, casual, outdoors, sitting", "target_prompt": "1boy, casual, outdoors, standing", "guidance_scale": 7.5, "masactrl_start_step": 4, "num_inference_steps": 50, "masactrl_start_layer": 10 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-05T10:13:45.252553Z", "created_at": "2023-12-05T10:13:23.700571Z", "data_removed": false, "error": null, "id": "puk7yk3b7aqhegw24tbbb7i6ry", "input": { "source_prompt": "1boy, casual, outdoors, sitting", "target_prompt": "1boy, casual, outdoors, standing", "guidance_scale": 7.5, "masactrl_start_step": 4, "num_inference_steps": 50, "masactrl_start_layer": 10 }, "logs": "/root/.pyenv/versions/3.11.6/lib/python3.11/site-packages/lightning_fabric/utilities/seed.py:40: No seed found, seed set to 3047472308\nSeed set to 3047472308\nMasaCtrl at denoising steps: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]\nMasaCtrl at U-Net layers: [10, 11, 12, 13, 14, 15]\ninput text embeddings : torch.Size([2, 77, 768])\n/src/masactrl/diffuser_utils.py:139: 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'.\nlatents_shape = (batch_size, self.unet.in_channels, height//8, width//8)\nlatents shape: torch.Size([2, 4, 64, 64])\nDDIM Sampler: 0%| | 0/50 [00:00<?, ?it/s]\nDDIM Sampler: 2%|▏ | 1/50 [00:00<00:29, 1.65it/s]\nDDIM Sampler: 4%|▍ | 2/50 [00:00<00:19, 2.50it/s]\nDDIM Sampler: 6%|▌ | 3/50 [00:01<00:15, 2.95it/s]\nDDIM Sampler: 8%|▊ | 4/50 [00:01<00:14, 3.22it/s]\nDDIM Sampler: 10%|█ | 5/50 [00:01<00:13, 3.36it/s]\nDDIM Sampler: 12%|█▏ | 6/50 [00:01<00:13, 3.25it/s]\nDDIM Sampler: 14%|█▍ | 7/50 [00:02<00:13, 3.17it/s]\nDDIM Sampler: 16%|█▌ | 8/50 [00:02<00:13, 3.13it/s]\nDDIM Sampler: 18%|█▊ | 9/50 [00:02<00:13, 3.10it/s]\nDDIM Sampler: 20%|██ | 10/50 [00:03<00:12, 3.08it/s]\nDDIM Sampler: 22%|██▏ | 11/50 [00:03<00:12, 3.06it/s]\nDDIM Sampler: 24%|██▍ | 12/50 [00:03<00:12, 3.06it/s]\nDDIM Sampler: 26%|██▌ | 13/50 [00:04<00:12, 3.05it/s]\nDDIM Sampler: 28%|██▊ | 14/50 [00:04<00:11, 3.04it/s]\nDDIM Sampler: 30%|███ | 15/50 [00:04<00:11, 3.04it/s]\nDDIM Sampler: 32%|███▏ | 16/50 [00:05<00:11, 3.04it/s]\nDDIM Sampler: 34%|███▍ | 17/50 [00:05<00:10, 3.04it/s]\nDDIM Sampler: 36%|███▌ | 18/50 [00:05<00:10, 3.04it/s]\nDDIM Sampler: 38%|███▊ | 19/50 [00:06<00:10, 3.03it/s]\nDDIM Sampler: 40%|████ | 20/50 [00:06<00:09, 3.03it/s]\nDDIM Sampler: 42%|████▏ | 21/50 [00:06<00:09, 3.03it/s]\nDDIM Sampler: 44%|████▍ | 22/50 [00:07<00:09, 3.03it/s]\nDDIM Sampler: 46%|████▌ | 23/50 [00:07<00:08, 3.03it/s]\nDDIM Sampler: 48%|████▊ | 24/50 [00:07<00:08, 3.03it/s]\nDDIM Sampler: 50%|█████ | 25/50 [00:08<00:08, 3.03it/s]\nDDIM Sampler: 52%|█████▏ | 26/50 [00:08<00:07, 3.03it/s]\nDDIM Sampler: 54%|█████▍ | 27/50 [00:08<00:07, 3.03it/s]\nDDIM Sampler: 56%|█████▌ | 28/50 [00:09<00:07, 3.03it/s]\nDDIM Sampler: 58%|█████▊ | 29/50 [00:09<00:06, 3.03it/s]\nDDIM Sampler: 60%|██████ | 30/50 [00:09<00:06, 3.03it/s]\nDDIM Sampler: 62%|██████▏ | 31/50 [00:10<00:06, 3.03it/s]\nDDIM Sampler: 64%|██████▍ | 32/50 [00:10<00:05, 3.03it/s]\nDDIM Sampler: 66%|██████▌ | 33/50 [00:10<00:05, 3.03it/s]\nDDIM Sampler: 68%|██████▊ | 34/50 [00:11<00:05, 3.03it/s]\nDDIM Sampler: 70%|███████ | 35/50 [00:11<00:04, 3.03it/s]\nDDIM Sampler: 72%|███████▏ | 36/50 [00:11<00:04, 3.03it/s]\nDDIM Sampler: 74%|███████▍ | 37/50 [00:12<00:04, 3.03it/s]\nDDIM Sampler: 76%|███████▌ | 38/50 [00:12<00:03, 3.03it/s]\nDDIM Sampler: 78%|███████▊ | 39/50 [00:12<00:03, 3.03it/s]\nDDIM Sampler: 80%|████████ | 40/50 [00:13<00:03, 3.03it/s]\nDDIM Sampler: 82%|████████▏ | 41/50 [00:13<00:02, 3.03it/s]\nDDIM Sampler: 84%|████████▍ | 42/50 [00:13<00:02, 3.04it/s]\nDDIM Sampler: 86%|████████▌ | 43/50 [00:14<00:02, 3.04it/s]\nDDIM Sampler: 88%|████████▊ | 44/50 [00:14<00:01, 3.04it/s]\nDDIM Sampler: 90%|█████████ | 45/50 [00:14<00:01, 3.04it/s]\nDDIM Sampler: 92%|█████████▏| 46/50 [00:15<00:01, 3.04it/s]\nDDIM Sampler: 94%|█████████▍| 47/50 [00:15<00:00, 3.04it/s]\nDDIM Sampler: 96%|█████████▌| 48/50 [00:15<00:00, 3.04it/s]\nDDIM Sampler: 98%|█████████▊| 49/50 [00:16<00:00, 3.04it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:16<00:00, 3.04it/s]\nDDIM Sampler: 100%|██████████| 50/50 [00:16<00:00, 3.03it/s]", "metrics": { "predict_time": 19.410476, "total_time": 21.551982 }, "output": [ "https://replicate.delivery/pbxt/UuTJV6Ue3gWBcCf3zMY8yErLd7VEXuBOFDaQ1IbfTjFuOVeHB/synt_0.png", "https://replicate.delivery/pbxt/Pgiaeeu4h9gVWEbXWo2IFYCC0zKVofM5gPD8E12SrATwOVeHB/synt_1.png" ], "started_at": "2023-12-05T10:13:25.842077Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/puk7yk3b7aqhegw24tbbb7i6ry", "cancel": "https://api.replicate.com/v1/predictions/puk7yk3b7aqhegw24tbbb7i6ry/cancel" }, "version": "9db49bf5fcedfb9279471e89b1daa326308ddaf633179d52f8695c47cfbe0800" }Generated in/root/.pyenv/versions/3.11.6/lib/python3.11/site-packages/lightning_fabric/utilities/seed.py:40: No seed found, seed set to 3047472308 Seed set to 3047472308 MasaCtrl at denoising steps: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49] MasaCtrl at U-Net layers: [10, 11, 12, 13, 14, 15] input text embeddings : torch.Size([2, 77, 768]) /src/masactrl/diffuser_utils.py:139: 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'. latents_shape = (batch_size, self.unet.in_channels, height//8, width//8) latents shape: torch.Size([2, 4, 64, 64]) DDIM Sampler: 0%| | 0/50 [00:00<?, ?it/s] DDIM Sampler: 2%|▏ | 1/50 [00:00<00:29, 1.65it/s] DDIM Sampler: 4%|▍ | 2/50 [00:00<00:19, 2.50it/s] DDIM Sampler: 6%|▌ | 3/50 [00:01<00:15, 2.95it/s] DDIM Sampler: 8%|▊ | 4/50 [00:01<00:14, 3.22it/s] DDIM Sampler: 10%|█ | 5/50 [00:01<00:13, 3.36it/s] DDIM Sampler: 12%|█▏ | 6/50 [00:01<00:13, 3.25it/s] DDIM Sampler: 14%|█▍ | 7/50 [00:02<00:13, 3.17it/s] DDIM Sampler: 16%|█▌ | 8/50 [00:02<00:13, 3.13it/s] DDIM Sampler: 18%|█▊ | 9/50 [00:02<00:13, 3.10it/s] DDIM Sampler: 20%|██ | 10/50 [00:03<00:12, 3.08it/s] DDIM Sampler: 22%|██▏ | 11/50 [00:03<00:12, 3.06it/s] DDIM Sampler: 24%|██▍ | 12/50 [00:03<00:12, 3.06it/s] DDIM Sampler: 26%|██▌ | 13/50 [00:04<00:12, 3.05it/s] DDIM Sampler: 28%|██▊ | 14/50 [00:04<00:11, 3.04it/s] DDIM Sampler: 30%|███ | 15/50 [00:04<00:11, 3.04it/s] DDIM Sampler: 32%|███▏ | 16/50 [00:05<00:11, 3.04it/s] DDIM Sampler: 34%|███▍ | 17/50 [00:05<00:10, 3.04it/s] DDIM Sampler: 36%|███▌ | 18/50 [00:05<00:10, 3.04it/s] DDIM Sampler: 38%|███▊ | 19/50 [00:06<00:10, 3.03it/s] DDIM Sampler: 40%|████ | 20/50 [00:06<00:09, 3.03it/s] DDIM Sampler: 42%|████▏ | 21/50 [00:06<00:09, 3.03it/s] DDIM Sampler: 44%|████▍ | 22/50 [00:07<00:09, 3.03it/s] DDIM Sampler: 46%|████▌ | 23/50 [00:07<00:08, 3.03it/s] DDIM Sampler: 48%|████▊ | 24/50 [00:07<00:08, 3.03it/s] DDIM Sampler: 50%|█████ | 25/50 [00:08<00:08, 3.03it/s] DDIM Sampler: 52%|█████▏ | 26/50 [00:08<00:07, 3.03it/s] DDIM Sampler: 54%|█████▍ | 27/50 [00:08<00:07, 3.03it/s] DDIM Sampler: 56%|█████▌ | 28/50 [00:09<00:07, 3.03it/s] DDIM Sampler: 58%|█████▊ | 29/50 [00:09<00:06, 3.03it/s] DDIM Sampler: 60%|██████ | 30/50 [00:09<00:06, 3.03it/s] DDIM Sampler: 62%|██████▏ | 31/50 [00:10<00:06, 3.03it/s] DDIM Sampler: 64%|██████▍ | 32/50 [00:10<00:05, 3.03it/s] DDIM Sampler: 66%|██████▌ | 33/50 [00:10<00:05, 3.03it/s] DDIM Sampler: 68%|██████▊ | 34/50 [00:11<00:05, 3.03it/s] DDIM Sampler: 70%|███████ | 35/50 [00:11<00:04, 3.03it/s] DDIM Sampler: 72%|███████▏ | 36/50 [00:11<00:04, 3.03it/s] DDIM Sampler: 74%|███████▍ | 37/50 [00:12<00:04, 3.03it/s] DDIM Sampler: 76%|███████▌ | 38/50 [00:12<00:03, 3.03it/s] DDIM Sampler: 78%|███████▊ | 39/50 [00:12<00:03, 3.03it/s] DDIM Sampler: 80%|████████ | 40/50 [00:13<00:03, 3.03it/s] DDIM Sampler: 82%|████████▏ | 41/50 [00:13<00:02, 3.03it/s] DDIM Sampler: 84%|████████▍ | 42/50 [00:13<00:02, 3.04it/s] DDIM Sampler: 86%|████████▌ | 43/50 [00:14<00:02, 3.04it/s] DDIM Sampler: 88%|████████▊ | 44/50 [00:14<00:01, 3.04it/s] DDIM Sampler: 90%|█████████ | 45/50 [00:14<00:01, 3.04it/s] DDIM Sampler: 92%|█████████▏| 46/50 [00:15<00:01, 3.04it/s] DDIM Sampler: 94%|█████████▍| 47/50 [00:15<00:00, 3.04it/s] DDIM Sampler: 96%|█████████▌| 48/50 [00:15<00:00, 3.04it/s] DDIM Sampler: 98%|█████████▊| 49/50 [00:16<00:00, 3.04it/s] DDIM Sampler: 100%|██████████| 50/50 [00:16<00:00, 3.04it/s] DDIM Sampler: 100%|██████████| 50/50 [00:16<00:00, 3.03it/s]
Want to make some of these yourself?
Run this model
