pollinations / tune-a-video
About Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
- Public
- 2.9K runs
-
A100 (80GB)
- GitHub
Prediction
pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25Input
- steps
- 300
- video
- width
- 512
- height
- 512
- length
- 5
- source_prompt
- a man surfing
- target_prompts
- a panda surfing a cartoon sloth surfing
{ "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing", "target_prompts": "a panda surfing\na cartoon sloth surfing" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25", { input: { steps: 300, video: "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", width: 512, height: 512, length: 5, source_prompt: "a man surfing", target_prompts: "a panda surfing\na cartoon sloth surfing" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25", input={ "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing", "target_prompts": "a panda surfing\na cartoon sloth surfing" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run pollinations/tune-a-video 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": "pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing", "target_prompts": "a panda surfing\\na cartoon sloth surfing" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-01-29T09:25:48.749584Z", "created_at": "2023-01-29T09:17:55.514237Z", "data_removed": false, "error": null, "id": "qam52xrlizd65jir376amhwp4u", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing", "target_prompts": "a panda surfing\na cartoon sloth surfing" }, "logs": "predict\nThe following values were not passed to `accelerate launch` and had defaults used instead:\n`--num_processes` was set to a value of `1`\n`--num_machines` was set to a value of `1`\n`--mixed_precision` was set to a value of `'no'`\n`--dynamo_backend` was set to a value of `'no'`\nTo avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\nA matching Triton is not available, some optimizations will not be enabled.\nError caught was: No module named 'triton'\nrunning with config:\npretrained_model_path: ./checkpoints/stable-diffusion-v1-4\noutput_dir: /outputs\ntrain_data:\nn_sample_frames: 8\nsample_start_idx: 0\nprompt: a man surfing\nvideo_path: /tmp/tmpk5ml9tquman%20surfing.mp4\nwidth: 512\nheight: 512\nsample_frame_rate: 1\nvalidation_data:\nnum_inference_steps: 50\nguidance_scale: 7.5\nprompts:\n- a panda surfing\n- a cartoon sloth surfing\nvideo_length: 5\nwidth: 512\nheight: 512\nlearning_rate: 3.0e-05\ntrain_batch_size: 1\nmax_train_steps: 300\ncheckpointing_steps: 1000\nvalidation_steps: 100\ntrainable_modules:\n- attn1.to_q\n- attn2.to_q\n- attn_temp\nseed: 33\nmixed_precision: fp16\nuse_8bit_adam: false\ngradient_checkpointing: true\nenable_xformers_memory_efficient_attention: true\n01/29/2023 09:21:16 - INFO - __main__ - Distributed environment: NO\nNum processes: 1\nProcess index: 0\nLocal process index: 0\nDevice: cuda\nMixed precision type: fp16\n{'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values.\n{'norm_num_groups'} was not found in config. Values will be initialized to default values.\n{'class_embed_type', 'use_linear_projection', 'upcast_attention', 'only_cross_attention', 'dual_cross_attention', 'mid_block_type', 'num_class_embeds', 'resnet_time_scale_shift'} was not found in config. Values will be initialized to default values.\n{'prediction_type'} was not found in config. Values will be initialized to default values.\n01/29/2023 09:21:32 - INFO - __main__ - ***** Running training *****\n01/29/2023 09:21:32 - INFO - __main__ - Num examples = 1\n01/29/2023 09:21:32 - INFO - __main__ - Num Epochs = 300\n01/29/2023 09:21:32 - INFO - __main__ - Instantaneous batch size per device = 1\n01/29/2023 09:21:32 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1\n01/29/2023 09:21:32 - INFO - __main__ - Gradient Accumulation steps = 1\n01/29/2023 09:21:32 - INFO - __main__ - Total optimization steps = 300\n 0%| | 0/300 [00:00<?, ?it/s]\nSteps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\nwarnings.warn(\"None of the inputs have requires_grad=True. 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lr=3e-5, step_loss=0.0493]{'prediction_type'} was not found in config. Values will be initialized to default values.\nConfiguration saved in /outputs/model_index.json\nConfiguration saved in /outputs/vae/config.json\nModel weights saved in /outputs/vae/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/unet/config.json\nModel weights saved in /outputs/unet/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/scheduler/scheduler_config.json\nSteps: 100%|██████████| 300/300 [04:14<00:00, 1.18it/s, lr=3e-5, step_loss=0.0493]\ntotal 32\n-rw-r--r-- 1 root root 942 Jan 29 09:21 config.yaml\n-rw-r--r-- 1 root root 373 Jan 29 09:25 model_index.json\ndrwxr-xr-x 5 root root 4096 Jan 29 09:25 samples\ndrwxr-xr-x 2 root root 4096 Jan 29 09:25 scheduler\ndrwxr-xr-x 2 root root 4096 Jan 29 09:25 text_encoder\ndrwxr-xr-x 2 root root 4096 Jan 29 09:25 tokenizer\ndrwxr-xr-x 2 root root 4096 Jan 29 09:25 unet\ndrwxr-xr-x 2 root root 4096 Jan 29 09:25 vae", "metrics": { "predict_time": 276.096091, "total_time": 473.235347 }, "output": "https://replicate.delivery/pbxt/TX9HfInfssiYg0S6IMOXe9hAznAbcXsY2GsbFzIA3Qt2s9xgA/sample-300.gif", "started_at": "2023-01-29T09:21:12.653493Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qam52xrlizd65jir376amhwp4u", "cancel": "https://api.replicate.com/v1/predictions/qam52xrlizd65jir376amhwp4u/cancel" }, "version": "0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25" }
Generated inpredict The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `1` `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. A matching Triton is not available, some optimizations will not be enabled. Error caught was: No module named 'triton' running with config: pretrained_model_path: ./checkpoints/stable-diffusion-v1-4 output_dir: /outputs train_data: n_sample_frames: 8 sample_start_idx: 0 prompt: a man surfing video_path: /tmp/tmpk5ml9tquman%20surfing.mp4 width: 512 height: 512 sample_frame_rate: 1 validation_data: num_inference_steps: 50 guidance_scale: 7.5 prompts: - a panda surfing - a cartoon sloth surfing video_length: 5 width: 512 height: 512 learning_rate: 3.0e-05 train_batch_size: 1 max_train_steps: 300 checkpointing_steps: 1000 validation_steps: 100 trainable_modules: - attn1.to_q - attn2.to_q - attn_temp seed: 33 mixed_precision: fp16 use_8bit_adam: false gradient_checkpointing: true enable_xformers_memory_efficient_attention: true 01/29/2023 09:21:16 - INFO - __main__ - Distributed environment: NO Num processes: 1 Process index: 0 Local process index: 0 Device: cuda Mixed precision type: fp16 {'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values. {'norm_num_groups'} was not found in config. Values will be initialized to default values. {'class_embed_type', 'use_linear_projection', 'upcast_attention', 'only_cross_attention', 'dual_cross_attention', 'mid_block_type', 'num_class_embeds', 'resnet_time_scale_shift'} was not found in config. Values will be initialized to default values. {'prediction_type'} was not found in config. Values will be initialized to default values. 01/29/2023 09:21:32 - INFO - __main__ - ***** Running training ***** 01/29/2023 09:21:32 - INFO - __main__ - Num examples = 1 01/29/2023 09:21:32 - INFO - __main__ - Num Epochs = 300 01/29/2023 09:21:32 - INFO - __main__ - Instantaneous batch size per device = 1 01/29/2023 09:21:32 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1 01/29/2023 09:21:32 - INFO - __main__ - Gradient Accumulation steps = 1 01/29/2023 09:21:32 - INFO - __main__ - Total optimization steps = 300 0%| | 0/300 [00:00<?, ?it/s] Steps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn("None of the inputs have requires_grad=True. Gradients will be None") Steps: 0%| | 1/300 [00:02<10:26, 2.10s/it] Steps: 0%| | 1/300 [00:02<10:26, 2.10s/it, lr=3e-5, step_loss=0.0124] Steps: 1%| | 2/300 [00:02<06:10, 1.24s/it, lr=3e-5, step_loss=0.0124] Steps: 1%| | 2/300 [00:02<06:10, 1.24s/it, lr=3e-5, step_loss=0.194] Steps: 1%| | 3/300 [00:03<04:47, 1.03it/s, lr=3e-5, step_loss=0.194] Steps: 1%| | 3/300 [00:03<04:47, 1.03it/s, lr=3e-5, step_loss=0.125] Steps: 1%|▏ | 4/300 [00:04<04:05, 1.20it/s, lr=3e-5, step_loss=0.125] Steps: 1%|▏ | 4/300 [00:04<04:05, 1.20it/s, lr=3e-5, step_loss=0.0276] Steps: 2%|▏ | 5/300 [00:04<03:43, 1.32it/s, lr=3e-5, step_loss=0.0276] Steps: 2%|▏ | 5/300 [00:04<03:43, 1.32it/s, lr=3e-5, step_loss=0.0411] Steps: 2%|▏ | 6/300 [00:05<03:26, 1.42it/s, lr=3e-5, step_loss=0.0411] Steps: 2%|▏ | 6/300 [00:05<03:26, 1.42it/s, lr=3e-5, step_loss=0.122] Steps: 2%|▏ | 7/300 [00:05<03:19, 1.47it/s, lr=3e-5, step_loss=0.122] Steps: 2%|▏ | 7/300 [00:05<03:19, 1.47it/s, lr=3e-5, step_loss=0.314] Steps: 3%|▎ | 8/300 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/outputs/samples/sample-300.gif Steps: 100%|██████████| 300/300 [04:07<00:00, 1.69it/s, lr=3e-5, step_loss=0.0493]{'prediction_type'} was not found in config. Values will be initialized to default values. Configuration saved in /outputs/model_index.json Configuration saved in /outputs/vae/config.json Model weights saved in /outputs/vae/diffusion_pytorch_model.bin Configuration saved in /outputs/unet/config.json Model weights saved in /outputs/unet/diffusion_pytorch_model.bin Configuration saved in /outputs/scheduler/scheduler_config.json Steps: 100%|██████████| 300/300 [04:14<00:00, 1.18it/s, lr=3e-5, step_loss=0.0493] total 32 -rw-r--r-- 1 root root 942 Jan 29 09:21 config.yaml -rw-r--r-- 1 root root 373 Jan 29 09:25 model_index.json drwxr-xr-x 5 root root 4096 Jan 29 09:25 samples drwxr-xr-x 2 root root 4096 Jan 29 09:25 scheduler drwxr-xr-x 2 root root 4096 Jan 29 09:25 text_encoder drwxr-xr-x 2 root root 4096 Jan 29 09:25 tokenizer drwxr-xr-x 2 root root 4096 Jan 29 09:25 unet drwxr-xr-x 2 root root 4096 Jan 29 09:25 vae
Prediction
pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25Input
- steps
- 300
- video
- width
- 512
- height
- 512
- length
- 5
- source_prompt
- driving mountain highway
- target_prompts
- driving city highway vaporwave
{ "steps": 300, "video": "https://replicate.delivery/pbxt/IDjGY6vFwAUjevKGAzdiF7dQ4pzEU0tDVCdL3uxanOJdbXSY/4seconds_of_scott.mov", "width": 512, "height": 512, "length": 5, "source_prompt": "driving mountain highway", "target_prompts": "driving city highway vaporwave " }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25", { input: { steps: 300, video: "https://replicate.delivery/pbxt/IDjGY6vFwAUjevKGAzdiF7dQ4pzEU0tDVCdL3uxanOJdbXSY/4seconds_of_scott.mov", width: 512, height: 512, length: 5, source_prompt: "driving mountain highway", target_prompts: "driving city highway vaporwave " } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25", input={ "steps": 300, "video": "https://replicate.delivery/pbxt/IDjGY6vFwAUjevKGAzdiF7dQ4pzEU0tDVCdL3uxanOJdbXSY/4seconds_of_scott.mov", "width": 512, "height": 512, "length": 5, "source_prompt": "driving mountain highway", "target_prompts": "driving city highway vaporwave " } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run pollinations/tune-a-video 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": "pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDjGY6vFwAUjevKGAzdiF7dQ4pzEU0tDVCdL3uxanOJdbXSY/4seconds_of_scott.mov", "width": 512, "height": 512, "length": 5, "source_prompt": "driving mountain highway", "target_prompts": "driving city highway vaporwave " } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-01-29T10:32:53.820164Z", "created_at": "2023-01-29T10:24:28.573852Z", "data_removed": false, "error": null, "id": "ienqtefmqffu7kgikxwqr5lduq", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDjGY6vFwAUjevKGAzdiF7dQ4pzEU0tDVCdL3uxanOJdbXSY/4seconds_of_scott.mov", "width": 512, "height": 512, "length": 5, "source_prompt": "driving mountain highway", "target_prompts": "driving city highway vaporwave " }, "logs": "predict\nThe following values were not passed to `accelerate launch` and had defaults used instead:\n`--num_processes` was set to a value of `1`\n`--num_machines` was set to a value of `1`\n`--mixed_precision` was set to a value of `'no'`\n`--dynamo_backend` was set to a value of `'no'`\nTo avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\nA matching Triton is not available, some optimizations will not be enabled.\nError caught was: No module named 'triton'\nrunning with config:\npretrained_model_path: ./checkpoints/stable-diffusion-v1-4\noutput_dir: /outputs\ntrain_data:\nn_sample_frames: 8\nsample_start_idx: 0\nprompt: driving mountain highway\nvideo_path: /tmp/tmptyj6m1894seconds_of_scott.mov\nwidth: 512\nheight: 512\nsample_frame_rate: 1\nvalidation_data:\nnum_inference_steps: 50\nguidance_scale: 7.5\nprompts:\n- 'driving city highway vaporwave '\nvideo_length: 5\nwidth: 512\nheight: 512\nlearning_rate: 3.0e-05\ntrain_batch_size: 1\nmax_train_steps: 300\ncheckpointing_steps: 1000\nvalidation_steps: 100\ntrainable_modules:\n- attn1.to_q\n- attn2.to_q\n- attn_temp\nseed: 33\nmixed_precision: fp16\nuse_8bit_adam: false\ngradient_checkpointing: true\nenable_xformers_memory_efficient_attention: true\n01/29/2023 10:28:12 - INFO - __main__ - Distributed environment: NO\nNum processes: 1\nProcess index: 0\nLocal process index: 0\nDevice: cuda\nMixed precision type: fp16\n{'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values.\n{'norm_num_groups'} was not found in config. Values will be initialized to default values.\n{'upcast_attention', 'resnet_time_scale_shift', 'only_cross_attention', 'class_embed_type', 'num_class_embeds', 'dual_cross_attention', 'use_linear_projection', 'mid_block_type'} was not found in config. Values will be initialized to default values.\n{'prediction_type'} was not found in config. Values will be initialized to default values.\n01/29/2023 10:28:28 - INFO - __main__ - ***** Running training *****\n01/29/2023 10:28:28 - INFO - __main__ - Num examples = 1\n01/29/2023 10:28:28 - INFO - __main__ - Num Epochs = 300\n01/29/2023 10:28:28 - INFO - __main__ - Instantaneous batch size per device = 1\n01/29/2023 10:28:28 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1\n01/29/2023 10:28:28 - INFO - __main__ - Gradient Accumulation steps = 1\n01/29/2023 10:28:28 - INFO - __main__ - Total optimization steps = 300\n 0%| | 0/300 [00:00<?, ?it/s]\nSteps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\nwarnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\nSteps: 0%| | 1/300 [00:02<11:40, 2.34s/it]\nSteps: 0%| | 1/300 [00:02<11:40, 2.34s/it, lr=3e-5, step_loss=0.0134]\nSteps: 1%| | 2/300 [00:03<07:00, 1.41s/it, lr=3e-5, step_loss=0.0134]\nSteps: 1%| | 2/300 [00:03<07:00, 1.41s/it, lr=3e-5, step_loss=0.203] \nSteps: 1%| | 3/300 [00:03<05:29, 1.11s/it, lr=3e-5, step_loss=0.203]\nSteps: 1%| | 3/300 [00:03<05:29, 1.11s/it, lr=3e-5, step_loss=0.132]\nSteps: 1%|▏ | 4/300 [00:04<04:42, 1.05it/s, lr=3e-5, step_loss=0.132]\nSteps: 1%|▏ | 4/300 [00:04<04:42, 1.05it/s, lr=3e-5, step_loss=0.0293]\nSteps: 2%|▏ | 5/300 [00:05<04:19, 1.14it/s, lr=3e-5, step_loss=0.0293]\nSteps: 2%|▏ | 5/300 [00:05<04:19, 1.14it/s, lr=3e-5, step_loss=0.0453]\nSteps: 2%|▏ | 6/300 [00:06<04:04, 1.20it/s, lr=3e-5, step_loss=0.0453]\nSteps: 2%|▏ | 6/300 [00:06<04:04, 1.20it/s, lr=3e-5, step_loss=0.129] \nSteps: 2%|▏ | 7/300 [00:06<03:50, 1.27it/s, lr=3e-5, step_loss=0.129]\nSteps: 2%|▏ | 7/300 [00:06<03:50, 1.27it/s, lr=3e-5, 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Values will be initialized to default values.\nConfiguration saved in /outputs/model_index.json\nConfiguration saved in /outputs/vae/config.json\nModel weights saved in /outputs/vae/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/unet/config.json\nModel weights saved in /outputs/unet/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/scheduler/scheduler_config.json\nSteps: 100%|██████████| 300/300 [04:22<00:00, 1.14it/s, lr=3e-5, step_loss=0.0525]\ntotal 32\n-rw-r--r-- 1 root root 947 Jan 29 10:28 config.yaml\n-rw-r--r-- 1 root root 373 Jan 29 10:32 model_index.json\ndrwxr-xr-x 5 root root 4096 Jan 29 10:32 samples\ndrwxr-xr-x 2 root root 4096 Jan 29 10:32 scheduler\ndrwxr-xr-x 2 root root 4096 Jan 29 10:32 text_encoder\ndrwxr-xr-x 2 root root 4096 Jan 29 10:32 tokenizer\ndrwxr-xr-x 2 root root 4096 Jan 29 10:32 unet\ndrwxr-xr-x 2 root root 4096 Jan 29 10:32 vae", "metrics": { "predict_time": 285.901161, "total_time": 505.246312 }, "output": "https://replicate.delivery/pbxt/sOMrGKBd6dbVNRaRGfheLHhgYDvZ0dqpBuJRF5yKopUU1fxgA/sample-300.gif", "started_at": "2023-01-29T10:28:07.919003Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ienqtefmqffu7kgikxwqr5lduq", "cancel": "https://api.replicate.com/v1/predictions/ienqtefmqffu7kgikxwqr5lduq/cancel" }, "version": "0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25" }
Generated inpredict The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `1` `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. A matching Triton is not available, some optimizations will not be enabled. Error caught was: No module named 'triton' running with config: pretrained_model_path: ./checkpoints/stable-diffusion-v1-4 output_dir: /outputs train_data: n_sample_frames: 8 sample_start_idx: 0 prompt: driving mountain highway video_path: /tmp/tmptyj6m1894seconds_of_scott.mov width: 512 height: 512 sample_frame_rate: 1 validation_data: num_inference_steps: 50 guidance_scale: 7.5 prompts: - 'driving city highway vaporwave ' video_length: 5 width: 512 height: 512 learning_rate: 3.0e-05 train_batch_size: 1 max_train_steps: 300 checkpointing_steps: 1000 validation_steps: 100 trainable_modules: - attn1.to_q - attn2.to_q - attn_temp seed: 33 mixed_precision: fp16 use_8bit_adam: false gradient_checkpointing: true enable_xformers_memory_efficient_attention: true 01/29/2023 10:28:12 - INFO - __main__ - Distributed environment: NO Num processes: 1 Process index: 0 Local process index: 0 Device: cuda Mixed precision type: fp16 {'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values. {'norm_num_groups'} was not found in config. Values will be initialized to default values. {'upcast_attention', 'resnet_time_scale_shift', 'only_cross_attention', 'class_embed_type', 'num_class_embeds', 'dual_cross_attention', 'use_linear_projection', 'mid_block_type'} was not found in config. Values will be initialized to default values. {'prediction_type'} was not found in config. Values will be initialized to default values. 01/29/2023 10:28:28 - INFO - __main__ - ***** Running training ***** 01/29/2023 10:28:28 - INFO - __main__ - Num examples = 1 01/29/2023 10:28:28 - INFO - __main__ - Num Epochs = 300 01/29/2023 10:28:28 - INFO - __main__ - Instantaneous batch size per device = 1 01/29/2023 10:28:28 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1 01/29/2023 10:28:28 - INFO - __main__ - Gradient Accumulation steps = 1 01/29/2023 10:28:28 - INFO - __main__ - Total optimization steps = 300 0%| | 0/300 [00:00<?, ?it/s] Steps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn("None of the inputs have requires_grad=True. Gradients will be None") Steps: 0%| | 1/300 [00:02<11:40, 2.34s/it] Steps: 0%| | 1/300 [00:02<11:40, 2.34s/it, lr=3e-5, step_loss=0.0134] Steps: 1%| | 2/300 [00:03<07:00, 1.41s/it, lr=3e-5, step_loss=0.0134] Steps: 1%| | 2/300 [00:03<07:00, 1.41s/it, lr=3e-5, step_loss=0.203] Steps: 1%| | 3/300 [00:03<05:29, 1.11s/it, lr=3e-5, step_loss=0.203] Steps: 1%| | 3/300 [00:03<05:29, 1.11s/it, lr=3e-5, step_loss=0.132] Steps: 1%|▏ | 4/300 [00:04<04:42, 1.05it/s, lr=3e-5, step_loss=0.132] Steps: 1%|▏ | 4/300 [00:04<04:42, 1.05it/s, lr=3e-5, step_loss=0.0293] Steps: 2%|▏ | 5/300 [00:05<04:19, 1.14it/s, lr=3e-5, step_loss=0.0293] Steps: 2%|▏ | 5/300 [00:05<04:19, 1.14it/s, lr=3e-5, step_loss=0.0453] Steps: 2%|▏ | 6/300 [00:06<04:04, 1.20it/s, lr=3e-5, step_loss=0.0453] Steps: 2%|▏ | 6/300 [00:06<04:04, 1.20it/s, lr=3e-5, step_loss=0.129] Steps: 2%|▏ | 7/300 [00:06<03:50, 1.27it/s, lr=3e-5, step_loss=0.129] Steps: 2%|▏ | 7/300 [00:06<03:50, 1.27it/s, lr=3e-5, step_loss=0.323] Steps: 3%|▎ | 8/300 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Values will be initialized to default values. Configuration saved in /outputs/model_index.json Configuration saved in /outputs/vae/config.json Model weights saved in /outputs/vae/diffusion_pytorch_model.bin Configuration saved in /outputs/unet/config.json Model weights saved in /outputs/unet/diffusion_pytorch_model.bin Configuration saved in /outputs/scheduler/scheduler_config.json Steps: 100%|██████████| 300/300 [04:22<00:00, 1.14it/s, lr=3e-5, step_loss=0.0525] total 32 -rw-r--r-- 1 root root 947 Jan 29 10:28 config.yaml -rw-r--r-- 1 root root 373 Jan 29 10:32 model_index.json drwxr-xr-x 5 root root 4096 Jan 29 10:32 samples drwxr-xr-x 2 root root 4096 Jan 29 10:32 scheduler drwxr-xr-x 2 root root 4096 Jan 29 10:32 text_encoder drwxr-xr-x 2 root root 4096 Jan 29 10:32 tokenizer drwxr-xr-x 2 root root 4096 Jan 29 10:32 unet drwxr-xr-x 2 root root 4096 Jan 29 10:32 vae
Prediction
pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25IDr4bjwykpyzbwhdmnidqzun322yStatusSucceededSourceWebHardware–Total durationCreatedInput
- steps
- 300
- video
- width
- 512
- height
- 512
- length
- 10
- source_prompt
- a man surfing
- target_prompts
- a neon 80s gender-neutral synthwave star surfing a cat surfing on clouds
{ "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 10, "source_prompt": "a man surfing", "target_prompts": "a neon 80s gender-neutral synthwave star surfing\na cat surfing on clouds" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25", { input: { steps: 300, video: "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", width: 512, height: 512, length: 10, source_prompt: "a man surfing", target_prompts: "a neon 80s gender-neutral synthwave star surfing\na cat surfing on clouds" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25", input={ "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 10, "source_prompt": "a man surfing", "target_prompts": "a neon 80s gender-neutral synthwave star surfing\na cat surfing on clouds" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run pollinations/tune-a-video 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": "pollinations/tune-a-video:0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 10, "source_prompt": "a man surfing", "target_prompts": "a neon 80s gender-neutral synthwave star surfing\\na cat surfing on clouds" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-01-29T13:39:03.226344Z", "created_at": "2023-01-29T13:33:41.466594Z", "data_removed": false, "error": null, "id": "r4bjwykpyzbwhdmnidqzun322y", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 10, "source_prompt": "a man surfing", "target_prompts": "a neon 80s gender-neutral synthwave star surfing\na cat surfing on clouds" }, "logs": "predict\nThe following values were not passed to `accelerate launch` and had defaults used instead:\n`--num_processes` was set to a value of `1`\n`--num_machines` was set to a value of `1`\n`--mixed_precision` was set to a value of `'no'`\n`--dynamo_backend` was set to a value of `'no'`\nTo avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\nA matching Triton is not available, some optimizations will not be enabled.\nError caught was: No module named 'triton'\nrunning with config:\npretrained_model_path: ./checkpoints/stable-diffusion-v1-4\noutput_dir: /outputs\ntrain_data:\nn_sample_frames: 8\nsample_start_idx: 0\nprompt: a man surfing\nvideo_path: /tmp/tmpol5tcz0lman%20surfing.mp4\nwidth: 512\nheight: 512\nsample_frame_rate: 1\nvalidation_data:\nnum_inference_steps: 50\nguidance_scale: 7.5\nprompts:\n- a neon 80s gender-neutral synthwave star surfing\n- a cat surfing on clouds\nvideo_length: 10\nwidth: 512\nheight: 512\nlearning_rate: 3.0e-05\ntrain_batch_size: 1\nmax_train_steps: 300\ncheckpointing_steps: 1000\nvalidation_steps: 100\ntrainable_modules:\n- attn1.to_q\n- attn2.to_q\n- attn_temp\nseed: 33\nmixed_precision: fp16\nuse_8bit_adam: false\ngradient_checkpointing: true\nenable_xformers_memory_efficient_attention: true\n01/29/2023 13:33:46 - INFO - __main__ - Distributed environment: NO\nNum processes: 1\nProcess index: 0\nLocal process index: 0\nDevice: cuda\nMixed precision type: fp16\n{'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values.\n{'norm_num_groups'} was not found in config. Values will be initialized to default values.\n{'upcast_attention', 'resnet_time_scale_shift', 'num_class_embeds', 'class_embed_type', 'only_cross_attention', 'mid_block_type', 'dual_cross_attention', 'use_linear_projection'} was not found in config. Values will be initialized to default values.\n{'prediction_type'} was not found in config. Values will be initialized to default values.\n01/29/2023 13:34:02 - INFO - __main__ - ***** Running training *****\n01/29/2023 13:34:02 - INFO - __main__ - Num examples = 1\n01/29/2023 13:34:02 - INFO - __main__ - Num Epochs = 300\n01/29/2023 13:34:02 - INFO - __main__ - Instantaneous batch size per device = 1\n01/29/2023 13:34:02 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1\n01/29/2023 13:34:02 - INFO - __main__ - Gradient Accumulation steps = 1\n01/29/2023 13:34:02 - INFO - __main__ - Total optimization steps = 300\n 0%| | 0/300 [00:00<?, ?it/s]\nSteps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\nwarnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\nSteps: 0%| | 1/300 [00:02<10:14, 2.06s/it]\nSteps: 0%| | 1/300 [00:02<10:14, 2.06s/it, lr=3e-5, step_loss=0.0124]\nSteps: 1%| | 2/300 [00:02<05:59, 1.21s/it, lr=3e-5, step_loss=0.0124]\nSteps: 1%| | 2/300 [00:02<05:59, 1.21s/it, lr=3e-5, step_loss=0.194] \nSteps: 1%| | 3/300 [00:03<04:34, 1.08it/s, lr=3e-5, step_loss=0.194]\nSteps: 1%| | 3/300 [00:03<04:34, 1.08it/s, lr=3e-5, step_loss=0.125]\nSteps: 1%|▏ | 4/300 [00:03<03:54, 1.26it/s, lr=3e-5, step_loss=0.125]\nSteps: 1%|▏ | 4/300 [00:03<03:54, 1.26it/s, lr=3e-5, step_loss=0.0276]\nSteps: 2%|▏ | 5/300 [00:04<03:33, 1.38it/s, lr=3e-5, step_loss=0.0276]\nSteps: 2%|▏ | 5/300 [00:04<03:33, 1.38it/s, lr=3e-5, step_loss=0.0411]\nSteps: 2%|▏ | 6/300 [00:05<03:20, 1.46it/s, lr=3e-5, step_loss=0.0411]\nSteps: 2%|▏ | 6/300 [00:05<03:20, 1.46it/s, lr=3e-5, step_loss=0.122] \nSteps: 2%|▏ | 7/300 [00:05<03:14, 1.50it/s, lr=3e-5, step_loss=0.122]\nSteps: 2%|▏ | 7/300 [00:05<03:14, 1.50it/s, lr=3e-5, 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lr=3e-5, step_loss=0.0493]{'prediction_type'} was not found in config. Values will be initialized to default values.\nConfiguration saved in /outputs/model_index.json\nConfiguration saved in /outputs/vae/config.json\nModel weights saved in /outputs/vae/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/unet/config.json\nModel weights saved in /outputs/unet/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/scheduler/scheduler_config.json\nSteps: 100%|██████████| 300/300 [04:57<00:00, 1.01it/s, lr=3e-5, step_loss=0.0493]\ntotal 32\n-rw-r--r-- 1 root root 976 Jan 29 13:33 config.yaml\n-rw-r--r-- 1 root root 373 Jan 29 13:38 model_index.json\ndrwxr-xr-x 5 root root 4096 Jan 29 13:38 samples\ndrwxr-xr-x 2 root root 4096 Jan 29 13:39 scheduler\ndrwxr-xr-x 2 root root 4096 Jan 29 13:38 text_encoder\ndrwxr-xr-x 2 root root 4096 Jan 29 13:38 tokenizer\ndrwxr-xr-x 2 root root 4096 Jan 29 13:38 unet\ndrwxr-xr-x 2 root root 4096 Jan 29 13:38 vae", "metrics": { "predict_time": 321.708507, "total_time": 321.75975 }, "output": "https://replicate.delivery/pbxt/OTBWSitpe72oHyGrNVBMETekcPTkFZjYBcCJRL6JrTb2jCZQA/sample-300.gif", "started_at": "2023-01-29T13:33:41.517837Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/r4bjwykpyzbwhdmnidqzun322y", "cancel": "https://api.replicate.com/v1/predictions/r4bjwykpyzbwhdmnidqzun322y/cancel" }, "version": "0c355f5c186696ccf0b111456fadc97fb5dae664d66e65229a43e5948364fc25" }
Generated inpredict The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `1` `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. A matching Triton is not available, some optimizations will not be enabled. Error caught was: No module named 'triton' running with config: pretrained_model_path: ./checkpoints/stable-diffusion-v1-4 output_dir: /outputs train_data: n_sample_frames: 8 sample_start_idx: 0 prompt: a man surfing video_path: /tmp/tmpol5tcz0lman%20surfing.mp4 width: 512 height: 512 sample_frame_rate: 1 validation_data: num_inference_steps: 50 guidance_scale: 7.5 prompts: - a neon 80s gender-neutral synthwave star surfing - a cat surfing on clouds video_length: 10 width: 512 height: 512 learning_rate: 3.0e-05 train_batch_size: 1 max_train_steps: 300 checkpointing_steps: 1000 validation_steps: 100 trainable_modules: - attn1.to_q - attn2.to_q - attn_temp seed: 33 mixed_precision: fp16 use_8bit_adam: false gradient_checkpointing: true enable_xformers_memory_efficient_attention: true 01/29/2023 13:33:46 - INFO - __main__ - Distributed environment: NO Num processes: 1 Process index: 0 Local process index: 0 Device: cuda Mixed precision type: fp16 {'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values. {'norm_num_groups'} was not found in config. Values will be initialized to default values. {'upcast_attention', 'resnet_time_scale_shift', 'num_class_embeds', 'class_embed_type', 'only_cross_attention', 'mid_block_type', 'dual_cross_attention', 'use_linear_projection'} was not found in config. Values will be initialized to default values. {'prediction_type'} was not found in config. Values will be initialized to default values. 01/29/2023 13:34:02 - INFO - __main__ - ***** Running training ***** 01/29/2023 13:34:02 - INFO - __main__ - Num examples = 1 01/29/2023 13:34:02 - INFO - __main__ - Num Epochs = 300 01/29/2023 13:34:02 - INFO - __main__ - Instantaneous batch size per device = 1 01/29/2023 13:34:02 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1 01/29/2023 13:34:02 - INFO - __main__ - Gradient Accumulation steps = 1 01/29/2023 13:34:02 - INFO - __main__ - Total optimization steps = 300 0%| | 0/300 [00:00<?, ?it/s] Steps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn("None of the inputs have requires_grad=True. Gradients will be None") Steps: 0%| | 1/300 [00:02<10:14, 2.06s/it] Steps: 0%| | 1/300 [00:02<10:14, 2.06s/it, lr=3e-5, step_loss=0.0124] Steps: 1%| | 2/300 [00:02<05:59, 1.21s/it, lr=3e-5, step_loss=0.0124] Steps: 1%| | 2/300 [00:02<05:59, 1.21s/it, lr=3e-5, step_loss=0.194] Steps: 1%| | 3/300 [00:03<04:34, 1.08it/s, lr=3e-5, step_loss=0.194] Steps: 1%| | 3/300 [00:03<04:34, 1.08it/s, lr=3e-5, step_loss=0.125] Steps: 1%|▏ | 4/300 [00:03<03:54, 1.26it/s, lr=3e-5, step_loss=0.125] Steps: 1%|▏ | 4/300 [00:03<03:54, 1.26it/s, lr=3e-5, step_loss=0.0276] Steps: 2%|▏ | 5/300 [00:04<03:33, 1.38it/s, lr=3e-5, step_loss=0.0276] Steps: 2%|▏ | 5/300 [00:04<03:33, 1.38it/s, lr=3e-5, step_loss=0.0411] Steps: 2%|▏ | 6/300 [00:05<03:20, 1.46it/s, lr=3e-5, step_loss=0.0411] Steps: 2%|▏ | 6/300 [00:05<03:20, 1.46it/s, lr=3e-5, step_loss=0.122] Steps: 2%|▏ | 7/300 [00:05<03:14, 1.50it/s, lr=3e-5, step_loss=0.122] Steps: 2%|▏ | 7/300 [00:05<03:14, 1.50it/s, lr=3e-5, step_loss=0.314] Steps: 3%|▎ | 8/300 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/outputs/samples/sample-300.gif Steps: 100%|██████████| 300/300 [04:51<00:00, 1.69it/s, lr=3e-5, step_loss=0.0493]{'prediction_type'} was not found in config. Values will be initialized to default values. Configuration saved in /outputs/model_index.json Configuration saved in /outputs/vae/config.json Model weights saved in /outputs/vae/diffusion_pytorch_model.bin Configuration saved in /outputs/unet/config.json Model weights saved in /outputs/unet/diffusion_pytorch_model.bin Configuration saved in /outputs/scheduler/scheduler_config.json Steps: 100%|██████████| 300/300 [04:57<00:00, 1.01it/s, lr=3e-5, step_loss=0.0493] total 32 -rw-r--r-- 1 root root 976 Jan 29 13:33 config.yaml -rw-r--r-- 1 root root 373 Jan 29 13:38 model_index.json drwxr-xr-x 5 root root 4096 Jan 29 13:38 samples drwxr-xr-x 2 root root 4096 Jan 29 13:39 scheduler drwxr-xr-x 2 root root 4096 Jan 29 13:38 text_encoder drwxr-xr-x 2 root root 4096 Jan 29 13:38 tokenizer drwxr-xr-x 2 root root 4096 Jan 29 13:38 unet drwxr-xr-x 2 root root 4096 Jan 29 13:38 vae
Prediction
pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1IDogi5hs5offf6bbsaw3ucplr5jaStatusSucceededSourceWebHardware–Total durationCreatedInput
- steps
- 300
- video
- width
- 512
- height
- 512
- length
- 5
- source_prompt
- a man surfing
- target_prompts
- a dominatrix surfing a voxel triangle surfing
- sample_frame_rate
- 10
{ "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing", "target_prompts": "a dominatrix surfing\na voxel triangle surfing", "sample_frame_rate": 10 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1", { input: { steps: 300, video: "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", width: 512, height: 512, length: 5, source_prompt: "a man surfing", target_prompts: "a dominatrix surfing\na voxel triangle surfing", sample_frame_rate: 10 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1", input={ "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing", "target_prompts": "a dominatrix surfing\na voxel triangle surfing", "sample_frame_rate": 10 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run pollinations/tune-a-video 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": "pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing", "target_prompts": "a dominatrix surfing\\na voxel triangle surfing", "sample_frame_rate": 10 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-01-29T14:03:07.732612Z", "created_at": "2023-01-29T13:56:53.436321Z", "data_removed": false, "error": null, "id": "ogi5hs5offf6bbsaw3ucplr5ja", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing", "target_prompts": "a dominatrix surfing\na voxel triangle surfing", "sample_frame_rate": 10 }, "logs": "predict\nThe following values were not passed to `accelerate launch` and had defaults used instead:\n`--num_processes` was set to a value of `1`\n`--num_machines` was set to a value of `1`\n`--mixed_precision` was set to a value of `'no'`\n`--dynamo_backend` was set to a value of `'no'`\nTo avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\nA matching Triton is not available, some optimizations will not be enabled.\nError caught was: No module named 'triton'\nrunning with config:\npretrained_model_path: ./checkpoints/stable-diffusion-v1-4\noutput_dir: /outputs\ntrain_data:\nn_sample_frames: 8\nsample_start_idx: 0\nprompt: a man surfing\nvideo_path: /tmp/tmphs5vcpr4man%20surfing.mp4\nwidth: 512\nheight: 512\nsample_frame_rate: 10\nvalidation_data:\nnum_inference_steps: 50\nguidance_scale: 7.5\nprompts:\n- a dominatrix surfing\n- a voxel triangle surfing\nvideo_length: 5\nwidth: 512\nheight: 512\nlearning_rate: 3.0e-05\ntrain_batch_size: 1\nmax_train_steps: 300\ncheckpointing_steps: 1000\nvalidation_steps: 100\ntrainable_modules:\n- attn1.to_q\n- attn2.to_q\n- attn_temp\nseed: 33\nmixed_precision: fp16\nuse_8bit_adam: false\ngradient_checkpointing: true\nenable_xformers_memory_efficient_attention: true\n01/29/2023 13:59:42 - INFO - __main__ - Distributed environment: NO\nNum processes: 1\nProcess index: 0\nLocal process index: 0\nDevice: cuda\nMixed precision type: fp16\n{'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values.\n{'norm_num_groups'} was not found in config. Values will be initialized to default values.\n{'mid_block_type', 'resnet_time_scale_shift', 'dual_cross_attention', 'num_class_embeds', 'use_linear_projection', 'class_embed_type', 'upcast_attention', 'only_cross_attention'} was not found in config. Values will be initialized to default values.\n{'prediction_type'} was not found in config. Values will be initialized to default values.\n01/29/2023 13:59:58 - INFO - __main__ - ***** Running training *****\n01/29/2023 13:59:58 - INFO - __main__ - Num examples = 1\n01/29/2023 13:59:58 - INFO - __main__ - Num Epochs = 300\n01/29/2023 13:59:58 - INFO - __main__ - Instantaneous batch size per device = 1\n01/29/2023 13:59:58 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1\n01/29/2023 13:59:58 - INFO - __main__ - Gradient Accumulation steps = 1\n01/29/2023 13:59:58 - INFO - __main__ - Total optimization steps = 300\n0%| | 0/300 [00:00<?, ?it/s]\nSteps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\nwarnings.warn(\"None of the inputs have requires_grad=True. 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lr=3e-5, step_loss=0.0533]{'prediction_type'} was not found in config. Values will be initialized to default values.\nConfiguration saved in /outputs/model_index.json\nConfiguration saved in /outputs/vae/config.json\nModel weights saved in /outputs/vae/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/unet/config.json\nModel weights saved in /outputs/unet/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/scheduler/scheduler_config.json\nSteps: 100%|██████████| 300/300 [03:06<00:00, 1.61it/s, lr=3e-5, step_loss=0.0533]\ntotal 32\n-rw-r--r-- 1 root root 949 Jan 29 13:59 config.yaml\n-rw-r--r-- 1 root root 373 Jan 29 14:02 model_index.json\ndrwxr-xr-x 5 root root 4096 Jan 29 14:02 samples\ndrwxr-xr-x 2 root root 4096 Jan 29 14:03 scheduler\ndrwxr-xr-x 2 root root 4096 Jan 29 14:02 text_encoder\ndrwxr-xr-x 2 root root 4096 Jan 29 14:02 tokenizer\ndrwxr-xr-x 2 root root 4096 Jan 29 14:02 unet\ndrwxr-xr-x 2 root root 4096 Jan 29 14:02 vae", "metrics": { "predict_time": 209.416022, "total_time": 374.296291 }, "output": "https://replicate.delivery/pbxt/oKN40WVImUKYDVEtFyn8BItgZm2epRgg7CfYQWxNUrsa6CZQA/sample-300.gif", "started_at": "2023-01-29T13:59:38.316590Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ogi5hs5offf6bbsaw3ucplr5ja", "cancel": "https://api.replicate.com/v1/predictions/ogi5hs5offf6bbsaw3ucplr5ja/cancel" }, "version": "558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1" }
Generated inpredict The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `1` `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. A matching Triton is not available, some optimizations will not be enabled. Error caught was: No module named 'triton' running with config: pretrained_model_path: ./checkpoints/stable-diffusion-v1-4 output_dir: /outputs train_data: n_sample_frames: 8 sample_start_idx: 0 prompt: a man surfing video_path: /tmp/tmphs5vcpr4man%20surfing.mp4 width: 512 height: 512 sample_frame_rate: 10 validation_data: num_inference_steps: 50 guidance_scale: 7.5 prompts: - a dominatrix surfing - a voxel triangle surfing video_length: 5 width: 512 height: 512 learning_rate: 3.0e-05 train_batch_size: 1 max_train_steps: 300 checkpointing_steps: 1000 validation_steps: 100 trainable_modules: - attn1.to_q - attn2.to_q - attn_temp seed: 33 mixed_precision: fp16 use_8bit_adam: false gradient_checkpointing: true enable_xformers_memory_efficient_attention: true 01/29/2023 13:59:42 - INFO - __main__ - Distributed environment: NO Num processes: 1 Process index: 0 Local process index: 0 Device: cuda Mixed precision type: fp16 {'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values. {'norm_num_groups'} was not found in config. Values will be initialized to default values. {'mid_block_type', 'resnet_time_scale_shift', 'dual_cross_attention', 'num_class_embeds', 'use_linear_projection', 'class_embed_type', 'upcast_attention', 'only_cross_attention'} was not found in config. Values will be initialized to default values. {'prediction_type'} was not found in config. Values will be initialized to default values. 01/29/2023 13:59:58 - INFO - __main__ - ***** Running training ***** 01/29/2023 13:59:58 - INFO - __main__ - Num examples = 1 01/29/2023 13:59:58 - INFO - __main__ - Num Epochs = 300 01/29/2023 13:59:58 - INFO - __main__ - Instantaneous batch size per device = 1 01/29/2023 13:59:58 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1 01/29/2023 13:59:58 - INFO - __main__ - Gradient Accumulation steps = 1 01/29/2023 13:59:58 - INFO - __main__ - Total optimization steps = 300 0%| | 0/300 [00:00<?, ?it/s] Steps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn("None of the inputs have requires_grad=True. Gradients will be None") Steps: 0%| | 1/300 [00:02<10:36, 2.13s/it] Steps: 0%| | 1/300 [00:02<10:36, 2.13s/it, lr=3e-5, step_loss=0.0125] Steps: 1%| | 2/300 [00:02<05:36, 1.13s/it, lr=3e-5, step_loss=0.0125] Steps: 1%| | 2/300 [00:02<05:36, 1.13s/it, lr=3e-5, step_loss=0.2] Steps: 1%| | 3/300 [00:02<03:52, 1.28it/s, lr=3e-5, step_loss=0.2] Steps: 1%| | 3/300 [00:02<03:52, 1.28it/s, lr=3e-5, step_loss=0.131] Steps: 1%|▏ | 4/300 [00:03<03:03, 1.61it/s, lr=3e-5, step_loss=0.131] Steps: 1%|▏ | 4/300 [00:03<03:03, 1.61it/s, lr=3e-5, step_loss=0.0287] Steps: 2%|▏ | 5/300 [00:03<02:41, 1.83it/s, lr=3e-5, step_loss=0.0287] Steps: 2%|▏ | 5/300 [00:03<02:41, 1.83it/s, lr=3e-5, step_loss=0.0431] Steps: 2%|▏ | 6/300 [00:04<02:25, 2.02it/s, lr=3e-5, step_loss=0.0431] Steps: 2%|▏ | 6/300 [00:04<02:25, 2.02it/s, lr=3e-5, step_loss=0.126] Steps: 2%|▏ | 7/300 [00:04<02:15, 2.16it/s, lr=3e-5, step_loss=0.126] Steps: 2%|▏ | 7/300 [00:04<02:15, 2.16it/s, lr=3e-5, step_loss=0.332] Steps: 3%|▎ | 8/300 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samples to /outputs/samples/sample-300.gif Steps: 100%|██████████| 300/300 [02:58<00:00, 2.76it/s, lr=3e-5, step_loss=0.0533]{'prediction_type'} was not found in config. Values will be initialized to default values. Configuration saved in /outputs/model_index.json Configuration saved in /outputs/vae/config.json Model weights saved in /outputs/vae/diffusion_pytorch_model.bin Configuration saved in /outputs/unet/config.json Model weights saved in /outputs/unet/diffusion_pytorch_model.bin Configuration saved in /outputs/scheduler/scheduler_config.json Steps: 100%|██████████| 300/300 [03:06<00:00, 1.61it/s, lr=3e-5, step_loss=0.0533] total 32 -rw-r--r-- 1 root root 949 Jan 29 13:59 config.yaml -rw-r--r-- 1 root root 373 Jan 29 14:02 model_index.json drwxr-xr-x 5 root root 4096 Jan 29 14:02 samples drwxr-xr-x 2 root root 4096 Jan 29 14:03 scheduler drwxr-xr-x 2 root root 4096 Jan 29 14:02 text_encoder drwxr-xr-x 2 root root 4096 Jan 29 14:02 tokenizer drwxr-xr-x 2 root root 4096 Jan 29 14:02 unet drwxr-xr-x 2 root root 4096 Jan 29 14:02 vae
Prediction
pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1IDy7sy6fd4gjbzrisbfeieetvvtuStatusSucceededSourceWebHardware–Total durationCreatedInput
- steps
- 300
- video
- width
- 512
- height
- 512
- length
- 5
- source_prompt
- a man surfing on water
- target_prompts
- a robot surfing on the moon
- sample_frame_rate
- 3
{ "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing on water", "target_prompts": "a robot surfing on the moon", "sample_frame_rate": 3 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1", { input: { steps: 300, video: "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", width: 512, height: 512, length: 5, source_prompt: "a man surfing on water", target_prompts: "a robot surfing on the moon", sample_frame_rate: 3 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1", input={ "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing on water", "target_prompts": "a robot surfing on the moon", "sample_frame_rate": 3 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run pollinations/tune-a-video 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": "pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing on water", "target_prompts": "a robot surfing on the moon", "sample_frame_rate": 3 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-01-29T15:04:21.536039Z", "created_at": "2023-01-29T14:56:33.594951Z", "data_removed": false, "error": null, "id": "y7sy6fd4gjbzrisbfeieetvvtu", "input": { "steps": 300, "video": "https://replicate.delivery/pbxt/IDiFVPWBr2LV9rZs1RP0GZGSz0SKSRidowzRZSSc8il25wmE/man%20surfing.mp4", "width": 512, "height": 512, "length": 5, "source_prompt": "a man surfing on water", "target_prompts": "a robot surfing on the moon", "sample_frame_rate": 3 }, "logs": "predict\nThe following values were not passed to `accelerate launch` and had defaults used instead:\n`--num_processes` was set to a value of `1`\n`--num_machines` was set to a value of `1`\n`--mixed_precision` was set to a value of `'no'`\n`--dynamo_backend` was set to a value of `'no'`\nTo avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\nA matching Triton is not available, some optimizations will not be enabled.\nError caught was: No module named 'triton'\nrunning with config:\npretrained_model_path: ./checkpoints/stable-diffusion-v1-4\noutput_dir: /outputs\ntrain_data:\nn_sample_frames: 8\nsample_start_idx: 0\nprompt: a man surfing on water\nvideo_path: /tmp/tmpczjkm6zyman%20surfing.mp4\nwidth: 512\nheight: 512\nsample_frame_rate: 3\nvalidation_data:\nnum_inference_steps: 50\nguidance_scale: 7.5\nprompts:\n- a robot surfing on the moon\nvideo_length: 5\nwidth: 512\nheight: 512\nlearning_rate: 3.0e-05\ntrain_batch_size: 1\nmax_train_steps: 300\ncheckpointing_steps: 1000\nvalidation_steps: 100\ntrainable_modules:\n- attn1.to_q\n- attn2.to_q\n- attn_temp\nseed: 33\nmixed_precision: fp16\nuse_8bit_adam: false\ngradient_checkpointing: true\nenable_xformers_memory_efficient_attention: true\n01/29/2023 15:00:37 - INFO - __main__ - Distributed environment: NO\nNum processes: 1\nProcess index: 0\nLocal process index: 0\nDevice: cuda\nMixed precision type: fp16\n{'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values.\n{'norm_num_groups'} was not found in config. Values will be initialized to default values.\n{'use_linear_projection', 'num_class_embeds', 'resnet_time_scale_shift', 'only_cross_attention', 'dual_cross_attention', 'mid_block_type', 'upcast_attention', 'class_embed_type'} was not found in config. Values will be initialized to default values.\n{'prediction_type'} was not found in config. Values will be initialized to default values.\n01/29/2023 15:00:54 - INFO - __main__ - ***** Running training *****\n01/29/2023 15:00:54 - INFO - __main__ - Num examples = 1\n01/29/2023 15:00:54 - INFO - __main__ - Num Epochs = 300\n01/29/2023 15:00:54 - INFO - __main__ - Instantaneous batch size per device = 1\n01/29/2023 15:00:54 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1\n01/29/2023 15:00:54 - INFO - __main__ - Gradient Accumulation steps = 1\n01/29/2023 15:00:54 - INFO - __main__ - Total optimization steps = 300\n 0%| | 0/300 [00:00<?, ?it/s]\nSteps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\nwarnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\nSteps: 0%| | 1/300 [00:02<11:00, 2.21s/it]\nSteps: 0%| | 1/300 [00:02<11:00, 2.21s/it, lr=3e-5, step_loss=0.0126]\nSteps: 1%| | 2/300 [00:02<06:13, 1.25s/it, lr=3e-5, step_loss=0.0126]\nSteps: 1%| | 2/300 [00:02<06:13, 1.25s/it, lr=3e-5, step_loss=0.198] \nSteps: 1%| | 3/300 [00:03<04:36, 1.07it/s, lr=3e-5, step_loss=0.198]\nSteps: 1%| | 3/300 [00:03<04:36, 1.07it/s, lr=3e-5, step_loss=0.127]\nSteps: 1%|▏ | 4/300 [00:03<03:54, 1.26it/s, lr=3e-5, step_loss=0.127]\nSteps: 1%|▏ | 4/300 [00:03<03:54, 1.26it/s, lr=3e-5, step_loss=0.028]\nSteps: 2%|▏ | 5/300 [00:04<03:28, 1.42it/s, lr=3e-5, step_loss=0.028]\nSteps: 2%|▏ | 5/300 [00:04<03:28, 1.42it/s, lr=3e-5, step_loss=0.0419]\nSteps: 2%|▏ | 6/300 [00:05<03:15, 1.50it/s, lr=3e-5, step_loss=0.0419]\nSteps: 2%|▏ | 6/300 [00:05<03:15, 1.50it/s, lr=3e-5, step_loss=0.124] \nSteps: 2%|▏ | 7/300 [00:05<03:05, 1.58it/s, lr=3e-5, step_loss=0.124]\nSteps: 2%|▏ | 7/300 [00:05<03:05, 1.58it/s, lr=3e-5, 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Values will be initialized to default values.\nConfiguration saved in /outputs/model_index.json\nConfiguration saved in /outputs/vae/config.json\nModel weights saved in /outputs/vae/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/unet/config.json\nModel weights saved in /outputs/unet/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/scheduler/scheduler_config.json\nSteps: 100%|██████████| 300/300 [03:24<00:00, 1.47it/s, lr=3e-5, step_loss=0.0516]\ntotal 32\n-rw-r--r-- 1 root root 935 Jan 29 15:00 config.yaml\n-rw-r--r-- 1 root root 373 Jan 29 15:04 model_index.json\ndrwxr-xr-x 5 root root 4096 Jan 29 15:04 samples\ndrwxr-xr-x 2 root root 4096 Jan 29 15:04 scheduler\ndrwxr-xr-x 2 root root 4096 Jan 29 15:04 text_encoder\ndrwxr-xr-x 2 root root 4096 Jan 29 15:04 tokenizer\ndrwxr-xr-x 2 root root 4096 Jan 29 15:04 unet\ndrwxr-xr-x 2 root root 4096 Jan 29 15:04 vae", "metrics": { "predict_time": 228.472234, "total_time": 467.941088 }, "output": "https://replicate.delivery/pbxt/mAONIem3fFqFsU2SkQp1Nufwtip6IPboSkJjjkGJaJUpnHygA/sample-300.gif", "started_at": "2023-01-29T15:00:33.063805Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/y7sy6fd4gjbzrisbfeieetvvtu", "cancel": "https://api.replicate.com/v1/predictions/y7sy6fd4gjbzrisbfeieetvvtu/cancel" }, "version": "558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1" }
Generated inpredict The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `1` `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. A matching Triton is not available, some optimizations will not be enabled. Error caught was: No module named 'triton' running with config: pretrained_model_path: ./checkpoints/stable-diffusion-v1-4 output_dir: /outputs train_data: n_sample_frames: 8 sample_start_idx: 0 prompt: a man surfing on water video_path: /tmp/tmpczjkm6zyman%20surfing.mp4 width: 512 height: 512 sample_frame_rate: 3 validation_data: num_inference_steps: 50 guidance_scale: 7.5 prompts: - a robot surfing on the moon video_length: 5 width: 512 height: 512 learning_rate: 3.0e-05 train_batch_size: 1 max_train_steps: 300 checkpointing_steps: 1000 validation_steps: 100 trainable_modules: - attn1.to_q - attn2.to_q - attn_temp seed: 33 mixed_precision: fp16 use_8bit_adam: false gradient_checkpointing: true enable_xformers_memory_efficient_attention: true 01/29/2023 15:00:37 - INFO - __main__ - Distributed environment: NO Num processes: 1 Process index: 0 Local process index: 0 Device: cuda Mixed precision type: fp16 {'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values. {'norm_num_groups'} was not found in config. Values will be initialized to default values. {'use_linear_projection', 'num_class_embeds', 'resnet_time_scale_shift', 'only_cross_attention', 'dual_cross_attention', 'mid_block_type', 'upcast_attention', 'class_embed_type'} was not found in config. Values will be initialized to default values. {'prediction_type'} was not found in config. Values will be initialized to default values. 01/29/2023 15:00:54 - INFO - __main__ - ***** Running training ***** 01/29/2023 15:00:54 - INFO - __main__ - Num examples = 1 01/29/2023 15:00:54 - INFO - __main__ - Num Epochs = 300 01/29/2023 15:00:54 - INFO - __main__ - Instantaneous batch size per device = 1 01/29/2023 15:00:54 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1 01/29/2023 15:00:54 - INFO - __main__ - Gradient Accumulation steps = 1 01/29/2023 15:00:54 - INFO - __main__ - Total optimization steps = 300 0%| | 0/300 [00:00<?, ?it/s] Steps: 0%| | 0/300 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn("None of the inputs have requires_grad=True. 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Values will be initialized to default values. Configuration saved in /outputs/model_index.json Configuration saved in /outputs/vae/config.json Model weights saved in /outputs/vae/diffusion_pytorch_model.bin Configuration saved in /outputs/unet/config.json Model weights saved in /outputs/unet/diffusion_pytorch_model.bin Configuration saved in /outputs/scheduler/scheduler_config.json Steps: 100%|██████████| 300/300 [03:24<00:00, 1.47it/s, lr=3e-5, step_loss=0.0516] total 32 -rw-r--r-- 1 root root 935 Jan 29 15:00 config.yaml -rw-r--r-- 1 root root 373 Jan 29 15:04 model_index.json drwxr-xr-x 5 root root 4096 Jan 29 15:04 samples drwxr-xr-x 2 root root 4096 Jan 29 15:04 scheduler drwxr-xr-x 2 root root 4096 Jan 29 15:04 text_encoder drwxr-xr-x 2 root root 4096 Jan 29 15:04 tokenizer drwxr-xr-x 2 root root 4096 Jan 29 15:04 unet drwxr-xr-x 2 root root 4096 Jan 29 15:04 vae
Prediction
pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1ID6na6gonncne6fbgyjvkkdu7ybqStatusSucceededSourceWebHardware–Total durationCreatedInput
- steps
- 100
- video
- width
- 512
- height
- 384
- length
- 15
- source_prompt
- a girl with a disco ball
- target_prompts
- a girl with a pufferfish
- sample_frame_rate
- 4
{ "steps": 100, "video": "https://replicate.delivery/pbxt/IDq3jZ2shDvEdhapAxSylO32rLLz1lE2k8DjuXbUB2ftEze7/saekovid.mov", "width": 512, "height": 384, "length": 15, "source_prompt": "a girl with a disco ball", "target_prompts": "a girl with a pufferfish", "sample_frame_rate": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1", { input: { steps: 100, video: "https://replicate.delivery/pbxt/IDq3jZ2shDvEdhapAxSylO32rLLz1lE2k8DjuXbUB2ftEze7/saekovid.mov", width: 512, height: 384, length: 15, source_prompt: "a girl with a disco ball", target_prompts: "a girl with a pufferfish", sample_frame_rate: 4 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run pollinations/tune-a-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1", input={ "steps": 100, "video": "https://replicate.delivery/pbxt/IDq3jZ2shDvEdhapAxSylO32rLLz1lE2k8DjuXbUB2ftEze7/saekovid.mov", "width": 512, "height": 384, "length": 15, "source_prompt": "a girl with a disco ball", "target_prompts": "a girl with a pufferfish", "sample_frame_rate": 4 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run pollinations/tune-a-video 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": "pollinations/tune-a-video:558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1", "input": { "steps": 100, "video": "https://replicate.delivery/pbxt/IDq3jZ2shDvEdhapAxSylO32rLLz1lE2k8DjuXbUB2ftEze7/saekovid.mov", "width": 512, "height": 384, "length": 15, "source_prompt": "a girl with a disco ball", "target_prompts": "a girl with a pufferfish", "sample_frame_rate": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-01-29T17:50:50.010023Z", "created_at": "2023-01-29T17:48:57.428582Z", "data_removed": false, "error": null, "id": "6na6gonncne6fbgyjvkkdu7ybq", "input": { "steps": 100, "video": "https://replicate.delivery/pbxt/IDq3jZ2shDvEdhapAxSylO32rLLz1lE2k8DjuXbUB2ftEze7/saekovid.mov", "width": 512, "height": 384, "length": 15, "source_prompt": "a girl with a disco ball", "target_prompts": "a girl with a pufferfish", "sample_frame_rate": 4 }, "logs": "predict\nThe following values were not passed to `accelerate launch` and had defaults used instead:\n`--num_processes` was set to a value of `1`\n`--num_machines` was set to a value of `1`\n`--mixed_precision` was set to a value of `'no'`\n`--dynamo_backend` was set to a value of `'no'`\nTo avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\nA matching Triton is not available, some optimizations will not be enabled.\nError caught was: No module named 'triton'\nrunning with config:\npretrained_model_path: ./checkpoints/stable-diffusion-v1-4\noutput_dir: /outputs\ntrain_data:\nn_sample_frames: 8\nsample_start_idx: 0\nprompt: a girl with a disco ball\nvideo_path: /tmp/tmp9n99tvgusaekovid.mov\nwidth: 512\nheight: 384\nsample_frame_rate: 4\nvalidation_data:\nnum_inference_steps: 50\nguidance_scale: 7.5\nprompts:\n- a girl with a pufferfish\nvideo_length: 15\nwidth: 512\nheight: 384\nlearning_rate: 3.0e-05\ntrain_batch_size: 1\nmax_train_steps: 100\ncheckpointing_steps: 1000\nvalidation_steps: 100\ntrainable_modules:\n- attn1.to_q\n- attn2.to_q\n- attn_temp\nseed: 33\nmixed_precision: fp16\nuse_8bit_adam: false\ngradient_checkpointing: true\nenable_xformers_memory_efficient_attention: true\n01/29/2023 17:49:01 - INFO - __main__ - Distributed environment: NO\nNum processes: 1\nProcess index: 0\nLocal process index: 0\nDevice: cuda\nMixed precision type: fp16\n{'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values.\n{'norm_num_groups'} was not found in config. Values will be initialized to default values.\n{'upcast_attention', 'resnet_time_scale_shift', 'use_linear_projection', 'class_embed_type', 'only_cross_attention', 'mid_block_type', 'dual_cross_attention', 'num_class_embeds'} was not found in config. Values will be initialized to default values.\n{'prediction_type'} was not found in config. Values will be initialized to default values.\n01/29/2023 17:49:17 - INFO - __main__ - ***** Running training *****\n01/29/2023 17:49:17 - INFO - __main__ - Num examples = 1\n01/29/2023 17:49:17 - INFO - __main__ - Num Epochs = 100\n01/29/2023 17:49:17 - INFO - __main__ - Instantaneous batch size per device = 1\n01/29/2023 17:49:17 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1\n01/29/2023 17:49:17 - INFO - __main__ - Gradient Accumulation steps = 1\n01/29/2023 17:49:17 - INFO - __main__ - Total optimization steps = 100\n 0%| | 0/100 [00:00<?, ?it/s]\nSteps: 0%| | 0/100 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\nwarnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\nSteps: 1%| | 1/100 [00:02<03:36, 2.19s/it]\nSteps: 1%| | 1/100 [00:02<03:36, 2.19s/it, lr=3e-5, step_loss=0.0119]\nSteps: 2%|▏ | 2/100 [00:02<02:03, 1.26s/it, lr=3e-5, step_loss=0.0119]\nSteps: 2%|▏ | 2/100 [00:02<02:03, 1.26s/it, lr=3e-5, step_loss=0.226] \nSteps: 3%|▎ | 3/100 [00:03<01:31, 1.06it/s, lr=3e-5, step_loss=0.226]\nSteps: 3%|▎ | 3/100 [00:03<01:31, 1.06it/s, lr=3e-5, step_loss=0.153]\nSteps: 4%|▍ | 4/100 [00:03<01:16, 1.25it/s, lr=3e-5, step_loss=0.153]\nSteps: 4%|▍ | 4/100 [00:03<01:16, 1.25it/s, lr=3e-5, step_loss=0.0315]\nSteps: 5%|▌ | 5/100 [00:04<01:07, 1.40it/s, lr=3e-5, step_loss=0.0315]\nSteps: 5%|▌ | 5/100 [00:04<01:07, 1.40it/s, lr=3e-5, step_loss=0.049] \nSteps: 6%|▌ | 6/100 [00:05<01:02, 1.50it/s, lr=3e-5, step_loss=0.049]\nSteps: 6%|▌ | 6/100 [00:05<01:02, 1.50it/s, lr=3e-5, step_loss=0.146]\nSteps: 7%|▋ | 7/100 [00:05<00:58, 1.58it/s, lr=3e-5, step_loss=0.146]\nSteps: 7%|▋ | 7/100 [00:05<00:58, 1.58it/s, lr=3e-5, 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[00:14<00:01, 3.11it/s]\u001b[A\n 94%|█████████▍| 47/50 [00:15<00:00, 3.13it/s]\u001b[A\n 96%|█████████▌| 48/50 [00:15<00:00, 3.12it/s]\u001b[A\n 98%|█████████▊| 49/50 [00:15<00:00, 3.12it/s]\u001b[A\n100%|██████████| 50/50 [00:16<00:00, 3.13it/s]\u001b[A\n100%|██████████| 50/50 [00:16<00:00, 3.11it/s]\n01/29/2023 17:50:38 - INFO - __main__ - Saved samples to /outputs/samples/sample-100.gif\nSteps: 100%|██████████| 100/100 [01:20<00:00, 1.72it/s, lr=3e-5, step_loss=0.214]{'prediction_type'} was not found in config. Values will be initialized to default values.\nConfiguration saved in /outputs/model_index.json\nConfiguration saved in /outputs/vae/config.json\nModel weights saved in /outputs/vae/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/unet/config.json\nModel weights saved in /outputs/unet/diffusion_pytorch_model.bin\nConfiguration saved in /outputs/scheduler/scheduler_config.json\nSteps: 100%|██████████| 100/100 [01:30<00:00, 1.11it/s, lr=3e-5, step_loss=0.214]\ntotal 32\n-rw-r--r-- 1 root root 930 Jan 29 17:49 config.yaml\n-rw-r--r-- 1 root root 373 Jan 29 17:50 model_index.json\ndrwxr-xr-x 3 root root 4096 Jan 29 17:50 samples\ndrwxr-xr-x 2 root root 4096 Jan 29 17:50 scheduler\ndrwxr-xr-x 2 root root 4096 Jan 29 17:50 text_encoder\ndrwxr-xr-x 2 root root 4096 Jan 29 17:50 tokenizer\ndrwxr-xr-x 2 root root 4096 Jan 29 17:50 unet\ndrwxr-xr-x 2 root root 4096 Jan 29 17:50 vae", "metrics": { "predict_time": 112.53809, "total_time": 112.581441 }, "output": "https://replicate.delivery/pbxt/5tleTsLVmjRoDifvrTS0eltf7pJfGK1Oujqfrdt8vjGf8HjMIA/sample-100.gif", "started_at": "2023-01-29T17:48:57.471933Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6na6gonncne6fbgyjvkkdu7ybq", "cancel": "https://api.replicate.com/v1/predictions/6na6gonncne6fbgyjvkkdu7ybq/cancel" }, "version": "558a8a71c36e900af4a9aec372cc77ebd93dc668fa3d3244e689b9a971d158e1" }
Generated inpredict The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `1` `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. A matching Triton is not available, some optimizations will not be enabled. Error caught was: No module named 'triton' running with config: pretrained_model_path: ./checkpoints/stable-diffusion-v1-4 output_dir: /outputs train_data: n_sample_frames: 8 sample_start_idx: 0 prompt: a girl with a disco ball video_path: /tmp/tmp9n99tvgusaekovid.mov width: 512 height: 384 sample_frame_rate: 4 validation_data: num_inference_steps: 50 guidance_scale: 7.5 prompts: - a girl with a pufferfish video_length: 15 width: 512 height: 384 learning_rate: 3.0e-05 train_batch_size: 1 max_train_steps: 100 checkpointing_steps: 1000 validation_steps: 100 trainable_modules: - attn1.to_q - attn2.to_q - attn_temp seed: 33 mixed_precision: fp16 use_8bit_adam: false gradient_checkpointing: true enable_xformers_memory_efficient_attention: true 01/29/2023 17:49:01 - INFO - __main__ - Distributed environment: NO Num processes: 1 Process index: 0 Local process index: 0 Device: cuda Mixed precision type: fp16 {'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values. {'norm_num_groups'} was not found in config. Values will be initialized to default values. {'upcast_attention', 'resnet_time_scale_shift', 'use_linear_projection', 'class_embed_type', 'only_cross_attention', 'mid_block_type', 'dual_cross_attention', 'num_class_embeds'} was not found in config. Values will be initialized to default values. {'prediction_type'} was not found in config. Values will be initialized to default values. 01/29/2023 17:49:17 - INFO - __main__ - ***** Running training ***** 01/29/2023 17:49:17 - INFO - __main__ - Num examples = 1 01/29/2023 17:49:17 - INFO - __main__ - Num Epochs = 100 01/29/2023 17:49:17 - INFO - __main__ - Instantaneous batch size per device = 1 01/29/2023 17:49:17 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 1 01/29/2023 17:49:17 - INFO - __main__ - Gradient Accumulation steps = 1 01/29/2023 17:49:17 - INFO - __main__ - Total optimization steps = 100 0%| | 0/100 [00:00<?, ?it/s] Steps: 0%| | 0/100 [00:00<?, ?it/s]/root/.pyenv/versions/3.7.16/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn("None of the inputs have requires_grad=True. Gradients will be None") Steps: 1%| | 1/100 [00:02<03:36, 2.19s/it] Steps: 1%| | 1/100 [00:02<03:36, 2.19s/it, lr=3e-5, step_loss=0.0119] Steps: 2%|▏ | 2/100 [00:02<02:03, 1.26s/it, lr=3e-5, step_loss=0.0119] Steps: 2%|▏ | 2/100 [00:02<02:03, 1.26s/it, lr=3e-5, step_loss=0.226] Steps: 3%|▎ | 3/100 [00:03<01:31, 1.06it/s, lr=3e-5, step_loss=0.226] Steps: 3%|▎ | 3/100 [00:03<01:31, 1.06it/s, lr=3e-5, step_loss=0.153] Steps: 4%|▍ | 4/100 [00:03<01:16, 1.25it/s, lr=3e-5, step_loss=0.153] Steps: 4%|▍ | 4/100 [00:03<01:16, 1.25it/s, lr=3e-5, step_loss=0.0315] Steps: 5%|▌ | 5/100 [00:04<01:07, 1.40it/s, lr=3e-5, step_loss=0.0315] Steps: 5%|▌ | 5/100 [00:04<01:07, 1.40it/s, lr=3e-5, step_loss=0.049] Steps: 6%|▌ | 6/100 [00:05<01:02, 1.50it/s, lr=3e-5, step_loss=0.049] Steps: 6%|▌ | 6/100 [00:05<01:02, 1.50it/s, lr=3e-5, step_loss=0.146] Steps: 7%|▋ | 7/100 [00:05<00:58, 1.58it/s, lr=3e-5, step_loss=0.146] Steps: 7%|▋ | 7/100 [00:05<00:58, 1.58it/s, lr=3e-5, step_loss=0.346] Steps: 8%|▊ | 8/100 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Values will be initialized to default values. Configuration saved in /outputs/model_index.json Configuration saved in /outputs/vae/config.json Model weights saved in /outputs/vae/diffusion_pytorch_model.bin Configuration saved in /outputs/unet/config.json Model weights saved in /outputs/unet/diffusion_pytorch_model.bin Configuration saved in /outputs/scheduler/scheduler_config.json Steps: 100%|██████████| 100/100 [01:30<00:00, 1.11it/s, lr=3e-5, step_loss=0.214] total 32 -rw-r--r-- 1 root root 930 Jan 29 17:49 config.yaml -rw-r--r-- 1 root root 373 Jan 29 17:50 model_index.json drwxr-xr-x 3 root root 4096 Jan 29 17:50 samples drwxr-xr-x 2 root root 4096 Jan 29 17:50 scheduler drwxr-xr-x 2 root root 4096 Jan 29 17:50 text_encoder drwxr-xr-x 2 root root 4096 Jan 29 17:50 tokenizer drwxr-xr-x 2 root root 4096 Jan 29 17:50 unet drwxr-xr-x 2 root root 4096 Jan 29 17:50 vae
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