nightmareai
/
disco-diffusion
Generate images using a variety of techniques - Powered by Discoart
Prediction
nightmareai/disco-diffusion:3c128f65Input
- RN50
- steps
- 100
- width
- 1280
- ViTB16
- ViTB32
- height
- 768
- prompt
- A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.
- init_scale
- 1000
- skip_steps
- 10
- range_scale
- 150
- cutn_batches
- 4
- display_rate
- 20
- target_scale
- 20000
- diffusion_model
- 512x512_diffusion_uncond_finetune_008100
- clip_guidance_scale
- 5000
- use_secondary_model
- diffusion_sampling_mode
- plms
{ "RN50": true, "steps": 100, "width": 1280, "ViTB16": true, "ViTB32": true, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "plms" }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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", { input: { RN50: true, steps: 100, width: 1280, ViTB16: true, ViTB32: true, height: 768, prompt: "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", init_scale: 1000, skip_steps: 10, range_scale: 150, cutn_batches: 4, display_rate: 20, target_scale: 20000, diffusion_model: "512x512_diffusion_uncond_finetune_008100", clip_guidance_scale: 5000, use_secondary_model: true, diffusion_sampling_mode: "plms" } } ); 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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", input={ "RN50": True, "steps": 100, "width": 1280, "ViTB16": True, "ViTB32": True, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": True, "diffusion_sampling_mode": "plms" } ) # The nightmareai/disco-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/disco-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/disco-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": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", "input": { "RN50": true, "steps": 100, "width": 1280, "ViTB16": true, "ViTB32": true, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "plms" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T11:00:04.156176Z", "created_at": "2022-07-10T10:48:48.096392Z", "data_removed": false, "error": null, "id": "m7srykonyvf6ni3v47eob2amqi", "input": { "RN50": true, "steps": 100, "width": 1280, "ViTB16": true, "ViTB32": true, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "plms" }, "logs": "2022-07-10 10:53:00.447 | INFO | dd:start_run:2224 - 💼 1 jobs found.\n2022-07-10 10:53:00.448 | INFO | dd:start_run:2236 - ⚒️ Session 53211f30-715a-45e9-99c2-6be3ee8bd590 started...\n2022-07-10 10:53:00.448 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1...\n2022-07-10 10:53:00.449 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion...\n2022-07-10 10:53:00.450 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 1876651774\n2022-07-10 10:53:00.451 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-10 10:53:00.451 | INFO | dd:do_run:1190 - 💻 Starting Run: 6af357b1-11dc-4954-ad45-4f17b3bc1889(0) at frame 0\n2022-07-10 10:53:00.451 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'...\n2022-07-10 10:53:08.770 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'...\n2022-07-10 10:53:14.198 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN50'...\n2022-07-10 10:53:18.328 | INFO | dd:do_run:1216 - 🤖 Loading secondary model...\n2022-07-10 10:53:18.629 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS...\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n warnings.warn(\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n\u001b[2K\n2022-07-10 10:53:20.486 | INFO | dd:disco:1502 - timestep_respacing : ddim100 | diffusion_steps: 1000\n2022-07-10 10:53:20.486 | INFO | dd:prepModels:1054 - Prepping models...\n2022-07-10 10:53:35.310 | INFO | dd:disco:1532 - Running job '42093ca4-2d79-44ab-8182-ddbf268da620'...\n2022-07-10 10:53:35.310 | INFO | dd:disco:1545 - 🌱 Seed used: 1876651774\n2022-07-10 10:53:35.321 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.']\n\u001b[2K\n\u001b[2K\n\u001b[2K\nBatches: 0%| | 0/1 [00:00<?, ?it/s]\nOutput()\n2022-07-10 10:53:37.281 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n\n\n 0%| | 0/90 [00:00<?, ?it/s]\u001b[A\n\n\nBatch 0, step 0, output 0:\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image 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saved to '/src/images_out/6af357b1-11dc-4954-ad45-4f17b3bc1889/6af357b1-11dc-4954-ad45-4f17b3bc1889(0)_0.png'\n\n\n100%|██████████| 90/90 [06:21<00:00, 4.44s/it]\u001b[A\n100%|██████████| 90/90 [06:21<00:00, 4.24s/it]\n2022-07-10 10:59:58.705 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-10 10:59:58.706 | SUCCESS | dd:start_run:2245 - ✅ Session 53211f30-715a-45e9-99c2-6be3ee8bd590 finished by user.\n2022-07-10 10:59:58.706 | DEBUG | dd:sendSMS:2530 - Not sending SMS\n2022-07-10 10:59:58.706 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...", "metrics": { "predict_time": 423.675935, "total_time": 676.059784 }, "output": [ "https://replicate.delivery/mgxm/1101ed75-d65e-4a45-959b-048a7debc248/progress.png", "https://replicate.delivery/mgxm/bc90146f-71a3-4afd-81fd-5995d2e674d7/progress.png", "https://replicate.delivery/mgxm/3e18da08-f286-472b-b5c3-325b3ee793ac/progress.png", "https://replicate.delivery/mgxm/120ed27b-2f50-4453-9c41-f317e46dcaa4/progress.png", "https://replicate.delivery/mgxm/4a98c507-b6a5-43c7-b689-39b7486cd1c0/progress.png", "https://replicate.delivery/mgxm/c1288e23-2428-43fe-b5a7-6851453826b1/progress.png", "https://replicate.delivery/mgxm/5800f828-94d5-46a0-9eb2-96b51d7846e3/6af357b1-11dc-4954-ad45-4f17b3bc18890_0.png" ], "started_at": "2022-07-10T10:53:00.480241Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m7srykonyvf6ni3v47eob2amqi", "cancel": "https://api.replicate.com/v1/predictions/m7srykonyvf6ni3v47eob2amqi/cancel" }, "version": "bc78822513db3dd6fc3be978d68472258b6f86a3a8ebf6aa8c5bc60b66bbae58" }Generated in2022-07-10 10:53:00.447 | INFO | dd:start_run:2224 - 💼 1 jobs found. 2022-07-10 10:53:00.448 | INFO | dd:start_run:2236 - ⚒️ Session 53211f30-715a-45e9-99c2-6be3ee8bd590 started... 2022-07-10 10:53:00.448 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1... 2022-07-10 10:53:00.449 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion... 2022-07-10 10:53:00.450 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 1876651774 2022-07-10 10:53:00.451 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-10 10:53:00.451 | INFO | dd:do_run:1190 - 💻 Starting Run: 6af357b1-11dc-4954-ad45-4f17b3bc1889(0) at frame 0 2022-07-10 10:53:00.451 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'... 2022-07-10 10:53:08.770 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'... 2022-07-10 10:53:14.198 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN50'... 2022-07-10 10:53:18.328 | INFO | dd:do_run:1216 - 🤖 Loading secondary model... 2022-07-10 10:53:18.629 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS... /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) 2022-07-10 10:53:20.486 | INFO | dd:disco:1502 - timestep_respacing : ddim100 | diffusion_steps: 1000 2022-07-10 10:53:20.486 | INFO | dd:prepModels:1054 - Prepping models... 2022-07-10 10:53:35.310 | INFO | dd:disco:1532 - Running job '42093ca4-2d79-44ab-8182-ddbf268da620'... 2022-07-10 10:53:35.310 | INFO | dd:disco:1545 - 🌱 Seed used: 1876651774 2022-07-10 10:53:35.321 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.'] Batches: 0%| | 0/1 [00:00<?, ?it/s] Output() 2022-07-10 10:53:37.281 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 0%| | 0/90 [00:00<?, ?it/s] Batch 0, step 0, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F4BB3E45E50> 0%| | 0/90 [00:11<?, ?it/s] 1%| | 1/90 [00:11<17:03, 11.50s/it] 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SMS 2022-07-10 10:59:58.706 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...Prediction
nightmareai/disco-diffusion:3c128f65Input
- RN50
- steps
- 250
- width
- 1280
- ViTB16
- ViTB32
- height
- 768
- prompt
- A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.
- init_scale
- 1000
- skip_steps
- 10
- range_scale
- 150
- cutn_batches
- 4
- display_rate
- 20
- target_scale
- 20000
- diffusion_model
- 512x512_diffusion_uncond_finetune_008100
- clip_guidance_scale
- 5000
- use_secondary_model
- diffusion_sampling_mode
- ddim
{ "RN50": true, "steps": 250, "width": 1280, "ViTB16": true, "ViTB32": true, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" }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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", { input: { RN50: true, steps: 250, width: 1280, ViTB16: true, ViTB32: true, height: 768, prompt: "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", init_scale: 1000, skip_steps: 10, range_scale: 150, cutn_batches: 4, display_rate: 20, target_scale: 20000, diffusion_model: "512x512_diffusion_uncond_finetune_008100", clip_guidance_scale: 5000, use_secondary_model: true, diffusion_sampling_mode: "ddim" } } ); 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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", input={ "RN50": True, "steps": 250, "width": 1280, "ViTB16": True, "ViTB32": True, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": True, "diffusion_sampling_mode": "ddim" } ) # The nightmareai/disco-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/disco-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/disco-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": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", "input": { "RN50": true, "steps": 250, "width": 1280, "ViTB16": true, "ViTB32": true, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T11:17:36.820113Z", "created_at": "2022-07-10T11:00:26.481418Z", "data_removed": false, "error": null, "id": "bzp5mhiyarcmvoinbxpubjhzou", "input": { "RN50": true, "steps": 250, "width": 1280, "ViTB16": true, "ViTB32": true, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" }, "logs": "2022-07-10 11:00:26.617 | INFO | dd:start_run:2224 - 💼 1 jobs found.\n2022-07-10 11:00:26.618 | INFO | dd:start_run:2236 - ⚒️ Session 0ed00f4d-91e7-4ca9-acff-88072c74069e started...\n2022-07-10 11:00:26.619 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1...\n2022-07-10 11:00:26.619 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion...\n2022-07-10 11:00:26.619 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 2866669839\n2022-07-10 11:00:26.620 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-10 11:00:26.620 | INFO | dd:do_run:1190 - 💻 Starting Run: ba70ca3b-8ed1-4ac4-a09f-3cc046dd3dea(0) at frame 0\n2022-07-10 11:00:26.620 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'...\n2022-07-10 11:00:31.138 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'...\n2022-07-10 11:00:35.131 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN50'...\n2022-07-10 11:00:38.346 | INFO | dd:do_run:1216 - 🤖 Loading secondary model...\n2022-07-10 11:00:38.634 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS...\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n warnings.warn(\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n\u001b[2K\n2022-07-10 11:00:42.292 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000\n2022-07-10 11:00:42.293 | INFO | dd:prepModels:1054 - Prepping models...\n2022-07-10 11:00:55.511 | INFO | dd:disco:1532 - Running job '854105ed-bc42-4cf6-a569-59bf14495d4e'...\n2022-07-10 11:00:55.511 | INFO | dd:disco:1545 - 🌱 Seed used: 2866669839\n2022-07-10 11:00:55.513 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.']\n\u001b[2K\n\u001b[2K\n\u001b[2K\nBatches: 0%| | 0/1 [00:00<?, ?it/s]\nOutput()\n2022-07-10 11:00:55.578 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n\n\n 0%| | 0/240 [00:00<?, ?it/s]\u001b[A\nBatch 0, step 0, output 0:\n\n\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image 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'/src/images_out/ba70ca3b-8ed1-4ac4-a09f-3cc046dd3dea/ba70ca3b-8ed1-4ac4-a09f-3cc046dd3dea(0)_0.png'\n\n\n100%|██████████| 240/240 [16:35<00:00, 4.50s/it]\u001b[A\n100%|██████████| 240/240 [16:35<00:00, 4.15s/it]\n2022-07-10 11:17:31.558 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-10 11:17:31.558 | SUCCESS | dd:start_run:2245 - ✅ Session 0ed00f4d-91e7-4ca9-acff-88072c74069e finished by user.\n2022-07-10 11:17:31.558 | DEBUG | dd:sendSMS:2530 - Not sending SMS\n2022-07-10 11:17:31.558 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...", "metrics": { "predict_time": 1030.13119, "total_time": 1030.338695 }, "output": [ "https://replicate.delivery/mgxm/d3e8443d-cb0e-45b4-b712-4b6a5f9c2a81/progress.png", "https://replicate.delivery/mgxm/79840fdf-a9f6-4a30-9b0a-7a5e084ca39b/progress.png", "https://replicate.delivery/mgxm/6c8d7bfb-d183-4b51-9278-979fd6231d78/progress.png", "https://replicate.delivery/mgxm/2fa3d15f-3ed5-422e-9ae4-04138f906a0a/progress.png", "https://replicate.delivery/mgxm/c416110b-f805-4f94-a74d-7978f84a94e3/progress.png", "https://replicate.delivery/mgxm/a8d6cf08-f57d-41b2-a8b7-45955f0097df/progress.png", "https://replicate.delivery/mgxm/8278c91e-ad9d-4eb6-8357-6ffd22266fa5/progress.png", "https://replicate.delivery/mgxm/4383264d-01e3-41d9-9c37-beae099c20c1/progress.png", "https://replicate.delivery/mgxm/b6341fb5-4546-46b4-b4c3-ae17f6aef37d/progress.png", "https://replicate.delivery/mgxm/d277784c-7819-4ac3-a9f4-8bf9bd756813/progress.png", "https://replicate.delivery/mgxm/d99647d9-f1c3-44e0-9b48-d2724c25044b/progress.png", "https://replicate.delivery/mgxm/e6217cfd-ff8b-44b6-b3af-d6ff767dad2b/progress.png", "https://replicate.delivery/mgxm/e9e07816-6147-4a86-8b27-10058e3b4620/progress.png", "https://replicate.delivery/mgxm/eb8dc56a-1e37-4e6a-8fd7-51ad12b399f5/ba70ca3b-8ed1-4ac4-a09f-3cc046dd3dea0_0.png" ], "started_at": "2022-07-10T11:00:26.688923Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bzp5mhiyarcmvoinbxpubjhzou", "cancel": "https://api.replicate.com/v1/predictions/bzp5mhiyarcmvoinbxpubjhzou/cancel" }, "version": "bc78822513db3dd6fc3be978d68472258b6f86a3a8ebf6aa8c5bc60b66bbae58" }Generated in2022-07-10 11:00:26.617 | INFO | dd:start_run:2224 - 💼 1 jobs found. 2022-07-10 11:00:26.618 | INFO | dd:start_run:2236 - ⚒️ Session 0ed00f4d-91e7-4ca9-acff-88072c74069e started... 2022-07-10 11:00:26.619 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1... 2022-07-10 11:00:26.619 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion... 2022-07-10 11:00:26.619 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 2866669839 2022-07-10 11:00:26.620 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-10 11:00:26.620 | INFO | dd:do_run:1190 - 💻 Starting Run: ba70ca3b-8ed1-4ac4-a09f-3cc046dd3dea(0) at frame 0 2022-07-10 11:00:26.620 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'... 2022-07-10 11:00:31.138 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'... 2022-07-10 11:00:35.131 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN50'... 2022-07-10 11:00:38.346 | INFO | dd:do_run:1216 - 🤖 Loading secondary model... 2022-07-10 11:00:38.634 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS... /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) 2022-07-10 11:00:42.292 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000 2022-07-10 11:00:42.293 | INFO | dd:prepModels:1054 - Prepping models... 2022-07-10 11:00:55.511 | INFO | dd:disco:1532 - Running job '854105ed-bc42-4cf6-a569-59bf14495d4e'... 2022-07-10 11:00:55.511 | INFO | dd:disco:1545 - 🌱 Seed used: 2866669839 2022-07-10 11:00:55.513 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.'] Batches: 0%| | 0/1 [00:00<?, ?it/s] Output() 2022-07-10 11:00:55.578 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 0%| | 0/240 [00:00<?, ?it/s] Batch 0, step 0, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F4BB3D99280> 0%| | 0/240 [00:03<?, ?it/s] 0%| | 1/240 [00:04<16:41, 4.19s/it] 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<PIL.Image.Image image mode=RGB size=1280x768 at 0x7F4BB3D996A0> 100%|█████████▉| 239/240 [16:35<00:04, 4.23s/it]2022-07-10 11:17:31.125 | INFO | dd:disco:1820 - Image render completed. 2022-07-10 11:17:31.557 | INFO | dd:disco:1840 - Image saved to '/src/images_out/ba70ca3b-8ed1-4ac4-a09f-3cc046dd3dea/ba70ca3b-8ed1-4ac4-a09f-3cc046dd3dea(0)_0.png' 100%|██████████| 240/240 [16:35<00:00, 4.50s/it] 100%|██████████| 240/240 [16:35<00:00, 4.15s/it] 2022-07-10 11:17:31.558 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-10 11:17:31.558 | SUCCESS | dd:start_run:2245 - ✅ Session 0ed00f4d-91e7-4ca9-acff-88072c74069e finished by user. 2022-07-10 11:17:31.558 | DEBUG | dd:sendSMS:2530 - Not sending SMS 2022-07-10 11:17:31.558 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...Prediction
nightmareai/disco-diffusion:3c128f65Input
- RN50
- steps
- 250
- width
- 1280
- ViTB16
- ViTB32
- height
- 768
- prompt
- A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.
- init_scale
- 1000
- skip_steps
- 10
- range_scale
- 150
- cutn_batches
- 4
- display_rate
- 20
- target_scale
- 20000
- diffusion_model
- pixel_art_diffusion_soft_256
- clip_guidance_scale
- 5000
- use_secondary_model
- diffusion_sampling_mode
- ddim
{ "RN50": true, "steps": 250, "width": 1280, "ViTB16": true, "ViTB32": true, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "pixel_art_diffusion_soft_256", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" }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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", { input: { RN50: true, steps: 250, width: 1280, ViTB16: true, ViTB32: true, height: 768, prompt: "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", init_scale: 1000, skip_steps: 10, range_scale: 150, cutn_batches: 4, display_rate: 20, target_scale: 20000, diffusion_model: "pixel_art_diffusion_soft_256", clip_guidance_scale: 5000, use_secondary_model: true, diffusion_sampling_mode: "ddim" } } ); 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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", input={ "RN50": True, "steps": 250, "width": 1280, "ViTB16": True, "ViTB32": True, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "pixel_art_diffusion_soft_256", "clip_guidance_scale": 5000, "use_secondary_model": True, "diffusion_sampling_mode": "ddim" } ) # The nightmareai/disco-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/disco-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/disco-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": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", "input": { "RN50": true, "steps": 250, "width": 1280, "ViTB16": true, "ViTB32": true, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "pixel_art_diffusion_soft_256", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T11:35:51.730281Z", "created_at": "2022-07-10T11:20:38.423114Z", "data_removed": false, "error": null, "id": "wxy2gmscjne6dg42wxq36um7je", "input": { "RN50": true, "steps": 250, "width": 1280, "ViTB16": true, "ViTB32": true, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "pixel_art_diffusion_soft_256", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" }, "logs": "2022-07-10 11:21:10.149 | INFO | dd:start_run:2224 - 💼 1 jobs found.\n2022-07-10 11:21:10.151 | INFO | dd:start_run:2236 - ⚒️ Session d9e07535-6ade-417e-8d4e-572b990c5021 started...\n2022-07-10 11:21:10.151 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1...\n2022-07-10 11:21:10.151 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion...\n2022-07-10 11:21:10.153 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 1242111724\n2022-07-10 11:21:10.153 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-10 11:21:10.154 | INFO | dd:do_run:1190 - 💻 Starting Run: 3a2cdff6-0268-4c70-bb7b-961b92ada908(0) at frame 0\n2022-07-10 11:21:10.154 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'...\n2022-07-10 11:21:18.182 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'...\n2022-07-10 11:21:23.718 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN50'...\n2022-07-10 11:21:27.937 | INFO | dd:do_run:1216 - 🤖 Loading secondary model...\n2022-07-10 11:21:28.212 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS...\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n warnings.warn(\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n\u001b[2K\n2022-07-10 11:21:30.168 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000\n2022-07-10 11:21:30.168 | INFO | dd:prepModels:1054 - Prepping models...\n2022-07-10 11:21:31.806 | INFO | dd:disco:1532 - Running job '2a862698-f845-496f-832d-67938da6318e'...\n2022-07-10 11:21:31.807 | INFO | dd:disco:1545 - 🌱 Seed used: 1242111724\n2022-07-10 11:21:31.816 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.']\n\u001b[2K\n\u001b[2K\n\u001b[2K\nBatches: 0%| | 0/1 [00:00<?, ?it/s]\nOutput()\n2022-07-10 11:21:33.533 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n\n\n 0%| | 0/240 [00:00<?, ?it/s]\u001b[A\nBatch 0, step 0, output 0:\n\n\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image 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'/src/images_out/3a2cdff6-0268-4c70-bb7b-961b92ada908/3a2cdff6-0268-4c70-bb7b-961b92ada908(0)_0.png'\n\n\n100%|██████████| 240/240 [14:12<00:00, 3.91s/it]\u001b[A\n100%|██████████| 240/240 [14:12<00:00, 3.55s/it]\n2022-07-10 11:35:45.745 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-10 11:35:45.745 | SUCCESS | dd:start_run:2245 - ✅ Session d9e07535-6ade-417e-8d4e-572b990c5021 finished by user.\n2022-07-10 11:35:45.745 | DEBUG | dd:sendSMS:2530 - Not sending SMS\n2022-07-10 11:35:45.745 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...", "metrics": { "predict_time": 881.544729, "total_time": 913.307167 }, "output": [ "https://replicate.delivery/mgxm/0cd9f858-a826-4cec-84e3-119f087b4a2e/progress.png", "https://replicate.delivery/mgxm/4941a2c6-124d-451b-8447-66da6e290432/progress.png", "https://replicate.delivery/mgxm/e08feb0a-03c1-41c2-9078-c21d292b5222/progress.png", "https://replicate.delivery/mgxm/9014c001-a4b1-42e7-bf54-a522281b470f/progress.png", "https://replicate.delivery/mgxm/a96f71af-3cd0-4857-b159-49f4ddb94bff/progress.png", "https://replicate.delivery/mgxm/d6d1c8f6-c2be-4c79-8b42-886f3af3e48b/progress.png", "https://replicate.delivery/mgxm/fa2be4cc-df3d-40e5-970a-5c3857231c7c/progress.png", "https://replicate.delivery/mgxm/7cdd6c6d-70db-42be-b00f-9cffd35cb5dc/progress.png", "https://replicate.delivery/mgxm/cd8ccbaa-0a8c-4292-b657-2e1e01fd799d/progress.png", "https://replicate.delivery/mgxm/410a6671-83c9-43ab-a554-90fd12255d79/progress.png", "https://replicate.delivery/mgxm/a3386c78-58d2-40be-8bfe-2193bac5da17/progress.png", "https://replicate.delivery/mgxm/dee3ee91-6e03-4c21-9bde-ada04ea9c30d/progress.png", "https://replicate.delivery/mgxm/8d60f574-0aca-4331-b4e5-9bdc84279aae/progress.png", "https://replicate.delivery/mgxm/357f257e-397a-463f-9c54-561239a9baf0/progress.png" ], "started_at": "2022-07-10T11:21:10.185552Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wxy2gmscjne6dg42wxq36um7je", "cancel": "https://api.replicate.com/v1/predictions/wxy2gmscjne6dg42wxq36um7je/cancel" }, "version": "bc78822513db3dd6fc3be978d68472258b6f86a3a8ebf6aa8c5bc60b66bbae58" }Generated in2022-07-10 11:21:10.149 | INFO | dd:start_run:2224 - 💼 1 jobs found. 2022-07-10 11:21:10.151 | INFO | dd:start_run:2236 - ⚒️ Session d9e07535-6ade-417e-8d4e-572b990c5021 started... 2022-07-10 11:21:10.151 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1... 2022-07-10 11:21:10.151 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion... 2022-07-10 11:21:10.153 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 1242111724 2022-07-10 11:21:10.153 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-10 11:21:10.154 | INFO | dd:do_run:1190 - 💻 Starting Run: 3a2cdff6-0268-4c70-bb7b-961b92ada908(0) at frame 0 2022-07-10 11:21:10.154 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'... 2022-07-10 11:21:18.182 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'... 2022-07-10 11:21:23.718 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN50'... 2022-07-10 11:21:27.937 | INFO | dd:do_run:1216 - 🤖 Loading secondary model... 2022-07-10 11:21:28.212 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS... /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) 2022-07-10 11:21:30.168 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000 2022-07-10 11:21:30.168 | INFO | dd:prepModels:1054 - Prepping models... 2022-07-10 11:21:31.806 | INFO | dd:disco:1532 - Running job '2a862698-f845-496f-832d-67938da6318e'... 2022-07-10 11:21:31.807 | INFO | dd:disco:1545 - 🌱 Seed used: 1242111724 2022-07-10 11:21:31.816 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.'] Batches: 0%| | 0/1 [00:00<?, ?it/s] Output() 2022-07-10 11:21:33.533 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 0%| | 0/240 [00:00<?, ?it/s] Batch 0, step 0, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F85CD253550> 0%| | 0/240 [00:07<?, ?it/s] 0%| | 1/240 [00:07<30:40, 7.70s/it] 1%| | 2/240 [00:10<19:47, 4.99s/it] 1%|▏ | 3/240 [00:13<16:19, 4.13s/it] 2%|▏ | 4/240 [00:17<14:41, 3.73s/it] 2%|▏ | 5/240 [00:20<13:46, 3.52s/it] 2%|▎ | 6/240 [00:23<13:13, 3.39s/it] 3%|▎ | 7/240 [00:26<12:51, 3.31s/it] 3%|▎ | 8/240 [00:29<12:37, 3.26s/it] 4%|▍ | 9/240 [00:32<12:27, 3.24s/it] 4%|▍ | 10/240 [00:35<12:21, 3.22s/it] 5%|▍ | 11/240 [00:39<12:16, 3.22s/it] 5%|▌ | 12/240 [00:42<12:14, 3.22s/it] 5%|▌ | 13/240 [00:45<12:11, 3.22s/it] 6%|▌ | 14/240 [00:48<12:10, 3.23s/it] 6%|▋ | 15/240 [00:52<12:08, 3.24s/it] 7%|▋ | 16/240 [00:55<12:08, 3.25s/it] 7%|▋ | 17/240 [00:58<12:08, 3.27s/it] 8%|▊ | 18/240 [01:02<12:07, 3.28s/it] 8%|▊ | 19/240 [01:05<12:07, 3.29s/it] 8%|▊ | 20/240 [01:08<12:06, 3.30s/it] Batch 0, step 20, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F85CD253580> 8%|▊ | 20/240 [01:12<12:06, 3.30s/it] 9%|▉ | 21/240 [01:12<12:23, 3.39s/it] 9%|▉ | 22/240 [01:15<12:18, 3.39s/it] 10%|▉ | 23/240 [01:19<12:15, 3.39s/it] 10%|█ | 24/240 [01:22<12:14, 3.40s/it] 10%|█ | 25/240 [01:25<12:13, 3.41s/it] 11%|█ | 26/240 [01:29<12:10, 3.41s/it] 11%|█▏ | 27/240 [01:32<12:09, 3.42s/it] 12%|█▏ | 28/240 [01:36<12:05, 3.42s/it] 12%|█▏ | 29/240 [01:39<12:00, 3.41s/it] 12%|█▎ | 30/240 [01:42<11:54, 3.40s/it] 13%|█▎ | 31/240 [01:46<11:49, 3.39s/it] 13%|█▎ | 32/240 [01:49<11:44, 3.39s/it] 14%|█▍ | 33/240 [01:53<11:38, 3.37s/it] 14%|█▍ | 34/240 [01:56<11:33, 3.37s/it] 15%|█▍ | 35/240 [01:59<11:29, 3.36s/it] 15%|█▌ | 36/240 [02:03<11:24, 3.35s/it] 15%|█▌ | 37/240 [02:06<11:19, 3.35s/it] 16%|█▌ | 38/240 [02:09<11:15, 3.34s/it] 16%|█▋ | 39/240 [02:13<11:12, 3.34s/it] 17%|█▋ | 40/240 [02:16<11:07, 3.34s/it] Batch 0, step 40, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F85CD294070> 17%|█▋ | 40/240 [02:19<11:07, 3.34s/it] 17%|█▋ | 41/240 [02:20<11:17, 3.41s/it] 18%|█▊ | 42/240 [02:23<11:09, 3.38s/it] 18%|█▊ | 43/240 [02:26<11:02, 3.36s/it] 18%|█▊ | 44/240 [02:29<10:56, 3.35s/it] 19%|█▉ | 45/240 [02:33<10:52, 3.35s/it] 19%|█▉ | 46/240 [02:36<10:49, 3.35s/it] 20%|█▉ | 47/240 [02:40<10:46, 3.35s/it] 20%|██ | 48/240 [02:43<10:42, 3.35s/it] 20%|██ | 49/240 [02:46<10:39, 3.35s/it] 21%|██ | 50/240 [02:50<10:36, 3.35s/it] 21%|██▏ | 51/240 [02:53<10:34, 3.36s/it] 22%|██▏ | 52/240 [02:56<10:31, 3.36s/it] 22%|██▏ | 53/240 [03:00<10:28, 3.36s/it] 22%|██▎ | 54/240 [03:03<10:26, 3.37s/it] 23%|██▎ | 55/240 [03:06<10:22, 3.36s/it] 23%|██▎ | 56/240 [03:10<10:18, 3.36s/it] 24%|██▍ | 57/240 [03:13<10:16, 3.37s/it] 24%|██▍ | 58/240 [03:16<10:12, 3.36s/it] 25%|██▍ | 59/240 [03:20<10:08, 3.36s/it] 25%|██▌ | 60/240 [03:23<10:04, 3.36s/it] Batch 0, step 60, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F85CD253490> 25%|██▌ | 60/240 [03:27<10:04, 3.36s/it] 25%|██▌ | 61/240 [03:27<10:14, 3.43s/it] 26%|██▌ | 62/240 [03:30<10:07, 3.41s/it] 26%|██▋ | 63/240 [03:34<10:01, 3.40s/it] 27%|██▋ | 64/240 [03:37<09:55, 3.38s/it] 27%|██▋ | 65/240 [03:40<09:50, 3.37s/it] 28%|██▊ | 66/240 [03:44<09:45, 3.36s/it] 28%|██▊ | 67/240 [03:47<09:39, 3.35s/it] 28%|██▊ | 68/240 [03:50<09:35, 3.35s/it] 29%|██▉ | 69/240 [03:54<09:32, 3.35s/it] 29%|██▉ | 70/240 [03:57<09:28, 3.35s/it] 30%|██▉ | 71/240 [04:00<09:25, 3.35s/it] 30%|███ | 72/240 [04:04<09:21, 3.34s/it] 30%|███ | 73/240 [04:07<09:18, 3.34s/it] 31%|███ | 74/240 [04:10<09:15, 3.35s/it] 31%|███▏ | 75/240 [04:14<09:12, 3.35s/it] 32%|███▏ | 76/240 [04:17<09:09, 3.35s/it] 32%|███▏ | 77/240 [04:20<09:05, 3.35s/it] 32%|███▎ | 78/240 [04:24<09:03, 3.35s/it] 33%|███▎ | 79/240 [04:27<08:59, 3.35s/it] 33%|███▎ | 80/240 [04:30<08:56, 3.35s/it] Batch 0, step 80, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F85CD294070> 33%|███▎ | 80/240 [04:34<08:56, 3.35s/it] 34%|███▍ | 81/240 [04:34<09:08, 3.45s/it] 34%|███▍ | 82/240 [04:37<09:01, 3.43s/it] 35%|███▍ | 83/240 [04:41<08:54, 3.40s/it] 35%|███▌ | 84/240 [04:44<08:48, 3.39s/it] 35%|███▌ | 85/240 [04:48<08:44, 3.38s/it] 36%|███▌ | 86/240 [04:51<08:40, 3.38s/it] 36%|███▋ | 87/240 [04:54<08:36, 3.37s/it] 37%|███▋ | 88/240 [04:58<08:31, 3.37s/it] 37%|███▋ | 89/240 [05:01<08:27, 3.36s/it] 38%|███▊ | 90/240 [05:04<08:24, 3.36s/it] 38%|███▊ | 91/240 [05:08<08:31, 3.43s/it] 38%|███▊ | 92/240 [05:12<08:36, 3.49s/it] 39%|███▉ | 93/240 [05:15<08:38, 3.53s/it] 39%|███▉ | 94/240 [05:19<08:39, 3.56s/it] 40%|███▉ | 95/240 [05:22<08:38, 3.57s/it] 40%|████ | 96/240 [05:26<08:35, 3.58s/it] 40%|████ | 97/240 [05:30<08:33, 3.59s/it] 41%|████ | 98/240 [05:33<08:30, 3.59s/it] 41%|████▏ | 99/240 [05:37<08:27, 3.60s/it] 42%|████▏ | 100/240 [05:40<08:24, 3.61s/it] Batch 0, step 100, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F85CD253550> 42%|████▏ | 100/240 [05:44<08:24, 3.61s/it] 42%|████▏ | 101/240 [05:44<08:36, 3.72s/it] 42%|████▎ | 102/240 [05:48<08:29, 3.69s/it] 43%|████▎ | 103/240 [05:52<08:22, 3.67s/it] 43%|████▎ | 104/240 [05:55<08:15, 3.64s/it] 44%|████▍ | 105/240 [05:59<08:09, 3.63s/it] 44%|████▍ | 106/240 [06:03<08:05, 3.63s/it] 45%|████▍ | 107/240 [06:06<08:01, 3.62s/it] 45%|████▌ | 108/240 [06:10<07:56, 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| 128/240 [07:23<06:46, 3.63s/it] 54%|█████▍ | 129/240 [07:26<06:41, 3.62s/it] 54%|█████▍ | 130/240 [07:30<06:38, 3.62s/it] 55%|█████▍ | 131/240 [07:33<06:34, 3.62s/it] 55%|█████▌ | 132/240 [07:37<06:30, 3.62s/it] 55%|█████▌ | 133/240 [07:41<06:27, 3.62s/it] 56%|█████▌ | 134/240 [07:44<06:23, 3.62s/it] 56%|█████▋ | 135/240 [07:48<06:19, 3.62s/it] 57%|█████▋ | 136/240 [07:51<06:15, 3.62s/it] 57%|█████▋ | 137/240 [07:55<06:12, 3.61s/it] 57%|█████▊ | 138/240 [07:59<06:09, 3.62s/it] 58%|█████▊ | 139/240 [08:02<06:06, 3.62s/it] 58%|█████▊ | 140/240 [08:06<06:02, 3.62s/it] Batch 0, step 140, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F85CD294070> 58%|█████▊ | 140/240 [08:10<06:02, 3.62s/it] 59%|█████▉ | 141/240 [08:10<06:20, 3.84s/it] 59%|█████▉ | 142/240 [08:14<06:10, 3.78s/it] 60%|█████▉ | 143/240 [08:18<06:02, 3.74s/it] 60%|██████ | 144/240 [08:21<05:55, 3.70s/it] 60%|██████ | 145/240 [08:25<05:48, 3.67s/it] 61%|██████ | 146/240 [08:28<05:43, 3.65s/it] 61%|██████▏ | 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92%|█████████▏| 220/240 [13:02<01:12, 3.62s/it] 92%|█████████▏| 221/240 [13:02<01:11, 3.75s/it] 92%|█████████▎| 222/240 [13:06<01:06, 3.72s/it] 93%|█████████▎| 223/240 [13:09<01:02, 3.69s/it] 93%|█████████▎| 224/240 [13:13<00:58, 3.67s/it] 94%|█████████▍| 225/240 [13:16<00:54, 3.65s/it] 94%|█████████▍| 226/240 [13:20<00:50, 3.64s/it] 95%|█████████▍| 227/240 [13:24<00:47, 3.63s/it] 95%|█████████▌| 228/240 [13:27<00:43, 3.63s/it] 95%|█████████▌| 229/240 [13:31<00:39, 3.63s/it] 96%|█████████▌| 230/240 [13:35<00:36, 3.63s/it] 96%|█████████▋| 231/240 [13:38<00:32, 3.63s/it] 97%|█████████▋| 232/240 [13:42<00:28, 3.62s/it] 97%|█████████▋| 233/240 [13:45<00:25, 3.62s/it] 98%|█████████▊| 234/240 [13:49<00:21, 3.61s/it] 98%|█████████▊| 235/240 [13:53<00:18, 3.61s/it] 98%|█████████▊| 236/240 [13:56<00:14, 3.61s/it] 99%|█████████▉| 237/240 [14:00<00:10, 3.62s/it] 99%|█████████▉| 238/240 [14:03<00:07, 3.62s/it] 100%|█████████▉| 239/240 [14:07<00:03, 3.63s/it] Batch 0, step 239, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F85CD294520> 100%|█████████▉| 239/240 [14:11<00:03, 3.63s/it]2022-07-10 11:35:45.291 | INFO | dd:disco:1820 - Image render completed. 2022-07-10 11:35:45.744 | INFO | dd:disco:1840 - Image saved to '/src/images_out/3a2cdff6-0268-4c70-bb7b-961b92ada908/3a2cdff6-0268-4c70-bb7b-961b92ada908(0)_0.png' 100%|██████████| 240/240 [14:12<00:00, 3.91s/it] 100%|██████████| 240/240 [14:12<00:00, 3.55s/it] 2022-07-10 11:35:45.745 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-10 11:35:45.745 | SUCCESS | dd:start_run:2245 - ✅ Session d9e07535-6ade-417e-8d4e-572b990c5021 finished by user. 2022-07-10 11:35:45.745 | DEBUG | dd:sendSMS:2530 - Not sending SMS 2022-07-10 11:35:45.745 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...Prediction
nightmareai/disco-diffusion:3c128f65Input
- RN50
- RN101
- steps
- 100
- width
- 1280
- ViTB16
- ViTB32
- ViTL14
- height
- 768
- prompt
- a splendid day at the fair with a hot dog cart on fire, watercolor, trending on artstation
- init_scale
- 1000
- skip_steps
- 10
- range_scale
- 150
- cutn_batches
- 4
- display_rate
- 20
- target_scale
- 20000
- diffusion_model
- 512x512_diffusion_uncond_finetune_008100
- clip_guidance_scale
- 5000
- use_secondary_model
- diffusion_sampling_mode
- plms
{ "RN50": false, "RN101": true, "steps": 100, "width": 1280, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 768, "prompt": "a splendid day at the fair with a hot dog cart on fire, watercolor, trending on artstation", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "plms" }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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", { input: { RN50: false, RN101: true, steps: 100, width: 1280, ViTB16: false, ViTB32: true, ViTL14: true, height: 768, prompt: "a splendid day at the fair with a hot dog cart on fire, watercolor, trending on artstation", init_scale: 1000, skip_steps: 10, range_scale: 150, cutn_batches: 4, display_rate: 20, target_scale: 20000, diffusion_model: "512x512_diffusion_uncond_finetune_008100", clip_guidance_scale: 5000, use_secondary_model: true, diffusion_sampling_mode: "plms" } } ); 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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", input={ "RN50": False, "RN101": True, "steps": 100, "width": 1280, "ViTB16": False, "ViTB32": True, "ViTL14": True, "height": 768, "prompt": "a splendid day at the fair with a hot dog cart on fire, watercolor, trending on artstation", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": True, "diffusion_sampling_mode": "plms" } ) # The nightmareai/disco-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/disco-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/disco-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": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", "input": { "RN50": false, "RN101": true, "steps": 100, "width": 1280, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 768, "prompt": "a splendid day at the fair with a hot dog cart on fire, watercolor, trending on artstation", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "plms" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T17:49:56.102752Z", "created_at": "2022-07-10T17:38:39.033607Z", "data_removed": false, "error": null, "id": "ffhmt2midvhq7g4vqkc6hwodjy", "input": { "RN50": false, "RN101": true, "steps": 100, "width": 1280, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 768, "prompt": "a splendid day at the fair with a hot dog cart on fire, watercolor, trending on artstation", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "plms" }, "logs": "2022-07-10 17:38:39.134 | INFO | dd:start_run:2224 - 💼 1 jobs found.\n2022-07-10 17:38:39.135 | INFO | dd:start_run:2236 - ⚒️ Session 3016cd34-a976-4120-bea9-ab4f516bcb1b started...\n2022-07-10 17:38:39.136 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1...\n2022-07-10 17:38:39.136 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion...\n2022-07-10 17:38:39.137 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 2061879424\n2022-07-10 17:38:39.138 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-10 17:38:39.138 | INFO | dd:do_run:1190 - 💻 Starting Run: 5c946cae-4a65-4e82-93d4-c75d5ac763d8(0) at frame 0\n2022-07-10 17:38:39.138 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'...\n2022-07-10 17:38:48.730 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-L/14'...\n2022-07-10 17:39:03.368 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN101'...\n2022-07-10 17:39:08.345 | INFO | dd:do_run:1216 - 🤖 Loading secondary model...\n2022-07-10 17:39:08.651 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS...\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n warnings.warn(\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n\u001b[2K\n2022-07-10 17:39:14.258 | INFO | dd:disco:1502 - timestep_respacing : ddim100 | diffusion_steps: 1000\n2022-07-10 17:39:14.259 | INFO | dd:prepModels:1054 - Prepping models...\n2022-07-10 17:39:28.494 | INFO | dd:disco:1532 - Running job '2c1c3bcc-ee12-444a-bca3-97f8b27af298'...\n2022-07-10 17:39:28.495 | INFO | dd:disco:1545 - 🌱 Seed used: 2061879424\n2022-07-10 17:39:28.506 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['a splendid day at the fair with a hot dog cart on fire, watercolor, trending on artstation']\n\u001b[2K\n\u001b[2K\n\u001b[2K\nBatches: 0%| | 0/1 [00:00<?, ?it/s]\nOutput()\n2022-07-10 17:39:30.302 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n\n\n 0%| | 0/90 [00:00<?, ?it/s]\u001b[A\n\n\nBatch 0, step 0, output 0:\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image mode=RGB size=1280x768 at 0x7F64E7D37B50>\n 0%| | 0/90 [00:15<?, 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'/src/images_out/5c946cae-4a65-4e82-93d4-c75d5ac763d8/5c946cae-4a65-4e82-93d4-c75d5ac763d8(0)_0.png'\n\n\n100%|██████████| 90/90 [10:19<00:00, 7.15s/it]\u001b[A\n100%|██████████| 90/90 [10:19<00:00, 6.88s/it]\n2022-07-10 17:49:49.737 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-10 17:49:49.738 | SUCCESS | dd:start_run:2245 - ✅ Session 3016cd34-a976-4120-bea9-ab4f516bcb1b finished by user.\n2022-07-10 17:49:49.738 | DEBUG | dd:sendSMS:2530 - Not sending SMS\n2022-07-10 17:49:49.738 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...", "metrics": { "predict_time": 676.842882, "total_time": 677.069145 }, "output": [ "https://replicate.delivery/mgxm/7871aabb-1e03-4fb4-b7fc-9bee49b5e884/progress.png", "https://replicate.delivery/mgxm/c5227933-7026-480f-91e8-5b15e78f01d0/progress.png", "https://replicate.delivery/mgxm/f31bd350-4cfa-4e0b-8915-0e8f56c087e5/progress.png", "https://replicate.delivery/mgxm/8f95d8e3-51ea-482e-9312-baa02335a122/progress.png", "https://replicate.delivery/mgxm/c9c26b18-04cb-4f5c-9753-e783ff5cb437/progress.png", "https://replicate.delivery/mgxm/d84bb6fc-96a6-4ab4-b811-cb8f8ff715e5/progress.png", "https://replicate.delivery/mgxm/38ef3c75-8a67-4b00-829e-b8d4a1e51b16/progress.png" ], "started_at": "2022-07-10T17:38:39.259870Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ffhmt2midvhq7g4vqkc6hwodjy", "cancel": "https://api.replicate.com/v1/predictions/ffhmt2midvhq7g4vqkc6hwodjy/cancel" }, "version": "3db339922ea7421159874c71c20123079fa9c696849fb7a84da283ca5a9d5904" }Generated in2022-07-10 17:38:39.134 | INFO | dd:start_run:2224 - 💼 1 jobs found. 2022-07-10 17:38:39.135 | INFO | dd:start_run:2236 - ⚒️ Session 3016cd34-a976-4120-bea9-ab4f516bcb1b started... 2022-07-10 17:38:39.136 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1... 2022-07-10 17:38:39.136 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion... 2022-07-10 17:38:39.137 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 2061879424 2022-07-10 17:38:39.138 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-10 17:38:39.138 | INFO | dd:do_run:1190 - 💻 Starting Run: 5c946cae-4a65-4e82-93d4-c75d5ac763d8(0) at frame 0 2022-07-10 17:38:39.138 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'... 2022-07-10 17:38:48.730 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-L/14'... 2022-07-10 17:39:03.368 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN101'... 2022-07-10 17:39:08.345 | INFO | dd:do_run:1216 - 🤖 Loading secondary model... 2022-07-10 17:39:08.651 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS... /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) 2022-07-10 17:39:14.258 | INFO | dd:disco:1502 - timestep_respacing : ddim100 | diffusion_steps: 1000 2022-07-10 17:39:14.259 | INFO | dd:prepModels:1054 - Prepping models... 2022-07-10 17:39:28.494 | INFO | dd:disco:1532 - Running job '2c1c3bcc-ee12-444a-bca3-97f8b27af298'... 2022-07-10 17:39:28.495 | INFO | dd:disco:1545 - 🌱 Seed used: 2061879424 2022-07-10 17:39:28.506 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['a splendid day at the fair with a hot dog cart on fire, watercolor, trending on artstation'] Batches: 0%| | 0/1 [00:00<?, ?it/s] Output() 2022-07-10 17:39:30.302 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 0%| | 0/90 [00:00<?, ?it/s] Batch 0, step 0, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7F64E7D37B50> 0%| | 0/90 [00:15<?, ?it/s] 1%| | 1/90 [00:15<22:56, 15.47s/it] 2%|▏ | 2/90 [00:21<14:34, 9.94s/it] 3%|▎ | 3/90 [00:27<11:53, 8.20s/it] 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cache on cuda:0...Prediction
nightmareai/disco-diffusion:3c128f65IDm526w6eambff7bu2dcvuxjfvhqStatusSucceededSourceWebHardware–Total durationCreatedInput
- RN50
- RN101
- steps
- 250
- width
- 1280
- ViTB16
- ViTB32
- ViTL14
- height
- 768
- prompt
- a mysterious galaxy far away from our own
- ViTL14_336
- init_scale
- 1000
- skip_steps
- 10
- range_scale
- 150
- cutn_batches
- 4
- display_rate
- 20
- target_scale
- 20000
- diffusion_model
- 512x512_diffusion_uncond_finetune_008100
- clip_guidance_scale
- 5000
- use_secondary_model
- diffusion_sampling_mode
- ddim
{ "RN50": false, "RN101": true, "steps": 250, "width": 1280, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 768, "prompt": "a mysterious galaxy far away from our own", "ViTL14_336": false, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" }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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", { input: { RN50: false, RN101: true, steps: 250, width: 1280, ViTB16: false, ViTB32: true, ViTL14: true, height: 768, prompt: "a mysterious galaxy far away from our own", ViTL14_336: false, init_scale: 1000, skip_steps: 10, range_scale: 150, cutn_batches: 4, display_rate: 20, target_scale: 20000, diffusion_model: "512x512_diffusion_uncond_finetune_008100", clip_guidance_scale: 5000, use_secondary_model: true, diffusion_sampling_mode: "ddim" } } ); 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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", input={ "RN50": False, "RN101": True, "steps": 250, "width": 1280, "ViTB16": False, "ViTB32": True, "ViTL14": True, "height": 768, "prompt": "a mysterious galaxy far away from our own", "ViTL14_336": False, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": True, "diffusion_sampling_mode": "ddim" } ) # The nightmareai/disco-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/disco-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/disco-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": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", "input": { "RN50": false, "RN101": true, "steps": 250, "width": 1280, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 768, "prompt": "a mysterious galaxy far away from our own", "ViTL14_336": false, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T14:43:39.385884Z", "created_at": "2022-07-11T14:34:20.388204Z", "data_removed": false, "error": null, "id": "m526w6eambff7bu2dcvuxjfvhq", "input": { "RN50": false, "RN101": true, "steps": 250, "width": 1280, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 768, "prompt": "a mysterious galaxy far away from our own", "ViTL14_336": false, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" }, "logs": "2022-07-11 14:34:20.879 | INFO | dd:start_run:2224 - 💼 1 jobs found.\n2022-07-11 14:34:20.880 | INFO | dd:start_run:2236 - ⚒️ Session 814a8239-fcf8-4caa-a1a9-8419026649ab started...\n2022-07-11 14:34:20.881 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1...\n2022-07-11 14:34:20.881 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion...\n2022-07-11 14:34:20.882 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 198216267\n2022-07-11 14:34:20.882 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-11 14:34:20.882 | INFO | dd:do_run:1190 - 💻 Starting Run: af45141f-67ca-4500-b7d7-050733aecd13(0) at frame 0\n2022-07-11 14:34:20.882 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'...\n2022-07-11 14:34:27.372 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-L/14'...\n2022-07-11 14:34:38.203 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN101'...\n2022-07-11 14:34:42.145 | INFO | dd:do_run:1216 - 🤖 Loading secondary model...\n2022-07-11 14:34:42.292 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS...\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n warnings.warn(\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n\u001b[2K\n2022-07-11 14:34:44.072 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000\n2022-07-11 14:34:44.072 | INFO | dd:prepModels:1054 - Prepping models...\n2022-07-11 14:34:50.526 | INFO | dd:disco:1532 - Running job '47d4a737-45e6-437b-b13d-1c052202eb94'...\n2022-07-11 14:34:50.527 | INFO | dd:disco:1545 - 🌱 Seed used: 198216267\n2022-07-11 14:34:50.528 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['a mysterious galaxy far away from our own']\n\u001b[2K\n\u001b[2K\n\u001b[2K\nBatches: 0%| | 0/1 [00:00<?, ?it/s]\nOutput()\n2022-07-11 14:34:51.355 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n\n\n 0%| | 0/240 [00:00<?, ?it/s]\u001b[A\n\n\nBatch 0, step 0, output 0:\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FF441489B20>\n 0%| | 0/240 [00:02<?, ?it/s]\u001b[A\n\n 0%| | 1/240 [00:02<09:07, 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[08:42<00:00, 2.18s/it]\n2022-07-11 14:43:33.663 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-11 14:43:33.663 | SUCCESS | dd:start_run:2245 - ✅ Session 814a8239-fcf8-4caa-a1a9-8419026649ab finished by user.\n2022-07-11 14:43:33.663 | DEBUG | dd:sendSMS:2530 - Not sending SMS\n2022-07-11 14:43:33.664 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...", "metrics": { "predict_time": 558.472413, "total_time": 558.99768 }, "output": [ "https://replicate.delivery/mgxm/f4618b8f-fbd8-4e17-be83-a354f6797f7e/progress.png", "https://replicate.delivery/mgxm/f3fe273c-425b-43cd-8136-5b0e60522aeb/progress.png", "https://replicate.delivery/mgxm/8e829124-9f77-471f-80cb-78acc445507c/progress.png", "https://replicate.delivery/mgxm/b89a9cc6-c5f0-4049-89cf-a5bb05970658/progress.png", "https://replicate.delivery/mgxm/a772ab22-c334-4917-89d7-6d88905540c0/progress.png", "https://replicate.delivery/mgxm/bcc360ff-b544-4edd-bae4-47be4496b458/progress.png", "https://replicate.delivery/mgxm/7c82c6d8-1525-4edf-8d91-2d7cc5594219/progress.png", "https://replicate.delivery/mgxm/f69ed71f-531b-402a-8907-5473158a4894/progress.png", "https://replicate.delivery/mgxm/483c1852-32a4-4307-95bf-879eb1167aaa/progress.png", "https://replicate.delivery/mgxm/9650922a-9d2a-4e38-8508-23ad6264e716/progress.png", "https://replicate.delivery/mgxm/ca755510-37f0-4c14-ad57-741224b88997/progress.png", "https://replicate.delivery/mgxm/db96fcf7-366c-4003-aead-a8a791b5ff3f/progress.png", "https://replicate.delivery/mgxm/5b3880d1-e68b-4069-bef1-dc3b9b5c7c77/progress.png", "https://replicate.delivery/mgxm/36ee41af-46ed-46e4-9f94-15ac3dca5b04/progress.png" ], "started_at": "2022-07-11T14:34:20.913471Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m526w6eambff7bu2dcvuxjfvhq", "cancel": "https://api.replicate.com/v1/predictions/m526w6eambff7bu2dcvuxjfvhq/cancel" }, "version": "3db339922ea7421159874c71c20123079fa9c696849fb7a84da283ca5a9d5904" }Generated in2022-07-11 14:34:20.879 | INFO | dd:start_run:2224 - 💼 1 jobs found. 2022-07-11 14:34:20.880 | INFO | dd:start_run:2236 - ⚒️ Session 814a8239-fcf8-4caa-a1a9-8419026649ab started... 2022-07-11 14:34:20.881 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1... 2022-07-11 14:34:20.881 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion... 2022-07-11 14:34:20.882 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 198216267 2022-07-11 14:34:20.882 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-11 14:34:20.882 | INFO | dd:do_run:1190 - 💻 Starting Run: af45141f-67ca-4500-b7d7-050733aecd13(0) at frame 0 2022-07-11 14:34:20.882 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'... 2022-07-11 14:34:27.372 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-L/14'... 2022-07-11 14:34:38.203 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN101'... 2022-07-11 14:34:42.145 | INFO | dd:do_run:1216 - 🤖 Loading secondary model... 2022-07-11 14:34:42.292 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS... /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) 2022-07-11 14:34:44.072 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000 2022-07-11 14:34:44.072 | INFO | dd:prepModels:1054 - Prepping models... 2022-07-11 14:34:50.526 | INFO | dd:disco:1532 - Running job '47d4a737-45e6-437b-b13d-1c052202eb94'... 2022-07-11 14:34:50.527 | INFO | dd:disco:1545 - 🌱 Seed used: 198216267 2022-07-11 14:34:50.528 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['a mysterious galaxy far away from our own'] Batches: 0%| | 0/1 [00:00<?, ?it/s] Output() 2022-07-11 14:34:51.355 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 0%| | 0/240 [00:00<?, ?it/s] Batch 0, step 0, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7FF441489B20> 0%| | 0/240 [00:02<?, ?it/s] 0%| | 1/240 [00:02<09:07, 2.29s/it] 1%| | 2/240 [00:04<08:27, 2.13s/it] 1%|▏ | 3/240 [00:06<08:12, 2.08s/it] 2%|▏ | 4/240 [00:08<08:06, 2.06s/it] 2%|▏ | 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2.22s/it]2022-07-11 14:43:33.221 | INFO | dd:disco:1820 - Image render completed. 2022-07-11 14:43:33.662 | INFO | dd:disco:1840 - Image saved to '/src/images_out/af45141f-67ca-4500-b7d7-050733aecd13/af45141f-67ca-4500-b7d7-050733aecd13(0)_0.png' 100%|██████████| 240/240 [08:42<00:00, 2.50s/it] 100%|██████████| 240/240 [08:42<00:00, 2.18s/it] 2022-07-11 14:43:33.663 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-11 14:43:33.663 | SUCCESS | dd:start_run:2245 - ✅ Session 814a8239-fcf8-4caa-a1a9-8419026649ab finished by user. 2022-07-11 14:43:33.663 | DEBUG | dd:sendSMS:2530 - Not sending SMS 2022-07-11 14:43:33.664 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...Prediction
nightmareai/disco-diffusion:3c128f65Input
- RN50
- RN101
- steps
- 250
- width
- 1280
- ViTB16
- ViTB32
- ViTL14
- height
- 768
- prompt
- a submarine sandwich lost at sea
- ViTL14_336
- init_scale
- 1000
- skip_steps
- 10
- range_scale
- 150
- cutn_batches
- 4
- display_rate
- 20
- target_scale
- 20000
- diffusion_model
- 512x512_diffusion_uncond_finetune_008100
- clip_guidance_scale
- 5000
- use_secondary_model
- diffusion_sampling_mode
- ddim
{ "RN50": false, "RN101": true, "steps": 250, "width": 1280, "ViTB16": true, "ViTB32": true, "ViTL14": false, "height": 768, "prompt": "a submarine sandwich lost at sea", "ViTL14_336": false, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" }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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", { input: { RN50: false, RN101: true, steps: 250, width: 1280, ViTB16: true, ViTB32: true, ViTL14: false, height: 768, prompt: "a submarine sandwich lost at sea", ViTL14_336: false, init_scale: 1000, skip_steps: 10, range_scale: 150, cutn_batches: 4, display_rate: 20, target_scale: 20000, diffusion_model: "512x512_diffusion_uncond_finetune_008100", clip_guidance_scale: 5000, use_secondary_model: true, diffusion_sampling_mode: "ddim" } } ); 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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", input={ "RN50": False, "RN101": True, "steps": 250, "width": 1280, "ViTB16": True, "ViTB32": True, "ViTL14": False, "height": 768, "prompt": "a submarine sandwich lost at sea", "ViTL14_336": False, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": True, "diffusion_sampling_mode": "ddim" } ) # The nightmareai/disco-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/disco-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/disco-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": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", "input": { "RN50": false, "RN101": true, "steps": 250, "width": 1280, "ViTB16": true, "ViTB32": true, "ViTL14": false, "height": 768, "prompt": "a submarine sandwich lost at sea", "ViTL14_336": false, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T15:05:37.539941Z", "created_at": "2022-07-11T14:46:54.122608Z", "data_removed": false, "error": null, "id": "g3zigqtpjjffxbx6u3ayjmulg4", "input": { "RN50": false, "RN101": true, "steps": 250, "width": 1280, "ViTB16": true, "ViTB32": true, "ViTL14": false, "height": 768, "prompt": "a submarine sandwich lost at sea", "ViTL14_336": false, "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 5000, "use_secondary_model": true, "diffusion_sampling_mode": "ddim" }, "logs": "2022-07-11 14:57:30.637 | INFO | dd:start_run:2224 - 💼 1 jobs found.\n2022-07-11 14:57:30.638 | INFO | dd:start_run:2236 - ⚒️ Session 11c90c0a-f2d8-4aa4-acd8-60fe77c69493 started...\n2022-07-11 14:57:30.638 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1...\n2022-07-11 14:57:30.638 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion...\n2022-07-11 14:57:30.639 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 662228741\n2022-07-11 14:57:30.639 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-11 14:57:30.639 | INFO | dd:do_run:1190 - 💻 Starting Run: f79cddc5-2ed9-4497-a3e4-fe01c89eb2c1(0) at frame 0\n2022-07-11 14:57:30.640 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'...\n2022-07-11 14:57:35.640 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'...\n2022-07-11 14:57:39.823 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN101'...\n2022-07-11 14:57:43.869 | INFO | dd:do_run:1216 - 🤖 Loading secondary model...\n2022-07-11 14:57:44.018 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS...\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n warnings.warn(\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n\u001b[2K\n2022-07-11 14:57:45.803 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000\n2022-07-11 14:57:45.803 | INFO | dd:prepModels:1054 - Prepping models...\n2022-07-11 14:57:53.329 | INFO | dd:disco:1532 - Running job '857b2d27-d54e-4ec7-8496-39cf3c4b6fee'...\n2022-07-11 14:57:53.329 | INFO | dd:disco:1545 - 🌱 Seed used: 662228741\n2022-07-11 14:57:53.332 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['a submarine sandwich lost at sea']\n\u001b[2K\n\u001b[2K\n\u001b[2K\nBatches: 0%| | 0/1 [00:00<?, ?it/s]\nOutput()\n2022-07-11 14:57:53.398 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n\n\n 0%| | 0/240 [00:00<?, ?it/s]\u001b[A\n\n\n\u001b[A\nBatch 0, step 0, output 0:\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CF460>\n 0%| | 0/240 [00:01<?, ?it/s]\u001b[A\n\n 0%| | 1/240 [00:02<08:06, 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[07:38<00:00, 1.91s/it]\n2022-07-11 15:05:32.314 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...\n2022-07-11 15:05:32.315 | SUCCESS | dd:start_run:2245 - ✅ Session 11c90c0a-f2d8-4aa4-acd8-60fe77c69493 finished by user.\n2022-07-11 15:05:32.315 | DEBUG | dd:sendSMS:2530 - Not sending SMS\n2022-07-11 15:05:32.315 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...", "metrics": { "predict_time": 486.934334, "total_time": 1123.417333 }, "output": [ "https://replicate.delivery/mgxm/62e55545-211d-4102-a966-3a2167ef4749/progress.png", "https://replicate.delivery/mgxm/62c97272-942e-4997-a836-47359a05c24b/progress.png", "https://replicate.delivery/mgxm/1a19001c-569c-4340-8a7c-ff353db1ba54/progress.png", "https://replicate.delivery/mgxm/6a4d8d6f-2baf-4594-9dd7-0ab87aae879b/progress.png", "https://replicate.delivery/mgxm/044e1d04-fc5a-4ddf-be0d-9b0d2f5267fd/progress.png", "https://replicate.delivery/mgxm/a4a894e1-7e5e-4ad8-9186-fb5a2d5c2c12/progress.png", 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"3db339922ea7421159874c71c20123079fa9c696849fb7a84da283ca5a9d5904" }Generated in2022-07-11 14:57:30.637 | INFO | dd:start_run:2224 - 💼 1 jobs found. 2022-07-11 14:57:30.638 | INFO | dd:start_run:2236 - ⚒️ Session 11c90c0a-f2d8-4aa4-acd8-60fe77c69493 started... 2022-07-11 14:57:30.638 | INFO | dd:start_run:2239 - 💼 Processing job 1 of 1... 2022-07-11 14:57:30.638 | INFO | dd:start_run:2242 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion... 2022-07-11 14:57:30.639 | INFO | dd:processBatch:2329 - 🌱 Randomly using seed: 662228741 2022-07-11 14:57:30.639 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-11 14:57:30.639 | INFO | dd:do_run:1190 - 💻 Starting Run: f79cddc5-2ed9-4497-a3e4-fe01c89eb2c1(0) at frame 0 2022-07-11 14:57:30.640 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/32'... 2022-07-11 14:57:35.640 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'ViT-B/16'... 2022-07-11 14:57:39.823 | INFO | dd:clipLoad:1182 - 🤖 Loading model 'RN101'... 2022-07-11 14:57:43.869 | INFO | dd:do_run:1216 - 🤖 Loading secondary model... 2022-07-11 14:57:44.018 | INFO | dd:do_run:1222 - 🤖 Loading LPIPS... /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) 2022-07-11 14:57:45.803 | INFO | dd:disco:1502 - timestep_respacing : ddim250 | diffusion_steps: 1000 2022-07-11 14:57:45.803 | INFO | dd:prepModels:1054 - Prepping models... 2022-07-11 14:57:53.329 | INFO | dd:disco:1532 - Running job '857b2d27-d54e-4ec7-8496-39cf3c4b6fee'... 2022-07-11 14:57:53.329 | INFO | dd:disco:1545 - 🌱 Seed used: 662228741 2022-07-11 14:57:53.332 | INFO | dd:disco:1571 - Frame 0 📝 Prompt: ['a submarine sandwich lost at sea'] Batches: 0%| | 0/1 [00:00<?, ?it/s] Output() 2022-07-11 14:57:53.398 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 0%| | 0/240 [00:00<?, ?it/s] Batch 0, step 0, output 0: <PIL.Image.Image image mode=RGB size=1280x768 at 0x7FBDA37CF460> 0%| | 0/240 [00:01<?, ?it/s] 0%| | 1/240 [00:02<08:06, 2.04s/it] 1%| | 2/240 [00:03<07:26, 1.88s/it] 1%|▏ | 3/240 [00:05<07:13, 1.83s/it] 2%|▏ | 4/240 [00:07<07:04, 1.80s/it] 2%|▏ | 5/240 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1.97s/it]2022-07-11 15:05:31.942 | INFO | dd:disco:1820 - Image render completed. 2022-07-11 15:05:32.314 | INFO | dd:disco:1840 - Image saved to '/src/images_out/f79cddc5-2ed9-4497-a3e4-fe01c89eb2c1/f79cddc5-2ed9-4497-a3e4-fe01c89eb2c1(0)_0.png' 100%|██████████| 240/240 [07:38<00:00, 2.20s/it] 100%|██████████| 240/240 [07:38<00:00, 1.91s/it] 2022-07-11 15:05:32.314 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0... 2022-07-11 15:05:32.315 | SUCCESS | dd:start_run:2245 - ✅ Session 11c90c0a-f2d8-4aa4-acd8-60fe77c69493 finished by user. 2022-07-11 15:05:32.315 | DEBUG | dd:sendSMS:2530 - Not sending SMS 2022-07-11 15:05:32.315 | INFO | dd:free_mem:71 - Clearing CUDA cache on cuda:0...Prediction
nightmareai/disco-diffusion:3c128f65Input
- RN50
- seed
- "2874114942"
- steps
- "200"
- width
- 400
- RN50x4
- ViTB16
- ViTB32
- ViTL14
- height
- 400
- prompt
- I feel so alive with these phantoms of night and I know that this life isn't safe, but it's wild and it's free, intricate detailed painting, artstation deviantart, absolutely mind blowing beautiful fine art, haunting, whimsical
- tv_scale
- 2000
- sat_scale
- 1000
- skip_augs
- RN50_cc12m
- ViTL14_336
- init_scale
- 1000
- skip_steps
- 4
- range_scale
- 200
- cutn_batches
- 4
- display_rate
- 60
- target_scale
- 20000
- diffusion_model
- 512x512_diffusion_uncond_finetune_008100
- clip_guidance_scale
- 80000
- use_secondary_model
- ViTB32_laion400m_e31
- ViTB32_laion400m_e32
- diffusion_sampling_mode
- plms
{ "RN50": false, "seed": "2874114942", "steps": "200", "width": 400, "RN50x4": true, "ViTB16": true, "ViTB32": true, "ViTL14": true, "height": 400, "prompt": "I feel so alive with these phantoms of night and I know that this life isn't safe, but it's wild and it's free, intricate detailed painting, artstation deviantart, absolutely mind blowing beautiful fine art, haunting, whimsical", "tv_scale": 2000, "sat_scale": 1000, "skip_augs": false, "RN50_cc12m": false, "ViTL14_336": true, "init_scale": 1000, "skip_steps": 4, "range_scale": 200, "cutn_batches": 4, "display_rate": 60, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 80000, "use_secondary_model": false, "ViTB32_laion400m_e31": false, "ViTB32_laion400m_e32": false, "diffusion_sampling_mode": "plms" }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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", { input: { RN50: false, seed: "2874114942", steps: "200", width: 400, RN50x4: true, ViTB16: true, ViTB32: true, ViTL14: true, height: 400, prompt: "I feel so alive with these phantoms of night and I know that this life isn't safe, but it's wild and it's free, intricate detailed painting, artstation deviantart, absolutely mind blowing beautiful fine art, haunting, whimsical", tv_scale: 2000, sat_scale: 1000, skip_augs: false, RN50_cc12m: false, ViTL14_336: true, init_scale: 1000, skip_steps: 4, range_scale: 200, cutn_batches: 4, display_rate: 60, target_scale: 20000, diffusion_model: "512x512_diffusion_uncond_finetune_008100", clip_guidance_scale: 80000, use_secondary_model: false, ViTB32_laion400m_e31: false, ViTB32_laion400m_e32: false, diffusion_sampling_mode: "plms" } } ); 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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", input={ "RN50": False, "seed": "2874114942", "steps": "200", "width": 400, "RN50x4": True, "ViTB16": True, "ViTB32": True, "ViTL14": True, "height": 400, "prompt": "I feel so alive with these phantoms of night and I know that this life isn't safe, but it's wild and it's free, intricate detailed painting, artstation deviantart, absolutely mind blowing beautiful fine art, haunting, whimsical", "tv_scale": 2000, "sat_scale": 1000, "skip_augs": False, "RN50_cc12m": False, "ViTL14_336": True, "init_scale": 1000, "skip_steps": 4, "range_scale": 200, "cutn_batches": 4, "display_rate": 60, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 80000, "use_secondary_model": False, "ViTB32_laion400m_e31": False, "ViTB32_laion400m_e32": False, "diffusion_sampling_mode": "plms" } ) # The nightmareai/disco-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/disco-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/disco-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": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", "input": { "RN50": false, "seed": "2874114942", "steps": "200", "width": 400, "RN50x4": true, "ViTB16": true, "ViTB32": true, "ViTL14": true, "height": 400, "prompt": "I feel so alive with these phantoms of night and I know that this life isn\'t safe, but it\'s wild and it\'s free, intricate detailed painting, artstation deviantart, absolutely mind blowing beautiful fine art, haunting, whimsical", "tv_scale": 2000, "sat_scale": 1000, "skip_augs": false, "RN50_cc12m": false, "ViTL14_336": true, "init_scale": 1000, "skip_steps": 4, "range_scale": 200, "cutn_batches": 4, "display_rate": 60, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 80000, "use_secondary_model": false, "ViTB32_laion400m_e31": false, "ViTB32_laion400m_e32": false, "diffusion_sampling_mode": "plms" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-21T17:48:29.543933Z", "created_at": "2022-07-21T17:28:59.227783Z", "data_removed": false, "error": null, "id": "q6olw26osbbpnme33qfhp73zo4", "input": { "RN50": false, "seed": "2874114942", "steps": "200", "width": 400, "RN50x4": true, "ViTB16": true, "ViTB32": true, "ViTL14": true, "height": 400, "prompt": "I feel so alive with these phantoms of night and I know that this life isn't safe, but it's wild and it's free, intricate detailed painting, artstation deviantart, absolutely mind blowing beautiful fine art, haunting, whimsical", "tv_scale": 2000, "sat_scale": 1000, "skip_augs": false, "RN50_cc12m": false, "ViTL14_336": true, "init_scale": 1000, "skip_steps": 4, "range_scale": 200, "cutn_batches": 4, "display_rate": 60, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "clip_guidance_scale": 80000, "use_secondary_model": false, "ViTB32_laion400m_e31": false, "ViTB32_laion400m_e32": false, "diffusion_sampling_mode": "plms" }, "logs": "2022-07-21 17:34:29.702 | INFO | dd:getDevice:2590 - ✅ Using device: cuda:0\n2022-07-21 17:34:29.703 | INFO | dd:getDevice:2600 - Disabling CUDNN for A100 gpu\n2022-07-21 17:34:29.706 | INFO | dd:start_run:2288 - 💼 1 jobs found.\n2022-07-21 17:34:29.706 | INFO | dd:start_run:2300 - ⚒️ Session 27801dd6-f5ec-4d4d-86c5-fc9d5ac6a5d0 started...\n2022-07-21 17:34:29.706 | INFO | dd:start_run:2303 - 💼 Processing job 1 of 1...\n2022-07-21 17:34:29.707 | INFO | dd:start_run:2306 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion...\n2022-07-21 17:34:29.708 | INFO | dd:processBatch:2414 - 🌱 Using starting seed: 2874114942\n2022-07-21 17:34:29.708 | INFO | dd:free_mem:73 - Clearing CUDA cache on cuda:0...\n2022-07-21 17:34:29.709 | INFO | dd:do_run:1188 - 💻 Starting Run: eb1a4d44-6054-4861-8a2e-1d4927542eb2(0) at frame 0\n2022-07-21 17:34:29.709 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'ViT-B/32'...\n2022-07-21 17:34:36.185 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'ViT-B/16'...\n2022-07-21 17:34:40.345 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'ViT-L/14'...\n2022-07-21 17:34:50.961 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'ViT-L/14@336px'...\n2022-07-21 17:35:00.966 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'RN50x4'...\n2022-07-21 17:35:06.323 | INFO | dd:do_run:1246 - 🤖 Loading LPIPS...\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n warnings.warn(\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n\u001b[2K\n2022-07-21 17:35:08.161 | WARNING | dd:disco:1497 - Changing output size to 384x384. Dimensions must by multiples of 64.\n2022-07-21 17:35:08.162 | INFO | dd:disco:1556 - timestep_respacing : ddim200 | diffusion_steps: 1000\n2022-07-21 17:35:08.162 | INFO | dd:prepModels:1071 - Prepping models...\n2022-07-21 17:35:14.292 | INFO | dd:disco:1586 - Running job '7c31ba4e-c6d4-4b9c-a375-7be4f1202798'...\n2022-07-21 17:35:14.293 | INFO | dd:disco:1600 - 🌱 Seed used: 2874114942\n2022-07-21 17:35:14.302 | INFO | dd:disco:1626 - Frame 0 📝 Prompt: [\"I feel so alive with these phantoms of night and I know that this life isn't safe, but it's wild and it's free, intricate detailed painting, artstation deviantart, absolutely mind blowing beautiful fine art, haunting, whimsical\"]\n\u001b[2K\n\u001b[2K\n\u001b[2K\nBatches: 0%| | 0/1 [00:00<?, ?it/s]\nOutput()\n2022-07-21 17:35:17.031 | INFO | dd:free_mem:73 - Clearing CUDA cache on cuda:0...\n\n\n 0%| | 0/196 [00:00<?, ?it/s]\u001b[A\nBatch 0, step 0, output 0:\n\n\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBF70>\n 0%| | 0/196 [00:07<?, ?it/s]\u001b[A\n\n 1%| | 1/196 [00:07<25:06, 7.73s/it]\u001b[A\n\n 1%| | 2/196 [00:11<17:33, 5.43s/it]\u001b[A\n\n 2%|▏ | 3/196 [00:15<15:04, 4.68s/it]\u001b[A\n\n 2%|▏ | 4/196 [00:19<13:52, 4.34s/it]\u001b[A\n\n 3%|▎ | 5/196 [00:22<13:10, 4.14s/it]\u001b[A\n\n 3%|▎ | 6/196 [00:26<12:44, 4.03s/it]\u001b[A\n\n 4%|▎ | 7/196 [00:30<12:26, 3.95s/it]\u001b[A\n\n 4%|▍ | 8/196 [00:34<12:13, 3.90s/it]\u001b[A\n\n 5%|▍ | 9/196 [00:38<12:03, 3.87s/it]\u001b[A\n\n 5%|▌ | 10/196 [00:41<11:55, 3.84s/it]\u001b[A\n\n 6%|▌ | 11/196 [00:45<11:48, 3.83s/it]\u001b[A\n\n 6%|▌ | 12/196 [00:49<11:43, 3.82s/it]\u001b[A\n\n 7%|▋ | 13/196 [00:53<11:37, 3.81s/it]\u001b[A\n\n 7%|▋ | 14/196 [00:57<11:33, 3.81s/it]\u001b[A\n\n 8%|▊ | 15/196 [01:00<11:29, 3.81s/it]\u001b[A\n\n 8%|▊ | 16/196 [01:04<11:25, 3.81s/it]\u001b[A\n\n 9%|▊ | 17/196 [01:08<11:21, 3.80s/it]\u001b[A\n\n 9%|▉ | 18/196 [01:12<11:16, 3.80s/it]\u001b[A\n\n 10%|▉ | 19/196 [01:16<11:11, 3.79s/it]\u001b[A\n\n 10%|█ | 20/196 [01:19<11:09, 3.80s/it]\u001b[A\n\n 11%|█ | 21/196 [01:23<11:05, 3.80s/it]\u001b[A\n\n 11%|█ | 22/196 [01:27<11:01, 3.80s/it]\u001b[A\n\n 12%|█▏ | 23/196 [01:31<10:56, 3.80s/it]\u001b[A\n\n 12%|█▏ | 24/196 [01:35<10:52, 3.80s/it]\u001b[A\n\n 13%|█▎ | 25/196 [01:38<10:49, 3.80s/it]\u001b[A\n\n 13%|█▎ | 26/196 [01:42<10:45, 3.80s/it]\u001b[A\n\n 14%|█▍ | 27/196 [01:46<10:41, 3.80s/it]\u001b[A\n\n 14%|█▍ | 28/196 [01:50<10:37, 3.79s/it]\u001b[A\n\n 15%|█▍ | 29/196 [01:54<10:34, 3.80s/it]\u001b[A\n\n 15%|█▌ | 30/196 [01:57<10:30, 3.80s/it]\u001b[A\n\n 16%|█▌ | 31/196 [02:01<10:27, 3.80s/it]\u001b[A\n\n 16%|█▋ | 32/196 [02:05<10:23, 3.80s/it]\u001b[A\n\n 17%|█▋ | 33/196 [02:09<10:20, 3.81s/it]\u001b[A\n\n 17%|█▋ | 34/196 [02:13<10:17, 3.81s/it]\u001b[A\n\n 18%|█▊ | 35/196 [02:16<10:13, 3.81s/it]\u001b[A\n\n 18%|█▊ | 36/196 [02:20<10:10, 3.81s/it]\u001b[A\n\n 19%|█▉ | 37/196 [02:24<10:06, 3.81s/it]\u001b[A\n\n 19%|█▉ | 38/196 [02:28<10:02, 3.81s/it]\u001b[A\n\n 20%|█▉ | 39/196 [02:32<09:58, 3.81s/it]\u001b[A\n\n 20%|██ | 40/196 [02:36<09:55, 3.81s/it]\u001b[A\n\n 21%|██ | 41/196 [02:39<09:51, 3.81s/it]\u001b[A\n\n 21%|██▏ | 42/196 [02:43<09:48, 3.82s/it]\u001b[A\n\n 22%|██▏ | 43/196 [02:47<09:44, 3.82s/it]\u001b[A\n\n 22%|██▏ | 44/196 [02:51<09:40, 3.82s/it]\u001b[A\n\n 23%|██▎ | 45/196 [02:55<09:36, 3.82s/it]\u001b[A\n\n 23%|██▎ | 46/196 [02:58<09:33, 3.82s/it]\u001b[A\n\n 24%|██▍ | 47/196 [03:02<09:29, 3.82s/it]\u001b[A\n\n 24%|██▍ | 48/196 [03:06<09:24, 3.81s/it]\u001b[A\n\n 25%|██▌ | 49/196 [03:10<09:21, 3.82s/it]\u001b[A\n\n 26%|██▌ | 50/196 [03:14<09:18, 3.83s/it]\u001b[A\n\n 26%|██▌ | 51/196 [03:18<09:13, 3.82s/it]\u001b[A\n\n 27%|██▋ | 52/196 [03:21<09:10, 3.82s/it]\u001b[A\n\n 27%|██▋ | 53/196 [03:25<09:07, 3.83s/it]\u001b[A\n\n 28%|██▊ | 54/196 [03:29<09:04, 3.83s/it]\u001b[A\n\n 28%|██▊ | 55/196 [03:33<08:59, 3.83s/it]\u001b[A\n\n 29%|██▊ | 56/196 [03:37<08:55, 3.83s/it]\u001b[A\n\n 29%|██▉ | 57/196 [03:41<08:51, 3.82s/it]\u001b[A\n\n 30%|██▉ | 58/196 [03:44<08:46, 3.82s/it]\u001b[A\n\n 30%|███ | 59/196 [03:48<08:42, 3.82s/it]\u001b[A\n\n 31%|███ | 60/196 [03:52<08:40, 3.82s/it]\u001b[A\n\n\nBatch 0, step 60, output 0:\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBEE0>\n 31%|███ | 60/196 [03:56<08:40, 3.82s/it]\u001b[A\n\n 31%|███ | 61/196 [03:56<08:37, 3.83s/it]\u001b[A\n\n 32%|███▏ | 62/196 [04:00<08:33, 3.83s/it]\u001b[A\n\n 32%|███▏ | 63/196 [04:03<08:29, 3.83s/it]\u001b[A\n\n 33%|███▎ | 64/196 [04:07<08:26, 3.83s/it]\u001b[A\n\n 33%|███▎ | 65/196 [04:11<08:23, 3.84s/it]\u001b[A\n\n 34%|███▎ | 66/196 [04:15<08:19, 3.84s/it]\u001b[A\n\n 34%|███▍ | 67/196 [04:19<08:15, 3.84s/it]\u001b[A\n\n 35%|███▍ | 68/196 [04:23<08:12, 3.85s/it]\u001b[A\n\n 35%|███▌ | 69/196 [04:27<08:08, 3.85s/it]\u001b[A2022-07-21 17:39:47.733 | DEBUG | dd:cond_fn:2061 - NaN'd\n\n\n 36%|███▌ | 70/196 [04:30<07:56, 3.78s/it]\u001b[A2022-07-21 17:39:51.365 | DEBUG | dd:cond_fn:2061 - NaN'd\n\n\n 36%|███▌ | 71/196 [04:34<07:47, 3.74s/it]\u001b[A\n\n 37%|███▋ | 72/196 [04:38<07:47, 3.77s/it]\u001b[A\n\n 37%|███▋ | 73/196 [04:42<07:47, 3.80s/it]\u001b[A\n\n 38%|███▊ | 74/196 [04:45<07:45, 3.82s/it]\u001b[A\n\n 38%|███▊ | 75/196 [04:49<07:43, 3.83s/it]\u001b[A\n\n 39%|███▉ | 76/196 [04:53<07:39, 3.83s/it]\u001b[A\n\n 39%|███▉ | 77/196 [04:57<07:46, 3.92s/it]\u001b[A\n\n 40%|███▉ | 78/196 [05:01<07:51, 3.99s/it]\u001b[A\n\n 40%|████ | 79/196 [05:06<07:51, 4.03s/it]\u001b[A\n\n 41%|████ | 80/196 [05:10<07:51, 4.06s/it]\u001b[A\n\n 41%|████▏ | 81/196 [05:14<07:50, 4.09s/it]\u001b[A\n\n 42%|████▏ | 82/196 [05:18<07:47, 4.10s/it]\u001b[A\n\n 42%|████▏ | 83/196 [05:22<07:44, 4.11s/it]\u001b[A\n\n 43%|████▎ | 84/196 [05:26<07:41, 4.12s/it]\u001b[A\n\n 43%|████▎ | 85/196 [05:30<07:37, 4.12s/it]\u001b[A\n\n 44%|████▍ | 86/196 [05:34<07:33, 4.13s/it]\u001b[A\n\n 44%|████▍ | 87/196 [05:39<07:29, 4.13s/it]\u001b[A\n\n 45%|████▍ | 88/196 [05:43<07:25, 4.13s/it]\u001b[A\n\n 45%|████▌ | 89/196 [05:47<07:20, 4.12s/it]\u001b[A\n\n 46%|████▌ | 90/196 [05:51<07:16, 4.12s/it]\u001b[A\n\n 46%|████▋ | 91/196 [05:55<07:12, 4.12s/it]\u001b[A\n\n 47%|████▋ | 92/196 [05:59<07:07, 4.11s/it]\u001b[A\n\n 47%|████▋ | 93/196 [06:03<07:03, 4.12s/it]\u001b[A\n\n 48%|████▊ | 94/196 [06:07<06:59, 4.11s/it]\u001b[A\n\n 48%|████▊ | 95/196 [06:12<06:55, 4.11s/it]\u001b[A\n\n 49%|████▉ | 96/196 [06:16<06:50, 4.11s/it]\u001b[A\n\n 49%|████▉ | 97/196 [06:20<06:46, 4.10s/it]\u001b[A\n\n 50%|█████ | 98/196 [06:24<06:42, 4.11s/it]\u001b[A\n\n 51%|█████ | 99/196 [06:28<06:38, 4.11s/it]\u001b[A\n\n 51%|█████ | 100/196 [06:32<06:34, 4.11s/it]\u001b[A\n\n 52%|█████▏ | 101/196 [06:36<06:30, 4.11s/it]\u001b[A\n\n 52%|█████▏ | 102/196 [06:40<06:26, 4.11s/it]\u001b[A\n\n 53%|█████▎ | 103/196 [06:44<06:22, 4.11s/it]\u001b[A\n\n 53%|█████▎ | 104/196 [06:49<06:18, 4.12s/it]\u001b[A\n\n 54%|█████▎ | 105/196 [06:53<06:14, 4.11s/it]\u001b[A\n\n 54%|█████▍ | 106/196 [06:57<06:09, 4.11s/it]\u001b[A\n\n 55%|█████▍ | 107/196 [07:01<06:05, 4.11s/it]\u001b[A\n\n 55%|█████▌ | 108/196 [07:05<06:00, 4.10s/it]\u001b[A\n\n 56%|█████▌ | 109/196 [07:09<05:56, 4.10s/it]\u001b[A\n\n 56%|█████▌ | 110/196 [07:13<05:52, 4.10s/it]\u001b[A\n\n 57%|█████▋ | 111/196 [07:17<05:48, 4.10s/it]\u001b[A\n\n 57%|█████▋ | 112/196 [07:21<05:45, 4.11s/it]\u001b[A\n\n 58%|█████▊ | 113/196 [07:25<05:41, 4.11s/it]\u001b[A\n\n 58%|█████▊ | 114/196 [07:30<05:36, 4.11s/it]\u001b[A\n\n 59%|█████▊ | 115/196 [07:34<05:32, 4.11s/it]\u001b[A\n\n 59%|█████▉ | 116/196 [07:38<05:28, 4.11s/it]\u001b[A\n\n 60%|█████▉ | 117/196 [07:42<05:24, 4.11s/it]\u001b[A\n\n 60%|██████ | 118/196 [07:46<05:20, 4.10s/it]\u001b[A\n\n 61%|██████ | 119/196 [07:50<05:16, 4.11s/it]\u001b[A\n\n 61%|██████ | 120/196 [07:54<05:12, 4.12s/it]\u001b[A\n\nBatch 0, step 120, output 0:\n\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBDF0>\n 61%|██████ | 120/196 [07:58<05:12, 4.12s/it]\u001b[A\n\n 62%|██████▏ | 121/196 [07:58<05:10, 4.14s/it]\u001b[A\n\n 62%|██████▏ | 122/196 [08:03<05:05, 4.13s/it]\u001b[A\n\n 63%|██████▎ | 123/196 [08:07<05:01, 4.13s/it]\u001b[A\n\n 63%|██████▎ | 124/196 [08:11<04:57, 4.13s/it]\u001b[A\n\n 64%|██████▍ | 125/196 [08:15<04:53, 4.13s/it]\u001b[A\n\n 64%|██████▍ | 126/196 [08:19<04:49, 4.13s/it]\u001b[A\n\n 65%|██████▍ | 127/196 [08:23<04:45, 4.13s/it]\u001b[A2022-07-21 17:43:44.640 | DEBUG | dd:cond_fn:2061 - NaN'd\n\n\n 65%|██████▌ | 128/196 [08:27<04:36, 4.07s/it]\u001b[A\n\n 66%|██████▌ | 129/196 [08:31<04:34, 4.09s/it]\u001b[A2022-07-21 17:43:52.669 | DEBUG | dd:cond_fn:2061 - NaN'd\n\n\n 66%|██████▋ | 130/196 [08:35<04:26, 4.03s/it]\u001b[A\n\n 67%|██████▋ | 131/196 [08:39<04:23, 4.06s/it]\u001b[A\n\n 67%|██████▋ | 132/196 [08:43<04:21, 4.08s/it]\u001b[A\n\n 68%|██████▊ | 133/196 [08:48<04:17, 4.10s/it]\u001b[A\n\n 68%|██████▊ | 134/196 [08:52<04:14, 4.10s/it]\u001b[A\n\n 69%|██████▉ | 135/196 [08:56<04:10, 4.11s/it]\u001b[A\n\n 69%|██████▉ | 136/196 [09:00<04:07, 4.12s/it]\u001b[A\n\n 70%|██████▉ | 137/196 [09:04<04:03, 4.12s/it]\u001b[A\n\n 70%|███████ | 138/196 [09:08<03:59, 4.13s/it]\u001b[A\n\n 71%|███████ | 139/196 [09:12<03:55, 4.13s/it]\u001b[A\n\n 71%|███████▏ | 140/196 [09:16<03:50, 4.12s/it]\u001b[A\n\n 72%|███████▏ | 141/196 [09:21<03:46, 4.12s/it]\u001b[A\n\n 72%|███████▏ | 142/196 [09:25<03:41, 4.11s/it]\u001b[A\n\n 73%|███████▎ | 143/196 [09:29<03:37, 4.10s/it]\u001b[A\n\n 73%|███████▎ | 144/196 [09:33<03:33, 4.10s/it]\u001b[A\n\n 74%|███████▍ | 145/196 [09:37<03:28, 4.09s/it]\u001b[A\n\n 74%|███████▍ | 146/196 [09:41<03:24, 4.09s/it]\u001b[A\n\n 75%|███████▌ | 147/196 [09:45<03:20, 4.10s/it]\u001b[A\n\n 76%|███████▌ | 148/196 [09:49<03:16, 4.10s/it]\u001b[A\n\n 76%|███████▌ | 149/196 [09:53<03:12, 4.10s/it]\u001b[A\n\n 77%|███████▋ | 150/196 [09:57<03:08, 4.10s/it]\u001b[A\n\n 77%|███████▋ | 151/196 [10:01<03:04, 4.10s/it]\u001b[A\n\n 78%|███████▊ | 152/196 [10:06<03:00, 4.10s/it]\u001b[A2022-07-21 17:45:26.967 | DEBUG | dd:cond_fn:2061 - NaN'd\n\n\n 78%|███████▊ | 153/196 [10:09<02:53, 4.03s/it]\u001b[A\n\n 79%|███████▊ | 154/196 [10:14<02:50, 4.06s/it]\u001b[A\n\n 79%|███████▉ | 155/196 [10:18<02:47, 4.08s/it]\u001b[A\n\n 80%|███████▉ | 156/196 [10:22<02:43, 4.09s/it]\u001b[A2022-07-21 17:45:43.251 | DEBUG | dd:cond_fn:2061 - NaN'd\n\n\n 80%|████████ | 157/196 [10:26<02:37, 4.04s/it]\u001b[A\n\n 81%|████████ | 158/196 [10:30<02:34, 4.06s/it]\u001b[A\n\n 81%|████████ | 159/196 [10:34<02:31, 4.08s/it]\u001b[A\n\n 82%|████████▏ | 160/196 [10:38<02:28, 4.11s/it]\u001b[A\n\n 82%|████████▏ | 161/196 [10:42<02:24, 4.13s/it]\u001b[A\n\n 83%|████████▎ | 162/196 [10:46<02:20, 4.14s/it]\u001b[A\n\n 83%|████████▎ | 163/196 [10:51<02:16, 4.14s/it]\u001b[A\n\n 84%|████████▎ | 164/196 [10:55<02:12, 4.14s/it]\u001b[A\n\n 84%|████████▍ | 165/196 [10:59<02:08, 4.15s/it]\u001b[A\n\n 85%|████████▍ | 166/196 [11:03<02:04, 4.15s/it]\u001b[A\n\n 85%|████████▌ | 167/196 [11:07<02:00, 4.15s/it]\u001b[A\n\n 86%|████████▌ | 168/196 [11:11<01:56, 4.15s/it]\u001b[A\n\n 86%|████████▌ | 169/196 [11:16<01:51, 4.14s/it]\u001b[A\n\n 87%|████████▋ | 170/196 [11:20<01:47, 4.14s/it]\u001b[A\n\n 87%|████████▋ | 171/196 [11:24<01:43, 4.14s/it]\u001b[A\n\n 88%|████████▊ | 172/196 [11:28<01:39, 4.14s/it]\u001b[A\n\n 88%|████████▊ | 173/196 [11:32<01:35, 4.13s/it]\u001b[A\n\n 89%|████████▉ | 174/196 [11:36<01:30, 4.13s/it]\u001b[A2022-07-21 17:46:57.609 | DEBUG | dd:cond_fn:2061 - NaN'd\n\n\n 89%|████████▉ | 175/196 [11:40<01:25, 4.06s/it]\u001b[A\n\n 90%|████████▉ | 176/196 [11:44<01:21, 4.08s/it]\u001b[A\n\n 90%|█████████ | 177/196 [11:48<01:17, 4.10s/it]\u001b[A\n\n 91%|█████████ | 178/196 [11:52<01:13, 4.11s/it]\u001b[A\n\n 91%|█████████▏| 179/196 [11:57<01:09, 4.12s/it]\u001b[A\n\nBatch 0, step 180, output 0:\n 92%|█████████▏| 180/196 [12:01<01:05, 4.12s/it]\u001b[A\n\n\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBEE0>\n 92%|█████████▏| 180/196 [12:05<01:05, 4.12s/it]\u001b[A\n\n 92%|█████████▏| 181/196 [12:05<01:01, 4.13s/it]\u001b[A\n\n 93%|█████████▎| 182/196 [12:09<00:57, 4.13s/it]\u001b[A\n\n 93%|█████████▎| 183/196 [12:13<00:53, 4.13s/it]\u001b[A\n\n 94%|█████████▍| 184/196 [12:17<00:49, 4.12s/it]\u001b[A\n\n 94%|█████████▍| 185/196 [12:21<00:45, 4.12s/it]\u001b[A\n\n 95%|█████████▍| 186/196 [12:25<00:41, 4.12s/it]\u001b[A\n\n 95%|█████████▌| 187/196 [12:30<00:37, 4.11s/it]\u001b[A\n\n 96%|█████████▌| 188/196 [12:34<00:32, 4.11s/it]\u001b[A\n\n 96%|█████████▋| 189/196 [12:38<00:28, 4.11s/it]\u001b[A\n\n 97%|█████████▋| 190/196 [12:42<00:24, 4.11s/it]\u001b[A\n\n 97%|█████████▋| 191/196 [12:46<00:20, 4.10s/it]\u001b[A2022-07-21 17:48:07.399 | DEBUG | dd:cond_fn:2061 - NaN'd\n\n\n 98%|█████████▊| 192/196 [12:50<00:16, 4.04s/it]\u001b[A\n\n 98%|█████████▊| 193/196 [12:54<00:12, 4.06s/it]\u001b[A\n\n 99%|█████████▉| 194/196 [12:58<00:08, 4.07s/it]\u001b[A\n\nBatch 0, step 195, output 0:\n 99%|█████████▉| 195/196 [13:02<00:04, 4.07s/it]\u001b[A\n\n\n\u001b[A\n\n\u001b[2K\n\u001b[2K\n<PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBDF0>\n 99%|█████████▉| 195/196 [13:06<00:04, 4.07s/it]\u001b[A2022-07-21 17:48:23.857 | INFO | dd:disco:1882 - Image render completed.\n2022-07-21 17:48:23.899 | INFO | dd:disco:1902 - Image saved to '/src/images_out/eb1a4d44-6054-4861-8a2e-1d4927542eb2/eb1a4d44-6054-4861-8a2e-1d4927542eb2(0)_0.png'\n\n\n100%|██████████| 196/196 [13:06<00:00, 4.11s/it]\u001b[A\n100%|██████████| 196/196 [13:06<00:00, 4.01s/it]\n2022-07-21 17:48:23.900 | INFO | dd:free_mem:73 - Clearing CUDA cache on cuda:0...\n2022-07-21 17:48:23.900 | SUCCESS | dd:start_run:2309 - ✅ Session 27801dd6-f5ec-4d4d-86c5-fc9d5ac6a5d0 finished by user.\n2022-07-21 17:48:23.900 | DEBUG | dd:sendSMS:2628 - Not sending SMS\n2022-07-21 17:48:23.900 | INFO | dd:free_mem:73 - Clearing CUDA cache on cuda:0...", "metrics": { "predict_time": 839.771437, "total_time": 1170.31615 }, "output": [ "https://replicate.delivery/mgxm/c8408ef2-c223-42df-925d-3331e370b5b7/progress.png", "https://replicate.delivery/mgxm/6e6af868-966c-4538-a0d2-ab822bda65e3/progress.png", "https://replicate.delivery/mgxm/9b9848ac-9bfd-4ebc-afc1-83ae32f9224d/progress.png", "https://replicate.delivery/mgxm/31c43e34-ed3c-4189-9de5-660300f00ec9/progress.png", "https://replicate.delivery/mgxm/9f246d09-b852-4384-be80-9bba5f9697af/progress.png", "https://replicate.delivery/mgxm/718506fb-f089-4f10-afd0-ba0321a71226/eb1a4d44-6054-4861-8a2e-1d4927542eb20_0.png" ], "started_at": "2022-07-21T17:34:29.772496Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/q6olw26osbbpnme33qfhp73zo4", "cancel": "https://api.replicate.com/v1/predictions/q6olw26osbbpnme33qfhp73zo4/cancel" }, "version": "cc730cf65f83d7ffed2aa6d47bc9a538b628617be5a4c2db27e7aee6a6391920" }Generated in2022-07-21 17:34:29.702 | INFO | dd:getDevice:2590 - ✅ Using device: cuda:0 2022-07-21 17:34:29.703 | INFO | dd:getDevice:2600 - Disabling CUDNN for A100 gpu 2022-07-21 17:34:29.706 | INFO | dd:start_run:2288 - 💼 1 jobs found. 2022-07-21 17:34:29.706 | INFO | dd:start_run:2300 - ⚒️ Session 27801dd6-f5ec-4d4d-86c5-fc9d5ac6a5d0 started... 2022-07-21 17:34:29.706 | INFO | dd:start_run:2303 - 💼 Processing job 1 of 1... 2022-07-21 17:34:29.707 | INFO | dd:start_run:2306 - 🌲 Setting TORCH_HOME environment variable to /root/.cache/disco-diffusion... 2022-07-21 17:34:29.708 | INFO | dd:processBatch:2414 - 🌱 Using starting seed: 2874114942 2022-07-21 17:34:29.708 | INFO | dd:free_mem:73 - Clearing CUDA cache on cuda:0... 2022-07-21 17:34:29.709 | INFO | dd:do_run:1188 - 💻 Starting Run: eb1a4d44-6054-4861-8a2e-1d4927542eb2(0) at frame 0 2022-07-21 17:34:29.709 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'ViT-B/32'... 2022-07-21 17:34:36.185 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'ViT-B/16'... 2022-07-21 17:34:40.345 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'ViT-L/14'... 2022-07-21 17:34:50.961 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'ViT-L/14@336px'... 2022-07-21 17:35:00.966 | INFO | dd:clipLoad:1174 - 🤖 Loading model 'RN50x4'... 2022-07-21 17:35:06.323 | INFO | dd:do_run:1246 - 🤖 Loading LPIPS... /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) 2022-07-21 17:35:08.161 | WARNING | dd:disco:1497 - Changing output size to 384x384. Dimensions must by multiples of 64. 2022-07-21 17:35:08.162 | INFO | dd:disco:1556 - timestep_respacing : ddim200 | diffusion_steps: 1000 2022-07-21 17:35:08.162 | INFO | dd:prepModels:1071 - Prepping models... 2022-07-21 17:35:14.292 | INFO | dd:disco:1586 - Running job '7c31ba4e-c6d4-4b9c-a375-7be4f1202798'... 2022-07-21 17:35:14.293 | INFO | dd:disco:1600 - 🌱 Seed used: 2874114942 2022-07-21 17:35:14.302 | INFO | dd:disco:1626 - Frame 0 📝 Prompt: ["I feel so alive with these phantoms of night and I know that this life isn't safe, but it's wild and it's free, intricate detailed painting, artstation deviantart, absolutely mind blowing beautiful fine art, haunting, whimsical"] Batches: 0%| | 0/1 [00:00<?, ?it/s] Output() 2022-07-21 17:35:17.031 | INFO | dd:free_mem:73 - Clearing CUDA cache on cuda:0... 0%| | 0/196 [00:00<?, ?it/s] Batch 0, step 0, output 0: <PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBF70> 0%| | 0/196 [00:07<?, ?it/s] 1%| | 1/196 [00:07<25:06, 7.73s/it] 1%| | 2/196 [00:11<17:33, 5.43s/it] 2%|▏ | 3/196 [00:15<15:04, 4.68s/it] 2%|▏ | 4/196 [00:19<13:52, 4.34s/it] 3%|▎ | 5/196 [00:22<13:10, 4.14s/it] 3%|▎ | 6/196 [00:26<12:44, 4.03s/it] 4%|▎ | 7/196 [00:30<12:26, 3.95s/it] 4%|▍ | 8/196 [00:34<12:13, 3.90s/it] 5%|▍ | 9/196 [00:38<12:03, 3.87s/it] 5%|▌ | 10/196 [00:41<11:55, 3.84s/it] 6%|▌ | 11/196 [00:45<11:48, 3.83s/it] 6%|▌ | 12/196 [00:49<11:43, 3.82s/it] 7%|▋ | 13/196 [00:53<11:37, 3.81s/it] 7%|▋ | 14/196 [00:57<11:33, 3.81s/it] 8%|▊ | 15/196 [01:00<11:29, 3.81s/it] 8%|▊ | 16/196 [01:04<11:25, 3.81s/it] 9%|▊ | 17/196 [01:08<11:21, 3.80s/it] 9%|▉ | 18/196 [01:12<11:16, 3.80s/it] 10%|▉ | 19/196 [01:16<11:11, 3.79s/it] 10%|█ | 20/196 [01:19<11:09, 3.80s/it] 11%|█ | 21/196 [01:23<11:05, 3.80s/it] 11%|█ | 22/196 [01:27<11:01, 3.80s/it] 12%|█▏ | 23/196 [01:31<10:56, 3.80s/it] 12%|█▏ | 24/196 [01:35<10:52, 3.80s/it] 13%|█▎ | 25/196 [01:38<10:49, 3.80s/it] 13%|█▎ | 26/196 [01:42<10:45, 3.80s/it] 14%|█▍ | 27/196 [01:46<10:41, 3.80s/it] 14%|█▍ | 28/196 [01:50<10:37, 3.79s/it] 15%|█▍ | 29/196 [01:54<10:34, 3.80s/it] 15%|█▌ | 30/196 [01:57<10:30, 3.80s/it] 16%|█▌ | 31/196 [02:01<10:27, 3.80s/it] 16%|█▋ | 32/196 [02:05<10:23, 3.80s/it] 17%|█▋ | 33/196 [02:09<10:20, 3.81s/it] 17%|█▋ | 34/196 [02:13<10:17, 3.81s/it] 18%|█▊ | 35/196 [02:16<10:13, 3.81s/it] 18%|█▊ | 36/196 [02:20<10:10, 3.81s/it] 19%|█▉ | 37/196 [02:24<10:06, 3.81s/it] 19%|█▉ | 38/196 [02:28<10:02, 3.81s/it] 20%|█▉ | 39/196 [02:32<09:58, 3.81s/it] 20%|██ | 40/196 [02:36<09:55, 3.81s/it] 21%|██ | 41/196 [02:39<09:51, 3.81s/it] 21%|██▏ | 42/196 [02:43<09:48, 3.82s/it] 22%|██▏ | 43/196 [02:47<09:44, 3.82s/it] 22%|██▏ | 44/196 [02:51<09:40, 3.82s/it] 23%|██▎ | 45/196 [02:55<09:36, 3.82s/it] 23%|██▎ | 46/196 [02:58<09:33, 3.82s/it] 24%|██▍ | 47/196 [03:02<09:29, 3.82s/it] 24%|██▍ | 48/196 [03:06<09:24, 3.81s/it] 25%|██▌ | 49/196 [03:10<09:21, 3.82s/it] 26%|██▌ | 50/196 [03:14<09:18, 3.83s/it] 26%|██▌ | 51/196 [03:18<09:13, 3.82s/it] 27%|██▋ | 52/196 [03:21<09:10, 3.82s/it] 27%|██▋ | 53/196 [03:25<09:07, 3.83s/it] 28%|██▊ | 54/196 [03:29<09:04, 3.83s/it] 28%|██▊ | 55/196 [03:33<08:59, 3.83s/it] 29%|██▊ | 56/196 [03:37<08:55, 3.83s/it] 29%|██▉ | 57/196 [03:41<08:51, 3.82s/it] 30%|██▉ | 58/196 [03:44<08:46, 3.82s/it] 30%|███ | 59/196 [03:48<08:42, 3.82s/it] 31%|███ | 60/196 [03:52<08:40, 3.82s/it] Batch 0, step 60, output 0: <PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBEE0> 31%|███ | 60/196 [03:56<08:40, 3.82s/it] 31%|███ | 61/196 [03:56<08:37, 3.83s/it] 32%|███▏ | 62/196 [04:00<08:33, 3.83s/it] 32%|███▏ | 63/196 [04:03<08:29, 3.83s/it] 33%|███▎ | 64/196 [04:07<08:26, 3.83s/it] 33%|███▎ | 65/196 [04:11<08:23, 3.84s/it] 34%|███▎ | 66/196 [04:15<08:19, 3.84s/it] 34%|███▍ | 67/196 [04:19<08:15, 3.84s/it] 35%|███▍ | 68/196 [04:23<08:12, 3.85s/it] 35%|███▌ | 69/196 [04:27<08:08, 3.85s/it]2022-07-21 17:39:47.733 | DEBUG | dd:cond_fn:2061 - NaN'd 36%|███▌ | 70/196 [04:30<07:56, 3.78s/it]2022-07-21 17:39:51.365 | DEBUG | dd:cond_fn:2061 - NaN'd 36%|███▌ | 71/196 [04:34<07:47, 3.74s/it] 37%|███▋ | 72/196 [04:38<07:47, 3.77s/it] 37%|███▋ | 73/196 [04:42<07:47, 3.80s/it] 38%|███▊ | 74/196 [04:45<07:45, 3.82s/it] 38%|███▊ | 75/196 [04:49<07:43, 3.83s/it] 39%|███▉ | 76/196 [04:53<07:39, 3.83s/it] 39%|███▉ | 77/196 [04:57<07:46, 3.92s/it] 40%|███▉ | 78/196 [05:01<07:51, 3.99s/it] 40%|████ | 79/196 [05:06<07:51, 4.03s/it] 41%|████ | 80/196 [05:10<07:51, 4.06s/it] 41%|████▏ | 81/196 [05:14<07:50, 4.09s/it] 42%|████▏ | 82/196 [05:18<07:47, 4.10s/it] 42%|████▏ | 83/196 [05:22<07:44, 4.11s/it] 43%|████▎ | 84/196 [05:26<07:41, 4.12s/it] 43%|████▎ | 85/196 [05:30<07:37, 4.12s/it] 44%|████▍ | 86/196 [05:34<07:33, 4.13s/it] 44%|████▍ | 87/196 [05:39<07:29, 4.13s/it] 45%|████▍ | 88/196 [05:43<07:25, 4.13s/it] 45%|████▌ | 89/196 [05:47<07:20, 4.12s/it] 46%|████▌ | 90/196 [05:51<07:16, 4.12s/it] 46%|████▋ | 91/196 [05:55<07:12, 4.12s/it] 47%|████▋ | 92/196 [05:59<07:07, 4.11s/it] 47%|████▋ | 93/196 [06:03<07:03, 4.12s/it] 48%|████▊ | 94/196 [06:07<06:59, 4.11s/it] 48%|████▊ | 95/196 [06:12<06:55, 4.11s/it] 49%|████▉ | 96/196 [06:16<06:50, 4.11s/it] 49%|████▉ | 97/196 [06:20<06:46, 4.10s/it] 50%|█████ | 98/196 [06:24<06:42, 4.11s/it] 51%|█████ | 99/196 [06:28<06:38, 4.11s/it] 51%|█████ | 100/196 [06:32<06:34, 4.11s/it] 52%|█████▏ | 101/196 [06:36<06:30, 4.11s/it] 52%|█████▏ | 102/196 [06:40<06:26, 4.11s/it] 53%|█████▎ | 103/196 [06:44<06:22, 4.11s/it] 53%|█████▎ | 104/196 [06:49<06:18, 4.12s/it] 54%|█████▎ | 105/196 [06:53<06:14, 4.11s/it] 54%|█████▍ | 106/196 [06:57<06:09, 4.11s/it] 55%|█████▍ | 107/196 [07:01<06:05, 4.11s/it] 55%|█████▌ | 108/196 [07:05<06:00, 4.10s/it] 56%|█████▌ | 109/196 [07:09<05:56, 4.10s/it] 56%|█████▌ | 110/196 [07:13<05:52, 4.10s/it] 57%|█████▋ | 111/196 [07:17<05:48, 4.10s/it] 57%|█████▋ | 112/196 [07:21<05:45, 4.11s/it] 58%|█████▊ | 113/196 [07:25<05:41, 4.11s/it] 58%|█████▊ | 114/196 [07:30<05:36, 4.11s/it] 59%|█████▊ | 115/196 [07:34<05:32, 4.11s/it] 59%|█████▉ | 116/196 [07:38<05:28, 4.11s/it] 60%|█████▉ | 117/196 [07:42<05:24, 4.11s/it] 60%|██████ | 118/196 [07:46<05:20, 4.10s/it] 61%|██████ | 119/196 [07:50<05:16, 4.11s/it] 61%|██████ | 120/196 [07:54<05:12, 4.12s/it] Batch 0, step 120, output 0: <PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBDF0> 61%|██████ | 120/196 [07:58<05:12, 4.12s/it] 62%|██████▏ | 121/196 [07:58<05:10, 4.14s/it] 62%|██████▏ | 122/196 [08:03<05:05, 4.13s/it] 63%|██████▎ | 123/196 [08:07<05:01, 4.13s/it] 63%|██████▎ | 124/196 [08:11<04:57, 4.13s/it] 64%|██████▍ | 125/196 [08:15<04:53, 4.13s/it] 64%|██████▍ | 126/196 [08:19<04:49, 4.13s/it] 65%|██████▍ | 127/196 [08:23<04:45, 4.13s/it]2022-07-21 17:43:44.640 | DEBUG | dd:cond_fn:2061 - 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NaN'd 89%|████████▉ | 175/196 [11:40<01:25, 4.06s/it] 90%|████████▉ | 176/196 [11:44<01:21, 4.08s/it] 90%|█████████ | 177/196 [11:48<01:17, 4.10s/it] 91%|█████████ | 178/196 [11:52<01:13, 4.11s/it] 91%|█████████▏| 179/196 [11:57<01:09, 4.12s/it] Batch 0, step 180, output 0: 92%|█████████▏| 180/196 [12:01<01:05, 4.12s/it] <PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBEE0> 92%|█████████▏| 180/196 [12:05<01:05, 4.12s/it] 92%|█████████▏| 181/196 [12:05<01:01, 4.13s/it] 93%|█████████▎| 182/196 [12:09<00:57, 4.13s/it] 93%|█████████▎| 183/196 [12:13<00:53, 4.13s/it] 94%|█████████▍| 184/196 [12:17<00:49, 4.12s/it] 94%|█████████▍| 185/196 [12:21<00:45, 4.12s/it] 95%|█████████▍| 186/196 [12:25<00:41, 4.12s/it] 95%|█████████▌| 187/196 [12:30<00:37, 4.11s/it] 96%|█████████▌| 188/196 [12:34<00:32, 4.11s/it] 96%|█████████▋| 189/196 [12:38<00:28, 4.11s/it] 97%|█████████▋| 190/196 [12:42<00:24, 4.11s/it] 97%|█████████▋| 191/196 [12:46<00:20, 4.10s/it]2022-07-21 17:48:07.399 | DEBUG | dd:cond_fn:2061 - NaN'd 98%|█████████▊| 192/196 [12:50<00:16, 4.04s/it] 98%|█████████▊| 193/196 [12:54<00:12, 4.06s/it] 99%|█████████▉| 194/196 [12:58<00:08, 4.07s/it] Batch 0, step 195, output 0: 99%|█████████▉| 195/196 [13:02<00:04, 4.07s/it] <PIL.Image.Image image mode=RGB size=384x384 at 0x7FBEA89FBDF0> 99%|█████████▉| 195/196 [13:06<00:04, 4.07s/it]2022-07-21 17:48:23.857 | INFO | dd:disco:1882 - Image render completed. 2022-07-21 17:48:23.899 | INFO | dd:disco:1902 - Image saved to '/src/images_out/eb1a4d44-6054-4861-8a2e-1d4927542eb2/eb1a4d44-6054-4861-8a2e-1d4927542eb2(0)_0.png' 100%|██████████| 196/196 [13:06<00:00, 4.11s/it] 100%|██████████| 196/196 [13:06<00:00, 4.01s/it] 2022-07-21 17:48:23.900 | INFO | dd:free_mem:73 - Clearing CUDA cache on cuda:0... 2022-07-21 17:48:23.900 | SUCCESS | dd:start_run:2309 - ✅ Session 27801dd6-f5ec-4d4d-86c5-fc9d5ac6a5d0 finished by user. 2022-07-21 17:48:23.900 | DEBUG | dd:sendSMS:2628 - Not sending SMS 2022-07-21 17:48:23.900 | INFO | dd:free_mem:73 - Clearing CUDA cache on cuda:0...Prediction
nightmareai/disco-diffusion:3c128f65Input
- RN50
- RN101
- steps
- 250
- width
- 512
- ViTB16
- ViTB32
- ViTL14
- height
- 512
- prompt
- a bed of roses
- init_scale
- 1000
- skip_steps
- 10
- range_scale
- 150
- cutn_batches
- 4
- display_rate
- 20
- target_scale
- 20000
- diffusion_model
- floraldiffusion
- ViTB32_laion2b_e16
- clip_guidance_scale
- 5000
- use_secondary_model
- ViTB16_laion400m_e32
- diffusion_sampling_mode
- ddim
{ "RN50": false, "RN101": true, "steps": 250, "width": 512, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 512, "prompt": "a bed of roses", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "floraldiffusion", "ViTB32_laion2b_e16": false, "clip_guidance_scale": 5000, "use_secondary_model": true, "ViTB16_laion400m_e32": false, "diffusion_sampling_mode": "ddim" }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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", { input: { RN50: false, RN101: true, steps: 250, width: 512, ViTB16: false, ViTB32: true, ViTL14: true, height: 512, prompt: "a bed of roses", init_scale: 1000, skip_steps: 10, range_scale: 150, cutn_batches: 4, display_rate: 20, target_scale: 20000, diffusion_model: "floraldiffusion", ViTB32_laion2b_e16: false, clip_guidance_scale: 5000, use_secondary_model: true, ViTB16_laion400m_e32: false, diffusion_sampling_mode: "ddim" } } ); 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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", input={ "RN50": False, "RN101": True, "steps": 250, "width": 512, "ViTB16": False, "ViTB32": True, "ViTL14": True, "height": 512, "prompt": "a bed of roses", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "floraldiffusion", "ViTB32_laion2b_e16": False, "clip_guidance_scale": 5000, "use_secondary_model": True, "ViTB16_laion400m_e32": False, "diffusion_sampling_mode": "ddim" } ) # The nightmareai/disco-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/disco-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/disco-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": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", "input": { "RN50": false, "RN101": true, "steps": 250, "width": 512, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 512, "prompt": "a bed of roses", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "floraldiffusion", "ViTB32_laion2b_e16": false, "clip_guidance_scale": 5000, "use_secondary_model": true, "ViTB16_laion400m_e32": false, "diffusion_sampling_mode": "ddim" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-29T12:52:38.955238Z", "created_at": "2022-07-29T12:50:03.255761Z", "data_removed": false, "error": null, "id": "33sobkznq5a5ncm7awdjwbxnxy", "input": { "RN50": false, "RN101": true, "steps": 250, "width": 512, "ViTB16": false, "ViTB32": true, "ViTL14": true, "height": 512, "prompt": "a bed of roses", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 20, "target_scale": 20000, "diffusion_model": "floraldiffusion", "ViTB32_laion2b_e16": false, "clip_guidance_scale": 5000, "use_secondary_model": true, "ViTB16_laion400m_e32": false, "diffusion_sampling_mode": "ddim" }, "logs": "2022-07-29 12:50:24,447 - discoart - WARNING - floraldiffusion is recommended to have width_height [512, 448], but you are using [512, 512]. This may lead to suboptimal results.\n2022-07-29 12:50:25,947 - discoart - INFO -\n looks like you are using a custom diffusion model,\n to override default diffusion model config, you can specify `create(diffusion_model_config={...}, ...)` as well,\n\n2022-07-29 12:50:42,544 - discoart - INFO - preparing models...\nSetting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]\nLoading model from: /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth\n2022-07-29 12:50:44,357 - discoart - INFO - creating artworks...\n\n 0%| | 0/240 [00:00<?, ?it/s]\n\n 0%| | 1/240 [00:00<01:17, 3.10it/s]\n 1%| | 2/240 [00:00<01:14, 3.21it/s]\n 1%|▏ | 3/240 [00:00<01:12, 3.25it/s]\n 2%|▏ | 4/240 [00:01<01:11, 3.31it/s]\n 2%|▏ | 5/240 [00:01<01:11, 3.30it/s]\n 2%|▎ | 6/240 [00:01<01:11, 3.30it/s]\n 3%|▎ | 7/240 [00:02<01:10, 3.32it/s]\n 3%|▎ | 8/240 [00:02<01:09, 3.33it/s]\n 4%|▍ | 9/240 [00:02<01:08, 3.35it/s]\n 4%|▍ | 10/240 [00:03<01:08, 3.35it/s]\n 5%|▍ | 11/240 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"https://replicate.delivery/mgxm/87e2ddca-f24f-4829-a356-0e61868436e9/0-step-20-0.png", "https://replicate.delivery/mgxm/5280fc04-250b-4193-ac05-5d11900d450e/0-step-40-0.png", "https://replicate.delivery/mgxm/9901e8c3-6c46-42e3-9bd3-d0d28c26bea8/0-step-60-0.png", "https://replicate.delivery/mgxm/e163ce28-5588-40bc-a129-5a7cd34e808d/0-step-80-0.png", "https://replicate.delivery/mgxm/b4a5e5e0-2447-42c1-82f4-6647fbb6090d/0-step-100-0.png", "https://replicate.delivery/mgxm/075c99d3-6899-42bb-8345-f9ffa9d573e3/0-step-120-0.png", "https://replicate.delivery/mgxm/29a6142c-9240-457c-a52c-7fe4a37c9b9b/0-step-140-0.png", "https://replicate.delivery/mgxm/f47fabfe-35a6-4427-8640-b2de22c71350/0-step-160-0.png", "https://replicate.delivery/mgxm/12a70af3-0508-4eb1-bc1c-33bf1d82dd84/0-step-180-0.png", "https://replicate.delivery/mgxm/0e8bdeb9-f051-48d0-8e6a-726150fa7d7b/0-step-200-0.png", "https://replicate.delivery/mgxm/e9cedfa9-c97f-4723-b8f6-509e51f84a32/0-step-220-0.png", "https://replicate.delivery/mgxm/68db8a8d-bae8-46d3-8ffc-21b7456cc70c/discoart-result.png" ], "started_at": "2022-07-29T12:50:24.396287Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/33sobkznq5a5ncm7awdjwbxnxy", "cancel": "https://api.replicate.com/v1/predictions/33sobkznq5a5ncm7awdjwbxnxy/cancel" }, "version": "ed429a3a5b9b9f40c772cc8e0d2eab060c5e1d004ea576e32909ae1dfd220b33" }Generated in2022-07-29 12:50:24,447 - discoart - WARNING - floraldiffusion is recommended to have width_height [512, 448], but you are using [512, 512]. This may lead to suboptimal results. 2022-07-29 12:50:25,947 - discoart - INFO - looks like you are using a custom diffusion model, to override default diffusion model config, you can specify `create(diffusion_model_config={...}, ...)` as well, 2022-07-29 12:50:42,544 - discoart - INFO - preparing models... Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off] Loading model from: /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth 2022-07-29 12:50:44,357 - discoart - INFO - creating artworks... 0%| | 0/240 [00:00<?, ?it/s] 0%| | 1/240 [00:00<01:17, 3.10it/s] 1%| | 2/240 [00:00<01:14, 3.21it/s] 1%|▏ | 3/240 [00:00<01:12, 3.25it/s] 2%|▏ | 4/240 [00:01<01:11, 3.31it/s] 2%|▏ | 5/240 [00:01<01:11, 3.30it/s] 2%|▎ | 6/240 [00:01<01:11, 3.30it/s] 3%|▎ | 7/240 [00:02<01:10, 3.32it/s] 3%|▎ | 8/240 [00:02<01:09, 3.33it/s] 4%|▍ | 9/240 [00:02<01:08, 3.35it/s] 4%|▍ | 10/240 [00:03<01:08, 3.35it/s] 5%|▍ | 11/240 [00:03<01:08, 3.35it/s] 5%|▌ | 12/240 [00:03<01:07, 3.37it/s] 5%|▌ | 13/240 [00:03<01:07, 3.36it/s] 6%|▌ | 14/240 [00:04<01:08, 3.30it/s] 6%|▋ | 15/240 [00:04<01:08, 3.30it/s] 7%|▋ | 16/240 [00:04<01:07, 3.32it/s] 7%|▋ | 17/240 [00:05<01:06, 3.35it/s] 8%|▊ | 18/240 [00:05<01:06, 3.35it/s] 8%|▊ | 19/240 [00:05<01:05, 3.36it/s] 8%|▊ | 20/240 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nightmareai/disco-diffusion:3c128f65Input
- RN50
- steps
- 250
- width
- 1280
- ViTB16
- ViTB32
- height
- 768
- prompt
- A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.
- init_scale
- 1000
- skip_steps
- 10
- range_scale
- 150
- cutn_batches
- 4
- display_rate
- 5
- target_scale
- 20000
- diffusion_model
- 512x512_diffusion_uncond_finetune_008100
- ViTB32_laion2b_e16
- clip_guidance_scale
- 5000
- use_secondary_model
- ViTB16_laion400m_e31
- ViTB16_laion400m_e32
- diffusion_sampling_mode
- ddim
{ "RN50": true, "steps": 250, "width": 1280, "ViTB16": false, "ViTB32": false, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 5, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "ViTB32_laion2b_e16": true, "clip_guidance_scale": 5000, "use_secondary_model": true, "ViTB16_laion400m_e31": false, "ViTB16_laion400m_e32": true, "diffusion_sampling_mode": "ddim" }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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", { input: { RN50: true, steps: 250, width: 1280, ViTB16: false, ViTB32: false, height: 768, prompt: "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", init_scale: 1000, skip_steps: 10, range_scale: 150, cutn_batches: 4, display_rate: 5, target_scale: 20000, diffusion_model: "512x512_diffusion_uncond_finetune_008100", ViTB32_laion2b_e16: true, clip_guidance_scale: 5000, use_secondary_model: true, ViTB16_laion400m_e31: false, ViTB16_laion400m_e32: true, diffusion_sampling_mode: "ddim" } } ); 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/disco-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nightmareai/disco-diffusion:3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", input={ "RN50": True, "steps": 250, "width": 1280, "ViTB16": False, "ViTB32": False, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 5, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "ViTB32_laion2b_e16": True, "clip_guidance_scale": 5000, "use_secondary_model": True, "ViTB16_laion400m_e31": False, "ViTB16_laion400m_e32": True, "diffusion_sampling_mode": "ddim" } ) # The nightmareai/disco-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/disco-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/disco-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": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507", "input": { "RN50": true, "steps": 250, "width": 1280, "ViTB16": false, "ViTB32": false, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 5, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "ViTB32_laion2b_e16": true, "clip_guidance_scale": 5000, "use_secondary_model": true, "ViTB16_laion400m_e31": false, "ViTB16_laion400m_e32": true, "diffusion_sampling_mode": "ddim" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-30T07:47:22.306258Z", "created_at": "2022-07-30T07:41:56.690889Z", "data_removed": false, "error": null, "id": "x5dpmo7ukfbz5fdb3u47ogddqe", "input": { "RN50": true, "steps": 250, "width": 1280, "ViTB16": false, "ViTB32": false, "height": 768, "prompt": "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "init_scale": 1000, "skip_steps": 10, "range_scale": 150, "cutn_batches": 4, "display_rate": 5, "target_scale": 20000, "diffusion_model": "512x512_diffusion_uncond_finetune_008100", "ViTB32_laion2b_e16": true, "clip_guidance_scale": 5000, "use_secondary_model": true, "ViTB16_laion400m_e31": false, "ViTB16_laion400m_e32": true, "diffusion_sampling_mode": "ddim" }, "logs": "2022-07-30 07:42:01,778 - discoart - INFO - preparing models...\nSetting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]\nLoading model from: /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth\n2022-07-30 07:42:03,652 - discoart - INFO - creating artworks...\n\n 0%| | 0/230 [00:00<?, ?it/s]\n\n 0%| | 1/230 [00:00<01:59, 1.92it/s]\n 1%| | 2/230 [00:01<02:09, 1.76it/s]\n 1%|▏ | 3/230 [00:01<02:03, 1.84it/s]\n 2%|▏ | 4/230 [00:02<02:00, 1.88it/s]\n 2%|▏ | 5/230 [00:02<01:59, 1.88it/s]\n\n\n 3%|▎ | 6/230 [00:03<02:00, 1.87it/s]\n 3%|▎ | 7/230 [00:03<02:12, 1.69it/s]\n\n 3%|▎ | 8/230 [00:04<02:12, 1.68it/s]\n 4%|▍ | 9/230 [00:05<02:10, 1.69it/s]\n 4%|▍ | 10/230 [00:05<02:04, 1.77it/s]\n\n\n 5%|▍ | 11/230 [00:06<02:01, 1.80it/s]\n 5%|▌ | 12/230 [00:06<02:10, 1.67it/s]\n 6%|▌ | 13/230 [00:07<02:10, 1.67it/s]\n 6%|▌ | 14/230 [00:08<02:08, 1.69it/s]\n 7%|▋ | 15/230 [00:08<02:06, 1.70it/s]\n\n\n\n 7%|▋ | 16/230 [00:09<02:06, 1.70it/s]\n 7%|▋ | 17/230 [00:10<02:19, 1.53it/s]\n 8%|▊ | 18/230 [00:10<02:15, 1.57it/s]\n 8%|▊ | 19/230 [00:11<02:10, 1.62it/s]\n 9%|▊ | 20/230 [00:11<02:07, 1.65it/s]\n\n\n 9%|▉ 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230/230 [04:24<00:00, 1.15s/it]\n\n\n\n\n\n\n\n\n\n2022-07-30 07:47:16,523 - discoart - INFO - done! discoart-946056f7b2ee8423bf7026e4e751863a\n", "metrics": { "predict_time": 325.416633, "total_time": 325.615369 }, "output": [ "https://replicate.delivery/mgxm/ea79a252-84cc-4a32-9aa5-04443702c7a8/0-step-0-0.png", "https://replicate.delivery/mgxm/835fa5b0-2034-4bc4-b01e-2271fc4bd08c/0-step-5-0.png", "https://replicate.delivery/mgxm/4216a7f9-2cdc-4d09-88a5-2e3ffbfcef73/0-step-10-0.png", "https://replicate.delivery/mgxm/ace81e25-109f-4cdd-8d14-39cadb4d2059/0-step-15-0.png", "https://replicate.delivery/mgxm/c5bcf3c5-bacf-44d2-9682-d13409564dd5/0-step-20-0.png", "https://replicate.delivery/mgxm/4e39cc64-1610-4afd-bf00-85daae62d959/0-step-25-0.png", "https://replicate.delivery/mgxm/2da390d3-1ae3-4cba-8bfa-1f5fe54742bc/0-step-30-0.png", "https://replicate.delivery/mgxm/d8bf5e15-436d-47d9-a87d-4a1f65928937/0-step-35-0.png", "https://replicate.delivery/mgxm/aeaa8fa1-ebac-4085-830e-facdea8b676f/0-step-40-0.png", "https://replicate.delivery/mgxm/b9edeefb-9499-4238-a6b9-318a6bb46d94/0-step-45-0.png", "https://replicate.delivery/mgxm/02ad509e-4158-430a-b3b4-a8b1a41b4400/0-step-50-0.png", "https://replicate.delivery/mgxm/0acce0ad-ec5f-425e-b70b-d8d497ce615b/0-step-55-0.png", "https://replicate.delivery/mgxm/e65fd83b-cca8-48f1-a606-d76880ac9816/0-step-60-0.png", "https://replicate.delivery/mgxm/0554fff7-c597-4e5b-9ee3-56bffe45b21a/0-step-65-0.png", "https://replicate.delivery/mgxm/4e52474a-65dc-44c1-a4ff-81cbde5b9601/0-step-70-0.png", "https://replicate.delivery/mgxm/fbdfe062-e581-46d3-85e1-9c460fdf0f00/0-step-75-0.png", "https://replicate.delivery/mgxm/baf8b171-a710-4bb9-b0bf-89c5a830cec5/0-step-80-0.png", "https://replicate.delivery/mgxm/b8fde20a-57a9-48c2-84bd-c3c7361402e9/0-step-85-0.png", "https://replicate.delivery/mgxm/330aa6f3-4e91-4424-b939-75fcef96e82c/0-step-90-0.png", "https://replicate.delivery/mgxm/fb2b107d-586a-4cd0-8ad6-d7eae4adb463/0-step-95-0.png", "https://replicate.delivery/mgxm/f6a4978c-4f5c-4e49-98e3-19cef68b7003/0-step-100-0.png", "https://replicate.delivery/mgxm/77515473-a67b-4aaf-bf2a-be1adb21e282/0-step-105-0.png", "https://replicate.delivery/mgxm/76a3d9cb-4a9e-490f-95fc-338a086e146b/0-step-110-0.png", "https://replicate.delivery/mgxm/d5341533-500f-4eda-bb40-b32ed1738eda/0-step-115-0.png", "https://replicate.delivery/mgxm/6a2adaa6-115e-4337-9be0-5c4f245012d1/0-step-120-0.png", "https://replicate.delivery/mgxm/0196f8d4-041e-4450-849e-9aafb9016e37/0-step-125-0.png", "https://replicate.delivery/mgxm/ea45d0d2-db96-4367-aaed-6bcba47a8881/0-step-130-0.png", "https://replicate.delivery/mgxm/8744e43b-57e9-47c8-93ae-d9eb028c6fe4/0-step-135-0.png", "https://replicate.delivery/mgxm/2ce53633-51f1-4af2-9c42-b5398edf7d0a/0-step-140-0.png", "https://replicate.delivery/mgxm/6f4ed834-cf6b-4e1f-a0aa-dc4b3c678cb9/0-step-145-0.png", "https://replicate.delivery/mgxm/299de4c8-2607-4b23-8a30-bcfc72019714/0-step-150-0.png", "https://replicate.delivery/mgxm/b72bc9fe-d25e-4223-916f-faa217a52359/0-step-155-0.png", "https://replicate.delivery/mgxm/60f4d026-aa28-4007-86cc-fdc6842d9d15/0-step-160-0.png", "https://replicate.delivery/mgxm/6c8157c4-b16f-44e6-b9dc-5ce0ce37582f/0-step-165-0.png", "https://replicate.delivery/mgxm/62bc93e4-6f5d-45ed-ba33-827ef68c68cd/0-step-170-0.png", "https://replicate.delivery/mgxm/e9aa3ebd-8a10-491a-8baf-ad9dfdd33610/0-step-175-0.png", "https://replicate.delivery/mgxm/4fe07120-9790-4a5b-bb83-70d4a84e539f/0-step-180-0.png", "https://replicate.delivery/mgxm/e2b08d1b-cec2-4913-8731-a5a7a8e305f1/0-step-185-0.png", "https://replicate.delivery/mgxm/6fbb9fde-ecb6-4eb1-9282-0e31fa8641ae/0-step-190-0.png", "https://replicate.delivery/mgxm/ba293406-c0a5-47e2-b90d-1f0ab537fb0f/0-step-195-0.png", "https://replicate.delivery/mgxm/8ecc94bd-3afc-4cbb-a38a-1b1919c4ea76/0-step-200-0.png", "https://replicate.delivery/mgxm/152439db-6960-47e4-979a-bba0bbaddaa0/0-step-205-0.png", "https://replicate.delivery/mgxm/871f6fa3-cd4c-431b-9c5b-86621806eee6/0-step-210-0.png", "https://replicate.delivery/mgxm/9cb1b185-5605-4f1f-81c5-faa14499b02d/0-step-215-0.png", "https://replicate.delivery/mgxm/079f1c63-59ed-46cb-b003-6326f2624f23/0-step-220-0.png", "https://replicate.delivery/mgxm/6d60e748-9d47-41b6-b49b-956784293dc4/0-step-225-0.png", "https://replicate.delivery/mgxm/a5f07613-267a-4ee3-b762-ed4200a64c08/discoart-result.png" ], "started_at": "2022-07-30T07:41:56.889625Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/x5dpmo7ukfbz5fdb3u47ogddqe", "cancel": "https://api.replicate.com/v1/predictions/x5dpmo7ukfbz5fdb3u47ogddqe/cancel" }, "version": "3c128f652e9f24e72896ac0b019e47facfd6bccf93104d50f09f1f2196325507" }Generated in2022-07-30 07:42:01,778 - discoart - INFO - preparing models... Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off] Loading model from: /root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth 2022-07-30 07:42:03,652 - discoart - INFO - creating artworks... 0%| | 0/230 [00:00<?, ?it/s] 0%| | 1/230 [00:00<01:59, 1.92it/s] 1%| | 2/230 [00:01<02:09, 1.76it/s] 1%|▏ | 3/230 [00:01<02:03, 1.84it/s] 2%|▏ | 4/230 [00:02<02:00, 1.88it/s] 2%|▏ | 5/230 [00:02<01:59, 1.88it/s] 3%|▎ | 6/230 [00:03<02:00, 1.87it/s] 3%|▎ | 7/230 [00:03<02:12, 1.69it/s] 3%|▎ | 8/230 [00:04<02:12, 1.68it/s] 4%|▍ | 9/230 [00:05<02:10, 1.69it/s] 4%|▍ | 10/230 [00:05<02:04, 1.77it/s] 5%|▍ | 11/230 [00:06<02:01, 1.80it/s] 5%|▌ | 12/230 [00:06<02:10, 1.67it/s] 6%|▌ | 13/230 [00:07<02:10, 1.67it/s] 6%|▌ | 14/230 [00:08<02:08, 1.69it/s] 7%|▋ | 15/230 [00:08<02:06, 1.70it/s] 7%|▋ | 16/230 [00:09<02:06, 1.70it/s] 7%|▋ | 17/230 [00:10<02:19, 1.53it/s] 8%|▊ | 18/230 [00:10<02:15, 1.57it/s] 8%|▊ | 19/230 [00:11<02:10, 1.62it/s] 9%|▊ | 20/230 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