cjwbw
/
wavyfusion
dreambooth trained on a very diverse dataset ranging from photographs to paintings
- Public
- 3.7K runs
-
T4
Prediction
cjwbw/wavyfusion:3a38e179IDsrdc4zbrofb4rnwhrxukyirza4StatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- batman wa-vy style
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "batman wa-vy style", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/wavyfusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/wavyfusion:3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9", { input: { width: 512, height: 512, prompt: "batman wa-vy style", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun cjwbw/wavyfusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/wavyfusion:3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9", input={ "width": 512, "height": 512, "prompt": "batman wa-vy style", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run cjwbw/wavyfusion 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": "3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9", "input": { "width": 512, "height": 512, "prompt": "batman wa-vy style", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "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.
Install Cogbrew install cogIf you don’t have Homebrew, there are other installation options available.
Pull and run cjwbw/wavyfusion using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/chenxwh/wavyfusion@sha256:3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9 \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="batman wa-vy style"' \ -i 'scheduler="DPMSolverMultistep"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Pull and run cjwbw/wavyfusion using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/chenxwh/wavyfusion@sha256:3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "batman wa-vy style", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsOutput
{ "completed_at": "2022-12-08T22:39:38.054324Z", "created_at": "2022-12-08T22:35:34.417392Z", "data_removed": false, "error": null, "id": "srdc4zbrofb4rnwhrxukyirza4", "input": { "width": 512, "height": 512, "prompt": "batman wa-vy style", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 1205\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<01:52, 2.30s/it]\n 4%|▍ | 2/50 [00:02<00:52, 1.09s/it]\n 6%|▌ | 3/50 [00:02<00:32, 1.43it/s]\n 8%|▊ | 4/50 [00:03<00:23, 1.94it/s]\n 10%|█ | 5/50 [00:03<00:18, 2.41it/s]\n 12%|█▏ | 6/50 [00:03<00:15, 2.82it/s]\n 14%|█▍ | 7/50 [00:03<00:13, 3.18it/s]\n 16%|█▌ | 8/50 [00:03<00:12, 3.46it/s]\n 18%|█▊ | 9/50 [00:04<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:04<00:10, 3.81it/s]\n 22%|██▏ | 11/50 [00:04<00:09, 3.95it/s]\n 24%|██▍ | 12/50 [00:04<00:09, 4.04it/s]\n 26%|██▌ | 13/50 [00:05<00:09, 4.10it/s]\n 28%|██▊ | 14/50 [00:05<00:08, 4.14it/s]\n 30%|███ | 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[00:10<00:03, 4.23it/s]\n 76%|███████▌ | 38/50 [00:11<00:02, 4.23it/s]\n 78%|███████▊ | 39/50 [00:11<00:02, 4.22it/s]\n 80%|████████ | 40/50 [00:11<00:02, 4.22it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 4.22it/s]\n 84%|████████▍ | 42/50 [00:11<00:01, 4.22it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 4.21it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 4.21it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 4.22it/s]\n 92%|█████████▏| 46/50 [00:12<00:00, 4.21it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 4.21it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 4.22it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 4.22it/s]\n100%|██████████| 50/50 [00:13<00:00, 4.22it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]", "metrics": { "predict_time": 16.287517, "total_time": 243.636932 }, "output": [ "https://replicate.delivery/pbxt/X3QiWcxedlyrekButiNlJqVDdfRfcy9merjX6yfOSzAaqZACE/out-0.png" ], "started_at": "2022-12-08T22:39:21.766807Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/srdc4zbrofb4rnwhrxukyirza4", "cancel": "https://api.replicate.com/v1/predictions/srdc4zbrofb4rnwhrxukyirza4/cancel" }, "version": "3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9" }Generated inUsing seed: 1205 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<01:52, 2.30s/it] 4%|▍ | 2/50 [00:02<00:52, 1.09s/it] 6%|▌ | 3/50 [00:02<00:32, 1.43it/s] 8%|▊ | 4/50 [00:03<00:23, 1.94it/s] 10%|█ | 5/50 [00:03<00:18, 2.41it/s] 12%|█▏ | 6/50 [00:03<00:15, 2.82it/s] 14%|█▍ | 7/50 [00:03<00:13, 3.18it/s] 16%|█▌ | 8/50 [00:03<00:12, 3.46it/s] 18%|█▊ | 9/50 [00:04<00:11, 3.66it/s] 20%|██ | 10/50 [00:04<00:10, 3.81it/s] 22%|██▏ | 11/50 [00:04<00:09, 3.95it/s] 24%|██▍ | 12/50 [00:04<00:09, 4.04it/s] 26%|██▌ | 13/50 [00:05<00:09, 4.10it/s] 28%|██▊ | 14/50 [00:05<00:08, 4.14it/s] 30%|███ | 15/50 [00:05<00:08, 4.16it/s] 32%|███▏ | 16/50 [00:05<00:08, 4.19it/s] 34%|███▍ | 17/50 [00:06<00:07, 4.21it/s] 36%|███▌ | 18/50 [00:06<00:07, 4.21it/s] 38%|███▊ 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