Instructions to use deathlegionteam/LEGION-Video-Gen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use deathlegionteam/LEGION-Video-Gen with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("deathlegionteam/LEGION-Video-Gen", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 2,116 Bytes
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"_class_name": "HunyuanVideo15Pipeline",
"_diffusers_version": "0.36.0.dev0",
"_name_or_path": "deathlegionteam/LEGION-Video-Gen",
"architectures": [
"HunyuanVideo15ForConditionalGeneration"
],
"model_type": "hunyuan_video",
"license": "apache-2.0",
"library_name": "diffusers",
"pipeline_tag": "text-to-video",
"base_model": "deathlegionteam/LEGION-Video-Gen",
"guider": [
"diffusers",
"ClassifierFreeGuidance"
],
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Qwen2_5_VLTextModel"
],
"text_encoder_2": [
"transformers",
"T5EncoderModel"
],
"tokenizer": [
"transformers",
"Qwen2TokenizerFast"
],
"tokenizer_2": [
"transformers",
"ByT5Tokenizer"
],
"transformer": [
"diffusers",
"HunyuanVideo15Transformer3DModel"
],
"vae": [
"diffusers",
"AutoencoderKLHunyuanVideo15"
],
"transformer_config": {
"_class_name": "HunyuanVideo15Transformer3DModel",
"attention_head_dim": 128,
"image_embed_dim": 1152,
"in_channels": 65,
"mlp_ratio": 4.0,
"num_attention_heads": 16,
"num_layers": 54,
"num_refiner_layers": 2,
"out_channels": 32,
"patch_size": 1,
"patch_size_t": 1,
"qk_norm": "rms_norm",
"rope_axes_dim": [16, 56, 56],
"rope_theta": 256.0,
"target_size": 640,
"task_type": "t2v",
"text_embed_2_dim": 1472,
"text_embed_dim": 3584,
"use_meanflow": false
},
"vae_config": {
"_class_name": "AutoencoderKLHunyuanVideo15",
"in_channels": 3,
"out_channels": 3,
"latent_channels": 32,
"block_out_channels": [128, 256, 512, 512],
"layers_per_block": 2,
"norm_num_groups": 32,
"scaling_factor": 0.476986,
"force_upcast": true,
"use_quant_conv": false
},
"scheduler_config": {
"_class_name": "FlowMatchEulerDiscreteScheduler",
"num_train_timesteps": 1000,
"shift": 7.0,
"use_dynamic_shifting": false,
"base_shift": 0.5,
"max_shift": 1.15,
"base_image_seq_len": 256,
"max_image_seq_len": 4096
}
} |