shimmercam / animatediff-v3
AnimateDiff v3 + SparseCtrl: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning. Created with Shimmer.
Prediction
shimmercam/animatediff-v3:dd87e0a6eed441ad5944d9b10a2020245889b6b85fe97bd9abeb76334d2c95ecIDpf4uuybbagzq4z7w5p6tkec2geStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- -1
- steps
- 25
- length
- 16
- prompt
- husky running in the snow
- guidance
- 8.5
- negative_prompt
- worst quality, low quality, letterboxed
- dreambooth_model
- None
{ "seed": -1, "steps": 25, "length": 16, "prompt": "husky running in the snow", "guidance": 8.5, "negative_prompt": "worst quality, low quality, letterboxed", "dreambooth_model": "None" }Install Replicate’s Node.js client library:npm install replicateImport 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 shimmercam/animatediff-v3 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shimmercam/animatediff-v3:dd87e0a6eed441ad5944d9b10a2020245889b6b85fe97bd9abeb76334d2c95ec", { input: { seed: -1, steps: 25, length: 16, prompt: "husky running in the snow", guidance: 8.5, negative_prompt: "worst quality, low quality, letterboxed", dreambooth_model: "None" } } ); // 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 replicateImport the client:import replicateRun shimmercam/animatediff-v3 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shimmercam/animatediff-v3:dd87e0a6eed441ad5944d9b10a2020245889b6b85fe97bd9abeb76334d2c95ec", input={ "seed": -1, "steps": 25, "length": 16, "prompt": "husky running in the snow", "guidance": 8.5, "negative_prompt": "worst quality, low quality, letterboxed", "dreambooth_model": "None" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shimmercam/animatediff-v3 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": "shimmercam/animatediff-v3:dd87e0a6eed441ad5944d9b10a2020245889b6b85fe97bd9abeb76334d2c95ec", "input": { "seed": -1, "steps": 25, "length": 16, "prompt": "husky running in the snow", "guidance": 8.5, "negative_prompt": "worst quality, low quality, letterboxed", "dreambooth_model": "None" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-16T20:58:17.238586Z", "created_at": "2023-12-16T20:54:44.468540Z", "data_removed": false, "error": null, "id": "pf4uuybbagzq4z7w5p6tkec2ge", "input": { "seed": -1, "steps": 25, "length": 16, "prompt": "husky running in the snow", "guidance": 8.5, "negative_prompt": "worst quality, low quality, letterboxed", "dreambooth_model": "None" }, "logs": "loading controlnet checkpoint from /AnimateDiff/models/SparseCtrl/v3_sd15_sparsectrl_rgb.ckpt ...\nload motion module from /AnimateDiff/models/Motion_Module/v3_sd15_mm.ckpt\nload domain lora from /AnimateDiff/models/Motion_Module/v3_sd15_adapter.ckpt\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:02<00:49, 2.07s/it]\n 8%|▊ | 2/25 [00:03<00:40, 1.78s/it]\n 12%|█▏ | 3/25 [00:05<00:37, 1.68s/it]\n 16%|█▌ | 4/25 [00:06<00:34, 1.64s/it]\n 20%|██ | 5/25 [00:08<00:32, 1.61s/it]\n 24%|██▍ | 6/25 [00:09<00:30, 1.60s/it]\n 28%|██▊ | 7/25 [00:11<00:28, 1.59s/it]\n 32%|███▏ | 8/25 [00:13<00:26, 1.58s/it]\n 36%|███▌ | 9/25 [00:14<00:25, 1.58s/it]\n 40%|████ | 10/25 [00:16<00:23, 1.58s/it]\n 44%|████▍ | 11/25 [00:17<00:22, 1.58s/it]\n 48%|████▊ | 12/25 [00:19<00:20, 1.57s/it]\n 52%|█████▏ | 13/25 [00:20<00:18, 1.57s/it]\n 56%|█████▌ | 14/25 [00:22<00:17, 1.57s/it]\n 60%|██████ | 15/25 [00:24<00:15, 1.57s/it]\n 64%|██████▍ | 16/25 [00:25<00:14, 1.57s/it]\n 68%|██████▊ | 17/25 [00:27<00:12, 1.57s/it]\n 72%|███████▏ | 18/25 [00:28<00:11, 1.57s/it]\n 76%|███████▌ | 19/25 [00:30<00:09, 1.57s/it]\n 80%|████████ | 20/25 [00:31<00:07, 1.57s/it]\n 84%|████████▍ | 21/25 [00:33<00:06, 1.57s/it]\n 88%|████████▊ | 22/25 [00:35<00:04, 1.57s/it]\n 92%|█████████▏| 23/25 [00:36<00:03, 1.57s/it]\n 96%|█████████▌| 24/25 [00:38<00:01, 1.57s/it]\n100%|██████████| 25/25 [00:39<00:00, 1.57s/it]\n100%|██████████| 25/25 [00:39<00:00, 1.59s/it]\n 0%| | 0/16 [00:00<?, ?it/s]\n 31%|███▏ | 5/16 [00:00<00:00, 44.00it/s]\n 62%|██████▎ | 10/16 [00:00<00:00, 23.75it/s]\n 81%|████████▏ | 13/16 [00:00<00:00, 21.43it/s]\n100%|██████████| 16/16 [00:00<00:00, 20.17it/s]\n100%|██████████| 16/16 [00:00<00:00, 22.03it/s]", "metrics": { "predict_time": 51.429076, "total_time": 212.770046 }, "output": "https://replicate.delivery/pbxt/NPJe0gaetTmb1Ee6rebJ6fFjnFfgAh1dCjp9UySp3UNOaBvgE/output.gif", "started_at": "2023-12-16T20:57:25.809510Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pf4uuybbagzq4z7w5p6tkec2ge", "cancel": "https://api.replicate.com/v1/predictions/pf4uuybbagzq4z7w5p6tkec2ge/cancel" }, "version": "dd87e0a6eed441ad5944d9b10a2020245889b6b85fe97bd9abeb76334d2c95ec" }Generated inloading controlnet checkpoint from /AnimateDiff/models/SparseCtrl/v3_sd15_sparsectrl_rgb.ckpt ... load motion module from /AnimateDiff/models/Motion_Module/v3_sd15_mm.ckpt load domain lora from /AnimateDiff/models/Motion_Module/v3_sd15_adapter.ckpt 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:02<00:49, 2.07s/it] 8%|▊ | 2/25 [00:03<00:40, 1.78s/it] 12%|█▏ | 3/25 [00:05<00:37, 1.68s/it] 16%|█▌ | 4/25 [00:06<00:34, 1.64s/it] 20%|██ | 5/25 [00:08<00:32, 1.61s/it] 24%|██▍ | 6/25 [00:09<00:30, 1.60s/it] 28%|██▊ | 7/25 [00:11<00:28, 1.59s/it] 32%|███▏ | 8/25 [00:13<00:26, 1.58s/it] 36%|███▌ | 9/25 [00:14<00:25, 1.58s/it] 40%|████ | 10/25 [00:16<00:23, 1.58s/it] 44%|████▍ | 11/25 [00:17<00:22, 1.58s/it] 48%|████▊ | 12/25 [00:19<00:20, 1.57s/it] 52%|█████▏ | 13/25 [00:20<00:18, 1.57s/it] 56%|█████▌ | 14/25 [00:22<00:17, 1.57s/it] 60%|██████ | 15/25 [00:24<00:15, 1.57s/it] 64%|██████▍ | 16/25 [00:25<00:14, 1.57s/it] 68%|██████▊ | 17/25 [00:27<00:12, 1.57s/it] 72%|███████▏ | 18/25 [00:28<00:11, 1.57s/it] 76%|███████▌ | 19/25 [00:30<00:09, 1.57s/it] 80%|████████ | 20/25 [00:31<00:07, 1.57s/it] 84%|████████▍ | 21/25 [00:33<00:06, 1.57s/it] 88%|████████▊ | 22/25 [00:35<00:04, 1.57s/it] 92%|█████████▏| 23/25 [00:36<00:03, 1.57s/it] 96%|█████████▌| 24/25 [00:38<00:01, 1.57s/it] 100%|██████████| 25/25 [00:39<00:00, 1.57s/it] 100%|██████████| 25/25 [00:39<00:00, 1.59s/it] 0%| | 0/16 [00:00<?, ?it/s] 31%|███▏ | 5/16 [00:00<00:00, 44.00it/s] 62%|██████▎ | 10/16 [00:00<00:00, 23.75it/s] 81%|████████▏ | 13/16 [00:00<00:00, 21.43it/s] 100%|██████████| 16/16 [00:00<00:00, 20.17it/s] 100%|██████████| 16/16 [00:00<00:00, 22.03it/s]Prediction
shimmercam/animatediff-v3:5e0d88c259792ad95a3c596ed1997de772c424dd0222802b830d43a36f861763IDpxl7jdlbirpfuq2g3bdrbcukvqStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- -1
- steps
- 25
- length
- 16
- prompt
- close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot
- guidance
- 7
- negative_prompt
- (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck
- dreambooth_model
- Realistic Vision
{ "seed": -1, "steps": 25, "length": 16, "prompt": "close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot", "guidance": 7, "negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck", "dreambooth_model": "Realistic Vision" }Install Replicate’s Node.js client library:npm install replicateImport 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 shimmercam/animatediff-v3 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shimmercam/animatediff-v3:5e0d88c259792ad95a3c596ed1997de772c424dd0222802b830d43a36f861763", { input: { seed: -1, steps: 25, length: 16, prompt: "close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot", guidance: 7, negative_prompt: "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck", dreambooth_model: "Realistic Vision" } } ); // 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 replicateImport the client:import replicateRun shimmercam/animatediff-v3 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shimmercam/animatediff-v3:5e0d88c259792ad95a3c596ed1997de772c424dd0222802b830d43a36f861763", input={ "seed": -1, "steps": 25, "length": 16, "prompt": "close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot", "guidance": 7, "negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck", "dreambooth_model": "Realistic Vision" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shimmercam/animatediff-v3 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": "shimmercam/animatediff-v3:5e0d88c259792ad95a3c596ed1997de772c424dd0222802b830d43a36f861763", "input": { "seed": -1, "steps": 25, "length": 16, "prompt": "close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot", "guidance": 7, "negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck", "dreambooth_model": "Realistic Vision" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-16T21:26:14.986192Z", "created_at": "2023-12-16T21:21:40.999806Z", "data_removed": false, "error": null, "id": "pxl7jdlbirpfuq2g3bdrbcukvq", "input": { "seed": -1, "steps": 25, "length": 16, "prompt": "close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot", "guidance": 7, "negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck", "dreambooth_model": "Realistic Vision" }, "logs": "loading controlnet checkpoint from /AnimateDiff/models/SparseCtrl/v3_sd15_sparsectrl_rgb.ckpt ...\nload motion module from /AnimateDiff/models/Motion_Module/v3_sd15_mm.ckpt\nload dreambooth model from /AnimateDiff/models/DreamBooth_LoRA/realisticVisionV60B1_v51VAE.safetensors\nconfig.json: 0%| | 0.00/4.52k [00:00<?, ?B/s]\nconfig.json: 100%|██████████| 4.52k/4.52k [00:00<00:00, 18.9MB/s]\nmodel.safetensors: 0%| | 0.00/1.71G [00:00<?, ?B/s]\nmodel.safetensors: 1%| | 10.5M/1.71G [00:00<01:08, 25.0MB/s]\nmodel.safetensors: 2%|▏ | 41.9M/1.71G [00:00<00:18, 91.7MB/s]\nmodel.safetensors: 4%|▍ | 73.4M/1.71G [00:00<00:11, 146MB/s] \nmodel.safetensors: 6%|▌ | 105M/1.71G [00:00<00:08, 179MB/s] \nmodel.safetensors: 8%|▊ | 136M/1.71G [00:00<00:07, 202MB/s]\nmodel.safetensors: 10%|▉ | 168M/1.71G [00:01<00:07, 206MB/s]\nmodel.safetensors: 12%|█▏ | 199M/1.71G [00:01<00:06, 223MB/s]\nmodel.safetensors: 13%|█▎ | 231M/1.71G [00:01<00:06, 237MB/s]\nmodel.safetensors: 15%|█▌ | 262M/1.71G [00:01<00:05, 248MB/s]\nmodel.safetensors: 17%|█▋ | 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1.70G/1.71G [00:07<00:00, 261MB/s]\nmodel.safetensors: 100%|██████████| 1.71G/1.71G [00:07<00:00, 242MB/s]\nSome weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.14.mlp.fc1.weight', 'vision_model.encoder.layers.8.self_attn.out_proj.bias', 'vision_model.encoder.layers.19.layer_norm2.weight', 'vision_model.encoder.layers.5.mlp.fc2.weight', 'vision_model.encoder.layers.16.mlp.fc1.bias', 'vision_model.encoder.layers.7.layer_norm1.bias', 'vision_model.encoder.layers.16.self_attn.v_proj.bias', 'vision_model.encoder.layers.0.self_attn.q_proj.bias', 'vision_model.encoder.layers.5.self_attn.k_proj.bias', 'vision_model.encoder.layers.21.self_attn.v_proj.weight', 'vision_model.encoder.layers.5.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.self_attn.v_proj.bias', 'vision_model.encoder.layers.10.self_attn.v_proj.weight', 'vision_model.encoder.layers.22.self_attn.out_proj.bias', 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'vision_model.encoder.layers.7.self_attn.out_proj.weight', 'vision_model.encoder.layers.20.mlp.fc2.weight', 'vision_model.encoder.layers.9.self_attn.k_proj.bias', 'vision_model.encoder.layers.3.self_attn.q_proj.weight', 'vision_model.encoder.layers.14.self_attn.v_proj.bias', 'vision_model.encoder.layers.23.self_attn.out_proj.weight', 'vision_model.encoder.layers.17.layer_norm1.weight', 'vision_model.encoder.layers.19.mlp.fc2.weight', 'vision_model.encoder.layers.13.mlp.fc2.bias', 'vision_model.encoder.layers.3.layer_norm2.bias', 'vision_model.encoder.layers.18.mlp.fc1.bias', 'vision_model.encoder.layers.9.layer_norm1.weight', 'vision_model.encoder.layers.8.self_attn.q_proj.bias', 'vision_model.encoder.layers.9.mlp.fc2.weight', 'vision_model.encoder.layers.6.self_attn.k_proj.weight', 'vision_model.encoder.layers.10.mlp.fc1.weight', 'vision_model.encoder.layers.1.layer_norm1.weight', 'vision_model.encoder.layers.20.layer_norm2.bias', 'vision_model.encoder.layers.11.layer_norm1.weight', 'vision_model.encoder.layers.22.layer_norm2.bias', 'vision_model.encoder.layers.13.mlp.fc1.bias', 'vision_model.encoder.layers.23.self_attn.k_proj.bias', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 'vision_model.encoder.layers.9.self_attn.out_proj.bias', 'vision_model.encoder.layers.14.mlp.fc2.weight', 'vision_model.encoder.layers.3.layer_norm1.weight', 'vision_model.encoder.layers.1.self_attn.out_proj.weight', 'vision_model.encoder.layers.15.self_attn.v_proj.bias', 'vision_model.encoder.layers.13.self_attn.q_proj.bias', 'vision_model.encoder.layers.2.layer_norm2.weight', 'vision_model.encoder.layers.11.layer_norm1.bias', 'vision_model.encoder.layers.10.self_attn.k_proj.weight', 'vision_model.encoder.layers.21.self_attn.out_proj.weight', 'vision_model.pre_layrnorm.weight', 'vision_model.encoder.layers.15.self_attn.k_proj.bias', 'vision_model.encoder.layers.19.self_attn.out_proj.weight', 'vision_model.encoder.layers.19.self_attn.q_proj.weight', 'vision_model.encoder.layers.3.self_attn.v_proj.weight', 'vision_model.encoder.layers.20.layer_norm1.bias', 'vision_model.encoder.layers.9.layer_norm2.weight', 'vision_model.encoder.layers.6.self_attn.out_proj.bias', 'vision_model.encoder.layers.2.self_attn.v_proj.weight', 'vision_model.encoder.layers.8.self_attn.out_proj.weight', 'vision_model.encoder.layers.19.self_attn.q_proj.bias', 'vision_model.encoder.layers.3.layer_norm2.weight', 'vision_model.encoder.layers.18.self_attn.k_proj.weight', 'vision_model.encoder.layers.4.mlp.fc1.bias', 'vision_model.encoder.layers.20.layer_norm2.weight', 'vision_model.encoder.layers.1.self_attn.k_proj.weight']\n- This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\nload domain lora from /AnimateDiff/models/Motion_Module/v3_sd15_adapter.ckpt\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:01<00:36, 1.54s/it]\n 8%|▊ | 2/25 [00:03<00:35, 1.54s/it]\n 12%|█▏ | 3/25 [00:04<00:33, 1.54s/it]\n 16%|█▌ | 4/25 [00:06<00:32, 1.54s/it]\n 20%|██ | 5/25 [00:07<00:30, 1.54s/it]\n 24%|██▍ | 6/25 [00:09<00:29, 1.54s/it]\n 28%|██▊ | 7/25 [00:10<00:27, 1.54s/it]\n 32%|███▏ | 8/25 [00:12<00:26, 1.54s/it]\n 36%|███▌ | 9/25 [00:13<00:24, 1.54s/it]\n 40%|████ | 10/25 [00:15<00:23, 1.54s/it]\n 44%|████▍ | 11/25 [00:16<00:21, 1.54s/it]\n 48%|████▊ | 12/25 [00:18<00:20, 1.54s/it]\n 52%|█████▏ | 13/25 [00:20<00:18, 1.54s/it]\n 56%|█████▌ | 14/25 [00:21<00:16, 1.54s/it]\n 60%|██████ | 15/25 [00:23<00:15, 1.54s/it]\n 64%|██████▍ | 16/25 [00:24<00:13, 1.54s/it]\n 68%|██████▊ | 17/25 [00:26<00:12, 1.54s/it]\n 72%|███████▏ | 18/25 [00:27<00:10, 1.54s/it]\n 76%|███████▌ | 19/25 [00:29<00:09, 1.54s/it]\n 80%|████████ | 20/25 [00:30<00:07, 1.54s/it]\n 84%|████████▍ | 21/25 [00:32<00:06, 1.55s/it]\n 88%|████████▊ | 22/25 [00:33<00:04, 1.54s/it]\n 92%|█████████▏| 23/25 [00:35<00:03, 1.55s/it]\n 96%|█████████▌| 24/25 [00:37<00:01, 1.54s/it]\n100%|██████████| 25/25 [00:38<00:00, 1.55s/it]\n100%|██████████| 25/25 [00:38<00:00, 1.54s/it]\n 0%| | 0/16 [00:00<?, ?it/s]\n 31%|███▏ | 5/16 [00:00<00:00, 46.58it/s]\n 62%|██████▎ | 10/16 [00:00<00:00, 24.95it/s]\n 88%|████████▊ | 14/16 [00:00<00:00, 22.06it/s]\n100%|██████████| 16/16 [00:00<00:00, 23.13it/s]", "metrics": { "predict_time": 56.183088, "total_time": 273.986386 }, "output": "https://replicate.delivery/pbxt/GqRNcQFu03qcFpj5cHQ6dXKLHsfIATWmm9KRbOhwA6N7PeCSA/output.gif", "started_at": "2023-12-16T21:25:18.803104Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pxl7jdlbirpfuq2g3bdrbcukvq", "cancel": "https://api.replicate.com/v1/predictions/pxl7jdlbirpfuq2g3bdrbcukvq/cancel" }, "version": "5e0d88c259792ad95a3c596ed1997de772c424dd0222802b830d43a36f861763" }Generated inloading controlnet checkpoint from /AnimateDiff/models/SparseCtrl/v3_sd15_sparsectrl_rgb.ckpt ... load motion module from /AnimateDiff/models/Motion_Module/v3_sd15_mm.ckpt load dreambooth model from /AnimateDiff/models/DreamBooth_LoRA/realisticVisionV60B1_v51VAE.safetensors config.json: 0%| | 0.00/4.52k [00:00<?, ?B/s] config.json: 100%|██████████| 4.52k/4.52k [00:00<00:00, 18.9MB/s] model.safetensors: 0%| | 0.00/1.71G [00:00<?, ?B/s] model.safetensors: 1%| | 10.5M/1.71G [00:00<01:08, 25.0MB/s] model.safetensors: 2%|▏ | 41.9M/1.71G [00:00<00:18, 91.7MB/s] model.safetensors: 4%|▍ | 73.4M/1.71G [00:00<00:11, 146MB/s] model.safetensors: 6%|▌ | 105M/1.71G [00:00<00:08, 179MB/s] model.safetensors: 8%|▊ | 136M/1.71G [00:00<00:07, 202MB/s] model.safetensors: 10%|▉ | 168M/1.71G [00:01<00:07, 206MB/s] model.safetensors: 12%|█▏ | 199M/1.71G [00:01<00:06, 223MB/s] model.safetensors: 13%|█▎ | 231M/1.71G [00:01<00:06, 237MB/s] model.safetensors: 15%|█▌ | 262M/1.71G [00:01<00:05, 248MB/s] model.safetensors: 17%|█▋ | 294M/1.71G [00:01<00:06, 217MB/s] model.safetensors: 19%|█▉ | 325M/1.71G [00:01<00:05, 232MB/s] model.safetensors: 21%|██ | 357M/1.71G [00:01<00:05, 245MB/s] model.safetensors: 23%|██▎ | 388M/1.71G [00:01<00:05, 233MB/s] model.safetensors: 25%|██▍ | 419M/1.71G [00:02<00:05, 238MB/s] model.safetensors: 26%|██▋ | 451M/1.71G [00:02<00:05, 243MB/s] model.safetensors: 28%|██▊ | 482M/1.71G [00:02<00:04, 247MB/s] model.safetensors: 31%|███ | 524M/1.71G [00:02<00:04, 264MB/s] model.safetensors: 32%|███▏ | 556M/1.71G [00:02<00:04, 256MB/s] model.safetensors: 34%|███▍ | 587M/1.71G [00:02<00:04, 262MB/s] model.safetensors: 36%|███▌ | 619M/1.71G [00:02<00:04, 254MB/s] model.safetensors: 38%|███▊ | 650M/1.71G [00:02<00:04, 256MB/s] model.safetensors: 40%|███▉ | 682M/1.71G [00:03<00:03, 262MB/s] model.safetensors: 42%|████▏ | 713M/1.71G [00:03<00:03, 265MB/s] model.safetensors: 44%|████▎ | 744M/1.71G [00:03<00:03, 258MB/s] model.safetensors: 45%|████▌ | 776M/1.71G [00:03<00:03, 256MB/s] model.safetensors: 47%|████▋ | 807M/1.71G [00:03<00:03, 257MB/s] model.safetensors: 49%|████▉ | 839M/1.71G [00:03<00:03, 262MB/s] model.safetensors: 51%|█████ | 870M/1.71G [00:03<00:03, 264MB/s] model.safetensors: 53%|█████▎ | 902M/1.71G [00:03<00:03, 260MB/s] model.safetensors: 55%|█████▍ | 933M/1.71G [00:04<00:03, 256MB/s] model.safetensors: 56%|█████▋ | 965M/1.71G [00:04<00:02, 258MB/s] model.safetensors: 58%|█████▊ | 996M/1.71G [00:04<00:02, 259MB/s] model.safetensors: 60%|██████ | 1.03G/1.71G [00:04<00:02, 262MB/s] model.safetensors: 62%|██████▏ | 1.06G/1.71G [00:04<00:02, 262MB/s] model.safetensors: 64%|██████▍ | 1.09G/1.71G [00:04<00:02, 260MB/s] model.safetensors: 66%|██████▌ | 1.12G/1.71G [00:04<00:02, 260MB/s] model.safetensors: 67%|██████▋ | 1.15G/1.71G [00:04<00:02, 255MB/s] model.safetensors: 69%|██████▉ | 1.18G/1.71G [00:05<00:02, 257MB/s] model.safetensors: 71%|███████ | 1.22G/1.71G [00:05<00:01, 261MB/s] model.safetensors: 73%|███████▎ | 1.25G/1.71G [00:05<00:01, 255MB/s] model.safetensors: 75%|███████▍ | 1.28G/1.71G [00:05<00:01, 260MB/s] model.safetensors: 77%|███████▋ | 1.31G/1.71G [00:05<00:01, 264MB/s] model.safetensors: 78%|███████▊ | 1.34G/1.71G [00:05<00:01, 253MB/s] model.safetensors: 80%|████████ | 1.37G/1.71G [00:05<00:01, 261MB/s] model.safetensors: 82%|████████▏ | 1.41G/1.71G [00:05<00:01, 267MB/s] model.safetensors: 84%|████████▍ | 1.44G/1.71G [00:05<00:01, 263MB/s] model.safetensors: 86%|████████▌ | 1.47G/1.71G [00:06<00:00, 262MB/s] model.safetensors: 88%|████████▊ | 1.50G/1.71G [00:06<00:00, 248MB/s] model.safetensors: 89%|████████▉ | 1.53G/1.71G [00:06<00:00, 256MB/s] model.safetensors: 92%|█████████▏| 1.57G/1.71G [00:06<00:00, 271MB/s] model.safetensors: 94%|█████████▍| 1.60G/1.71G [00:06<00:00, 257MB/s] model.safetensors: 96%|█████████▌| 1.64G/1.71G [00:06<00:00, 257MB/s] model.safetensors: 97%|█████████▋| 1.67G/1.71G [00:06<00:00, 258MB/s] model.safetensors: 99%|█████████▉| 1.70G/1.71G [00:07<00:00, 261MB/s] model.safetensors: 100%|██████████| 1.71G/1.71G [00:07<00:00, 242MB/s] Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.14.mlp.fc1.weight', 'vision_model.encoder.layers.8.self_attn.out_proj.bias', 'vision_model.encoder.layers.19.layer_norm2.weight', 'vision_model.encoder.layers.5.mlp.fc2.weight', 'vision_model.encoder.layers.16.mlp.fc1.bias', 'vision_model.encoder.layers.7.layer_norm1.bias', 'vision_model.encoder.layers.16.self_attn.v_proj.bias', 'vision_model.encoder.layers.0.self_attn.q_proj.bias', 'vision_model.encoder.layers.5.self_attn.k_proj.bias', 'vision_model.encoder.layers.21.self_attn.v_proj.weight', 'vision_model.encoder.layers.5.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.self_attn.v_proj.bias', 'vision_model.encoder.layers.10.self_attn.v_proj.weight', 'vision_model.encoder.layers.22.self_attn.out_proj.bias', 'vision_model.encoder.layers.1.self_attn.v_proj.weight', 'vision_model.encoder.layers.20.self_attn.out_proj.weight', 'vision_model.encoder.layers.6.self_attn.q_proj.bias', 'vision_model.encoder.layers.4.self_attn.q_proj.weight', 'vision_model.encoder.layers.19.layer_norm1.weight', 'vision_model.encoder.layers.0.self_attn.v_proj.weight', 'vision_model.encoder.layers.9.self_attn.v_proj.weight', 'vision_model.encoder.layers.15.layer_norm1.bias', 'vision_model.encoder.layers.7.self_attn.v_proj.bias', 'vision_model.encoder.layers.20.self_attn.k_proj.bias', 'vision_model.encoder.layers.15.mlp.fc1.bias', 'vision_model.encoder.layers.8.self_attn.v_proj.weight', 'vision_model.encoder.layers.8.mlp.fc1.weight', 'vision_model.encoder.layers.21.self_attn.q_proj.weight', 'vision_model.encoder.layers.10.self_attn.k_proj.bias', 'vision_model.encoder.layers.23.layer_norm1.bias', 'vision_model.encoder.layers.20.mlp.fc2.bias', 'vision_model.encoder.layers.7.mlp.fc1.bias', 'vision_model.encoder.layers.12.layer_norm2.bias', 'vision_model.encoder.layers.21.mlp.fc1.bias', 'vision_model.encoder.layers.2.self_attn.out_proj.bias', 'vision_model.encoder.layers.13.layer_norm1.weight', 'vision_model.encoder.layers.5.layer_norm1.weight', 'vision_model.encoder.layers.13.self_attn.k_proj.weight', 'vision_model.encoder.layers.19.self_attn.k_proj.weight', 'vision_model.encoder.layers.13.self_attn.v_proj.bias', 'vision_model.encoder.layers.12.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.self_attn.q_proj.bias', 'vision_model.encoder.layers.5.self_attn.q_proj.weight', 'vision_model.encoder.layers.11.self_attn.q_proj.weight', 'vision_model.encoder.layers.18.self_attn.v_proj.weight', 'vision_model.encoder.layers.14.layer_norm2.bias', 'vision_model.encoder.layers.21.self_attn.k_proj.weight', 'vision_model.encoder.layers.6.mlp.fc2.weight', 'visual_projection.weight', 'vision_model.encoder.layers.17.layer_norm1.bias', 'vision_model.encoder.layers.15.layer_norm2.weight', 'vision_model.encoder.layers.12.layer_norm2.weight', 'vision_model.encoder.layers.15.self_attn.out_proj.bias', 'vision_model.encoder.layers.20.mlp.fc1.weight', 'vision_model.encoder.layers.18.self_attn.v_proj.bias', 'vision_model.encoder.layers.8.layer_norm1.bias', 'vision_model.encoder.layers.8.mlp.fc2.weight', 'vision_model.encoder.layers.6.mlp.fc2.bias', 'vision_model.encoder.layers.10.mlp.fc2.weight', 'vision_model.encoder.layers.22.mlp.fc1.weight', 'vision_model.encoder.layers.11.self_attn.v_proj.bias', 'vision_model.post_layernorm.bias', 'vision_model.encoder.layers.4.layer_norm1.weight', 'vision_model.encoder.layers.8.self_attn.k_proj.weight', 'vision_model.encoder.layers.22.self_attn.q_proj.bias', 'vision_model.encoder.layers.22.self_attn.out_proj.weight', 'vision_model.pre_layrnorm.bias', 'vision_model.encoder.layers.12.mlp.fc2.weight', 'vision_model.encoder.layers.9.mlp.fc2.bias', 'vision_model.encoder.layers.8.self_attn.v_proj.bias', 'vision_model.encoder.layers.17.self_attn.out_proj.weight', 'vision_model.encoder.layers.18.layer_norm2.weight', 'vision_model.encoder.layers.18.mlp.fc2.weight', 'vision_model.encoder.layers.20.mlp.fc1.bias', 'vision_model.encoder.layers.5.layer_norm1.bias', 'vision_model.encoder.layers.6.layer_norm2.weight', 'vision_model.encoder.layers.0.self_attn.q_proj.weight', 'vision_model.encoder.layers.22.mlp.fc1.bias', 'vision_model.encoder.layers.3.mlp.fc1.bias', 'vision_model.encoder.layers.17.layer_norm2.bias', 'vision_model.encoder.layers.13.layer_norm2.bias', 'vision_model.encoder.layers.21.self_attn.v_proj.bias', 'vision_model.encoder.layers.8.layer_norm1.weight', 'vision_model.encoder.layers.23.mlp.fc1.bias', 'vision_model.encoder.layers.3.self_attn.v_proj.bias', 'vision_model.encoder.layers.5.mlp.fc2.bias', 'vision_model.encoder.layers.8.mlp.fc1.bias', 'vision_model.encoder.layers.20.self_attn.k_proj.weight', 'vision_model.encoder.layers.8.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.mlp.fc1.bias', 'vision_model.encoder.layers.7.self_attn.k_proj.weight', 'vision_model.encoder.layers.1.self_attn.out_proj.bias', 'vision_model.encoder.layers.2.mlp.fc2.weight', 'vision_model.encoder.layers.0.self_attn.k_proj.weight', 'vision_model.encoder.layers.3.mlp.fc1.weight', 'vision_model.encoder.layers.10.layer_norm2.bias', 'vision_model.encoder.layers.7.layer_norm2.weight', 'vision_model.encoder.layers.19.self_attn.v_proj.bias', 'vision_model.encoder.layers.0.layer_norm2.bias', 'vision_model.encoder.layers.7.layer_norm1.weight', 'vision_model.encoder.layers.22.self_attn.k_proj.bias', 'vision_model.encoder.layers.13.self_attn.v_proj.weight', 'vision_model.encoder.layers.5.self_attn.v_proj.bias', 'vision_model.encoder.layers.0.self_attn.out_proj.bias', 'vision_model.encoder.layers.17.self_attn.out_proj.bias', 'vision_model.encoder.layers.16.layer_norm2.weight', 'vision_model.encoder.layers.10.self_attn.q_proj.weight', 'vision_model.encoder.layers.19.self_attn.out_proj.bias', 'text_projection.weight', 'vision_model.encoder.layers.9.self_attn.q_proj.weight', 'vision_model.encoder.layers.5.self_attn.q_proj.bias', 'vision_model.encoder.layers.12.self_attn.k_proj.bias', 'vision_model.encoder.layers.4.mlp.fc1.weight', 'vision_model.encoder.layers.21.layer_norm1.bias', 'vision_model.encoder.layers.16.self_attn.q_proj.bias', 'vision_model.encoder.layers.23.self_attn.q_proj.bias', 'vision_model.encoder.layers.16.layer_norm1.weight', 'vision_model.encoder.layers.23.mlp.fc2.weight', 'vision_model.encoder.layers.7.mlp.fc2.bias', 'vision_model.encoder.layers.14.layer_norm2.weight', 'vision_model.encoder.layers.18.self_attn.q_proj.weight', 'vision_model.encoder.layers.23.mlp.fc2.bias', 'vision_model.encoder.layers.23.layer_norm1.weight', 'vision_model.encoder.layers.19.self_attn.v_proj.weight', 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'vision_model.encoder.layers.11.layer_norm1.weight', 'vision_model.encoder.layers.22.layer_norm2.bias', 'vision_model.encoder.layers.13.mlp.fc1.bias', 'vision_model.encoder.layers.23.self_attn.k_proj.bias', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 'vision_model.encoder.layers.9.self_attn.out_proj.bias', 'vision_model.encoder.layers.14.mlp.fc2.weight', 'vision_model.encoder.layers.3.layer_norm1.weight', 'vision_model.encoder.layers.1.self_attn.out_proj.weight', 'vision_model.encoder.layers.15.self_attn.v_proj.bias', 'vision_model.encoder.layers.13.self_attn.q_proj.bias', 'vision_model.encoder.layers.2.layer_norm2.weight', 'vision_model.encoder.layers.11.layer_norm1.bias', 'vision_model.encoder.layers.10.self_attn.k_proj.weight', 'vision_model.encoder.layers.21.self_attn.out_proj.weight', 'vision_model.pre_layrnorm.weight', 'vision_model.encoder.layers.15.self_attn.k_proj.bias', 'vision_model.encoder.layers.19.self_attn.out_proj.weight', 'vision_model.encoder.layers.19.self_attn.q_proj.weight', 'vision_model.encoder.layers.3.self_attn.v_proj.weight', 'vision_model.encoder.layers.20.layer_norm1.bias', 'vision_model.encoder.layers.9.layer_norm2.weight', 'vision_model.encoder.layers.6.self_attn.out_proj.bias', 'vision_model.encoder.layers.2.self_attn.v_proj.weight', 'vision_model.encoder.layers.8.self_attn.out_proj.weight', 'vision_model.encoder.layers.19.self_attn.q_proj.bias', 'vision_model.encoder.layers.3.layer_norm2.weight', 'vision_model.encoder.layers.18.self_attn.k_proj.weight', 'vision_model.encoder.layers.4.mlp.fc1.bias', 'vision_model.encoder.layers.20.layer_norm2.weight', 'vision_model.encoder.layers.1.self_attn.k_proj.weight'] - This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). load domain lora from /AnimateDiff/models/Motion_Module/v3_sd15_adapter.ckpt 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:01<00:36, 1.54s/it] 8%|▊ | 2/25 [00:03<00:35, 1.54s/it] 12%|█▏ | 3/25 [00:04<00:33, 1.54s/it] 16%|█▌ | 4/25 [00:06<00:32, 1.54s/it] 20%|██ | 5/25 [00:07<00:30, 1.54s/it] 24%|██▍ | 6/25 [00:09<00:29, 1.54s/it] 28%|██▊ | 7/25 [00:10<00:27, 1.54s/it] 32%|███▏ | 8/25 [00:12<00:26, 1.54s/it] 36%|███▌ | 9/25 [00:13<00:24, 1.54s/it] 40%|████ | 10/25 [00:15<00:23, 1.54s/it] 44%|████▍ | 11/25 [00:16<00:21, 1.54s/it] 48%|████▊ | 12/25 [00:18<00:20, 1.54s/it] 52%|█████▏ | 13/25 [00:20<00:18, 1.54s/it] 56%|█████▌ | 14/25 [00:21<00:16, 1.54s/it] 60%|██████ | 15/25 [00:23<00:15, 1.54s/it] 64%|██████▍ | 16/25 [00:24<00:13, 1.54s/it] 68%|██████▊ | 17/25 [00:26<00:12, 1.54s/it] 72%|███████▏ | 18/25 [00:27<00:10, 1.54s/it] 76%|███████▌ | 19/25 [00:29<00:09, 1.54s/it] 80%|████████ | 20/25 [00:30<00:07, 1.54s/it] 84%|████████▍ | 21/25 [00:32<00:06, 1.55s/it] 88%|████████▊ | 22/25 [00:33<00:04, 1.54s/it] 92%|█████████▏| 23/25 [00:35<00:03, 1.55s/it] 96%|█████████▌| 24/25 [00:37<00:01, 1.54s/it] 100%|██████████| 25/25 [00:38<00:00, 1.55s/it] 100%|██████████| 25/25 [00:38<00:00, 1.54s/it] 0%| | 0/16 [00:00<?, ?it/s] 31%|███▏ | 5/16 [00:00<00:00, 46.58it/s] 62%|██████▎ | 10/16 [00:00<00:00, 24.95it/s] 88%|████████▊ | 14/16 [00:00<00:00, 22.06it/s] 100%|██████████| 16/16 [00:00<00:00, 23.13it/s]
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