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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Experiments with Text-To-Video Zero Pipeline"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/awu/dev/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import torch\n",
"import imageio\n",
"from diffusers import TextToVideoZeroPipeline, ControlNetModel, StableDiffusionControlNetPipeline, TextToVideoZeroPipeline\n",
"from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor\n",
"from huggingface_hub import hf_hub_download\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"sys.path.insert(0, \"..\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import jax\n",
"jax.local_devices()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Text-To-Video"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_id = \"tuwonga/zukki_style\"\n",
"pipe = TextToVideoZeroPipeline.from_pretrained(model_id)\n",
"\n",
"prompt = \"A person taking a walk through the city at night\"\n",
"result = pipe(prompt=prompt).images\n",
"result = [(r * 255).astype(\"uint8\") for r in result]\n",
"imageio.mimsave(\"video.mp4\", result, fps=4)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Text-To-Video with Pose Control"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_id = \"runwayml/stable-diffusion-v1-5\" # base model\n",
"video_path = \"__assets__/dance1_corr.mp4\" # pose video\n",
"\n",
"reader = imageio.get_reader(video_path, \"ffmpeg\")\n",
"frame_count = 8\n",
"pose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]\n",
"\n",
"controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-openpose\")\n",
"pipe = StableDiffusionControlNetPipeline.from_pretrained(model_id, controlnet=controlnet)\n",
"\n",
"# Set the attention processor\n",
"pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n",
"pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n",
"\n",
"# fix latents for all frames\n",
"latents = torch.randn((1, 4, 64, 64)).repeat(len(pose_images), 1, 1, 1)\n",
"\n",
"prompt = \"Darth Vader dancing in a desert\"\n",
"result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images\n",
"imageio.mimsave(\"video.mp4\", result, fps=4)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Text-To-Video with Safetensors"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checkpoint path: /home/awu/.cache/huggingface/hub/models--breakcore2--ligne_claire_anime_diffusion/snapshots/0e89c2e14030f1afdc77b208e35aaf4a597238d9/ligne_claire_anime_diffusion_v1.safetensors\n",
"global_step key not found in model\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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'vision_model.encoder.layers.2.self_attn.v_proj.bias', 'vision_model.encoder.layers.16.mlp.fc2.weight', 'vision_model.encoder.layers.8.self_attn.k_proj.bias', 'vision_model.encoder.layers.13.self_attn.k_proj.weight', 'vision_model.encoder.layers.18.self_attn.q_proj.weight', 'vision_model.encoder.layers.19.mlp.fc1.weight', 'vision_model.encoder.layers.21.mlp.fc2.bias', 'vision_model.encoder.layers.16.layer_norm2.weight', 'vision_model.encoder.layers.18.mlp.fc1.weight', 'vision_model.encoder.layers.16.self_attn.out_proj.weight', 'vision_model.encoder.layers.4.layer_norm1.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).\n",
"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"id2label\"]` will be overriden.\n",
"/home/awu/dev/lib/python3.8/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt\n",
"from huggingface_hub import hf_hub_download\n",
"\n",
"ckpt_path = hf_hub_download(repo_id=\"breakcore2/ligne_claire_anime_diffusion\", filename=\"ligne_claire_anime_diffusion_v1.safetensors\")\n",
"\n",
"print(f\"Checkpoint path: {ckpt_path}\")\n",
"\n",
"# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\n",
"pipe = download_from_original_stable_diffusion_ckpt(\n",
" checkpoint_path=ckpt_path,\n",
" original_config_file=\"configs/v1-inference.yaml\",\n",
" from_safetensors=True\n",
")\n",
"\n",
"# pipe.save_pretrained(\"./models/ligne_claire\", safe_serialization=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StableDiffusionPipeline {\n",
" \"_class_name\": \"StableDiffusionPipeline\",\n",
" \"_diffusers_version\": \"0.16.0.dev0\",\n",
" \"feature_extractor\": [\n",
" \"transformers\",\n",
" \"CLIPFeatureExtractor\"\n",
" ],\n",
" \"requires_safety_checker\": true,\n",
" \"safety_checker\": [\n",
" \"stable_diffusion\",\n",
" \"StableDiffusionSafetyChecker\"\n",
" ],\n",
" \"scheduler\": [\n",
" \"diffusers\",\n",
" \"PNDMScheduler\"\n",
" ],\n",
" \"text_encoder\": [\n",
" \"transformers\",\n",
" \"CLIPTextModel\"\n",
" ],\n",
" \"tokenizer\": [\n",
" \"transformers\",\n",
" \"CLIPTokenizer\"\n",
" ],\n",
" \"unet\": [\n",
" \"diffusers\",\n",
" \"UNet2DConditionModel\"\n",
" ],\n",
" \"vae\": [\n",
" \"diffusers\",\n",
" \"AutoencoderKL\"\n",
" ]\n",
"}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipe"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"UNet2DConditionModel(\n",
" (conv_in): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_proj): Timesteps()\n",
" (time_embedding): TimestepEmbedding(\n",
" (linear_1): Linear(in_features=320, out_features=1280, bias=True)\n",
" (act): SiLU()\n",
" (linear_2): Linear(in_features=1280, out_features=1280, bias=True)\n",
" )\n",
" (down_blocks): ModuleList(\n",
" (0): CrossAttnDownBlock2D(\n",
" (attentions): ModuleList(\n",
" (0-1): 2 x Transformer2DModel(\n",
" (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\n",
" (proj_in): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\n",
" (transformer_blocks): ModuleList(\n",
" (0): BasicTransformerBlock(\n",
" (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
" (attn1): Attention(\n",
" (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
" (to_k): Linear(in_features=320, out_features=320, bias=False)\n",
" (to_v): Linear(in_features=320, out_features=320, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=320, out_features=320, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
" (attn2): Attention(\n",
" (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
" (to_k): Linear(in_features=768, out_features=320, bias=False)\n",
" (to_v): Linear(in_features=768, out_features=320, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=320, out_features=320, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
" (ff): FeedForward(\n",
" (net): ModuleList(\n",
" (0): GEGLU(\n",
" (proj): Linear(in_features=320, out_features=2560, bias=True)\n",
" )\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" (2): Linear(in_features=1280, out_features=320, bias=True)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (proj_out): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (resnets): ModuleList(\n",
" (0-1): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
" (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (downsamplers): ModuleList(\n",
" (0): Downsample2D(\n",
" (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (1): CrossAttnDownBlock2D(\n",
" (attentions): ModuleList(\n",
" (0-1): 2 x Transformer2DModel(\n",
" (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\n",
" (proj_in): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\n",
" (transformer_blocks): ModuleList(\n",
" (0): BasicTransformerBlock(\n",
" (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
" (attn1): Attention(\n",
" (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
" (to_k): Linear(in_features=640, out_features=640, bias=False)\n",
" (to_v): Linear(in_features=640, out_features=640, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=640, out_features=640, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
" (attn2): Attention(\n",
" (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
" (to_k): Linear(in_features=768, out_features=640, bias=False)\n",
" (to_v): Linear(in_features=768, out_features=640, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=640, out_features=640, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
" (ff): FeedForward(\n",
" (net): ModuleList(\n",
" (0): GEGLU(\n",
" (proj): Linear(in_features=640, out_features=5120, bias=True)\n",
" )\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" (2): Linear(in_features=2560, out_features=640, bias=True)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (proj_out): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
" (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(320, 640, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
" (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (downsamplers): ModuleList(\n",
" (0): Downsample2D(\n",
" (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (2): CrossAttnDownBlock2D(\n",
" (attentions): ModuleList(\n",
" (0-1): 2 x Transformer2DModel(\n",
" (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
" (proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" (transformer_blocks): ModuleList(\n",
" (0): BasicTransformerBlock(\n",
" (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
" (attn1): Attention(\n",
" (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
" (attn2): Attention(\n",
" (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_k): Linear(in_features=768, out_features=1280, bias=False)\n",
" (to_v): Linear(in_features=768, out_features=1280, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
" (ff): FeedForward(\n",
" (net): ModuleList(\n",
" (0): GEGLU(\n",
" (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
" )\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(640, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (downsamplers): ModuleList(\n",
" (0): Downsample2D(\n",
" (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (3): DownBlock2D(\n",
" (resnets): ModuleList(\n",
" (0-1): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (up_blocks): ModuleList(\n",
" (0): UpBlock2D(\n",
" (resnets): ModuleList(\n",
" (0-2): 3 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (upsamplers): ModuleList(\n",
" (0): Upsample2D(\n",
" (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (1): CrossAttnUpBlock2D(\n",
" (attentions): ModuleList(\n",
" (0-2): 3 x Transformer2DModel(\n",
" (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
" (proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" (transformer_blocks): ModuleList(\n",
" (0): BasicTransformerBlock(\n",
" (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
" (attn1): Attention(\n",
" (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
" (attn2): Attention(\n",
" (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_k): Linear(in_features=768, out_features=1280, bias=False)\n",
" (to_v): Linear(in_features=768, out_features=1280, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
" (ff): FeedForward(\n",
" (net): ModuleList(\n",
" (0): GEGLU(\n",
" (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
" )\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (resnets): ModuleList(\n",
" (0-1): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (2): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(1920, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (upsamplers): ModuleList(\n",
" (0): Upsample2D(\n",
" (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (2): CrossAttnUpBlock2D(\n",
" (attentions): ModuleList(\n",
" (0-2): 3 x Transformer2DModel(\n",
" (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\n",
" (proj_in): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\n",
" (transformer_blocks): ModuleList(\n",
" (0): BasicTransformerBlock(\n",
" (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
" (attn1): Attention(\n",
" (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
" (to_k): Linear(in_features=640, out_features=640, bias=False)\n",
" (to_v): Linear(in_features=640, out_features=640, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=640, out_features=640, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
" (attn2): Attention(\n",
" (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
" (to_k): Linear(in_features=768, out_features=640, bias=False)\n",
" (to_v): Linear(in_features=768, out_features=640, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=640, out_features=640, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
" (ff): FeedForward(\n",
" (net): ModuleList(\n",
" (0): GEGLU(\n",
" (proj): Linear(in_features=640, out_features=5120, bias=True)\n",
" )\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" (2): Linear(in_features=2560, out_features=640, bias=True)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (proj_out): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
" (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(1920, 640, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
" (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(1280, 640, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (2): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
" (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(960, 640, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (upsamplers): ModuleList(\n",
" (0): Upsample2D(\n",
" (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (3): CrossAttnUpBlock2D(\n",
" (attentions): ModuleList(\n",
" (0-2): 3 x Transformer2DModel(\n",
" (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\n",
" (proj_in): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\n",
" (transformer_blocks): ModuleList(\n",
" (0): BasicTransformerBlock(\n",
" (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
" (attn1): Attention(\n",
" (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
" (to_k): Linear(in_features=320, out_features=320, bias=False)\n",
" (to_v): Linear(in_features=320, out_features=320, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=320, out_features=320, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
" (attn2): Attention(\n",
" (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
" (to_k): Linear(in_features=768, out_features=320, bias=False)\n",
" (to_v): Linear(in_features=768, out_features=320, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=320, out_features=320, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
" (ff): FeedForward(\n",
" (net): ModuleList(\n",
" (0): GEGLU(\n",
" (proj): Linear(in_features=320, out_features=2560, bias=True)\n",
" )\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" (2): Linear(in_features=1280, out_features=320, bias=True)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (proj_out): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(960, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
" (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1-2): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
" (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(640, 320, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (mid_block): UNetMidBlock2DCrossAttn(\n",
" (attentions): ModuleList(\n",
" (0): Transformer2DModel(\n",
" (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
" (proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" (transformer_blocks): ModuleList(\n",
" (0): BasicTransformerBlock(\n",
" (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
" (attn1): Attention(\n",
" (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
" (attn2): Attention(\n",
" (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
" (to_k): Linear(in_features=768, out_features=1280, bias=False)\n",
" (to_v): Linear(in_features=768, out_features=1280, bias=False)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
" (ff): FeedForward(\n",
" (net): ModuleList(\n",
" (0): GEGLU(\n",
" (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
" )\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" )\n",
" (resnets): ModuleList(\n",
" (0-1): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
" (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" )\n",
" (conv_norm_out): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
" (conv_act): SiLU()\n",
" (conv_out): Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
")"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipe.unet"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Models...\n",
"Generating Animation...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 50/50 [51:51<00:00, 62.23s/it] \n"
]
}
],
"source": [
"video_path = \"../__assets__/dance2_corr.mp4\" # pose video\n",
"\n",
"reader = imageio.get_reader(video_path, \"ffmpeg\")\n",
"frame_count = 16\n",
"pose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]\n",
"\n",
"print(\"Loading Models...\")\n",
"controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-openpose\")\n",
"pipe = StableDiffusionControlNetPipeline.from_pretrained(\"../models/ligne_claire\", controlnet=controlnet)\n",
"\n",
"# Set the attention processor\n",
"pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n",
"pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n",
"\n",
"# fix latents for all frames\n",
"latents = torch.randn((1, 4, 64, 64)).repeat(len(pose_images), 1, 1, 1)\n",
"\n",
"\n",
"print(\"Generating Animation...\")\n",
"prompt = \"(ligne claire), girl walking through a city of sky scrapers at night\"\n",
"result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images\n",
"imageio.mimsave(\"video.mp4\", result, fps=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "dev",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|