Commit
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4a1207e
1
Parent(s):
6b1a06f
TemporalNet2 initial changes
Browse files- README.md +36 -0
- cldm_v15.yaml +79 -0
- config.json +42 -0
- crop_all_images.py +33 -0
- temporalnetversion2.ckpt +3 -0
- temporalnetversion2.yaml +79 -0
- temporalvideo.py +213 -0
README.md
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---
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license: openrail
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---
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---
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license: openrail
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tags:
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- controlnet
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- stable-diffusion
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- diffusers
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base_model: runwayml/stable-diffusion-v1-5
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---
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Introducing the Beta Version of TemporalNet
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TemporalNet is a ControlNet model designed to enhance the temporal consistency of generated outputs, as demonstrated in this example: https://twitter.com/CiaraRowles1/status/1637486561917906944. While it does not eliminate all flickering, it significantly reduces it, particularly at higher denoise levels. For optimal results, it is recommended to use TemporalNet in combination with other methods.
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Instructions for Use:
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1) Add the model "diff_control_sd15_temporalnet_fp16.safetensors" to your models folder in the ControlNet extension in Automatic1111's Web UI.
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2) Create a folder that contains:
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- A subfolder named "Input_Images" with the input frames
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- A PNG file called "init.png" that is pre-stylized in your desired style
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- The "temporalvideo.py" script
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3) Customize the "temporalvideo.py" script according to your preferences, such as the image resolution, prompt, and control net settings.
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4) Launch Automatic1111's Web UI with the --api setting enabled.
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5) Execute the Python script.
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*Please note that the "init.png" image will not significantly influence the style of the output video. Its primary purpose is to prevent a drastic change in aesthetics during the first few frames.*
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Also, I highly recommend you use this in conjunction with the hed model, the settings are already in the script.
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ToDo:
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Write an Extension for the web ui.
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Write a feature that automatically generates an "init.png" image if none is provided.
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̶C̶h̶a̶n̶g̶e̶ ̶t̶h̶e̶ ̶e̶x̶t̶e̶n̶s̶i̶o̶n̶ ̶t̶o̶ ̶.̶s̶a̶f̶e̶t̶e̶n̶s̶o̶r̶s̶ ̶a̶n̶d̶ ̶i̶n̶v̶e̶s̶t̶i̶g̶a̶t̶e̶ ̶c̶o̶m̶p̶r̶e̶s̶s̶i̶o̶n̶.̶
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cldm_v15.yaml
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model:
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target: cldm.cldm.ControlLDM
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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control_key: "hint"
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image_size: 64
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channels: 4
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cond_stage_trainable: false
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False
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only_mid_control: False
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control_stage_config:
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target: cldm.cldm.ControlNet
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params:
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image_size: 32 # unused
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in_channels: 4
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hint_channels: 3
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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unet_config:
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target: cldm.cldm.ControlledUnetModel
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params:
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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double_z: true
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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config.json
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{
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"_class_name": "ControlNetModel",
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"_diffusers_version": "0.15.0.dev0",
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"_name_or_path": "./",
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"act_fn": "silu",
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"attention_head_dim": 8,
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"block_out_channels": [
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320,
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640,
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1280,
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1280
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],
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"class_embed_type": null,
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"conditioning_embedding_out_channels": [
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16,
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32,
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96,
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256
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],
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"controlnet_conditioning_channel_order": "rgb",
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"cross_attention_dim": 768,
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"down_block_types": [
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D"
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],
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"downsample_padding": 1,
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"flip_sin_to_cos": true,
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"freq_shift": 0,
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"in_channels": 4,
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"layers_per_block": 2,
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"mid_block_scale_factor": 1,
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"norm_eps": 1e-05,
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"norm_num_groups": 32,
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"num_class_embeds": null,
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"only_cross_attention": false,
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"projection_class_embeddings_input_dim": null,
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"resnet_time_scale_shift": "default",
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"upcast_attention": false,
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"use_linear_projection": false
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}
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crop_all_images.py
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import cv2
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import os
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y_folder = "./Input_Images"
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def crop_and_save_images(folder_path):
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# Get a list of all files in the folder
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files = os.listdir(folder_path)
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for file in files:
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# Construct the full file path
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file_path = os.path.join(folder_path, file)
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# Load the image
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img = cv2.imread(file_path)
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# Get the dimensions of the image
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h, w = img.shape[:2]
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# Determine the size of the crop
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crop_size = min(h, w)
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# Calculate the start coordinates of the crop
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start_y = (h - crop_size) // 2
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start_x = (w - crop_size) // 2
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# Perform the crop
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img_cropped = img[start_y : start_y + crop_size, start_x : start_x + crop_size]
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# Save the cropped image, overwriting the original image
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cv2.imwrite(file_path, img_cropped)
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# Example usage:
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crop_and_save_images(y_folder)
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temporalnetversion2.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:51eea78ec529e554291e5c6f66f5a37c27ae8565594b22da297e28eb0f47ab27
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size 12688113093
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temporalnetversion2.yaml
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model:
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target: cldm.cldm.ControlLDM
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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control_key: "hint"
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image_size: 64
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channels: 4
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cond_stage_trainable: false
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False
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only_mid_control: False
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control_stage_config:
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target: cldm.cldm.ControlNet
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params:
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image_size: 32 # unused
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in_channels: 4
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hint_channels: 6
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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unet_config:
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target: cldm.cldm.ControlledUnetModel
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params:
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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double_z: true
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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+
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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temporalvideo.py
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|
1 |
+
import os
|
2 |
+
import glob
|
3 |
+
import requests
|
4 |
+
import json
|
5 |
+
import cv2
|
6 |
+
import numpy as np
|
7 |
+
import sys
|
8 |
+
import torch
|
9 |
+
from PIL import Image
|
10 |
+
from pprint import pprint
|
11 |
+
import base64
|
12 |
+
from io import BytesIO
|
13 |
+
import torchvision.transforms.functional as F
|
14 |
+
from torchvision.io import read_video, read_image, ImageReadMode
|
15 |
+
from torchvision.models.optical_flow import Raft_Large_Weights
|
16 |
+
from torchvision.models.optical_flow import raft_large
|
17 |
+
from torchvision.io import read_video, read_image, ImageReadMode
|
18 |
+
from torchvision.utils import flow_to_image
|
19 |
+
import cv2
|
20 |
+
from torchvision.io import write_jpeg
|
21 |
+
import pickle
|
22 |
+
|
23 |
+
|
24 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
25 |
+
|
26 |
+
model = raft_large(weights=Raft_Large_Weights.DEFAULT, progress=False).to(device)
|
27 |
+
model = model.eval()
|
28 |
+
|
29 |
+
# Replace with the actual path to your image file and folder
|
30 |
+
x_path = "./init.png"
|
31 |
+
y_folder = "./Input_Images"
|
32 |
+
|
33 |
+
output_folder = "output"
|
34 |
+
os.makedirs(output_folder, exist_ok=True)
|
35 |
+
|
36 |
+
def get_image_paths(folder):
|
37 |
+
image_extensions = ("*.jpg", "*.jpeg", "*.png", "*.bmp")
|
38 |
+
files = []
|
39 |
+
for ext in image_extensions:
|
40 |
+
files.extend(glob.glob(os.path.join(folder, ext)))
|
41 |
+
return sorted(files)
|
42 |
+
|
43 |
+
y_paths = get_image_paths(y_folder)
|
44 |
+
|
45 |
+
def send_request(last_image_path, optical_flow_path,current_image_path):
|
46 |
+
url = "http://localhost:7860/sdapi/v1/img2img"
|
47 |
+
|
48 |
+
with open(last_image_path, "rb") as b:
|
49 |
+
last_image_encoded = base64.b64encode(b.read()).decode("utf-8")
|
50 |
+
|
51 |
+
# Load and process the last image
|
52 |
+
last_image = cv2.imread(last_image_path)
|
53 |
+
last_image = cv2.cvtColor(last_image, cv2.COLOR_BGR2RGB)
|
54 |
+
last_image = cv2.resize(last_image, (512, 512))
|
55 |
+
|
56 |
+
# Load and process the optical flow image
|
57 |
+
flow_image = cv2.imread(optical_flow_path)
|
58 |
+
flow_image = cv2.cvtColor(flow_image, cv2.COLOR_BGR2RGB)
|
59 |
+
|
60 |
+
# Load and process the current image
|
61 |
+
with open(current_image_path, "rb") as b:
|
62 |
+
current_image = base64.b64encode(b.read()).decode("utf-8")
|
63 |
+
|
64 |
+
|
65 |
+
# Concatenating the three images to make a 6-channel image
|
66 |
+
six_channel_image = np.dstack((last_image, flow_image))
|
67 |
+
|
68 |
+
# Serializing the 6-channel image
|
69 |
+
serialized_image = pickle.dumps(six_channel_image)
|
70 |
+
|
71 |
+
# Encoding the serialized image
|
72 |
+
encoded_image = base64.b64encode(serialized_image).decode('utf-8')
|
73 |
+
|
74 |
+
data = {
|
75 |
+
"init_images": [current_image],
|
76 |
+
"inpainting_fill": 0,
|
77 |
+
"inpaint_full_res": True,
|
78 |
+
"inpaint_full_res_padding": 1,
|
79 |
+
"inpainting_mask_invert": 1,
|
80 |
+
"resize_mode": 0,
|
81 |
+
"denoising_strength": 0.4,
|
82 |
+
"prompt": "1girl, woman",
|
83 |
+
"negative_prompt": "",
|
84 |
+
"alwayson_scripts": {
|
85 |
+
"ControlNet":{
|
86 |
+
"args": [
|
87 |
+
{
|
88 |
+
"input_image": current_image,
|
89 |
+
"module": "hed",
|
90 |
+
"model": "control_hed-fp16 [13fee50b]",
|
91 |
+
"weight": 0.7,
|
92 |
+
"guidance": 1,
|
93 |
+
},
|
94 |
+
{
|
95 |
+
"input_image": encoded_image,
|
96 |
+
"model": "temporalnetversion2 [b146ac48]",
|
97 |
+
"module": "none",
|
98 |
+
"weight": 0.6,
|
99 |
+
"guidance": 1,
|
100 |
+
},
|
101 |
+
{
|
102 |
+
"input_image": current_image,
|
103 |
+
"model": "control_v11p_sd15_openpose [cab727d4]",
|
104 |
+
"module": "openpose_full",
|
105 |
+
"weight": 0.7,
|
106 |
+
"guidance":1,
|
107 |
+
}
|
108 |
+
|
109 |
+
|
110 |
+
]
|
111 |
+
}
|
112 |
+
},
|
113 |
+
"seed": 4123457655,
|
114 |
+
"subseed": -1,
|
115 |
+
"subseed_strength": -1,
|
116 |
+
"sampler_index": "Euler a",
|
117 |
+
"batch_size": 1,
|
118 |
+
"n_iter": 1,
|
119 |
+
"steps": 20,
|
120 |
+
"cfg_scale": 6,
|
121 |
+
"width": 512,
|
122 |
+
"height": 512,
|
123 |
+
"restore_faces": True,
|
124 |
+
"include_init_images": True,
|
125 |
+
"override_settings": {},
|
126 |
+
"override_settings_restore_afterwards": True
|
127 |
+
}
|
128 |
+
response = requests.post(url, json=data)
|
129 |
+
if response.status_code == 200:
|
130 |
+
return response.content
|
131 |
+
else:
|
132 |
+
try:
|
133 |
+
error_data = response.json()
|
134 |
+
print("Error:")
|
135 |
+
print(str(error_data))
|
136 |
+
|
137 |
+
except json.JSONDecodeError:
|
138 |
+
print(f"Error: Unable to parse JSON error data.")
|
139 |
+
return None
|
140 |
+
|
141 |
+
|
142 |
+
|
143 |
+
def infer(frameA, frameB):
|
144 |
+
|
145 |
+
|
146 |
+
input_frame_1 = read_image(str(frameA), ImageReadMode.RGB)
|
147 |
+
|
148 |
+
input_frame_2 = read_image(str(frameB), ImageReadMode.RGB)
|
149 |
+
|
150 |
+
|
151 |
+
#img1_batch = torch.stack([frames[0]])
|
152 |
+
#img2_batch = torch.stack([frames[1]])
|
153 |
+
|
154 |
+
img1_batch = torch.stack([input_frame_1])
|
155 |
+
img2_batch = torch.stack([input_frame_2])
|
156 |
+
|
157 |
+
|
158 |
+
weights = Raft_Large_Weights.DEFAULT
|
159 |
+
transforms = weights.transforms()
|
160 |
+
|
161 |
+
|
162 |
+
def preprocess(img1_batch, img2_batch):
|
163 |
+
img1_batch = F.resize(img1_batch, size=[512, 512])
|
164 |
+
img2_batch = F.resize(img2_batch, size=[512, 512])
|
165 |
+
return transforms(img1_batch, img2_batch)
|
166 |
+
|
167 |
+
|
168 |
+
img1_batch, img2_batch = preprocess(img1_batch, img2_batch)
|
169 |
+
|
170 |
+
|
171 |
+
list_of_flows = model(img1_batch.to(device), img2_batch.to(device))
|
172 |
+
|
173 |
+
predicted_flows = list_of_flows[-1]
|
174 |
+
|
175 |
+
|
176 |
+
#flow_imgs = flow_to_image(predicted_flows)
|
177 |
+
|
178 |
+
#print(flow_imgs)
|
179 |
+
|
180 |
+
predicted_flow = list_of_flows[-1][0]
|
181 |
+
opitcal_flow_path = os.path.join(output_folder, f"flow_{i}.png")
|
182 |
+
flow_img = flow_to_image(predicted_flow).to("cpu")
|
183 |
+
write_jpeg(flow_img,opitcal_flow_path)
|
184 |
+
|
185 |
+
|
186 |
+
return opitcal_flow_path
|
187 |
+
|
188 |
+
output_images = []
|
189 |
+
output_paths = []
|
190 |
+
|
191 |
+
# Initialize with the first image path
|
192 |
+
|
193 |
+
result = x_path
|
194 |
+
output_image_path = os.path.join(output_folder, f"output_image_0.png")
|
195 |
+
|
196 |
+
#with open(output_image_path, "wb") as f:
|
197 |
+
# f.write(result)
|
198 |
+
|
199 |
+
last_image_path = x_path
|
200 |
+
for i in range(1, len(y_paths)):
|
201 |
+
# Use the last image path and optical flow map to generate the next input
|
202 |
+
optical_flow = infer(y_paths[i - 1], y_paths[i])
|
203 |
+
|
204 |
+
# Modify your send_request to use the last_image_path
|
205 |
+
result = send_request(last_image_path, optical_flow, y_paths[i])
|
206 |
+
data = json.loads(result)
|
207 |
+
encoded_image = data["images"][0]
|
208 |
+
output_image_path = os.path.join(output_folder, f"output_image_{i}.png")
|
209 |
+
last_image_path = output_image_path
|
210 |
+
with open(output_image_path, "wb") as f:
|
211 |
+
f.write(base64.b64decode(encoded_image))
|
212 |
+
print(f"Written data for frame {i}:")
|
213 |
+
|