asiryan / sdxl
SDXL Model (Text2Img, Img2Img and Inpainting)
Prediction
asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6IDxr4tevlb2a6ln6w23x6mr3kigaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 16010
- image
- width
- 768
- height
- 768
- prompt
- Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera.
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.6
- num_inference_steps
- 30
{ "seed": 16010, "image": "https://replicate.delivery/pbxt/JvqeLjqDJRHsORHxZr2jAc2I3PYd6i9WxGCYWlSxdM28meTC/astro.png", "width": 768, "height": 768, "prompt": "Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera.", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 }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 asiryan/sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6", { input: { seed: 16010, image: "https://replicate.delivery/pbxt/JvqeLjqDJRHsORHxZr2jAc2I3PYd6i9WxGCYWlSxdM28meTC/astro.png", width: 768, height: 768, prompt: "Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera.", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, prompt_strength: 0.6, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun asiryan/sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6", input={ "seed": 16010, "image": "https://replicate.delivery/pbxt/JvqeLjqDJRHsORHxZr2jAc2I3PYd6i9WxGCYWlSxdM28meTC/astro.png", "width": 768, "height": 768, "prompt": "Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera.", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run asiryan/sdxl 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": "asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6", "input": { "seed": 16010, "image": "https://replicate.delivery/pbxt/JvqeLjqDJRHsORHxZr2jAc2I3PYd6i9WxGCYWlSxdM28meTC/astro.png", "width": 768, "height": 768, "prompt": "Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera.", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 } }' \ 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/asiryan/sdxl@sha256:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6 \ -i 'seed=16010' \ -i 'image="https://replicate.delivery/pbxt/JvqeLjqDJRHsORHxZr2jAc2I3PYd6i9WxGCYWlSxdM28meTC/astro.png"' \ -i 'width=768' \ -i 'height=768' \ -i 'prompt="Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera."' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER_ANCESTRAL"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=false' \ -i 'high_noise_frac=0.8' \ -i 'prompt_strength=0.6' \ -i 'num_inference_steps=30'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/asiryan/sdxl@sha256:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 16010, "image": "https://replicate.delivery/pbxt/JvqeLjqDJRHsORHxZr2jAc2I3PYd6i9WxGCYWlSxdM28meTC/astro.png", "width": 768, "height": 768, "prompt": "Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera.", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-24T09:32:25.012996Z", "created_at": "2023-11-24T09:32:21.724211Z", "data_removed": false, "error": null, "id": "xr4tevlb2a6ln6w23x6mr3kiga", "input": { "seed": 16010, "image": "https://replicate.delivery/pbxt/JvqeLjqDJRHsORHxZr2jAc2I3PYd6i9WxGCYWlSxdM28meTC/astro.png", "width": 768, "height": 768, "prompt": "Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera.", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 }, "logs": "Using seed: 16010\nPrompt: Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera.\nimg2img mode\n 0%| | 0/18 [00:00<?, ?it/s]\n 11%|█ | 2/18 [00:00<00:01, 13.18it/s]\n 22%|██▏ | 4/18 [00:00<00:01, 13.07it/s]\n 33%|███▎ | 6/18 [00:00<00:00, 13.06it/s]\n 44%|████▍ | 8/18 [00:00<00:00, 13.02it/s]\n 56%|█████▌ | 10/18 [00:00<00:00, 13.02it/s]\n 67%|██████▋ | 12/18 [00:00<00:00, 13.00it/s]\n 78%|███████▊ | 14/18 [00:01<00:00, 13.00it/s]\n 89%|████████▉ | 16/18 [00:01<00:00, 12.98it/s]\n100%|██████████| 18/18 [00:01<00:00, 12.97it/s]\n100%|██████████| 18/18 [00:01<00:00, 13.00it/s]", "metrics": { "predict_time": 3.250703, "total_time": 3.288785 }, "output": [ "https://replicate.delivery/pbxt/y2z09uUMMM6pMVafUVKcWiQEyEFeud3hZCwA3ljS66boeD3jA/out-0.png" ], "started_at": "2023-11-24T09:32:21.762293Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xr4tevlb2a6ln6w23x6mr3kiga", "cancel": "https://api.replicate.com/v1/predictions/xr4tevlb2a6ln6w23x6mr3kiga/cancel" }, "version": "49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6" }Generated inUsing seed: 16010 Prompt: Photo of a lone astronaut standing on a barren planet, looking up at the stars, surrounded by remnants of a destroyed spaceship. Deep blue filter, harsh shadows, intense stare, gritty texture, captured by a Sony Alpha 7S III camera. img2img mode 0%| | 0/18 [00:00<?, ?it/s] 11%|█ | 2/18 [00:00<00:01, 13.18it/s] 22%|██▏ | 4/18 [00:00<00:01, 13.07it/s] 33%|███▎ | 6/18 [00:00<00:00, 13.06it/s] 44%|████▍ | 8/18 [00:00<00:00, 13.02it/s] 56%|█████▌ | 10/18 [00:00<00:00, 13.02it/s] 67%|██████▋ | 12/18 [00:00<00:00, 13.00it/s] 78%|███████▊ | 14/18 [00:01<00:00, 13.00it/s] 89%|████████▉ | 16/18 [00:01<00:00, 12.98it/s] 100%|██████████| 18/18 [00:01<00:00, 12.97it/s] 100%|██████████| 18/18 [00:01<00:00, 13.00it/s]Prediction
asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6IDm4c5kblbhqo2gzujevvkjvdlcqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- mask
- seed
- 16010
- image
- width
- 1280
- height
- 1280
- prompt
- a tabby cat, high resolution, sitting on a park bench
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.6
- num_inference_steps
- 30
{ "mask": "https://replicate.delivery/pbxt/JvqffKrR2WvfN9Tb6eCv3GEgv5wjOuhuwy7Q4EKkxPE2n1xD/mask.png", "seed": 16010, "image": "https://replicate.delivery/pbxt/Jvqffzzgb0TNKqaHDGVYjdg5poR28Uvn0j8FsyxurU44CjzQ/demo.png", "width": 1280, "height": 1280, "prompt": "a tabby cat, high resolution, sitting on a park bench", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 }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 asiryan/sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6", { input: { mask: "https://replicate.delivery/pbxt/JvqffKrR2WvfN9Tb6eCv3GEgv5wjOuhuwy7Q4EKkxPE2n1xD/mask.png", seed: 16010, image: "https://replicate.delivery/pbxt/Jvqffzzgb0TNKqaHDGVYjdg5poR28Uvn0j8FsyxurU44CjzQ/demo.png", width: 1280, height: 1280, prompt: "a tabby cat, high resolution, sitting on a park bench", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, prompt_strength: 0.6, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun asiryan/sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6", input={ "mask": "https://replicate.delivery/pbxt/JvqffKrR2WvfN9Tb6eCv3GEgv5wjOuhuwy7Q4EKkxPE2n1xD/mask.png", "seed": 16010, "image": "https://replicate.delivery/pbxt/Jvqffzzgb0TNKqaHDGVYjdg5poR28Uvn0j8FsyxurU44CjzQ/demo.png", "width": 1280, "height": 1280, "prompt": "a tabby cat, high resolution, sitting on a park bench", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run asiryan/sdxl 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": "asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6", "input": { "mask": "https://replicate.delivery/pbxt/JvqffKrR2WvfN9Tb6eCv3GEgv5wjOuhuwy7Q4EKkxPE2n1xD/mask.png", "seed": 16010, "image": "https://replicate.delivery/pbxt/Jvqffzzgb0TNKqaHDGVYjdg5poR28Uvn0j8FsyxurU44CjzQ/demo.png", "width": 1280, "height": 1280, "prompt": "a tabby cat, high resolution, sitting on a park bench", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 } }' \ 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/asiryan/sdxl@sha256:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6 \ -i 'mask="https://replicate.delivery/pbxt/JvqffKrR2WvfN9Tb6eCv3GEgv5wjOuhuwy7Q4EKkxPE2n1xD/mask.png"' \ -i 'seed=16010' \ -i 'image="https://replicate.delivery/pbxt/Jvqffzzgb0TNKqaHDGVYjdg5poR28Uvn0j8FsyxurU44CjzQ/demo.png"' \ -i 'width=1280' \ -i 'height=1280' \ -i 'prompt="a tabby cat, high resolution, sitting on a park bench"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER_ANCESTRAL"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=false' \ -i 'high_noise_frac=0.8' \ -i 'prompt_strength=0.6' \ -i 'num_inference_steps=30'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/asiryan/sdxl@sha256:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "mask": "https://replicate.delivery/pbxt/JvqffKrR2WvfN9Tb6eCv3GEgv5wjOuhuwy7Q4EKkxPE2n1xD/mask.png", "seed": 16010, "image": "https://replicate.delivery/pbxt/Jvqffzzgb0TNKqaHDGVYjdg5poR28Uvn0j8FsyxurU44CjzQ/demo.png", "width": 1280, "height": 1280, "prompt": "a tabby cat, high resolution, sitting on a park bench", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-24T09:33:55.446034Z", "created_at": "2023-11-24T09:33:45.372899Z", "data_removed": false, "error": null, "id": "m4c5kblbhqo2gzujevvkjvdlcq", "input": { "mask": "https://replicate.delivery/pbxt/JvqffKrR2WvfN9Tb6eCv3GEgv5wjOuhuwy7Q4EKkxPE2n1xD/mask.png", "seed": 16010, "image": "https://replicate.delivery/pbxt/Jvqffzzgb0TNKqaHDGVYjdg5poR28Uvn0j8FsyxurU44CjzQ/demo.png", "width": 1280, "height": 1280, "prompt": "a tabby cat, high resolution, sitting on a park bench", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.6, "num_inference_steps": 30 }, "logs": "Using seed: 16010\nPrompt: a tabby cat, high resolution, sitting on a park bench\ninpainting mode\n 0%| | 0/18 [00:00<?, ?it/s]\n 6%|▌ | 1/18 [00:00<00:05, 2.96it/s]\n 11%|█ | 2/18 [00:00<00:05, 2.98it/s]\n 17%|█▋ | 3/18 [00:01<00:05, 2.97it/s]\n 22%|██▏ | 4/18 [00:01<00:04, 2.98it/s]\n 28%|██▊ | 5/18 [00:01<00:04, 2.99it/s]\n 33%|███▎ | 6/18 [00:02<00:04, 2.98it/s]\n 39%|███▉ | 7/18 [00:02<00:03, 2.98it/s]\n 44%|████▍ | 8/18 [00:02<00:03, 2.99it/s]\n 50%|█████ | 9/18 [00:03<00:03, 2.99it/s]\n 56%|█████▌ | 10/18 [00:03<00:02, 2.99it/s]\n 61%|██████ | 11/18 [00:03<00:02, 3.00it/s]\n 67%|██████▋ | 12/18 [00:04<00:01, 3.00it/s]\n 72%|███████▏ | 13/18 [00:04<00:01, 3.00it/s]\n 78%|███████▊ | 14/18 [00:04<00:01, 3.00it/s]\n 83%|████████▎ | 15/18 [00:05<00:00, 3.00it/s]\n 89%|████████▉ | 16/18 [00:05<00:00, 3.00it/s]\n 94%|█████████▍| 17/18 [00:05<00:00, 3.00it/s]\n100%|██████████| 18/18 [00:06<00:00, 3.00it/s]\n100%|██████████| 18/18 [00:06<00:00, 2.99it/s]", "metrics": { "predict_time": 10.014939, "total_time": 10.073135 }, "output": [ "https://replicate.delivery/pbxt/0UoaCYNqc9a7KlgjvNPymtBYVFNtHWavW20AsgWD5LpAg4eIA/out-0.png" ], "started_at": "2023-11-24T09:33:45.431095Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m4c5kblbhqo2gzujevvkjvdlcq", "cancel": "https://api.replicate.com/v1/predictions/m4c5kblbhqo2gzujevvkjvdlcq/cancel" }, "version": "49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6" }Generated inUsing seed: 16010 Prompt: a tabby cat, high resolution, sitting on a park bench inpainting mode 0%| | 0/18 [00:00<?, ?it/s] 6%|▌ | 1/18 [00:00<00:05, 2.96it/s] 11%|█ | 2/18 [00:00<00:05, 2.98it/s] 17%|█▋ | 3/18 [00:01<00:05, 2.97it/s] 22%|██▏ | 4/18 [00:01<00:04, 2.98it/s] 28%|██▊ | 5/18 [00:01<00:04, 2.99it/s] 33%|███▎ | 6/18 [00:02<00:04, 2.98it/s] 39%|███▉ | 7/18 [00:02<00:03, 2.98it/s] 44%|████▍ | 8/18 [00:02<00:03, 2.99it/s] 50%|█████ | 9/18 [00:03<00:03, 2.99it/s] 56%|█████▌ | 10/18 [00:03<00:02, 2.99it/s] 61%|██████ | 11/18 [00:03<00:02, 3.00it/s] 67%|██████▋ | 12/18 [00:04<00:01, 3.00it/s] 72%|███████▏ | 13/18 [00:04<00:01, 3.00it/s] 78%|███████▊ | 14/18 [00:04<00:01, 3.00it/s] 83%|████████▎ | 15/18 [00:05<00:00, 3.00it/s] 89%|████████▉ | 16/18 [00:05<00:00, 3.00it/s] 94%|█████████▍| 17/18 [00:05<00:00, 3.00it/s] 100%|██████████| 18/18 [00:06<00:00, 3.00it/s] 100%|██████████| 18/18 [00:06<00:00, 2.99it/s]Prediction
asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6IDgp4lpqtb54sunfasp6aczcqsoyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 16010
- width
- 768
- height
- 768
- prompt
- An astronaut riding a rainbow unicorn, cinematic, dramatic
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "seed": 16010, "width": 768, "height": 768, "prompt": "An astronaut riding a rainbow unicorn, cinematic, dramatic", "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, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }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 asiryan/sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6", { input: { seed: 16010, width: 768, height: 768, prompt: "An astronaut riding a rainbow unicorn, cinematic, dramatic", 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, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 25 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun asiryan/sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6", input={ "seed": 16010, "width": 768, "height": 768, "prompt": "An astronaut riding a rainbow unicorn, cinematic, dramatic", "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, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run asiryan/sdxl 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": "asiryan/sdxl:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6", "input": { "seed": 16010, "width": 768, "height": 768, "prompt": "An astronaut riding a rainbow unicorn, cinematic, dramatic", "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, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ 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/asiryan/sdxl@sha256:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6 \ -i 'seed=16010' \ -i 'width=768' \ -i 'height=768' \ -i 'prompt="An astronaut riding a rainbow unicorn, cinematic, dramatic"' \ -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 'negative_prompt=""' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=25'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/asiryan/sdxl@sha256:49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 16010, "width": 768, "height": 768, "prompt": "An astronaut riding a rainbow unicorn, cinematic, dramatic", "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, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-14T22:48:35.589518Z", "created_at": "2023-11-14T22:48:30.786421Z", "data_removed": false, "error": null, "id": "gp4lpqtb54sunfasp6aczcqsoy", "input": { "seed": 16010, "width": 768, "height": 768, "prompt": "An astronaut riding a rainbow unicorn, cinematic, dramatic", "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, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 16010\nPrompt: An astronaut riding a rainbow unicorn, cinematic, dramatic\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:02, 8.29it/s]\n 8%|▊ | 2/25 [00:00<00:02, 8.19it/s]\n 12%|█▏ | 3/25 [00:00<00:02, 8.15it/s]\n 16%|█▌ | 4/25 [00:00<00:02, 8.14it/s]\n 20%|██ | 5/25 [00:00<00:02, 8.13it/s]\n 24%|██▍ | 6/25 [00:00<00:02, 8.13it/s]\n 28%|██▊ | 7/25 [00:00<00:02, 8.14it/s]\n 32%|███▏ | 8/25 [00:00<00:02, 8.14it/s]\n 36%|███▌ | 9/25 [00:01<00:01, 8.14it/s]\n 40%|████ | 10/25 [00:01<00:01, 8.13it/s]\n 44%|████▍ | 11/25 [00:01<00:01, 8.12it/s]\n 48%|████▊ | 12/25 [00:01<00:01, 8.12it/s]\n 52%|█████▏ | 13/25 [00:01<00:01, 8.13it/s]\n 56%|█████▌ | 14/25 [00:01<00:01, 8.13it/s]\n 60%|██████ | 15/25 [00:01<00:01, 8.12it/s]\n 64%|██████▍ | 16/25 [00:01<00:01, 8.12it/s]\n 68%|██████▊ | 17/25 [00:02<00:00, 8.12it/s]\n 72%|███████▏ | 18/25 [00:02<00:00, 8.11it/s]\n 76%|███████▌ | 19/25 [00:02<00:00, 8.12it/s]\n 80%|████████ | 20/25 [00:02<00:00, 8.12it/s]\n 84%|████████▍ | 21/25 [00:02<00:00, 8.12it/s]\n 88%|████████▊ | 22/25 [00:02<00:00, 8.11it/s]\n 92%|█████████▏| 23/25 [00:02<00:00, 8.10it/s]\n 96%|█████████▌| 24/25 [00:02<00:00, 8.11it/s]\n100%|██████████| 25/25 [00:03<00:00, 8.13it/s]\n100%|██████████| 25/25 [00:03<00:00, 8.13it/s]", "metrics": { "predict_time": 4.765814, "total_time": 4.803097 }, "output": [ "https://replicate.delivery/pbxt/eEeoxW8fGRGxso3OJBcNH8I5LFCmZztqTMkT9kzwvFzFa1wjA/out-0.png" ], "started_at": "2023-11-14T22:48:30.823704Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gp4lpqtb54sunfasp6aczcqsoy", "cancel": "https://api.replicate.com/v1/predictions/gp4lpqtb54sunfasp6aczcqsoy/cancel" }, "version": "49d55f8ecdf23a74f0a2d2e70254b68948e20f08e33a0347b82dd106519731e6" }Generated inUsing seed: 16010 Prompt: An astronaut riding a rainbow unicorn, cinematic, dramatic txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:02, 8.29it/s] 8%|▊ | 2/25 [00:00<00:02, 8.19it/s] 12%|█▏ | 3/25 [00:00<00:02, 8.15it/s] 16%|█▌ | 4/25 [00:00<00:02, 8.14it/s] 20%|██ | 5/25 [00:00<00:02, 8.13it/s] 24%|██▍ | 6/25 [00:00<00:02, 8.13it/s] 28%|██▊ | 7/25 [00:00<00:02, 8.14it/s] 32%|███▏ | 8/25 [00:00<00:02, 8.14it/s] 36%|███▌ | 9/25 [00:01<00:01, 8.14it/s] 40%|████ | 10/25 [00:01<00:01, 8.13it/s] 44%|████▍ | 11/25 [00:01<00:01, 8.12it/s] 48%|████▊ | 12/25 [00:01<00:01, 8.12it/s] 52%|█████▏ | 13/25 [00:01<00:01, 8.13it/s] 56%|█████▌ | 14/25 [00:01<00:01, 8.13it/s] 60%|██████ | 15/25 [00:01<00:01, 8.12it/s] 64%|██████▍ | 16/25 [00:01<00:01, 8.12it/s] 68%|██████▊ | 17/25 [00:02<00:00, 8.12it/s] 72%|███████▏ | 18/25 [00:02<00:00, 8.11it/s] 76%|███████▌ | 19/25 [00:02<00:00, 8.12it/s] 80%|████████ | 20/25 [00:02<00:00, 8.12it/s] 84%|████████▍ | 21/25 [00:02<00:00, 8.12it/s] 88%|████████▊ | 22/25 [00:02<00:00, 8.11it/s] 92%|█████████▏| 23/25 [00:02<00:00, 8.10it/s] 96%|█████████▌| 24/25 [00:02<00:00, 8.11it/s] 100%|██████████| 25/25 [00:03<00:00, 8.13it/s] 100%|██████████| 25/25 [00:03<00:00, 8.13it/s]
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