nightmareai
/
majesty-diffusion
Generate images from text using CLIP guided latent diffusion
- Public
- 8.3K runs
-
A100 (80GB)
- GitHub
Prediction
nightmareai/majesty-diffusion:76f01b26Input
- model
- finetuned
- width
- "640"
- height
- "896"
- clip_scale
- 4000
- init_scale
- 1000
- clip_prompts
- The portrait of a Majestic Princess, trending on artstation
- latent_scale
- 5
- output_steps
- 10
- latent_prompt
- The portrait of a Majestic Princess, trending on artstation
- custom_settings
{ "model": "finetuned", "width": "640", "height": "896", "clip_scale": 4000, "init_scale": 1000, "clip_prompts": "The portrait of a Majestic Princess, trending on artstation\n", "latent_scale": 5, "output_steps": 10, "latent_prompt": "The portrait of a Majestic Princess, trending on artstation", "custom_settings": "\n" }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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", { input: { model: "finetuned", width: "640", height: "896", clip_scale: 4000, init_scale: 1000, clip_prompts: "The portrait of a Majestic Princess, trending on artstation\n", latent_scale: 5, output_steps: 10, latent_prompt: "The portrait of a Majestic Princess, trending on artstation", custom_settings: "\n" } } ); 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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", input={ "model": "finetuned", "width": "640", "height": "896", "clip_scale": 4000, "init_scale": 1000, "clip_prompts": "The portrait of a Majestic Princess, trending on artstation\n", "latent_scale": 5, "output_steps": 10, "latent_prompt": "The portrait of a Majestic Princess, trending on artstation", "custom_settings": "\n" } ) # The nightmareai/majesty-diffusion model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/nightmareai/majesty-diffusion/api#output-schema print(item)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 nightmareai/majesty-diffusion 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": "76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", "input": { "model": "finetuned", "width": "640", "height": "896", "clip_scale": 4000, "init_scale": 1000, "clip_prompts": "The portrait of a Majestic Princess, trending on artstation\\n", "latent_scale": 5, "output_steps": 10, "latent_prompt": "The portrait of a Majestic Princess, trending on artstation", "custom_settings": "\\n" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-26T13:32:26.837418Z", "created_at": "2022-07-26T13:28:57.746556Z", "data_removed": false, "error": null, "id": "jtb5lks5rrbdnglds7l27bkrjy", "input": { "model": "finetuned", "width": "640", "height": "896", "clip_scale": 4000, "init_scale": 1000, "clip_prompts": "The portrait of a Majestic Princess, trending on artstation\n", "latent_scale": 5, "output_steps": 10, "latent_prompt": "The portrait of a Majestic Princess, trending on artstation", "custom_settings": "\n" }, "logs": "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nSampling images 1/1\n\nData shape for DDIM sampling is (1, 4, 56, 40), eta 1.3\nRunning DDIM Sampling with 119 timesteps\nSampling: 0%| | 0/1 [00:00<?, ?it/s]\n\nDDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%| | 1/119 [00:05<10:39, 5.42s/it]\u001b[A\n\nDDIM Sampler: 2%|▏ | 2/119 [00:06<05:10, 2.66s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 3/119 [00:06<03:24, 1.76s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 4/119 [00:07<02:33, 1.34s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 5/119 [00:08<02:05, 1.10s/it]\u001b[A\n\nDDIM Sampler: 5%|▌ | 6/119 [00:08<01:48, 1.05it/s]\u001b[A\n\nDDIM Sampler: 6%|▌ | 7/119 [00:09<01:34, 1.19it/s]\u001b[A\n\nDDIM Sampler: 7%|▋ | 8/119 [00:10<01:27, 1.27it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 9/119 [00:10<01:22, 1.33it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 10/119 [00:11<01:19, 1.38it/s]\u001b[A\n\nDDIM Sampler: 9%|▉ | 11/119 [00:12<01:16, 1.41it/s]\u001b[A\n\nDDIM Sampler: 10%|█ | 12/119 [00:12<01:13, 1.45it/s]\u001b[A\n\nDDIM Sampler: 11%|█ | 13/119 [00:13<01:12, 1.46it/s]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 14/119 [00:14<01:10, 1.48it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 15/119 [00:14<01:10, 1.49it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 16/119 [00:15<01:09, 1.49it/s]\u001b[A\n\nDDIM Sampler: 14%|█▍ | 17/119 [00:16<01:08, 1.50it/s]\u001b[A\n\nDDIM Sampler: 15%|█▌ | 18/119 [00:16<01:07, 1.51it/s]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 19/119 [00:17<01:06, 1.51it/s]\u001b[A\n\nDDIM Sampler: 17%|█▋ | 20/119 [00:18<01:05, 1.50it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 21/119 [00:18<01:04, 1.51it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 22/119 [00:19<01:04, 1.50it/s]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 23/119 [00:20<01:03, 1.50it/s]\u001b[A\n\nDDIM Sampler: 20%|██ | 24/119 [00:20<01:03, 1.50it/s]\u001b[A\n\nDDIM Sampler: 21%|██ | 25/119 [00:21<01:02, 1.50it/s]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 26/119 [00:22<01:01, 1.51it/s]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 27/119 [00:22<01:01, 1.50it/s]\u001b[A\n\nDDIM Sampler: 24%|██▎ | 28/119 [00:23<01:00, 1.50it/s]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 29/119 [00:24<00:59, 1.51it/s]\u001b[A\n\nDDIM Sampler: 25%|██▌ | 30/119 [00:24<00:58, 1.51it/s]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 31/119 [00:25<00:58, 1.50it/s]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 32/119 [00:26<00:57, 1.51it/s]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 33/119 [00:26<00:57, 1.50it/s]\u001b[A\n\nDDIM Sampler: 29%|██▊ | 34/119 [00:27<00:56, 1.51it/s]\u001b[A\n\nDDIM Sampler: 29%|██▉ | 35/119 [00:28<00:55, 1.51it/s]\u001b[A\n\nDDIM Sampler: 30%|███ | 36/119 [00:28<00:55, 1.51it/s]\u001b[A\n\nDDIM Sampler: 31%|███ | 37/119 [00:29<00:54, 1.50it/s]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 38/119 [00:30<00:54, 1.50it/s]\u001b[A\n\nDDIM Sampler: 33%|███▎ | 39/119 [00:30<00:53, 1.49it/s]\u001b[A\n\nDDIM Sampler: 34%|███▎ | 40/119 [00:31<00:52, 1.50it/s]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 41/119 [00:32<00:52, 1.50it/s]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 42/119 [00:32<00:51, 1.49it/s]\u001b[A\n\nDDIM Sampler: 36%|███▌ | 43/119 [00:33<00:51, 1.48it/s]\u001b[A\n\nDDIM Sampler: 37%|███▋ | 44/119 [00:34<00:50, 1.49it/s]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 45/119 [00:34<00:49, 1.49it/s]\u001b[A\n\nDDIM Sampler: 39%|███▊ | 46/119 [00:35<00:48, 1.49it/s]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 47/119 [00:36<00:48, 1.49it/s]\u001b[A\n\nDDIM Sampler: 40%|████ | 48/119 [00:36<00:47, 1.49it/s]\u001b[A\n\nDDIM Sampler: 41%|████ | 49/119 [00:37<00:46, 1.49it/s]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 50/119 [00:38<00:46, 1.49it/s]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 51/119 [00:38<00:45, 1.49it/s]\u001b[A\n\nDDIM Sampler: 44%|████▎ | 52/119 [00:39<00:44, 1.49it/s]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 53/119 [00:40<00:44, 1.49it/s]\u001b[A\n\nDDIM Sampler: 45%|████▌ | 54/119 [00:40<00:43, 1.49it/s]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 55/119 [00:41<00:42, 1.50it/s]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 56/119 [00:42<00:42, 1.49it/s]\u001b[A\n\nDDIM Sampler: 48%|████▊ | 57/119 [00:42<00:41, 1.49it/s]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 58/119 [00:43<00:41, 1.48it/s]\u001b[A\n\nDDIM Sampler: 50%|████▉ | 59/119 [00:44<00:40, 1.48it/s]\u001b[A\n\nDDIM Sampler: 50%|█████ | 60/119 [00:44<00:39, 1.48it/s]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 61/119 [00:45<00:39, 1.49it/s]\u001b[A\n\nDDIM Sampler: 52%|█████▏ | 62/119 [00:46<00:38, 1.49it/s]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 63/119 [00:46<00:38, 1.47it/s]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 64/119 [00:47<00:38, 1.44it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▍ | 65/119 [00:48<00:37, 1.44it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 66/119 [00:49<00:36, 1.44it/s]\u001b[A\n\nDDIM Sampler: 56%|█████▋ | 67/119 [00:49<00:36, 1.44it/s]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 68/119 [00:50<00:35, 1.42it/s]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 69/119 [00:51<00:35, 1.42it/s]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 70/119 [00:51<00:34, 1.42it/s]\u001b[A\n\nDDIM Sampler: 60%|█████▉ | 71/119 [00:52<00:33, 1.42it/s]\u001b[A\n\nDDIM Sampler: 61%|██████ | 72/119 [00:53<00:33, 1.42it/s]\u001b[A\n\nDDIM Sampler: 61%|██████▏ | 73/119 [00:53<00:32, 1.42it/s]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 74/119 [00:54<00:31, 1.43it/s]\u001b[A\n\nDDIM Sampler: 63%|██████▎ | 75/119 [00:55<00:30, 1.43it/s]\u001b[A\n\nDDIM Sampler: 64%|██████▍ | 76/119 [00:56<00:30, 1.42it/s]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 77/119 [00:56<00:29, 1.42it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 78/119 [00:57<00:28, 1.43it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▋ | 79/119 [00:58<00:27, 1.43it/s]\u001b[A\n\nDDIM Sampler: 67%|██████▋ | 80/119 [00:58<00:27, 1.43it/s]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 81/119 [00:59<00:26, 1.44it/s]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 82/119 [01:00<00:25, 1.44it/s]\u001b[A\n\nDDIM Sampler: 70%|██████▉ | 83/119 [01:00<00:25, 1.44it/s]\u001b[A\n\nDDIM Sampler: 71%|███████ | 84/119 [01:01<00:24, 1.44it/s]\u001b[A\n\nDDIM Sampler: 71%|███████▏ | 85/119 [01:02<00:23, 1.44it/s]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 86/119 [01:03<00:22, 1.44it/s]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 87/119 [01:03<00:22, 1.43it/s]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 88/119 [01:04<00:21, 1.44it/s]\u001b[A\n\nDDIM Sampler: 75%|███████▍ | 89/119 [01:05<00:20, 1.44it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 90/119 [01:05<00:20, 1.43it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▋ | 91/119 [01:06<00:19, 1.43it/s]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 92/119 [01:07<00:18, 1.43it/s]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 93/119 [01:07<00:18, 1.43it/s]\u001b[A\n\nDDIM Sampler: 79%|███████▉ | 94/119 [01:08<00:17, 1.43it/s]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 95/119 [01:09<00:16, 1.43it/s]\u001b[A\n\nDDIM Sampler: 81%|████████ | 96/119 [01:09<00:15, 1.44it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 97/119 [01:10<00:15, 1.44it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 98/119 [01:11<00:14, 1.44it/s]\u001b[A\n\nDDIM Sampler: 83%|████████▎ | 99/119 [01:12<00:13, 1.44it/s]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 100/119 [01:12<00:13, 1.44it/s]\u001b[A\n\nDDIM Sampler: 85%|████████▍ | 101/119 [01:13<00:12, 1.44it/s]\u001b[A\n\nDDIM Sampler: 86%|████████▌ | 102/119 [01:14<00:11, 1.44it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 103/119 [01:14<00:11, 1.44it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 104/119 [01:15<00:10, 1.44it/s]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 105/119 [01:16<00:09, 1.44it/s]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 106/119 [01:16<00:09, 1.44it/s]\u001b[A\n\nDDIM Sampler: 90%|████████▉ | 107/119 [01:17<00:08, 1.45it/s]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 108/119 [01:18<00:07, 1.44it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 109/119 [01:19<00:06, 1.44it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 110/119 [01:19<00:06, 1.44it/s]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 111/119 [01:20<00:05, 1.45it/s]\u001b[A\n\nDDIM Sampler: 94%|█████████▍| 112/119 [01:21<00:04, 1.45it/s]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 113/119 [01:21<00:04, 1.45it/s]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 114/119 [01:22<00:03, 1.45it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 115/119 [01:23<00:02, 1.44it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 116/119 [01:23<00:02, 1.44it/s]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 117/119 [01:24<00:01, 1.44it/s]\u001b[A\n\nDDIM Sampler: 99%|█████████▉| 118/119 [01:25<00:00, 1.44it/s]\u001b[A\n\nDDIM Sampler: 100%|██████████| 119/119 [01:25<00:00, 1.43it/s]\u001b[A\nDDIM Sampler: 100%|██████████| 119/119 [01:25<00:00, 1.38it/s]\npython inference_gfpgan.py -i /tmp/tmp7a1octkkgfpgan/temp_1658842225.png -o results -v 1.3 -s 2\n\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nProcessing temp_1658842225.png ...\n\tTile 1/2\n\tTile 2/2\nResults are in the [results] folder.\nData shape for DDIM sampling is (1, 4, 56, 40), eta 1.1\nRunning DDIM Sampling with 74 timesteps\n\n\nDDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%|▏ | 1/74 [00:10<12:17, 10.10s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 2/74 [00:11<05:52, 4.89s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 3/74 [00:12<03:48, 3.22s/it]\u001b[A\n\nDDIM Sampler: 5%|▌ | 4/74 [00:13<02:50, 2.44s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 5/74 [00:15<02:18, 2.00s/it]\u001b[A\n\nDDIM Sampler: 8%|▊ | 6/74 [00:16<01:58, 1.74s/it]\u001b[A\n\nDDIM Sampler: 9%|▉ | 7/74 [00:17<01:46, 1.59s/it]\u001b[A\n\nDDIM Sampler: 11%|█ | 8/74 [00:18<01:37, 1.48s/it]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 9/74 [00:20<01:31, 1.41s/it]\u001b[A\n\nDDIM Sampler: 14%|█▎ | 10/74 [00:21<01:26, 1.36s/it]\u001b[A\n\nDDIM Sampler: 15%|█▍ | 11/74 [00:22<01:23, 1.32s/it]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 12/74 [00:23<01:21, 1.31s/it]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 13/74 [00:25<01:19, 1.30s/it]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 14/74 [00:26<01:17, 1.29s/it]\u001b[A\n\nDDIM Sampler: 20%|██ | 15/74 [00:27<01:15, 1.28s/it]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 16/74 [00:28<01:14, 1.28s/it]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 17/74 [00:30<01:12, 1.28s/it]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 18/74 [00:31<01:11, 1.27s/it]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 19/74 [00:32<01:10, 1.28s/it]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 20/74 [00:33<01:08, 1.27s/it]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 21/74 [00:35<01:07, 1.27s/it]\u001b[A\n\nDDIM Sampler: 30%|██▉ | 22/74 [00:36<01:06, 1.28s/it]\u001b[A\n\nDDIM Sampler: 31%|███ | 23/74 [00:37<01:05, 1.28s/it]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 24/74 [00:39<01:04, 1.28s/it]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 25/74 [00:40<01:03, 1.29s/it]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 26/74 [00:41<01:01, 1.29s/it]\u001b[A\n\nDDIM Sampler: 36%|███▋ | 27/74 [00:42<01:00, 1.29s/it]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 28/74 [00:44<00:59, 1.29s/it]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 29/74 [00:45<00:57, 1.29s/it]\u001b[A\n\nDDIM Sampler: 41%|████ | 30/74 [00:46<00:56, 1.29s/it]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 31/74 [00:48<00:55, 1.29s/it]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 32/74 [00:49<00:54, 1.29s/it]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 33/74 [00:50<00:52, 1.29s/it]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 34/74 [00:52<00:51, 1.30s/it]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 35/74 [00:53<00:50, 1.30s/it]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 36/74 [00:54<00:49, 1.30s/it]\u001b[A\n\nDDIM Sampler: 50%|█████ | 37/74 [00:55<00:48, 1.30s/it]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 38/74 [00:57<00:46, 1.30s/it]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 39/74 [00:58<00:45, 1.30s/it]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 40/74 [00:59<00:44, 1.30s/it]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 41/74 [01:01<00:42, 1.30s/it]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 42/74 [01:02<00:41, 1.30s/it]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 43/74 [01:03<00:40, 1.30s/it]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 44/74 [01:05<00:39, 1.30s/it]\u001b[A\n\nDDIM Sampler: 61%|██████ | 45/74 [01:06<00:37, 1.31s/it]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 46/74 [01:07<00:36, 1.31s/it]\u001b[A\n\nDDIM Sampler: 64%|██████▎ | 47/74 [01:08<00:35, 1.31s/it]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 48/74 [01:10<00:33, 1.31s/it]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 49/74 [01:11<00:32, 1.31s/it]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 50/74 [01:12<00:31, 1.31s/it]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 51/74 [01:14<00:30, 1.31s/it]\u001b[A\n\nDDIM Sampler: 70%|███████ | 52/74 [01:15<00:28, 1.31s/it]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 53/74 [01:16<00:27, 1.31s/it]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 54/74 [01:18<00:26, 1.32s/it]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 55/74 [01:19<00:24, 1.31s/it]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 56/74 [01:20<00:23, 1.32s/it]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 57/74 [01:22<00:22, 1.32s/it]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 58/74 [01:23<00:21, 1.32s/it]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 59/74 [01:24<00:19, 1.32s/it]\u001b[A\n\nDDIM Sampler: 81%|████████ | 60/74 [01:26<00:18, 1.32s/it]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 61/74 [01:27<00:17, 1.32s/it]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 62/74 [01:28<00:15, 1.32s/it]\u001b[A\n\nDDIM Sampler: 85%|████████▌ | 63/74 [01:30<00:14, 1.32s/it]\u001b[A\n\nDDIM Sampler: 86%|████████▋ | 64/74 [01:31<00:13, 1.32s/it]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 65/74 [01:32<00:11, 1.32s/it]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 66/74 [01:34<00:10, 1.32s/it]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 67/74 [01:35<00:09, 1.32s/it]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 68/74 [01:36<00:07, 1.31s/it]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 69/74 [01:37<00:06, 1.31s/it]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 70/74 [01:39<00:05, 1.31s/it]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 71/74 [01:40<00:03, 1.31s/it]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 72/74 [01:41<00:02, 1.32s/it]\u001b[A\n\nDDIM Sampler: 99%|█████████▊| 73/74 [01:43<00:01, 1.31s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 74/74 [01:44<00:00, 1.32s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 74/74 [01:44<00:00, 1.41s/it]\n\nSampling: 100%|██████████| 1/1 [03:27<00:00, 207.93s/it]\nSampling: 100%|██████████| 1/1 [03:27<00:00, 207.93s/it]", "metrics": { "predict_time": 208.923517, "total_time": 209.090862 }, "output": [ "https://replicate.delivery/mgxm/378b409d-1dce-4972-8bf1-834f68d7b6e9/0.png", "https://replicate.delivery/mgxm/79c7e4b0-0002-41cf-a842-ca2ec48a4276/10.png", "https://replicate.delivery/mgxm/75f3c13b-c020-43b3-b176-8e17e83628b8/20.png", "https://replicate.delivery/mgxm/7426ca01-3d23-4a4c-820d-8e05472bc2f2/30.png", "https://replicate.delivery/mgxm/fa173ee2-df7c-4de4-ba0e-faa33b0419b2/40.png", "https://replicate.delivery/mgxm/7f781feb-5730-421a-b794-a35147b4f2a8/50.png", "https://replicate.delivery/mgxm/f618dccd-05b8-4aa2-a82e-9aec5ba9ccb2/60.png", "https://replicate.delivery/mgxm/1f6cc666-1800-4a75-96f6-0f8b3d19eb20/70.png", "https://replicate.delivery/mgxm/269aa8b8-fa87-44a8-b2a2-467507e5afff/80.png", "https://replicate.delivery/mgxm/d68b9360-cc51-4ccc-b92c-fc2d0f8378c0/90.png", "https://replicate.delivery/mgxm/2ceffeb5-d127-4747-88d8-e9a950e84e96/100.png", "https://replicate.delivery/mgxm/7c2e1b88-7e6d-402e-b2ee-2fb8f6a80223/110.png", "https://replicate.delivery/mgxm/a6686201-38a1-4a67-8222-1bb636c64e4f/120.png", "https://replicate.delivery/mgxm/2d7092ce-c965-4b59-80ed-07a915d5645c/130.png", "https://replicate.delivery/mgxm/8107cb00-d16f-400c-b7f9-c3a75a2dc2b3/140.png", "https://replicate.delivery/mgxm/42ac2aef-9de6-4212-a8ec-44d3daf5bd5c/150.png", "https://replicate.delivery/mgxm/6932370f-3be5-4e5d-a487-48bcf351b832/160.png", "https://replicate.delivery/mgxm/0705323f-7250-413a-beb5-12e2b52aa11f/170.png", "https://replicate.delivery/mgxm/be5f1644-c4f7-4c77-a219-ac8d0bd820d2/180.png", "https://replicate.delivery/mgxm/f38fda6d-79af-4532-9cf6-f779233c0ff7/190.png", "https://replicate.delivery/mgxm/2acf5067-4f9b-4d23-a888-19b5a74b2f81/1658842342.png" ], "started_at": "2022-07-26T13:28:57.913901Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jtb5lks5rrbdnglds7l27bkrjy", "cancel": "https://api.replicate.com/v1/predictions/jtb5lks5rrbdnglds7l27bkrjy/cancel" }, "version": "3522493255734d9ff6ba0ff4325a37c85ea2ae73dbc6c2d9817f700e229b23fd" }Generated inhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Sampling images 1/1 Data shape for DDIM sampling is (1, 4, 56, 40), eta 1.3 Running DDIM Sampling with 119 timesteps Sampling: 0%| | 0/1 [00:00<?, ?it/s] DDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s] DDIM Sampler: 1%| | 1/119 [00:05<10:39, 5.42s/it] DDIM Sampler: 2%|▏ | 2/119 [00:06<05:10, 2.66s/it] DDIM Sampler: 3%|▎ | 3/119 [00:06<03:24, 1.76s/it] DDIM Sampler: 3%|▎ | 4/119 [00:07<02:33, 1.34s/it] DDIM Sampler: 4%|▍ | 5/119 [00:08<02:05, 1.10s/it] DDIM Sampler: 5%|▌ | 6/119 [00:08<01:48, 1.05it/s] DDIM Sampler: 6%|▌ | 7/119 [00:09<01:34, 1.19it/s] DDIM Sampler: 7%|▋ | 8/119 [00:10<01:27, 1.27it/s] DDIM Sampler: 8%|▊ | 9/119 [00:10<01:22, 1.33it/s] DDIM Sampler: 8%|▊ | 10/119 [00:11<01:19, 1.38it/s] DDIM Sampler: 9%|▉ | 11/119 [00:12<01:16, 1.41it/s] DDIM Sampler: 10%|█ | 12/119 [00:12<01:13, 1.45it/s] DDIM Sampler: 11%|█ | 13/119 [00:13<01:12, 1.46it/s] DDIM Sampler: 12%|█▏ | 14/119 [00:14<01:10, 1.48it/s] DDIM Sampler: 13%|█▎ | 15/119 [00:14<01:10, 1.49it/s] DDIM Sampler: 13%|█▎ | 16/119 [00:15<01:09, 1.49it/s] DDIM Sampler: 14%|█▍ | 17/119 [00:16<01:08, 1.50it/s] DDIM Sampler: 15%|█▌ | 18/119 [00:16<01:07, 1.51it/s] DDIM Sampler: 16%|█▌ | 19/119 [00:17<01:06, 1.51it/s] DDIM Sampler: 17%|█▋ | 20/119 [00:18<01:05, 1.50it/s] DDIM Sampler: 18%|█▊ | 21/119 [00:18<01:04, 1.51it/s] DDIM Sampler: 18%|█▊ | 22/119 [00:19<01:04, 1.50it/s] DDIM Sampler: 19%|█▉ | 23/119 [00:20<01:03, 1.50it/s] DDIM Sampler: 20%|██ | 24/119 [00:20<01:03, 1.50it/s] DDIM Sampler: 21%|██ | 25/119 [00:21<01:02, 1.50it/s] DDIM Sampler: 22%|██▏ | 26/119 [00:22<01:01, 1.51it/s] DDIM Sampler: 23%|██▎ | 27/119 [00:22<01:01, 1.50it/s] DDIM Sampler: 24%|██▎ | 28/119 [00:23<01:00, 1.50it/s] DDIM Sampler: 24%|██▍ | 29/119 [00:24<00:59, 1.51it/s] DDIM Sampler: 25%|██▌ | 30/119 [00:24<00:58, 1.51it/s] DDIM Sampler: 26%|██▌ | 31/119 [00:25<00:58, 1.50it/s] DDIM Sampler: 27%|██▋ | 32/119 [00:26<00:57, 1.51it/s] DDIM Sampler: 28%|██▊ | 33/119 [00:26<00:57, 1.50it/s] DDIM Sampler: 29%|██▊ | 34/119 [00:27<00:56, 1.51it/s] DDIM Sampler: 29%|██▉ | 35/119 [00:28<00:55, 1.51it/s] DDIM Sampler: 30%|███ | 36/119 [00:28<00:55, 1.51it/s] DDIM Sampler: 31%|███ | 37/119 [00:29<00:54, 1.50it/s] DDIM Sampler: 32%|███▏ | 38/119 [00:30<00:54, 1.50it/s] DDIM Sampler: 33%|███▎ | 39/119 [00:30<00:53, 1.49it/s] DDIM Sampler: 34%|███▎ | 40/119 [00:31<00:52, 1.50it/s] DDIM Sampler: 34%|███▍ | 41/119 [00:32<00:52, 1.50it/s] DDIM Sampler: 35%|███▌ | 42/119 [00:32<00:51, 1.49it/s] DDIM Sampler: 36%|███▌ | 43/119 [00:33<00:51, 1.48it/s] DDIM Sampler: 37%|███▋ | 44/119 [00:34<00:50, 1.49it/s] DDIM Sampler: 38%|███▊ | 45/119 [00:34<00:49, 1.49it/s] DDIM Sampler: 39%|███▊ | 46/119 [00:35<00:48, 1.49it/s] DDIM Sampler: 39%|███▉ | 47/119 [00:36<00:48, 1.49it/s] DDIM Sampler: 40%|████ | 48/119 [00:36<00:47, 1.49it/s] DDIM Sampler: 41%|████ | 49/119 [00:37<00:46, 1.49it/s] DDIM Sampler: 42%|████▏ | 50/119 [00:38<00:46, 1.49it/s] DDIM Sampler: 43%|████▎ | 51/119 [00:38<00:45, 1.49it/s] DDIM Sampler: 44%|████▎ | 52/119 [00:39<00:44, 1.49it/s] DDIM Sampler: 45%|████▍ | 53/119 [00:40<00:44, 1.49it/s] DDIM Sampler: 45%|████▌ | 54/119 [00:40<00:43, 1.49it/s] DDIM Sampler: 46%|████▌ | 55/119 [00:41<00:42, 1.50it/s] DDIM Sampler: 47%|████▋ | 56/119 [00:42<00:42, 1.49it/s] DDIM Sampler: 48%|████▊ | 57/119 [00:42<00:41, 1.49it/s] DDIM Sampler: 49%|████▊ | 58/119 [00:43<00:41, 1.48it/s] DDIM Sampler: 50%|████▉ | 59/119 [00:44<00:40, 1.48it/s] DDIM Sampler: 50%|█████ | 60/119 [00:44<00:39, 1.48it/s] DDIM Sampler: 51%|█████▏ | 61/119 [00:45<00:39, 1.49it/s] DDIM Sampler: 52%|█████▏ | 62/119 [00:46<00:38, 1.49it/s] DDIM Sampler: 53%|█████▎ | 63/119 [00:46<00:38, 1.47it/s] DDIM Sampler: 54%|█████▍ | 64/119 [00:47<00:38, 1.44it/s] DDIM Sampler: 55%|█████▍ | 65/119 [00:48<00:37, 1.44it/s] DDIM Sampler: 55%|█████▌ | 66/119 [00:49<00:36, 1.44it/s] DDIM Sampler: 56%|█████▋ | 67/119 [00:49<00:36, 1.44it/s] DDIM Sampler: 57%|█████▋ | 68/119 [00:50<00:35, 1.42it/s] DDIM Sampler: 58%|█████▊ | 69/119 [00:51<00:35, 1.42it/s] DDIM Sampler: 59%|█████▉ | 70/119 [00:51<00:34, 1.42it/s] DDIM Sampler: 60%|█████▉ | 71/119 [00:52<00:33, 1.42it/s] DDIM Sampler: 61%|██████ | 72/119 [00:53<00:33, 1.42it/s] DDIM Sampler: 61%|██████▏ | 73/119 [00:53<00:32, 1.42it/s] DDIM Sampler: 62%|██████▏ | 74/119 [00:54<00:31, 1.43it/s] DDIM Sampler: 63%|██████▎ | 75/119 [00:55<00:30, 1.43it/s] DDIM Sampler: 64%|██████▍ | 76/119 [00:56<00:30, 1.42it/s] DDIM Sampler: 65%|██████▍ | 77/119 [00:56<00:29, 1.42it/s] DDIM Sampler: 66%|██████▌ | 78/119 [00:57<00:28, 1.43it/s] DDIM Sampler: 66%|██████▋ | 79/119 [00:58<00:27, 1.43it/s] DDIM Sampler: 67%|██████▋ | 80/119 [00:58<00:27, 1.43it/s] DDIM Sampler: 68%|██████▊ | 81/119 [00:59<00:26, 1.44it/s] DDIM Sampler: 69%|██████▉ | 82/119 [01:00<00:25, 1.44it/s] DDIM Sampler: 70%|██████▉ | 83/119 [01:00<00:25, 1.44it/s] DDIM Sampler: 71%|███████ | 84/119 [01:01<00:24, 1.44it/s] DDIM Sampler: 71%|███████▏ | 85/119 [01:02<00:23, 1.44it/s] DDIM Sampler: 72%|███████▏ | 86/119 [01:03<00:22, 1.44it/s] DDIM Sampler: 73%|███████▎ | 87/119 [01:03<00:22, 1.43it/s] DDIM Sampler: 74%|███████▍ | 88/119 [01:04<00:21, 1.44it/s] DDIM Sampler: 75%|███████▍ | 89/119 [01:05<00:20, 1.44it/s] DDIM Sampler: 76%|███████▌ | 90/119 [01:05<00:20, 1.43it/s] DDIM Sampler: 76%|███████▋ | 91/119 [01:06<00:19, 1.43it/s] DDIM Sampler: 77%|███████▋ | 92/119 [01:07<00:18, 1.43it/s] DDIM Sampler: 78%|███████▊ | 93/119 [01:07<00:18, 1.43it/s] DDIM Sampler: 79%|███████▉ | 94/119 [01:08<00:17, 1.43it/s] DDIM Sampler: 80%|███████▉ | 95/119 [01:09<00:16, 1.43it/s] DDIM Sampler: 81%|████████ | 96/119 [01:09<00:15, 1.44it/s] DDIM Sampler: 82%|████████▏ | 97/119 [01:10<00:15, 1.44it/s] DDIM Sampler: 82%|████████▏ | 98/119 [01:11<00:14, 1.44it/s] DDIM Sampler: 83%|████████▎ | 99/119 [01:12<00:13, 1.44it/s] DDIM Sampler: 84%|████████▍ | 100/119 [01:12<00:13, 1.44it/s] DDIM Sampler: 85%|████████▍ | 101/119 [01:13<00:12, 1.44it/s] DDIM Sampler: 86%|████████▌ | 102/119 [01:14<00:11, 1.44it/s] DDIM Sampler: 87%|████████▋ | 103/119 [01:14<00:11, 1.44it/s] DDIM Sampler: 87%|████████▋ | 104/119 [01:15<00:10, 1.44it/s] DDIM Sampler: 88%|████████▊ | 105/119 [01:16<00:09, 1.44it/s] DDIM Sampler: 89%|████████▉ | 106/119 [01:16<00:09, 1.44it/s] DDIM Sampler: 90%|████████▉ | 107/119 [01:17<00:08, 1.45it/s] DDIM Sampler: 91%|█████████ | 108/119 [01:18<00:07, 1.44it/s] DDIM Sampler: 92%|█████████▏| 109/119 [01:19<00:06, 1.44it/s] DDIM Sampler: 92%|█████████▏| 110/119 [01:19<00:06, 1.44it/s] DDIM Sampler: 93%|█████████▎| 111/119 [01:20<00:05, 1.45it/s] DDIM Sampler: 94%|█████████▍| 112/119 [01:21<00:04, 1.45it/s] DDIM Sampler: 95%|█████████▍| 113/119 [01:21<00:04, 1.45it/s] DDIM Sampler: 96%|█████████▌| 114/119 [01:22<00:03, 1.45it/s] DDIM Sampler: 97%|█████████▋| 115/119 [01:23<00:02, 1.44it/s] DDIM Sampler: 97%|█████████▋| 116/119 [01:23<00:02, 1.44it/s] DDIM Sampler: 98%|█████████▊| 117/119 [01:24<00:01, 1.44it/s] DDIM Sampler: 99%|█████████▉| 118/119 [01:25<00:00, 1.44it/s] DDIM Sampler: 100%|██████████| 119/119 [01:25<00:00, 1.43it/s] DDIM Sampler: 100%|██████████| 119/119 [01:25<00:00, 1.38it/s] python inference_gfpgan.py -i /tmp/tmp7a1octkkgfpgan/temp_1658842225.png -o results -v 1.3 -s 2 huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Processing temp_1658842225.png ... Tile 1/2 Tile 2/2 Results are in the [results] folder. Data shape for DDIM sampling is (1, 4, 56, 40), eta 1.1 Running DDIM Sampling with 74 timesteps DDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s] DDIM Sampler: 1%|▏ | 1/74 [00:10<12:17, 10.10s/it] DDIM Sampler: 3%|▎ | 2/74 [00:11<05:52, 4.89s/it] DDIM Sampler: 4%|▍ | 3/74 [00:12<03:48, 3.22s/it] DDIM Sampler: 5%|▌ | 4/74 [00:13<02:50, 2.44s/it] DDIM Sampler: 7%|▋ | 5/74 [00:15<02:18, 2.00s/it] DDIM Sampler: 8%|▊ | 6/74 [00:16<01:58, 1.74s/it] DDIM Sampler: 9%|▉ | 7/74 [00:17<01:46, 1.59s/it] DDIM Sampler: 11%|█ | 8/74 [00:18<01:37, 1.48s/it] DDIM Sampler: 12%|█▏ | 9/74 [00:20<01:31, 1.41s/it] DDIM Sampler: 14%|█▎ | 10/74 [00:21<01:26, 1.36s/it] DDIM Sampler: 15%|█▍ | 11/74 [00:22<01:23, 1.32s/it] DDIM Sampler: 16%|█▌ | 12/74 [00:23<01:21, 1.31s/it] DDIM Sampler: 18%|█▊ | 13/74 [00:25<01:19, 1.30s/it] DDIM Sampler: 19%|█▉ | 14/74 [00:26<01:17, 1.29s/it] DDIM Sampler: 20%|██ | 15/74 [00:27<01:15, 1.28s/it] DDIM Sampler: 22%|██▏ | 16/74 [00:28<01:14, 1.28s/it] DDIM Sampler: 23%|██▎ | 17/74 [00:30<01:12, 1.28s/it] DDIM Sampler: 24%|██▍ | 18/74 [00:31<01:11, 1.27s/it] DDIM Sampler: 26%|██▌ | 19/74 [00:32<01:10, 1.28s/it] DDIM Sampler: 27%|██▋ | 20/74 [00:33<01:08, 1.27s/it] DDIM Sampler: 28%|██▊ | 21/74 [00:35<01:07, 1.27s/it] DDIM Sampler: 30%|██▉ | 22/74 [00:36<01:06, 1.28s/it] DDIM Sampler: 31%|███ | 23/74 [00:37<01:05, 1.28s/it] DDIM Sampler: 32%|███▏ | 24/74 [00:39<01:04, 1.28s/it] DDIM Sampler: 34%|███▍ | 25/74 [00:40<01:03, 1.29s/it] DDIM Sampler: 35%|███▌ | 26/74 [00:41<01:01, 1.29s/it] DDIM Sampler: 36%|███▋ | 27/74 [00:42<01:00, 1.29s/it] DDIM Sampler: 38%|███▊ | 28/74 [00:44<00:59, 1.29s/it] DDIM Sampler: 39%|███▉ | 29/74 [00:45<00:57, 1.29s/it] DDIM Sampler: 41%|████ | 30/74 [00:46<00:56, 1.29s/it] DDIM Sampler: 42%|████▏ | 31/74 [00:48<00:55, 1.29s/it] DDIM Sampler: 43%|████▎ | 32/74 [00:49<00:54, 1.29s/it] DDIM Sampler: 45%|████▍ | 33/74 [00:50<00:52, 1.29s/it] DDIM Sampler: 46%|████▌ | 34/74 [00:52<00:51, 1.30s/it] DDIM Sampler: 47%|████▋ | 35/74 [00:53<00:50, 1.30s/it] DDIM Sampler: 49%|████▊ | 36/74 [00:54<00:49, 1.30s/it] DDIM Sampler: 50%|█████ | 37/74 [00:55<00:48, 1.30s/it] DDIM Sampler: 51%|█████▏ | 38/74 [00:57<00:46, 1.30s/it] DDIM Sampler: 53%|█████▎ | 39/74 [00:58<00:45, 1.30s/it] DDIM Sampler: 54%|█████▍ | 40/74 [00:59<00:44, 1.30s/it] DDIM Sampler: 55%|█████▌ | 41/74 [01:01<00:42, 1.30s/it] DDIM Sampler: 57%|█████▋ | 42/74 [01:02<00:41, 1.30s/it] DDIM Sampler: 58%|█████▊ | 43/74 [01:03<00:40, 1.30s/it] DDIM Sampler: 59%|█████▉ | 44/74 [01:05<00:39, 1.30s/it] DDIM Sampler: 61%|██████ | 45/74 [01:06<00:37, 1.31s/it] DDIM Sampler: 62%|██████▏ | 46/74 [01:07<00:36, 1.31s/it] DDIM Sampler: 64%|██████▎ | 47/74 [01:08<00:35, 1.31s/it] DDIM Sampler: 65%|██████▍ | 48/74 [01:10<00:33, 1.31s/it] DDIM Sampler: 66%|██████▌ | 49/74 [01:11<00:32, 1.31s/it] DDIM Sampler: 68%|██████▊ | 50/74 [01:12<00:31, 1.31s/it] DDIM Sampler: 69%|██████▉ | 51/74 [01:14<00:30, 1.31s/it] DDIM Sampler: 70%|███████ | 52/74 [01:15<00:28, 1.31s/it] DDIM Sampler: 72%|███████▏ | 53/74 [01:16<00:27, 1.31s/it] DDIM Sampler: 73%|███████▎ | 54/74 [01:18<00:26, 1.32s/it] DDIM Sampler: 74%|███████▍ | 55/74 [01:19<00:24, 1.31s/it] DDIM Sampler: 76%|███████▌ | 56/74 [01:20<00:23, 1.32s/it] DDIM Sampler: 77%|███████▋ | 57/74 [01:22<00:22, 1.32s/it] DDIM Sampler: 78%|███████▊ | 58/74 [01:23<00:21, 1.32s/it] DDIM Sampler: 80%|███████▉ | 59/74 [01:24<00:19, 1.32s/it] DDIM Sampler: 81%|████████ | 60/74 [01:26<00:18, 1.32s/it] DDIM Sampler: 82%|████████▏ | 61/74 [01:27<00:17, 1.32s/it] DDIM Sampler: 84%|████████▍ | 62/74 [01:28<00:15, 1.32s/it] DDIM Sampler: 85%|████████▌ | 63/74 [01:30<00:14, 1.32s/it] DDIM Sampler: 86%|████████▋ | 64/74 [01:31<00:13, 1.32s/it] DDIM Sampler: 88%|████████▊ | 65/74 [01:32<00:11, 1.32s/it] DDIM Sampler: 89%|████████▉ | 66/74 [01:34<00:10, 1.32s/it] DDIM Sampler: 91%|█████████ | 67/74 [01:35<00:09, 1.32s/it] DDIM Sampler: 92%|█████████▏| 68/74 [01:36<00:07, 1.31s/it] DDIM Sampler: 93%|█████████▎| 69/74 [01:37<00:06, 1.31s/it] DDIM Sampler: 95%|█████████▍| 70/74 [01:39<00:05, 1.31s/it] DDIM Sampler: 96%|█████████▌| 71/74 [01:40<00:03, 1.31s/it] DDIM Sampler: 97%|█████████▋| 72/74 [01:41<00:02, 1.32s/it] DDIM Sampler: 99%|█████████▊| 73/74 [01:43<00:01, 1.31s/it] DDIM Sampler: 100%|██████████| 74/74 [01:44<00:00, 1.32s/it] DDIM Sampler: 100%|██████████| 74/74 [01:44<00:00, 1.41s/it] Sampling: 100%|██████████| 1/1 [03:27<00:00, 207.93s/it] Sampling: 100%|██████████| 1/1 [03:27<00:00, 207.93s/it]Prediction
nightmareai/majesty-diffusion:76f01b26IDwzqm4jqxondazkup763nv7eeiiStatusSucceededSourceAPIHardware–Total durationCreatedInput
- width
- 640
- height
- 768
- clip_scale
- 16000
- clip_prompts
- This image has a fairy-like feel to it with colorful water and tree leaves. The forest in the background adds a mystical feeling, making this an ideal image for relaxation purposes.
- latent_scale
- 12
- latent_prompt
- a magical pool in a forest
- aesthetic_loss_scale
- 400
{ "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "This image has a fairy-like feel to it with colorful water and tree leaves. The forest in the background adds a mystical feeling, making this an ideal image for relaxation purposes.", "latent_scale": 12, "latent_prompt": "a magical pool in a forest", "aesthetic_loss_scale": 400 }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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", { input: { width: 640, height: 768, clip_scale: 16000, clip_prompts: "This image has a fairy-like feel to it with colorful water and tree leaves. The forest in the background adds a mystical feeling, making this an ideal image for relaxation purposes.", latent_scale: 12, latent_prompt: "a magical pool in a forest", aesthetic_loss_scale: 400 } } ); 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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", input={ "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "This image has a fairy-like feel to it with colorful water and tree leaves. The forest in the background adds a mystical feeling, making this an ideal image for relaxation purposes.", "latent_scale": 12, "latent_prompt": "a magical pool in a forest", "aesthetic_loss_scale": 400 } ) # The nightmareai/majesty-diffusion model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/nightmareai/majesty-diffusion/api#output-schema print(item)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 nightmareai/majesty-diffusion 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": "76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", "input": { "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "This image has a fairy-like feel to it with colorful water and tree leaves. The forest in the background adds a mystical feeling, making this an ideal image for relaxation purposes.", "latent_scale": 12, "latent_prompt": "a magical pool in a forest", "aesthetic_loss_scale": 400 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-26T14:42:36.616736Z", "created_at": "2022-07-26T14:37:28.910951Z", "data_removed": false, "error": null, "id": "wzqm4jqxondazkup763nv7eeii", "input": { "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "This image has a fairy-like feel to it with colorful water and tree leaves. The forest in the background adds a mystical feeling, making this an ideal image for relaxation purposes.", "latent_scale": 12, "latent_prompt": "a magical pool in a forest", "aesthetic_loss_scale": 400 }, "logs": "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nSampling images 1/1\n\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.3\nRunning DDIM Sampling with 119 timesteps\nSampling: 0%| | 0/1 [00:00<?, ?it/s]\n\nDDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%| | 1/119 [00:00<01:15, 1.57it/s]\u001b[A\n\nDDIM Sampler: 2%|▏ | 2/119 [00:01<01:12, 1.60it/s]\u001b[A\n\nDDIM Sampler: 3%|▎ | 3/119 [00:01<01:11, 1.62it/s]\u001b[A\n\nDDIM Sampler: 3%|▎ | 4/119 [00:02<01:10, 1.62it/s]\u001b[A\n\nDDIM Sampler: 4%|▍ | 5/119 [00:03<01:10, 1.63it/s]\u001b[A\n\nDDIM Sampler: 5%|▌ | 6/119 [00:03<01:09, 1.62it/s]\u001b[A\n\nDDIM Sampler: 6%|▌ | 7/119 [00:04<01:08, 1.63it/s]\u001b[A\n\nDDIM Sampler: 7%|▋ | 8/119 [00:04<01:07, 1.64it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 9/119 [00:05<01:06, 1.65it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 10/119 [00:06<01:05, 1.65it/s]\u001b[A\n\nDDIM Sampler: 9%|▉ | 11/119 [00:06<01:05, 1.66it/s]\u001b[A\n\nDDIM Sampler: 10%|█ | 12/119 [00:07<01:04, 1.66it/s]\u001b[A\n\nDDIM Sampler: 11%|█ | 13/119 [00:07<01:03, 1.66it/s]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 14/119 [00:08<01:03, 1.66it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 15/119 [00:09<01:02, 1.66it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 16/119 [00:09<01:02, 1.65it/s]\u001b[A\n\nDDIM Sampler: 14%|█▍ | 17/119 [00:10<01:01, 1.66it/s]\u001b[A\n\nDDIM Sampler: 15%|█▌ | 18/119 [00:10<01:00, 1.66it/s]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 19/119 [00:11<01:00, 1.66it/s]\u001b[A\n\nDDIM Sampler: 17%|█▋ | 20/119 [00:12<00:59, 1.67it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 21/119 [00:12<00:58, 1.67it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 22/119 [00:13<00:58, 1.66it/s]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 23/119 [00:13<00:57, 1.67it/s]\u001b[A\n\nDDIM Sampler: 20%|██ | 24/119 [00:14<00:56, 1.67it/s]\u001b[A\n\nDDIM Sampler: 21%|██ | 25/119 [00:15<00:56, 1.67it/s]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 26/119 [00:15<00:55, 1.67it/s]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 27/119 [00:16<00:55, 1.67it/s]\u001b[A\n\nDDIM Sampler: 24%|██▎ | 28/119 [00:16<00:54, 1.67it/s]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 29/119 [00:17<00:53, 1.67it/s]\u001b[A\n\nDDIM Sampler: 25%|██▌ | 30/119 [00:18<00:53, 1.68it/s]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 31/119 [00:18<00:52, 1.67it/s]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 32/119 [00:19<00:52, 1.67it/s]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 33/119 [00:19<00:51, 1.67it/s]\u001b[A\n\nDDIM Sampler: 29%|██▊ | 34/119 [00:20<00:50, 1.67it/s]\u001b[A\n\nDDIM Sampler: 29%|██▉ | 35/119 [00:21<00:50, 1.67it/s]\u001b[A\n\nDDIM Sampler: 30%|███ | 36/119 [00:21<00:49, 1.67it/s]\u001b[A\n\nDDIM Sampler: 31%|███ | 37/119 [00:22<00:49, 1.66it/s]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 38/119 [00:22<00:48, 1.66it/s]\u001b[A\n\nDDIM Sampler: 33%|███▎ | 39/119 [00:23<00:47, 1.67it/s]\u001b[A\n\nDDIM Sampler: 34%|███▎ | 40/119 [00:24<00:47, 1.68it/s]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 41/119 [00:24<00:46, 1.67it/s]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 42/119 [00:25<00:45, 1.68it/s]\u001b[A\n\nDDIM Sampler: 36%|███▌ | 43/119 [00:25<00:45, 1.68it/s]\u001b[A\n\nDDIM Sampler: 37%|███▋ | 44/119 [00:26<00:44, 1.69it/s]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 45/119 [00:27<00:43, 1.68it/s]\u001b[A\n\nDDIM Sampler: 39%|███▊ | 46/119 [00:27<00:43, 1.68it/s]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 47/119 [00:28<00:42, 1.68it/s]\u001b[A\n\nDDIM Sampler: 40%|████ | 48/119 [00:28<00:42, 1.69it/s]\u001b[A\n\nDDIM Sampler: 41%|████ | 49/119 [00:29<00:41, 1.69it/s]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 50/119 [00:30<00:40, 1.69it/s]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 51/119 [00:30<00:40, 1.69it/s]\u001b[A\n\nDDIM Sampler: 44%|████▎ | 52/119 [00:31<00:39, 1.69it/s]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 53/119 [00:31<00:38, 1.70it/s]\u001b[A\n\nDDIM Sampler: 45%|████▌ | 54/119 [00:32<00:38, 1.71it/s]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 55/119 [00:32<00:37, 1.71it/s]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 56/119 [00:33<00:36, 1.71it/s]\u001b[A\n\nDDIM Sampler: 48%|████▊ | 57/119 [00:34<00:36, 1.72it/s]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 58/119 [00:34<00:35, 1.71it/s]\u001b[A\n\nDDIM Sampler: 50%|████▉ | 59/119 [00:35<00:35, 1.71it/s]\u001b[A\n\nDDIM Sampler: 50%|█████ | 60/119 [00:35<00:34, 1.71it/s]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 61/119 [00:36<00:33, 1.71it/s]\u001b[A\n\nDDIM Sampler: 52%|█████▏ | 62/119 [00:37<00:33, 1.71it/s]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 63/119 [00:37<00:33, 1.68it/s]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 64/119 [00:38<00:32, 1.67it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▍ | 65/119 [00:38<00:32, 1.64it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 66/119 [00:39<00:32, 1.64it/s]\u001b[A\n\nDDIM Sampler: 56%|█████▋ | 67/119 [00:40<00:31, 1.64it/s]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 68/119 [00:40<00:31, 1.64it/s]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 69/119 [00:41<00:30, 1.63it/s]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 70/119 [00:41<00:30, 1.63it/s]\u001b[A\n\nDDIM Sampler: 60%|█████▉ | 71/119 [00:42<00:29, 1.63it/s]\u001b[A\n\nDDIM Sampler: 61%|██████ | 72/119 [00:43<00:28, 1.63it/s]\u001b[A\n\nDDIM Sampler: 61%|██████▏ | 73/119 [00:43<00:28, 1.63it/s]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 74/119 [00:44<00:27, 1.63it/s]\u001b[A\n\nDDIM Sampler: 63%|██████▎ | 75/119 [00:45<00:26, 1.63it/s]\u001b[A\n\nDDIM Sampler: 64%|██████▍ | 76/119 [00:45<00:26, 1.64it/s]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 77/119 [00:46<00:25, 1.64it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 78/119 [00:46<00:25, 1.64it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▋ | 79/119 [00:47<00:24, 1.64it/s]\u001b[A\n\nDDIM Sampler: 67%|██████▋ | 80/119 [00:48<00:23, 1.64it/s]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 81/119 [00:48<00:23, 1.63it/s]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 82/119 [00:49<00:22, 1.63it/s]\u001b[A\n\nDDIM Sampler: 70%|██████▉ | 83/119 [00:49<00:21, 1.64it/s]\u001b[A\n\nDDIM Sampler: 71%|███████ | 84/119 [00:50<00:21, 1.64it/s]\u001b[A\n\nDDIM Sampler: 71%|███████▏ | 85/119 [00:51<00:20, 1.64it/s]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 86/119 [00:51<00:20, 1.64it/s]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 87/119 [00:52<00:19, 1.64it/s]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 88/119 [00:52<00:18, 1.64it/s]\u001b[A\n\nDDIM Sampler: 75%|███████▍ | 89/119 [00:53<00:18, 1.62it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 90/119 [00:54<00:17, 1.63it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▋ | 91/119 [00:54<00:17, 1.63it/s]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 92/119 [00:55<00:16, 1.63it/s]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 93/119 [00:56<00:15, 1.63it/s]\u001b[A\n\nDDIM Sampler: 79%|███████▉ | 94/119 [00:56<00:15, 1.64it/s]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 95/119 [00:57<00:14, 1.64it/s]\u001b[A\n\nDDIM Sampler: 81%|████████ | 96/119 [00:57<00:14, 1.64it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 97/119 [00:58<00:13, 1.64it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 98/119 [00:59<00:12, 1.63it/s]\u001b[A\n\nDDIM Sampler: 83%|████████▎ | 99/119 [00:59<00:12, 1.63it/s]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 100/119 [01:00<00:11, 1.63it/s]\u001b[A\n\nDDIM Sampler: 85%|████████▍ | 101/119 [01:00<00:11, 1.62it/s]\u001b[A\n\nDDIM Sampler: 86%|████████▌ | 102/119 [01:01<00:10, 1.62it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 103/119 [01:02<00:09, 1.62it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 104/119 [01:02<00:09, 1.62it/s]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 105/119 [01:03<00:08, 1.62it/s]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 106/119 [01:04<00:08, 1.62it/s]\u001b[A\n\nDDIM Sampler: 90%|████████▉ | 107/119 [01:04<00:07, 1.63it/s]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 108/119 [01:05<00:06, 1.62it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 109/119 [01:05<00:06, 1.62it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 110/119 [01:06<00:05, 1.63it/s]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 111/119 [01:07<00:04, 1.63it/s]\u001b[A\n\nDDIM Sampler: 94%|█████████▍| 112/119 [01:07<00:04, 1.63it/s]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 113/119 [01:08<00:03, 1.63it/s]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 114/119 [01:08<00:03, 1.63it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 115/119 [01:09<00:02, 1.64it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 116/119 [01:10<00:01, 1.64it/s]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 117/119 [01:10<00:01, 1.64it/s]\u001b[A\n\nDDIM Sampler: 99%|█████████▉| 118/119 [01:11<00:00, 1.64it/s]\u001b[A\n\nDDIM Sampler: 100%|██████████| 119/119 [01:11<00:00, 1.64it/s]\u001b[A\nDDIM Sampler: 100%|██████████| 119/119 [01:11<00:00, 1.65it/s]\npython inference_gfpgan.py -i /tmp/tmp5313lkf4gfpgan/temp_1658846460.png -o results -v 1.3 -s 2\n\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nProcessing temp_1658846460.png ...\n\tTile 1/1\nResults are in the [results] folder.\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.1\nRunning DDIM Sampling with 74 timesteps\n\n\nDDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%|▏ | 1/74 [00:01<01:20, 1.10s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 2/74 [00:02<01:18, 1.09s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 3/74 [00:03<01:17, 1.09s/it]\u001b[A\n\nDDIM Sampler: 5%|▌ | 4/74 [00:04<01:15, 1.08s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 5/74 [00:05<01:14, 1.08s/it]\u001b[A\n\nDDIM Sampler: 8%|▊ | 6/74 [00:06<01:13, 1.08s/it]\u001b[A\n\nDDIM Sampler: 9%|▉ | 7/74 [00:07<01:12, 1.08s/it]\u001b[A\n\nDDIM Sampler: 11%|█ | 8/74 [00:08<01:11, 1.08s/it]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 9/74 [00:09<01:10, 1.08s/it]\u001b[A\n\nDDIM Sampler: 14%|█▎ | 10/74 [00:10<01:09, 1.08s/it]\u001b[A\n\nDDIM Sampler: 15%|█▍ | 11/74 [00:11<01:08, 1.08s/it]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 12/74 [00:12<01:06, 1.08s/it]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 13/74 [00:14<01:06, 1.08s/it]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 14/74 [00:15<01:05, 1.09s/it]\u001b[A\n\nDDIM Sampler: 20%|██ | 15/74 [00:16<01:04, 1.09s/it]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 16/74 [00:17<01:02, 1.09s/it]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 17/74 [00:18<01:01, 1.09s/it]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 18/74 [00:19<01:00, 1.09s/it]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 19/74 [00:20<00:59, 1.09s/it]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 20/74 [00:21<00:58, 1.08s/it]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 21/74 [00:22<00:57, 1.08s/it]\u001b[A\n\nDDIM Sampler: 30%|██▉ | 22/74 [00:23<00:56, 1.08s/it]\u001b[A\n\nDDIM Sampler: 31%|███ | 23/74 [00:24<00:55, 1.08s/it]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 24/74 [00:26<00:54, 1.08s/it]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 25/74 [00:27<00:53, 1.08s/it]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 26/74 [00:28<00:51, 1.08s/it]\u001b[A\n\nDDIM Sampler: 36%|███▋ | 27/74 [00:29<00:50, 1.08s/it]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 28/74 [00:30<00:49, 1.09s/it]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 29/74 [00:31<00:48, 1.08s/it]\u001b[A\n\nDDIM Sampler: 41%|████ | 30/74 [00:32<00:47, 1.08s/it]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 31/74 [00:33<00:46, 1.08s/it]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 32/74 [00:34<00:45, 1.08s/it]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 33/74 [00:35<00:44, 1.08s/it]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 34/74 [00:36<00:43, 1.08s/it]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 35/74 [00:37<00:42, 1.08s/it]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 36/74 [00:39<00:41, 1.08s/it]\u001b[A\n\nDDIM Sampler: 50%|█████ | 37/74 [00:40<00:40, 1.09s/it]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 38/74 [00:41<00:39, 1.09s/it]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 39/74 [00:42<00:37, 1.08s/it]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 40/74 [00:43<00:36, 1.08s/it]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 41/74 [00:44<00:35, 1.08s/it]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 42/74 [00:45<00:34, 1.08s/it]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 43/74 [00:46<00:33, 1.08s/it]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 44/74 [00:47<00:32, 1.08s/it]\u001b[A\n\nDDIM Sampler: 61%|██████ | 45/74 [00:48<00:31, 1.08s/it]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 46/74 [00:49<00:30, 1.08s/it]\u001b[A\n\nDDIM Sampler: 64%|██████▎ | 47/74 [00:50<00:29, 1.09s/it]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 48/74 [00:52<00:28, 1.09s/it]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 49/74 [00:53<00:27, 1.09s/it]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 50/74 [00:54<00:26, 1.09s/it]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 51/74 [00:55<00:24, 1.09s/it]\u001b[A\n\nDDIM Sampler: 70%|███████ | 52/74 [00:56<00:23, 1.09s/it]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 53/74 [00:57<00:22, 1.08s/it]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 54/74 [00:58<00:21, 1.08s/it]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 55/74 [00:59<00:20, 1.08s/it]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 56/74 [01:00<00:19, 1.08s/it]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 57/74 [01:01<00:18, 1.08s/it]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 58/74 [01:02<00:17, 1.08s/it]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 59/74 [01:03<00:16, 1.08s/it]\u001b[A\n\nDDIM Sampler: 81%|████████ | 60/74 [01:05<00:15, 1.08s/it]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 61/74 [01:06<00:14, 1.08s/it]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 62/74 [01:07<00:12, 1.08s/it]\u001b[A\n\nDDIM Sampler: 85%|████████▌ | 63/74 [01:08<00:11, 1.08s/it]\u001b[A\n\nDDIM Sampler: 86%|████████▋ | 64/74 [01:09<00:10, 1.08s/it]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 65/74 [01:10<00:09, 1.08s/it]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 66/74 [01:11<00:08, 1.08s/it]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 67/74 [01:12<00:07, 1.08s/it]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 68/74 [01:13<00:06, 1.08s/it]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 69/74 [01:14<00:05, 1.08s/it]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 70/74 [01:15<00:04, 1.08s/it]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 71/74 [01:16<00:03, 1.08s/it]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 72/74 [01:18<00:02, 1.08s/it]\u001b[A\n\nDDIM Sampler: 99%|█████████▊| 73/74 [01:19<00:01, 1.08s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 74/74 [01:20<00:00, 1.08s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 74/74 [01:20<00:00, 1.08s/it]\n\nSampling: 100%|██████████| 1/1 [02:48<00:00, 168.10s/it]\nSampling: 100%|██████████| 1/1 [02:48<00:00, 168.10s/it]", "metrics": { "predict_time": 169.005694, "total_time": 307.705785 }, "output": [ "https://replicate.delivery/mgxm/bae18aec-4d20-4b86-a525-2a808290b2b9/0.png", "https://replicate.delivery/mgxm/fd236e6e-2198-4c03-8fe3-fd3a94a473f5/10.png", "https://replicate.delivery/mgxm/beb8b888-9113-4004-9d5b-933e88a275a4/20.png", "https://replicate.delivery/mgxm/5aaa3c7f-557a-490f-afb9-c7f80595eb7c/30.png", "https://replicate.delivery/mgxm/023b1f44-81b5-4af7-b837-2b3837697920/40.png", "https://replicate.delivery/mgxm/5809defd-5fe0-4ea2-b2d3-6c8779e8587d/50.png", "https://replicate.delivery/mgxm/c7949717-6e9e-4798-9636-142e2f01035c/60.png", "https://replicate.delivery/mgxm/c1794446-8def-460a-ab50-da8c2753e3dd/70.png", "https://replicate.delivery/mgxm/e93eeb35-1693-4654-9ccb-45d6bb8fdad2/80.png", "https://replicate.delivery/mgxm/953ed179-10d6-4c91-9c82-5f2af1ba2d76/90.png", "https://replicate.delivery/mgxm/cace5043-c596-41c9-bbaf-8e65498ed926/100.png", "https://replicate.delivery/mgxm/df4aa0ef-d8d6-47a5-a0da-73ed7f807030/110.png", "https://replicate.delivery/mgxm/7b26f335-7221-4a34-bc5b-018e31e5d0a2/120.png", "https://replicate.delivery/mgxm/1af09697-e9d2-42d6-ad2e-3fda05cf9fbd/130.png", "https://replicate.delivery/mgxm/f34bf04a-e78c-4ba4-847f-4f153585bf77/140.png", "https://replicate.delivery/mgxm/f8682444-c0ec-4d89-9756-a7c432cdf9e6/150.png", "https://replicate.delivery/mgxm/579f6dbd-49a7-4604-9ba3-24615bf14c3c/160.png", "https://replicate.delivery/mgxm/4a3ac416-9541-4603-9507-47a2c3619f2f/170.png", "https://replicate.delivery/mgxm/267bf2f1-b973-4c52-a2c1-eeacc4b6ca1e/180.png", "https://replicate.delivery/mgxm/1b7b2f96-69f1-44ca-8eff-3b71a15d6a61/190.png", "https://replicate.delivery/mgxm/6b553484-a0fa-4164-a219-f7e8c7000c82/1658846552.png" ], "started_at": "2022-07-26T14:39:47.611042Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wzqm4jqxondazkup763nv7eeii", "cancel": "https://api.replicate.com/v1/predictions/wzqm4jqxondazkup763nv7eeii/cancel" }, "version": "27d269c0f48d9583460fe190355035f273ec90180a84753bbf6c8a4ead4cc2a2" }Generated inhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Sampling images 1/1 Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.3 Running DDIM Sampling with 119 timesteps Sampling: 0%| | 0/1 [00:00<?, ?it/s] DDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s] DDIM Sampler: 1%| | 1/119 [00:00<01:15, 1.57it/s] DDIM Sampler: 2%|▏ | 2/119 [00:01<01:12, 1.60it/s] DDIM Sampler: 3%|▎ | 3/119 [00:01<01:11, 1.62it/s] DDIM Sampler: 3%|▎ | 4/119 [00:02<01:10, 1.62it/s] DDIM Sampler: 4%|▍ | 5/119 [00:03<01:10, 1.63it/s] DDIM Sampler: 5%|▌ | 6/119 [00:03<01:09, 1.62it/s] DDIM Sampler: 6%|▌ | 7/119 [00:04<01:08, 1.63it/s] DDIM Sampler: 7%|▋ | 8/119 [00:04<01:07, 1.64it/s] DDIM Sampler: 8%|▊ | 9/119 [00:05<01:06, 1.65it/s] DDIM Sampler: 8%|▊ | 10/119 [00:06<01:05, 1.65it/s] DDIM Sampler: 9%|▉ | 11/119 [00:06<01:05, 1.66it/s] DDIM Sampler: 10%|█ | 12/119 [00:07<01:04, 1.66it/s] DDIM Sampler: 11%|█ | 13/119 [00:07<01:03, 1.66it/s] DDIM Sampler: 12%|█▏ | 14/119 [00:08<01:03, 1.66it/s] DDIM Sampler: 13%|█▎ | 15/119 [00:09<01:02, 1.66it/s] DDIM Sampler: 13%|█▎ | 16/119 [00:09<01:02, 1.65it/s] DDIM Sampler: 14%|█▍ | 17/119 [00:10<01:01, 1.66it/s] DDIM Sampler: 15%|█▌ | 18/119 [00:10<01:00, 1.66it/s] DDIM Sampler: 16%|█▌ | 19/119 [00:11<01:00, 1.66it/s] DDIM Sampler: 17%|█▋ | 20/119 [00:12<00:59, 1.67it/s] DDIM Sampler: 18%|█▊ | 21/119 [00:12<00:58, 1.67it/s] DDIM Sampler: 18%|█▊ | 22/119 [00:13<00:58, 1.66it/s] DDIM Sampler: 19%|█▉ | 23/119 [00:13<00:57, 1.67it/s] DDIM Sampler: 20%|██ | 24/119 [00:14<00:56, 1.67it/s] DDIM Sampler: 21%|██ | 25/119 [00:15<00:56, 1.67it/s] DDIM Sampler: 22%|██▏ | 26/119 [00:15<00:55, 1.67it/s] DDIM Sampler: 23%|██▎ | 27/119 [00:16<00:55, 1.67it/s] DDIM Sampler: 24%|██▎ | 28/119 [00:16<00:54, 1.67it/s] DDIM Sampler: 24%|██▍ | 29/119 [00:17<00:53, 1.67it/s] DDIM Sampler: 25%|██▌ | 30/119 [00:18<00:53, 1.68it/s] DDIM Sampler: 26%|██▌ | 31/119 [00:18<00:52, 1.67it/s] DDIM Sampler: 27%|██▋ | 32/119 [00:19<00:52, 1.67it/s] DDIM Sampler: 28%|██▊ | 33/119 [00:19<00:51, 1.67it/s] DDIM Sampler: 29%|██▊ | 34/119 [00:20<00:50, 1.67it/s] DDIM Sampler: 29%|██▉ | 35/119 [00:21<00:50, 1.67it/s] DDIM Sampler: 30%|███ | 36/119 [00:21<00:49, 1.67it/s] DDIM Sampler: 31%|███ | 37/119 [00:22<00:49, 1.66it/s] DDIM Sampler: 32%|███▏ | 38/119 [00:22<00:48, 1.66it/s] DDIM Sampler: 33%|███▎ | 39/119 [00:23<00:47, 1.67it/s] DDIM Sampler: 34%|███▎ | 40/119 [00:24<00:47, 1.68it/s] DDIM Sampler: 34%|███▍ | 41/119 [00:24<00:46, 1.67it/s] DDIM Sampler: 35%|███▌ | 42/119 [00:25<00:45, 1.68it/s] DDIM Sampler: 36%|███▌ | 43/119 [00:25<00:45, 1.68it/s] DDIM Sampler: 37%|███▋ | 44/119 [00:26<00:44, 1.69it/s] DDIM Sampler: 38%|███▊ | 45/119 [00:27<00:43, 1.68it/s] DDIM Sampler: 39%|███▊ | 46/119 [00:27<00:43, 1.68it/s] DDIM Sampler: 39%|███▉ | 47/119 [00:28<00:42, 1.68it/s] DDIM Sampler: 40%|████ | 48/119 [00:28<00:42, 1.69it/s] DDIM Sampler: 41%|████ | 49/119 [00:29<00:41, 1.69it/s] DDIM Sampler: 42%|████▏ | 50/119 [00:30<00:40, 1.69it/s] DDIM Sampler: 43%|████▎ | 51/119 [00:30<00:40, 1.69it/s] DDIM Sampler: 44%|████▎ | 52/119 [00:31<00:39, 1.69it/s] DDIM Sampler: 45%|████▍ | 53/119 [00:31<00:38, 1.70it/s] DDIM Sampler: 45%|████▌ | 54/119 [00:32<00:38, 1.71it/s] DDIM Sampler: 46%|████▌ | 55/119 [00:32<00:37, 1.71it/s] DDIM Sampler: 47%|████▋ | 56/119 [00:33<00:36, 1.71it/s] DDIM Sampler: 48%|████▊ | 57/119 [00:34<00:36, 1.72it/s] DDIM Sampler: 49%|████▊ | 58/119 [00:34<00:35, 1.71it/s] DDIM Sampler: 50%|████▉ | 59/119 [00:35<00:35, 1.71it/s] DDIM Sampler: 50%|█████ | 60/119 [00:35<00:34, 1.71it/s] DDIM Sampler: 51%|█████▏ | 61/119 [00:36<00:33, 1.71it/s] DDIM Sampler: 52%|█████▏ | 62/119 [00:37<00:33, 1.71it/s] DDIM Sampler: 53%|█████▎ | 63/119 [00:37<00:33, 1.68it/s] DDIM Sampler: 54%|█████▍ | 64/119 [00:38<00:32, 1.67it/s] DDIM Sampler: 55%|█████▍ | 65/119 [00:38<00:32, 1.64it/s] DDIM Sampler: 55%|█████▌ | 66/119 [00:39<00:32, 1.64it/s] DDIM Sampler: 56%|█████▋ | 67/119 [00:40<00:31, 1.64it/s] DDIM Sampler: 57%|█████▋ | 68/119 [00:40<00:31, 1.64it/s] DDIM Sampler: 58%|█████▊ | 69/119 [00:41<00:30, 1.63it/s] DDIM Sampler: 59%|█████▉ | 70/119 [00:41<00:30, 1.63it/s] DDIM Sampler: 60%|█████▉ | 71/119 [00:42<00:29, 1.63it/s] DDIM Sampler: 61%|██████ | 72/119 [00:43<00:28, 1.63it/s] DDIM Sampler: 61%|██████▏ | 73/119 [00:43<00:28, 1.63it/s] DDIM Sampler: 62%|██████▏ | 74/119 [00:44<00:27, 1.63it/s] DDIM Sampler: 63%|██████▎ | 75/119 [00:45<00:26, 1.63it/s] DDIM Sampler: 64%|██████▍ | 76/119 [00:45<00:26, 1.64it/s] DDIM Sampler: 65%|██████▍ | 77/119 [00:46<00:25, 1.64it/s] DDIM Sampler: 66%|██████▌ | 78/119 [00:46<00:25, 1.64it/s] DDIM Sampler: 66%|██████▋ | 79/119 [00:47<00:24, 1.64it/s] DDIM Sampler: 67%|██████▋ | 80/119 [00:48<00:23, 1.64it/s] DDIM Sampler: 68%|██████▊ | 81/119 [00:48<00:23, 1.63it/s] DDIM Sampler: 69%|██████▉ | 82/119 [00:49<00:22, 1.63it/s] DDIM Sampler: 70%|██████▉ | 83/119 [00:49<00:21, 1.64it/s] DDIM Sampler: 71%|███████ | 84/119 [00:50<00:21, 1.64it/s] DDIM Sampler: 71%|███████▏ | 85/119 [00:51<00:20, 1.64it/s] DDIM Sampler: 72%|███████▏ | 86/119 [00:51<00:20, 1.64it/s] DDIM Sampler: 73%|███████▎ | 87/119 [00:52<00:19, 1.64it/s] DDIM Sampler: 74%|███████▍ | 88/119 [00:52<00:18, 1.64it/s] DDIM Sampler: 75%|███████▍ | 89/119 [00:53<00:18, 1.62it/s] DDIM Sampler: 76%|███████▌ | 90/119 [00:54<00:17, 1.63it/s] DDIM Sampler: 76%|███████▋ | 91/119 [00:54<00:17, 1.63it/s] DDIM Sampler: 77%|███████▋ | 92/119 [00:55<00:16, 1.63it/s] DDIM Sampler: 78%|███████▊ | 93/119 [00:56<00:15, 1.63it/s] DDIM Sampler: 79%|███████▉ | 94/119 [00:56<00:15, 1.64it/s] DDIM Sampler: 80%|███████▉ | 95/119 [00:57<00:14, 1.64it/s] DDIM Sampler: 81%|████████ | 96/119 [00:57<00:14, 1.64it/s] DDIM Sampler: 82%|████████▏ | 97/119 [00:58<00:13, 1.64it/s] DDIM Sampler: 82%|████████▏ | 98/119 [00:59<00:12, 1.63it/s] DDIM Sampler: 83%|████████▎ | 99/119 [00:59<00:12, 1.63it/s] DDIM Sampler: 84%|████████▍ | 100/119 [01:00<00:11, 1.63it/s] DDIM Sampler: 85%|████████▍ | 101/119 [01:00<00:11, 1.62it/s] DDIM Sampler: 86%|████████▌ | 102/119 [01:01<00:10, 1.62it/s] DDIM Sampler: 87%|████████▋ | 103/119 [01:02<00:09, 1.62it/s] DDIM Sampler: 87%|████████▋ | 104/119 [01:02<00:09, 1.62it/s] DDIM Sampler: 88%|████████▊ | 105/119 [01:03<00:08, 1.62it/s] DDIM Sampler: 89%|████████▉ | 106/119 [01:04<00:08, 1.62it/s] DDIM Sampler: 90%|████████▉ | 107/119 [01:04<00:07, 1.63it/s] DDIM Sampler: 91%|█████████ | 108/119 [01:05<00:06, 1.62it/s] DDIM Sampler: 92%|█████████▏| 109/119 [01:05<00:06, 1.62it/s] DDIM Sampler: 92%|█████████▏| 110/119 [01:06<00:05, 1.63it/s] DDIM Sampler: 93%|█████████▎| 111/119 [01:07<00:04, 1.63it/s] DDIM Sampler: 94%|█████████▍| 112/119 [01:07<00:04, 1.63it/s] DDIM Sampler: 95%|█████████▍| 113/119 [01:08<00:03, 1.63it/s] DDIM Sampler: 96%|█████████▌| 114/119 [01:08<00:03, 1.63it/s] DDIM Sampler: 97%|█████████▋| 115/119 [01:09<00:02, 1.64it/s] DDIM Sampler: 97%|█████████▋| 116/119 [01:10<00:01, 1.64it/s] DDIM Sampler: 98%|█████████▊| 117/119 [01:10<00:01, 1.64it/s] DDIM Sampler: 99%|█████████▉| 118/119 [01:11<00:00, 1.64it/s] DDIM Sampler: 100%|██████████| 119/119 [01:11<00:00, 1.64it/s] DDIM Sampler: 100%|██████████| 119/119 [01:11<00:00, 1.65it/s] python inference_gfpgan.py -i /tmp/tmp5313lkf4gfpgan/temp_1658846460.png -o results -v 1.3 -s 2 huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Processing temp_1658846460.png ... Tile 1/1 Results are in the [results] folder. Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.1 Running DDIM Sampling with 74 timesteps DDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s] DDIM Sampler: 1%|▏ | 1/74 [00:01<01:20, 1.10s/it] DDIM Sampler: 3%|▎ | 2/74 [00:02<01:18, 1.09s/it] DDIM Sampler: 4%|▍ | 3/74 [00:03<01:17, 1.09s/it] DDIM Sampler: 5%|▌ | 4/74 [00:04<01:15, 1.08s/it] DDIM Sampler: 7%|▋ | 5/74 [00:05<01:14, 1.08s/it] DDIM Sampler: 8%|▊ | 6/74 [00:06<01:13, 1.08s/it] DDIM Sampler: 9%|▉ | 7/74 [00:07<01:12, 1.08s/it] DDIM Sampler: 11%|█ | 8/74 [00:08<01:11, 1.08s/it] DDIM Sampler: 12%|█▏ | 9/74 [00:09<01:10, 1.08s/it] DDIM Sampler: 14%|█▎ | 10/74 [00:10<01:09, 1.08s/it] DDIM Sampler: 15%|█▍ | 11/74 [00:11<01:08, 1.08s/it] DDIM Sampler: 16%|█▌ | 12/74 [00:12<01:06, 1.08s/it] DDIM Sampler: 18%|█▊ | 13/74 [00:14<01:06, 1.08s/it] DDIM Sampler: 19%|█▉ | 14/74 [00:15<01:05, 1.09s/it] DDIM Sampler: 20%|██ | 15/74 [00:16<01:04, 1.09s/it] DDIM Sampler: 22%|██▏ | 16/74 [00:17<01:02, 1.09s/it] DDIM Sampler: 23%|██▎ | 17/74 [00:18<01:01, 1.09s/it] DDIM Sampler: 24%|██▍ | 18/74 [00:19<01:00, 1.09s/it] DDIM Sampler: 26%|██▌ | 19/74 [00:20<00:59, 1.09s/it] DDIM Sampler: 27%|██▋ | 20/74 [00:21<00:58, 1.08s/it] DDIM Sampler: 28%|██▊ | 21/74 [00:22<00:57, 1.08s/it] DDIM Sampler: 30%|██▉ | 22/74 [00:23<00:56, 1.08s/it] DDIM Sampler: 31%|███ | 23/74 [00:24<00:55, 1.08s/it] DDIM Sampler: 32%|███▏ | 24/74 [00:26<00:54, 1.08s/it] DDIM Sampler: 34%|███▍ | 25/74 [00:27<00:53, 1.08s/it] DDIM Sampler: 35%|███▌ | 26/74 [00:28<00:51, 1.08s/it] DDIM Sampler: 36%|███▋ | 27/74 [00:29<00:50, 1.08s/it] DDIM Sampler: 38%|███▊ | 28/74 [00:30<00:49, 1.09s/it] DDIM Sampler: 39%|███▉ | 29/74 [00:31<00:48, 1.08s/it] DDIM Sampler: 41%|████ | 30/74 [00:32<00:47, 1.08s/it] DDIM Sampler: 42%|████▏ | 31/74 [00:33<00:46, 1.08s/it] DDIM Sampler: 43%|████▎ | 32/74 [00:34<00:45, 1.08s/it] DDIM Sampler: 45%|████▍ | 33/74 [00:35<00:44, 1.08s/it] DDIM Sampler: 46%|████▌ | 34/74 [00:36<00:43, 1.08s/it] DDIM Sampler: 47%|████▋ | 35/74 [00:37<00:42, 1.08s/it] DDIM Sampler: 49%|████▊ | 36/74 [00:39<00:41, 1.08s/it] DDIM Sampler: 50%|█████ | 37/74 [00:40<00:40, 1.09s/it] DDIM Sampler: 51%|█████▏ | 38/74 [00:41<00:39, 1.09s/it] DDIM Sampler: 53%|█████▎ | 39/74 [00:42<00:37, 1.08s/it] DDIM Sampler: 54%|█████▍ | 40/74 [00:43<00:36, 1.08s/it] DDIM Sampler: 55%|█████▌ | 41/74 [00:44<00:35, 1.08s/it] DDIM Sampler: 57%|█████▋ | 42/74 [00:45<00:34, 1.08s/it] DDIM Sampler: 58%|█████▊ | 43/74 [00:46<00:33, 1.08s/it] DDIM Sampler: 59%|█████▉ | 44/74 [00:47<00:32, 1.08s/it] DDIM Sampler: 61%|██████ | 45/74 [00:48<00:31, 1.08s/it] DDIM Sampler: 62%|██████▏ | 46/74 [00:49<00:30, 1.08s/it] DDIM Sampler: 64%|██████▎ | 47/74 [00:50<00:29, 1.09s/it] DDIM Sampler: 65%|██████▍ | 48/74 [00:52<00:28, 1.09s/it] DDIM Sampler: 66%|██████▌ | 49/74 [00:53<00:27, 1.09s/it] DDIM Sampler: 68%|██████▊ | 50/74 [00:54<00:26, 1.09s/it] DDIM Sampler: 69%|██████▉ | 51/74 [00:55<00:24, 1.09s/it] DDIM Sampler: 70%|███████ | 52/74 [00:56<00:23, 1.09s/it] DDIM Sampler: 72%|███████▏ | 53/74 [00:57<00:22, 1.08s/it] DDIM Sampler: 73%|███████▎ | 54/74 [00:58<00:21, 1.08s/it] DDIM Sampler: 74%|███████▍ | 55/74 [00:59<00:20, 1.08s/it] DDIM Sampler: 76%|███████▌ | 56/74 [01:00<00:19, 1.08s/it] DDIM Sampler: 77%|███████▋ | 57/74 [01:01<00:18, 1.08s/it] DDIM Sampler: 78%|███████▊ | 58/74 [01:02<00:17, 1.08s/it] DDIM Sampler: 80%|███████▉ | 59/74 [01:03<00:16, 1.08s/it] DDIM Sampler: 81%|████████ | 60/74 [01:05<00:15, 1.08s/it] DDIM Sampler: 82%|████████▏ | 61/74 [01:06<00:14, 1.08s/it] DDIM Sampler: 84%|████████▍ | 62/74 [01:07<00:12, 1.08s/it] DDIM Sampler: 85%|████████▌ | 63/74 [01:08<00:11, 1.08s/it] DDIM Sampler: 86%|████████▋ | 64/74 [01:09<00:10, 1.08s/it] DDIM Sampler: 88%|████████▊ | 65/74 [01:10<00:09, 1.08s/it] DDIM Sampler: 89%|████████▉ | 66/74 [01:11<00:08, 1.08s/it] DDIM Sampler: 91%|█████████ | 67/74 [01:12<00:07, 1.08s/it] DDIM Sampler: 92%|█████████▏| 68/74 [01:13<00:06, 1.08s/it] DDIM Sampler: 93%|█████████▎| 69/74 [01:14<00:05, 1.08s/it] DDIM Sampler: 95%|█████████▍| 70/74 [01:15<00:04, 1.08s/it] DDIM Sampler: 96%|█████████▌| 71/74 [01:16<00:03, 1.08s/it] DDIM Sampler: 97%|█████████▋| 72/74 [01:18<00:02, 1.08s/it] DDIM Sampler: 99%|█████████▊| 73/74 [01:19<00:01, 1.08s/it] DDIM Sampler: 100%|██████████| 74/74 [01:20<00:00, 1.08s/it] DDIM Sampler: 100%|██████████| 74/74 [01:20<00:00, 1.08s/it] Sampling: 100%|██████████| 1/1 [02:48<00:00, 168.10s/it] Sampling: 100%|██████████| 1/1 [02:48<00:00, 168.10s/it]Prediction
nightmareai/majesty-diffusion:76f01b26Input
- width
- 640
- height
- 768
- clip_scale
- 16000
- clip_prompts
- The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.
- latent_scale
- 12
- latent_prompt
- vaporwave princess
- aesthetic_loss_scale
- 400
{ "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", "latent_scale": 12, "latent_prompt": "vaporwave princess", "aesthetic_loss_scale": 400 }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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", { input: { width: 640, height: 768, clip_scale: 16000, clip_prompts: "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", latent_scale: 12, latent_prompt: "vaporwave princess", aesthetic_loss_scale: 400 } } ); 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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", input={ "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", "latent_scale": 12, "latent_prompt": "vaporwave princess", "aesthetic_loss_scale": 400 } ) # The nightmareai/majesty-diffusion model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/nightmareai/majesty-diffusion/api#output-schema print(item)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 nightmareai/majesty-diffusion 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": "76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", "input": { "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", "latent_scale": 12, "latent_prompt": "vaporwave princess", "aesthetic_loss_scale": 400 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-26T15:06:02.303289Z", "created_at": "2022-07-26T15:01:50.303671Z", "data_removed": false, "error": null, "id": "ziyebr5lqvagdfrkzwqeaoxahy", "input": { "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", "latent_scale": 12, "latent_prompt": "vaporwave princess", "aesthetic_loss_scale": 400 }, "logs": "Sampling images 1/1\n\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.3\nRunning DDIM Sampling with 119 timesteps\nSampling: 0%| | 0/1 [00:00<?, ?it/s]\n\nDDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%| | 1/119 [00:11<22:48, 11.59s/it]\u001b[A\n\nDDIM Sampler: 2%|▏ | 2/119 [00:12<10:03, 5.16s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 3/119 [00:12<05:59, 3.10s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 4/119 [00:13<04:04, 2.13s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 5/119 [00:14<03:00, 1.58s/it]\u001b[A\n\nDDIM Sampler: 5%|▌ | 6/119 [00:14<02:23, 1.27s/it]\u001b[A\n\nDDIM Sampler: 6%|▌ | 7/119 [00:15<01:59, 1.07s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 8/119 [00:16<01:43, 1.07it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 9/119 [00:16<01:31, 1.21it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 10/119 [00:17<01:23, 1.30it/s]\u001b[A\n\nDDIM Sampler: 9%|▉ | 11/119 [00:17<01:18, 1.37it/s]\u001b[A\n\nDDIM Sampler: 10%|█ | 12/119 [00:18<01:15, 1.42it/s]\u001b[A\n\nDDIM Sampler: 11%|█ | 13/119 [00:19<01:12, 1.47it/s]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 14/119 [00:19<01:10, 1.49it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 15/119 [00:20<01:08, 1.52it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 16/119 [00:21<01:06, 1.55it/s]\u001b[A\n\nDDIM Sampler: 14%|█▍ | 17/119 [00:21<01:05, 1.55it/s]\u001b[A\n\nDDIM Sampler: 15%|█▌ | 18/119 [00:22<01:04, 1.56it/s]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 19/119 [00:23<01:03, 1.57it/s]\u001b[A\n\nDDIM Sampler: 17%|█▋ | 20/119 [00:23<01:02, 1.57it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 21/119 [00:24<01:02, 1.57it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 22/119 [00:24<01:02, 1.56it/s]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 23/119 [00:25<01:01, 1.57it/s]\u001b[A\n\nDDIM Sampler: 20%|██ | 24/119 [00:26<01:00, 1.57it/s]\u001b[A\n\nDDIM Sampler: 21%|██ | 25/119 [00:26<00:59, 1.57it/s]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 26/119 [00:27<00:59, 1.57it/s]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 27/119 [00:28<00:58, 1.57it/s]\u001b[A\n\nDDIM Sampler: 24%|██▎ | 28/119 [00:28<00:57, 1.59it/s]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 29/119 [00:29<00:56, 1.60it/s]\u001b[A\n\nDDIM Sampler: 25%|██▌ | 30/119 [00:29<00:55, 1.61it/s]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 31/119 [00:30<00:54, 1.61it/s]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 32/119 [00:31<00:53, 1.62it/s]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 33/119 [00:31<00:52, 1.63it/s]\u001b[A\n\nDDIM Sampler: 29%|██▊ | 34/119 [00:32<00:51, 1.64it/s]\u001b[A\n\nDDIM Sampler: 29%|██▉ | 35/119 [00:33<00:50, 1.65it/s]\u001b[A\n\nDDIM Sampler: 30%|███ | 36/119 [00:33<00:50, 1.65it/s]\u001b[A\n\nDDIM Sampler: 31%|███ | 37/119 [00:34<00:49, 1.65it/s]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 38/119 [00:34<00:49, 1.65it/s]\u001b[A\n\nDDIM Sampler: 33%|███▎ | 39/119 [00:35<00:48, 1.64it/s]\u001b[A\n\nDDIM Sampler: 34%|███▎ | 40/119 [00:36<00:48, 1.64it/s]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 41/119 [00:36<00:47, 1.63it/s]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 42/119 [00:37<00:47, 1.62it/s]\u001b[A\n\nDDIM Sampler: 36%|███▌ | 43/119 [00:37<00:47, 1.59it/s]\u001b[A\n\nDDIM Sampler: 37%|███▋ | 44/119 [00:38<00:47, 1.58it/s]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 45/119 [00:39<00:47, 1.57it/s]\u001b[A\n\nDDIM Sampler: 39%|███▊ | 46/119 [00:39<00:46, 1.57it/s]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 47/119 [00:40<00:45, 1.58it/s]\u001b[A\n\nDDIM Sampler: 40%|████ | 48/119 [00:41<00:44, 1.58it/s]\u001b[A\n\nDDIM Sampler: 41%|████ | 49/119 [00:41<00:44, 1.58it/s]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 50/119 [00:42<00:43, 1.58it/s]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 51/119 [00:43<00:42, 1.58it/s]\u001b[A\n\nDDIM Sampler: 44%|████▎ | 52/119 [00:43<00:42, 1.58it/s]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 53/119 [00:44<00:41, 1.58it/s]\u001b[A\n\nDDIM Sampler: 45%|████▌ | 54/119 [00:44<00:41, 1.57it/s]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 55/119 [00:45<00:40, 1.58it/s]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 56/119 [00:46<00:39, 1.58it/s]\u001b[A\n\nDDIM Sampler: 48%|████▊ | 57/119 [00:46<00:39, 1.58it/s]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 58/119 [00:47<00:38, 1.58it/s]\u001b[A\n\nDDIM Sampler: 50%|████▉ | 59/119 [00:48<00:37, 1.58it/s]\u001b[A\n\nDDIM Sampler: 50%|█████ | 60/119 [00:48<00:37, 1.57it/s]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 61/119 [00:49<00:36, 1.58it/s]\u001b[A\n\nDDIM Sampler: 52%|█████▏ | 62/119 [00:50<00:36, 1.57it/s]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 63/119 [00:50<00:36, 1.55it/s]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 64/119 [00:51<00:35, 1.54it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▍ | 65/119 [00:52<00:35, 1.53it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 66/119 [00:52<00:34, 1.53it/s]\u001b[A\n\nDDIM Sampler: 56%|█████▋ | 67/119 [00:53<00:34, 1.52it/s]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 68/119 [00:54<00:33, 1.52it/s]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 69/119 [00:54<00:33, 1.51it/s]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 70/119 [00:55<00:32, 1.51it/s]\u001b[A\n\nDDIM Sampler: 60%|█████▉ | 71/119 [00:55<00:31, 1.51it/s]\u001b[A\n\nDDIM Sampler: 61%|██████ | 72/119 [00:56<00:31, 1.51it/s]\u001b[A\n\nDDIM Sampler: 61%|██████▏ | 73/119 [00:57<00:30, 1.51it/s]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 74/119 [00:57<00:29, 1.51it/s]\u001b[A\n\nDDIM Sampler: 63%|██████▎ | 75/119 [00:58<00:29, 1.51it/s]\u001b[A\n\nDDIM Sampler: 64%|██████▍ | 76/119 [00:59<00:28, 1.51it/s]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 77/119 [00:59<00:27, 1.51it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 78/119 [01:00<00:27, 1.51it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▋ | 79/119 [01:01<00:26, 1.51it/s]\u001b[A\n\nDDIM Sampler: 67%|██████▋ | 80/119 [01:01<00:25, 1.50it/s]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 81/119 [01:02<00:25, 1.50it/s]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 82/119 [01:03<00:24, 1.50it/s]\u001b[A\n\nDDIM Sampler: 70%|██████▉ | 83/119 [01:03<00:23, 1.50it/s]\u001b[A\n\nDDIM Sampler: 71%|███████ | 84/119 [01:04<00:23, 1.50it/s]\u001b[A\n\nDDIM Sampler: 71%|███████▏ | 85/119 [01:05<00:22, 1.50it/s]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 86/119 [01:05<00:22, 1.49it/s]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 87/119 [01:06<00:21, 1.49it/s]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 88/119 [01:07<00:20, 1.49it/s]\u001b[A\n\nDDIM Sampler: 75%|███████▍ | 89/119 [01:07<00:20, 1.50it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 90/119 [01:08<00:19, 1.50it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▋ | 91/119 [01:09<00:18, 1.49it/s]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 92/119 [01:10<00:18, 1.49it/s]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 93/119 [01:10<00:17, 1.49it/s]\u001b[A\n\nDDIM Sampler: 79%|███████▉ | 94/119 [01:11<00:16, 1.49it/s]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 95/119 [01:12<00:16, 1.49it/s]\u001b[A\n\nDDIM Sampler: 81%|████████ | 96/119 [01:12<00:15, 1.49it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 97/119 [01:13<00:14, 1.48it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 98/119 [01:14<00:14, 1.49it/s]\u001b[A\n\nDDIM Sampler: 83%|████████▎ | 99/119 [01:14<00:13, 1.48it/s]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 100/119 [01:15<00:12, 1.49it/s]\u001b[A\n\nDDIM Sampler: 85%|████████▍ | 101/119 [01:16<00:12, 1.49it/s]\u001b[A\n\nDDIM Sampler: 86%|████████▌ | 102/119 [01:16<00:11, 1.50it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 103/119 [01:17<00:10, 1.50it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 104/119 [01:18<00:10, 1.49it/s]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 105/119 [01:18<00:09, 1.50it/s]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 106/119 [01:19<00:08, 1.51it/s]\u001b[A\n\nDDIM Sampler: 90%|████████▉ | 107/119 [01:20<00:07, 1.51it/s]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 108/119 [01:20<00:07, 1.51it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 109/119 [01:21<00:06, 1.51it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 110/119 [01:22<00:05, 1.51it/s]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 111/119 [01:22<00:05, 1.51it/s]\u001b[A\n\nDDIM Sampler: 94%|█████████▍| 112/119 [01:23<00:04, 1.50it/s]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 113/119 [01:24<00:03, 1.51it/s]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 114/119 [01:24<00:03, 1.51it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 115/119 [01:25<00:02, 1.51it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 116/119 [01:25<00:01, 1.51it/s]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 117/119 [01:26<00:01, 1.51it/s]\u001b[A\n\nDDIM Sampler: 99%|█████████▉| 118/119 [01:27<00:00, 1.51it/s]\u001b[A\n\nDDIM Sampler: 100%|██████████| 119/119 [01:27<00:00, 1.51it/s]\u001b[A\nDDIM Sampler: 100%|██████████| 119/119 [01:27<00:00, 1.35it/s]\npython inference_gfpgan.py -i /tmp/tmprqvpx06_gfpgan/temp_1658847856.png -o results -v 1.3 -s 2\n\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nDownloading: \"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth\" to /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/realesrgan/weights/RealESRGAN_x2plus.pth\n\n\n 0%| | 0.00/64.0M [00:00<?, ?B/s]\n 16%|█▌ | 10.1M/64.0M [00:00<00:00, 106MB/s]\n 58%|█████▊ | 37.1M/64.0M [00:00<00:00, 210MB/s]\n100%|█████████▉| 63.8M/64.0M [00:00<00:00, 242MB/s]\n100%|██████████| 64.0M/64.0M [00:00<00:00, 223MB/s]\nProcessing temp_1658847856.png ...\n\tTile 1/1\nResults are in the [results] folder.\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.1\nRunning DDIM Sampling with 74 timesteps\n\n\nDDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%|▏ | 1/74 [00:09<11:30, 9.46s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 2/74 [00:10<05:25, 4.53s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 3/74 [00:11<03:29, 2.95s/it]\u001b[A\n\nDDIM Sampler: 5%|▌ | 4/74 [00:12<02:34, 2.21s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 5/74 [00:13<02:04, 1.80s/it]\u001b[A\n\nDDIM Sampler: 8%|▊ | 6/74 [00:14<01:45, 1.55s/it]\u001b[A\n\nDDIM Sampler: 9%|▉ | 7/74 [00:15<01:33, 1.40s/it]\u001b[A\n\nDDIM Sampler: 11%|█ | 8/74 [00:16<01:25, 1.30s/it]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 9/74 [00:18<01:19, 1.23s/it]\u001b[A\n\nDDIM Sampler: 14%|█▎ | 10/74 [00:19<01:15, 1.18s/it]\u001b[A\n\nDDIM Sampler: 15%|█▍ | 11/74 [00:20<01:12, 1.14s/it]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 12/74 [00:21<01:09, 1.12s/it]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 13/74 [00:22<01:07, 1.11s/it]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 14/74 [00:23<01:05, 1.10s/it]\u001b[A\n\nDDIM Sampler: 20%|██ | 15/74 [00:24<01:04, 1.09s/it]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 16/74 [00:25<01:02, 1.08s/it]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 17/74 [00:26<01:01, 1.08s/it]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 18/74 [00:27<01:00, 1.08s/it]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 19/74 [00:28<00:59, 1.08s/it]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 20/74 [00:29<00:58, 1.08s/it]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 21/74 [00:30<00:56, 1.07s/it]\u001b[A\n\nDDIM Sampler: 30%|██▉ | 22/74 [00:31<00:56, 1.08s/it]\u001b[A\n\nDDIM Sampler: 31%|███ | 23/74 [00:33<00:54, 1.08s/it]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 24/74 [00:34<00:53, 1.07s/it]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 25/74 [00:35<00:52, 1.07s/it]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 26/74 [00:36<00:51, 1.07s/it]\u001b[A\n\nDDIM Sampler: 36%|███▋ | 27/74 [00:37<00:50, 1.07s/it]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 28/74 [00:38<00:49, 1.07s/it]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 29/74 [00:39<00:48, 1.07s/it]\u001b[A\n\nDDIM Sampler: 41%|████ | 30/74 [00:40<00:47, 1.07s/it]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 31/74 [00:41<00:46, 1.07s/it]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 32/74 [00:42<00:45, 1.08s/it]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 33/74 [00:43<00:44, 1.08s/it]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 34/74 [00:44<00:42, 1.07s/it]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 35/74 [00:45<00:41, 1.08s/it]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 36/74 [00:46<00:40, 1.07s/it]\u001b[A\n\nDDIM Sampler: 50%|█████ | 37/74 [00:48<00:39, 1.08s/it]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 38/74 [00:49<00:38, 1.08s/it]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 39/74 [00:50<00:37, 1.08s/it]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 40/74 [00:51<00:36, 1.08s/it]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 41/74 [00:52<00:35, 1.09s/it]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 42/74 [00:53<00:34, 1.09s/it]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 43/74 [00:54<00:33, 1.09s/it]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 44/74 [00:55<00:32, 1.09s/it]\u001b[A\n\nDDIM Sampler: 61%|██████ | 45/74 [00:56<00:31, 1.09s/it]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 46/74 [00:57<00:30, 1.09s/it]\u001b[A\n\nDDIM Sampler: 64%|██████▎ | 47/74 [00:58<00:29, 1.09s/it]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 48/74 [01:00<00:28, 1.10s/it]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 49/74 [01:01<00:27, 1.10s/it]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 50/74 [01:02<00:26, 1.10s/it]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 51/74 [01:03<00:25, 1.10s/it]\u001b[A\n\nDDIM Sampler: 70%|███████ | 52/74 [01:04<00:24, 1.10s/it]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 53/74 [01:05<00:23, 1.10s/it]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 54/74 [01:06<00:22, 1.10s/it]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 55/74 [01:07<00:20, 1.10s/it]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 56/74 [01:08<00:19, 1.10s/it]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 57/74 [01:09<00:18, 1.10s/it]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 58/74 [01:11<00:17, 1.10s/it]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 59/74 [01:12<00:16, 1.10s/it]\u001b[A\n\nDDIM Sampler: 81%|████████ | 60/74 [01:13<00:15, 1.10s/it]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 61/74 [01:14<00:14, 1.10s/it]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 62/74 [01:15<00:13, 1.10s/it]\u001b[A\n\nDDIM Sampler: 85%|████████▌ | 63/74 [01:16<00:12, 1.10s/it]\u001b[A\n\nDDIM Sampler: 86%|████████▋ | 64/74 [01:17<00:11, 1.10s/it]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 65/74 [01:18<00:09, 1.10s/it]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 66/74 [01:19<00:08, 1.10s/it]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 67/74 [01:21<00:07, 1.10s/it]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 68/74 [01:22<00:06, 1.10s/it]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 69/74 [01:23<00:05, 1.10s/it]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 70/74 [01:24<00:04, 1.10s/it]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 71/74 [01:25<00:03, 1.11s/it]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 72/74 [01:26<00:02, 1.11s/it]\u001b[A\n\nDDIM Sampler: 99%|█████████▊| 73/74 [01:27<00:01, 1.11s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 74/74 [01:28<00:00, 1.11s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 74/74 [01:28<00:00, 1.20s/it]\n\nSampling: 100%|██████████| 1/1 [03:14<00:00, 194.40s/it]\nSampling: 100%|██████████| 1/1 [03:14<00:00, 194.40s/it]", "metrics": { "predict_time": 195.271336, "total_time": 251.999618 }, "output": [ "https://replicate.delivery/mgxm/05c51477-135f-4bf5-8347-71ef182e444f/0.png", "https://replicate.delivery/mgxm/44c843b4-c91a-43a2-a34a-82413ae01556/10.png", "https://replicate.delivery/mgxm/465b41cb-ace8-4f2b-ac33-b18205ad8b47/20.png", "https://replicate.delivery/mgxm/2e55d006-9e01-4f96-a55b-7b8f7a51a405/30.png", "https://replicate.delivery/mgxm/ec799994-5cf9-45a4-a5da-0dc8492dbc21/40.png", "https://replicate.delivery/mgxm/892673a8-30c1-4de3-81d2-817d43c23c85/50.png", "https://replicate.delivery/mgxm/7a9a13c5-f575-445f-a967-8e7d93805ea9/60.png", "https://replicate.delivery/mgxm/83afa9a1-df65-4306-b9f3-e0060b507e50/70.png", "https://replicate.delivery/mgxm/5475a2e9-f8df-4575-b3d9-27d2845c1767/80.png", "https://replicate.delivery/mgxm/e48ab18e-7a1e-42ec-baa4-405e5ca13a18/90.png", "https://replicate.delivery/mgxm/390bd57f-94e2-4070-97cb-1d0371878cc8/100.png", "https://replicate.delivery/mgxm/791567d3-e994-45ab-afdc-56aa8c73e601/110.png", "https://replicate.delivery/mgxm/5a8297cd-2da5-463d-8e30-a36dc3d8b97a/120.png", "https://replicate.delivery/mgxm/9326daeb-8302-4d88-872d-3a12e4c82c7a/130.png", "https://replicate.delivery/mgxm/b995b6b5-e030-4c1b-906f-786d6379b699/140.png", "https://replicate.delivery/mgxm/c75d995c-3234-4281-b9bd-67efac64f484/150.png", "https://replicate.delivery/mgxm/4de3c739-de10-4c99-9d28-eec0c5cffc0a/160.png", "https://replicate.delivery/mgxm/6fbde938-897a-4d32-9390-0f05873de607/170.png", "https://replicate.delivery/mgxm/2ce0cb66-ff04-4c00-8090-b991a0342059/180.png", "https://replicate.delivery/mgxm/5d538fbb-a119-4a13-9d4f-ebde6bee3ed3/190.png", "https://replicate.delivery/mgxm/b95b22ef-54d0-4248-94ef-268635aac529/1658847957.png" ], "started_at": "2022-07-26T15:02:47.031953Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ziyebr5lqvagdfrkzwqeaoxahy", "cancel": "https://api.replicate.com/v1/predictions/ziyebr5lqvagdfrkzwqeaoxahy/cancel" }, "version": "73bfb38ec5c159e3dcbe2dc69d2db245c1cc07a94377829c49dded3a7ddb7e3a" }Generated inSampling images 1/1 Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.3 Running DDIM Sampling with 119 timesteps Sampling: 0%| | 0/1 [00:00<?, ?it/s] DDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s] DDIM Sampler: 1%| | 1/119 [00:11<22:48, 11.59s/it] DDIM Sampler: 2%|▏ | 2/119 [00:12<10:03, 5.16s/it] DDIM Sampler: 3%|▎ | 3/119 [00:12<05:59, 3.10s/it] DDIM Sampler: 3%|▎ | 4/119 [00:13<04:04, 2.13s/it] DDIM Sampler: 4%|▍ | 5/119 [00:14<03:00, 1.58s/it] DDIM Sampler: 5%|▌ | 6/119 [00:14<02:23, 1.27s/it] DDIM Sampler: 6%|▌ | 7/119 [00:15<01:59, 1.07s/it] DDIM Sampler: 7%|▋ | 8/119 [00:16<01:43, 1.07it/s] DDIM Sampler: 8%|▊ | 9/119 [00:16<01:31, 1.21it/s] DDIM Sampler: 8%|▊ | 10/119 [00:17<01:23, 1.30it/s] DDIM Sampler: 9%|▉ | 11/119 [00:17<01:18, 1.37it/s] DDIM Sampler: 10%|█ | 12/119 [00:18<01:15, 1.42it/s] DDIM Sampler: 11%|█ | 13/119 [00:19<01:12, 1.47it/s] DDIM Sampler: 12%|█▏ | 14/119 [00:19<01:10, 1.49it/s] DDIM Sampler: 13%|█▎ | 15/119 [00:20<01:08, 1.52it/s] DDIM Sampler: 13%|█▎ | 16/119 [00:21<01:06, 1.55it/s] DDIM Sampler: 14%|█▍ | 17/119 [00:21<01:05, 1.55it/s] DDIM Sampler: 15%|█▌ | 18/119 [00:22<01:04, 1.56it/s] DDIM Sampler: 16%|█▌ | 19/119 [00:23<01:03, 1.57it/s] DDIM Sampler: 17%|█▋ | 20/119 [00:23<01:02, 1.57it/s] DDIM Sampler: 18%|█▊ | 21/119 [00:24<01:02, 1.57it/s] DDIM Sampler: 18%|█▊ | 22/119 [00:24<01:02, 1.56it/s] DDIM Sampler: 19%|█▉ | 23/119 [00:25<01:01, 1.57it/s] DDIM Sampler: 20%|██ | 24/119 [00:26<01:00, 1.57it/s] DDIM Sampler: 21%|██ | 25/119 [00:26<00:59, 1.57it/s] DDIM Sampler: 22%|██▏ | 26/119 [00:27<00:59, 1.57it/s] DDIM Sampler: 23%|██▎ | 27/119 [00:28<00:58, 1.57it/s] DDIM Sampler: 24%|██▎ | 28/119 [00:28<00:57, 1.59it/s] DDIM Sampler: 24%|██▍ | 29/119 [00:29<00:56, 1.60it/s] DDIM Sampler: 25%|██▌ | 30/119 [00:29<00:55, 1.61it/s] DDIM Sampler: 26%|██▌ | 31/119 [00:30<00:54, 1.61it/s] DDIM Sampler: 27%|██▋ | 32/119 [00:31<00:53, 1.62it/s] DDIM Sampler: 28%|██▊ | 33/119 [00:31<00:52, 1.63it/s] DDIM Sampler: 29%|██▊ | 34/119 [00:32<00:51, 1.64it/s] DDIM Sampler: 29%|██▉ | 35/119 [00:33<00:50, 1.65it/s] DDIM Sampler: 30%|███ | 36/119 [00:33<00:50, 1.65it/s] DDIM Sampler: 31%|███ | 37/119 [00:34<00:49, 1.65it/s] DDIM Sampler: 32%|███▏ | 38/119 [00:34<00:49, 1.65it/s] DDIM Sampler: 33%|███▎ | 39/119 [00:35<00:48, 1.64it/s] DDIM Sampler: 34%|███▎ | 40/119 [00:36<00:48, 1.64it/s] DDIM Sampler: 34%|███▍ | 41/119 [00:36<00:47, 1.63it/s] DDIM Sampler: 35%|███▌ | 42/119 [00:37<00:47, 1.62it/s] DDIM Sampler: 36%|███▌ | 43/119 [00:37<00:47, 1.59it/s] DDIM Sampler: 37%|███▋ | 44/119 [00:38<00:47, 1.58it/s] DDIM Sampler: 38%|███▊ | 45/119 [00:39<00:47, 1.57it/s] DDIM Sampler: 39%|███▊ | 46/119 [00:39<00:46, 1.57it/s] DDIM Sampler: 39%|███▉ | 47/119 [00:40<00:45, 1.58it/s] DDIM Sampler: 40%|████ | 48/119 [00:41<00:44, 1.58it/s] DDIM Sampler: 41%|████ | 49/119 [00:41<00:44, 1.58it/s] DDIM Sampler: 42%|████▏ | 50/119 [00:42<00:43, 1.58it/s] DDIM Sampler: 43%|████▎ | 51/119 [00:43<00:42, 1.58it/s] DDIM Sampler: 44%|████▎ | 52/119 [00:43<00:42, 1.58it/s] DDIM Sampler: 45%|████▍ | 53/119 [00:44<00:41, 1.58it/s] DDIM Sampler: 45%|████▌ | 54/119 [00:44<00:41, 1.57it/s] DDIM Sampler: 46%|████▌ | 55/119 [00:45<00:40, 1.58it/s] DDIM Sampler: 47%|████▋ | 56/119 [00:46<00:39, 1.58it/s] DDIM Sampler: 48%|████▊ | 57/119 [00:46<00:39, 1.58it/s] DDIM Sampler: 49%|████▊ | 58/119 [00:47<00:38, 1.58it/s] DDIM Sampler: 50%|████▉ | 59/119 [00:48<00:37, 1.58it/s] DDIM Sampler: 50%|█████ | 60/119 [00:48<00:37, 1.57it/s] DDIM Sampler: 51%|█████▏ | 61/119 [00:49<00:36, 1.58it/s] DDIM Sampler: 52%|█████▏ | 62/119 [00:50<00:36, 1.57it/s] DDIM Sampler: 53%|█████▎ | 63/119 [00:50<00:36, 1.55it/s] DDIM Sampler: 54%|█████▍ | 64/119 [00:51<00:35, 1.54it/s] DDIM Sampler: 55%|█████▍ | 65/119 [00:52<00:35, 1.53it/s] DDIM Sampler: 55%|█████▌ | 66/119 [00:52<00:34, 1.53it/s] DDIM Sampler: 56%|█████▋ | 67/119 [00:53<00:34, 1.52it/s] DDIM Sampler: 57%|█████▋ | 68/119 [00:54<00:33, 1.52it/s] DDIM Sampler: 58%|█████▊ | 69/119 [00:54<00:33, 1.51it/s] DDIM Sampler: 59%|█████▉ | 70/119 [00:55<00:32, 1.51it/s] DDIM Sampler: 60%|█████▉ | 71/119 [00:55<00:31, 1.51it/s] DDIM Sampler: 61%|██████ | 72/119 [00:56<00:31, 1.51it/s] DDIM Sampler: 61%|██████▏ | 73/119 [00:57<00:30, 1.51it/s] DDIM Sampler: 62%|██████▏ | 74/119 [00:57<00:29, 1.51it/s] DDIM Sampler: 63%|██████▎ | 75/119 [00:58<00:29, 1.51it/s] DDIM Sampler: 64%|██████▍ | 76/119 [00:59<00:28, 1.51it/s] DDIM Sampler: 65%|██████▍ | 77/119 [00:59<00:27, 1.51it/s] DDIM Sampler: 66%|██████▌ | 78/119 [01:00<00:27, 1.51it/s] DDIM Sampler: 66%|██████▋ | 79/119 [01:01<00:26, 1.51it/s] DDIM Sampler: 67%|██████▋ | 80/119 [01:01<00:25, 1.50it/s] DDIM Sampler: 68%|██████▊ | 81/119 [01:02<00:25, 1.50it/s] DDIM Sampler: 69%|██████▉ | 82/119 [01:03<00:24, 1.50it/s] DDIM Sampler: 70%|██████▉ | 83/119 [01:03<00:23, 1.50it/s] DDIM Sampler: 71%|███████ | 84/119 [01:04<00:23, 1.50it/s] DDIM Sampler: 71%|███████▏ | 85/119 [01:05<00:22, 1.50it/s] DDIM Sampler: 72%|███████▏ | 86/119 [01:05<00:22, 1.49it/s] DDIM Sampler: 73%|███████▎ | 87/119 [01:06<00:21, 1.49it/s] DDIM Sampler: 74%|███████▍ | 88/119 [01:07<00:20, 1.49it/s] DDIM Sampler: 75%|███████▍ | 89/119 [01:07<00:20, 1.50it/s] DDIM Sampler: 76%|███████▌ | 90/119 [01:08<00:19, 1.50it/s] DDIM Sampler: 76%|███████▋ | 91/119 [01:09<00:18, 1.49it/s] DDIM Sampler: 77%|███████▋ | 92/119 [01:10<00:18, 1.49it/s] DDIM Sampler: 78%|███████▊ | 93/119 [01:10<00:17, 1.49it/s] DDIM Sampler: 79%|███████▉ | 94/119 [01:11<00:16, 1.49it/s] DDIM Sampler: 80%|███████▉ | 95/119 [01:12<00:16, 1.49it/s] DDIM Sampler: 81%|████████ | 96/119 [01:12<00:15, 1.49it/s] DDIM Sampler: 82%|████████▏ | 97/119 [01:13<00:14, 1.48it/s] DDIM Sampler: 82%|████████▏ | 98/119 [01:14<00:14, 1.49it/s] DDIM Sampler: 83%|████████▎ | 99/119 [01:14<00:13, 1.48it/s] DDIM Sampler: 84%|████████▍ | 100/119 [01:15<00:12, 1.49it/s] DDIM Sampler: 85%|████████▍ | 101/119 [01:16<00:12, 1.49it/s] DDIM Sampler: 86%|████████▌ | 102/119 [01:16<00:11, 1.50it/s] DDIM Sampler: 87%|████████▋ | 103/119 [01:17<00:10, 1.50it/s] DDIM Sampler: 87%|████████▋ | 104/119 [01:18<00:10, 1.49it/s] DDIM Sampler: 88%|████████▊ | 105/119 [01:18<00:09, 1.50it/s] DDIM Sampler: 89%|████████▉ | 106/119 [01:19<00:08, 1.51it/s] DDIM Sampler: 90%|████████▉ | 107/119 [01:20<00:07, 1.51it/s] DDIM Sampler: 91%|█████████ | 108/119 [01:20<00:07, 1.51it/s] DDIM Sampler: 92%|█████████▏| 109/119 [01:21<00:06, 1.51it/s] DDIM Sampler: 92%|█████████▏| 110/119 [01:22<00:05, 1.51it/s] DDIM Sampler: 93%|█████████▎| 111/119 [01:22<00:05, 1.51it/s] DDIM Sampler: 94%|█████████▍| 112/119 [01:23<00:04, 1.50it/s] DDIM Sampler: 95%|█████████▍| 113/119 [01:24<00:03, 1.51it/s] DDIM Sampler: 96%|█████████▌| 114/119 [01:24<00:03, 1.51it/s] DDIM Sampler: 97%|█████████▋| 115/119 [01:25<00:02, 1.51it/s] DDIM Sampler: 97%|█████████▋| 116/119 [01:25<00:01, 1.51it/s] DDIM Sampler: 98%|█████████▊| 117/119 [01:26<00:01, 1.51it/s] DDIM Sampler: 99%|█████████▉| 118/119 [01:27<00:00, 1.51it/s] DDIM Sampler: 100%|██████████| 119/119 [01:27<00:00, 1.51it/s] DDIM Sampler: 100%|██████████| 119/119 [01:27<00:00, 1.35it/s] python inference_gfpgan.py -i /tmp/tmprqvpx06_gfpgan/temp_1658847856.png -o results -v 1.3 -s 2 huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Downloading: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth" to /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/realesrgan/weights/RealESRGAN_x2plus.pth 0%| | 0.00/64.0M [00:00<?, ?B/s] 16%|█▌ | 10.1M/64.0M [00:00<00:00, 106MB/s] 58%|█████▊ | 37.1M/64.0M [00:00<00:00, 210MB/s] 100%|█████████▉| 63.8M/64.0M [00:00<00:00, 242MB/s] 100%|██████████| 64.0M/64.0M [00:00<00:00, 223MB/s] Processing temp_1658847856.png ... Tile 1/1 Results are in the [results] folder. Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.1 Running DDIM Sampling with 74 timesteps DDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s] DDIM Sampler: 1%|▏ | 1/74 [00:09<11:30, 9.46s/it] DDIM Sampler: 3%|▎ | 2/74 [00:10<05:25, 4.53s/it] DDIM Sampler: 4%|▍ | 3/74 [00:11<03:29, 2.95s/it] DDIM Sampler: 5%|▌ | 4/74 [00:12<02:34, 2.21s/it] DDIM Sampler: 7%|▋ | 5/74 [00:13<02:04, 1.80s/it] DDIM Sampler: 8%|▊ | 6/74 [00:14<01:45, 1.55s/it] DDIM Sampler: 9%|▉ | 7/74 [00:15<01:33, 1.40s/it] DDIM Sampler: 11%|█ | 8/74 [00:16<01:25, 1.30s/it] DDIM Sampler: 12%|█▏ | 9/74 [00:18<01:19, 1.23s/it] DDIM Sampler: 14%|█▎ | 10/74 [00:19<01:15, 1.18s/it] DDIM Sampler: 15%|█▍ | 11/74 [00:20<01:12, 1.14s/it] DDIM Sampler: 16%|█▌ | 12/74 [00:21<01:09, 1.12s/it] DDIM Sampler: 18%|█▊ | 13/74 [00:22<01:07, 1.11s/it] DDIM Sampler: 19%|█▉ | 14/74 [00:23<01:05, 1.10s/it] DDIM Sampler: 20%|██ | 15/74 [00:24<01:04, 1.09s/it] DDIM Sampler: 22%|██▏ | 16/74 [00:25<01:02, 1.08s/it] DDIM Sampler: 23%|██▎ | 17/74 [00:26<01:01, 1.08s/it] DDIM Sampler: 24%|██▍ | 18/74 [00:27<01:00, 1.08s/it] DDIM Sampler: 26%|██▌ | 19/74 [00:28<00:59, 1.08s/it] DDIM Sampler: 27%|██▋ | 20/74 [00:29<00:58, 1.08s/it] DDIM Sampler: 28%|██▊ | 21/74 [00:30<00:56, 1.07s/it] DDIM Sampler: 30%|██▉ | 22/74 [00:31<00:56, 1.08s/it] DDIM Sampler: 31%|███ | 23/74 [00:33<00:54, 1.08s/it] DDIM Sampler: 32%|███▏ | 24/74 [00:34<00:53, 1.07s/it] DDIM Sampler: 34%|███▍ | 25/74 [00:35<00:52, 1.07s/it] DDIM Sampler: 35%|███▌ | 26/74 [00:36<00:51, 1.07s/it] DDIM Sampler: 36%|███▋ | 27/74 [00:37<00:50, 1.07s/it] DDIM Sampler: 38%|███▊ | 28/74 [00:38<00:49, 1.07s/it] DDIM Sampler: 39%|███▉ | 29/74 [00:39<00:48, 1.07s/it] DDIM Sampler: 41%|████ | 30/74 [00:40<00:47, 1.07s/it] DDIM Sampler: 42%|████▏ | 31/74 [00:41<00:46, 1.07s/it] DDIM Sampler: 43%|████▎ | 32/74 [00:42<00:45, 1.08s/it] DDIM Sampler: 45%|████▍ | 33/74 [00:43<00:44, 1.08s/it] DDIM Sampler: 46%|████▌ | 34/74 [00:44<00:42, 1.07s/it] DDIM Sampler: 47%|████▋ | 35/74 [00:45<00:41, 1.08s/it] DDIM Sampler: 49%|████▊ | 36/74 [00:46<00:40, 1.07s/it] DDIM Sampler: 50%|█████ | 37/74 [00:48<00:39, 1.08s/it] DDIM Sampler: 51%|█████▏ | 38/74 [00:49<00:38, 1.08s/it] DDIM Sampler: 53%|█████▎ | 39/74 [00:50<00:37, 1.08s/it] DDIM Sampler: 54%|█████▍ | 40/74 [00:51<00:36, 1.08s/it] DDIM Sampler: 55%|█████▌ | 41/74 [00:52<00:35, 1.09s/it] DDIM Sampler: 57%|█████▋ | 42/74 [00:53<00:34, 1.09s/it] DDIM Sampler: 58%|█████▊ | 43/74 [00:54<00:33, 1.09s/it] DDIM Sampler: 59%|█████▉ | 44/74 [00:55<00:32, 1.09s/it] DDIM Sampler: 61%|██████ | 45/74 [00:56<00:31, 1.09s/it] DDIM Sampler: 62%|██████▏ | 46/74 [00:57<00:30, 1.09s/it] DDIM Sampler: 64%|██████▎ | 47/74 [00:58<00:29, 1.09s/it] DDIM Sampler: 65%|██████▍ | 48/74 [01:00<00:28, 1.10s/it] DDIM Sampler: 66%|██████▌ | 49/74 [01:01<00:27, 1.10s/it] DDIM Sampler: 68%|██████▊ | 50/74 [01:02<00:26, 1.10s/it] DDIM Sampler: 69%|██████▉ | 51/74 [01:03<00:25, 1.10s/it] DDIM Sampler: 70%|███████ | 52/74 [01:04<00:24, 1.10s/it] DDIM Sampler: 72%|███████▏ | 53/74 [01:05<00:23, 1.10s/it] DDIM Sampler: 73%|███████▎ | 54/74 [01:06<00:22, 1.10s/it] DDIM Sampler: 74%|███████▍ | 55/74 [01:07<00:20, 1.10s/it] DDIM Sampler: 76%|███████▌ | 56/74 [01:08<00:19, 1.10s/it] DDIM Sampler: 77%|███████▋ | 57/74 [01:09<00:18, 1.10s/it] DDIM Sampler: 78%|███████▊ | 58/74 [01:11<00:17, 1.10s/it] DDIM Sampler: 80%|███████▉ | 59/74 [01:12<00:16, 1.10s/it] DDIM Sampler: 81%|████████ | 60/74 [01:13<00:15, 1.10s/it] DDIM Sampler: 82%|████████▏ | 61/74 [01:14<00:14, 1.10s/it] DDIM Sampler: 84%|████████▍ | 62/74 [01:15<00:13, 1.10s/it] DDIM Sampler: 85%|████████▌ | 63/74 [01:16<00:12, 1.10s/it] DDIM Sampler: 86%|████████▋ | 64/74 [01:17<00:11, 1.10s/it] DDIM Sampler: 88%|████████▊ | 65/74 [01:18<00:09, 1.10s/it] DDIM Sampler: 89%|████████▉ | 66/74 [01:19<00:08, 1.10s/it] DDIM Sampler: 91%|█████████ | 67/74 [01:21<00:07, 1.10s/it] DDIM Sampler: 92%|█████████▏| 68/74 [01:22<00:06, 1.10s/it] DDIM Sampler: 93%|█████████▎| 69/74 [01:23<00:05, 1.10s/it] DDIM Sampler: 95%|█████████▍| 70/74 [01:24<00:04, 1.10s/it] DDIM Sampler: 96%|█████████▌| 71/74 [01:25<00:03, 1.11s/it] DDIM Sampler: 97%|█████████▋| 72/74 [01:26<00:02, 1.11s/it] DDIM Sampler: 99%|█████████▊| 73/74 [01:27<00:01, 1.11s/it] DDIM Sampler: 100%|██████████| 74/74 [01:28<00:00, 1.11s/it] DDIM Sampler: 100%|██████████| 74/74 [01:28<00:00, 1.20s/it] Sampling: 100%|██████████| 1/1 [03:14<00:00, 194.40s/it] Sampling: 100%|██████████| 1/1 [03:14<00:00, 194.40s/it]Prediction
nightmareai/majesty-diffusion:76f01b26Input
- width
- 640
- height
- 768
- clip_scale
- 16000
- clip_prompts
- The image has a gritty, urban feel with bright colors used to make the subject more visible. There is a lot of graffiti and street art featured in the shot, giving it an edgy look.
- latent_scale
- 12
- latent_prompt
- do ya work
- aesthetic_loss_scale
- 400
{ "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The image has a gritty, urban feel with bright colors used to make the subject more visible. There is a lot of graffiti and street art featured in the shot, giving it an edgy look.", "latent_scale": 12, "latent_prompt": "do ya work", "aesthetic_loss_scale": 400 }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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", { input: { width: 640, height: 768, clip_scale: 16000, clip_prompts: "The image has a gritty, urban feel with bright colors used to make the subject more visible. There is a lot of graffiti and street art featured in the shot, giving it an edgy look.", latent_scale: 12, latent_prompt: "do ya work", aesthetic_loss_scale: 400 } } ); 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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", input={ "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The image has a gritty, urban feel with bright colors used to make the subject more visible. There is a lot of graffiti and street art featured in the shot, giving it an edgy look.", "latent_scale": 12, "latent_prompt": "do ya work", "aesthetic_loss_scale": 400 } ) # The nightmareai/majesty-diffusion model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/nightmareai/majesty-diffusion/api#output-schema print(item)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 nightmareai/majesty-diffusion 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": "76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", "input": { "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The image has a gritty, urban feel with bright colors used to make the subject more visible. There is a lot of graffiti and street art featured in the shot, giving it an edgy look.", "latent_scale": 12, "latent_prompt": "do ya work", "aesthetic_loss_scale": 400 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-26T14:21:23.743950Z", "created_at": "2022-07-26T14:13:10.227368Z", "data_removed": false, "error": null, "id": "ieuask3xbzcs7hllttvttes6le", "input": { "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The image has a gritty, urban feel with bright colors used to make the subject more visible. There is a lot of graffiti and street art featured in the shot, giving it an edgy look.", "latent_scale": 12, "latent_prompt": "do ya work", "aesthetic_loss_scale": 400 }, "logs": "\nSampling images 1/1\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.3\nRunning DDIM Sampling with 119 timesteps\nSampling: 0%| | 0/1 [00:00<?, ?it/s]\n\nDDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%| | 1/119 [00:11<22:41, 11.54s/it]\u001b[A\n\nDDIM Sampler: 2%|▏ | 2/119 [00:12<10:00, 5.13s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 3/119 [00:12<05:57, 3.08s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 4/119 [00:13<04:03, 2.12s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 5/119 [00:14<03:00, 1.58s/it]\u001b[A\n\nDDIM Sampler: 5%|▌ | 6/119 [00:14<02:22, 1.26s/it]\u001b[A\n\nDDIM Sampler: 6%|▌ | 7/119 [00:15<01:58, 1.06s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 8/119 [00:15<01:42, 1.09it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 9/119 [00:16<01:31, 1.21it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 10/119 [00:17<01:23, 1.30it/s]\u001b[A\n\nDDIM Sampler: 9%|▉ | 11/119 [00:17<01:18, 1.37it/s]\u001b[A\n\nDDIM Sampler: 10%|█ | 12/119 [00:18<01:15, 1.42it/s]\u001b[A\n\nDDIM Sampler: 11%|█ | 13/119 [00:19<01:12, 1.47it/s]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 14/119 [00:19<01:10, 1.50it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 15/119 [00:20<01:07, 1.53it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 16/119 [00:21<01:06, 1.55it/s]\u001b[A\n\nDDIM Sampler: 14%|█▍ | 17/119 [00:21<01:04, 1.58it/s]\u001b[A\n\nDDIM Sampler: 15%|█▌ | 18/119 [00:22<01:03, 1.58it/s]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 19/119 [00:22<01:03, 1.57it/s]\u001b[A\n\nDDIM Sampler: 17%|█▋ | 20/119 [00:23<01:02, 1.58it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 21/119 [00:24<01:02, 1.57it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 22/119 [00:24<01:01, 1.57it/s]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 23/119 [00:25<01:00, 1.57it/s]\u001b[A\n\nDDIM Sampler: 20%|██ | 24/119 [00:26<01:00, 1.57it/s]\u001b[A\n\nDDIM Sampler: 21%|██ | 25/119 [00:26<00:59, 1.58it/s]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 26/119 [00:27<00:58, 1.58it/s]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 27/119 [00:28<00:57, 1.59it/s]\u001b[A\n\nDDIM Sampler: 24%|██▎ | 28/119 [00:28<00:57, 1.58it/s]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 29/119 [00:29<00:56, 1.59it/s]\u001b[A\n\nDDIM Sampler: 25%|██▌ | 30/119 [00:29<00:55, 1.59it/s]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 31/119 [00:30<00:55, 1.60it/s]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 32/119 [00:31<00:54, 1.59it/s]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 33/119 [00:31<00:53, 1.60it/s]\u001b[A\n\nDDIM Sampler: 29%|██▊ | 34/119 [00:32<00:53, 1.58it/s]\u001b[A\n\nDDIM Sampler: 29%|██▉ | 35/119 [00:33<00:52, 1.60it/s]\u001b[A\n\nDDIM Sampler: 30%|███ | 36/119 [00:33<00:52, 1.59it/s]\u001b[A\n\nDDIM Sampler: 31%|███ | 37/119 [00:34<00:51, 1.59it/s]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 38/119 [00:34<00:50, 1.59it/s]\u001b[A\n\nDDIM Sampler: 33%|███▎ | 39/119 [00:35<00:50, 1.59it/s]\u001b[A\n\nDDIM Sampler: 34%|███▎ | 40/119 [00:36<00:49, 1.59it/s]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 41/119 [00:36<00:48, 1.59it/s]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 42/119 [00:37<00:48, 1.60it/s]\u001b[A\n\nDDIM Sampler: 36%|███▌ | 43/119 [00:38<00:47, 1.60it/s]\u001b[A\n\nDDIM Sampler: 37%|███▋ | 44/119 [00:38<00:47, 1.59it/s]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 45/119 [00:39<00:46, 1.59it/s]\u001b[A\n\nDDIM Sampler: 39%|███▊ | 46/119 [00:39<00:46, 1.58it/s]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 47/119 [00:40<00:45, 1.58it/s]\u001b[A\n\nDDIM Sampler: 40%|████ | 48/119 [00:41<00:44, 1.59it/s]\u001b[A\n\nDDIM Sampler: 41%|████ | 49/119 [00:41<00:44, 1.59it/s]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 50/119 [00:42<00:43, 1.59it/s]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 51/119 [00:43<00:42, 1.58it/s]\u001b[A\n\nDDIM Sampler: 44%|████▎ | 52/119 [00:43<00:42, 1.58it/s]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 53/119 [00:44<00:41, 1.58it/s]\u001b[A\n\nDDIM Sampler: 45%|████▌ | 54/119 [00:45<00:41, 1.58it/s]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 55/119 [00:45<00:40, 1.58it/s]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 56/119 [00:46<00:39, 1.58it/s]\u001b[A\n\nDDIM Sampler: 48%|████▊ | 57/119 [00:46<00:39, 1.57it/s]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 58/119 [00:47<00:38, 1.58it/s]\u001b[A\n\nDDIM Sampler: 50%|████▉ | 59/119 [00:48<00:38, 1.58it/s]\u001b[A\n\nDDIM Sampler: 50%|█████ | 60/119 [00:48<00:37, 1.58it/s]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 61/119 [00:49<00:36, 1.58it/s]\u001b[A\n\nDDIM Sampler: 52%|█████▏ | 62/119 [00:50<00:36, 1.58it/s]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 63/119 [00:50<00:35, 1.56it/s]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 64/119 [00:51<00:35, 1.54it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▍ | 65/119 [00:52<00:35, 1.53it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 66/119 [00:52<00:34, 1.52it/s]\u001b[A\n\nDDIM Sampler: 56%|█████▋ | 67/119 [00:53<00:34, 1.52it/s]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 68/119 [00:54<00:33, 1.52it/s]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 69/119 [00:54<00:32, 1.52it/s]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 70/119 [00:55<00:32, 1.52it/s]\u001b[A\n\nDDIM Sampler: 60%|█████▉ | 71/119 [00:56<00:31, 1.52it/s]\u001b[A\n\nDDIM Sampler: 61%|██████ | 72/119 [00:56<00:30, 1.52it/s]\u001b[A\n\nDDIM Sampler: 61%|██████▏ | 73/119 [00:57<00:30, 1.52it/s]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 74/119 [00:58<00:29, 1.51it/s]\u001b[A\n\nDDIM Sampler: 63%|██████▎ | 75/119 [00:58<00:29, 1.51it/s]\u001b[A\n\nDDIM Sampler: 64%|██████▍ | 76/119 [00:59<00:28, 1.51it/s]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 77/119 [00:59<00:27, 1.52it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 78/119 [01:00<00:27, 1.52it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▋ | 79/119 [01:01<00:26, 1.52it/s]\u001b[A\n\nDDIM Sampler: 67%|██████▋ | 80/119 [01:01<00:25, 1.51it/s]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 81/119 [01:02<00:25, 1.51it/s]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 82/119 [01:03<00:24, 1.51it/s]\u001b[A\n\nDDIM Sampler: 70%|██████▉ | 83/119 [01:03<00:23, 1.51it/s]\u001b[A\n\nDDIM Sampler: 71%|███████ | 84/119 [01:04<00:23, 1.52it/s]\u001b[A\n\nDDIM Sampler: 71%|███████▏ | 85/119 [01:05<00:22, 1.52it/s]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 86/119 [01:05<00:21, 1.51it/s]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 87/119 [01:06<00:21, 1.51it/s]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 88/119 [01:07<00:20, 1.52it/s]\u001b[A\n\nDDIM Sampler: 75%|███████▍ | 89/119 [01:07<00:19, 1.51it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 90/119 [01:08<00:19, 1.52it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▋ | 91/119 [01:09<00:18, 1.52it/s]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 92/119 [01:09<00:17, 1.52it/s]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 93/119 [01:10<00:17, 1.52it/s]\u001b[A\n\nDDIM Sampler: 79%|███████▉ | 94/119 [01:11<00:16, 1.52it/s]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 95/119 [01:11<00:15, 1.52it/s]\u001b[A\n\nDDIM Sampler: 81%|████████ | 96/119 [01:12<00:15, 1.51it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 97/119 [01:13<00:14, 1.51it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 98/119 [01:13<00:14, 1.49it/s]\u001b[A\n\nDDIM Sampler: 83%|████████▎ | 99/119 [01:14<00:13, 1.50it/s]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 100/119 [01:15<00:12, 1.51it/s]\u001b[A\n\nDDIM Sampler: 85%|████████▍ | 101/119 [01:15<00:11, 1.51it/s]\u001b[A\n\nDDIM Sampler: 86%|████████▌ | 102/119 [01:16<00:11, 1.51it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 103/119 [01:17<00:10, 1.51it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 104/119 [01:17<00:09, 1.51it/s]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 105/119 [01:18<00:09, 1.51it/s]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 106/119 [01:19<00:08, 1.52it/s]\u001b[A\n\nDDIM Sampler: 90%|████████▉ | 107/119 [01:19<00:07, 1.52it/s]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 108/119 [01:20<00:07, 1.52it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 109/119 [01:21<00:06, 1.52it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 110/119 [01:21<00:05, 1.51it/s]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 111/119 [01:22<00:05, 1.50it/s]\u001b[A\n\nDDIM Sampler: 94%|█████████▍| 112/119 [01:23<00:04, 1.50it/s]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 113/119 [01:23<00:03, 1.51it/s]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 114/119 [01:24<00:03, 1.51it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 115/119 [01:25<00:02, 1.51it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 116/119 [01:25<00:01, 1.50it/s]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 117/119 [01:26<00:01, 1.51it/s]\u001b[A\n\nDDIM Sampler: 99%|█████████▉| 118/119 [01:27<00:00, 1.51it/s]\u001b[A\n\nDDIM Sampler: 100%|██████████| 119/119 [01:27<00:00, 1.52it/s]\u001b[A\nDDIM Sampler: 100%|██████████| 119/119 [01:27<00:00, 1.36it/s]\npython inference_gfpgan.py -i /tmp/tmpps7jgki4gfpgan/temp_1658845173.png -o results -v 1.3 -s 2\n\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nDownloading: \"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth\" to /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/realesrgan/weights/RealESRGAN_x2plus.pth\n\n\n 0%| | 0.00/64.0M [00:00<?, ?B/s]\n 1%| | 352k/64.0M [00:00<00:21, 3.14MB/s]\n 4%|▍ | 2.72M/64.0M [00:00<00:04, 15.0MB/s]\n 11%|█ | 7.09M/64.0M [00:00<00:02, 27.3MB/s]\n 17%|█▋ | 10.7M/64.0M [00:00<00:01, 30.3MB/s]\n 23%|██▎ | 14.7M/64.0M [00:00<00:01, 33.1MB/s]\n 28%|██▊ | 18.2M/64.0M [00:00<00:01, 34.2MB/s]\n 34%|███▍ | 22.0M/64.0M [00:00<00:01, 35.4MB/s]\n 41%|████ | 26.0M/64.0M [00:00<00:01, 37.4MB/s]\n 46%|████▋ | 29.6M/64.0M [00:00<00:00, 36.8MB/s]\n 52%|█████▏ | 33.1M/64.0M [00:01<00:00, 36.5MB/s]\n 57%|█████▋ | 36.7M/64.0M [00:01<00:00, 36.7MB/s]\n 64%|██████▎ | 40.7M/64.0M [00:01<00:00, 37.1MB/s]\n 70%|██████▉ | 44.7M/64.0M [00:01<00:00, 37.4MB/s]\n 75%|███████▌ | 48.2M/64.0M [00:01<00:00, 36.6MB/s]\n 82%|████████▏ | 52.7M/64.0M [00:01<00:00, 38.0MB/s]\n 89%|████████▊ | 56.7M/64.0M [00:01<00:00, 36.6MB/s]\n 95%|█████████▍| 60.7M/64.0M [00:01<00:00, 37.8MB/s]\n100%|██████████| 64.0M/64.0M [00:01<00:00, 35.2MB/s]\nProcessing temp_1658845173.png ...\n\tTile 1/1\nResults are in the [results] folder.\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.1\nRunning DDIM Sampling with 74 timesteps\n\n\nDDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%|▏ | 1/74 [00:09<11:35, 9.52s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 2/74 [00:10<05:29, 4.58s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 3/74 [00:11<03:32, 2.99s/it]\u001b[A\n\nDDIM Sampler: 5%|▌ | 4/74 [00:12<02:37, 2.25s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 5/74 [00:13<02:06, 1.84s/it]\u001b[A\n\nDDIM Sampler: 8%|▊ | 6/74 [00:15<01:48, 1.59s/it]\u001b[A\n\nDDIM Sampler: 9%|▉ | 7/74 [00:16<01:35, 1.43s/it]\u001b[A\n\nDDIM Sampler: 11%|█ | 8/74 [00:17<01:27, 1.33s/it]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 9/74 [00:18<01:21, 1.26s/it]\u001b[A\n\nDDIM Sampler: 14%|█▎ | 10/74 [00:19<01:17, 1.21s/it]\u001b[A\n\nDDIM Sampler: 15%|█▍ | 11/74 [00:20<01:14, 1.19s/it]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 12/74 [00:21<01:12, 1.17s/it]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 13/74 [00:22<01:10, 1.15s/it]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 14/74 [00:23<01:08, 1.14s/it]\u001b[A\n\nDDIM Sampler: 20%|██ | 15/74 [00:25<01:06, 1.13s/it]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 16/74 [00:26<01:05, 1.13s/it]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 17/74 [00:27<01:04, 1.13s/it]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 18/74 [00:28<01:02, 1.12s/it]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 19/74 [00:29<01:01, 1.12s/it]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 20/74 [00:30<01:00, 1.12s/it]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 21/74 [00:31<00:59, 1.12s/it]\u001b[A\n\nDDIM Sampler: 30%|██▉ | 22/74 [00:32<00:58, 1.12s/it]\u001b[A\n\nDDIM Sampler: 31%|███ | 23/74 [00:34<00:57, 1.12s/it]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 24/74 [00:35<00:55, 1.12s/it]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 25/74 [00:36<00:55, 1.12s/it]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 26/74 [00:37<00:53, 1.12s/it]\u001b[A\n\nDDIM Sampler: 36%|███▋ | 27/74 [00:38<00:52, 1.12s/it]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 28/74 [00:39<00:51, 1.12s/it]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 29/74 [00:40<00:50, 1.13s/it]\u001b[A\n\nDDIM Sampler: 41%|████ | 30/74 [00:41<00:49, 1.13s/it]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 31/74 [00:43<00:48, 1.13s/it]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 32/74 [00:44<00:47, 1.13s/it]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 33/74 [00:45<00:46, 1.13s/it]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 34/74 [00:46<00:45, 1.13s/it]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 35/74 [00:47<00:43, 1.13s/it]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 36/74 [00:48<00:42, 1.13s/it]\u001b[A\n\nDDIM Sampler: 50%|█████ | 37/74 [00:49<00:41, 1.13s/it]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 38/74 [00:50<00:40, 1.13s/it]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 39/74 [00:52<00:39, 1.13s/it]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 40/74 [00:53<00:38, 1.13s/it]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 41/74 [00:54<00:37, 1.13s/it]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 42/74 [00:55<00:36, 1.13s/it]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 43/74 [00:56<00:35, 1.13s/it]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 44/74 [00:57<00:33, 1.13s/it]\u001b[A\n\nDDIM Sampler: 61%|██████ | 45/74 [00:58<00:32, 1.13s/it]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 46/74 [01:00<00:31, 1.13s/it]\u001b[A\n\nDDIM Sampler: 64%|██████▎ | 47/74 [01:01<00:30, 1.13s/it]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 48/74 [01:02<00:29, 1.13s/it]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 49/74 [01:03<00:28, 1.13s/it]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 50/74 [01:04<00:27, 1.13s/it]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 51/74 [01:05<00:26, 1.13s/it]\u001b[A\n\nDDIM Sampler: 70%|███████ | 52/74 [01:06<00:24, 1.13s/it]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 53/74 [01:07<00:23, 1.13s/it]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 54/74 [01:09<00:22, 1.14s/it]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 55/74 [01:10<00:21, 1.13s/it]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 56/74 [01:11<00:20, 1.13s/it]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 57/74 [01:12<00:19, 1.13s/it]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 58/74 [01:13<00:18, 1.13s/it]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 59/74 [01:14<00:16, 1.13s/it]\u001b[A\n\nDDIM Sampler: 81%|████████ | 60/74 [01:15<00:15, 1.13s/it]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 61/74 [01:16<00:14, 1.13s/it]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 62/74 [01:18<00:13, 1.13s/it]\u001b[A\n\nDDIM Sampler: 85%|████████▌ | 63/74 [01:19<00:12, 1.13s/it]\u001b[A\n\nDDIM Sampler: 86%|████████▋ | 64/74 [01:20<00:11, 1.14s/it]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 65/74 [01:21<00:10, 1.14s/it]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 66/74 [01:22<00:09, 1.13s/it]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 67/74 [01:23<00:07, 1.13s/it]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 68/74 [01:24<00:06, 1.13s/it]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 69/74 [01:26<00:05, 1.13s/it]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 70/74 [01:27<00:04, 1.13s/it]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 71/74 [01:28<00:03, 1.13s/it]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 72/74 [01:29<00:02, 1.13s/it]\u001b[A\n\nDDIM Sampler: 99%|█████████▊| 73/74 [01:30<00:01, 1.14s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 74/74 [01:31<00:00, 1.14s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 74/74 [01:31<00:00, 1.24s/it]\n\nSampling: 100%|██████████| 1/1 [03:18<00:00, 198.71s/it]\nSampling: 100%|██████████| 1/1 [03:18<00:00, 198.71s/it]", "metrics": { "predict_time": 199.561053, "total_time": 493.516582 }, "output": [ "https://replicate.delivery/mgxm/bb1c1cdb-c60a-4dcb-9709-440318c6607e/0.png", "https://replicate.delivery/mgxm/1f471e82-92a5-4c78-99e5-483c4fddd3d9/10.png", "https://replicate.delivery/mgxm/ca5b8037-ce2d-4ab8-bcbd-549898c4be91/20.png", "https://replicate.delivery/mgxm/02d1fadd-4ab8-4cae-8cc7-8d88a22d36e3/30.png", "https://replicate.delivery/mgxm/f1d7c29c-4be1-4e52-ae0e-c4d17e37654b/40.png", "https://replicate.delivery/mgxm/1a4eeeb0-7be4-4bd4-bd29-c3c3f7e5af96/50.png", "https://replicate.delivery/mgxm/7e0d8cb1-5a6d-47f3-895b-58ae416ed34b/60.png", "https://replicate.delivery/mgxm/a0c49263-eddb-40d9-a3f4-a0e2e5657087/70.png", "https://replicate.delivery/mgxm/58c4c868-1798-4d15-85b3-8ae3cbcff1ac/80.png", "https://replicate.delivery/mgxm/5b12631f-1782-4b64-94de-4bfdd5a95a6f/90.png", "https://replicate.delivery/mgxm/aa895475-2c1d-4403-89bb-81e76c0a9b69/100.png", "https://replicate.delivery/mgxm/e0e98a1b-b7d5-427d-9e34-bf8bcb1ad95f/110.png", "https://replicate.delivery/mgxm/8e6413ee-abb5-4aa0-881f-2a0df2e23df5/120.png", "https://replicate.delivery/mgxm/84d86900-68af-4007-b8df-51a13e02a463/130.png", "https://replicate.delivery/mgxm/acbe2558-81e6-4505-aac0-ca85791f16be/140.png", "https://replicate.delivery/mgxm/12318c56-8ed5-465a-a72b-1ee0e96635bc/150.png", "https://replicate.delivery/mgxm/f2225254-34a3-4b24-be1e-e2cd7b0079e6/160.png", "https://replicate.delivery/mgxm/579a4fbf-2b82-425e-be7e-b7ad8b3ba2f2/170.png", "https://replicate.delivery/mgxm/11cbef20-579e-483c-9246-ca26061a319f/180.png", "https://replicate.delivery/mgxm/8c4805e6-4615-4adf-b058-dc067bdca5e8/190.png", "https://replicate.delivery/mgxm/47c5e40b-f356-446a-97a4-7b3789058eb4/1658845279.png" ], "started_at": "2022-07-26T14:18:04.182897Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ieuask3xbzcs7hllttvttes6le", "cancel": "https://api.replicate.com/v1/predictions/ieuask3xbzcs7hllttvttes6le/cancel" }, "version": "09b0d0f7e8b6bf2021873759444ef961d7fa9546223045b2ba79da23cd65f18d" }Generated inSampling images 1/1 Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.3 Running DDIM Sampling with 119 timesteps Sampling: 0%| | 0/1 [00:00<?, ?it/s] DDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s] DDIM Sampler: 1%| | 1/119 [00:11<22:41, 11.54s/it] DDIM Sampler: 2%|▏ | 2/119 [00:12<10:00, 5.13s/it] DDIM Sampler: 3%|▎ | 3/119 [00:12<05:57, 3.08s/it] DDIM Sampler: 3%|▎ | 4/119 [00:13<04:03, 2.12s/it] DDIM Sampler: 4%|▍ | 5/119 [00:14<03:00, 1.58s/it] DDIM Sampler: 5%|▌ | 6/119 [00:14<02:22, 1.26s/it] DDIM Sampler: 6%|▌ | 7/119 [00:15<01:58, 1.06s/it] DDIM Sampler: 7%|▋ | 8/119 [00:15<01:42, 1.09it/s] DDIM Sampler: 8%|▊ | 9/119 [00:16<01:31, 1.21it/s] DDIM Sampler: 8%|▊ | 10/119 [00:17<01:23, 1.30it/s] DDIM Sampler: 9%|▉ | 11/119 [00:17<01:18, 1.37it/s] DDIM Sampler: 10%|█ | 12/119 [00:18<01:15, 1.42it/s] DDIM Sampler: 11%|█ | 13/119 [00:19<01:12, 1.47it/s] DDIM Sampler: 12%|█▏ | 14/119 [00:19<01:10, 1.50it/s] DDIM Sampler: 13%|█▎ | 15/119 [00:20<01:07, 1.53it/s] DDIM Sampler: 13%|█▎ | 16/119 [00:21<01:06, 1.55it/s] DDIM Sampler: 14%|█▍ | 17/119 [00:21<01:04, 1.58it/s] DDIM Sampler: 15%|█▌ | 18/119 [00:22<01:03, 1.58it/s] DDIM Sampler: 16%|█▌ | 19/119 [00:22<01:03, 1.57it/s] DDIM Sampler: 17%|█▋ | 20/119 [00:23<01:02, 1.58it/s] DDIM Sampler: 18%|█▊ | 21/119 [00:24<01:02, 1.57it/s] DDIM Sampler: 18%|█▊ | 22/119 [00:24<01:01, 1.57it/s] DDIM Sampler: 19%|█▉ | 23/119 [00:25<01:00, 1.57it/s] DDIM Sampler: 20%|██ | 24/119 [00:26<01:00, 1.57it/s] DDIM Sampler: 21%|██ | 25/119 [00:26<00:59, 1.58it/s] DDIM Sampler: 22%|██▏ | 26/119 [00:27<00:58, 1.58it/s] DDIM Sampler: 23%|██▎ | 27/119 [00:28<00:57, 1.59it/s] DDIM Sampler: 24%|██▎ | 28/119 [00:28<00:57, 1.58it/s] DDIM Sampler: 24%|██▍ | 29/119 [00:29<00:56, 1.59it/s] DDIM Sampler: 25%|██▌ | 30/119 [00:29<00:55, 1.59it/s] DDIM Sampler: 26%|██▌ | 31/119 [00:30<00:55, 1.60it/s] DDIM Sampler: 27%|██▋ | 32/119 [00:31<00:54, 1.59it/s] DDIM Sampler: 28%|██▊ | 33/119 [00:31<00:53, 1.60it/s] DDIM Sampler: 29%|██▊ | 34/119 [00:32<00:53, 1.58it/s] DDIM Sampler: 29%|██▉ | 35/119 [00:33<00:52, 1.60it/s] DDIM Sampler: 30%|███ | 36/119 [00:33<00:52, 1.59it/s] DDIM Sampler: 31%|███ | 37/119 [00:34<00:51, 1.59it/s] DDIM Sampler: 32%|███▏ | 38/119 [00:34<00:50, 1.59it/s] DDIM Sampler: 33%|███▎ | 39/119 [00:35<00:50, 1.59it/s] DDIM Sampler: 34%|███▎ | 40/119 [00:36<00:49, 1.59it/s] DDIM Sampler: 34%|███▍ | 41/119 [00:36<00:48, 1.59it/s] DDIM Sampler: 35%|███▌ | 42/119 [00:37<00:48, 1.60it/s] DDIM Sampler: 36%|███▌ | 43/119 [00:38<00:47, 1.60it/s] DDIM Sampler: 37%|███▋ | 44/119 [00:38<00:47, 1.59it/s] DDIM Sampler: 38%|███▊ | 45/119 [00:39<00:46, 1.59it/s] DDIM Sampler: 39%|███▊ | 46/119 [00:39<00:46, 1.58it/s] DDIM Sampler: 39%|███▉ | 47/119 [00:40<00:45, 1.58it/s] DDIM Sampler: 40%|████ | 48/119 [00:41<00:44, 1.59it/s] DDIM Sampler: 41%|████ | 49/119 [00:41<00:44, 1.59it/s] DDIM Sampler: 42%|████▏ | 50/119 [00:42<00:43, 1.59it/s] DDIM Sampler: 43%|████▎ | 51/119 [00:43<00:42, 1.58it/s] DDIM Sampler: 44%|████▎ | 52/119 [00:43<00:42, 1.58it/s] DDIM Sampler: 45%|████▍ | 53/119 [00:44<00:41, 1.58it/s] DDIM Sampler: 45%|████▌ | 54/119 [00:45<00:41, 1.58it/s] DDIM Sampler: 46%|████▌ | 55/119 [00:45<00:40, 1.58it/s] DDIM Sampler: 47%|████▋ | 56/119 [00:46<00:39, 1.58it/s] DDIM Sampler: 48%|████▊ | 57/119 [00:46<00:39, 1.57it/s] DDIM Sampler: 49%|████▊ | 58/119 [00:47<00:38, 1.58it/s] DDIM Sampler: 50%|████▉ | 59/119 [00:48<00:38, 1.58it/s] DDIM Sampler: 50%|█████ | 60/119 [00:48<00:37, 1.58it/s] DDIM Sampler: 51%|█████▏ | 61/119 [00:49<00:36, 1.58it/s] DDIM Sampler: 52%|█████▏ | 62/119 [00:50<00:36, 1.58it/s] DDIM Sampler: 53%|█████▎ | 63/119 [00:50<00:35, 1.56it/s] DDIM Sampler: 54%|█████▍ | 64/119 [00:51<00:35, 1.54it/s] DDIM Sampler: 55%|█████▍ | 65/119 [00:52<00:35, 1.53it/s] DDIM Sampler: 55%|█████▌ | 66/119 [00:52<00:34, 1.52it/s] DDIM Sampler: 56%|█████▋ | 67/119 [00:53<00:34, 1.52it/s] DDIM Sampler: 57%|█████▋ | 68/119 [00:54<00:33, 1.52it/s] DDIM Sampler: 58%|█████▊ | 69/119 [00:54<00:32, 1.52it/s] DDIM Sampler: 59%|█████▉ | 70/119 [00:55<00:32, 1.52it/s] DDIM Sampler: 60%|█████▉ | 71/119 [00:56<00:31, 1.52it/s] DDIM Sampler: 61%|██████ | 72/119 [00:56<00:30, 1.52it/s] DDIM Sampler: 61%|██████▏ | 73/119 [00:57<00:30, 1.52it/s] DDIM Sampler: 62%|██████▏ | 74/119 [00:58<00:29, 1.51it/s] DDIM Sampler: 63%|██████▎ | 75/119 [00:58<00:29, 1.51it/s] DDIM Sampler: 64%|██████▍ | 76/119 [00:59<00:28, 1.51it/s] DDIM Sampler: 65%|██████▍ | 77/119 [00:59<00:27, 1.52it/s] DDIM Sampler: 66%|██████▌ | 78/119 [01:00<00:27, 1.52it/s] DDIM Sampler: 66%|██████▋ | 79/119 [01:01<00:26, 1.52it/s] DDIM Sampler: 67%|██████▋ | 80/119 [01:01<00:25, 1.51it/s] DDIM Sampler: 68%|██████▊ | 81/119 [01:02<00:25, 1.51it/s] DDIM Sampler: 69%|██████▉ | 82/119 [01:03<00:24, 1.51it/s] DDIM Sampler: 70%|██████▉ | 83/119 [01:03<00:23, 1.51it/s] DDIM Sampler: 71%|███████ | 84/119 [01:04<00:23, 1.52it/s] DDIM Sampler: 71%|███████▏ | 85/119 [01:05<00:22, 1.52it/s] DDIM Sampler: 72%|███████▏ | 86/119 [01:05<00:21, 1.51it/s] DDIM Sampler: 73%|███████▎ | 87/119 [01:06<00:21, 1.51it/s] DDIM Sampler: 74%|███████▍ | 88/119 [01:07<00:20, 1.52it/s] DDIM Sampler: 75%|███████▍ | 89/119 [01:07<00:19, 1.51it/s] DDIM Sampler: 76%|███████▌ | 90/119 [01:08<00:19, 1.52it/s] DDIM Sampler: 76%|███████▋ | 91/119 [01:09<00:18, 1.52it/s] DDIM Sampler: 77%|███████▋ | 92/119 [01:09<00:17, 1.52it/s] DDIM Sampler: 78%|███████▊ | 93/119 [01:10<00:17, 1.52it/s] DDIM Sampler: 79%|███████▉ | 94/119 [01:11<00:16, 1.52it/s] DDIM Sampler: 80%|███████▉ | 95/119 [01:11<00:15, 1.52it/s] DDIM Sampler: 81%|████████ | 96/119 [01:12<00:15, 1.51it/s] DDIM Sampler: 82%|████████▏ | 97/119 [01:13<00:14, 1.51it/s] DDIM Sampler: 82%|████████▏ | 98/119 [01:13<00:14, 1.49it/s] DDIM Sampler: 83%|████████▎ | 99/119 [01:14<00:13, 1.50it/s] DDIM Sampler: 84%|████████▍ | 100/119 [01:15<00:12, 1.51it/s] DDIM Sampler: 85%|████████▍ | 101/119 [01:15<00:11, 1.51it/s] DDIM Sampler: 86%|████████▌ | 102/119 [01:16<00:11, 1.51it/s] DDIM Sampler: 87%|████████▋ | 103/119 [01:17<00:10, 1.51it/s] DDIM Sampler: 87%|████████▋ | 104/119 [01:17<00:09, 1.51it/s] DDIM Sampler: 88%|████████▊ | 105/119 [01:18<00:09, 1.51it/s] DDIM Sampler: 89%|████████▉ | 106/119 [01:19<00:08, 1.52it/s] DDIM Sampler: 90%|████████▉ | 107/119 [01:19<00:07, 1.52it/s] DDIM Sampler: 91%|█████████ | 108/119 [01:20<00:07, 1.52it/s] DDIM Sampler: 92%|█████████▏| 109/119 [01:21<00:06, 1.52it/s] DDIM Sampler: 92%|█████████▏| 110/119 [01:21<00:05, 1.51it/s] DDIM Sampler: 93%|█████████▎| 111/119 [01:22<00:05, 1.50it/s] DDIM Sampler: 94%|█████████▍| 112/119 [01:23<00:04, 1.50it/s] DDIM Sampler: 95%|█████████▍| 113/119 [01:23<00:03, 1.51it/s] DDIM Sampler: 96%|█████████▌| 114/119 [01:24<00:03, 1.51it/s] DDIM Sampler: 97%|█████████▋| 115/119 [01:25<00:02, 1.51it/s] DDIM Sampler: 97%|█████████▋| 116/119 [01:25<00:01, 1.50it/s] DDIM Sampler: 98%|█████████▊| 117/119 [01:26<00:01, 1.51it/s] DDIM Sampler: 99%|█████████▉| 118/119 [01:27<00:00, 1.51it/s] DDIM Sampler: 100%|██████████| 119/119 [01:27<00:00, 1.52it/s] DDIM Sampler: 100%|██████████| 119/119 [01:27<00:00, 1.36it/s] python inference_gfpgan.py -i /tmp/tmpps7jgki4gfpgan/temp_1658845173.png -o results -v 1.3 -s 2 huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Downloading: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth" to /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/realesrgan/weights/RealESRGAN_x2plus.pth 0%| | 0.00/64.0M [00:00<?, ?B/s] 1%| | 352k/64.0M [00:00<00:21, 3.14MB/s] 4%|▍ | 2.72M/64.0M [00:00<00:04, 15.0MB/s] 11%|█ | 7.09M/64.0M [00:00<00:02, 27.3MB/s] 17%|█▋ | 10.7M/64.0M [00:00<00:01, 30.3MB/s] 23%|██▎ | 14.7M/64.0M [00:00<00:01, 33.1MB/s] 28%|██▊ | 18.2M/64.0M [00:00<00:01, 34.2MB/s] 34%|███▍ | 22.0M/64.0M [00:00<00:01, 35.4MB/s] 41%|████ | 26.0M/64.0M [00:00<00:01, 37.4MB/s] 46%|████▋ | 29.6M/64.0M [00:00<00:00, 36.8MB/s] 52%|█████▏ | 33.1M/64.0M [00:01<00:00, 36.5MB/s] 57%|█████▋ | 36.7M/64.0M [00:01<00:00, 36.7MB/s] 64%|██████▎ | 40.7M/64.0M [00:01<00:00, 37.1MB/s] 70%|██████▉ | 44.7M/64.0M [00:01<00:00, 37.4MB/s] 75%|███████▌ | 48.2M/64.0M [00:01<00:00, 36.6MB/s] 82%|████████▏ | 52.7M/64.0M [00:01<00:00, 38.0MB/s] 89%|████████▊ | 56.7M/64.0M [00:01<00:00, 36.6MB/s] 95%|█████████▍| 60.7M/64.0M [00:01<00:00, 37.8MB/s] 100%|██████████| 64.0M/64.0M [00:01<00:00, 35.2MB/s] Processing temp_1658845173.png ... Tile 1/1 Results are in the [results] folder. Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.1 Running DDIM Sampling with 74 timesteps DDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s] DDIM Sampler: 1%|▏ | 1/74 [00:09<11:35, 9.52s/it] DDIM Sampler: 3%|▎ | 2/74 [00:10<05:29, 4.58s/it] DDIM Sampler: 4%|▍ | 3/74 [00:11<03:32, 2.99s/it] DDIM Sampler: 5%|▌ | 4/74 [00:12<02:37, 2.25s/it] DDIM Sampler: 7%|▋ | 5/74 [00:13<02:06, 1.84s/it] DDIM Sampler: 8%|▊ | 6/74 [00:15<01:48, 1.59s/it] DDIM Sampler: 9%|▉ | 7/74 [00:16<01:35, 1.43s/it] DDIM Sampler: 11%|█ | 8/74 [00:17<01:27, 1.33s/it] DDIM Sampler: 12%|█▏ | 9/74 [00:18<01:21, 1.26s/it] DDIM Sampler: 14%|█▎ | 10/74 [00:19<01:17, 1.21s/it] DDIM Sampler: 15%|█▍ | 11/74 [00:20<01:14, 1.19s/it] DDIM Sampler: 16%|█▌ | 12/74 [00:21<01:12, 1.17s/it] DDIM Sampler: 18%|█▊ | 13/74 [00:22<01:10, 1.15s/it] DDIM Sampler: 19%|█▉ | 14/74 [00:23<01:08, 1.14s/it] DDIM Sampler: 20%|██ | 15/74 [00:25<01:06, 1.13s/it] DDIM Sampler: 22%|██▏ | 16/74 [00:26<01:05, 1.13s/it] DDIM Sampler: 23%|██▎ | 17/74 [00:27<01:04, 1.13s/it] DDIM Sampler: 24%|██▍ | 18/74 [00:28<01:02, 1.12s/it] DDIM Sampler: 26%|██▌ | 19/74 [00:29<01:01, 1.12s/it] DDIM Sampler: 27%|██▋ | 20/74 [00:30<01:00, 1.12s/it] DDIM Sampler: 28%|██▊ | 21/74 [00:31<00:59, 1.12s/it] DDIM Sampler: 30%|██▉ | 22/74 [00:32<00:58, 1.12s/it] DDIM Sampler: 31%|███ | 23/74 [00:34<00:57, 1.12s/it] DDIM Sampler: 32%|███▏ | 24/74 [00:35<00:55, 1.12s/it] DDIM Sampler: 34%|███▍ | 25/74 [00:36<00:55, 1.12s/it] DDIM Sampler: 35%|███▌ | 26/74 [00:37<00:53, 1.12s/it] DDIM Sampler: 36%|███▋ | 27/74 [00:38<00:52, 1.12s/it] DDIM Sampler: 38%|███▊ | 28/74 [00:39<00:51, 1.12s/it] DDIM Sampler: 39%|███▉ | 29/74 [00:40<00:50, 1.13s/it] DDIM Sampler: 41%|████ | 30/74 [00:41<00:49, 1.13s/it] DDIM Sampler: 42%|████▏ | 31/74 [00:43<00:48, 1.13s/it] DDIM Sampler: 43%|████▎ | 32/74 [00:44<00:47, 1.13s/it] DDIM Sampler: 45%|████▍ | 33/74 [00:45<00:46, 1.13s/it] DDIM Sampler: 46%|████▌ | 34/74 [00:46<00:45, 1.13s/it] DDIM Sampler: 47%|████▋ | 35/74 [00:47<00:43, 1.13s/it] DDIM Sampler: 49%|████▊ | 36/74 [00:48<00:42, 1.13s/it] DDIM Sampler: 50%|█████ | 37/74 [00:49<00:41, 1.13s/it] DDIM Sampler: 51%|█████▏ | 38/74 [00:50<00:40, 1.13s/it] DDIM Sampler: 53%|█████▎ | 39/74 [00:52<00:39, 1.13s/it] DDIM Sampler: 54%|█████▍ | 40/74 [00:53<00:38, 1.13s/it] DDIM Sampler: 55%|█████▌ | 41/74 [00:54<00:37, 1.13s/it] DDIM Sampler: 57%|█████▋ | 42/74 [00:55<00:36, 1.13s/it] DDIM Sampler: 58%|█████▊ | 43/74 [00:56<00:35, 1.13s/it] DDIM Sampler: 59%|█████▉ | 44/74 [00:57<00:33, 1.13s/it] DDIM Sampler: 61%|██████ | 45/74 [00:58<00:32, 1.13s/it] DDIM Sampler: 62%|██████▏ | 46/74 [01:00<00:31, 1.13s/it] DDIM Sampler: 64%|██████▎ | 47/74 [01:01<00:30, 1.13s/it] DDIM Sampler: 65%|██████▍ | 48/74 [01:02<00:29, 1.13s/it] DDIM Sampler: 66%|██████▌ | 49/74 [01:03<00:28, 1.13s/it] DDIM Sampler: 68%|██████▊ | 50/74 [01:04<00:27, 1.13s/it] DDIM Sampler: 69%|██████▉ | 51/74 [01:05<00:26, 1.13s/it] DDIM Sampler: 70%|███████ | 52/74 [01:06<00:24, 1.13s/it] DDIM Sampler: 72%|███████▏ | 53/74 [01:07<00:23, 1.13s/it] DDIM Sampler: 73%|███████▎ | 54/74 [01:09<00:22, 1.14s/it] DDIM Sampler: 74%|███████▍ | 55/74 [01:10<00:21, 1.13s/it] DDIM Sampler: 76%|███████▌ | 56/74 [01:11<00:20, 1.13s/it] DDIM Sampler: 77%|███████▋ | 57/74 [01:12<00:19, 1.13s/it] DDIM Sampler: 78%|███████▊ | 58/74 [01:13<00:18, 1.13s/it] DDIM Sampler: 80%|███████▉ | 59/74 [01:14<00:16, 1.13s/it] DDIM Sampler: 81%|████████ | 60/74 [01:15<00:15, 1.13s/it] DDIM Sampler: 82%|████████▏ | 61/74 [01:16<00:14, 1.13s/it] DDIM Sampler: 84%|████████▍ | 62/74 [01:18<00:13, 1.13s/it] DDIM Sampler: 85%|████████▌ | 63/74 [01:19<00:12, 1.13s/it] DDIM Sampler: 86%|████████▋ | 64/74 [01:20<00:11, 1.14s/it] DDIM Sampler: 88%|████████▊ | 65/74 [01:21<00:10, 1.14s/it] DDIM Sampler: 89%|████████▉ | 66/74 [01:22<00:09, 1.13s/it] DDIM Sampler: 91%|█████████ | 67/74 [01:23<00:07, 1.13s/it] DDIM Sampler: 92%|█████████▏| 68/74 [01:24<00:06, 1.13s/it] DDIM Sampler: 93%|█████████▎| 69/74 [01:26<00:05, 1.13s/it] DDIM Sampler: 95%|█████████▍| 70/74 [01:27<00:04, 1.13s/it] DDIM Sampler: 96%|█████████▌| 71/74 [01:28<00:03, 1.13s/it] DDIM Sampler: 97%|█████████▋| 72/74 [01:29<00:02, 1.13s/it] DDIM Sampler: 99%|█████████▊| 73/74 [01:30<00:01, 1.14s/it] DDIM Sampler: 100%|██████████| 74/74 [01:31<00:00, 1.14s/it] DDIM Sampler: 100%|██████████| 74/74 [01:31<00:00, 1.24s/it] Sampling: 100%|██████████| 1/1 [03:18<00:00, 198.71s/it] Sampling: 100%|██████████| 1/1 [03:18<00:00, 198.71s/it]Prediction
nightmareai/majesty-diffusion:76f01b26ID3hlkpt5amfh6xoue3gjh7anoyaStatusSucceededSourceAPIHardware–Total durationCreatedInput
- width
- 640
- height
- 768
- clip_scale
- 16000
- clip_prompts
- The visual style of this image is mostly bright and colorful, with a few mysterious elements. It has a futuristic feel to it, and might be inspired by science fiction movies or books.
- latent_scale
- 12
- latent_prompt
- weird and wonderful AI art
- aesthetic_loss_scale
- 400
{ "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The visual style of this image is mostly bright and colorful, with a few mysterious elements. It has a futuristic feel to it, and might be inspired by science fiction movies or books.", "latent_scale": 12, "latent_prompt": "weird and wonderful AI art", "aesthetic_loss_scale": 400 }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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", { input: { width: 640, height: 768, clip_scale: 16000, clip_prompts: "The visual style of this image is mostly bright and colorful, with a few mysterious elements. It has a futuristic feel to it, and might be inspired by science fiction movies or books.", latent_scale: 12, latent_prompt: "weird and wonderful AI art", aesthetic_loss_scale: 400 } } ); 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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", input={ "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The visual style of this image is mostly bright and colorful, with a few mysterious elements. It has a futuristic feel to it, and might be inspired by science fiction movies or books.", "latent_scale": 12, "latent_prompt": "weird and wonderful AI art", "aesthetic_loss_scale": 400 } ) # The nightmareai/majesty-diffusion model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/nightmareai/majesty-diffusion/api#output-schema print(item)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 nightmareai/majesty-diffusion 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": "76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", "input": { "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The visual style of this image is mostly bright and colorful, with a few mysterious elements. It has a futuristic feel to it, and might be inspired by science fiction movies or books.", "latent_scale": 12, "latent_prompt": "weird and wonderful AI art", "aesthetic_loss_scale": 400 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-26T14:59:01.896580Z", "created_at": "2022-07-26T14:53:33.665732Z", "data_removed": false, "error": null, "id": "3hlkpt5amfh6xoue3gjh7anoya", "input": { "width": 640, "height": 768, "clip_scale": 16000, "clip_prompts": "The visual style of this image is mostly bright and colorful, with a few mysterious elements. It has a futuristic feel to it, and might be inspired by science fiction movies or books.", "latent_scale": 12, "latent_prompt": "weird and wonderful AI art", "aesthetic_loss_scale": 400 }, "logs": "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nSampling images 1/1\n\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.3\nRunning DDIM Sampling with 119 timesteps\nSampling: 0%| | 0/1 [00:00<?, ?it/s]\n\nDDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%| | 1/119 [00:05<10:08, 5.16s/it]\u001b[A\n\nDDIM Sampler: 2%|▏ | 2/119 [00:05<04:51, 2.49s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 3/119 [00:06<03:08, 1.62s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 4/119 [00:06<02:20, 1.22s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 5/119 [00:07<01:53, 1.00it/s]\u001b[A\n\nDDIM Sampler: 5%|▌ | 6/119 [00:08<01:37, 1.16it/s]\u001b[A\n\nDDIM Sampler: 6%|▌ | 7/119 [00:08<01:26, 1.29it/s]\u001b[A\n\nDDIM Sampler: 7%|▋ | 8/119 [00:09<01:19, 1.40it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 9/119 [00:09<01:14, 1.48it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 10/119 [00:10<01:11, 1.53it/s]\u001b[A\n\nDDIM Sampler: 9%|▉ | 11/119 [00:11<01:09, 1.56it/s]\u001b[A\n\nDDIM Sampler: 10%|█ | 12/119 [00:11<01:08, 1.57it/s]\u001b[A\n\nDDIM Sampler: 11%|█ | 13/119 [00:12<01:06, 1.59it/s]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 14/119 [00:13<01:05, 1.61it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 15/119 [00:13<01:05, 1.59it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 16/119 [00:14<01:04, 1.60it/s]\u001b[A\n\nDDIM Sampler: 14%|█▍ | 17/119 [00:14<01:03, 1.61it/s]\u001b[A\n\nDDIM Sampler: 15%|█▌ | 18/119 [00:15<01:02, 1.62it/s]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 19/119 [00:16<01:01, 1.62it/s]\u001b[A\n\nDDIM Sampler: 17%|█▋ | 20/119 [00:16<01:01, 1.61it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 21/119 [00:17<01:01, 1.60it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 22/119 [00:18<01:01, 1.58it/s]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 23/119 [00:18<01:01, 1.57it/s]\u001b[A\n\nDDIM Sampler: 20%|██ | 24/119 [00:19<01:00, 1.56it/s]\u001b[A\n\nDDIM Sampler: 21%|██ | 25/119 [00:19<01:00, 1.55it/s]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 26/119 [00:20<01:00, 1.55it/s]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 27/119 [00:21<00:59, 1.55it/s]\u001b[A\n\nDDIM Sampler: 24%|██▎ | 28/119 [00:21<00:59, 1.53it/s]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 29/119 [00:22<00:58, 1.54it/s]\u001b[A\n\nDDIM Sampler: 25%|██▌ | 30/119 [00:23<00:57, 1.55it/s]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 31/119 [00:23<00:56, 1.55it/s]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 32/119 [00:24<00:55, 1.56it/s]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 33/119 [00:25<00:55, 1.56it/s]\u001b[A\n\nDDIM Sampler: 29%|██▊ | 34/119 [00:25<00:54, 1.56it/s]\u001b[A\n\nDDIM Sampler: 29%|██▉ | 35/119 [00:26<00:53, 1.56it/s]\u001b[A\n\nDDIM Sampler: 30%|███ | 36/119 [00:27<00:53, 1.55it/s]\u001b[A\n\nDDIM Sampler: 31%|███ | 37/119 [00:27<00:52, 1.56it/s]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 38/119 [00:28<00:51, 1.56it/s]\u001b[A\n\nDDIM Sampler: 33%|███▎ | 39/119 [00:28<00:51, 1.57it/s]\u001b[A\n\nDDIM Sampler: 34%|███▎ | 40/119 [00:29<00:50, 1.57it/s]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 41/119 [00:30<00:49, 1.58it/s]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 42/119 [00:30<00:48, 1.58it/s]\u001b[A\n\nDDIM Sampler: 36%|███▌ | 43/119 [00:31<00:48, 1.58it/s]\u001b[A\n\nDDIM Sampler: 37%|███▋ | 44/119 [00:32<00:47, 1.57it/s]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 45/119 [00:32<00:46, 1.58it/s]\u001b[A\n\nDDIM Sampler: 39%|███▊ | 46/119 [00:33<00:46, 1.58it/s]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 47/119 [00:34<00:45, 1.58it/s]\u001b[A\n\nDDIM Sampler: 40%|████ | 48/119 [00:34<00:44, 1.58it/s]\u001b[A\n\nDDIM Sampler: 41%|████ | 49/119 [00:35<00:44, 1.57it/s]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 50/119 [00:35<00:43, 1.57it/s]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 51/119 [00:36<00:43, 1.57it/s]\u001b[A\n\nDDIM Sampler: 44%|████▎ | 52/119 [00:37<00:42, 1.56it/s]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 53/119 [00:37<00:42, 1.56it/s]\u001b[A\n\nDDIM Sampler: 45%|████▌ | 54/119 [00:38<00:41, 1.56it/s]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 55/119 [00:39<00:41, 1.56it/s]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 56/119 [00:39<00:40, 1.56it/s]\u001b[A\n\nDDIM Sampler: 48%|████▊ | 57/119 [00:40<00:39, 1.56it/s]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 58/119 [00:41<00:39, 1.55it/s]\u001b[A\n\nDDIM Sampler: 50%|████▉ | 59/119 [00:41<00:38, 1.55it/s]\u001b[A\n\nDDIM Sampler: 50%|█████ | 60/119 [00:42<00:38, 1.55it/s]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 61/119 [00:43<00:37, 1.55it/s]\u001b[A\n\nDDIM Sampler: 52%|█████▏ | 62/119 [00:43<00:36, 1.55it/s]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 63/119 [00:44<00:36, 1.53it/s]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 64/119 [00:45<00:36, 1.51it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▍ | 65/119 [00:45<00:35, 1.50it/s]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 66/119 [00:46<00:35, 1.50it/s]\u001b[A\n\nDDIM Sampler: 56%|█████▋ | 67/119 [00:47<00:34, 1.49it/s]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 68/119 [00:47<00:34, 1.48it/s]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 69/119 [00:48<00:33, 1.47it/s]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 70/119 [00:49<00:33, 1.47it/s]\u001b[A\n\nDDIM Sampler: 60%|█████▉ | 71/119 [00:49<00:32, 1.47it/s]\u001b[A\n\nDDIM Sampler: 61%|██████ | 72/119 [00:50<00:31, 1.48it/s]\u001b[A\n\nDDIM Sampler: 61%|██████▏ | 73/119 [00:51<00:31, 1.47it/s]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 74/119 [00:51<00:30, 1.47it/s]\u001b[A\n\nDDIM Sampler: 63%|██████▎ | 75/119 [00:52<00:29, 1.47it/s]\u001b[A\n\nDDIM Sampler: 64%|██████▍ | 76/119 [00:53<00:29, 1.47it/s]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 77/119 [00:53<00:28, 1.48it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 78/119 [00:54<00:27, 1.48it/s]\u001b[A\n\nDDIM Sampler: 66%|██████▋ | 79/119 [00:55<00:27, 1.47it/s]\u001b[A\n\nDDIM Sampler: 67%|██████▋ | 80/119 [00:55<00:26, 1.47it/s]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 81/119 [00:56<00:25, 1.47it/s]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 82/119 [00:57<00:25, 1.47it/s]\u001b[A\n\nDDIM Sampler: 70%|██████▉ | 83/119 [00:57<00:24, 1.47it/s]\u001b[A\n\nDDIM Sampler: 71%|███████ | 84/119 [00:58<00:23, 1.47it/s]\u001b[A\n\nDDIM Sampler: 71%|███████▏ | 85/119 [00:59<00:23, 1.47it/s]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 86/119 [00:59<00:22, 1.48it/s]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 87/119 [01:00<00:21, 1.48it/s]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 88/119 [01:01<00:21, 1.47it/s]\u001b[A\n\nDDIM Sampler: 75%|███████▍ | 89/119 [01:02<00:20, 1.46it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 90/119 [01:02<00:19, 1.47it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▋ | 91/119 [01:03<00:19, 1.47it/s]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 92/119 [01:04<00:18, 1.48it/s]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 93/119 [01:04<00:17, 1.47it/s]\u001b[A\n\nDDIM Sampler: 79%|███████▉ | 94/119 [01:05<00:16, 1.47it/s]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 95/119 [01:06<00:16, 1.48it/s]\u001b[A\n\nDDIM Sampler: 81%|████████ | 96/119 [01:06<00:15, 1.47it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 97/119 [01:07<00:14, 1.47it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 98/119 [01:08<00:14, 1.48it/s]\u001b[A\n\nDDIM Sampler: 83%|████████▎ | 99/119 [01:08<00:13, 1.48it/s]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 100/119 [01:09<00:12, 1.48it/s]\u001b[A\n\nDDIM Sampler: 85%|████████▍ | 101/119 [01:10<00:12, 1.48it/s]\u001b[A\n\nDDIM Sampler: 86%|████████▌ | 102/119 [01:10<00:11, 1.48it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 103/119 [01:11<00:10, 1.48it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 104/119 [01:12<00:10, 1.48it/s]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 105/119 [01:12<00:09, 1.48it/s]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 106/119 [01:13<00:08, 1.48it/s]\u001b[A\n\nDDIM Sampler: 90%|████████▉ | 107/119 [01:14<00:08, 1.48it/s]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 108/119 [01:14<00:07, 1.48it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 109/119 [01:15<00:06, 1.48it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 110/119 [01:16<00:06, 1.48it/s]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 111/119 [01:16<00:05, 1.48it/s]\u001b[A\n\nDDIM Sampler: 94%|█████████▍| 112/119 [01:17<00:04, 1.47it/s]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 113/119 [01:18<00:04, 1.48it/s]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 114/119 [01:18<00:03, 1.48it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 115/119 [01:19<00:02, 1.47it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 116/119 [01:20<00:02, 1.48it/s]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 117/119 [01:20<00:01, 1.48it/s]\u001b[A\n\nDDIM Sampler: 99%|█████████▉| 118/119 [01:21<00:00, 1.48it/s]\u001b[A\n\nDDIM Sampler: 100%|██████████| 119/119 [01:22<00:00, 1.48it/s]\u001b[A\nDDIM Sampler: 100%|██████████| 119/119 [01:22<00:00, 1.45it/s]\npython inference_gfpgan.py -i /tmp/tmpnu3kk771gfpgan/temp_1658847432.png -o results -v 1.3 -s 2\n\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nProcessing temp_1658847432.png ...\n\tTile 1/1\nResults are in the [results] folder.\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.1\nRunning DDIM Sampling with 74 timesteps\n\n\nDDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%|▏ | 1/74 [00:09<11:33, 9.50s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 2/74 [00:10<05:28, 4.56s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 3/74 [00:11<03:31, 2.98s/it]\u001b[A\n\nDDIM Sampler: 5%|▌ | 4/74 [00:12<02:37, 2.25s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 5/74 [00:13<02:06, 1.84s/it]\u001b[A\n\nDDIM Sampler: 8%|▊ | 6/74 [00:15<01:48, 1.59s/it]\u001b[A\n\nDDIM Sampler: 9%|▉ | 7/74 [00:16<01:36, 1.43s/it]\u001b[A\n\nDDIM Sampler: 11%|█ | 8/74 [00:17<01:28, 1.33s/it]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 9/74 [00:18<01:22, 1.26s/it]\u001b[A\n\nDDIM Sampler: 14%|█▎ | 10/74 [00:19<01:17, 1.22s/it]\u001b[A\n\nDDIM Sampler: 15%|█▍ | 11/74 [00:20<01:14, 1.19s/it]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 12/74 [00:21<01:12, 1.16s/it]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 13/74 [00:22<01:10, 1.15s/it]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 14/74 [00:23<01:08, 1.14s/it]\u001b[A\n\nDDIM Sampler: 20%|██ | 15/74 [00:25<01:06, 1.13s/it]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 16/74 [00:26<01:05, 1.13s/it]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 17/74 [00:27<01:04, 1.13s/it]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 18/74 [00:28<01:03, 1.13s/it]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 19/74 [00:29<01:02, 1.13s/it]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 20/74 [00:30<01:00, 1.13s/it]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 21/74 [00:31<00:59, 1.12s/it]\u001b[A\n\nDDIM Sampler: 30%|██▉ | 22/74 [00:32<00:58, 1.12s/it]\u001b[A\n\nDDIM Sampler: 31%|███ | 23/74 [00:34<00:57, 1.12s/it]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 24/74 [00:35<00:56, 1.12s/it]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 25/74 [00:36<00:55, 1.13s/it]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 26/74 [00:37<00:54, 1.13s/it]\u001b[A\n\nDDIM Sampler: 36%|███▋ | 27/74 [00:38<00:53, 1.13s/it]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 28/74 [00:39<00:52, 1.14s/it]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 29/74 [00:40<00:51, 1.13s/it]\u001b[A\n\nDDIM Sampler: 41%|████ | 30/74 [00:42<00:49, 1.13s/it]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 31/74 [00:43<00:48, 1.14s/it]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 32/74 [00:44<00:47, 1.14s/it]\u001b[A\n\nDDIM Sampler: 45%|████▍ | 33/74 [00:45<00:46, 1.14s/it]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 34/74 [00:46<00:45, 1.14s/it]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 35/74 [00:47<00:44, 1.14s/it]\u001b[A\n\nDDIM Sampler: 49%|████▊ | 36/74 [00:48<00:43, 1.14s/it]\u001b[A\n\nDDIM Sampler: 50%|█████ | 37/74 [00:50<00:42, 1.14s/it]\u001b[A\n\nDDIM Sampler: 51%|█████▏ | 38/74 [00:51<00:41, 1.14s/it]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 39/74 [00:52<00:39, 1.14s/it]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 40/74 [00:53<00:38, 1.14s/it]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 41/74 [00:54<00:37, 1.14s/it]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 42/74 [00:55<00:36, 1.14s/it]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 43/74 [00:56<00:35, 1.15s/it]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 44/74 [00:58<00:34, 1.15s/it]\u001b[A\n\nDDIM Sampler: 61%|██████ | 45/74 [00:59<00:33, 1.15s/it]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 46/74 [01:00<00:32, 1.15s/it]\u001b[A\n\nDDIM Sampler: 64%|██████▎ | 47/74 [01:01<00:31, 1.15s/it]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 48/74 [01:02<00:29, 1.15s/it]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 49/74 [01:03<00:28, 1.15s/it]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 50/74 [01:04<00:27, 1.15s/it]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 51/74 [01:06<00:26, 1.15s/it]\u001b[A\n\nDDIM Sampler: 70%|███████ | 52/74 [01:07<00:25, 1.15s/it]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 53/74 [01:08<00:24, 1.15s/it]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 54/74 [01:09<00:23, 1.15s/it]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 55/74 [01:10<00:21, 1.15s/it]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 56/74 [01:11<00:20, 1.15s/it]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 57/74 [01:13<00:19, 1.16s/it]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 58/74 [01:14<00:18, 1.16s/it]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 59/74 [01:15<00:17, 1.16s/it]\u001b[A\n\nDDIM Sampler: 81%|████████ | 60/74 [01:16<00:16, 1.16s/it]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 61/74 [01:17<00:15, 1.16s/it]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 62/74 [01:18<00:13, 1.16s/it]\u001b[A\n\nDDIM Sampler: 85%|████████▌ | 63/74 [01:19<00:12, 1.16s/it]\u001b[A\n\nDDIM Sampler: 86%|████████▋ | 64/74 [01:21<00:11, 1.16s/it]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 65/74 [01:22<00:10, 1.16s/it]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 66/74 [01:23<00:09, 1.16s/it]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 67/74 [01:24<00:08, 1.16s/it]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 68/74 [01:25<00:06, 1.17s/it]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 69/74 [01:26<00:05, 1.17s/it]\u001b[A\n\nDDIM Sampler: 95%|█████████▍| 70/74 [01:28<00:04, 1.17s/it]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 71/74 [01:29<00:03, 1.17s/it]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 72/74 [01:30<00:02, 1.16s/it]\u001b[A\n\nDDIM Sampler: 99%|█████████▊| 73/74 [01:31<00:01, 1.16s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 74/74 [01:32<00:00, 1.17s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 74/74 [01:32<00:00, 1.25s/it]\n\nSampling: 100%|██████████| 1/1 [03:12<00:00, 192.10s/it]\nSampling: 100%|██████████| 1/1 [03:12<00:00, 192.10s/it]", "metrics": { "predict_time": 193.118235, "total_time": 328.230848 }, "output": [ "https://replicate.delivery/mgxm/74577741-e407-417f-b33c-70d75d2a75b9/0.png", "https://replicate.delivery/mgxm/671b9a15-0159-4116-abf3-18708c6b88a1/10.png", "https://replicate.delivery/mgxm/3a79fa42-bc5c-4c9a-af60-facdabd73ac1/20.png", "https://replicate.delivery/mgxm/69f5196c-3baf-4927-bedc-44c2ac4b5e89/30.png", "https://replicate.delivery/mgxm/eee071fe-e735-49fa-baea-fec1c139ac48/40.png", "https://replicate.delivery/mgxm/35a8fc8d-c128-4e54-9572-ad1919bcbb9f/50.png", "https://replicate.delivery/mgxm/128c6904-8fe0-4352-be4e-01eeed4e7d86/60.png", "https://replicate.delivery/mgxm/bff1a7e0-548a-4e68-b950-8ba534601bb2/70.png", "https://replicate.delivery/mgxm/abade3d1-c58e-43e5-b349-9e93a2ad6ccb/80.png", "https://replicate.delivery/mgxm/4a503202-f05e-4530-af2b-1be34f787237/90.png", "https://replicate.delivery/mgxm/7666607e-c9fd-45a0-a4e5-9d754a9b28b4/100.png", "https://replicate.delivery/mgxm/5258c986-a726-4dcc-84eb-ec5a792e78c0/110.png", "https://replicate.delivery/mgxm/1d0e1d89-517c-4120-b4d2-3104e08d927d/120.png", "https://replicate.delivery/mgxm/93be934b-9712-48a5-8e14-a16772a74533/130.png", "https://replicate.delivery/mgxm/9d6c6275-78ed-4220-90d4-ea148b824fcc/140.png", "https://replicate.delivery/mgxm/cdfb8c93-aa39-4c50-925e-065857210077/150.png", "https://replicate.delivery/mgxm/c17297ef-32ca-40aa-b20d-8bcf6b116a4f/160.png", "https://replicate.delivery/mgxm/44607cfe-a081-442b-b0d9-283b1742ff42/170.png", "https://replicate.delivery/mgxm/cfa4cefc-a546-4a33-b3ec-ea9fc507b50f/180.png", "https://replicate.delivery/mgxm/b0f06fc4-37ba-41ae-8664-b5764135fec6/190.png", "https://replicate.delivery/mgxm/7c8f302a-6c66-42e6-b262-a8f83c220512/1658847537.png" ], "started_at": "2022-07-26T14:55:48.778345Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3hlkpt5amfh6xoue3gjh7anoya", "cancel": "https://api.replicate.com/v1/predictions/3hlkpt5amfh6xoue3gjh7anoya/cancel" }, "version": "73bfb38ec5c159e3dcbe2dc69d2db245c1cc07a94377829c49dded3a7ddb7e3a" }Generated inhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Sampling images 1/1 Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.3 Running DDIM Sampling with 119 timesteps Sampling: 0%| | 0/1 [00:00<?, ?it/s] DDIM Sampler: 0%| | 0/119 [00:00<?, ?it/s] DDIM Sampler: 1%| | 1/119 [00:05<10:08, 5.16s/it] DDIM Sampler: 2%|▏ | 2/119 [00:05<04:51, 2.49s/it] DDIM Sampler: 3%|▎ | 3/119 [00:06<03:08, 1.62s/it] DDIM Sampler: 3%|▎ | 4/119 [00:06<02:20, 1.22s/it] DDIM Sampler: 4%|▍ | 5/119 [00:07<01:53, 1.00it/s] DDIM Sampler: 5%|▌ | 6/119 [00:08<01:37, 1.16it/s] DDIM Sampler: 6%|▌ | 7/119 [00:08<01:26, 1.29it/s] DDIM Sampler: 7%|▋ | 8/119 [00:09<01:19, 1.40it/s] DDIM Sampler: 8%|▊ | 9/119 [00:09<01:14, 1.48it/s] DDIM Sampler: 8%|▊ | 10/119 [00:10<01:11, 1.53it/s] DDIM Sampler: 9%|▉ | 11/119 [00:11<01:09, 1.56it/s] DDIM Sampler: 10%|█ | 12/119 [00:11<01:08, 1.57it/s] DDIM Sampler: 11%|█ | 13/119 [00:12<01:06, 1.59it/s] DDIM Sampler: 12%|█▏ | 14/119 [00:13<01:05, 1.61it/s] DDIM Sampler: 13%|█▎ | 15/119 [00:13<01:05, 1.59it/s] DDIM Sampler: 13%|█▎ | 16/119 [00:14<01:04, 1.60it/s] DDIM Sampler: 14%|█▍ | 17/119 [00:14<01:03, 1.61it/s] DDIM Sampler: 15%|█▌ | 18/119 [00:15<01:02, 1.62it/s] DDIM Sampler: 16%|█▌ | 19/119 [00:16<01:01, 1.62it/s] DDIM Sampler: 17%|█▋ | 20/119 [00:16<01:01, 1.61it/s] DDIM Sampler: 18%|█▊ | 21/119 [00:17<01:01, 1.60it/s] DDIM Sampler: 18%|█▊ | 22/119 [00:18<01:01, 1.58it/s] DDIM Sampler: 19%|█▉ | 23/119 [00:18<01:01, 1.57it/s] DDIM Sampler: 20%|██ | 24/119 [00:19<01:00, 1.56it/s] DDIM Sampler: 21%|██ | 25/119 [00:19<01:00, 1.55it/s] DDIM Sampler: 22%|██▏ | 26/119 [00:20<01:00, 1.55it/s] DDIM Sampler: 23%|██▎ | 27/119 [00:21<00:59, 1.55it/s] DDIM Sampler: 24%|██▎ | 28/119 [00:21<00:59, 1.53it/s] DDIM Sampler: 24%|██▍ | 29/119 [00:22<00:58, 1.54it/s] DDIM Sampler: 25%|██▌ | 30/119 [00:23<00:57, 1.55it/s] DDIM Sampler: 26%|██▌ | 31/119 [00:23<00:56, 1.55it/s] DDIM Sampler: 27%|██▋ | 32/119 [00:24<00:55, 1.56it/s] DDIM Sampler: 28%|██▊ | 33/119 [00:25<00:55, 1.56it/s] DDIM Sampler: 29%|██▊ | 34/119 [00:25<00:54, 1.56it/s] DDIM Sampler: 29%|██▉ | 35/119 [00:26<00:53, 1.56it/s] DDIM Sampler: 30%|███ | 36/119 [00:27<00:53, 1.55it/s] DDIM Sampler: 31%|███ | 37/119 [00:27<00:52, 1.56it/s] DDIM Sampler: 32%|███▏ | 38/119 [00:28<00:51, 1.56it/s] DDIM Sampler: 33%|███▎ | 39/119 [00:28<00:51, 1.57it/s] DDIM Sampler: 34%|███▎ | 40/119 [00:29<00:50, 1.57it/s] DDIM Sampler: 34%|███▍ | 41/119 [00:30<00:49, 1.58it/s] DDIM Sampler: 35%|███▌ | 42/119 [00:30<00:48, 1.58it/s] DDIM Sampler: 36%|███▌ | 43/119 [00:31<00:48, 1.58it/s] DDIM Sampler: 37%|███▋ | 44/119 [00:32<00:47, 1.57it/s] DDIM Sampler: 38%|███▊ | 45/119 [00:32<00:46, 1.58it/s] DDIM Sampler: 39%|███▊ | 46/119 [00:33<00:46, 1.58it/s] DDIM Sampler: 39%|███▉ | 47/119 [00:34<00:45, 1.58it/s] DDIM Sampler: 40%|████ | 48/119 [00:34<00:44, 1.58it/s] DDIM Sampler: 41%|████ | 49/119 [00:35<00:44, 1.57it/s] DDIM Sampler: 42%|████▏ | 50/119 [00:35<00:43, 1.57it/s] DDIM Sampler: 43%|████▎ | 51/119 [00:36<00:43, 1.57it/s] DDIM Sampler: 44%|████▎ | 52/119 [00:37<00:42, 1.56it/s] DDIM Sampler: 45%|████▍ | 53/119 [00:37<00:42, 1.56it/s] DDIM Sampler: 45%|████▌ | 54/119 [00:38<00:41, 1.56it/s] DDIM Sampler: 46%|████▌ | 55/119 [00:39<00:41, 1.56it/s] DDIM Sampler: 47%|████▋ | 56/119 [00:39<00:40, 1.56it/s] DDIM Sampler: 48%|████▊ | 57/119 [00:40<00:39, 1.56it/s] DDIM Sampler: 49%|████▊ | 58/119 [00:41<00:39, 1.55it/s] DDIM Sampler: 50%|████▉ | 59/119 [00:41<00:38, 1.55it/s] DDIM Sampler: 50%|█████ | 60/119 [00:42<00:38, 1.55it/s] DDIM Sampler: 51%|█████▏ | 61/119 [00:43<00:37, 1.55it/s] DDIM Sampler: 52%|█████▏ | 62/119 [00:43<00:36, 1.55it/s] DDIM Sampler: 53%|█████▎ | 63/119 [00:44<00:36, 1.53it/s] DDIM Sampler: 54%|█████▍ | 64/119 [00:45<00:36, 1.51it/s] DDIM Sampler: 55%|█████▍ | 65/119 [00:45<00:35, 1.50it/s] DDIM Sampler: 55%|█████▌ | 66/119 [00:46<00:35, 1.50it/s] DDIM Sampler: 56%|█████▋ | 67/119 [00:47<00:34, 1.49it/s] DDIM Sampler: 57%|█████▋ | 68/119 [00:47<00:34, 1.48it/s] DDIM Sampler: 58%|█████▊ | 69/119 [00:48<00:33, 1.47it/s] DDIM Sampler: 59%|█████▉ | 70/119 [00:49<00:33, 1.47it/s] DDIM Sampler: 60%|█████▉ | 71/119 [00:49<00:32, 1.47it/s] DDIM Sampler: 61%|██████ | 72/119 [00:50<00:31, 1.48it/s] DDIM Sampler: 61%|██████▏ | 73/119 [00:51<00:31, 1.47it/s] DDIM Sampler: 62%|██████▏ | 74/119 [00:51<00:30, 1.47it/s] DDIM Sampler: 63%|██████▎ | 75/119 [00:52<00:29, 1.47it/s] DDIM Sampler: 64%|██████▍ | 76/119 [00:53<00:29, 1.47it/s] DDIM Sampler: 65%|██████▍ | 77/119 [00:53<00:28, 1.48it/s] DDIM Sampler: 66%|██████▌ | 78/119 [00:54<00:27, 1.48it/s] DDIM Sampler: 66%|██████▋ | 79/119 [00:55<00:27, 1.47it/s] DDIM Sampler: 67%|██████▋ | 80/119 [00:55<00:26, 1.47it/s] DDIM Sampler: 68%|██████▊ | 81/119 [00:56<00:25, 1.47it/s] DDIM Sampler: 69%|██████▉ | 82/119 [00:57<00:25, 1.47it/s] DDIM Sampler: 70%|██████▉ | 83/119 [00:57<00:24, 1.47it/s] DDIM Sampler: 71%|███████ | 84/119 [00:58<00:23, 1.47it/s] DDIM Sampler: 71%|███████▏ | 85/119 [00:59<00:23, 1.47it/s] DDIM Sampler: 72%|███████▏ | 86/119 [00:59<00:22, 1.48it/s] DDIM Sampler: 73%|███████▎ | 87/119 [01:00<00:21, 1.48it/s] DDIM Sampler: 74%|███████▍ | 88/119 [01:01<00:21, 1.47it/s] DDIM Sampler: 75%|███████▍ | 89/119 [01:02<00:20, 1.46it/s] DDIM Sampler: 76%|███████▌ | 90/119 [01:02<00:19, 1.47it/s] DDIM Sampler: 76%|███████▋ | 91/119 [01:03<00:19, 1.47it/s] DDIM Sampler: 77%|███████▋ | 92/119 [01:04<00:18, 1.48it/s] DDIM Sampler: 78%|███████▊ | 93/119 [01:04<00:17, 1.47it/s] DDIM Sampler: 79%|███████▉ | 94/119 [01:05<00:16, 1.47it/s] DDIM Sampler: 80%|███████▉ | 95/119 [01:06<00:16, 1.48it/s] DDIM Sampler: 81%|████████ | 96/119 [01:06<00:15, 1.47it/s] DDIM Sampler: 82%|████████▏ | 97/119 [01:07<00:14, 1.47it/s] DDIM Sampler: 82%|████████▏ | 98/119 [01:08<00:14, 1.48it/s] DDIM Sampler: 83%|████████▎ | 99/119 [01:08<00:13, 1.48it/s] DDIM Sampler: 84%|████████▍ | 100/119 [01:09<00:12, 1.48it/s] DDIM Sampler: 85%|████████▍ | 101/119 [01:10<00:12, 1.48it/s] DDIM Sampler: 86%|████████▌ | 102/119 [01:10<00:11, 1.48it/s] DDIM Sampler: 87%|████████▋ | 103/119 [01:11<00:10, 1.48it/s] DDIM Sampler: 87%|████████▋ | 104/119 [01:12<00:10, 1.48it/s] DDIM Sampler: 88%|████████▊ | 105/119 [01:12<00:09, 1.48it/s] DDIM Sampler: 89%|████████▉ | 106/119 [01:13<00:08, 1.48it/s] DDIM Sampler: 90%|████████▉ | 107/119 [01:14<00:08, 1.48it/s] DDIM Sampler: 91%|█████████ | 108/119 [01:14<00:07, 1.48it/s] DDIM Sampler: 92%|█████████▏| 109/119 [01:15<00:06, 1.48it/s] DDIM Sampler: 92%|█████████▏| 110/119 [01:16<00:06, 1.48it/s] DDIM Sampler: 93%|█████████▎| 111/119 [01:16<00:05, 1.48it/s] DDIM Sampler: 94%|█████████▍| 112/119 [01:17<00:04, 1.47it/s] DDIM Sampler: 95%|█████████▍| 113/119 [01:18<00:04, 1.48it/s] DDIM Sampler: 96%|█████████▌| 114/119 [01:18<00:03, 1.48it/s] DDIM Sampler: 97%|█████████▋| 115/119 [01:19<00:02, 1.47it/s] DDIM Sampler: 97%|█████████▋| 116/119 [01:20<00:02, 1.48it/s] DDIM Sampler: 98%|█████████▊| 117/119 [01:20<00:01, 1.48it/s] DDIM Sampler: 99%|█████████▉| 118/119 [01:21<00:00, 1.48it/s] DDIM Sampler: 100%|██████████| 119/119 [01:22<00:00, 1.48it/s] DDIM Sampler: 100%|██████████| 119/119 [01:22<00:00, 1.45it/s] python inference_gfpgan.py -i /tmp/tmpnu3kk771gfpgan/temp_1658847432.png -o results -v 1.3 -s 2 huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Processing temp_1658847432.png ... Tile 1/1 Results are in the [results] folder. Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.1 Running DDIM Sampling with 74 timesteps DDIM Sampler: 0%| | 0/74 [00:00<?, ?it/s] DDIM Sampler: 1%|▏ | 1/74 [00:09<11:33, 9.50s/it] DDIM Sampler: 3%|▎ | 2/74 [00:10<05:28, 4.56s/it] DDIM Sampler: 4%|▍ | 3/74 [00:11<03:31, 2.98s/it] DDIM Sampler: 5%|▌ | 4/74 [00:12<02:37, 2.25s/it] DDIM Sampler: 7%|▋ | 5/74 [00:13<02:06, 1.84s/it] DDIM Sampler: 8%|▊ | 6/74 [00:15<01:48, 1.59s/it] DDIM Sampler: 9%|▉ | 7/74 [00:16<01:36, 1.43s/it] DDIM Sampler: 11%|█ | 8/74 [00:17<01:28, 1.33s/it] DDIM Sampler: 12%|█▏ | 9/74 [00:18<01:22, 1.26s/it] DDIM Sampler: 14%|█▎ | 10/74 [00:19<01:17, 1.22s/it] DDIM Sampler: 15%|█▍ | 11/74 [00:20<01:14, 1.19s/it] DDIM Sampler: 16%|█▌ | 12/74 [00:21<01:12, 1.16s/it] DDIM Sampler: 18%|█▊ | 13/74 [00:22<01:10, 1.15s/it] DDIM Sampler: 19%|█▉ | 14/74 [00:23<01:08, 1.14s/it] DDIM Sampler: 20%|██ | 15/74 [00:25<01:06, 1.13s/it] DDIM Sampler: 22%|██▏ | 16/74 [00:26<01:05, 1.13s/it] DDIM Sampler: 23%|██▎ | 17/74 [00:27<01:04, 1.13s/it] DDIM Sampler: 24%|██▍ | 18/74 [00:28<01:03, 1.13s/it] DDIM Sampler: 26%|██▌ | 19/74 [00:29<01:02, 1.13s/it] DDIM Sampler: 27%|██▋ | 20/74 [00:30<01:00, 1.13s/it] DDIM Sampler: 28%|██▊ | 21/74 [00:31<00:59, 1.12s/it] DDIM Sampler: 30%|██▉ | 22/74 [00:32<00:58, 1.12s/it] DDIM Sampler: 31%|███ | 23/74 [00:34<00:57, 1.12s/it] DDIM Sampler: 32%|███▏ | 24/74 [00:35<00:56, 1.12s/it] DDIM Sampler: 34%|███▍ | 25/74 [00:36<00:55, 1.13s/it] DDIM Sampler: 35%|███▌ | 26/74 [00:37<00:54, 1.13s/it] DDIM Sampler: 36%|███▋ | 27/74 [00:38<00:53, 1.13s/it] DDIM Sampler: 38%|███▊ | 28/74 [00:39<00:52, 1.14s/it] DDIM Sampler: 39%|███▉ | 29/74 [00:40<00:51, 1.13s/it] DDIM Sampler: 41%|████ | 30/74 [00:42<00:49, 1.13s/it] DDIM Sampler: 42%|████▏ | 31/74 [00:43<00:48, 1.14s/it] DDIM Sampler: 43%|████▎ | 32/74 [00:44<00:47, 1.14s/it] DDIM Sampler: 45%|████▍ | 33/74 [00:45<00:46, 1.14s/it] DDIM Sampler: 46%|████▌ | 34/74 [00:46<00:45, 1.14s/it] DDIM Sampler: 47%|████▋ | 35/74 [00:47<00:44, 1.14s/it] DDIM Sampler: 49%|████▊ | 36/74 [00:48<00:43, 1.14s/it] DDIM Sampler: 50%|█████ | 37/74 [00:50<00:42, 1.14s/it] DDIM Sampler: 51%|█████▏ | 38/74 [00:51<00:41, 1.14s/it] DDIM Sampler: 53%|█████▎ | 39/74 [00:52<00:39, 1.14s/it] DDIM Sampler: 54%|█████▍ | 40/74 [00:53<00:38, 1.14s/it] DDIM Sampler: 55%|█████▌ | 41/74 [00:54<00:37, 1.14s/it] DDIM Sampler: 57%|█████▋ | 42/74 [00:55<00:36, 1.14s/it] DDIM Sampler: 58%|█████▊ | 43/74 [00:56<00:35, 1.15s/it] DDIM Sampler: 59%|█████▉ | 44/74 [00:58<00:34, 1.15s/it] DDIM Sampler: 61%|██████ | 45/74 [00:59<00:33, 1.15s/it] DDIM Sampler: 62%|██████▏ | 46/74 [01:00<00:32, 1.15s/it] DDIM Sampler: 64%|██████▎ | 47/74 [01:01<00:31, 1.15s/it] DDIM Sampler: 65%|██████▍ | 48/74 [01:02<00:29, 1.15s/it] DDIM Sampler: 66%|██████▌ | 49/74 [01:03<00:28, 1.15s/it] DDIM Sampler: 68%|██████▊ | 50/74 [01:04<00:27, 1.15s/it] DDIM Sampler: 69%|██████▉ | 51/74 [01:06<00:26, 1.15s/it] DDIM Sampler: 70%|███████ | 52/74 [01:07<00:25, 1.15s/it] DDIM Sampler: 72%|███████▏ | 53/74 [01:08<00:24, 1.15s/it] DDIM Sampler: 73%|███████▎ | 54/74 [01:09<00:23, 1.15s/it] DDIM Sampler: 74%|███████▍ | 55/74 [01:10<00:21, 1.15s/it] DDIM Sampler: 76%|███████▌ | 56/74 [01:11<00:20, 1.15s/it] DDIM Sampler: 77%|███████▋ | 57/74 [01:13<00:19, 1.16s/it] DDIM Sampler: 78%|███████▊ | 58/74 [01:14<00:18, 1.16s/it] DDIM Sampler: 80%|███████▉ | 59/74 [01:15<00:17, 1.16s/it] DDIM Sampler: 81%|████████ | 60/74 [01:16<00:16, 1.16s/it] DDIM Sampler: 82%|████████▏ | 61/74 [01:17<00:15, 1.16s/it] DDIM Sampler: 84%|████████▍ | 62/74 [01:18<00:13, 1.16s/it] DDIM Sampler: 85%|████████▌ | 63/74 [01:19<00:12, 1.16s/it] DDIM Sampler: 86%|████████▋ | 64/74 [01:21<00:11, 1.16s/it] DDIM Sampler: 88%|████████▊ | 65/74 [01:22<00:10, 1.16s/it] DDIM Sampler: 89%|████████▉ | 66/74 [01:23<00:09, 1.16s/it] DDIM Sampler: 91%|█████████ | 67/74 [01:24<00:08, 1.16s/it] DDIM Sampler: 92%|█████████▏| 68/74 [01:25<00:06, 1.17s/it] DDIM Sampler: 93%|█████████▎| 69/74 [01:26<00:05, 1.17s/it] DDIM Sampler: 95%|█████████▍| 70/74 [01:28<00:04, 1.17s/it] DDIM Sampler: 96%|█████████▌| 71/74 [01:29<00:03, 1.17s/it] DDIM Sampler: 97%|█████████▋| 72/74 [01:30<00:02, 1.16s/it] DDIM Sampler: 99%|█████████▊| 73/74 [01:31<00:01, 1.16s/it] DDIM Sampler: 100%|██████████| 74/74 [01:32<00:00, 1.17s/it] DDIM Sampler: 100%|██████████| 74/74 [01:32<00:00, 1.25s/it] Sampling: 100%|██████████| 1/1 [03:12<00:00, 192.10s/it] Sampling: 100%|██████████| 1/1 [03:12<00:00, 192.10s/it]Prediction
nightmareai/majesty-diffusion:76f01b26Input
- model
- finetuned
- width
- 640
- height
- 768
- clip_scale
- 16000
- init_scale
- 1000
- clip_prompts
- The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.
- latent_scale
- 12
- output_steps
- 10
- latent_prompt
- vaporwave princess
- custom_settings
- # // PenguinAnimations Settings // # Work better with + RN50 # Have a fun!!! i took weeks to make that. # PLEASE! DO PROMPT ENGINEERING!!! or things gonna be messy!! # Try to cut artist names and places of latent. [advanced_settings] clip_guidance_schedule = [6200]*1800 #clip_guidance_scale = 8500 custom_schedule_setting = [[30, 1000, 8], 'gfpgan:2.0','noise:0.3', [30, 250, 8], 'gfpgan:1.0','noise:0.3', [1, 125, 4], 'gfpgan:1.0','noise:0.3', [1, 80, 4]] clamp_index = [0.61, 0.45] latent_diffusion_guidance_scale = 9 cut_overview = [8]*200 + [10]*200 + [8]*200 + [6]*200 + [1]*200 cut_innercut = [2]*200 + [6]*200 + [8]*200 + [10]*200 + [14]*200 cut_ic_pow = 0.5 cut_icgray_p = [0.87]*100+[0.78]*50+[0.73]*50+[0.64]*60+[0.56]*40+[0.50]*50+[0.33]*100+[0.19]*150+[0]*400 cutn_batches = 1 range_index = [0]*200 + [50000.0]*400 + [0]*1000 active_function = "softsign" tv_scales = [0]*1000 latent_tv_loss = True symmetric_loss_scale = 0.8 compress_steps = 200 compress_factor = 0.1 punish_steps = 200 punish_factor = 0.5 #Experimental aesthetic embeddings, work only with OpenAI ViT-B/32 and ViT-L/14 experimental_aesthetic_embeddings = True #How much you want this to influence your result experimental_aesthetic_embeddings_weight = 0.3 #9 are good aesthetic embeddings, 0 are bad ones experimental_aesthetic_embeddings_score = 8
- starting_timestep
- 0.9
- aesthetic_loss_scale
- 400
{ "model": "finetuned", "width": 640, "height": 768, "clip_scale": 16000, "init_scale": 1000, "clip_prompts": "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", "latent_scale": 12, "output_steps": 10, "latent_prompt": "vaporwave princess", "custom_settings": "\n# // PenguinAnimations Settings //\n# Work better with + RN50 \n# Have a fun!!! i took weeks to make that.\n\n# PLEASE! DO PROMPT ENGINEERING!!! or things gonna be messy!!\n# Try to cut artist names and places of latent.\n\n[advanced_settings]\nclip_guidance_schedule = [6200]*1800\n#clip_guidance_scale = 8500\ncustom_schedule_setting = [[30, 1000, 8], 'gfpgan:2.0','noise:0.3', [30, 250, 8], 'gfpgan:1.0','noise:0.3', [1, 125, 4], 'gfpgan:1.0','noise:0.3', [1, 80, 4]]\nclamp_index = [0.61, 0.45]\nlatent_diffusion_guidance_scale = 9\ncut_overview = [8]*200 + [10]*200 + [8]*200 + [6]*200 + [1]*200\ncut_innercut = [2]*200 + [6]*200 + [8]*200 + [10]*200 + [14]*200\ncut_ic_pow = 0.5\ncut_icgray_p = [0.87]*100+[0.78]*50+[0.73]*50+[0.64]*60+[0.56]*40+[0.50]*50+[0.33]*100+[0.19]*150+[0]*400\ncutn_batches = 1\nrange_index = [0]*200 + [50000.0]*400 + [0]*1000\nactive_function = \"softsign\"\ntv_scales = [0]*1000\nlatent_tv_loss = True\nsymmetric_loss_scale = 0.8\ncompress_steps = 200\ncompress_factor = 0.1\npunish_steps = 200\npunish_factor = 0.5\n#Experimental aesthetic embeddings, work only with OpenAI ViT-B/32 and ViT-L/14\nexperimental_aesthetic_embeddings = True\n#How much you want this to influence your result\nexperimental_aesthetic_embeddings_weight = 0.3\n#9 are good aesthetic embeddings, 0 are bad ones\nexperimental_aesthetic_embeddings_score = 8\n", "starting_timestep": 0.9, "aesthetic_loss_scale": 400 }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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", { input: { model: "finetuned", width: 640, height: 768, clip_scale: 16000, init_scale: 1000, clip_prompts: "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", latent_scale: 12, output_steps: 10, latent_prompt: "vaporwave princess", custom_settings: "\n# // PenguinAnimations Settings //\n# Work better with + RN50 \n# Have a fun!!! i took weeks to make that.\n\n# PLEASE! DO PROMPT ENGINEERING!!! or things gonna be messy!!\n# Try to cut artist names and places of latent.\n\n[advanced_settings]\nclip_guidance_schedule = [6200]*1800\n#clip_guidance_scale = 8500\ncustom_schedule_setting = [[30, 1000, 8], 'gfpgan:2.0','noise:0.3', [30, 250, 8], 'gfpgan:1.0','noise:0.3', [1, 125, 4], 'gfpgan:1.0','noise:0.3', [1, 80, 4]]\nclamp_index = [0.61, 0.45]\nlatent_diffusion_guidance_scale = 9\ncut_overview = [8]*200 + [10]*200 + [8]*200 + [6]*200 + [1]*200\ncut_innercut = [2]*200 + [6]*200 + [8]*200 + [10]*200 + [14]*200\ncut_ic_pow = 0.5\ncut_icgray_p = [0.87]*100+[0.78]*50+[0.73]*50+[0.64]*60+[0.56]*40+[0.50]*50+[0.33]*100+[0.19]*150+[0]*400\ncutn_batches = 1\nrange_index = [0]*200 + [50000.0]*400 + [0]*1000\nactive_function = \"softsign\"\ntv_scales = [0]*1000\nlatent_tv_loss = True\nsymmetric_loss_scale = 0.8\ncompress_steps = 200\ncompress_factor = 0.1\npunish_steps = 200\npunish_factor = 0.5\n#Experimental aesthetic embeddings, work only with OpenAI ViT-B/32 and ViT-L/14\nexperimental_aesthetic_embeddings = True\n#How much you want this to influence your result\nexperimental_aesthetic_embeddings_weight = 0.3\n#9 are good aesthetic embeddings, 0 are bad ones\nexperimental_aesthetic_embeddings_score = 8\n", starting_timestep: 0.9, aesthetic_loss_scale: 400 } } ); 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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/majesty-diffusion:76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", input={ "model": "finetuned", "width": 640, "height": 768, "clip_scale": 16000, "init_scale": 1000, "clip_prompts": "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", "latent_scale": 12, "output_steps": 10, "latent_prompt": "vaporwave princess", "custom_settings": "\n# // PenguinAnimations Settings //\n# Work better with + RN50 \n# Have a fun!!! i took weeks to make that.\n\n# PLEASE! DO PROMPT ENGINEERING!!! or things gonna be messy!!\n# Try to cut artist names and places of latent.\n\n[advanced_settings]\nclip_guidance_schedule = [6200]*1800\n#clip_guidance_scale = 8500\ncustom_schedule_setting = [[30, 1000, 8], 'gfpgan:2.0','noise:0.3', [30, 250, 8], 'gfpgan:1.0','noise:0.3', [1, 125, 4], 'gfpgan:1.0','noise:0.3', [1, 80, 4]]\nclamp_index = [0.61, 0.45]\nlatent_diffusion_guidance_scale = 9\ncut_overview = [8]*200 + [10]*200 + [8]*200 + [6]*200 + [1]*200\ncut_innercut = [2]*200 + [6]*200 + [8]*200 + [10]*200 + [14]*200\ncut_ic_pow = 0.5\ncut_icgray_p = [0.87]*100+[0.78]*50+[0.73]*50+[0.64]*60+[0.56]*40+[0.50]*50+[0.33]*100+[0.19]*150+[0]*400\ncutn_batches = 1\nrange_index = [0]*200 + [50000.0]*400 + [0]*1000\nactive_function = \"softsign\"\ntv_scales = [0]*1000\nlatent_tv_loss = True\nsymmetric_loss_scale = 0.8\ncompress_steps = 200\ncompress_factor = 0.1\npunish_steps = 200\npunish_factor = 0.5\n#Experimental aesthetic embeddings, work only with OpenAI ViT-B/32 and ViT-L/14\nexperimental_aesthetic_embeddings = True\n#How much you want this to influence your result\nexperimental_aesthetic_embeddings_weight = 0.3\n#9 are good aesthetic embeddings, 0 are bad ones\nexperimental_aesthetic_embeddings_score = 8\n", "starting_timestep": 0.9, "aesthetic_loss_scale": 400 } ) # The nightmareai/majesty-diffusion model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/nightmareai/majesty-diffusion/api#output-schema print(item)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 nightmareai/majesty-diffusion 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": "76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d", "input": { "model": "finetuned", "width": 640, "height": 768, "clip_scale": 16000, "init_scale": 1000, "clip_prompts": "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", "latent_scale": 12, "output_steps": 10, "latent_prompt": "vaporwave princess", "custom_settings": "\\n# // PenguinAnimations Settings //\\n# Work better with + RN50 \\n# Have a fun!!! i took weeks to make that.\\n\\n# PLEASE! DO PROMPT ENGINEERING!!! or things gonna be messy!!\\n# Try to cut artist names and places of latent.\\n\\n[advanced_settings]\\nclip_guidance_schedule = [6200]*1800\\n#clip_guidance_scale = 8500\\ncustom_schedule_setting = [[30, 1000, 8], \'gfpgan:2.0\',\'noise:0.3\', [30, 250, 8], \'gfpgan:1.0\',\'noise:0.3\', [1, 125, 4], \'gfpgan:1.0\',\'noise:0.3\', [1, 80, 4]]\\nclamp_index = [0.61, 0.45]\\nlatent_diffusion_guidance_scale = 9\\ncut_overview = [8]*200 + [10]*200 + [8]*200 + [6]*200 + [1]*200\\ncut_innercut = [2]*200 + [6]*200 + [8]*200 + [10]*200 + [14]*200\\ncut_ic_pow = 0.5\\ncut_icgray_p = [0.87]*100+[0.78]*50+[0.73]*50+[0.64]*60+[0.56]*40+[0.50]*50+[0.33]*100+[0.19]*150+[0]*400\\ncutn_batches = 1\\nrange_index = [0]*200 + [50000.0]*400 + [0]*1000\\nactive_function = \\"softsign\\"\\ntv_scales = [0]*1000\\nlatent_tv_loss = True\\nsymmetric_loss_scale = 0.8\\ncompress_steps = 200\\ncompress_factor = 0.1\\npunish_steps = 200\\npunish_factor = 0.5\\n#Experimental aesthetic embeddings, work only with OpenAI ViT-B/32 and ViT-L/14\\nexperimental_aesthetic_embeddings = True\\n#How much you want this to influence your result\\nexperimental_aesthetic_embeddings_weight = 0.3\\n#9 are good aesthetic embeddings, 0 are bad ones\\nexperimental_aesthetic_embeddings_score = 8\\n", "starting_timestep": 0.9, "aesthetic_loss_scale": 400 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-28T23:36:51.049642Z", "created_at": "2022-07-28T23:30:56.842108Z", "data_removed": false, "error": null, "id": "jlkz4yfmfzdhdh5idjie2bhm64", "input": { "model": "finetuned", "width": 640, "height": 768, "clip_scale": 16000, "init_scale": 1000, "clip_prompts": "The visual style of the image is akin to a retro 8-bit look, with clean lines and flat colors. There is a cartoonish quality to some of the characters and objects, as if they are depictions of vaporwave tropes or influences.", "latent_scale": 12, "output_steps": 10, "latent_prompt": "vaporwave princess", "custom_settings": "\n# // PenguinAnimations Settings //\n# Work better with + RN50 \n# Have a fun!!! i took weeks to make that.\n\n# PLEASE! DO PROMPT ENGINEERING!!! or things gonna be messy!!\n# Try to cut artist names and places of latent.\n\n[advanced_settings]\nclip_guidance_schedule = [6200]*1800\n#clip_guidance_scale = 8500\ncustom_schedule_setting = [[30, 1000, 8], 'gfpgan:2.0','noise:0.3', [30, 250, 8], 'gfpgan:1.0','noise:0.3', [1, 125, 4], 'gfpgan:1.0','noise:0.3', [1, 80, 4]]\nclamp_index = [0.61, 0.45]\nlatent_diffusion_guidance_scale = 9\ncut_overview = [8]*200 + [10]*200 + [8]*200 + [6]*200 + [1]*200\ncut_innercut = [2]*200 + [6]*200 + [8]*200 + [10]*200 + [14]*200\ncut_ic_pow = 0.5\ncut_icgray_p = [0.87]*100+[0.78]*50+[0.73]*50+[0.64]*60+[0.56]*40+[0.50]*50+[0.33]*100+[0.19]*150+[0]*400\ncutn_batches = 1\nrange_index = [0]*200 + [50000.0]*400 + [0]*1000\nactive_function = \"softsign\"\ntv_scales = [0]*1000\nlatent_tv_loss = True\nsymmetric_loss_scale = 0.8\ncompress_steps = 200\ncompress_factor = 0.1\npunish_steps = 200\npunish_factor = 0.5\n#Experimental aesthetic embeddings, work only with OpenAI ViT-B/32 and ViT-L/14\nexperimental_aesthetic_embeddings = True\n#How much you want this to influence your result\nexperimental_aesthetic_embeddings_weight = 0.3\n#9 are good aesthetic embeddings, 0 are bad ones\nexperimental_aesthetic_embeddings_score = 8\n", "starting_timestep": 0.9, "aesthetic_loss_scale": 400 }, "logs": "Loaded /tmp/tmpc3q6g38tmajesty/settings.cfg\nSampling images 1/1\n\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.3\nRunning DDIM Sampling with 122 timesteps\nSampling: 0%| | 0/1 [00:00<?, ?it/s]\n\nDDIM Sampler: 0%| | 0/122 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%| | 1/122 [00:13<26:37, 13.20s/it]\u001b[A\n\nDDIM Sampler: 2%|▏ | 2/122 [00:13<11:46, 5.89s/it]\u001b[A\n\nDDIM Sampler: 2%|▏ | 3/122 [00:14<07:02, 3.55s/it]\u001b[A\n\nDDIM Sampler: 3%|▎ | 4/122 [00:15<04:49, 2.45s/it]\u001b[A\n\nDDIM Sampler: 4%|▍ | 5/122 [00:16<03:36, 1.85s/it]\u001b[A\n\nDDIM Sampler: 5%|▍ | 6/122 [00:17<02:52, 1.48s/it]\u001b[A\n\nDDIM Sampler: 6%|▌ | 7/122 [00:17<02:23, 1.25s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 8/122 [00:18<02:05, 1.10s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 9/122 [00:19<01:52, 1.01it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 10/122 [00:20<01:43, 1.08it/s]\u001b[A\n\nDDIM Sampler: 9%|▉ | 11/122 [00:20<01:37, 1.14it/s]\u001b[A\n\nDDIM Sampler: 10%|▉ | 12/122 [00:21<01:33, 1.18it/s]\u001b[A\n\nDDIM Sampler: 11%|█ | 13/122 [00:22<01:29, 1.21it/s]\u001b[A\n\nDDIM Sampler: 11%|█▏ | 14/122 [00:23<01:27, 1.24it/s]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 15/122 [00:23<01:25, 1.25it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 16/122 [00:24<01:24, 1.26it/s]\u001b[A\n\nDDIM Sampler: 14%|█▍ | 17/122 [00:25<01:22, 1.27it/s]\u001b[A\n\nDDIM Sampler: 15%|█▍ | 18/122 [00:26<01:21, 1.27it/s]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 19/122 [00:27<01:20, 1.28it/s]\u001b[A\n\nDDIM Sampler: 16%|█▋ | 20/122 [00:27<01:19, 1.28it/s]\u001b[A\n\nDDIM Sampler: 17%|█▋ | 21/122 [00:28<01:18, 1.28it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 22/122 [00:29<01:17, 1.29it/s]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 23/122 [00:30<01:17, 1.29it/s]\u001b[A\n\nDDIM Sampler: 20%|█▉ | 24/122 [00:30<01:15, 1.29it/s]\u001b[A\n\nDDIM Sampler: 20%|██ | 25/122 [00:31<01:14, 1.30it/s]\u001b[A\n\nDDIM Sampler: 21%|██▏ | 26/122 [00:43<06:42, 4.19s/it]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 27/122 [00:44<05:07, 3.23s/it]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 28/122 [00:45<04:00, 2.56s/it]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 29/122 [00:46<03:14, 2.10s/it]\u001b[A\n\nDDIM Sampler: 25%|██▍ | 30/122 [00:47<02:42, 1.77s/it]\u001b[A\n\nDDIM Sampler: 25%|██▌ | 31/122 [00:48<02:19, 1.54s/it]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 32/122 [00:49<02:03, 1.38s/it]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 33/122 [00:50<01:53, 1.27s/it]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 34/122 [00:51<01:44, 1.19s/it]\u001b[A\n\nDDIM Sampler: 29%|██▊ | 35/122 [00:52<01:38, 1.13s/it]\u001b[A\n\nDDIM Sampler: 30%|██▉ | 36/122 [00:53<01:33, 1.09s/it]\u001b[A\n\nDDIM Sampler: 30%|███ | 37/122 [00:54<01:30, 1.07s/it]\u001b[A\n\nDDIM Sampler: 31%|███ | 38/122 [00:55<01:27, 1.04s/it]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 39/122 [00:56<01:25, 1.04s/it]\u001b[A\n\nDDIM Sampler: 33%|███▎ | 40/122 [00:57<01:24, 1.03s/it]\u001b[A\n\nDDIM Sampler: 34%|███▎ | 41/122 [00:58<01:22, 1.02s/it]\u001b[A\n\nDDIM Sampler: 34%|███▍ | 42/122 [00:59<01:21, 1.01s/it]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 43/122 [01:00<01:19, 1.01s/it]\u001b[A\n\nDDIM Sampler: 36%|███▌ | 44/122 [01:01<01:18, 1.01s/it]\u001b[A\n\nDDIM Sampler: 37%|███▋ | 45/122 [01:02<01:17, 1.00s/it]\u001b[A\n\nDDIM Sampler: 38%|███▊ | 46/122 [01:03<01:15, 1.00it/s]\u001b[A\n\nDDIM Sampler: 39%|███▊ | 47/122 [01:04<01:14, 1.01it/s]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 48/122 [01:05<01:13, 1.01it/s]\u001b[A\n\nDDIM Sampler: 40%|████ | 49/122 [01:06<01:12, 1.01it/s]\u001b[A\n\nDDIM Sampler: 41%|████ | 50/122 [01:07<01:11, 1.01it/s]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 51/122 [01:08<01:10, 1.01it/s]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 52/122 [01:09<01:09, 1.00it/s]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 53/122 [01:10<01:09, 1.00s/it]\u001b[A\n\nDDIM Sampler: 44%|████▍ | 54/122 [01:11<01:08, 1.01s/it]\u001b[A\n\nDDIM Sampler: 45%|████▌ | 55/122 [01:12<01:07, 1.01s/it]\u001b[A\n\nDDIM Sampler: 46%|████▌ | 56/122 [01:13<01:06, 1.01s/it]\u001b[A\n\nDDIM Sampler: 47%|████▋ | 57/122 [01:14<01:05, 1.00s/it]\u001b[A\n\nDDIM Sampler: 48%|████▊ | 58/122 [01:15<01:04, 1.00s/it]\u001b[A\n\nDDIM Sampler: 48%|████▊ | 59/122 [01:16<01:03, 1.00s/it]\u001b[A\n\nDDIM Sampler: 49%|████▉ | 60/122 [01:17<01:02, 1.00s/it]\u001b[A\n\nDDIM Sampler: 50%|█████ | 61/122 [01:18<01:01, 1.00s/it]\u001b[A\n\nDDIM Sampler: 51%|█████ | 62/122 [01:19<01:00, 1.00s/it]\u001b[A\n\nDDIM Sampler: 52%|█████▏ | 63/122 [01:20<00:59, 1.00s/it]\u001b[A\n\nDDIM Sampler: 52%|█████▏ | 64/122 [01:21<00:58, 1.00s/it]\u001b[A\n\nDDIM Sampler: 53%|█████▎ | 65/122 [01:22<00:56, 1.00it/s]\u001b[A\n\nDDIM Sampler: 54%|█████▍ | 66/122 [01:23<00:56, 1.00s/it]\u001b[A\n\nDDIM Sampler: 55%|█████▍ | 67/122 [01:24<00:55, 1.00s/it]\u001b[A\n\nDDIM Sampler: 56%|█████▌ | 68/122 [01:25<00:54, 1.00s/it]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 69/122 [01:26<00:52, 1.00it/s]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 70/122 [01:27<00:51, 1.00it/s]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 71/122 [01:28<00:50, 1.00it/s]\u001b[A\n\nDDIM Sampler: 59%|█████▉ | 72/122 [01:29<00:49, 1.00it/s]\u001b[A\n\nDDIM Sampler: 60%|█████▉ | 73/122 [01:30<00:49, 1.00s/it]\u001b[A\n\nDDIM Sampler: 61%|██████ | 74/122 [01:31<00:47, 1.00it/s]\u001b[A\n\nDDIM Sampler: 61%|██████▏ | 75/122 [01:32<00:46, 1.01it/s]\u001b[A\n\nDDIM Sampler: 62%|██████▏ | 76/122 [01:33<00:45, 1.00it/s]\u001b[A\n\nDDIM Sampler: 63%|██████▎ | 77/122 [01:34<00:45, 1.00s/it]\u001b[A\n\nDDIM Sampler: 64%|██████▍ | 78/122 [01:35<00:44, 1.00s/it]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 79/122 [01:36<00:43, 1.00s/it]\u001b[A\n\nDDIM Sampler: 66%|██████▌ | 80/122 [01:37<00:42, 1.00s/it]\u001b[A\n\nDDIM Sampler: 66%|██████▋ | 81/122 [01:38<00:41, 1.00s/it]\u001b[A\n\nDDIM Sampler: 67%|██████▋ | 82/122 [01:39<00:40, 1.00s/it]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 83/122 [01:40<00:39, 1.01s/it]\u001b[A\n\nDDIM Sampler: 69%|██████▉ | 84/122 [01:41<00:38, 1.00s/it]\u001b[A\n\nDDIM Sampler: 70%|██████▉ | 85/122 [01:42<00:37, 1.00s/it]\u001b[A\n\nDDIM Sampler: 70%|███████ | 86/122 [01:43<00:35, 1.00it/s]\u001b[A\n\nDDIM Sampler: 71%|███████▏ | 87/122 [01:44<00:34, 1.00it/s]\u001b[A\n\nDDIM Sampler: 72%|███████▏ | 88/122 [01:45<00:33, 1.00it/s]\u001b[A\n\nDDIM Sampler: 73%|███████▎ | 89/122 [01:46<00:32, 1.00it/s]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 90/122 [01:47<00:31, 1.01it/s]\u001b[A\n\nDDIM Sampler: 75%|███████▍ | 91/122 [01:48<00:30, 1.01it/s]\u001b[A\n\nDDIM Sampler: 75%|███████▌ | 92/122 [01:49<00:29, 1.01it/s]\u001b[A\n\nDDIM Sampler: 76%|███████▌ | 93/122 [01:50<00:28, 1.00it/s]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 94/122 [01:51<00:27, 1.00it/s]\u001b[A\n\nDDIM Sampler: 78%|███████▊ | 95/122 [01:52<00:26, 1.00it/s]\u001b[A\n\nDDIM Sampler: 79%|███████▊ | 96/122 [01:53<00:25, 1.00it/s]\u001b[A\n\nDDIM Sampler: 80%|███████▉ | 97/122 [01:54<00:24, 1.00it/s]\u001b[A\n\nDDIM Sampler: 80%|████████ | 98/122 [01:55<00:23, 1.00it/s]\u001b[A\n\nDDIM Sampler: 81%|████████ | 99/122 [01:56<00:22, 1.00it/s]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 100/122 [01:57<00:21, 1.01it/s]\u001b[A\n\nDDIM Sampler: 83%|████████▎ | 101/122 [02:09<01:26, 4.14s/it]\u001b[A\n\nDDIM Sampler: 84%|████████▎ | 102/122 [02:10<01:04, 3.20s/it]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 103/122 [02:11<00:48, 2.54s/it]\u001b[A\n\nDDIM Sampler: 85%|████████▌ | 104/122 [02:12<00:37, 2.08s/it]\u001b[A\n\nDDIM Sampler: 86%|████████▌ | 105/122 [02:13<00:29, 1.76s/it]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 106/122 [02:14<00:24, 1.53s/it]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 107/122 [02:15<00:20, 1.37s/it]\u001b[A\n\nDDIM Sampler: 89%|████████▊ | 108/122 [02:16<00:17, 1.26s/it]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 109/122 [02:17<00:15, 1.18s/it]\u001b[A\n\nDDIM Sampler: 90%|█████████ | 110/122 [02:18<00:13, 1.13s/it]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 111/122 [02:19<00:11, 1.09s/it]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 112/122 [02:20<00:10, 1.06s/it]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 113/122 [02:21<00:09, 1.04s/it]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 114/122 [02:22<00:08, 1.03s/it]\u001b[A\n\nDDIM Sampler: 94%|█████████▍| 115/122 [02:23<00:07, 1.02s/it]\u001b[A\n\nDDIM Sampler: 95%|█████████▌| 116/122 [02:24<00:06, 1.01s/it]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 117/122 [02:25<00:05, 1.01s/it]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 118/122 [02:26<00:04, 1.01s/it]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 119/122 [02:27<00:03, 1.01s/it]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 120/122 [02:28<00:02, 1.00s/it]\u001b[A\n\nDDIM Sampler: 99%|█████████▉| 121/122 [02:29<00:01, 1.00s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 122/122 [02:30<00:00, 1.00s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 122/122 [02:30<00:00, 1.23s/it]\npython inference_gfpgan.py -i /tmp/tmpa1_mn0gkgfpgan/temp_1659051240.png -o results -v 1.3 -s 2\n\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nDownloading: \"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth\" to /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/realesrgan/weights/RealESRGAN_x2plus.pth\n\n\n 0%| | 0.00/64.0M [00:00<?, ?B/s]\n 6%|▋ | 4.01M/64.0M [00:00<00:01, 35.7MB/s]\n 13%|█▎ | 8.01M/64.0M [00:00<00:01, 34.5MB/s]\n 19%|█▉ | 12.0M/64.0M [00:00<00:01, 33.8MB/s]\n 25%|██▌ | 16.0M/64.0M [00:00<00:01, 34.5MB/s]\n 31%|███▏ | 20.0M/64.0M [00:00<00:01, 34.4MB/s]\n 38%|███▊ | 24.0M/64.0M [00:00<00:01, 34.4MB/s]\n 44%|████▍ | 28.0M/64.0M [00:00<00:01, 34.0MB/s]\n 50%|█████ | 32.0M/64.0M [00:00<00:00, 34.0MB/s]\n 56%|█████▋ | 36.0M/64.0M [00:01<00:00, 33.2MB/s]\n 61%|██████▏ | 39.2M/64.0M [00:01<00:00, 33.0MB/s]\n 69%|██████▉ | 44.0M/64.0M [00:01<00:00, 35.0MB/s]\n 75%|███████▌ | 48.0M/64.0M [00:01<00:00, 34.6MB/s]\n 81%|████████▏ | 52.0M/64.0M [00:01<00:00, 34.8MB/s]\n 87%|████████▋ | 55.9M/64.0M [00:01<00:00, 36.4MB/s]\n 94%|█████████▎| 59.9M/64.0M [00:01<00:00, 37.0MB/s]\n100%|█████████▉| 63.9M/64.0M [00:01<00:00, 36.9MB/s]\n100%|██████████| 64.0M/64.0M [00:01<00:00, 35.0MB/s]\nProcessing temp_1659051240.png ...\n\tTile 1/1\nResults are in the [results] folder.\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.1\nRunning DDIM Sampling with 28 timesteps\n\n\nDDIM Sampler: 0%| | 0/28 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 4%|▎ | 1/28 [00:09<04:28, 9.95s/it]\u001b[A\n\nDDIM Sampler: 7%|▋ | 2/28 [00:11<02:09, 4.99s/it]\u001b[A\n\nDDIM Sampler: 11%|█ | 3/28 [00:12<01:24, 3.40s/it]\u001b[A\n\nDDIM Sampler: 14%|█▍ | 4/28 [00:14<01:03, 2.65s/it]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 5/28 [00:16<00:51, 2.24s/it]\u001b[A\n\nDDIM Sampler: 21%|██▏ | 6/28 [00:17<00:43, 2.00s/it]\u001b[A\n\nDDIM Sampler: 25%|██▌ | 7/28 [00:19<00:38, 1.84s/it]\u001b[A\n\nDDIM Sampler: 29%|██▊ | 8/28 [00:20<00:34, 1.74s/it]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 9/28 [00:22<00:31, 1.67s/it]\u001b[A\n\nDDIM Sampler: 36%|███▌ | 10/28 [00:23<00:29, 1.63s/it]\u001b[A\n\nDDIM Sampler: 39%|███▉ | 11/28 [00:25<00:27, 1.60s/it]\u001b[A\n\nDDIM Sampler: 43%|████▎ | 12/28 [00:26<00:25, 1.58s/it]\u001b[A\n\nDDIM Sampler: 46%|████▋ | 13/28 [00:28<00:23, 1.57s/it]\u001b[A\n\nDDIM Sampler: 50%|█████ | 14/28 [00:29<00:21, 1.56s/it]\u001b[A\n\nDDIM Sampler: 54%|█████▎ | 15/28 [00:31<00:20, 1.55s/it]\u001b[A\n\nDDIM Sampler: 57%|█████▋ | 16/28 [00:32<00:18, 1.55s/it]\u001b[A\n\nDDIM Sampler: 61%|██████ | 17/28 [00:34<00:16, 1.54s/it]\u001b[A\n\nDDIM Sampler: 64%|██████▍ | 18/28 [00:35<00:15, 1.54s/it]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 19/28 [00:37<00:13, 1.54s/it]\u001b[A\n\nDDIM Sampler: 71%|███████▏ | 20/28 [00:38<00:12, 1.54s/it]\u001b[A\n\nDDIM Sampler: 75%|███████▌ | 21/28 [00:40<00:10, 1.54s/it]\u001b[A\n\nDDIM Sampler: 79%|███████▊ | 22/28 [00:42<00:09, 1.54s/it]\u001b[A\n\nDDIM Sampler: 82%|████████▏ | 23/28 [00:43<00:07, 1.54s/it]\u001b[A\n\nDDIM Sampler: 86%|████████▌ | 24/28 [00:45<00:06, 1.54s/it]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 25/28 [00:46<00:04, 1.54s/it]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 26/28 [00:48<00:03, 1.54s/it]\u001b[A\n\nDDIM Sampler: 96%|█████████▋| 27/28 [00:49<00:01, 1.54s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 28/28 [00:51<00:00, 1.54s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 28/28 [00:51<00:00, 1.83s/it]\npython inference_gfpgan.py -i /tmp/tmp8k90e3etgfpgan/temp_1659051305.png -o results -v 1.3 -s 1\n\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nProcessing temp_1659051305.png ...\n\tTile 1/4\n\tTile 2/4\n\tTile 3/4\n\tTile 4/4\nResults are in the [results] folder.\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.1\nRunning DDIM Sampling with 31 timesteps\n\n\nDDIM Sampler: 0%| | 0/31 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 3%|▎ | 1/31 [00:01<00:45, 1.51s/it]\u001b[A\n\nDDIM Sampler: 6%|▋ | 2/31 [00:03<00:44, 1.52s/it]\u001b[A\n\nDDIM Sampler: 10%|▉ | 3/31 [00:04<00:42, 1.52s/it]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 4/31 [00:06<00:40, 1.51s/it]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 5/31 [00:07<00:39, 1.51s/it]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 6/31 [00:09<00:37, 1.52s/it]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 7/31 [00:10<00:36, 1.52s/it]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 8/31 [00:12<00:34, 1.51s/it]\u001b[A\n\nDDIM Sampler: 29%|██▉ | 9/31 [00:13<00:33, 1.51s/it]\u001b[A\n\nDDIM Sampler: 32%|███▏ | 10/31 [00:15<00:31, 1.51s/it]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 11/31 [00:16<00:30, 1.51s/it]\u001b[A\n\nDDIM Sampler: 39%|███▊ | 12/31 [00:18<00:28, 1.52s/it]\u001b[A\n\nDDIM Sampler: 42%|████▏ | 13/31 [00:19<00:27, 1.52s/it]\u001b[A\n\nDDIM Sampler: 45%|████▌ | 14/31 [00:21<00:25, 1.52s/it]\u001b[A\n\nDDIM Sampler: 48%|████▊ | 15/31 [00:22<00:24, 1.52s/it]\u001b[A\n\nDDIM Sampler: 52%|█████▏ | 16/31 [00:24<00:22, 1.52s/it]\u001b[A\n\nDDIM Sampler: 55%|█████▍ | 17/31 [00:25<00:21, 1.52s/it]\u001b[A\n\nDDIM Sampler: 58%|█████▊ | 18/31 [00:27<00:19, 1.52s/it]\u001b[A\n\nDDIM Sampler: 61%|██████▏ | 19/31 [00:28<00:18, 1.52s/it]\u001b[A\n\nDDIM Sampler: 65%|██████▍ | 20/31 [00:30<00:16, 1.52s/it]\u001b[A\n\nDDIM Sampler: 68%|██████▊ | 21/31 [00:31<00:15, 1.51s/it]\u001b[A\n\nDDIM Sampler: 71%|███████ | 22/31 [00:33<00:13, 1.52s/it]\u001b[A\n\nDDIM Sampler: 74%|███████▍ | 23/31 [00:34<00:12, 1.52s/it]\u001b[A\n\nDDIM Sampler: 77%|███████▋ | 24/31 [00:36<00:10, 1.52s/it]\u001b[A\n\nDDIM Sampler: 81%|████████ | 25/31 [00:37<00:09, 1.52s/it]\u001b[A\n\nDDIM Sampler: 84%|████████▍ | 26/31 [00:39<00:07, 1.52s/it]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 27/31 [00:40<00:06, 1.52s/it]\u001b[A\n\nDDIM Sampler: 90%|█████████ | 28/31 [00:42<00:04, 1.52s/it]\u001b[A\n\nDDIM Sampler: 94%|█████████▎| 29/31 [00:43<00:03, 1.51s/it]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 30/31 [00:45<00:01, 1.51s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 31/31 [00:46<00:00, 1.51s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 31/31 [00:46<00:00, 1.52s/it]\npython inference_gfpgan.py -i /tmp/tmpaa3kz4qsgfpgan/temp_1659051364.png -o results -v 1.3 -s 1\n\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nProcessing temp_1659051364.png ...\n\tTile 1/4\n\tTile 2/4\n\tTile 3/4\n\tTile 4/4\nResults are in the [results] folder.\nData shape for DDIM sampling is (1, 4, 48, 40), eta 1.1\nRunning DDIM Sampling with 20 timesteps\n\n\nDDIM Sampler: 0%| | 0/20 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 5%|▌ | 1/20 [00:01<00:28, 1.51s/it]\u001b[A\n\nDDIM Sampler: 10%|█ | 2/20 [00:03<00:27, 1.51s/it]\u001b[A\n\nDDIM Sampler: 15%|█▌ | 3/20 [00:04<00:25, 1.50s/it]\u001b[A\n\nDDIM Sampler: 20%|██ | 4/20 [00:06<00:24, 1.50s/it]\u001b[A\n\nDDIM Sampler: 25%|██▌ | 5/20 [00:07<00:22, 1.50s/it]\u001b[A\n\nDDIM Sampler: 30%|███ | 6/20 [00:09<00:21, 1.51s/it]\u001b[A\n\nDDIM Sampler: 35%|███▌ | 7/20 [00:10<00:19, 1.51s/it]\u001b[A\n\nDDIM Sampler: 40%|████ | 8/20 [00:12<00:18, 1.50s/it]\u001b[A\n\nDDIM Sampler: 45%|████▌ | 9/20 [00:13<00:16, 1.50s/it]\u001b[A\n\nDDIM Sampler: 50%|█████ | 10/20 [00:15<00:15, 1.50s/it]\u001b[A\n\nDDIM Sampler: 55%|█████▌ | 11/20 [00:16<00:13, 1.50s/it]\u001b[A\n\nDDIM Sampler: 60%|██████ | 12/20 [00:18<00:11, 1.50s/it]\u001b[A\n\nDDIM Sampler: 65%|██████▌ | 13/20 [00:19<00:10, 1.50s/it]\u001b[A\n\nDDIM Sampler: 70%|███████ | 14/20 [00:21<00:08, 1.50s/it]\u001b[A\n\nDDIM Sampler: 75%|███████▌ | 15/20 [00:22<00:07, 1.49s/it]\u001b[A\n\nDDIM Sampler: 80%|████████ | 16/20 [00:24<00:06, 1.50s/it]\u001b[A\n\nDDIM Sampler: 85%|████████▌ | 17/20 [00:25<00:04, 1.50s/it]\u001b[A\n\nDDIM Sampler: 90%|█████████ | 18/20 [00:27<00:02, 1.50s/it]\u001b[A\n\nDDIM Sampler: 95%|█████████▌| 19/20 [00:28<00:01, 1.50s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 20/20 [00:29<00:00, 1.49s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 20/20 [00:29<00:00, 1.50s/it]\n\nSampling: 100%|██████████| 1/1 [05:21<00:00, 321.72s/it]\nSampling: 100%|██████████| 1/1 [05:21<00:00, 321.72s/it]", "metrics": { "predict_time": 322.567232, "total_time": 354.207534 }, "output": [ "https://replicate.delivery/mgxm/17931c6a-02de-447a-8ecd-83dd4e65f47a/0.png", "https://replicate.delivery/mgxm/02e7369b-3d8b-46c2-b3c6-12b76c506951/10.png", "https://replicate.delivery/mgxm/407dc054-d59c-4e0d-a6a5-eed4e167a6d5/20.png", "https://replicate.delivery/mgxm/77ab8077-15f0-46b6-86d1-fa5eced914e0/30.png", "https://replicate.delivery/mgxm/e4e320c2-b8be-49a7-b135-1d286e733ec1/40.png", "https://replicate.delivery/mgxm/5b63c308-43ef-4cbf-8190-3205b23eb1d5/50.png", "https://replicate.delivery/mgxm/c944650c-c219-4761-a85a-6e00dd702528/60.png", "https://replicate.delivery/mgxm/ca7ca9b9-aff8-429c-84b7-b63d93cf6d4a/70.png", "https://replicate.delivery/mgxm/4f599e7d-944f-4308-9919-dd7495c635b7/80.png", "https://replicate.delivery/mgxm/c62d05ac-1bab-48ac-aa18-66a1370c0d15/90.png", "https://replicate.delivery/mgxm/5cfadc4e-d5f3-4231-9cfb-51085520bfd5/100.png", "https://replicate.delivery/mgxm/7011cb76-6fb3-4a34-bb65-0215d45142b8/110.png", "https://replicate.delivery/mgxm/713a4c31-b77b-465f-9fa9-88c1474f4242/120.png", "https://replicate.delivery/mgxm/0d40062d-fd16-4f28-868e-526f44bd0da2/130.png", "https://replicate.delivery/mgxm/a35a227d-7e68-42a9-967f-1c43052a96ea/140.png", "https://replicate.delivery/mgxm/289bc2d1-6507-46ba-b0a0-60dd2797c831/150.png", "https://replicate.delivery/mgxm/d785cef6-b6de-4a89-989e-88f05dd7d32f/160.png", "https://replicate.delivery/mgxm/069dc8c2-6a1c-4069-9f3e-4d5a9b8ae94a/170.png", "https://replicate.delivery/mgxm/ea1684db-e883-4127-a474-0c7ff58f7674/180.png", "https://replicate.delivery/mgxm/567caaca-7310-4ab0-93b4-5172d6d9dbd2/190.png", "https://replicate.delivery/mgxm/ecd48799-0cb0-429e-a7bd-d6695dc3e694/200.png", "https://replicate.delivery/mgxm/0320d250-20dd-4669-8353-cb4351b7aa50/1659051406.png" ], "started_at": "2022-07-28T23:31:28.482410Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jlkz4yfmfzdhdh5idjie2bhm64", "cancel": "https://api.replicate.com/v1/predictions/jlkz4yfmfzdhdh5idjie2bhm64/cancel" }, "version": "76f01b269da5b6f12d201700e3e7d253b7decefe4d9d8ae9417df5977f63fb8d" }Generated inLoaded /tmp/tmpc3q6g38tmajesty/settings.cfg Sampling images 1/1 Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.3 Running DDIM Sampling with 122 timesteps Sampling: 0%| | 0/1 [00:00<?, ?it/s] DDIM Sampler: 0%| | 0/122 [00:00<?, ?it/s] DDIM Sampler: 1%| | 1/122 [00:13<26:37, 13.20s/it] DDIM Sampler: 2%|▏ | 2/122 [00:13<11:46, 5.89s/it] DDIM Sampler: 2%|▏ | 3/122 [00:14<07:02, 3.55s/it] DDIM Sampler: 3%|▎ | 4/122 [00:15<04:49, 2.45s/it] DDIM Sampler: 4%|▍ | 5/122 [00:16<03:36, 1.85s/it] DDIM Sampler: 5%|▍ | 6/122 [00:17<02:52, 1.48s/it] DDIM Sampler: 6%|▌ | 7/122 [00:17<02:23, 1.25s/it] DDIM Sampler: 7%|▋ | 8/122 [00:18<02:05, 1.10s/it] DDIM Sampler: 7%|▋ | 9/122 [00:19<01:52, 1.01it/s] DDIM Sampler: 8%|▊ | 10/122 [00:20<01:43, 1.08it/s] DDIM Sampler: 9%|▉ | 11/122 [00:20<01:37, 1.14it/s] DDIM Sampler: 10%|▉ | 12/122 [00:21<01:33, 1.18it/s] DDIM Sampler: 11%|█ | 13/122 [00:22<01:29, 1.21it/s] DDIM Sampler: 11%|█▏ | 14/122 [00:23<01:27, 1.24it/s] DDIM Sampler: 12%|█▏ | 15/122 [00:23<01:25, 1.25it/s] DDIM Sampler: 13%|█▎ | 16/122 [00:24<01:24, 1.26it/s] DDIM Sampler: 14%|█▍ | 17/122 [00:25<01:22, 1.27it/s] DDIM Sampler: 15%|█▍ | 18/122 [00:26<01:21, 1.27it/s] DDIM Sampler: 16%|█▌ | 19/122 [00:27<01:20, 1.28it/s] DDIM Sampler: 16%|█▋ | 20/122 [00:27<01:19, 1.28it/s] DDIM Sampler: 17%|█▋ | 21/122 [00:28<01:18, 1.28it/s] DDIM Sampler: 18%|█▊ | 22/122 [00:29<01:17, 1.29it/s] DDIM Sampler: 19%|█▉ | 23/122 [00:30<01:17, 1.29it/s] DDIM Sampler: 20%|█▉ | 24/122 [00:30<01:15, 1.29it/s] DDIM Sampler: 20%|██ | 25/122 [00:31<01:14, 1.30it/s] DDIM Sampler: 21%|██▏ | 26/122 [00:43<06:42, 4.19s/it] DDIM Sampler: 22%|██▏ | 27/122 [00:44<05:07, 3.23s/it] DDIM Sampler: 23%|██▎ | 28/122 [00:45<04:00, 2.56s/it] DDIM Sampler: 24%|██▍ | 29/122 [00:46<03:14, 2.10s/it] DDIM Sampler: 25%|██▍ | 30/122 [00:47<02:42, 1.77s/it] DDIM Sampler: 25%|██▌ | 31/122 [00:48<02:19, 1.54s/it] DDIM Sampler: 26%|██▌ | 32/122 [00:49<02:03, 1.38s/it] DDIM Sampler: 27%|██▋ | 33/122 [00:50<01:53, 1.27s/it] DDIM Sampler: 28%|██▊ | 34/122 [00:51<01:44, 1.19s/it] DDIM Sampler: 29%|██▊ | 35/122 [00:52<01:38, 1.13s/it] DDIM Sampler: 30%|██▉ | 36/122 [00:53<01:33, 1.09s/it] DDIM Sampler: 30%|███ | 37/122 [00:54<01:30, 1.07s/it] DDIM Sampler: 31%|███ | 38/122 [00:55<01:27, 1.04s/it] DDIM Sampler: 32%|███▏ | 39/122 [00:56<01:25, 1.04s/it] DDIM Sampler: 33%|███▎ | 40/122 [00:57<01:24, 1.03s/it] DDIM Sampler: 34%|███▎ | 41/122 [00:58<01:22, 1.02s/it] DDIM Sampler: 34%|███▍ | 42/122 [00:59<01:21, 1.01s/it] DDIM Sampler: 35%|███▌ | 43/122 [01:00<01:19, 1.01s/it] DDIM Sampler: 36%|███▌ | 44/122 [01:01<01:18, 1.01s/it] DDIM Sampler: 37%|███▋ | 45/122 [01:02<01:17, 1.00s/it] DDIM Sampler: 38%|███▊ | 46/122 [01:03<01:15, 1.00it/s] DDIM Sampler: 39%|███▊ | 47/122 [01:04<01:14, 1.01it/s] DDIM Sampler: 39%|███▉ | 48/122 [01:05<01:13, 1.01it/s] DDIM Sampler: 40%|████ | 49/122 [01:06<01:12, 1.01it/s] DDIM Sampler: 41%|████ | 50/122 [01:07<01:11, 1.01it/s] DDIM Sampler: 42%|████▏ | 51/122 [01:08<01:10, 1.01it/s] DDIM Sampler: 43%|████▎ | 52/122 [01:09<01:09, 1.00it/s] DDIM Sampler: 43%|████▎ | 53/122 [01:10<01:09, 1.00s/it] DDIM Sampler: 44%|████▍ | 54/122 [01:11<01:08, 1.01s/it] DDIM Sampler: 45%|████▌ | 55/122 [01:12<01:07, 1.01s/it] DDIM Sampler: 46%|████▌ | 56/122 [01:13<01:06, 1.01s/it] DDIM Sampler: 47%|████▋ | 57/122 [01:14<01:05, 1.00s/it] DDIM Sampler: 48%|████▊ | 58/122 [01:15<01:04, 1.00s/it] DDIM Sampler: 48%|████▊ | 59/122 [01:16<01:03, 1.00s/it] DDIM Sampler: 49%|████▉ | 60/122 [01:17<01:02, 1.00s/it] DDIM Sampler: 50%|█████ | 61/122 [01:18<01:01, 1.00s/it] DDIM Sampler: 51%|█████ | 62/122 [01:19<01:00, 1.00s/it] DDIM Sampler: 52%|█████▏ | 63/122 [01:20<00:59, 1.00s/it] DDIM Sampler: 52%|█████▏ | 64/122 [01:21<00:58, 1.00s/it] DDIM Sampler: 53%|█████▎ | 65/122 [01:22<00:56, 1.00it/s] DDIM Sampler: 54%|█████▍ | 66/122 [01:23<00:56, 1.00s/it] DDIM Sampler: 55%|█████▍ | 67/122 [01:24<00:55, 1.00s/it] DDIM Sampler: 56%|█████▌ | 68/122 [01:25<00:54, 1.00s/it] DDIM Sampler: 57%|█████▋ | 69/122 [01:26<00:52, 1.00it/s] DDIM Sampler: 57%|█████▋ | 70/122 [01:27<00:51, 1.00it/s] DDIM Sampler: 58%|█████▊ | 71/122 [01:28<00:50, 1.00it/s] DDIM Sampler: 59%|█████▉ | 72/122 [01:29<00:49, 1.00it/s] DDIM Sampler: 60%|█████▉ | 73/122 [01:30<00:49, 1.00s/it] DDIM Sampler: 61%|██████ | 74/122 [01:31<00:47, 1.00it/s] DDIM Sampler: 61%|██████▏ | 75/122 [01:32<00:46, 1.01it/s] DDIM Sampler: 62%|██████▏ | 76/122 [01:33<00:45, 1.00it/s] DDIM Sampler: 63%|██████▎ | 77/122 [01:34<00:45, 1.00s/it] DDIM Sampler: 64%|██████▍ | 78/122 [01:35<00:44, 1.00s/it] DDIM Sampler: 65%|██████▍ | 79/122 [01:36<00:43, 1.00s/it] DDIM Sampler: 66%|██████▌ | 80/122 [01:37<00:42, 1.00s/it] DDIM Sampler: 66%|██████▋ | 81/122 [01:38<00:41, 1.00s/it] DDIM Sampler: 67%|██████▋ | 82/122 [01:39<00:40, 1.00s/it] DDIM Sampler: 68%|██████▊ | 83/122 [01:40<00:39, 1.01s/it] DDIM Sampler: 69%|██████▉ | 84/122 [01:41<00:38, 1.00s/it] DDIM Sampler: 70%|██████▉ | 85/122 [01:42<00:37, 1.00s/it] DDIM Sampler: 70%|███████ | 86/122 [01:43<00:35, 1.00it/s] DDIM Sampler: 71%|███████▏ | 87/122 [01:44<00:34, 1.00it/s] DDIM Sampler: 72%|███████▏ | 88/122 [01:45<00:33, 1.00it/s] DDIM Sampler: 73%|███████▎ | 89/122 [01:46<00:32, 1.00it/s] DDIM Sampler: 74%|███████▍ | 90/122 [01:47<00:31, 1.01it/s] DDIM Sampler: 75%|███████▍ | 91/122 [01:48<00:30, 1.01it/s] DDIM Sampler: 75%|███████▌ | 92/122 [01:49<00:29, 1.01it/s] DDIM Sampler: 76%|███████▌ | 93/122 [01:50<00:28, 1.00it/s] DDIM Sampler: 77%|███████▋ | 94/122 [01:51<00:27, 1.00it/s] DDIM Sampler: 78%|███████▊ | 95/122 [01:52<00:26, 1.00it/s] DDIM Sampler: 79%|███████▊ | 96/122 [01:53<00:25, 1.00it/s] DDIM Sampler: 80%|███████▉ | 97/122 [01:54<00:24, 1.00it/s] DDIM Sampler: 80%|████████ | 98/122 [01:55<00:23, 1.00it/s] DDIM Sampler: 81%|████████ | 99/122 [01:56<00:22, 1.00it/s] DDIM Sampler: 82%|████████▏ | 100/122 [01:57<00:21, 1.01it/s] DDIM Sampler: 83%|████████▎ | 101/122 [02:09<01:26, 4.14s/it] DDIM Sampler: 84%|████████▎ | 102/122 [02:10<01:04, 3.20s/it] DDIM Sampler: 84%|████████▍ | 103/122 [02:11<00:48, 2.54s/it] DDIM Sampler: 85%|████████▌ | 104/122 [02:12<00:37, 2.08s/it] DDIM Sampler: 86%|████████▌ | 105/122 [02:13<00:29, 1.76s/it] DDIM Sampler: 87%|████████▋ | 106/122 [02:14<00:24, 1.53s/it] DDIM Sampler: 88%|████████▊ | 107/122 [02:15<00:20, 1.37s/it] DDIM Sampler: 89%|████████▊ | 108/122 [02:16<00:17, 1.26s/it] DDIM Sampler: 89%|████████▉ | 109/122 [02:17<00:15, 1.18s/it] DDIM Sampler: 90%|█████████ | 110/122 [02:18<00:13, 1.13s/it] DDIM Sampler: 91%|█████████ | 111/122 [02:19<00:11, 1.09s/it] DDIM Sampler: 92%|█████████▏| 112/122 [02:20<00:10, 1.06s/it] DDIM Sampler: 93%|█████████▎| 113/122 [02:21<00:09, 1.04s/it] DDIM Sampler: 93%|█████████▎| 114/122 [02:22<00:08, 1.03s/it] DDIM Sampler: 94%|█████████▍| 115/122 [02:23<00:07, 1.02s/it] DDIM Sampler: 95%|█████████▌| 116/122 [02:24<00:06, 1.01s/it] DDIM Sampler: 96%|█████████▌| 117/122 [02:25<00:05, 1.01s/it] DDIM Sampler: 97%|█████████▋| 118/122 [02:26<00:04, 1.01s/it] DDIM Sampler: 98%|█████████▊| 119/122 [02:27<00:03, 1.01s/it] DDIM Sampler: 98%|█████████▊| 120/122 [02:28<00:02, 1.00s/it] DDIM Sampler: 99%|█████████▉| 121/122 [02:29<00:01, 1.00s/it] DDIM Sampler: 100%|██████████| 122/122 [02:30<00:00, 1.00s/it] DDIM Sampler: 100%|██████████| 122/122 [02:30<00:00, 1.23s/it] python inference_gfpgan.py -i /tmp/tmpa1_mn0gkgfpgan/temp_1659051240.png -o results -v 1.3 -s 2 huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Downloading: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth" to /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/realesrgan/weights/RealESRGAN_x2plus.pth 0%| | 0.00/64.0M [00:00<?, ?B/s] 6%|▋ | 4.01M/64.0M [00:00<00:01, 35.7MB/s] 13%|█▎ | 8.01M/64.0M [00:00<00:01, 34.5MB/s] 19%|█▉ | 12.0M/64.0M [00:00<00:01, 33.8MB/s] 25%|██▌ | 16.0M/64.0M [00:00<00:01, 34.5MB/s] 31%|███▏ | 20.0M/64.0M [00:00<00:01, 34.4MB/s] 38%|███▊ | 24.0M/64.0M [00:00<00:01, 34.4MB/s] 44%|████▍ | 28.0M/64.0M [00:00<00:01, 34.0MB/s] 50%|█████ | 32.0M/64.0M [00:00<00:00, 34.0MB/s] 56%|█████▋ | 36.0M/64.0M [00:01<00:00, 33.2MB/s] 61%|██████▏ | 39.2M/64.0M [00:01<00:00, 33.0MB/s] 69%|██████▉ | 44.0M/64.0M [00:01<00:00, 35.0MB/s] 75%|███████▌ | 48.0M/64.0M [00:01<00:00, 34.6MB/s] 81%|████████▏ | 52.0M/64.0M [00:01<00:00, 34.8MB/s] 87%|████████▋ | 55.9M/64.0M [00:01<00:00, 36.4MB/s] 94%|█████████▎| 59.9M/64.0M [00:01<00:00, 37.0MB/s] 100%|█████████▉| 63.9M/64.0M [00:01<00:00, 36.9MB/s] 100%|██████████| 64.0M/64.0M [00:01<00:00, 35.0MB/s] Processing temp_1659051240.png ... Tile 1/1 Results are in the [results] folder. Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.1 Running DDIM Sampling with 28 timesteps DDIM Sampler: 0%| | 0/28 [00:00<?, ?it/s] DDIM Sampler: 4%|▎ | 1/28 [00:09<04:28, 9.95s/it] DDIM Sampler: 7%|▋ | 2/28 [00:11<02:09, 4.99s/it] DDIM Sampler: 11%|█ | 3/28 [00:12<01:24, 3.40s/it] DDIM Sampler: 14%|█▍ | 4/28 [00:14<01:03, 2.65s/it] DDIM Sampler: 18%|█▊ | 5/28 [00:16<00:51, 2.24s/it] DDIM Sampler: 21%|██▏ | 6/28 [00:17<00:43, 2.00s/it] DDIM Sampler: 25%|██▌ | 7/28 [00:19<00:38, 1.84s/it] DDIM Sampler: 29%|██▊ | 8/28 [00:20<00:34, 1.74s/it] DDIM Sampler: 32%|███▏ | 9/28 [00:22<00:31, 1.67s/it] DDIM Sampler: 36%|███▌ | 10/28 [00:23<00:29, 1.63s/it] DDIM Sampler: 39%|███▉ | 11/28 [00:25<00:27, 1.60s/it] DDIM Sampler: 43%|████▎ | 12/28 [00:26<00:25, 1.58s/it] DDIM Sampler: 46%|████▋ | 13/28 [00:28<00:23, 1.57s/it] DDIM Sampler: 50%|█████ | 14/28 [00:29<00:21, 1.56s/it] DDIM Sampler: 54%|█████▎ | 15/28 [00:31<00:20, 1.55s/it] DDIM Sampler: 57%|█████▋ | 16/28 [00:32<00:18, 1.55s/it] DDIM Sampler: 61%|██████ | 17/28 [00:34<00:16, 1.54s/it] DDIM Sampler: 64%|██████▍ | 18/28 [00:35<00:15, 1.54s/it] DDIM Sampler: 68%|██████▊ | 19/28 [00:37<00:13, 1.54s/it] DDIM Sampler: 71%|███████▏ | 20/28 [00:38<00:12, 1.54s/it] DDIM Sampler: 75%|███████▌ | 21/28 [00:40<00:10, 1.54s/it] DDIM Sampler: 79%|███████▊ | 22/28 [00:42<00:09, 1.54s/it] DDIM Sampler: 82%|████████▏ | 23/28 [00:43<00:07, 1.54s/it] DDIM Sampler: 86%|████████▌ | 24/28 [00:45<00:06, 1.54s/it] DDIM Sampler: 89%|████████▉ | 25/28 [00:46<00:04, 1.54s/it] DDIM Sampler: 93%|█████████▎| 26/28 [00:48<00:03, 1.54s/it] DDIM Sampler: 96%|█████████▋| 27/28 [00:49<00:01, 1.54s/it] DDIM Sampler: 100%|██████████| 28/28 [00:51<00:00, 1.54s/it] DDIM Sampler: 100%|██████████| 28/28 [00:51<00:00, 1.83s/it] python inference_gfpgan.py -i /tmp/tmp8k90e3etgfpgan/temp_1659051305.png -o results -v 1.3 -s 1 huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Processing temp_1659051305.png ... Tile 1/4 Tile 2/4 Tile 3/4 Tile 4/4 Results are in the [results] folder. Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.1 Running DDIM Sampling with 31 timesteps DDIM Sampler: 0%| | 0/31 [00:00<?, ?it/s] DDIM Sampler: 3%|▎ | 1/31 [00:01<00:45, 1.51s/it] DDIM Sampler: 6%|▋ | 2/31 [00:03<00:44, 1.52s/it] DDIM Sampler: 10%|▉ | 3/31 [00:04<00:42, 1.52s/it] DDIM Sampler: 13%|█▎ | 4/31 [00:06<00:40, 1.51s/it] DDIM Sampler: 16%|█▌ | 5/31 [00:07<00:39, 1.51s/it] DDIM Sampler: 19%|█▉ | 6/31 [00:09<00:37, 1.52s/it] DDIM Sampler: 23%|██▎ | 7/31 [00:10<00:36, 1.52s/it] DDIM Sampler: 26%|██▌ | 8/31 [00:12<00:34, 1.51s/it] DDIM Sampler: 29%|██▉ | 9/31 [00:13<00:33, 1.51s/it] DDIM Sampler: 32%|███▏ | 10/31 [00:15<00:31, 1.51s/it] DDIM Sampler: 35%|███▌ | 11/31 [00:16<00:30, 1.51s/it] DDIM Sampler: 39%|███▊ | 12/31 [00:18<00:28, 1.52s/it] DDIM Sampler: 42%|████▏ | 13/31 [00:19<00:27, 1.52s/it] DDIM Sampler: 45%|████▌ | 14/31 [00:21<00:25, 1.52s/it] DDIM Sampler: 48%|████▊ | 15/31 [00:22<00:24, 1.52s/it] DDIM Sampler: 52%|█████▏ | 16/31 [00:24<00:22, 1.52s/it] DDIM Sampler: 55%|█████▍ | 17/31 [00:25<00:21, 1.52s/it] DDIM Sampler: 58%|█████▊ | 18/31 [00:27<00:19, 1.52s/it] DDIM Sampler: 61%|██████▏ | 19/31 [00:28<00:18, 1.52s/it] DDIM Sampler: 65%|██████▍ | 20/31 [00:30<00:16, 1.52s/it] DDIM Sampler: 68%|██████▊ | 21/31 [00:31<00:15, 1.51s/it] DDIM Sampler: 71%|███████ | 22/31 [00:33<00:13, 1.52s/it] DDIM Sampler: 74%|███████▍ | 23/31 [00:34<00:12, 1.52s/it] DDIM Sampler: 77%|███████▋ | 24/31 [00:36<00:10, 1.52s/it] DDIM Sampler: 81%|████████ | 25/31 [00:37<00:09, 1.52s/it] DDIM Sampler: 84%|████████▍ | 26/31 [00:39<00:07, 1.52s/it] DDIM Sampler: 87%|████████▋ | 27/31 [00:40<00:06, 1.52s/it] DDIM Sampler: 90%|█████████ | 28/31 [00:42<00:04, 1.52s/it] DDIM Sampler: 94%|█████████▎| 29/31 [00:43<00:03, 1.51s/it] DDIM Sampler: 97%|█████████▋| 30/31 [00:45<00:01, 1.51s/it] DDIM Sampler: 100%|██████████| 31/31 [00:46<00:00, 1.51s/it] DDIM Sampler: 100%|██████████| 31/31 [00:46<00:00, 1.52s/it] python inference_gfpgan.py -i /tmp/tmpaa3kz4qsgfpgan/temp_1659051364.png -o results -v 1.3 -s 1 huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) Processing temp_1659051364.png ... Tile 1/4 Tile 2/4 Tile 3/4 Tile 4/4 Results are in the [results] folder. Data shape for DDIM sampling is (1, 4, 48, 40), eta 1.1 Running DDIM Sampling with 20 timesteps DDIM Sampler: 0%| | 0/20 [00:00<?, ?it/s] DDIM Sampler: 5%|▌ | 1/20 [00:01<00:28, 1.51s/it] DDIM Sampler: 10%|█ | 2/20 [00:03<00:27, 1.51s/it] DDIM Sampler: 15%|█▌ | 3/20 [00:04<00:25, 1.50s/it] DDIM Sampler: 20%|██ | 4/20 [00:06<00:24, 1.50s/it] DDIM Sampler: 25%|██▌ | 5/20 [00:07<00:22, 1.50s/it] DDIM Sampler: 30%|███ | 6/20 [00:09<00:21, 1.51s/it] DDIM Sampler: 35%|███▌ | 7/20 [00:10<00:19, 1.51s/it] DDIM Sampler: 40%|████ | 8/20 [00:12<00:18, 1.50s/it] DDIM Sampler: 45%|████▌ | 9/20 [00:13<00:16, 1.50s/it] DDIM Sampler: 50%|█████ | 10/20 [00:15<00:15, 1.50s/it] DDIM Sampler: 55%|█████▌ | 11/20 [00:16<00:13, 1.50s/it] DDIM Sampler: 60%|██████ | 12/20 [00:18<00:11, 1.50s/it] DDIM Sampler: 65%|██████▌ | 13/20 [00:19<00:10, 1.50s/it] DDIM Sampler: 70%|███████ | 14/20 [00:21<00:08, 1.50s/it] DDIM Sampler: 75%|███████▌ | 15/20 [00:22<00:07, 1.49s/it] DDIM Sampler: 80%|████████ | 16/20 [00:24<00:06, 1.50s/it] DDIM Sampler: 85%|████████▌ | 17/20 [00:25<00:04, 1.50s/it] DDIM Sampler: 90%|█████████ | 18/20 [00:27<00:02, 1.50s/it] DDIM Sampler: 95%|█████████▌| 19/20 [00:28<00:01, 1.50s/it] DDIM Sampler: 100%|██████████| 20/20 [00:29<00:00, 1.49s/it] DDIM Sampler: 100%|██████████| 20/20 [00:29<00:00, 1.50s/it] Sampling: 100%|██████████| 1/1 [05:21<00:00, 321.72s/it] Sampling: 100%|██████████| 1/1 [05:21<00:00, 321.72s/it]
Want to make some of these yourself?
Run this model