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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "b53f5b58",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3c8361d88484d9984ecd3746399940f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading pipeline components...: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d82356db6da94c5cb78b8f5681f87304",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5052153c971542aa9dc0a9561f040bc5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import torch\n",
"from diffusers import AutoencoderKLWan\n",
"from pipeline import WanPipeline\n",
"from diffusers.utils import export_to_video\n",
"\n",
"model_id = \"Wan-AI/Wan2.1-T2V-1.3B-Diffusers\"\n",
"vae = AutoencoderKLWan.from_pretrained(model_id, subfolder=\"vae\", torch_dtype=torch.float32)\n",
"pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)\n",
"pipe.load_lora_weights(\n",
" \"Kijai/WanVideo_comfy\",\n",
" weight_name=\"Wan21_CausVid_bidirect2_T2V_1_3B_lora_rank32.safetensors\",\n",
" adapter_name=\"lora\"\n",
") \n",
"pipe = pipe.to(\"cuda\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "cc3c8947",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"True\n",
"2 510\n",
"32760\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`num_frames - 1` has to be divisible by 4. Rounding to the nearest number.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2f9f2e39d9584aab8cc0f27e4f061dc1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/12 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
},
{
"data": {
"text/plain": [
"'vsf.mp4'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from processor import WanAttnProcessor2_0\n",
"\n",
"# prompt = \"A chef cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The cat is wearing a chef suit\"\n",
"# neg_prompt = \"chef hat\"\n",
"prompt = \"A cessna flying over a snowy mountain landscape, with a clear blue sky and fluffy white clouds. The plane is flying at a low altitude, casting a shadow on the snow-covered ground below. The mountains are rugged and steep, with patches of evergreen trees visible in the foreground.\"\n",
"neg_prompt = \"trees\"\n",
"\n",
"neg_prompt_embeds, _ = pipe.encode_prompt(\n",
" prompt=neg_prompt,\n",
" padding=False,\n",
" do_classifier_free_guidance=False,\n",
")\n",
"\n",
"pos_prompt_embeds, _ = pipe.encode_prompt( \n",
" prompt=prompt,\n",
" do_classifier_free_guidance=False, \n",
" max_sequence_length=512 - neg_prompt_embeds.shape[1],\n",
")\n",
"pipe.set_adapters(\"lora\", 0.5)\n",
"\n",
"\n",
"\n",
"neg_len = neg_prompt_embeds.shape[1]\n",
"pos_len = pos_prompt_embeds.shape[1]\n",
"print(neg_len, pos_len)\n",
"height = 480\n",
"width = 832\n",
"frames = 81\n",
"\n",
"img_len = (height//8) * (width//8) * 3 * (frames // 4 + 1) // 12\n",
"print(img_len)\n",
"mask = torch.zeros((1, img_len, pos_len+neg_len)).cuda()\n",
"mask[:, :, -neg_len:] = -torch.inf # this should be negative -torch.inf #\n",
"# mask[:, :, -neg_len:] = -0.2 # this should be negative -torch.inf #\n",
"\n",
"for block in pipe.transformer.blocks:\n",
" block.attn2.processor = WanAttnProcessor2_0(scale=1.7, neg_prompt_length=neg_len, attn_mask=mask)\n",
"# should we still do exploation in space \n",
"\n",
"prompt_embeds = torch.cat([pos_prompt_embeds, neg_prompt_embeds], dim=1)\n",
"\n",
"output = pipe(\n",
" prompt_embeds=prompt_embeds,\n",
" # prompt_embeds=pos_prompt_embeds,\n",
" negative_prompt=neg_prompt,\n",
" height=height,\n",
" width=width,\n",
" num_frames=frames + 1,\n",
" num_inference_steps=12,\n",
" guidance_scale=0.0, \n",
" generator=torch.Generator(device=\"cuda\").manual_seed(42),\n",
").frames[0]\n",
"export_to_video(output, \"vsf.mp4\", fps=15)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "neg",
"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": "3.10.17"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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