archievilliers
/
sdxl-picasso
An SDXL fine-tune based on Picasso's work
- Public
- 250 runs
-
L40S
- SDXL fine-tune
Prediction
archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3ID5ue44fdbntvlocy5i5oyzyvjqiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a man in the style of TOK
- 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
- 50
{ "width": 1024, "height": 1024, "prompt": "a man in the style of TOK", "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": 50 }
Install Replicateβs Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run archievilliers/sdxl-picasso using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3", { input: { width: 1024, height: 1024, prompt: "a man in the style of TOK", 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: 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 Replicateβs Python client library:pip install replicate
Import the client:import replicate
Run archievilliers/sdxl-picasso using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3", input={ "width": 1024, "height": 1024, "prompt": "a man in the style of TOK", "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": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run archievilliers/sdxl-picasso using Replicateβs 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": "archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3", "input": { "width": 1024, "height": 1024, "prompt": "a man in the style of TOK", "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": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicateβs HTTP API reference docs.
Output
{ "completed_at": "2024-01-04T15:59:07.214234Z", "created_at": "2024-01-04T15:58:36.630792Z", "data_removed": false, "error": null, "id": "5ue44fdbntvlocy5i5oyzyvjqi", "input": { "width": 1024, "height": 1024, "prompt": "a man in the style of TOK", "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": 50 }, "logs": "Using seed: 64057\nEnsuring enough disk space...\nFree disk space: 3020731977728\nDownloading weights: https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar\n2024-01-04T15:58:43Z | INFO | [ Initiating ] dest=/src/weights-cache/eeec9c4593292083 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar\n2024-01-04T15:58:51Z | INFO | [ Complete ] dest=/src/weights-cache/eeec9c4593292083 size=\"186 MB\" total_elapsed=7.424s url=https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar\nb''\nDownloaded weights in 7.54965353012085 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a man in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|β | 1/50 [00:00<00:13, 3.67it/s]\n 4%|β | 2/50 [00:00<00:13, 3.67it/s]\n 6%|β | 3/50 [00:00<00:12, 3.67it/s]\n 8%|β | 4/50 [00:01<00:12, 3.67it/s]\n 10%|β | 5/50 [00:01<00:12, 3.66it/s]\n 12%|ββ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|ββ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|ββ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|ββ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|ββ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|βββ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|βββ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|βββ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|βββ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|βββ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|ββββ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|ββββ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|ββββ | 18/50 [00:04<00:08, 3.65it/s]\n 38%|ββββ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|ββββ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|βββββ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|βββββ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|βββββ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|βββββ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|βββββ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|ββββββ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|ββββββ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|ββββββ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|ββββββ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|ββββββ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|βββββββ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|βββββββ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|βββββββ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|βββββββ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|βββββββ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|ββββββββ | 36/50 [00:09<00:03, 3.64it/s]\n 74%|ββββββββ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|ββββββββ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|ββββββββ | 39/50 [00:10<00:03, 3.64it/s]\n 80%|ββββββββ | 40/50 [00:10<00:02, 3.64it/s]\n 82%|βββββββββ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|βββββββββ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|βββββββββ | 43/50 [00:11<00:01, 3.64it/s]\n 88%|βββββββββ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|βββββββββ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|ββββββββββ| 46/50 [00:12<00:01, 3.63it/s]\n 94%|ββββββββββ| 47/50 [00:12<00:00, 3.64it/s]\n 96%|ββββββββββ| 48/50 [00:13<00:00, 3.63it/s]\n 98%|ββββββββββ| 49/50 [00:13<00:00, 3.63it/s]\n100%|ββββββββββ| 50/50 [00:13<00:00, 3.63it/s]\n100%|ββββββββββ| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 23.338297, "total_time": 30.583442 }, "output": [ "https://replicate.delivery/pbxt/b0YMHoVpJ47kLdN7yzJ8A0ftoCvfkWsVjtyf3Pf7yGBq8hkIB/out-0.png" ], "started_at": "2024-01-04T15:58:43.875937Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5ue44fdbntvlocy5i5oyzyvjqi", "cancel": "https://api.replicate.com/v1/predictions/5ue44fdbntvlocy5i5oyzyvjqi/cancel" }, "version": "3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3" }
Generated inUsing seed: 64057 Ensuring enough disk space... Free disk space: 3020731977728 Downloading weights: https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar 2024-01-04T15:58:43Z | INFO | [ Initiating ] dest=/src/weights-cache/eeec9c4593292083 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar 2024-01-04T15:58:51Z | INFO | [ Complete ] dest=/src/weights-cache/eeec9c4593292083 size="186 MB" total_elapsed=7.424s url=https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar b'' Downloaded weights in 7.54965353012085 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a man in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|β | 1/50 [00:00<00:13, 3.67it/s] 4%|β | 2/50 [00:00<00:13, 3.67it/s] 6%|β | 3/50 [00:00<00:12, 3.67it/s] 8%|β | 4/50 [00:01<00:12, 3.67it/s] 10%|β | 5/50 [00:01<00:12, 3.66it/s] 12%|ββ | 6/50 [00:01<00:12, 3.66it/s] 14%|ββ | 7/50 [00:01<00:11, 3.66it/s] 16%|ββ | 8/50 [00:02<00:11, 3.66it/s] 18%|ββ | 9/50 [00:02<00:11, 3.66it/s] 20%|ββ | 10/50 [00:02<00:10, 3.66it/s] 22%|βββ | 11/50 [00:03<00:10, 3.65it/s] 24%|βββ | 12/50 [00:03<00:10, 3.65it/s] 26%|βββ | 13/50 [00:03<00:10, 3.65it/s] 28%|βββ | 14/50 [00:03<00:09, 3.65it/s] 30%|βββ | 15/50 [00:04<00:09, 3.65it/s] 32%|ββββ | 16/50 [00:04<00:09, 3.65it/s] 34%|ββββ | 17/50 [00:04<00:09, 3.65it/s] 36%|ββββ | 18/50 [00:04<00:08, 3.65it/s] 38%|ββββ | 19/50 [00:05<00:08, 3.65it/s] 40%|ββββ | 20/50 [00:05<00:08, 3.65it/s] 42%|βββββ | 21/50 [00:05<00:07, 3.65it/s] 44%|βββββ | 22/50 [00:06<00:07, 3.64it/s] 46%|βββββ | 23/50 [00:06<00:07, 3.64it/s] 48%|βββββ | 24/50 [00:06<00:07, 3.64it/s] 50%|βββββ | 25/50 [00:06<00:06, 3.65it/s] 52%|ββββββ | 26/50 [00:07<00:06, 3.64it/s] 54%|ββββββ | 27/50 [00:07<00:06, 3.64it/s] 56%|ββββββ | 28/50 [00:07<00:06, 3.64it/s] 58%|ββββββ | 29/50 [00:07<00:05, 3.64it/s] 60%|ββββββ | 30/50 [00:08<00:05, 3.64it/s] 62%|βββββββ | 31/50 [00:08<00:05, 3.64it/s] 64%|βββββββ | 32/50 [00:08<00:04, 3.64it/s] 66%|βββββββ | 33/50 [00:09<00:04, 3.64it/s] 68%|βββββββ | 34/50 [00:09<00:04, 3.64it/s] 70%|βββββββ | 35/50 [00:09<00:04, 3.64it/s] 72%|ββββββββ | 36/50 [00:09<00:03, 3.64it/s] 74%|ββββββββ | 37/50 [00:10<00:03, 3.64it/s] 76%|ββββββββ | 38/50 [00:10<00:03, 3.64it/s] 78%|ββββββββ | 39/50 [00:10<00:03, 3.64it/s] 80%|ββββββββ | 40/50 [00:10<00:02, 3.64it/s] 82%|βββββββββ | 41/50 [00:11<00:02, 3.64it/s] 84%|βββββββββ | 42/50 [00:11<00:02, 3.63it/s] 86%|βββββββββ | 43/50 [00:11<00:01, 3.64it/s] 88%|βββββββββ | 44/50 [00:12<00:01, 3.64it/s] 90%|βββββββββ | 45/50 [00:12<00:01, 3.64it/s] 92%|ββββββββββ| 46/50 [00:12<00:01, 3.63it/s] 94%|ββββββββββ| 47/50 [00:12<00:00, 3.64it/s] 96%|ββββββββββ| 48/50 [00:13<00:00, 3.63it/s] 98%|ββββββββββ| 49/50 [00:13<00:00, 3.63it/s] 100%|ββββββββββ| 50/50 [00:13<00:00, 3.63it/s] 100%|ββββββββββ| 50/50 [00:13<00:00, 3.64it/s]
Prediction
archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3IDtj2nty3bx4zftoj7kmv3z5ijkqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a photo of TOK
- 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
- 50
{ "width": 1024, "height": 1024, "prompt": "a photo of TOK", "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": 50 }
Install Replicateβs Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run archievilliers/sdxl-picasso using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3", { input: { width: 1024, height: 1024, prompt: "a photo of TOK", 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: 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 Replicateβs Python client library:pip install replicate
Import the client:import replicate
Run archievilliers/sdxl-picasso using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3", input={ "width": 1024, "height": 1024, "prompt": "a photo of TOK", "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": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run archievilliers/sdxl-picasso using Replicateβs 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": "archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3", "input": { "width": 1024, "height": 1024, "prompt": "a photo of TOK", "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": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicateβs HTTP API reference docs.
Output
{ "completed_at": "2024-01-04T15:59:57.755009Z", "created_at": "2024-01-04T15:59:29.729352Z", "data_removed": false, "error": null, "id": "tj2nty3bx4zftoj7kmv3z5ijkq", "input": { "width": 1024, "height": 1024, "prompt": "a photo of TOK", "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": 50 }, "logs": "Using seed: 53930\nEnsuring enough disk space...\nFree disk space: 2035670163456\nDownloading weights: https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar\n2024-01-04T15:59:33Z | INFO | [ Initiating ] dest=/src/weights-cache/eeec9c4593292083 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar\n2024-01-04T15:59:41Z | INFO | [ Complete ] dest=/src/weights-cache/eeec9c4593292083 size=\"186 MB\" total_elapsed=8.415s url=https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar\nb''\nDownloaded weights in 8.596491813659668 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a photo of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|β | 1/50 [00:00<00:13, 3.67it/s]\n 4%|β | 2/50 [00:00<00:13, 3.65it/s]\n 6%|β | 3/50 [00:00<00:12, 3.65it/s]\n 8%|β | 4/50 [00:01<00:12, 3.64it/s]\n 10%|β | 5/50 [00:01<00:12, 3.64it/s]\n 12%|ββ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|ββ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|ββ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|ββ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|ββ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|βββ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|βββ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|βββ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|βββ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|βββ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|ββββ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|ββββ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|ββββ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|ββββ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|ββββ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|βββββ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|βββββ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|βββββ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|βββββ | 24/50 [00:06<00:07, 3.65it/s]\n 50%|βββββ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|ββββββ | 26/50 [00:07<00:06, 3.65it/s]\n 54%|ββββββ | 27/50 [00:07<00:06, 3.65it/s]\n 56%|ββββββ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|ββββββ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|ββββββ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|βββββββ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|βββββββ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|βββββββ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|βββββββ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|βββββββ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|ββββββββ | 36/50 [00:09<00:03, 3.64it/s]\n 74%|ββββββββ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|ββββββββ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|ββββββββ | 39/50 [00:10<00:03, 3.64it/s]\n 80%|ββββββββ | 40/50 [00:10<00:02, 3.64it/s]\n 82%|βββββββββ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|βββββββββ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|βββββββββ | 43/50 [00:11<00:01, 3.64it/s]\n 88%|βββββββββ | 44/50 [00:12<00:01, 3.63it/s]\n 90%|βββββββββ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|ββββββββββ| 46/50 [00:12<00:01, 3.64it/s]\n 94%|ββββββββββ| 47/50 [00:12<00:00, 3.64it/s]\n 96%|ββββββββββ| 48/50 [00:13<00:00, 3.64it/s]\n 98%|ββββββββββ| 49/50 [00:13<00:00, 3.64it/s]\n100%|ββββββββββ| 50/50 [00:13<00:00, 3.63it/s]\n100%|ββββββββββ| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 24.771562, "total_time": 28.025657 }, "output": [ "https://replicate.delivery/pbxt/BuJePrQKt7wHNKMaEJzE2mpHDF1M364eYl6w5xzaSko8fQSkA/out-0.png" ], "started_at": "2024-01-04T15:59:32.983447Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tj2nty3bx4zftoj7kmv3z5ijkq", "cancel": "https://api.replicate.com/v1/predictions/tj2nty3bx4zftoj7kmv3z5ijkq/cancel" }, "version": "3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3" }
Generated inUsing seed: 53930 Ensuring enough disk space... Free disk space: 2035670163456 Downloading weights: https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar 2024-01-04T15:59:33Z | INFO | [ Initiating ] dest=/src/weights-cache/eeec9c4593292083 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar 2024-01-04T15:59:41Z | INFO | [ Complete ] dest=/src/weights-cache/eeec9c4593292083 size="186 MB" total_elapsed=8.415s url=https://replicate.delivery/pbxt/7bR5yE6mhsK8N5zkqOeNngCRM4bxHf9ua07SSjjHwkeQ6QSkA/trained_model.tar b'' Downloaded weights in 8.596491813659668 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a photo of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|β | 1/50 [00:00<00:13, 3.67it/s] 4%|β | 2/50 [00:00<00:13, 3.65it/s] 6%|β | 3/50 [00:00<00:12, 3.65it/s] 8%|β | 4/50 [00:01<00:12, 3.64it/s] 10%|β | 5/50 [00:01<00:12, 3.64it/s] 12%|ββ | 6/50 [00:01<00:12, 3.64it/s] 14%|ββ | 7/50 [00:01<00:11, 3.64it/s] 16%|ββ | 8/50 [00:02<00:11, 3.63it/s] 18%|ββ | 9/50 [00:02<00:11, 3.63it/s] 20%|ββ | 10/50 [00:02<00:11, 3.63it/s] 22%|βββ | 11/50 [00:03<00:10, 3.63it/s] 24%|βββ | 12/50 [00:03<00:10, 3.63it/s] 26%|βββ | 13/50 [00:03<00:10, 3.63it/s] 28%|βββ | 14/50 [00:03<00:09, 3.64it/s] 30%|βββ | 15/50 [00:04<00:09, 3.64it/s] 32%|ββββ | 16/50 [00:04<00:09, 3.65it/s] 34%|ββββ | 17/50 [00:04<00:09, 3.64it/s] 36%|ββββ | 18/50 [00:04<00:08, 3.64it/s] 38%|ββββ | 19/50 [00:05<00:08, 3.65it/s] 40%|ββββ | 20/50 [00:05<00:08, 3.65it/s] 42%|βββββ | 21/50 [00:05<00:07, 3.65it/s] 44%|βββββ | 22/50 [00:06<00:07, 3.65it/s] 46%|βββββ | 23/50 [00:06<00:07, 3.65it/s] 48%|βββββ | 24/50 [00:06<00:07, 3.65it/s] 50%|βββββ | 25/50 [00:06<00:06, 3.65it/s] 52%|ββββββ | 26/50 [00:07<00:06, 3.65it/s] 54%|ββββββ | 27/50 [00:07<00:06, 3.65it/s] 56%|ββββββ | 28/50 [00:07<00:06, 3.64it/s] 58%|ββββββ | 29/50 [00:07<00:05, 3.64it/s] 60%|ββββββ | 30/50 [00:08<00:05, 3.64it/s] 62%|βββββββ | 31/50 [00:08<00:05, 3.64it/s] 64%|βββββββ | 32/50 [00:08<00:04, 3.65it/s] 66%|βββββββ | 33/50 [00:09<00:04, 3.65it/s] 68%|βββββββ | 34/50 [00:09<00:04, 3.65it/s] 70%|βββββββ | 35/50 [00:09<00:04, 3.64it/s] 72%|ββββββββ | 36/50 [00:09<00:03, 3.64it/s] 74%|ββββββββ | 37/50 [00:10<00:03, 3.64it/s] 76%|ββββββββ | 38/50 [00:10<00:03, 3.64it/s] 78%|ββββββββ | 39/50 [00:10<00:03, 3.64it/s] 80%|ββββββββ | 40/50 [00:10<00:02, 3.64it/s] 82%|βββββββββ | 41/50 [00:11<00:02, 3.64it/s] 84%|βββββββββ | 42/50 [00:11<00:02, 3.64it/s] 86%|βββββββββ | 43/50 [00:11<00:01, 3.64it/s] 88%|βββββββββ | 44/50 [00:12<00:01, 3.63it/s] 90%|βββββββββ | 45/50 [00:12<00:01, 3.64it/s] 92%|ββββββββββ| 46/50 [00:12<00:01, 3.64it/s] 94%|ββββββββββ| 47/50 [00:12<00:00, 3.64it/s] 96%|ββββββββββ| 48/50 [00:13<00:00, 3.64it/s] 98%|ββββββββββ| 49/50 [00:13<00:00, 3.64it/s] 100%|ββββββββββ| 50/50 [00:13<00:00, 3.63it/s] 100%|ββββββββββ| 50/50 [00:13<00:00, 3.64it/s]
Prediction
archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3IDg23fz5dbqwxv6uumfqrhnvtlnmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a painting of a man in the style of TOK framed at an art gallery
- 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
- 50
{ "width": 1024, "height": 1024, "prompt": "a painting of a man in the style of TOK framed at an art gallery", "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": 50 }
Install Replicateβs Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run archievilliers/sdxl-picasso using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3", { input: { width: 1024, height: 1024, prompt: "a painting of a man in the style of TOK framed at an art gallery", 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: 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 Replicateβs Python client library:pip install replicate
Import the client:import replicate
Run archievilliers/sdxl-picasso using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3", input={ "width": 1024, "height": 1024, "prompt": "a painting of a man in the style of TOK framed at an art gallery", "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": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run archievilliers/sdxl-picasso using Replicateβs 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": "archievilliers/sdxl-picasso:3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3", "input": { "width": 1024, "height": 1024, "prompt": "a painting of a man in the style of TOK framed at an art gallery", "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": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicateβs HTTP API reference docs.
Output
{ "completed_at": "2024-01-04T16:12:03.153790Z", "created_at": "2024-01-04T16:11:45.367567Z", "data_removed": false, "error": null, "id": "g23fz5dbqwxv6uumfqrhnvtlnm", "input": { "width": 1024, "height": 1024, "prompt": "a painting of a man in the style of TOK framed at an art gallery", "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": 50 }, "logs": "Using seed: 5982\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a painting of a man in the style of <s0><s1> framed at an art gallery\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|β | 1/50 [00:00<00:13, 3.66it/s]\n 4%|β | 2/50 [00:00<00:13, 3.66it/s]\n 6%|β | 3/50 [00:00<00:12, 3.66it/s]\n 8%|β | 4/50 [00:01<00:12, 3.66it/s]\n 10%|β | 5/50 [00:01<00:12, 3.66it/s]\n 12%|ββ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|ββ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|ββ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|ββ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|ββ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|βββ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|βββ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|βββ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|βββ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|βββ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|ββββ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|ββββ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|ββββ | 18/50 [00:04<00:08, 3.65it/s]\n 38%|ββββ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|ββββ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|βββββ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|βββββ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|βββββ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|βββββ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|βββββ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|ββββββ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|ββββββ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|ββββββ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|ββββββ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|ββββββ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|βββββββ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|βββββββ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|βββββββ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|βββββββ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|βββββββ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|ββββββββ | 36/50 [00:09<00:03, 3.64it/s]\n 74%|ββββββββ | 37/50 [00:10<00:03, 3.63it/s]\n 76%|ββββββββ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|ββββββββ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|ββββββββ | 40/50 [00:10<00:02, 3.64it/s]\n 82%|βββββββββ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|βββββββββ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|βββββββββ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|βββββββββ | 44/50 [00:12<00:01, 3.63it/s]\n 90%|βββββββββ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|ββββββββββ| 46/50 [00:12<00:01, 3.63it/s]\n 94%|ββββββββββ| 47/50 [00:12<00:00, 3.64it/s]\n 96%|ββββββββββ| 48/50 [00:13<00:00, 3.64it/s]\n 98%|ββββββββββ| 49/50 [00:13<00:00, 3.64it/s]\n100%|ββββββββββ| 50/50 [00:13<00:00, 3.63it/s]\n100%|ββββββββββ| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 15.607402, "total_time": 17.786223 }, "output": [ "https://replicate.delivery/pbxt/3uGR7So6IibTItAHEXaefxUYUYsConVohTO7Oz6whScSrIJSA/out-0.png" ], "started_at": "2024-01-04T16:11:47.546388Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/g23fz5dbqwxv6uumfqrhnvtlnm", "cancel": "https://api.replicate.com/v1/predictions/g23fz5dbqwxv6uumfqrhnvtlnm/cancel" }, "version": "3fabfe8f6d7bc5fea366113398404830e9e231df3d413585057edf8538c819a3" }
Generated inUsing seed: 5982 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a painting of a man in the style of <s0><s1> framed at an art gallery txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|β | 1/50 [00:00<00:13, 3.66it/s] 4%|β | 2/50 [00:00<00:13, 3.66it/s] 6%|β | 3/50 [00:00<00:12, 3.66it/s] 8%|β | 4/50 [00:01<00:12, 3.66it/s] 10%|β | 5/50 [00:01<00:12, 3.66it/s] 12%|ββ | 6/50 [00:01<00:12, 3.66it/s] 14%|ββ | 7/50 [00:01<00:11, 3.66it/s] 16%|ββ | 8/50 [00:02<00:11, 3.66it/s] 18%|ββ | 9/50 [00:02<00:11, 3.66it/s] 20%|ββ | 10/50 [00:02<00:10, 3.66it/s] 22%|βββ | 11/50 [00:03<00:10, 3.65it/s] 24%|βββ | 12/50 [00:03<00:10, 3.65it/s] 26%|βββ | 13/50 [00:03<00:10, 3.65it/s] 28%|βββ | 14/50 [00:03<00:09, 3.65it/s] 30%|βββ | 15/50 [00:04<00:09, 3.65it/s] 32%|ββββ | 16/50 [00:04<00:09, 3.65it/s] 34%|ββββ | 17/50 [00:04<00:09, 3.65it/s] 36%|ββββ | 18/50 [00:04<00:08, 3.65it/s] 38%|ββββ | 19/50 [00:05<00:08, 3.64it/s] 40%|ββββ | 20/50 [00:05<00:08, 3.65it/s] 42%|βββββ | 21/50 [00:05<00:07, 3.65it/s] 44%|βββββ | 22/50 [00:06<00:07, 3.65it/s] 46%|βββββ | 23/50 [00:06<00:07, 3.64it/s] 48%|βββββ | 24/50 [00:06<00:07, 3.64it/s] 50%|βββββ | 25/50 [00:06<00:06, 3.64it/s] 52%|ββββββ | 26/50 [00:07<00:06, 3.64it/s] 54%|ββββββ | 27/50 [00:07<00:06, 3.64it/s] 56%|ββββββ | 28/50 [00:07<00:06, 3.64it/s] 58%|ββββββ | 29/50 [00:07<00:05, 3.64it/s] 60%|ββββββ | 30/50 [00:08<00:05, 3.64it/s] 62%|βββββββ | 31/50 [00:08<00:05, 3.64it/s] 64%|βββββββ | 32/50 [00:08<00:04, 3.64it/s] 66%|βββββββ | 33/50 [00:09<00:04, 3.63it/s] 68%|βββββββ | 34/50 [00:09<00:04, 3.64it/s] 70%|βββββββ | 35/50 [00:09<00:04, 3.64it/s] 72%|ββββββββ | 36/50 [00:09<00:03, 3.64it/s] 74%|ββββββββ | 37/50 [00:10<00:03, 3.63it/s] 76%|ββββββββ | 38/50 [00:10<00:03, 3.64it/s] 78%|ββββββββ | 39/50 [00:10<00:03, 3.63it/s] 80%|ββββββββ | 40/50 [00:10<00:02, 3.64it/s] 82%|βββββββββ | 41/50 [00:11<00:02, 3.64it/s] 84%|βββββββββ | 42/50 [00:11<00:02, 3.63it/s] 86%|βββββββββ | 43/50 [00:11<00:01, 3.63it/s] 88%|βββββββββ | 44/50 [00:12<00:01, 3.63it/s] 90%|βββββββββ | 45/50 [00:12<00:01, 3.64it/s] 92%|ββββββββββ| 46/50 [00:12<00:01, 3.63it/s] 94%|ββββββββββ| 47/50 [00:12<00:00, 3.64it/s] 96%|ββββββββββ| 48/50 [00:13<00:00, 3.64it/s] 98%|ββββββββββ| 49/50 [00:13<00:00, 3.64it/s] 100%|ββββββββββ| 50/50 [00:13<00:00, 3.63it/s] 100%|ββββββββββ| 50/50 [00:13<00:00, 3.64it/s]
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