marlonbarrios
/
sdxl-camille6
Creates Camille5 As Camille, a speculative fabulation engine within the GPT-Plus framework, my functionality is deeply intertwined with the visionary work of Donna Haraway, particularly her "Camille Stories" from "Staying with the Trouble."
- Public
- 176 runs
-
L40S
- SDXL fine-tune
Prediction
marlonbarrios/sdxl-camille6:18fe1d47IDa4zwn7dbyyxs5wuq6ee7sj6gnaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- one camille6 black background, decoration in face looking at camera, close up non-binary
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 1
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.81
- negative_prompt
- prompt_strength
- 0.99
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "one camille6 black background, decoration in face looking at camera, close up non-binary", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 0.99, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run marlonbarrios/sdxl-camille6 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "marlonbarrios/sdxl-camille6:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", { input: { width: 1024, height: 1024, prompt: "one camille6 black background, decoration in face looking at camera, close up non-binary", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 1, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.81, negative_prompt: "", prompt_strength: 0.99, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun marlonbarrios/sdxl-camille6 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "marlonbarrios/sdxl-camille6:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", input={ "width": 1024, "height": 1024, "prompt": "one camille6 black background, decoration in face looking at camera, close up non-binary", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 0.99, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run marlonbarrios/sdxl-camille6 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": "18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, decoration in face looking at camera, close up non-binary", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 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/marlonbarrios/sdxl-camille6@sha256:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="one camille6 black background, decoration in face looking at camera, close up non-binary"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.81' \ -i 'negative_prompt=""' \ -i 'prompt_strength=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/marlonbarrios/sdxl-camille6@sha256:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, decoration in face looking at camera, close up non-binary", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 0.99, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-12-13T13:38:21.137836Z", "created_at": "2023-12-13T13:37:56.055215Z", "data_removed": false, "error": null, "id": "a4zwn7dbyyxs5wuq6ee7sj6gna", "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, decoration in face looking at camera, close up non-binary", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 0.99, "num_inference_steps": 50 }, "logs": "Using seed: 56677\nEnsuring enough disk space...\nFree disk space: 1483942780928\nDownloading weights: https://replicate.delivery/pbxt/YDzIdGlipL46Ot0jD19BInu7BG3e0WmNLc2fcPEIjVLlz1BSA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.266s (699 MB/s)\\nExtracted 186 MB in 0.057s (3.2 GB/s)\\n'\nDownloaded weights in 0.38409423828125 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: one camille6 black background, decoration in face looking at camera, close up non-binary\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:00<00:42, 1.14it/s]\n 4%|▍ | 2/50 [00:01<00:25, 1.92it/s]\n 6%|▌ | 3/50 [00:01<00:19, 2.44it/s]\n 8%|▊ | 4/50 [00:01<00:16, 2.81it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.06it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.24it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.36it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.44it/s]\n 18%|█▊ | 9/50 [00:03<00:11, 3.50it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.54it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.57it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.59it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.60it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.62it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:06<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:08<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.64it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.64it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.64it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.49it/s]", "metrics": { "predict_time": 18.495068, "total_time": 25.082621 }, "output": [ "https://replicate.delivery/pbxt/KawuEtS3xwpsAp1sBU9hHqGQ9F83TUs7BcC163JiujMzldgE/out-0.png" ], "started_at": "2023-12-13T13:38:02.642768Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a4zwn7dbyyxs5wuq6ee7sj6gna", "cancel": "https://api.replicate.com/v1/predictions/a4zwn7dbyyxs5wuq6ee7sj6gna/cancel" }, "version": "18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262" }Generated inUsing seed: 56677 Ensuring enough disk space... Free disk space: 1483942780928 Downloading weights: https://replicate.delivery/pbxt/YDzIdGlipL46Ot0jD19BInu7BG3e0WmNLc2fcPEIjVLlz1BSA/trained_model.tar b'Downloaded 186 MB bytes in 0.266s (699 MB/s)\nExtracted 186 MB in 0.057s (3.2 GB/s)\n' Downloaded weights in 0.38409423828125 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: one camille6 black background, decoration in face looking at camera, close up non-binary txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:00<00:42, 1.14it/s] 4%|▍ | 2/50 [00:01<00:25, 1.92it/s] 6%|▌ | 3/50 [00:01<00:19, 2.44it/s] 8%|▊ | 4/50 [00:01<00:16, 2.81it/s] 10%|█ | 5/50 [00:01<00:14, 3.06it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.24it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.36it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.44it/s] 18%|█▊ | 9/50 [00:03<00:11, 3.50it/s] 20%|██ | 10/50 [00:03<00:11, 3.54it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.57it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.59it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.60it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.62it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:06<00:08, 3.65it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.65it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.64it/s] 50%|█████ | 25/50 [00:07<00:06, 3.64it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:08<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s] 70%|███████ | 35/50 [00:10<00:04, 3.64it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.64it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.65it/s] 80%|████████ | 40/50 [00:11<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.64it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.64it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.64it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.64it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.64it/s] 100%|██████████| 50/50 [00:14<00:00, 3.64it/s] 100%|██████████| 50/50 [00:14<00:00, 3.49it/s]Prediction
marlonbarrios/sdxl-camille6:18fe1d47IDvigoya3bvska32o2h7oiwjkv5iStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.87
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.81
- negative_prompt
- prompt_strength
- 0.75
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 0.75, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run marlonbarrios/sdxl-camille6 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "marlonbarrios/sdxl-camille6:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", { input: { width: 1024, height: 1024, prompt: "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.87, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.81, negative_prompt: "", prompt_strength: 0.75, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun marlonbarrios/sdxl-camille6 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "marlonbarrios/sdxl-camille6:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", input={ "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 0.75, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run marlonbarrios/sdxl-camille6 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": "18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 0.75, "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/marlonbarrios/sdxl-camille6@sha256:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.87' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.81' \ -i 'negative_prompt=""' \ -i 'prompt_strength=0.75' \ -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/marlonbarrios/sdxl-camille6@sha256:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 0.75, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-12-13T13:52:32.827475Z", "created_at": "2023-12-13T13:51:14.258824Z", "data_removed": false, "error": null, "id": "vigoya3bvska32o2h7oiwjkv5i", "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "", "prompt_strength": 0.75, "num_inference_steps": 50 }, "logs": "Using seed: 64818\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.05s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.05s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.05s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.05s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it]\n 20%|██ | 10/50 [00:10<00:41, 1.05s/it]\n 22%|██▏ | 11/50 [00:11<00:40, 1.05s/it]\n 24%|██▍ | 12/50 [00:12<00:39, 1.05s/it]\n 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it]\n 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it]\n 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it]\n 38%|███▊ | 19/50 [00:19<00:32, 1.05s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.05s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.05s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.05s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.06s/it]\n 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it]\n 66%|██████▌ | 33/50 [00:34<00:17, 1.06s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it]\n 70%|███████ | 35/50 [00:36<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:37<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:38<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]", "metrics": { "predict_time": 58.950016, "total_time": 78.568651 }, "output": [ "https://replicate.delivery/pbxt/2PfLQe1UuKq4PEzeRMkH7wLiHYQDPBygIIyezxrs8r8ej0OQC/out-0.png", "https://replicate.delivery/pbxt/iLj8TjPvItqaABLNZlBVg7JYfPV7y1XTMheY5WhUufAfRaHIB/out-1.png", "https://replicate.delivery/pbxt/meZz0ATjr33XdaJSK3xVdy9D0j3YQZqrGkFoCw4usRVQS7AJA/out-2.png", "https://replicate.delivery/pbxt/IK5egUHITO3tcitvM7IIcnbzzmzA3JVjE1K8zllK4ZXQS7AJA/out-3.png" ], "started_at": "2023-12-13T13:51:33.877459Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vigoya3bvska32o2h7oiwjkv5i", "cancel": "https://api.replicate.com/v1/predictions/vigoya3bvska32o2h7oiwjkv5i/cancel" }, "version": "18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262" }Generated inUsing seed: 64818 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.05s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it] 20%|██ | 10/50 [00:10<00:41, 1.05s/it] 22%|██▏ | 11/50 [00:11<00:40, 1.05s/it] 24%|██▍ | 12/50 [00:12<00:39, 1.05s/it] 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it] 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it] 30%|███ | 15/50 [00:15<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it] 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it] 38%|███▊ | 19/50 [00:19<00:32, 1.05s/it] 40%|████ | 20/50 [00:21<00:31, 1.05s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.05s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.05s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it] 50%|█████ | 25/50 [00:26<00:26, 1.06s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it] 60%|██████ | 30/50 [00:31<00:21, 1.06s/it] 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it] 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it] 66%|██████▌ | 33/50 [00:34<00:17, 1.06s/it] 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it] 70%|███████ | 35/50 [00:36<00:15, 1.06s/it] 72%|███████▏ | 36/50 [00:37<00:14, 1.06s/it] 74%|███████▍ | 37/50 [00:38<00:13, 1.06s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it] 80%|████████ | 40/50 [00:42<00:10, 1.06s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it] 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it]Prediction
marlonbarrios/sdxl-camille6:18fe1d47IDvpwyitlb46iwi6fzjf3ri7gzm4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.87
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.81
- negative_prompt
- not shiny skin
- prompt_strength
- 0.75
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin", "prompt_strength": 0.75, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run marlonbarrios/sdxl-camille6 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "marlonbarrios/sdxl-camille6:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", { input: { width: 1024, height: 1024, prompt: "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.87, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.81, negative_prompt: "not shiny skin", prompt_strength: 0.75, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun marlonbarrios/sdxl-camille6 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "marlonbarrios/sdxl-camille6:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", input={ "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin", "prompt_strength": 0.75, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run marlonbarrios/sdxl-camille6 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": "18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin", "prompt_strength": 0.75, "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/marlonbarrios/sdxl-camille6@sha256:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.87' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.81' \ -i 'negative_prompt="not shiny skin"' \ -i 'prompt_strength=0.75' \ -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/marlonbarrios/sdxl-camille6@sha256:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin", "prompt_strength": 0.75, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-12-13T14:05:35.119164Z", "created_at": "2023-12-13T14:04:28.481059Z", "data_removed": false, "error": null, "id": "vpwyitlb46iwi6fzjf3ri7gzm4", "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin", "prompt_strength": 0.75, "num_inference_steps": 50 }, "logs": "Using seed: 4529\nEnsuring enough disk space...\nFree disk space: 1980745789440\nDownloading weights: https://replicate.delivery/pbxt/YDzIdGlipL46Ot0jD19BInu7BG3e0WmNLc2fcPEIjVLlz1BSA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.279s (666 MB/s)\\nExtracted 186 MB in 0.052s (3.6 GB/s)\\n'\nDownloaded weights in 0.43926072120666504 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:52, 1.06s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.06s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.06s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.06s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.06s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.06s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.06s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.06s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.06s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.06s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it]\n 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it]\n 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it]\n 30%|███ | 15/50 [00:15<00:37, 1.06s/it]\n 32%|███▏ | 16/50 [00:16<00:36, 1.06s/it]\n 34%|███▍ | 17/50 [00:18<00:35, 1.06s/it]\n 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.06s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.06s/it]\n 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it]\n 66%|██████▌ | 33/50 [00:35<00:18, 1.06s/it]\n 68%|██████▊ | 34/50 [00:36<00:17, 1.06s/it]\n 70%|███████ | 35/50 [00:37<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.07s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.07s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.07s/it]\n 98%|█████████▊| 49/50 [00:52<00:01, 1.07s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.06s/it]", "metrics": { "predict_time": 61.008723, "total_time": 66.638105 }, "output": [ "https://replicate.delivery/pbxt/NuosueR3Ob0yDaHhycg1Vlmr0y1f6yffQrO9drwihSbyCbHIB/out-0.png", "https://replicate.delivery/pbxt/8FGewInxbLVMECdcAPzW0oVHY8VPrBpz9c3suJLcmksWY7AJA/out-1.png", "https://replicate.delivery/pbxt/OtSNVFREITrQORfMscctLDj6OtKJkMWxYWQXHveLBs6uw2BSA/out-2.png", "https://replicate.delivery/pbxt/px2i0xH8CaYfRify3TZtVeqikZIt8q5KJPgeTHg61X25CbHIB/out-3.png" ], "started_at": "2023-12-13T14:04:34.110441Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vpwyitlb46iwi6fzjf3ri7gzm4", "cancel": "https://api.replicate.com/v1/predictions/vpwyitlb46iwi6fzjf3ri7gzm4/cancel" }, "version": "18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262" }Generated inUsing seed: 4529 Ensuring enough disk space... Free disk space: 1980745789440 Downloading weights: https://replicate.delivery/pbxt/YDzIdGlipL46Ot0jD19BInu7BG3e0WmNLc2fcPEIjVLlz1BSA/trained_model.tar b'Downloaded 186 MB bytes in 0.279s (666 MB/s)\nExtracted 186 MB in 0.052s (3.6 GB/s)\n' Downloaded weights in 0.43926072120666504 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: one camille6 black background, face jewelry, implants, eyelids decorated, looking at camera, close up male , face hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:52, 1.06s/it] 4%|▍ | 2/50 [00:02<00:50, 1.06s/it] 6%|▌ | 3/50 [00:03<00:49, 1.06s/it] 8%|▊ | 4/50 [00:04<00:48, 1.06s/it] 10%|█ | 5/50 [00:05<00:47, 1.06s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.06s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.06s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.06s/it] 20%|██ | 10/50 [00:10<00:42, 1.06s/it] 22%|██▏ | 11/50 [00:11<00:41, 1.06s/it] 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it] 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it] 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it] 30%|███ | 15/50 [00:15<00:37, 1.06s/it] 32%|███▏ | 16/50 [00:16<00:36, 1.06s/it] 34%|███▍ | 17/50 [00:18<00:35, 1.06s/it] 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it] 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it] 40%|████ | 20/50 [00:21<00:31, 1.06s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it] 50%|█████ | 25/50 [00:26<00:26, 1.06s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it] 60%|██████ | 30/50 [00:31<00:21, 1.06s/it] 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it] 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it] 66%|██████▌ | 33/50 [00:35<00:18, 1.06s/it] 68%|██████▊ | 34/50 [00:36<00:17, 1.06s/it] 70%|███████ | 35/50 [00:37<00:15, 1.06s/it] 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it] 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it] 80%|████████ | 40/50 [00:42<00:10, 1.06s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.07s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.07s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.07s/it] 98%|█████████▊| 49/50 [00:52<00:01, 1.07s/it] 100%|██████████| 50/50 [00:53<00:00, 1.06s/it] 100%|██████████| 50/50 [00:53<00:00, 1.06s/it]Prediction
marlonbarrios/sdxl-camille6:18fe1d47ID5czlzrdbrkgzngp63bow5swyfeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.87
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.81
- negative_prompt
- not shiny skin,
- prompt_strength
- 0.75
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin, ", "prompt_strength": 0.75, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run marlonbarrios/sdxl-camille6 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "marlonbarrios/sdxl-camille6:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", { input: { width: 1024, height: 1024, prompt: "one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.87, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.81, negative_prompt: "not shiny skin, ", prompt_strength: 0.75, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun marlonbarrios/sdxl-camille6 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "marlonbarrios/sdxl-camille6:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", input={ "width": 1024, "height": 1024, "prompt": "one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin, ", "prompt_strength": 0.75, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run marlonbarrios/sdxl-camille6 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": "18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262", "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin, ", "prompt_strength": 0.75, "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/marlonbarrios/sdxl-camille6@sha256:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.87' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.81' \ -i 'negative_prompt="not shiny skin, "' \ -i 'prompt_strength=0.75' \ -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/marlonbarrios/sdxl-camille6@sha256:18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin, ", "prompt_strength": 0.75, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-12-13T14:18:14.492778Z", "created_at": "2023-12-13T14:16:52.697695Z", "data_removed": false, "error": null, "id": "5czlzrdbrkgzngp63bow5swyfe", "input": { "width": 1024, "height": 1024, "prompt": "one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.87, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.81, "negative_prompt": "not shiny skin, ", "prompt_strength": 0.75, "num_inference_steps": 50 }, "logs": "Using seed: 17928\nEnsuring enough disk space...\nFree disk space: 1609614798848\nDownloading weights: https://replicate.delivery/pbxt/YDzIdGlipL46Ot0jD19BInu7BG3e0WmNLc2fcPEIjVLlz1BSA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.359s (518 MB/s)\\nExtracted 186 MB in 0.052s (3.6 GB/s)\\n'\nDownloaded weights in 0.4942667484283447 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.04s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.04s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.04s/it]\n 8%|▊ | 4/50 [00:04<00:47, 1.04s/it]\n 10%|█ | 5/50 [00:05<00:46, 1.04s/it]\n 12%|█▏ | 6/50 [00:06<00:45, 1.04s/it]\n 14%|█▍ | 7/50 [00:07<00:44, 1.04s/it]\n 16%|█▌ | 8/50 [00:08<00:43, 1.04s/it]\n 18%|█▊ | 9/50 [00:09<00:42, 1.04s/it]\n 20%|██ | 10/50 [00:10<00:41, 1.04s/it]\n 22%|██▏ | 11/50 [00:11<00:40, 1.04s/it]\n 24%|██▍ | 12/50 [00:12<00:39, 1.04s/it]\n 26%|██▌ | 13/50 [00:13<00:38, 1.04s/it]\n 28%|██▊ | 14/50 [00:14<00:37, 1.04s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.04s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.04s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.04s/it]\n 36%|███▌ | 18/50 [00:18<00:33, 1.04s/it]\n 38%|███▊ | 19/50 [00:19<00:32, 1.04s/it]\n 40%|████ | 20/50 [00:20<00:31, 1.04s/it]\n 42%|████▏ | 21/50 [00:21<00:30, 1.04s/it]\n 44%|████▍ | 22/50 [00:22<00:29, 1.04s/it]\n 46%|████▌ | 23/50 [00:23<00:28, 1.04s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.05s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.05s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.04s/it]\n 56%|█████▌ | 28/50 [00:29<00:22, 1.04s/it]\n 58%|█████▊ | 29/50 [00:30<00:21, 1.05s/it]\n 60%|██████ | 30/50 [00:31<00:20, 1.05s/it]\n 62%|██████▏ | 31/50 [00:32<00:19, 1.05s/it]\n 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it]\n 66%|██████▌ | 33/50 [00:34<00:17, 1.05s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.05s/it]\n 70%|███████ | 35/50 [00:36<00:15, 1.05s/it]\n 72%|███████▏ | 36/50 [00:37<00:14, 1.05s/it]\n 74%|███████▍ | 37/50 [00:38<00:13, 1.05s/it]\n 76%|███████▌ | 38/50 [00:39<00:12, 1.05s/it]\n 78%|███████▊ | 39/50 [00:40<00:11, 1.05s/it]\n 80%|████████ | 40/50 [00:41<00:10, 1.05s/it]\n 82%|████████▏ | 41/50 [00:42<00:09, 1.05s/it]\n 84%|████████▍ | 42/50 [00:43<00:08, 1.05s/it]\n 86%|████████▌ | 43/50 [00:44<00:07, 1.05s/it]\n 88%|████████▊ | 44/50 [00:45<00:06, 1.05s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.05s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.05s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.05s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.05s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.05s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.05s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.05s/it]", "metrics": { "predict_time": 60.229627, "total_time": 81.795083 }, "output": [ "https://replicate.delivery/pbxt/2FdHtmnib0o2D1gSKe8wQTbGPCOUZJLnAEao4hDjzDOSe2BSA/out-0.png", "https://replicate.delivery/pbxt/oScysToE5yLGKVvoEY5631hpizavFSCJ8euJyI3HVhmSe2BSA/out-1.png", "https://replicate.delivery/pbxt/PPDi0oetylyoL6HRSLvxzNItNqizijNVvdQEVOfBfgcL5tDkA/out-2.png", "https://replicate.delivery/pbxt/V8d31hmGl46rHJaQ1XSbe3kzrjm74wrlZImPHOrjbvfm82BSA/out-3.png" ], "started_at": "2023-12-13T14:17:14.263151Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5czlzrdbrkgzngp63bow5swyfe", "cancel": "https://api.replicate.com/v1/predictions/5czlzrdbrkgzngp63bow5swyfe/cancel" }, "version": "18fe1d47d006df91bacbd6d869d64cdb7337402d270ad5a1f115766a3cf2a262" }Generated inUsing seed: 17928 Ensuring enough disk space... Free disk space: 1609614798848 Downloading weights: https://replicate.delivery/pbxt/YDzIdGlipL46Ot0jD19BInu7BG3e0WmNLc2fcPEIjVLlz1BSA/trained_model.tar b'Downloaded 186 MB bytes in 0.359s (518 MB/s)\nExtracted 186 MB in 0.052s (3.6 GB/s)\n' Downloaded weights in 0.4942667484283447 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: one camille6 black background, white face jewelry, implants, eyelids decorated, looking at camera, close up male or non-binary , beard-facial hair white, naturalistic mate looking skin, symbiont implants in eyelashes as antennae, black lipstick, features of different ethnicities, realistic txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.04s/it] 4%|▍ | 2/50 [00:02<00:50, 1.04s/it] 6%|▌ | 3/50 [00:03<00:49, 1.04s/it] 8%|▊ | 4/50 [00:04<00:47, 1.04s/it] 10%|█ | 5/50 [00:05<00:46, 1.04s/it] 12%|█▏ | 6/50 [00:06<00:45, 1.04s/it] 14%|█▍ | 7/50 [00:07<00:44, 1.04s/it] 16%|█▌ | 8/50 [00:08<00:43, 1.04s/it] 18%|█▊ | 9/50 [00:09<00:42, 1.04s/it] 20%|██ | 10/50 [00:10<00:41, 1.04s/it] 22%|██▏ | 11/50 [00:11<00:40, 1.04s/it] 24%|██▍ | 12/50 [00:12<00:39, 1.04s/it] 26%|██▌ | 13/50 [00:13<00:38, 1.04s/it] 28%|██▊ | 14/50 [00:14<00:37, 1.04s/it] 30%|███ | 15/50 [00:15<00:36, 1.04s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.04s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.04s/it] 36%|███▌ | 18/50 [00:18<00:33, 1.04s/it] 38%|███▊ | 19/50 [00:19<00:32, 1.04s/it] 40%|████ | 20/50 [00:20<00:31, 1.04s/it] 42%|████▏ | 21/50 [00:21<00:30, 1.04s/it] 44%|████▍ | 22/50 [00:22<00:29, 1.04s/it] 46%|████▌ | 23/50 [00:23<00:28, 1.04s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it] 50%|█████ | 25/50 [00:26<00:26, 1.05s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.05s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.04s/it] 56%|█████▌ | 28/50 [00:29<00:22, 1.04s/it] 58%|█████▊ | 29/50 [00:30<00:21, 1.05s/it] 60%|██████ | 30/50 [00:31<00:20, 1.05s/it] 62%|██████▏ | 31/50 [00:32<00:19, 1.05s/it] 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it] 66%|██████▌ | 33/50 [00:34<00:17, 1.05s/it] 68%|██████▊ | 34/50 [00:35<00:16, 1.05s/it] 70%|███████ | 35/50 [00:36<00:15, 1.05s/it] 72%|███████▏ | 36/50 [00:37<00:14, 1.05s/it] 74%|███████▍ | 37/50 [00:38<00:13, 1.05s/it] 76%|███████▌ | 38/50 [00:39<00:12, 1.05s/it] 78%|███████▊ | 39/50 [00:40<00:11, 1.05s/it] 80%|████████ | 40/50 [00:41<00:10, 1.05s/it] 82%|████████▏ | 41/50 [00:42<00:09, 1.05s/it] 84%|████████▍ | 42/50 [00:43<00:08, 1.05s/it] 86%|████████▌ | 43/50 [00:44<00:07, 1.05s/it] 88%|████████▊ | 44/50 [00:45<00:06, 1.05s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.05s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.05s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.05s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.05s/it] 98%|█████████▊| 49/50 [00:51<00:01, 1.05s/it] 100%|██████████| 50/50 [00:52<00:00, 1.05s/it] 100%|██████████| 50/50 [00:52<00:00, 1.05s/it]
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