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[v2.0] AnimateDiff ControlNet Animation Refiner - LCM [Part 3]

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Description

New Update v2.1 !!! Available Here :  https://www.patreon.com/posts/update-v2-1-lcm-95056616

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A series of 5 workflows, for easy understanding, usage, and more clarity.

The Workflow is divided into 5 parts :


What this workflow does


This workflow can Refine Bad looking images from [Part 2] into detailed videos, with the help of animateDiff used as an Upscaler and Refiner.  


How to use this workflow


As you have already generated raw images from [Part 2] you can further enhance the details from this workflow.


  • 1) Enter the Paths in Purple Directory Nodes of the Raw Images
  • 2) Enter the Output path for saving the refined images.
  • 3) Enter Batch Range
  • 4) Overlapping Frames - 0 is Default (Put 5 or 10 for overlapping cross fade technique between batches)
  • 5) Skip Frame is 0 by default (Increase it after every batch)
  • 6) Batch Number Naming, Increase it by 1 after every batch for organizing.
  • 7) Refiner Upscale - Upscale value is 1.4 by default ( Use 1.4 - 1.8 for best result for SD models)
  • 8) Refiner Denoise - 0.45 by default (Use between 0.4 - 0.6 for best results, Higher value may change the image entirely) * Inject Additional Refiner Noise - Use value 0 - 1 (It enhances the minute details, use with add details lora)
  • 9) Enter Prompts for more accuracy.  
  • 10) Choose Model checkpoint you want to use for refiner.
  • 11) Loras work as usual (Some of not compatible with animatediff, test it first with 10 frames)


Tips about this workflow

You can also use this Refiner as :


1) Vid2Vid stylization


Stylized Video2Video Can also be made with this [Part 3] Refiner workflow:

  • Generate the passes from [Part 1} and it will also produce original frames in a separate folder.
  • Enter the ControlNet Softedge pass and Lineart pass in the controlnet directory node.
  • Use these original frames as raw input in the refiner workflow with a denoise of 0.5-0.7
  • Enter prompts and choose models, loras for stylization.


2) SVD / Normal Video Refiner


You can also refine Videos from SVD or even a low Res video of anything and convert it to a high res.

  • Generate the passes from [Part 1} and it will also produce original frames in a separate folder.
  • Enter the ControlNet Softedge pass and Lineart pass in the controlnet directory node.
  • Put Value around 1.5 in the Upscale node
  • Use these original frames as raw input in the refiner workflow with a denoise of 0.5-0.7
  • Enter prompts for moreaccuracy and choose proper models , realistic for real photos, and anime for anime images ...etc.

SVD with this refiner already built can be found here : SVD with AnimateDiff Refiner


🎥 Video demo link (optional)

All the Recent Uploaded Shorts are refined from this workflow :  https://www.youtube.com/@Ai_Davos/shorts  


Important Documented Tutorial:

https://www.patreon.com/posts/update-animate-94523632  


My Discord Server : https://discord.gg/z9rgJyfPWJ

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Primitive Nodes (18)

Evaluate Integers (1)

Note (4)

PrimitiveNode (3)

Reroute (10)

Custom Nodes (56)

AnimateDiff Evolved

  • - ADE_AnimateDiffUniformContextOptions (1)

  • - ADE_AnimateDiffLoaderWithContext (1)

  • - CR Integer To String (3)

  • - CR Apply LoRA Stack (1)

  • - CR LoRA Stack (2)

ComfyUI

  • - KSampler (1)

  • - PreviewImage (6)

  • - CheckpointLoaderSimple (1)

  • - LoraLoader (1)

  • - ModelSamplingDiscrete (1)

  • - ControlNetApplyAdvanced (2)

  • - VAELoader (1)

  • - ImageScaleBy (1)

  • - VAEDecode (1)

  • - VAEEncode (1)

  • - BNK_InjectNoise (1)

  • - ControlNetLoaderAdvanced (2)

  • - LoadImagesFromDirectory (3)

  • - VHS_SplitImages (6)

  • - FloatConstant (4)

  • - ColorMatch (1)

  • - Int Literal (2)

  • - Cfg Literal (1)

  • - Seed Generator (1)

  • - smZ CLIPTextEncode (2)

  • - GetImageSize (1)

  • - Text Multiline (2)

  • - Image Save (1)

  • - Text Concatenate (1)

  • - Text String (1)

  • - String to Text (1)

Checkpoints (1)

imp_v10.safetensors

LoRAs (1)

lcm_pytorch_lora_weights.safetensors