tgohblio / instant-id-multicontrolnet
InstantID. ControlNets. More base SDXL models. And the latest ByteDance's ⚡️SDXL-Lightning !⚡️
Prediction
tgohblio/instant-id-multicontrolnet:17ff70ef0f59242a00fc27431d0f70be0f851dd49221cd9bb5396cc465d92a65IDm77udxdbnijr4giab5shndgtsuStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- pose
- seed
- 0
- canny
- model
- AlbedoBase XL V2
- width
- 1024
- height
- 1024
- prompt
- woman as elven princess, with blue sheen dress, masterpiece
- depth_map
- num_steps
- 25
- scheduler
- DPMSolverMultistepScheduler
- pose_strength
- 0.5
- canny_strength
- 0.5
- depth_strength
- 0.5
- guidance_scale
- 7
- safety_checker
- face_image_path
- lightning_steps
- 4step
- negative_prompt
- ugly, low quality, deformed face, nsfw
- enable_fast_mode
- adapter_strength_ratio
- 0.8
- identitynet_strength_ratio
- 0.8
{ "pose": false, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "width": 1024, "height": 1024, "prompt": "woman as elven princess, with blue sheen dress, masterpiece", "depth_map": false, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.5, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": true, "adapter_strength_ratio": 0.8, "identitynet_strength_ratio": 0.8 }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 tgohblio/instant-id-multicontrolnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tgohblio/instant-id-multicontrolnet:17ff70ef0f59242a00fc27431d0f70be0f851dd49221cd9bb5396cc465d92a65", { input: { pose: false, seed: 0, canny: false, model: "AlbedoBase XL V2", width: 1024, height: 1024, prompt: "woman as elven princess, with blue sheen dress, masterpiece", depth_map: false, num_steps: 25, scheduler: "DPMSolverMultistepScheduler", pose_strength: 0.5, canny_strength: 0.5, depth_strength: 0.5, guidance_scale: 7, safety_checker: true, face_image_path: "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", lightning_steps: "4step", negative_prompt: "ugly, low quality, deformed face, nsfw", enable_fast_mode: true, adapter_strength_ratio: 0.8, identitynet_strength_ratio: 0.8 } } ); // 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 tgohblio/instant-id-multicontrolnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tgohblio/instant-id-multicontrolnet:17ff70ef0f59242a00fc27431d0f70be0f851dd49221cd9bb5396cc465d92a65", input={ "pose": False, "seed": 0, "canny": False, "model": "AlbedoBase XL V2", "width": 1024, "height": 1024, "prompt": "woman as elven princess, with blue sheen dress, masterpiece", "depth_map": False, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.5, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": True, "face_image_path": "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": True, "adapter_strength_ratio": 0.8, "identitynet_strength_ratio": 0.8 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tgohblio/instant-id-multicontrolnet 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": "tgohblio/instant-id-multicontrolnet:17ff70ef0f59242a00fc27431d0f70be0f851dd49221cd9bb5396cc465d92a65", "input": { "pose": false, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "width": 1024, "height": 1024, "prompt": "woman as elven princess, with blue sheen dress, masterpiece", "depth_map": false, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.5, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": true, "adapter_strength_ratio": 0.8, "identitynet_strength_ratio": 0.8 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-02-22T15:52:24.255139Z", "created_at": "2024-02-22T15:29:07.524002Z", "data_removed": false, "error": null, "id": "m77udxdbnijr4giab5shndgtsu", "input": { "pose": false, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "width": 1024, "height": 1024, "prompt": "woman as elven princess, with blue sheen dress, masterpiece", "depth_map": false, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.5, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": true, "adapter_strength_ratio": 0.8, "identitynet_strength_ratio": 0.8 }, "logs": "[!] Resizing output to 1024x1024\nset det-size: (1024, 1024)\nwarning: det_size is already set in detection model, ignore\n/root/.pyenv/versions/3.11.8/lib/python3.11/site-packages/insightface/utils/transform.py:68: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\nTo use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\nP = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:02, 1.22it/s]\n 50%|█████ | 2/4 [00:01<00:00, 2.07it/s]\n 75%|███████▌ | 3/4 [00:01<00:00, 2.67it/s]\n100%|██████████| 4/4 [00:01<00:00, 3.08it/s]\n100%|██████████| 4/4 [00:01<00:00, 2.56it/s]", "metrics": { "predict_time": 19.420978, "total_time": 1396.731137 }, "output": "https://replicate.delivery/pbxt/oXXi2h9fwi0qcqnRIRfAzwOWOWFvfXe8rF9g4ARjFNMc7HlJB/result.jpg", "started_at": "2024-02-22T15:52:04.834161Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m77udxdbnijr4giab5shndgtsu", "cancel": "https://api.replicate.com/v1/predictions/m77udxdbnijr4giab5shndgtsu/cancel" }, "version": "17ff70ef0f59242a00fc27431d0f70be0f851dd49221cd9bb5396cc465d92a65" }Generated in[!] Resizing output to 1024x1024 set det-size: (1024, 1024) warning: det_size is already set in detection model, ignore /root/.pyenv/versions/3.11.8/lib/python3.11/site-packages/insightface/utils/transform.py:68: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions. To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`. P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:02, 1.22it/s] 50%|█████ | 2/4 [00:01<00:00, 2.07it/s] 75%|███████▌ | 3/4 [00:01<00:00, 2.67it/s] 100%|██████████| 4/4 [00:01<00:00, 3.08it/s] 100%|██████████| 4/4 [00:01<00:00, 2.56it/s]Prediction
tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3cIDaz8jsv37t9rgp0cf9h48kaky64StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- pose
- seed
- 0
- canny
- model
- AlbedoBase XL V2
- prompt
- photograph of a. (bare chested) man as runner jumping in the air, drizzling rain, detailed face, detailed eyes, soft light, stadium background, 100mm
- depth_map
- num_steps
- 30
- scheduler
- DPMSolverMultistepScheduler
- pose_strength
- 0.8
- canny_strength
- 0.5
- depth_strength
- 0.5
- guidance_scale
- 7
- safety_checker
- face_image_path
- lightning_steps
- 4step
- negative_prompt
- (worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)
- pose_image_path
- enable_fast_mode
- adapter_strength_ratio
- 0.6
- enhance_non_face_region
- identitynet_strength_ratio
- 0.75
{ "pose": true, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "prompt": "photograph of a. (bare chested) man as runner jumping in the air, drizzling rain, detailed face, detailed eyes, soft light, stadium background, 100mm", "depth_map": false, "num_steps": 30, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.8, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/Krzj0oXh0WkpTbseXkw4RFHl0vKuQCZqxQNcwUKkw84SB9JA/t-c-512.jpg", "lightning_steps": "4step", "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)", "pose_image_path": "https://replicate.delivery/pbxt/Krzj1Gs52kaofLZFPm5SnyNmKYkL54KawYm4CoFU6fJtWjvM/jump-pose2.jpeg", "enable_fast_mode": false, "adapter_strength_ratio": 0.6, "enhance_non_face_region": false, "identitynet_strength_ratio": 0.75 }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 tgohblio/instant-id-multicontrolnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c", { input: { pose: true, seed: 0, canny: false, model: "AlbedoBase XL V2", prompt: "photograph of a. (bare chested) man as runner jumping in the air, drizzling rain, detailed face, detailed eyes, soft light, stadium background, 100mm", depth_map: false, num_steps: 30, scheduler: "DPMSolverMultistepScheduler", pose_strength: 0.8, canny_strength: 0.5, depth_strength: 0.5, guidance_scale: 7, safety_checker: true, face_image_path: "https://replicate.delivery/pbxt/Krzj0oXh0WkpTbseXkw4RFHl0vKuQCZqxQNcwUKkw84SB9JA/t-c-512.jpg", lightning_steps: "4step", negative_prompt: "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)", pose_image_path: "https://replicate.delivery/pbxt/Krzj1Gs52kaofLZFPm5SnyNmKYkL54KawYm4CoFU6fJtWjvM/jump-pose2.jpeg", enable_fast_mode: false, adapter_strength_ratio: 0.6, enhance_non_face_region: false, identitynet_strength_ratio: 0.75 } } ); // 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 tgohblio/instant-id-multicontrolnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c", input={ "pose": True, "seed": 0, "canny": False, "model": "AlbedoBase XL V2", "prompt": "photograph of a. (bare chested) man as runner jumping in the air, drizzling rain, detailed face, detailed eyes, soft light, stadium background, 100mm", "depth_map": False, "num_steps": 30, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.8, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": True, "face_image_path": "https://replicate.delivery/pbxt/Krzj0oXh0WkpTbseXkw4RFHl0vKuQCZqxQNcwUKkw84SB9JA/t-c-512.jpg", "lightning_steps": "4step", "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)", "pose_image_path": "https://replicate.delivery/pbxt/Krzj1Gs52kaofLZFPm5SnyNmKYkL54KawYm4CoFU6fJtWjvM/jump-pose2.jpeg", "enable_fast_mode": False, "adapter_strength_ratio": 0.6, "enhance_non_face_region": False, "identitynet_strength_ratio": 0.75 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tgohblio/instant-id-multicontrolnet 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": "tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c", "input": { "pose": true, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "prompt": "photograph of a. (bare chested) man as runner jumping in the air, drizzling rain, detailed face, detailed eyes, soft light, stadium background, 100mm", "depth_map": false, "num_steps": 30, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.8, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/Krzj0oXh0WkpTbseXkw4RFHl0vKuQCZqxQNcwUKkw84SB9JA/t-c-512.jpg", "lightning_steps": "4step", "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)", "pose_image_path": "https://replicate.delivery/pbxt/Krzj1Gs52kaofLZFPm5SnyNmKYkL54KawYm4CoFU6fJtWjvM/jump-pose2.jpeg", "enable_fast_mode": false, "adapter_strength_ratio": 0.6, "enhance_non_face_region": false, "identitynet_strength_ratio": 0.75 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-06T05:03:17.235709Z", "created_at": "2024-05-06T05:02:44.690000Z", "data_removed": false, "error": null, "id": "az8jsv37t9rgp0cf9h48kaky64", "input": { "pose": true, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "prompt": "photograph of a. (bare chested) man as runner jumping in the air, drizzling rain, detailed face, detailed eyes, soft light, stadium background, 100mm", "depth_map": false, "num_steps": 30, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.8, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/Krzj0oXh0WkpTbseXkw4RFHl0vKuQCZqxQNcwUKkw84SB9JA/t-c-512.jpg", "lightning_steps": "4step", "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)", "pose_image_path": "https://replicate.delivery/pbxt/Krzj1Gs52kaofLZFPm5SnyNmKYkL54KawYm4CoFU6fJtWjvM/jump-pose2.jpeg", "enable_fast_mode": false, "adapter_strength_ratio": 0.6, "enhance_non_face_region": false, "identitynet_strength_ratio": 0.75 }, "logs": "0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:07, 3.82it/s]\n 7%|▋ | 2/30 [00:00<00:06, 4.18it/s]\n 10%|█ | 3/30 [00:00<00:06, 4.01it/s]\n 13%|█▎ | 4/30 [00:01<00:06, 3.92it/s]\n 17%|█▋ | 5/30 [00:01<00:06, 3.88it/s]\n 20%|██ | 6/30 [00:01<00:06, 3.86it/s]\n 23%|██▎ | 7/30 [00:01<00:06, 3.83it/s]\n 27%|██▋ | 8/30 [00:02<00:05, 3.83it/s]\n 30%|███ | 9/30 [00:02<00:05, 3.83it/s]\n 33%|███▎ | 10/30 [00:02<00:05, 3.82it/s]\n 37%|███▋ | 11/30 [00:02<00:04, 3.81it/s]\n 40%|████ | 12/30 [00:03<00:04, 3.80it/s]\n 43%|████▎ | 13/30 [00:03<00:04, 3.81it/s]\n 47%|████▋ | 14/30 [00:03<00:04, 3.80it/s]\n 50%|█████ | 15/30 [00:03<00:03, 3.81it/s]\n 53%|█████▎ | 16/30 [00:04<00:03, 3.81it/s]\n 57%|█████▋ | 17/30 [00:04<00:03, 3.80it/s]\n 60%|██████ | 18/30 [00:04<00:03, 3.80it/s]\n 63%|██████▎ | 19/30 [00:04<00:02, 3.80it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.80it/s]\n 70%|███████ | 21/30 [00:05<00:02, 3.80it/s]\n 73%|███████▎ | 22/30 [00:05<00:02, 3.80it/s]\n 77%|███████▋ | 23/30 [00:06<00:01, 3.81it/s]\n 80%|████████ | 24/30 [00:06<00:01, 3.81it/s]\n 83%|████████▎ | 25/30 [00:06<00:01, 3.80it/s]\n 87%|████████▋ | 26/30 [00:06<00:01, 3.80it/s]\n 90%|█████████ | 27/30 [00:07<00:00, 3.80it/s]\n 93%|█████████▎| 28/30 [00:07<00:00, 3.81it/s]\n 97%|█████████▋| 29/30 [00:07<00:00, 3.82it/s]\n100%|██████████| 30/30 [00:07<00:00, 3.82it/s]\n100%|██████████| 30/30 [00:07<00:00, 3.83it/s]", "metrics": { "predict_time": 21.727375, "total_time": 32.545709 }, "output": "https://replicate.delivery/pbxt/jVn71Uvt8r4oBZI4Wy9Hny6sBCWPJJMWQdnIZN5jUmGlWYsE/result.jpg", "started_at": "2024-05-06T05:02:55.508334Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/az8jsv37t9rgp0cf9h48kaky64", "cancel": "https://api.replicate.com/v1/predictions/az8jsv37t9rgp0cf9h48kaky64/cancel" }, "version": "35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c" }Generated in0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:07, 3.82it/s] 7%|▋ | 2/30 [00:00<00:06, 4.18it/s] 10%|█ | 3/30 [00:00<00:06, 4.01it/s] 13%|█▎ | 4/30 [00:01<00:06, 3.92it/s] 17%|█▋ | 5/30 [00:01<00:06, 3.88it/s] 20%|██ | 6/30 [00:01<00:06, 3.86it/s] 23%|██▎ | 7/30 [00:01<00:06, 3.83it/s] 27%|██▋ | 8/30 [00:02<00:05, 3.83it/s] 30%|███ | 9/30 [00:02<00:05, 3.83it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.82it/s] 37%|███▋ | 11/30 [00:02<00:04, 3.81it/s] 40%|████ | 12/30 [00:03<00:04, 3.80it/s] 43%|████▎ | 13/30 [00:03<00:04, 3.81it/s] 47%|████▋ | 14/30 [00:03<00:04, 3.80it/s] 50%|█████ | 15/30 [00:03<00:03, 3.81it/s] 53%|█████▎ | 16/30 [00:04<00:03, 3.81it/s] 57%|█████▋ | 17/30 [00:04<00:03, 3.80it/s] 60%|██████ | 18/30 [00:04<00:03, 3.80it/s] 63%|██████▎ | 19/30 [00:04<00:02, 3.80it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.80it/s] 70%|███████ | 21/30 [00:05<00:02, 3.80it/s] 73%|███████▎ | 22/30 [00:05<00:02, 3.80it/s] 77%|███████▋ | 23/30 [00:06<00:01, 3.81it/s] 80%|████████ | 24/30 [00:06<00:01, 3.81it/s] 83%|████████▎ | 25/30 [00:06<00:01, 3.80it/s] 87%|████████▋ | 26/30 [00:06<00:01, 3.80it/s] 90%|█████████ | 27/30 [00:07<00:00, 3.80it/s] 93%|█████████▎| 28/30 [00:07<00:00, 3.81it/s] 97%|█████████▋| 29/30 [00:07<00:00, 3.82it/s] 100%|██████████| 30/30 [00:07<00:00, 3.82it/s] 100%|██████████| 30/30 [00:07<00:00, 3.83it/s]Prediction
tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3cIDm4p3dphw6hrgj0cf90wrc4g018StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- pose
- seed
- 0
- canny
- model
- AlbedoBase XL V2
- prompt
- man in tuxedo suit, with las vegas casino in the background
- depth_map
- num_steps
- 25
- scheduler
- DPMSolverMultistepScheduler
- pose_strength
- 1
- canny_strength
- 0.5
- depth_strength
- 0.5
- guidance_scale
- 7
- safety_checker
- face_image_path
- lightning_steps
- 4step
- negative_prompt
- ugly, low quality, deformed face, nsfw
- enable_fast_mode
- adapter_strength_ratio
- 0.8
- enhance_non_face_region
- identitynet_strength_ratio
- 0.8
{ "pose": false, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "prompt": "man in tuxedo suit, with las vegas casino in the background", "depth_map": false, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 1, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/KriNVidWYW5jdfMSpYiULn2NBoKyneExGUoplbSaKZC9vKPd/harrison_ford.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": true, "adapter_strength_ratio": 0.8, "enhance_non_face_region": true, "identitynet_strength_ratio": 0.8 }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 tgohblio/instant-id-multicontrolnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c", { input: { pose: false, seed: 0, canny: false, model: "AlbedoBase XL V2", prompt: "man in tuxedo suit, with las vegas casino in the background", depth_map: false, num_steps: 25, scheduler: "DPMSolverMultistepScheduler", pose_strength: 1, canny_strength: 0.5, depth_strength: 0.5, guidance_scale: 7, safety_checker: true, face_image_path: "https://replicate.delivery/pbxt/KriNVidWYW5jdfMSpYiULn2NBoKyneExGUoplbSaKZC9vKPd/harrison_ford.jpeg", lightning_steps: "4step", negative_prompt: "ugly, low quality, deformed face, nsfw", enable_fast_mode: true, adapter_strength_ratio: 0.8, enhance_non_face_region: true, identitynet_strength_ratio: 0.8 } } ); // 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 tgohblio/instant-id-multicontrolnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c", input={ "pose": False, "seed": 0, "canny": False, "model": "AlbedoBase XL V2", "prompt": "man in tuxedo suit, with las vegas casino in the background", "depth_map": False, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 1, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": True, "face_image_path": "https://replicate.delivery/pbxt/KriNVidWYW5jdfMSpYiULn2NBoKyneExGUoplbSaKZC9vKPd/harrison_ford.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": True, "adapter_strength_ratio": 0.8, "enhance_non_face_region": True, "identitynet_strength_ratio": 0.8 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tgohblio/instant-id-multicontrolnet 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": "tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c", "input": { "pose": false, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "prompt": "man in tuxedo suit, with las vegas casino in the background", "depth_map": false, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 1, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/KriNVidWYW5jdfMSpYiULn2NBoKyneExGUoplbSaKZC9vKPd/harrison_ford.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": true, "adapter_strength_ratio": 0.8, "enhance_non_face_region": true, "identitynet_strength_ratio": 0.8 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-05T10:32:06.278539Z", "created_at": "2024-05-05T10:07:41.620000Z", "data_removed": false, "error": null, "id": "m4p3dphw6hrgj0cf90wrc4g018", "input": { "pose": false, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "prompt": "man in tuxedo suit, with las vegas casino in the background", "depth_map": false, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 1, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/KriNVidWYW5jdfMSpYiULn2NBoKyneExGUoplbSaKZC9vKPd/harrison_ford.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": true, "adapter_strength_ratio": 0.8, "enhance_non_face_region": true, "identitynet_strength_ratio": 0.8 }, "logs": "0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:01, 2.53it/s]\n 50%|█████ | 2/4 [00:00<00:00, 3.39it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 3.77it/s]\n100%|██████████| 4/4 [00:01<00:00, 4.07it/s]\n100%|██████████| 4/4 [00:01<00:00, 3.75it/s]", "metrics": { "predict_time": 10.750353, "total_time": 1464.658539 }, "output": "https://replicate.delivery/pbxt/ghbXUhP00yYKCFNodTUTtfCfxE5ooJY5jkRO6Ajr4oNlIRxSA/result.jpg", "started_at": "2024-05-05T10:31:55.528186Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m4p3dphw6hrgj0cf90wrc4g018", "cancel": "https://api.replicate.com/v1/predictions/m4p3dphw6hrgj0cf90wrc4g018/cancel" }, "version": "35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c" }Prediction
tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3cIDq5ypk1g4yhrgg0cf90wskpb96rStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- pose
- seed
- 0
- canny
- model
- AlbedoBase XL V2
- prompt
- woman as elven princess, with blue sheen dress, masterpiece
- depth_map
- num_steps
- 25
- scheduler
- DPMSolverMultistepScheduler
- pose_strength
- 0.5
- canny_strength
- 0.5
- depth_strength
- 0.5
- guidance_scale
- 7
- safety_checker
- face_image_path
- lightning_steps
- 4step
- negative_prompt
- ugly, low quality, deformed face, nsfw
- enable_fast_mode
- adapter_strength_ratio
- 0.8
- enhance_non_face_region
- identitynet_strength_ratio
- 0.8
{ "pose": false, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "prompt": "woman as elven princess, with blue sheen dress, masterpiece", "depth_map": false, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.5, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": true, "adapter_strength_ratio": 0.8, "enhance_non_face_region": true, "identitynet_strength_ratio": 0.8 }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 tgohblio/instant-id-multicontrolnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c", { input: { pose: false, seed: 0, canny: false, model: "AlbedoBase XL V2", prompt: "woman as elven princess, with blue sheen dress, masterpiece", depth_map: false, num_steps: 25, scheduler: "DPMSolverMultistepScheduler", pose_strength: 0.5, canny_strength: 0.5, depth_strength: 0.5, guidance_scale: 7, safety_checker: true, face_image_path: "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", lightning_steps: "4step", negative_prompt: "ugly, low quality, deformed face, nsfw", enable_fast_mode: true, adapter_strength_ratio: 0.8, enhance_non_face_region: true, identitynet_strength_ratio: 0.8 } } ); // 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 tgohblio/instant-id-multicontrolnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c", input={ "pose": False, "seed": 0, "canny": False, "model": "AlbedoBase XL V2", "prompt": "woman as elven princess, with blue sheen dress, masterpiece", "depth_map": False, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.5, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": True, "face_image_path": "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": True, "adapter_strength_ratio": 0.8, "enhance_non_face_region": True, "identitynet_strength_ratio": 0.8 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tgohblio/instant-id-multicontrolnet 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": "tgohblio/instant-id-multicontrolnet:35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c", "input": { "pose": false, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "prompt": "woman as elven princess, with blue sheen dress, masterpiece", "depth_map": false, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.5, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": true, "adapter_strength_ratio": 0.8, "enhance_non_face_region": true, "identitynet_strength_ratio": 0.8 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-05T10:31:55.025763Z", "created_at": "2024-05-05T10:07:27.476000Z", "data_removed": false, "error": null, "id": "q5ypk1g4yhrgg0cf90wskpb96r", "input": { "pose": false, "seed": 0, "canny": false, "model": "AlbedoBase XL V2", "prompt": "woman as elven princess, with blue sheen dress, masterpiece", "depth_map": false, "num_steps": 25, "scheduler": "DPMSolverMultistepScheduler", "pose_strength": 0.5, "canny_strength": 0.5, "depth_strength": 0.5, "guidance_scale": 7, "safety_checker": true, "face_image_path": "https://replicate.delivery/pbxt/KRsl57SjTUo1WOBw1ir3UVI06jpQ7ybyEtdprpqF2qja40Wn/halle-berry.jpeg", "lightning_steps": "4step", "negative_prompt": "ugly, low quality, deformed face, nsfw", "enable_fast_mode": true, "adapter_strength_ratio": 0.8, "enhance_non_face_region": true, "identitynet_strength_ratio": 0.8 }, "logs": "/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/insightface/utils/transform.py:68: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\nTo use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\nP = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:02, 1.36it/s]\n 50%|█████ | 2/4 [00:00<00:00, 2.32it/s]\n 75%|███████▌ | 3/4 [00:01<00:00, 2.99it/s]\n100%|██████████| 4/4 [00:01<00:00, 3.45it/s]\n100%|██████████| 4/4 [00:01<00:00, 2.87it/s]", "metrics": { "predict_time": 9.760985, "total_time": 1467.549763 }, "output": "https://replicate.delivery/pbxt/ykhrJWQyyEqEDZQsySpruv5fRKgFaDtjT35GgL5JEYDNkoYJA/result.jpg", "started_at": "2024-05-05T10:31:45.264778Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/q5ypk1g4yhrgg0cf90wskpb96r", "cancel": "https://api.replicate.com/v1/predictions/q5ypk1g4yhrgg0cf90wskpb96r/cancel" }, "version": "35324a7df2397e6e57dfd8f4f9d2910425f5123109c8c3ed035e769aeff9ff3c" }Generated in/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/insightface/utils/transform.py:68: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions. To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`. P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:02, 1.36it/s] 50%|█████ | 2/4 [00:00<00:00, 2.32it/s] 75%|███████▌ | 3/4 [00:01<00:00, 2.99it/s] 100%|██████████| 4/4 [00:01<00:00, 3.45it/s] 100%|██████████| 4/4 [00:01<00:00, 2.87it/s]
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