brewwh
/
cog-a1111-ui
A collection of anime stable diffusion models with VAEs and LORAs.
- Public
- 3.7K runs
-
A100 (80GB)
Prediction
brewwh/cog-a1111-ui:15680039ID4dn2julbjkhmib5trn755xiz4mStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- vae
- ema_original.pt
- seed
- -1
- model
- anythingv.safetensors
- steps
- 15
- width
- 768
- height
- 768
- prompt
- masterpiece, illustration, portrait of a beautiful young woman
- hr_scale
- 2
- cfg_scale
- 7
- enable_hr
- batch_size
- 4
- hr_upscaler
- R-ESRGAN 4x+ Anime6B
- sampler_name
- DPM++ 2M Karras
- negative_prompt
- low quality, bad quality, worst quality
- denoising_strength
- 0.38
- hr_second_pass_steps
- 8
{ "vae": "ema_original.pt", "seed": -1, "model": "anythingv.safetensors", "steps": 15, "width": 768, "height": 768, "prompt": "masterpiece, illustration, portrait of a beautiful young woman", "hr_scale": 2, "cfg_scale": 7, "enable_hr": true, "batch_size": 4, "hr_upscaler": "R-ESRGAN 4x+ Anime6B", "sampler_name": "DPM++ 2M Karras", "negative_prompt": "low quality, bad quality, worst quality", "denoising_strength": 0.38, "hr_second_pass_steps": 8 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run brewwh/cog-a1111-ui using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "brewwh/cog-a1111-ui:15680039e93e5d25a863e8a5a4f82dcc5398011c321ca86362a20f71b09fb7ff", { input: { vae: "ema_original.pt", seed: -1, model: "anythingv.safetensors", steps: 15, width: 768, height: 768, prompt: "masterpiece, illustration, portrait of a beautiful young woman", hr_scale: 2, cfg_scale: 7, enable_hr: true, batch_size: 4, hr_upscaler: "R-ESRGAN 4x+ Anime6B", sampler_name: "DPM++ 2M Karras", negative_prompt: "low quality, bad quality, worst quality", denoising_strength: 0.38, hr_second_pass_steps: 8 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun brewwh/cog-a1111-ui using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "brewwh/cog-a1111-ui:15680039e93e5d25a863e8a5a4f82dcc5398011c321ca86362a20f71b09fb7ff", input={ "vae": "ema_original.pt", "seed": -1, "model": "anythingv.safetensors", "steps": 15, "width": 768, "height": 768, "prompt": "masterpiece, illustration, portrait of a beautiful young woman", "hr_scale": 2, "cfg_scale": 7, "enable_hr": True, "batch_size": 4, "hr_upscaler": "R-ESRGAN 4x+ Anime6B", "sampler_name": "DPM++ 2M Karras", "negative_prompt": "low quality, bad quality, worst quality", "denoising_strength": 0.38, "hr_second_pass_steps": 8 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run brewwh/cog-a1111-ui 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": "15680039e93e5d25a863e8a5a4f82dcc5398011c321ca86362a20f71b09fb7ff", "input": { "vae": "ema_original.pt", "seed": -1, "model": "anythingv.safetensors", "steps": 15, "width": 768, "height": 768, "prompt": "masterpiece, illustration, portrait of a beautiful young woman", "hr_scale": 2, "cfg_scale": 7, "enable_hr": true, "batch_size": 4, "hr_upscaler": "R-ESRGAN 4x+ Anime6B", "sampler_name": "DPM++ 2M Karras", "negative_prompt": "low quality, bad quality, worst quality", "denoising_strength": 0.38, "hr_second_pass_steps": 8 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-28T02:23:39.851271Z", "created_at": "2023-12-28T02:18:14.619446Z", "data_removed": false, "error": null, "id": "4dn2julbjkhmib5trn755xiz4m", "input": { "vae": "ema_original.pt", "seed": -1, "model": "anythingv.safetensors", "steps": 15, "width": 768, "height": 768, "prompt": "masterpiece, illustration, portrait of a beautiful young woman", "hr_scale": 2, "cfg_scale": 7, "enable_hr": true, "batch_size": 4, "hr_upscaler": "R-ESRGAN 4x+ Anime6B", "sampler_name": "DPM++ 2M Karras", "negative_prompt": "low quality, bad quality, worst quality", "denoising_strength": 0.38, "hr_second_pass_steps": 8 }, "logs": "Checking model and vae...\nPrevious model: xxmix.safetensors\nUpdating the model...\nLoading weights [1cb9f9c58a] from cache\nLoading VAE weights specified in settings: cached ema_original.pt\nApplying xformers cross attention optimization.\nWeights loaded in 0.6s (apply weights to model: 0.2s, move model to device: 0.3s).\nPrevious vae: ema_original.pt\nVAE is already up-to-date.\nDone, time used: 1.7655346393585205 seconds.\nTotal progress: 0it [00:00, ?it/s]\u001b[A\n 0%| | 0/15 [00:00<?, ?it/s]\nTotal progress: 1it [00:00, 1.24it/s]\u001b[A\n 7%|▋ | 1/15 [00:00<00:03, 4.21it/s]\nTotal progress: 2it [00:00, 2.32it/s]\u001b[A\n 13%|█▎ | 2/15 [00:00<00:02, 5.06it/s]\nTotal progress: 3it [00:01, 3.22it/s]\u001b[A\n 20%|██ | 3/15 [00:00<00:02, 5.42it/s]\nTotal progress: 4it [00:01, 3.92it/s]\u001b[A\n 27%|██▋ | 4/15 [00:00<00:01, 5.60it/s]\nTotal progress: 5it [00:01, 4.46it/s]\u001b[A\n 33%|███▎ | 5/15 [00:00<00:01, 5.70it/s]\nTotal progress: 6it [00:01, 4.86it/s]\u001b[A\n 40%|████ | 6/15 [00:01<00:01, 5.77it/s]\nTotal progress: 7it [00:01, 5.16it/s]\u001b[A\n 47%|████▋ | 7/15 [00:01<00:01, 5.81it/s]\nTotal progress: 8it [00:01, 5.37it/s]\u001b[A\n 53%|█████▎ | 8/15 [00:01<00:01, 5.84it/s]\nTotal progress: 9it [00:02, 5.52it/s]\u001b[A\n 60%|██████ | 9/15 [00:01<00:01, 5.85it/s]\nTotal progress: 10it [00:02, 5.63it/s]\u001b[A\n 67%|██████▋ | 10/15 [00:01<00:00, 5.86it/s]\nTotal progress: 11it [00:02, 5.71it/s]\u001b[A\n 73%|███████▎ | 11/15 [00:01<00:00, 5.87it/s]\nTotal progress: 12it [00:02, 5.76it/s]\u001b[A\n 80%|████████ | 12/15 [00:02<00:00, 5.88it/s]\nTotal progress: 13it [00:02, 5.81it/s]\u001b[A\n 87%|████████▋ | 13/15 [00:02<00:00, 5.89it/s]\nTotal progress: 14it [00:03, 5.84it/s]\u001b[A\n 93%|█████████▎| 14/15 [00:02<00:00, 5.89it/s]\n100%|██████████| 15/15 [00:02<00:00, 5.89it/s]\n100%|██████████| 15/15 [00:02<00:00, 5.74it/s]\nTotal progress: 15it [00:03, 5.85it/s]\u001b[ATile 1/25\nTile 2/25\nTile 3/25\nTile 4/25\nTile 5/25\nTile 6/25\nTile 7/25\nTile 8/25\nTile 9/25\nTile 10/25\nTile 11/25\nTile 12/25\nTile 13/25\nTile 14/25\nTile 15/25\nTile 16/25\nTile 17/25\nTile 18/25\nTile 19/25\nTile 20/25\nTile 21/25\nTile 22/25\nTile 23/25\nTile 24/25\nTile 25/25\nTile 1/25\nTile 2/25\nTile 3/25\nTile 4/25\nTile 5/25\nTile 6/25\nTile 7/25\nTile 8/25\nTile 9/25\nTile 10/25\nTile 11/25\nTile 12/25\nTile 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[00:03<00:05, 1.15s/it]\nTotal progress: 19it [00:12, 1.42s/it]\u001b[A\n 50%|█████ | 4/8 [00:04<00:04, 1.15s/it]\nTotal progress: 20it [00:13, 1.34s/it]\u001b[A\n 62%|██████▎ | 5/8 [00:05<00:03, 1.15s/it]\nTotal progress: 21it [00:14, 1.29s/it]\u001b[A\n 75%|███████▌ | 6/8 [00:06<00:02, 1.16s/it]\nTotal progress: 22it [00:16, 1.25s/it]\u001b[A\n 88%|████████▊ | 7/8 [00:08<00:01, 1.16s/it]\n100%|██████████| 8/8 [00:09<00:00, 1.16s/it]\n100%|██████████| 8/8 [00:09<00:00, 1.16s/it]\nTotal progress: 23it [00:17, 1.22s/it]\u001b[A", "metrics": { "predict_time": 27.555119, "total_time": 325.231825 }, "output": [ "https://replicate.delivery/pbxt/iFn9MIgCESagBZRGdFVgf12YzYfcF99VUs6cUkf7XghSxRNkA/1703730213_0.png", "https://replicate.delivery/pbxt/FkWNrmi5l4ZAJFTSRkUHMhAqSW4FKSQtE3NF6BeJLdaVcUDJA/1703730213_1.png", "https://replicate.delivery/pbxt/i8I5us1AlQ69PFT7gRi5eWeD21386C0vxpPEy8ooe9bVxRNkA/1703730213_2.png", "https://replicate.delivery/pbxt/q0cLoUVqGiLJIpYv1NuLhwPCoo97Y9GvkVcuqK7uZHyKOqhE/1703730213_3.png" ], "started_at": "2023-12-28T02:23:12.296152Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4dn2julbjkhmib5trn755xiz4m", "cancel": "https://api.replicate.com/v1/predictions/4dn2julbjkhmib5trn755xiz4m/cancel" }, "version": "181fdde502e52ce3df9c8e6626b549019e4d81a13971a5ea93874898ba4b3e32" }Generated inChecking model and vae... Previous model: xxmix.safetensors Updating the model... Loading weights [1cb9f9c58a] from cache Loading VAE weights specified in settings: cached ema_original.pt Applying xformers cross attention optimization. Weights loaded in 0.6s (apply weights to model: 0.2s, move model to device: 0.3s). Previous vae: ema_original.pt VAE is already up-to-date. Done, time used: 1.7655346393585205 seconds. Total progress: 0it [00:00, ?it/s] 0%| | 0/15 [00:00<?, ?it/s] Total progress: 1it [00:00, 1.24it/s] 7%|▋ | 1/15 [00:00<00:03, 4.21it/s] Total progress: 2it [00:00, 2.32it/s] 13%|█▎ | 2/15 [00:00<00:02, 5.06it/s] Total progress: 3it [00:01, 3.22it/s] 20%|██ | 3/15 [00:00<00:02, 5.42it/s] Total progress: 4it [00:01, 3.92it/s] 27%|██▋ | 4/15 [00:00<00:01, 5.60it/s] Total progress: 5it [00:01, 4.46it/s] 33%|███▎ | 5/15 [00:00<00:01, 5.70it/s] Total progress: 6it [00:01, 4.86it/s] 40%|████ | 6/15 [00:01<00:01, 5.77it/s] Total progress: 7it [00:01, 5.16it/s] 47%|████▋ | 7/15 [00:01<00:01, 5.81it/s] Total progress: 8it [00:01, 5.37it/s] 53%|█████▎ | 8/15 [00:01<00:01, 5.84it/s] Total progress: 9it [00:02, 5.52it/s] 60%|██████ | 9/15 [00:01<00:01, 5.85it/s] Total progress: 10it [00:02, 5.63it/s] 67%|██████▋ | 10/15 [00:01<00:00, 5.86it/s] Total progress: 11it [00:02, 5.71it/s] 73%|███████▎ | 11/15 [00:01<00:00, 5.87it/s] Total progress: 12it [00:02, 5.76it/s] 80%|████████ | 12/15 [00:02<00:00, 5.88it/s] Total progress: 13it [00:02, 5.81it/s] 87%|████████▋ | 13/15 [00:02<00:00, 5.89it/s] Total progress: 14it [00:03, 5.84it/s] 93%|█████████▎| 14/15 [00:02<00:00, 5.89it/s] 100%|██████████| 15/15 [00:02<00:00, 5.89it/s] 100%|██████████| 15/15 [00:02<00:00, 5.74it/s] Total progress: 15it [00:03, 5.85it/s]Tile 1/25 Tile 2/25 Tile 3/25 Tile 4/25 Tile 5/25 Tile 6/25 Tile 7/25 Tile 8/25 Tile 9/25 Tile 10/25 Tile 11/25 Tile 12/25 Tile 13/25 Tile 14/25 Tile 15/25 Tile 16/25 Tile 17/25 Tile 18/25 Tile 19/25 Tile 20/25 Tile 21/25 Tile 22/25 Tile 23/25 Tile 24/25 Tile 25/25 Tile 1/25 Tile 2/25 Tile 3/25 Tile 4/25 Tile 5/25 Tile 6/25 Tile 7/25 Tile 8/25 Tile 9/25 Tile 10/25 Tile 11/25 Tile 12/25 Tile 13/25 Tile 14/25 Tile 15/25 Tile 16/25 Tile 17/25 Tile 18/25 Tile 19/25 Tile 20/25 Tile 21/25 Tile 22/25 Tile 23/25 Tile 24/25 Tile 25/25 Tile 1/25 Tile 2/25 Tile 3/25 Tile 4/25 Tile 5/25 Tile 6/25 Tile 7/25 Tile 8/25 Tile 9/25 Tile 10/25 Tile 11/25 Tile 12/25 Tile 13/25 Tile 14/25 Tile 15/25 Tile 16/25 Tile 17/25 Tile 18/25 Tile 19/25 Tile 20/25 Tile 21/25 Tile 22/25 Tile 23/25 Tile 24/25 Tile 25/25 Tile 1/25 Tile 2/25 Tile 3/25 Tile 4/25 Tile 5/25 Tile 6/25 Tile 7/25 Tile 8/25 Tile 9/25 Tile 10/25 Tile 11/25 Tile 12/25 Tile 13/25 Tile 14/25 Tile 15/25 Tile 16/25 Tile 17/25 Tile 18/25 Tile 19/25 Tile 20/25 Tile 21/25 Tile 22/25 Tile 23/25 Tile 24/25 Tile 25/25 0%| | 0/8 [00:00<?, ?it/s] Total progress: 16it [00:09, 1.93s/it] 12%|█▎ | 1/8 [00:01<00:08, 1.16s/it] Total progress: 17it [00:10, 1.70s/it] 25%|██▌ | 2/8 [00:02<00:06, 1.15s/it] Total progress: 18it [00:11, 1.53s/it] 38%|███▊ | 3/8 [00:03<00:05, 1.15s/it] Total progress: 19it [00:12, 1.42s/it] 50%|█████ | 4/8 [00:04<00:04, 1.15s/it] Total progress: 20it [00:13, 1.34s/it] 62%|██████▎ | 5/8 [00:05<00:03, 1.15s/it] Total progress: 21it [00:14, 1.29s/it] 75%|███████▌ | 6/8 [00:06<00:02, 1.16s/it] Total progress: 22it [00:16, 1.25s/it] 88%|████████▊ | 7/8 [00:08<00:01, 1.16s/it] 100%|██████████| 8/8 [00:09<00:00, 1.16s/it] 100%|██████████| 8/8 [00:09<00:00, 1.16s/it] Total progress: 23it [00:17, 1.22s/it]
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