fofr/sdxl-reference
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sdxl-reference
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fofr/sdxl-reference
Make variations of an image with SDXL
fofr/star-trek-flan
flan-t5-xl trained on the Memory Alpha Star Trek Wiki
fofr/star-trek-gpt-j-6b
gpt-j-6b trained on the Memory Alpha Star Trek Wiki
fofr/star-trek-llama
llama-7b trained on the Memory Alpha Star Trek Wiki
fofr/star-trek-adventure
fofr/image-prompts
Generate image prompts for Midjourney. Prefix inputs with "Image: "
fofr/video-to-frames
Split a video into frames
fofr/frames-to-video
Convert a set of frames to a video
fofr/damo-text2video
fofr/audio-to-waveform
Create a waveform video from audio
fofr/sdxl-tron
A fine-tuned SDXL lora based on Tron Legacy
fofr/sdxl-barbie
A fine-tuned SDXL lora based on the Barbie movie
fofr/sdxl-mario-kart
fofr/sdxl-sonic-2
fofr/sdxl-text
Exploratory SDXL fine-tuning on text "FOFR"
fofr/sdxl-barbietron
SDXL fine-tuned on both Barbie and Tron Legacy
fofr/sdxl-vision-pro
An SDXL fine-tune on Apple Vision Pro
fofr/sdxl-cats-movie
fofr/sdxl-matrix-code
An SDXL fine-tune based on Matrix Code art
fofr/sdxl-simpsons
fofr/sdxl-demo
Prediction
fofr/sdxl-reference:255c5260c64a99de3c76bb349d517e75ef59280d97cae56b1b4118010075d98bIDjsiqxplbbhmdri7idhg2p2cgwuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 768
- height
- 768
- prompt
- a cat yawning
- scheduler
- K_EULER
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- negative_prompt
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/Jry0X2SDwNDCWxVpn9ndrlj4RciPlOntsGui7PY3JZtu5fmM/output-0-1.png", "width": 768, "height": 768, "prompt": "a cat yawning", "scheduler": "K_EULER", "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": "", "num_inference_steps": 50 }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 fofr/sdxl-reference using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-reference:255c5260c64a99de3c76bb349d517e75ef59280d97cae56b1b4118010075d98b", { input: { image: "https://replicate.delivery/pbxt/Jry0X2SDwNDCWxVpn9ndrlj4RciPlOntsGui7PY3JZtu5fmM/output-0-1.png", width: 768, height: 768, prompt: "a cat yawning", scheduler: "K_EULER", num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, negative_prompt: "", num_inference_steps: 50 } } ); // 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 fofr/sdxl-reference using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-reference:255c5260c64a99de3c76bb349d517e75ef59280d97cae56b1b4118010075d98b", input={ "image": "https://replicate.delivery/pbxt/Jry0X2SDwNDCWxVpn9ndrlj4RciPlOntsGui7PY3JZtu5fmM/output-0-1.png", "width": 768, "height": 768, "prompt": "a cat yawning", "scheduler": "K_EULER", "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "negative_prompt": "", "num_inference_steps": 50 } ) # 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 fofr/sdxl-reference 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": "fofr/sdxl-reference:255c5260c64a99de3c76bb349d517e75ef59280d97cae56b1b4118010075d98b", "input": { "image": "https://replicate.delivery/pbxt/Jry0X2SDwNDCWxVpn9ndrlj4RciPlOntsGui7PY3JZtu5fmM/output-0-1.png", "width": 768, "height": 768, "prompt": "a cat yawning", "scheduler": "K_EULER", "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": "", "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/fofr/sdxl-reference@sha256:255c5260c64a99de3c76bb349d517e75ef59280d97cae56b1b4118010075d98b \ -i 'image="https://replicate.delivery/pbxt/Jry0X2SDwNDCWxVpn9ndrlj4RciPlOntsGui7PY3JZtu5fmM/output-0-1.png"' \ -i 'width=768' \ -i 'height=768' \ -i 'prompt="a cat yawning"' \ -i 'scheduler="K_EULER"' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=false' \ -i 'negative_prompt=""' \ -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/fofr/sdxl-reference@sha256:255c5260c64a99de3c76bb349d517e75ef59280d97cae56b1b4118010075d98bcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/Jry0X2SDwNDCWxVpn9ndrlj4RciPlOntsGui7PY3JZtu5fmM/output-0-1.png", "width": 768, "height": 768, "prompt": "a cat yawning", "scheduler": "K_EULER", "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": "", "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-13T11:07:26.073442Z", "created_at": "2023-11-13T11:06:26.654904Z", "data_removed": false, "error": null, "id": "jsiqxplbbhmdri7idhg2p2cgwu", "input": { "image": "https://replicate.delivery/pbxt/Jry0X2SDwNDCWxVpn9ndrlj4RciPlOntsGui7PY3JZtu5fmM/output-0-1.png", "width": 768, "height": 768, "prompt": "a cat yawning", "scheduler": "K_EULER", "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "negative_prompt": "", "num_inference_steps": 50 }, "logs": "Using seed: 49968\nPrompt: a cat yawning\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<00:51, 1.07s/it]\n 6%|▌ | 3/50 [00:03<00:50, 1.07s/it]\n 8%|▊ | 4/50 [00:04<00:49, 1.07s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.07s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.07s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.07s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.07s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.07s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.07s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.07s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.07s/it]\n 26%|██▌ | 13/50 [00:13<00:39, 1.07s/it]\n 28%|██▊ | 14/50 [00:14<00:38, 1.07s/it]\n 30%|███ | 15/50 [00:16<00:37, 1.07s/it]\n 32%|███▏ | 16/50 [00:17<00:36, 1.07s/it]\n 34%|███▍ | 17/50 [00:18<00:35, 1.07s/it]\n 36%|███▌ | 18/50 [00:19<00:34, 1.07s/it]\n 38%|███▊ | 19/50 [00:20<00:33, 1.07s/it]\n 40%|████ | 20/50 [00:21<00:32, 1.08s/it]\n 42%|████▏ | 21/50 [00:22<00:31, 1.08s/it]\n 44%|████▍ | 22/50 [00:23<00:30, 1.08s/it]\n 46%|████▌ | 23/50 [00:24<00:29, 1.08s/it]\n 48%|████▊ | 24/50 [00:25<00:28, 1.08s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.08s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.08s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.08s/it]\n 56%|█████▌ | 28/50 [00:30<00:23, 1.08s/it]\n 58%|█████▊ | 29/50 [00:31<00:22, 1.08s/it]\n 60%|██████ | 30/50 [00:32<00:21, 1.08s/it]\n 62%|██████▏ | 31/50 [00:33<00:20, 1.08s/it]\n 64%|██████▍ | 32/50 [00:34<00:19, 1.08s/it]\n 66%|██████▌ | 33/50 [00:35<00:18, 1.08s/it]\n 68%|██████▊ | 34/50 [00:36<00:17, 1.08s/it]\n 70%|███████ | 35/50 [00:37<00:16, 1.08s/it]\n 72%|███████▏ | 36/50 [00:38<00:15, 1.08s/it]\n 74%|███████▍ | 37/50 [00:39<00:14, 1.08s/it]\n 76%|███████▌ | 38/50 [00:40<00:13, 1.08s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.08s/it]\n 80%|████████ | 40/50 [00:43<00:10, 1.08s/it]\n 82%|████████▏ | 41/50 [00:44<00:09, 1.08s/it]\n 84%|████████▍ | 42/50 [00:45<00:08, 1.08s/it]\n 86%|████████▌ | 43/50 [00:46<00:07, 1.09s/it]\n 88%|████████▊ | 44/50 [00:47<00:06, 1.09s/it]\n 90%|█████████ | 45/50 [00:48<00:05, 1.09s/it]\n 92%|█████████▏| 46/50 [00:49<00:04, 1.09s/it]\n 94%|█████████▍| 47/50 [00:50<00:03, 1.09s/it]\n 96%|█████████▌| 48/50 [00:51<00:02, 1.09s/it]\n 98%|█████████▊| 49/50 [00:52<00:01, 1.09s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.09s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.08s/it]", "metrics": { "predict_time": 59.474061, "total_time": 59.418538 }, "output": [ "https://replicate.delivery/pbxt/63GQh2TMiioaIxw3AtHLth11MIYKoLZtMLNgvwmHNw4a1e7IA/out-0.png", "https://replicate.delivery/pbxt/fzofLKIJ19qib0i8BJGMIFrM0Ycu0V4T21OIob7YxYfYr2vjA/out-1.png", "https://replicate.delivery/pbxt/ETibthKf60yfSkc6FeQSgBFdWtSdJBzdpctcfDtrAj63WtfOC/out-2.png", "https://replicate.delivery/pbxt/DEfR4Nu6AXS2GCBIbKJ5pvuK174abvTg9S9WkIBpkeQtV73RA/out-3.png" ], "started_at": "2023-11-13T11:06:26.599381Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jsiqxplbbhmdri7idhg2p2cgwu", "cancel": "https://api.replicate.com/v1/predictions/jsiqxplbbhmdri7idhg2p2cgwu/cancel" }, "version": "255c5260c64a99de3c76bb349d517e75ef59280d97cae56b1b4118010075d98b" }Generated inUsing seed: 49968 Prompt: a cat yawning 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:52, 1.06s/it] 4%|▍ | 2/50 [00:02<00:51, 1.07s/it] 6%|▌ | 3/50 [00:03<00:50, 1.07s/it] 8%|▊ | 4/50 [00:04<00:49, 1.07s/it] 10%|█ | 5/50 [00:05<00:47, 1.07s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.07s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.07s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.07s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.07s/it] 20%|██ | 10/50 [00:10<00:42, 1.07s/it] 22%|██▏ | 11/50 [00:11<00:41, 1.07s/it] 24%|██▍ | 12/50 [00:12<00:40, 1.07s/it] 26%|██▌ | 13/50 [00:13<00:39, 1.07s/it] 28%|██▊ | 14/50 [00:14<00:38, 1.07s/it] 30%|███ | 15/50 [00:16<00:37, 1.07s/it] 32%|███▏ | 16/50 [00:17<00:36, 1.07s/it] 34%|███▍ | 17/50 [00:18<00:35, 1.07s/it] 36%|███▌ | 18/50 [00:19<00:34, 1.07s/it] 38%|███▊ | 19/50 [00:20<00:33, 1.07s/it] 40%|████ | 20/50 [00:21<00:32, 1.08s/it] 42%|████▏ | 21/50 [00:22<00:31, 1.08s/it] 44%|████▍ | 22/50 [00:23<00:30, 1.08s/it] 46%|████▌ | 23/50 [00:24<00:29, 1.08s/it] 48%|████▊ | 24/50 [00:25<00:28, 1.08s/it] 50%|█████ | 25/50 [00:26<00:26, 1.08s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.08s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.08s/it] 56%|█████▌ | 28/50 [00:30<00:23, 1.08s/it] 58%|█████▊ | 29/50 [00:31<00:22, 1.08s/it] 60%|██████ | 30/50 [00:32<00:21, 1.08s/it] 62%|██████▏ | 31/50 [00:33<00:20, 1.08s/it] 64%|██████▍ | 32/50 [00:34<00:19, 1.08s/it] 66%|██████▌ | 33/50 [00:35<00:18, 1.08s/it] 68%|██████▊ | 34/50 [00:36<00:17, 1.08s/it] 70%|███████ | 35/50 [00:37<00:16, 1.08s/it] 72%|███████▏ | 36/50 [00:38<00:15, 1.08s/it] 74%|███████▍ | 37/50 [00:39<00:14, 1.08s/it] 76%|███████▌ | 38/50 [00:40<00:13, 1.08s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.08s/it] 80%|████████ | 40/50 [00:43<00:10, 1.08s/it] 82%|████████▏ | 41/50 [00:44<00:09, 1.08s/it] 84%|████████▍ | 42/50 [00:45<00:08, 1.08s/it] 86%|████████▌ | 43/50 [00:46<00:07, 1.09s/it] 88%|████████▊ | 44/50 [00:47<00:06, 1.09s/it] 90%|█████████ | 45/50 [00:48<00:05, 1.09s/it] 92%|█████████▏| 46/50 [00:49<00:04, 1.09s/it] 94%|█████████▍| 47/50 [00:50<00:03, 1.09s/it] 96%|█████████▌| 48/50 [00:51<00:02, 1.09s/it] 98%|█████████▊| 49/50 [00:52<00:01, 1.09s/it] 100%|██████████| 50/50 [00:53<00:00, 1.09s/it] 100%|██████████| 50/50 [00:53<00:00, 1.08s/it]
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