nightmareai
/
latent-sr
Upscale images with the latent diffusion superresolution model
Prediction
nightmareai/latent-sr:9117a98dInput
- up_f
- 4
- image
- steps
- 100
{ "up_f": 4, "image": "https://replicate.delivery/mgxm/5ab69372-977e-47b3-b408-b0b19a92e21f/firefox_n7EAvZoX3o.png", "steps": 100 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run nightmareai/latent-sr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/latent-sr:9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abc", { input: { up_f: 4, image: "https://replicate.delivery/mgxm/5ab69372-977e-47b3-b408-b0b19a92e21f/firefox_n7EAvZoX3o.png", steps: 100 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun nightmareai/latent-sr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/latent-sr:9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abc", input={ "up_f": 4, "image": "https://replicate.delivery/mgxm/5ab69372-977e-47b3-b408-b0b19a92e21f/firefox_n7EAvZoX3o.png", "steps": 100 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run nightmareai/latent-sr 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": "9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abc", "input": { "up_f": 4, "image": "https://replicate.delivery/mgxm/5ab69372-977e-47b3-b408-b0b19a92e21f/firefox_n7EAvZoX3o.png", "steps": 100 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cogIf you don’t have Homebrew, there are other installation options available.
Pull and run nightmareai/latent-sr using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/nightmareai/latent-sr@sha256:9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abc \ -i 'up_f=4' \ -i 'image="https://replicate.delivery/mgxm/5ab69372-977e-47b3-b408-b0b19a92e21f/firefox_n7EAvZoX3o.png"' \ -i 'steps=100'To learn more, take a look at the Cog documentation.
Pull and run nightmareai/latent-sr using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/nightmareai/latent-sr@sha256:9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abccurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "up_f": 4, "image": "https://replicate.delivery/mgxm/5ab69372-977e-47b3-b408-b0b19a92e21f/firefox_n7EAvZoX3o.png", "steps": 100 } }' \ http://localhost:5000/predictionsOutput
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/mgxm/5ab69372-977e-47b3-b408-b0b19a92e21f/firefox_n7EAvZoX3o.png", "outut": "https://replicate.delivery/mgxm/1001c645-8ffb-4f4c-a37d-f44e7b8f8641/tmpai02eqrl.png" }{ "completed_at": "2022-07-12T04:22:39.323367Z", "created_at": "2022-07-12T04:19:19.769563Z", "data_removed": false, "error": null, "id": "cweyeicmvzbjze6kmqcel7jdru", "input": { "up_f": 4, "image": "https://replicate.delivery/mgxm/5ab69372-977e-47b3-b408-b0b19a92e21f/firefox_n7EAvZoX3o.png", "steps": 100 }, "logs": "\nPlotting: Switched to EMA weights\nSampling with eta = 1.0; steps: 100\nData shape for DDIM sampling is (1, 3, 256, 256), eta 1.0\nRunning DDIM Sampling with 100 timesteps\nSampling: 0%| | 0/1 [00:00<?, ?it/s]\n\nDDIM Sampler: 0%| | 0/100 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%| | 1/100 [00:00<01:00, 1.65it/s]\u001b[A\n\nDDIM Sampler: 2%|▏ | 2/100 [00:01<00:59, 1.65it/s]\u001b[A\n\nDDIM Sampler: 3%|▎ | 3/100 [00:01<00:58, 1.64it/s]\u001b[A\n\nDDIM Sampler: 4%|▍ | 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[00:56<00:05, 1.57it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 92/100 [00:57<00:05, 1.57it/s]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 93/100 [00:57<00:04, 1.57it/s]\u001b[A\n\nDDIM Sampler: 94%|█████████▍| 94/100 [00:58<00:03, 1.57it/s]\u001b[A\n\nDDIM Sampler: 95%|█████████▌| 95/100 [00:59<00:03, 1.57it/s]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 96/100 [00:59<00:02, 1.57it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 97/100 [01:00<00:01, 1.57it/s]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 98/100 [01:01<00:01, 1.57it/s]\u001b[A\n\nDDIM Sampler: 99%|█████████▉| 99/100 [01:01<00:00, 1.56it/s]\u001b[A\n\nDDIM Sampler: 100%|██████████| 100/100 [01:02<00:00, 1.56it/s]\u001b[A\nDDIM Sampler: 100%|██████████| 100/100 [01:02<00:00, 1.60it/s]\nPlotting: Restored training weights\n\nSampling: 100%|██████████| 1/1 [01:21<00:00, 81.49s/it]\nSampling: 100%|██████████| 1/1 [01:21<00:00, 81.49s/it]", "metrics": { "predict_time": 86.890231, "total_time": 199.553804 }, "output": "https://replicate.delivery/mgxm/1001c645-8ffb-4f4c-a37d-f44e7b8f8641/tmpai02eqrl.png", "started_at": "2022-07-12T04:21:12.433136Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cweyeicmvzbjze6kmqcel7jdru", "cancel": "https://api.replicate.com/v1/predictions/cweyeicmvzbjze6kmqcel7jdru/cancel" }, "version": "87b274bb57818073a37236f9827d855ce355ae056f222b02467c1e64628d575f" }Generated inPlotting: Switched to EMA weights Sampling with eta = 1.0; steps: 100 Data shape for DDIM sampling is (1, 3, 256, 256), eta 1.0 Running DDIM Sampling with 100 timesteps Sampling: 0%| | 0/1 [00:00<?, ?it/s] DDIM Sampler: 0%| | 0/100 [00:00<?, ?it/s] DDIM Sampler: 1%| | 1/100 [00:00<01:00, 1.65it/s] DDIM Sampler: 2%|▏ | 2/100 [00:01<00:59, 1.65it/s] DDIM Sampler: 3%|▎ | 3/100 [00:01<00:58, 1.64it/s] DDIM Sampler: 4%|▍ | 4/100 [00:02<00:58, 1.64it/s] DDIM Sampler: 5%|▌ | 5/100 [00:03<00:57, 1.64it/s] DDIM Sampler: 6%|▌ | 6/100 [00:03<00:57, 1.64it/s] DDIM Sampler: 7%|▋ | 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nightmareai/latent-sr:9117a98dIDuvvgykoa5vga7fjkzvt7yu5q3mStatusSucceededSourceWebHardware–Total durationCreatedInput
- up_f
- 4
- image
- steps
- 100
{ "up_f": 4, "image": "https://replicate.delivery/mgxm/d4fea5b0-2ffb-4fe5-b651-de6058763080/4K-old-and-dusty-wood-floor-with-scratches-and-bumps-seamle.png", "steps": 100 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run nightmareai/latent-sr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/latent-sr:9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abc", { input: { up_f: 4, image: "https://replicate.delivery/mgxm/d4fea5b0-2ffb-4fe5-b651-de6058763080/4K-old-and-dusty-wood-floor-with-scratches-and-bumps-seamle.png", steps: 100 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun nightmareai/latent-sr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/latent-sr:9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abc", input={ "up_f": 4, "image": "https://replicate.delivery/mgxm/d4fea5b0-2ffb-4fe5-b651-de6058763080/4K-old-and-dusty-wood-floor-with-scratches-and-bumps-seamle.png", "steps": 100 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run nightmareai/latent-sr 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": "9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abc", "input": { "up_f": 4, "image": "https://replicate.delivery/mgxm/d4fea5b0-2ffb-4fe5-b651-de6058763080/4K-old-and-dusty-wood-floor-with-scratches-and-bumps-seamle.png", "steps": 100 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cogIf you don’t have Homebrew, there are other installation options available.
Pull and run nightmareai/latent-sr using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/nightmareai/latent-sr@sha256:9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abc \ -i 'up_f=4' \ -i 'image="https://replicate.delivery/mgxm/d4fea5b0-2ffb-4fe5-b651-de6058763080/4K-old-and-dusty-wood-floor-with-scratches-and-bumps-seamle.png"' \ -i 'steps=100'To learn more, take a look at the Cog documentation.
Pull and run nightmareai/latent-sr using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/nightmareai/latent-sr@sha256:9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abccurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "up_f": 4, "image": "https://replicate.delivery/mgxm/d4fea5b0-2ffb-4fe5-b651-de6058763080/4K-old-and-dusty-wood-floor-with-scratches-and-bumps-seamle.png", "steps": 100 } }' \ http://localhost:5000/predictionsOutput
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/mgxm/d4fea5b0-2ffb-4fe5-b651-de6058763080/4K-old-and-dusty-wood-floor-with-scratches-and-bumps-seamle.png", "outut": "https://replicate.delivery/mgxm/62ca1b90-9ec2-4303-94f0-92206ea75459/tmpmtljfr8t.png" }{ "completed_at": "2022-07-13T14:57:32.718149Z", "created_at": "2022-07-13T14:37:08.313818Z", "data_removed": false, "error": null, "id": "uvvgykoa5vga7fjkzvt7yu5q3m", "input": { "up_f": 4, "image": "https://replicate.delivery/mgxm/d4fea5b0-2ffb-4fe5-b651-de6058763080/4K-old-and-dusty-wood-floor-with-scratches-and-bumps-seamle.png", "steps": 100 }, "logs": "\nPlotting: Switched to EMA weights\nSampling with eta = 1.0; steps: 100\nData shape for DDIM sampling is (1, 3, 704, 704), eta 1.0\nRunning DDIM Sampling with 100 timesteps\nSampling: 0%| | 0/1 [00:00<?, ?it/s]\n\nDDIM Sampler: 0%| | 0/100 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%| | 1/100 [00:07<12:11, 7.39s/it]\u001b[A\n\nDDIM Sampler: 2%|▏ | 2/100 [00:14<12:04, 7.39s/it]\u001b[A\n\nDDIM 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[11:02<01:13, 7.37s/it]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 91/100 [11:09<01:06, 7.37s/it]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 92/100 [11:17<00:59, 7.38s/it]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 93/100 [11:24<00:51, 7.38s/it]\u001b[A\n\nDDIM Sampler: 94%|█████████▍| 94/100 [11:31<00:44, 7.38s/it]\u001b[A\n\nDDIM Sampler: 95%|█████████▌| 95/100 [11:39<00:36, 7.37s/it]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 96/100 [11:46<00:29, 7.37s/it]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 97/100 [11:53<00:22, 7.37s/it]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 98/100 [12:01<00:14, 7.37s/it]\u001b[A\n\nDDIM Sampler: 99%|█████████▉| 99/100 [12:08<00:07, 7.37s/it]\u001b[A\n\nDDIM Sampler: 100%|██████████| 100/100 [12:15<00:00, 7.36s/it]\u001b[A\nDDIM Sampler: 100%|██████████| 100/100 [12:15<00:00, 7.36s/it]\nPlotting: Restored training weights\n\nSampling: 100%|██████████| 1/1 [16:28<00:00, 988.03s/it]\nSampling: 100%|██████████| 1/1 [16:28<00:00, 988.03s/it]", "metrics": { "predict_time": 1007.693702, "total_time": 1224.404331 }, "output": "https://replicate.delivery/mgxm/62ca1b90-9ec2-4303-94f0-92206ea75459/tmpmtljfr8t.png", "started_at": "2022-07-13T14:40:45.024447Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uvvgykoa5vga7fjkzvt7yu5q3m", "cancel": "https://api.replicate.com/v1/predictions/uvvgykoa5vga7fjkzvt7yu5q3m/cancel" }, "version": "9117a98dd15e931011b8b960963a2dec20ab493c6c0d3a134525273da1616abc" }Generated inPlotting: Switched to EMA weights Sampling with eta = 1.0; steps: 100 Data shape for DDIM sampling is (1, 3, 704, 704), eta 1.0 Running DDIM Sampling with 100 timesteps Sampling: 0%| | 0/1 [00:00<?, ?it/s] DDIM Sampler: 0%| | 0/100 [00:00<?, ?it/s] DDIM Sampler: 1%| | 1/100 [00:07<12:11, 7.39s/it] DDIM Sampler: 2%|▏ | 2/100 [00:14<12:04, 7.39s/it] DDIM Sampler: 3%|▎ | 3/100 [00:22<11:56, 7.39s/it] DDIM Sampler: 4%|▍ | 4/100 [00:29<11:48, 7.38s/it] DDIM Sampler: 5%|▌ | 5/100 [00:36<11:39, 7.36s/it] DDIM Sampler: 6%|▌ | 6/100 [00:44<11:31, 7.35s/it] DDIM Sampler: 7%|▋ | 7/100 [00:51<11:23, 7.35s/it] DDIM Sampler: 8%|▊ | 8/100 [00:58<11:15, 7.35s/it] DDIM Sampler: 9%|▉ | 9/100 [01:06<11:08, 7.35s/it] DDIM Sampler: 10%|█ | 10/100 [01:13<11:02, 7.36s/it] DDIM Sampler: 11%|█ | 11/100 [01:20<10:55, 7.36s/it] DDIM Sampler: 12%|█▏ | 12/100 [01:28<10:48, 7.37s/it] DDIM Sampler: 13%|█▎ | 13/100 [01:35<10:41, 7.37s/it] DDIM Sampler: 14%|█▍ | 14/100 [01:43<10:33, 7.37s/it] DDIM Sampler: 15%|█▌ | 15/100 [01:50<10:26, 7.37s/it] DDIM Sampler: 16%|█▌ | 16/100 [01:57<10:18, 7.37s/it] DDIM Sampler: 17%|█▋ | 17/100 [02:05<10:10, 7.36s/it] DDIM Sampler: 18%|█▊ | 18/100 [02:12<10:03, 7.36s/it] DDIM Sampler: 19%|█▉ | 19/100 [02:19<09:55, 7.36s/it] DDIM Sampler: 20%|██ | 20/100 [02:27<09:48, 7.36s/it] DDIM Sampler: 21%|██ | 21/100 [02:34<09:41, 7.36s/it] DDIM Sampler: 22%|██▏ | 22/100 [02:41<09:33, 7.36s/it] DDIM Sampler: 23%|██▎ | 23/100 [02:49<09:26, 7.36s/it] DDIM Sampler: 24%|██▍ | 24/100 [02:56<09:19, 7.36s/it] DDIM Sampler: 25%|██▌ | 25/100 [03:04<09:11, 7.36s/it] DDIM Sampler: 26%|██▌ | 26/100 [03:11<09:04, 7.36s/it] DDIM Sampler: 27%|██▋ | 27/100 [03:18<08:57, 7.37s/it] DDIM Sampler: 28%|██▊ | 28/100 [03:26<08:50, 7.37s/it] DDIM Sampler: 29%|██▉ | 29/100 [03:33<08:43, 7.37s/it] DDIM Sampler: 30%|███ | 30/100 [03:40<08:36, 7.38s/it] DDIM Sampler: 31%|███ | 31/100 [03:48<08:29, 7.38s/it] DDIM Sampler: 32%|███▏ | 32/100 [03:55<08:21, 7.38s/it] DDIM Sampler: 33%|███▎ | 33/100 [04:03<08:14, 7.38s/it] DDIM Sampler: 34%|███▍ | 34/100 [04:10<08:06, 7.38s/it] DDIM Sampler: 35%|███▌ | 35/100 [04:17<07:59, 7.38s/it] DDIM Sampler: 36%|███▌ | 36/100 [04:25<07:51, 7.37s/it] DDIM Sampler: 37%|███▋ | 37/100 [04:32<07:44, 7.37s/it] DDIM Sampler: 38%|███▊ | 38/100 [04:39<07:36, 7.36s/it] DDIM Sampler: 39%|███▉ | 39/100 [04:47<07:29, 7.36s/it] DDIM Sampler: 40%|████ | 40/100 [04:54<07:21, 7.36s/it] DDIM Sampler: 41%|████ | 41/100 [05:01<07:13, 7.35s/it] DDIM Sampler: 42%|████▏ | 42/100 [05:09<07:06, 7.35s/it] DDIM Sampler: 43%|████▎ | 43/100 [05:16<06:58, 7.35s/it] DDIM Sampler: 44%|████▍ | 44/100 [05:23<06:51, 7.35s/it] DDIM Sampler: 45%|████▌ | 45/100 [05:31<06:44, 7.35s/it] DDIM Sampler: 46%|████▌ | 46/100 [05:38<06:36, 7.35s/it] DDIM Sampler: 47%|████▋ | 47/100 [05:46<06:29, 7.35s/it] DDIM Sampler: 48%|████▊ | 48/100 [05:53<06:22, 7.35s/it] DDIM Sampler: 49%|████▉ | 49/100 [06:00<06:14, 7.35s/it] DDIM Sampler: 50%|█████ | 50/100 [06:08<06:07, 7.35s/it] DDIM Sampler: 51%|█████ | 51/100 [06:15<06:00, 7.35s/it] DDIM Sampler: 52%|█████▏ | 52/100 [06:22<05:52, 7.35s/it] DDIM Sampler: 53%|█████▎ | 53/100 [06:30<05:45, 7.35s/it] DDIM Sampler: 54%|█████▍ | 54/100 [06:37<05:38, 7.35s/it] DDIM Sampler: 55%|█████▌ | 55/100 [06:44<05:30, 7.35s/it] DDIM Sampler: 56%|█████▌ | 56/100 [06:52<05:23, 7.35s/it] DDIM Sampler: 57%|█████▋ | 57/100 [06:59<05:16, 7.35s/it] DDIM Sampler: 58%|█████▊ | 58/100 [07:06<05:08, 7.35s/it] DDIM Sampler: 59%|█████▉ | 59/100 [07:14<05:01, 7.35s/it] DDIM Sampler: 60%|██████ | 60/100 [07:21<04:53, 7.35s/it] DDIM Sampler: 61%|██████ | 61/100 [07:28<04:46, 7.35s/it] DDIM Sampler: 62%|██████▏ | 62/100 [07:36<04:39, 7.35s/it] DDIM Sampler: 63%|██████▎ | 63/100 [07:43<04:32, 7.35s/it] DDIM Sampler: 64%|██████▍ | 64/100 [07:51<04:24, 7.35s/it] DDIM Sampler: 65%|██████▌ | 65/100 [07:58<04:17, 7.35s/it] DDIM Sampler: 66%|██████▌ | 66/100 [08:05<04:09, 7.35s/it] DDIM Sampler: 67%|██████▋ | 67/100 [08:13<04:02, 7.35s/it] DDIM Sampler: 68%|██████▊ | 68/100 [08:20<03:55, 7.35s/it] DDIM Sampler: 69%|██████▉ | 69/100 [08:27<03:47, 7.35s/it] DDIM Sampler: 70%|███████ | 70/100 [08:35<03:40, 7.35s/it] DDIM Sampler: 71%|███████ | 71/100 [08:42<03:33, 7.35s/it] DDIM Sampler: 72%|███████▏ | 72/100 [08:49<03:25, 7.35s/it] DDIM Sampler: 73%|███████▎ | 73/100 [08:57<03:18, 7.35s/it] DDIM Sampler: 74%|███████▍ | 74/100 [09:04<03:11, 7.35s/it] DDIM Sampler: 75%|███████▌ | 75/100 [09:11<03:03, 7.36s/it] DDIM Sampler: 76%|███████▌ | 76/100 [09:19<02:56, 7.36s/it] DDIM Sampler: 77%|███████▋ | 77/100 [09:26<02:49, 7.36s/it] DDIM Sampler: 78%|███████▊ | 78/100 [09:33<02:41, 7.36s/it] DDIM Sampler: 79%|███████▉ | 79/100 [09:41<02:34, 7.35s/it] DDIM Sampler: 80%|████████ | 80/100 [09:48<02:27, 7.35s/it] DDIM Sampler: 81%|████████ | 81/100 [09:56<02:19, 7.35s/it] DDIM Sampler: 82%|████████▏ | 82/100 [10:03<02:12, 7.35s/it] DDIM Sampler: 83%|████████▎ | 83/100 [10:10<02:04, 7.35s/it] DDIM Sampler: 84%|████████▍ | 84/100 [10:18<01:57, 7.35s/it] DDIM Sampler: 85%|████████▌ | 85/100 [10:25<01:50, 7.35s/it] DDIM Sampler: 86%|████████▌ | 86/100 [10:32<01:42, 7.35s/it] DDIM Sampler: 87%|████████▋ | 87/100 [10:40<01:35, 7.36s/it] DDIM Sampler: 88%|████████▊ | 88/100 [10:47<01:28, 7.36s/it] DDIM Sampler: 89%|████████▉ | 89/100 [10:54<01:21, 7.36s/it] DDIM Sampler: 90%|█████████ | 90/100 [11:02<01:13, 7.37s/it] DDIM Sampler: 91%|█████████ | 91/100 [11:09<01:06, 7.37s/it] DDIM Sampler: 92%|█████████▏| 92/100 [11:17<00:59, 7.38s/it] DDIM Sampler: 93%|█████████▎| 93/100 [11:24<00:51, 7.38s/it] DDIM Sampler: 94%|█████████▍| 94/100 [11:31<00:44, 7.38s/it] DDIM Sampler: 95%|█████████▌| 95/100 [11:39<00:36, 7.37s/it] DDIM Sampler: 96%|█████████▌| 96/100 [11:46<00:29, 7.37s/it] DDIM Sampler: 97%|█████████▋| 97/100 [11:53<00:22, 7.37s/it] DDIM Sampler: 98%|█████████▊| 98/100 [12:01<00:14, 7.37s/it] DDIM Sampler: 99%|█████████▉| 99/100 [12:08<00:07, 7.37s/it] DDIM Sampler: 100%|██████████| 100/100 [12:15<00:00, 7.36s/it] DDIM Sampler: 100%|██████████| 100/100 [12:15<00:00, 7.36s/it] Plotting: Restored training weights Sampling: 100%|██████████| 1/1 [16:28<00:00, 988.03s/it] Sampling: 100%|██████████| 1/1 [16:28<00:00, 988.03s/it]
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