justmalhar / sxdl-sketchnotes
SDXL Fine tuned on sketchnote style images. Prompt Prefix: a sketchnote photo of TOK
- Public
- 23K runs
-
L40S
- SDXL fine-tune
Prediction
justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7cIDf02z3qwb35rgg0cfm65rgz7q30StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a sketchnote photo of TOK explaining types of sorting algorithms
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining types of sorting algorithms", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "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 justmalhar/sxdl-sketchnotes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c", { input: { width: 1024, height: 1024, prompt: "a sketchnote photo of TOK explaining types of sorting algorithms", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, 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 justmalhar/sxdl-sketchnotes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c", input={ "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining types of sorting algorithms", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run justmalhar/sxdl-sketchnotes 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": "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c", "input": { "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining types of sorting algorithms", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-22T18:24:46.635355Z", "created_at": "2024-05-22T18:23:47.993000Z", "data_removed": false, "error": null, "id": "f02z3qwb35rgg0cfm65rgz7q30", "input": { "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining types of sorting algorithms", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 9272\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a sketchnote photo of <s0><s1> explaining types of sorting algorithms\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:35, 1.09it/s]\n 5%|▌ | 2/40 [00:01<00:34, 1.09it/s]\n 8%|▊ | 3/40 [00:02<00:34, 1.09it/s]\n 10%|█ | 4/40 [00:03<00:33, 1.09it/s]\n 12%|█▎ | 5/40 [00:04<00:32, 1.09it/s]\n 15%|█▌ | 6/40 [00:05<00:31, 1.08it/s]\n 18%|█▊ | 7/40 [00:06<00:30, 1.08it/s]\n 20%|██ | 8/40 [00:07<00:29, 1.08it/s]\n 22%|██▎ | 9/40 [00:08<00:28, 1.08it/s]\n 25%|██▌ | 10/40 [00:09<00:27, 1.08it/s]\n 28%|██▊ | 11/40 [00:10<00:26, 1.08it/s]\n 30%|███ | 12/40 [00:11<00:25, 1.08it/s]\n 32%|███▎ | 13/40 [00:11<00:24, 1.08it/s]\n 35%|███▌ | 14/40 [00:12<00:24, 1.08it/s]\n 38%|███▊ | 15/40 [00:13<00:23, 1.08it/s]\n 40%|████ | 16/40 [00:14<00:22, 1.08it/s]\n 42%|████▎ | 17/40 [00:15<00:21, 1.08it/s]\n 45%|████▌ | 18/40 [00:16<00:20, 1.08it/s]\n 48%|████▊ | 19/40 [00:17<00:19, 1.08it/s]\n 50%|█████ | 20/40 [00:18<00:18, 1.08it/s]\n 52%|█████▎ | 21/40 [00:19<00:17, 1.08it/s]\n 55%|█████▌ | 22/40 [00:20<00:16, 1.08it/s]\n 57%|█████▊ | 23/40 [00:21<00:15, 1.08it/s]\n 60%|██████ | 24/40 [00:22<00:14, 1.08it/s]\n 62%|██████▎ | 25/40 [00:23<00:13, 1.08it/s]\n 65%|██████▌ | 26/40 [00:24<00:12, 1.08it/s]\n 68%|██████▊ | 27/40 [00:24<00:12, 1.08it/s]\n 70%|███████ | 28/40 [00:25<00:11, 1.08it/s]\n 72%|███████▎ | 29/40 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[00:08<00:00, 1.08it/s]\n100%|██████████| 10/10 [00:09<00:00, 1.08it/s]\n100%|██████████| 10/10 [00:09<00:00, 1.08it/s]", "metrics": { "predict_time": 53.310049, "total_time": 58.642355 }, "output": [ "https://replicate.delivery/pbxt/J9or5jf3xZ31E6PsS4hA4lcRwzYs3faVmnJ6U3o8cOSspetlA/out-0.png", "https://replicate.delivery/pbxt/TSqUfoznfPp7TE39TLUeh3bYLEbTNWctNnve3oBHcE93m6bLB/out-1.png", "https://replicate.delivery/pbxt/I3dgpOKJR6Y7N1IeZi1xd2FWwHMSQAiuUsywJYeRg39tpetlA/out-2.png", "https://replicate.delivery/pbxt/gclBkGsjo4rOH9hfevFqMKFbuDSWZLHckJm95jtZuD3upetlA/out-3.png" ], "started_at": "2024-05-22T18:23:53.325306Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f02z3qwb35rgg0cfm65rgz7q30", "cancel": "https://api.replicate.com/v1/predictions/f02z3qwb35rgg0cfm65rgz7q30/cancel" }, "version": "988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c" }Generated inUsing seed: 9272 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a sketchnote photo of <s0><s1> explaining types of sorting algorithms txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:35, 1.09it/s] 5%|▌ | 2/40 [00:01<00:34, 1.09it/s] 8%|▊ | 3/40 [00:02<00:34, 1.09it/s] 10%|█ | 4/40 [00:03<00:33, 1.09it/s] 12%|█▎ | 5/40 [00:04<00:32, 1.09it/s] 15%|█▌ | 6/40 [00:05<00:31, 1.08it/s] 18%|█▊ | 7/40 [00:06<00:30, 1.08it/s] 20%|██ | 8/40 [00:07<00:29, 1.08it/s] 22%|██▎ | 9/40 [00:08<00:28, 1.08it/s] 25%|██▌ | 10/40 [00:09<00:27, 1.08it/s] 28%|██▊ | 11/40 [00:10<00:26, 1.08it/s] 30%|███ | 12/40 [00:11<00:25, 1.08it/s] 32%|███▎ | 13/40 [00:11<00:24, 1.08it/s] 35%|███▌ | 14/40 [00:12<00:24, 1.08it/s] 38%|███▊ | 15/40 [00:13<00:23, 1.08it/s] 40%|████ | 16/40 [00:14<00:22, 1.08it/s] 42%|████▎ | 17/40 [00:15<00:21, 1.08it/s] 45%|████▌ | 18/40 [00:16<00:20, 1.08it/s] 48%|████▊ | 19/40 [00:17<00:19, 1.08it/s] 50%|█████ | 20/40 [00:18<00:18, 1.08it/s] 52%|█████▎ | 21/40 [00:19<00:17, 1.08it/s] 55%|█████▌ | 22/40 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[00:02<00:06, 1.09it/s] 40%|████ | 4/10 [00:03<00:05, 1.08it/s] 50%|█████ | 5/10 [00:04<00:04, 1.08it/s] 60%|██████ | 6/10 [00:05<00:03, 1.08it/s] 70%|███████ | 7/10 [00:06<00:02, 1.08it/s] 80%|████████ | 8/10 [00:07<00:01, 1.08it/s] 90%|█████████ | 9/10 [00:08<00:00, 1.08it/s] 100%|██████████| 10/10 [00:09<00:00, 1.08it/s] 100%|██████████| 10/10 [00:09<00:00, 1.08it/s]Prediction
justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7cIDdyvzqd3pm9rgm0cfm64scyhzb8StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a sketchnote photo of TOK explaining how human brain works
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining how human brain works", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "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 justmalhar/sxdl-sketchnotes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c", { input: { width: 1024, height: 1024, prompt: "a sketchnote photo of TOK explaining how human brain works", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, 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 justmalhar/sxdl-sketchnotes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c", input={ "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining how human brain works", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run justmalhar/sxdl-sketchnotes 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": "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c", "input": { "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining how human brain works", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-22T18:22:35.158693Z", "created_at": "2024-05-22T18:21:31.682000Z", "data_removed": false, "error": null, "id": "dyvzqd3pm9rgm0cfm64scyhzb8", "input": { "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining how human brain works", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 21990\nEnsuring enough disk space...\nFree disk space: 1768129372160\nDownloading weights: https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar\n2024-05-22T18:21:35Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/864fe49e400e52a3 url=https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar\n2024-05-22T18:21:41Z | INFO | [ Complete ] dest=/src/weights-cache/864fe49e400e52a3 size=\"186 MB\" total_elapsed=6.113s url=https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar\nb''\nDownloaded weights in 6.26224160194397 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a sketchnote photo of <s0><s1> explaining how human brain works\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1946: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights`\ndeprecate(\n 2%|▎ | 1/40 [00:00<00:35, 1.09it/s]\n 5%|▌ | 2/40 [00:01<00:34, 1.09it/s]\n 8%|▊ | 3/40 [00:02<00:34, 1.09it/s]\n 10%|█ | 4/40 [00:03<00:33, 1.09it/s]\n 12%|█▎ | 5/40 [00:04<00:32, 1.09it/s]\n 15%|█▌ | 6/40 [00:05<00:31, 1.09it/s]\n 18%|█▊ | 7/40 [00:06<00:30, 1.09it/s]\n 20%|██ | 8/40 [00:07<00:29, 1.09it/s]\n 22%|██▎ | 9/40 [00:08<00:28, 1.09it/s]\n 25%|██▌ | 10/40 [00:09<00:27, 1.09it/s]\n 28%|██▊ | 11/40 [00:10<00:26, 1.09it/s]\n 30%|███ | 12/40 [00:11<00:25, 1.09it/s]\n 32%|███▎ | 13/40 [00:11<00:24, 1.09it/s]\n 35%|███▌ | 14/40 [00:12<00:23, 1.09it/s]\n 38%|███▊ | 15/40 [00:13<00:22, 1.09it/s]\n 40%|████ | 16/40 [00:14<00:21, 1.09it/s]\n 42%|████▎ | 17/40 [00:15<00:21, 1.09it/s]\n 45%|████▌ | 18/40 [00:16<00:20, 1.09it/s]\n 48%|████▊ | 19/40 [00:17<00:19, 1.09it/s]\n 50%|█████ | 20/40 [00:18<00:18, 1.09it/s]\n 52%|█████▎ | 21/40 [00:19<00:17, 1.09it/s]\n 55%|█████▌ | 22/40 [00:20<00:16, 1.09it/s]\n 57%|█████▊ | 23/40 [00:21<00:15, 1.09it/s]\n 60%|██████ | 24/40 [00:22<00:14, 1.09it/s]\n 62%|██████▎ | 25/40 [00:22<00:13, 1.08it/s]\n 65%|██████▌ | 26/40 [00:23<00:12, 1.08it/s]\n 68%|██████▊ | 27/40 [00:24<00:12, 1.08it/s]\n 70%|███████ | 28/40 [00:25<00:11, 1.08it/s]\n 72%|███████▎ | 29/40 [00:26<00:10, 1.08it/s]\n 75%|███████▌ | 30/40 [00:27<00:09, 1.08it/s]\n 78%|███████▊ | 31/40 [00:28<00:08, 1.08it/s]\n 80%|████████ | 32/40 [00:29<00:07, 1.08it/s]\n 82%|████████▎ | 33/40 [00:30<00:06, 1.08it/s]\n 85%|████████▌ | 34/40 [00:31<00:05, 1.08it/s]\n 88%|████████▊ | 35/40 [00:32<00:04, 1.08it/s]\n 90%|█████████ | 36/40 [00:33<00:03, 1.08it/s]\n 92%|█████████▎| 37/40 [00:34<00:02, 1.08it/s]\n 95%|█████████▌| 38/40 [00:34<00:01, 1.08it/s]\n 98%|█████████▊| 39/40 [00:35<00:00, 1.08it/s]\n100%|██████████| 40/40 [00:36<00:00, 1.08it/s]\n100%|██████████| 40/40 [00:36<00:00, 1.09it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:08, 1.09it/s]\n 20%|██ | 2/10 [00:01<00:07, 1.09it/s]\n 30%|███ | 3/10 [00:02<00:06, 1.09it/s]\n 40%|████ | 4/10 [00:03<00:05, 1.09it/s]\n 50%|█████ | 5/10 [00:04<00:04, 1.09it/s]\n 60%|██████ | 6/10 [00:05<00:03, 1.09it/s]\n 70%|███████ | 7/10 [00:06<00:02, 1.08it/s]\n 80%|████████ | 8/10 [00:07<00:01, 1.08it/s]\n 90%|█████████ | 9/10 [00:08<00:00, 1.08it/s]\n100%|██████████| 10/10 [00:09<00:00, 1.09it/s]\n100%|██████████| 10/10 [00:09<00:00, 1.09it/s]", "metrics": { "predict_time": 59.608088, "total_time": 63.476693 }, "output": [ "https://replicate.delivery/pbxt/fwyK9KfKg9ikeJSfkdmaJ0KibLtuZfAVfhqf0pNvcafaonetlA/out-0.png", "https://replicate.delivery/pbxt/GoFjPmlmI0LwFdBibV9ffDzrq68PHSYcgo61TW8g6TqpnetlA/out-1.png", "https://replicate.delivery/pbxt/1xT85IiTCMK2BRm4q770W5mQQoCnPSnH6vD9gxDvMlj6pvtE/out-2.png", "https://replicate.delivery/pbxt/DXiwEmvJCP5lFdp7wGpoZwUhcuyfcjX35g1AkierzuQqnetlA/out-3.png" ], "started_at": "2024-05-22T18:21:35.550605Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dyvzqd3pm9rgm0cfm64scyhzb8", "cancel": "https://api.replicate.com/v1/predictions/dyvzqd3pm9rgm0cfm64scyhzb8/cancel" }, "version": "988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c" }Generated inUsing seed: 21990 Ensuring enough disk space... Free disk space: 1768129372160 Downloading weights: https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar 2024-05-22T18:21:35Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/864fe49e400e52a3 url=https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar 2024-05-22T18:21:41Z | INFO | [ Complete ] dest=/src/weights-cache/864fe49e400e52a3 size="186 MB" total_elapsed=6.113s url=https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar b'' Downloaded weights in 6.26224160194397 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a sketchnote photo of <s0><s1> explaining how human brain works txt2img mode 0%| | 0/40 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1946: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights` deprecate( 2%|▎ | 1/40 [00:00<00:35, 1.09it/s] 5%|▌ | 2/40 [00:01<00:34, 1.09it/s] 8%|▊ | 3/40 [00:02<00:34, 1.09it/s] 10%|█ | 4/40 [00:03<00:33, 1.09it/s] 12%|█▎ | 5/40 [00:04<00:32, 1.09it/s] 15%|█▌ | 6/40 [00:05<00:31, 1.09it/s] 18%|█▊ | 7/40 [00:06<00:30, 1.09it/s] 20%|██ | 8/40 [00:07<00:29, 1.09it/s] 22%|██▎ | 9/40 [00:08<00:28, 1.09it/s] 25%|██▌ | 10/40 [00:09<00:27, 1.09it/s] 28%|██▊ | 11/40 [00:10<00:26, 1.09it/s] 30%|███ | 12/40 [00:11<00:25, 1.09it/s] 32%|███▎ | 13/40 [00:11<00:24, 1.09it/s] 35%|███▌ | 14/40 [00:12<00:23, 1.09it/s] 38%|███▊ | 15/40 [00:13<00:22, 1.09it/s] 40%|████ | 16/40 [00:14<00:21, 1.09it/s] 42%|████▎ | 17/40 [00:15<00:21, 1.09it/s] 45%|████▌ | 18/40 [00:16<00:20, 1.09it/s] 48%|████▊ | 19/40 [00:17<00:19, 1.09it/s] 50%|█████ | 20/40 [00:18<00:18, 1.09it/s] 52%|█████▎ | 21/40 [00:19<00:17, 1.09it/s] 55%|█████▌ | 22/40 [00:20<00:16, 1.09it/s] 57%|█████▊ | 23/40 [00:21<00:15, 1.09it/s] 60%|██████ | 24/40 [00:22<00:14, 1.09it/s] 62%|██████▎ | 25/40 [00:22<00:13, 1.08it/s] 65%|██████▌ | 26/40 [00:23<00:12, 1.08it/s] 68%|██████▊ | 27/40 [00:24<00:12, 1.08it/s] 70%|███████ | 28/40 [00:25<00:11, 1.08it/s] 72%|███████▎ | 29/40 [00:26<00:10, 1.08it/s] 75%|███████▌ | 30/40 [00:27<00:09, 1.08it/s] 78%|███████▊ | 31/40 [00:28<00:08, 1.08it/s] 80%|████████ | 32/40 [00:29<00:07, 1.08it/s] 82%|████████▎ | 33/40 [00:30<00:06, 1.08it/s] 85%|████████▌ | 34/40 [00:31<00:05, 1.08it/s] 88%|████████▊ | 35/40 [00:32<00:04, 1.08it/s] 90%|█████████ | 36/40 [00:33<00:03, 1.08it/s] 92%|█████████▎| 37/40 [00:34<00:02, 1.08it/s] 95%|█████████▌| 38/40 [00:34<00:01, 1.08it/s] 98%|█████████▊| 39/40 [00:35<00:00, 1.08it/s] 100%|██████████| 40/40 [00:36<00:00, 1.08it/s] 100%|██████████| 40/40 [00:36<00:00, 1.09it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:08, 1.09it/s] 20%|██ | 2/10 [00:01<00:07, 1.09it/s] 30%|███ | 3/10 [00:02<00:06, 1.09it/s] 40%|████ | 4/10 [00:03<00:05, 1.09it/s] 50%|█████ | 5/10 [00:04<00:04, 1.09it/s] 60%|██████ | 6/10 [00:05<00:03, 1.09it/s] 70%|███████ | 7/10 [00:06<00:02, 1.08it/s] 80%|████████ | 8/10 [00:07<00:01, 1.08it/s] 90%|█████████ | 9/10 [00:08<00:00, 1.08it/s] 100%|██████████| 10/10 [00:09<00:00, 1.09it/s] 100%|██████████| 10/10 [00:09<00:00, 1.09it/s]Prediction
justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7cID5wva6kf935rgj0cfm67tscg92mStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a sketchnote photo of TOK explaining what is Sketchnotes
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining what is Sketchnotes ", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "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 justmalhar/sxdl-sketchnotes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c", { input: { width: 1024, height: 1024, prompt: "a sketchnote photo of TOK explaining what is Sketchnotes ", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, 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 justmalhar/sxdl-sketchnotes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c", input={ "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining what is Sketchnotes ", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run justmalhar/sxdl-sketchnotes 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": "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c", "input": { "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining what is Sketchnotes ", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-22T18:30:25.496195Z", "created_at": "2024-05-22T18:28:34.201000Z", "data_removed": false, "error": null, "id": "5wva6kf935rgj0cfm67tscg92m", "input": { "width": 1024, "height": 1024, "prompt": "a sketchnote photo of TOK explaining what is Sketchnotes ", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 20959\nEnsuring enough disk space...\nFree disk space: 1886305222656\nDownloading weights: https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar\n2024-05-22T18:29:31Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/864fe49e400e52a3 url=https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar\n2024-05-22T18:29:32Z | INFO | [ Complete ] dest=/src/weights-cache/864fe49e400e52a3 size=\"186 MB\" total_elapsed=0.671s url=https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar\nb''\nDownloaded weights in 0.7931919097900391 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a sketchnote photo of <s0><s1> explaining what is Sketchnotes\ntxt2img mode\n 0%| | 0/40 [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/usr/local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1946: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights`\ndeprecate(\n 2%|▎ | 1/40 [00:01<00:41, 1.05s/it]\n 5%|▌ | 2/40 [00:01<00:36, 1.03it/s]\n 8%|▊ | 3/40 [00:02<00:34, 1.06it/s]\n 10%|█ | 4/40 [00:03<00:33, 1.07it/s]\n 12%|█▎ | 5/40 [00:04<00:32, 1.08it/s]\n 15%|█▌ | 6/40 [00:05<00:31, 1.08it/s]\n 18%|█▊ | 7/40 [00:06<00:30, 1.08it/s]\n 20%|██ | 8/40 [00:07<00:29, 1.09it/s]\n 22%|██▎ | 9/40 [00:08<00:28, 1.09it/s]\n 25%|██▌ | 10/40 [00:09<00:27, 1.09it/s]\n 28%|██▊ | 11/40 [00:10<00:26, 1.09it/s]\n 30%|███ | 12/40 [00:11<00:25, 1.09it/s]\n 32%|███▎ | 13/40 [00:12<00:24, 1.09it/s]\n 35%|███▌ | 14/40 [00:12<00:23, 1.09it/s]\n 38%|███▊ | 15/40 [00:13<00:22, 1.09it/s]\n 40%|████ | 16/40 [00:14<00:21, 1.09it/s]\n 42%|████▎ | 17/40 [00:15<00:21, 1.09it/s]\n 45%|████▌ | 18/40 [00:16<00:20, 1.09it/s]\n 48%|████▊ | 19/40 [00:17<00:19, 1.09it/s]\n 50%|█████ | 20/40 [00:18<00:18, 1.09it/s]\n 52%|█████▎ | 21/40 [00:19<00:17, 1.09it/s]\n 55%|█████▌ | 22/40 [00:20<00:16, 1.09it/s]\n 57%|█████▊ | 23/40 [00:21<00:15, 1.09it/s]\n 60%|██████ | 24/40 [00:22<00:14, 1.09it/s]\n 62%|██████▎ | 25/40 [00:23<00:13, 1.09it/s]\n 65%|██████▌ | 26/40 [00:23<00:12, 1.09it/s]\n 68%|██████▊ | 27/40 [00:24<00:11, 1.09it/s]\n 70%|███████ | 28/40 [00:25<00:11, 1.09it/s]\n 72%|███████▎ | 29/40 [00:26<00:10, 1.09it/s]\n 75%|███████▌ | 30/40 [00:27<00:09, 1.09it/s]\n 78%|███████▊ | 31/40 [00:28<00:08, 1.09it/s]\n 80%|████████ | 32/40 [00:29<00:07, 1.08it/s]\n 82%|████████▎ | 33/40 [00:30<00:06, 1.08it/s]\n 85%|████████▌ | 34/40 [00:31<00:05, 1.08it/s]\n 88%|████████▊ | 35/40 [00:32<00:04, 1.08it/s]\n 90%|█████████ | 36/40 [00:33<00:03, 1.08it/s]\n 92%|█████████▎| 37/40 [00:34<00:02, 1.08it/s]\n 95%|█████████▌| 38/40 [00:35<00:01, 1.08it/s]\n 98%|█████████▊| 39/40 [00:35<00:00, 1.08it/s]\n100%|██████████| 40/40 [00:36<00:00, 1.08it/s]\n100%|██████████| 40/40 [00:36<00:00, 1.08it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:08, 1.08it/s]\n 20%|██ | 2/10 [00:01<00:07, 1.09it/s]\n 30%|███ | 3/10 [00:02<00:06, 1.09it/s]\n 40%|████ | 4/10 [00:03<00:05, 1.09it/s]\n 50%|█████ | 5/10 [00:04<00:04, 1.08it/s]\n 60%|██████ | 6/10 [00:05<00:03, 1.09it/s]\n 70%|███████ | 7/10 [00:06<00:02, 1.09it/s]\n 80%|████████ | 8/10 [00:07<00:01, 1.09it/s]\n 90%|█████████ | 9/10 [00:08<00:00, 1.09it/s]\n100%|██████████| 10/10 [00:09<00:00, 1.09it/s]\n100%|██████████| 10/10 [00:09<00:00, 1.09it/s]", "metrics": { "predict_time": 53.751031, "total_time": 111.295195 }, "output": [ "https://replicate.delivery/pbxt/POdn5Yai9npsPZl4xuWXexOYJwpTDYuXyd7ebA1xq3wfd9tlA/out-0.png", "https://replicate.delivery/pbxt/5DY8wNw4dX42H5gMfmzQvlnrHwtJogaSf5WzCuMlYaqAvetlA/out-1.png", "https://replicate.delivery/pbxt/E0scZMp9Qk6zPZJrfmmnUpQcJrTnCOe9NNp1IK2ZubBAvetlA/out-2.png", "https://replicate.delivery/pbxt/nqU1dur06I5cMR5BIdsdiFzjSem0M685OfotOku9xIMBvetlA/out-3.png" ], "started_at": "2024-05-22T18:29:31.745164Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5wva6kf935rgj0cfm67tscg92m", "cancel": "https://api.replicate.com/v1/predictions/5wva6kf935rgj0cfm67tscg92m/cancel" }, "version": "988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c" }Generated inUsing seed: 20959 Ensuring enough disk space... Free disk space: 1886305222656 Downloading weights: https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar 2024-05-22T18:29:31Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/864fe49e400e52a3 url=https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar 2024-05-22T18:29:32Z | INFO | [ Complete ] dest=/src/weights-cache/864fe49e400e52a3 size="186 MB" total_elapsed=0.671s url=https://replicate.delivery/pbxt/SemDU9mXjlQsa6nDjLaQLdgfQNI4Loqw3xHB6lQAzeJfi5bLB/trained_model.tar b'' Downloaded weights in 0.7931919097900391 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a sketchnote photo of <s0><s1> explaining what is Sketchnotes txt2img mode 0%| | 0/40 [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, /usr/local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1946: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights` deprecate( 2%|▎ | 1/40 [00:01<00:41, 1.05s/it] 5%|▌ | 2/40 [00:01<00:36, 1.03it/s] 8%|▊ | 3/40 [00:02<00:34, 1.06it/s] 10%|█ | 4/40 [00:03<00:33, 1.07it/s] 12%|█▎ | 5/40 [00:04<00:32, 1.08it/s] 15%|█▌ | 6/40 [00:05<00:31, 1.08it/s] 18%|█▊ | 7/40 [00:06<00:30, 1.08it/s] 20%|██ | 8/40 [00:07<00:29, 1.09it/s] 22%|██▎ | 9/40 [00:08<00:28, 1.09it/s] 25%|██▌ | 10/40 [00:09<00:27, 1.09it/s] 28%|██▊ | 11/40 [00:10<00:26, 1.09it/s] 30%|███ | 12/40 [00:11<00:25, 1.09it/s] 32%|███▎ | 13/40 [00:12<00:24, 1.09it/s] 35%|███▌ | 14/40 [00:12<00:23, 1.09it/s] 38%|███▊ | 15/40 [00:13<00:22, 1.09it/s] 40%|████ | 16/40 [00:14<00:21, 1.09it/s] 42%|████▎ | 17/40 [00:15<00:21, 1.09it/s] 45%|████▌ | 18/40 [00:16<00:20, 1.09it/s] 48%|████▊ | 19/40 [00:17<00:19, 1.09it/s] 50%|█████ | 20/40 [00:18<00:18, 1.09it/s] 52%|█████▎ | 21/40 [00:19<00:17, 1.09it/s] 55%|█████▌ | 22/40 [00:20<00:16, 1.09it/s] 57%|█████▊ | 23/40 [00:21<00:15, 1.09it/s] 60%|██████ | 24/40 [00:22<00:14, 1.09it/s] 62%|██████▎ | 25/40 [00:23<00:13, 1.09it/s] 65%|██████▌ | 26/40 [00:23<00:12, 1.09it/s] 68%|██████▊ | 27/40 [00:24<00:11, 1.09it/s] 70%|███████ | 28/40 [00:25<00:11, 1.09it/s] 72%|███████▎ | 29/40 [00:26<00:10, 1.09it/s] 75%|███████▌ | 30/40 [00:27<00:09, 1.09it/s] 78%|███████▊ | 31/40 [00:28<00:08, 1.09it/s] 80%|████████ | 32/40 [00:29<00:07, 1.08it/s] 82%|████████▎ | 33/40 [00:30<00:06, 1.08it/s] 85%|████████▌ | 34/40 [00:31<00:05, 1.08it/s] 88%|████████▊ | 35/40 [00:32<00:04, 1.08it/s] 90%|█████████ | 36/40 [00:33<00:03, 1.08it/s] 92%|█████████▎| 37/40 [00:34<00:02, 1.08it/s] 95%|█████████▌| 38/40 [00:35<00:01, 1.08it/s] 98%|█████████▊| 39/40 [00:35<00:00, 1.08it/s] 100%|██████████| 40/40 [00:36<00:00, 1.08it/s] 100%|██████████| 40/40 [00:36<00:00, 1.08it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:08, 1.08it/s] 20%|██ | 2/10 [00:01<00:07, 1.09it/s] 30%|███ | 3/10 [00:02<00:06, 1.09it/s] 40%|████ | 4/10 [00:03<00:05, 1.09it/s] 50%|█████ | 5/10 [00:04<00:04, 1.08it/s] 60%|██████ | 6/10 [00:05<00:03, 1.09it/s] 70%|███████ | 7/10 [00:06<00:02, 1.09it/s] 80%|████████ | 8/10 [00:07<00:01, 1.09it/s] 90%|█████████ | 9/10 [00:08<00:00, 1.09it/s] 100%|██████████| 10/10 [00:09<00:00, 1.09it/s] 100%|██████████| 10/10 [00:09<00:00, 1.09it/s]
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