jschoormans
/
sdxl-inpainting-trainable
A SDXL inpainting model that can be used for Replicate finetuning
- Public
- 514 runs
-
L40S
Prediction
jschoormans/sdxl-inpainting-trainable:0d940144IDyerj3ilbgqt3nvgshbu224uoxyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- An astronaut riding a rainbow unicorn
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "An astronaut riding a rainbow unicorn", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jschoormans/sdxl-inpainting-trainable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jschoormans/sdxl-inpainting-trainable:0d940144ef2cf3dc4ecdcaff75855a297a4598ad80d2f4ca224658a6599600d9", { input: { width: 1024, height: 1024, prompt: "An astronaut riding a rainbow unicorn", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run jschoormans/sdxl-inpainting-trainable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jschoormans/sdxl-inpainting-trainable:0d940144ef2cf3dc4ecdcaff75855a297a4598ad80d2f4ca224658a6599600d9", input={ "width": 1024, "height": 1024, "prompt": "An astronaut riding a rainbow unicorn", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run jschoormans/sdxl-inpainting-trainable 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": "0d940144ef2cf3dc4ecdcaff75855a297a4598ad80d2f4ca224658a6599600d9", "input": { "width": 1024, "height": 1024, "prompt": "An astronaut riding a rainbow unicorn", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run jschoormans/sdxl-inpainting-trainable using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/jschoormans/sdxl-inpainting-trainable@sha256:0d940144ef2cf3dc4ecdcaff75855a297a4598ad80d2f4ca224658a6599600d9 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="An astronaut riding a rainbow unicorn"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'
To learn more, take a look at the Cog documentation.
Pull and run jschoormans/sdxl-inpainting-trainable using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/jschoormans/sdxl-inpainting-trainable@sha256:0d940144ef2cf3dc4ecdcaff75855a297a4598ad80d2f4ca224658a6599600d9
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "An astronaut riding a rainbow unicorn", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2023-09-10T18:57:28.560348Z", "created_at": "2023-09-10T18:54:42.800703Z", "data_removed": false, "error": null, "id": "yerj3ilbgqt3nvgshbu224uoxy", "input": { "width": 1024, "height": 1024, "prompt": "An astronaut riding a rainbow unicorn", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 57216\nPrompt: An astronaut riding a rainbow unicorn\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:34, 1.43it/s]\n 4%|▍ | 2/50 [00:00<00:19, 2.46it/s]\n 6%|▌ | 3/50 [00:01<00:14, 3.19it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.72it/s]\n 10%|█ | 5/50 [00:01<00:11, 4.08it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.34it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.52it/s]\n 16%|█▌ | 8/50 [00:02<00:09, 4.65it/s]\n 18%|█▊ | 9/50 [00:02<00:08, 4.74it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.80it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.84it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.88it/s]\n 26%|██▌ | 13/50 [00:03<00:07, 4.90it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.91it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.92it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 4.91it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.92it/s]\n 36%|███▌ | 18/50 [00:04<00:06, 4.93it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.94it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.94it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.93it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.93it/s]\n 46%|████▌ | 23/50 [00:05<00:05, 4.93it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.93it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.93it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.93it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.93it/s]\n 56%|█████▌ | 28/50 [00:06<00:04, 4.94it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.94it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.95it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.95it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 4.95it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.96it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.96it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.96it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.96it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 4.97it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.97it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.97it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.97it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.96it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 4.97it/s]\n 86%|████████▌ | 43/50 [00:09<00:01, 4.96it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.96it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.95it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.95it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.96it/s]\n 96%|█████████▌| 48/50 [00:10<00:00, 4.96it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.97it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.97it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.72it/s]", "metrics": { "predict_time": 13.285031, "total_time": 165.759645 }, "output": [ "https://replicate.delivery/pbxt/WweF1FitVuyQXCf6FwQD2xBaOeOqbdkKxcBLmnRbAUed5wLGB/out-0.png" ], "started_at": "2023-09-10T18:57:15.275317Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yerj3ilbgqt3nvgshbu224uoxy", "cancel": "https://api.replicate.com/v1/predictions/yerj3ilbgqt3nvgshbu224uoxy/cancel" }, "version": "81be943507a7c0d2d62c56ebce166edeb0635798841e096a898d4b4a57a43102" }
Generated inUsing seed: 57216 Prompt: An astronaut riding a rainbow unicorn txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:34, 1.43it/s] 4%|▍ | 2/50 [00:00<00:19, 2.46it/s] 6%|▌ | 3/50 [00:01<00:14, 3.19it/s] 8%|▊ | 4/50 [00:01<00:12, 3.72it/s] 10%|█ | 5/50 [00:01<00:11, 4.08it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.34it/s] 14%|█▍ | 7/50 [00:01<00:09, 4.52it/s] 16%|█▌ | 8/50 [00:02<00:09, 4.65it/s] 18%|█▊ | 9/50 [00:02<00:08, 4.74it/s] 20%|██ | 10/50 [00:02<00:08, 4.80it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.84it/s] 24%|██▍ | 12/50 [00:02<00:07, 4.88it/s] 26%|██▌ | 13/50 [00:03<00:07, 4.90it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.91it/s] 30%|███ | 15/50 [00:03<00:07, 4.92it/s] 32%|███▏ | 16/50 [00:03<00:06, 4.91it/s] 34%|███▍ | 17/50 [00:03<00:06, 4.92it/s] 36%|███▌ | 18/50 [00:04<00:06, 4.93it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.94it/s] 40%|████ | 20/50 [00:04<00:06, 4.94it/s] 42%|████▏ | 21/50 [00:04<00:05, 4.93it/s] 44%|████▍ | 22/50 [00:04<00:05, 4.93it/s] 46%|████▌ | 23/50 [00:05<00:05, 4.93it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.93it/s] 50%|█████ | 25/50 [00:05<00:05, 4.93it/s] 52%|█████▏ | 26/50 [00:05<00:04, 4.93it/s] 54%|█████▍ | 27/50 [00:05<00:04, 4.93it/s] 56%|█████▌ | 28/50 [00:06<00:04, 4.94it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.94it/s] 60%|██████ | 30/50 [00:06<00:04, 4.95it/s] 62%|██████▏ | 31/50 [00:06<00:03, 4.95it/s] 64%|██████▍ | 32/50 [00:06<00:03, 4.95it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.96it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.96it/s] 70%|███████ | 35/50 [00:07<00:03, 4.96it/s] 72%|███████▏ | 36/50 [00:07<00:02, 4.96it/s] 74%|███████▍ | 37/50 [00:07<00:02, 4.97it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.97it/s] 78%|███████▊ | 39/50 [00:08<00:02, 4.97it/s] 80%|████████ | 40/50 [00:08<00:02, 4.97it/s] 82%|████████▏ | 41/50 [00:08<00:01, 4.96it/s] 84%|████████▍ | 42/50 [00:08<00:01, 4.97it/s] 86%|████████▌ | 43/50 [00:09<00:01, 4.96it/s] 88%|████████▊ | 44/50 [00:09<00:01, 4.96it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.95it/s] 92%|█████████▏| 46/50 [00:09<00:00, 4.95it/s] 94%|█████████▍| 47/50 [00:09<00:00, 4.96it/s] 96%|█████████▌| 48/50 [00:10<00:00, 4.96it/s] 98%|█████████▊| 49/50 [00:10<00:00, 4.97it/s] 100%|██████████| 50/50 [00:10<00:00, 4.97it/s] 100%|██████████| 50/50 [00:10<00:00, 4.72it/s]
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