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finetuned models support
This commit is contained in:
DeepBeepMeep
parent
43aa414eaf
commit
3749d23d44
12
README.md
12
README.md
@ -20,6 +20,17 @@ WanGP supports the Wan (and derived models), Hunyuan Video and LTV Video models
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**Follow DeepBeepMeep on Twitter/X to get the Latest News**: https://x.com/deepbeepmeep
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## 🔥 Latest Updates
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### June 12 2025: WanGP v5.6
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👋 *Finetune models*: You find the 20 models supported by WanGP not sufficient ? Too impatient to wait for the next release to get the support for a newly released model ? Your prayers have been answered: if a new model is compatible with a model architecture supported by WanGP, you can add yourself the support for this model in WanGP by just creating Finetune model definition. You can then store this model in the cloud (for instance in Huggingface) and the very light finetune definition file can be easily shared with other users. WanGP will download automatically the finetuned model for them.
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To celebrate this new feature, I have provided 4 finetuned model definitions:
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- *Fast Hunyuan Video* : generate model t2v in only 6 steps
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- *Hunyuan Vido AccVideo* : generate model t2v in only 5 steps
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- *Wan FusioniX*: it is a combo of AccVideo / CausVid ans other models and can generate high quality Wan videos in only 8 steps
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- *Vace FusioniX*: the ultimate Vace model, as it is a combo of Vace / AccVideo / CausVid ans other models and can generate high quality Wan Controled videos in only 10 steps
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Check the *Finetune Guide* to create finetune models definitions and share them on the WanGP discord server.
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### June 11 2025: WanGP v5.5
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👋 *Hunyuan Video Custom Audio*: it is similar to Hunyuan Video Avatar except there isn't any lower limit on the number of frames and you can use your reference images in a different context than the image itself\
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*Hunyuan Video Custom Edit*: Hunyuan Video Controlnet, use it to do inpainting and replace a person in a video while still keeping his poses. Similar to Vace but less restricted than the Wan models in terms of content...
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@ -96,6 +107,7 @@ For detailed installation instructions for different GPU generations:
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### Advanced Features
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- **[Loras Guide](docs/LORAS.md)** - Using and managing Loras for customization
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- **[Finetunes](docs/FINETUNES.md)** - Add manually new models to WanGP
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- **[VACE ControlNet](docs/VACE.md)** - Advanced video control and manipulation
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- **[Command Line Reference](docs/CLI.md)** - All available command line options
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14
configs/flf2v_720p.json
Normal file
14
configs/flf2v_720p.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.30.0",
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"dim": 5120,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_dim": 36,
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"model_type": "i2v",
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"num_heads": 40,
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"num_layers": 40,
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"out_dim": 16,
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"text_len": 512
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}
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14
configs/i2v.json
Normal file
14
configs/i2v.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.30.0",
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"dim": 5120,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_dim": 36,
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"model_type": "i2v",
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"num_heads": 40,
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"num_layers": 40,
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"out_dim": 16,
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"text_len": 512
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}
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14
configs/i2v_720p.json
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14
configs/i2v_720p.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.30.0",
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"dim": 5120,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_dim": 36,
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"model_type": "i2v",
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"num_heads": 40,
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"num_layers": 40,
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"out_dim": 16,
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"text_len": 512
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}
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14
configs/phantom_1.3B.json
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14
configs/phantom_1.3B.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.30.0",
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"dim": 1536,
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"eps": 1e-06,
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"ffn_dim": 8960,
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"freq_dim": 256,
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"in_dim": 16,
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"model_type": "t2v",
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"num_heads": 12,
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"num_layers": 30,
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"out_dim": 16,
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"text_len": 512
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}
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14
configs/phantom_14B.json
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configs/phantom_14B.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.30.0",
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"dim": 5120,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_dim": 16,
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"model_type": "t2v",
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"num_heads": 40,
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"num_layers": 40,
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"out_dim": 16,
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"text_len": 512
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}
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14
configs/sky_df_1.3.json
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configs/sky_df_1.3.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.30.0",
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"dim": 1536,
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"eps": 1e-06,
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"ffn_dim": 8960,
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"freq_dim": 256,
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"in_dim": 16,
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"model_type": "t2v",
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"num_heads": 12,
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"num_layers": 30,
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"out_dim": 16,
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"text_len": 512
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}
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14
configs/sky_df_14B.json
Normal file
14
configs/sky_df_14B.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.30.0",
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"dim": 5120,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_dim": 16,
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"model_type": "t2v",
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"num_heads": 40,
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"num_layers": 40,
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"out_dim": 16,
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"text_len": 512
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}
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14
configs/t2v.json
Normal file
14
configs/t2v.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.30.0",
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"dim": 5120,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_dim": 16,
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"model_type": "t2v",
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"num_heads": 40,
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"num_layers": 40,
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"out_dim": 16,
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"text_len": 512
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}
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14
configs/t2v_1.3B.json
Normal file
14
configs/t2v_1.3B.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.30.0",
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"dim": 1536,
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"eps": 1e-06,
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"ffn_dim": 8960,
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"freq_dim": 256,
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"in_dim": 16,
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"model_type": "t2v",
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"num_heads": 12,
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"num_layers": 30,
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"out_dim": 16,
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"text_len": 512
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}
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16
configs/vace_1.3B.json
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configs/vace_1.3B.json
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{
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"_class_name": "VaceWanModel",
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"_diffusers_version": "0.30.0",
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"dim": 1536,
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"eps": 1e-06,
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"ffn_dim": 8960,
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"freq_dim": 256,
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"in_dim": 16,
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"model_type": "t2v",
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"num_heads": 12,
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"num_layers": 30,
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"out_dim": 16,
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"text_len": 512,
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"vace_layers": [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28],
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"vace_in_dim": 96
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}
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16
configs/vace_14B.json
Normal file
16
configs/vace_14B.json
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{
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"_class_name": "VaceWanModel",
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"_diffusers_version": "0.30.0",
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"dim": 5120,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_dim": 16,
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"model_type": "t2v",
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"num_heads": 40,
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"num_layers": 40,
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"out_dim": 16,
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"text_len": 512,
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"vace_layers": [0, 5, 10, 15, 20, 25, 30, 35],
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"vace_in_dim": 96
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}
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docs/FINETUNES.md
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docs/FINETUNES.md
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# FINETUNES
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A Finetuned model is model that shares the same architecture of one specific model but has derived weights from this model. Some finetuned models have been created by combining multiple finetuned models.
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As there are potentially an infinite number of finetunes, specific finetuned models are not known by default by WanGP, however you can create a finetuned model definition that will tell WanGP about the existence of this finetuned model and WanGP will do as usual all the work for you: autodownload the model and build the user interface.
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Finetune models definitions are light json files that can be easily shared. You can find some of them on the WanGP *discord* server https://discord.gg/g7efUW9jGV
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Finetuned models have been tested so far with Wan2.1 text2video, Wan2.1 image2video, Hunyuan Video text2video. There isn't currently any support for LTX Video finetunes.
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## Create a new Finetune Model Definition
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All the finetune models definitions are json files stored in the **finetunes** sub folder. All the corresponding finetune model weights will be stored in the *ckpts* subfolder and will sit next to the base models.
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WanGP comes with a few prebuilt finetune models that you can use as starting points and to get an idea of the structure of the definition file.
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A definition is built from a *settings file* that can contains all the default parameters for a video generation. On top of this file a subtree named **model** contains all the information regarding the finetune (URLs to download model, corresponding base model id, ...).
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You can obtain a settings file in several ways:
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- In the subfolder **settings**, get the json file that corresponds to the base model of your finetune (see the next section for the list of ids of base models)
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- From the user interface, go to the base model and click **export settings**
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Here are steps:
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1) Create a *settings file*
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2) Add a **model** subtree with the finetune description
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3) Save this file in the subfolder **finetunes**. The name used for the file will be used as its id. It is a good practise to prefix the name of this file with the base model. For instance for a finetune named **Fast*** based on Hunyuan Text 2 Video model *hunyuan_t2v_fast.json*. In this example the Id is *hunyuan_t2v_fast*.
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4) Restart WanGP
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## Base Models Ids
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A finetune is derived from a base model and will inherit all the user interface and corresponding model capabilities, here are the Ids:
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- *t2v*: Wan 2.1 Video text 2
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- *i2v*: Wan 2.1 Video image 2 480p
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- *i2v_720p*: Wan 2.1 Video image 2 720p
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- *vace_14B*: Wan 2.1 Vace 14B
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- *hunyuan*: Hunyuan Video text 2 video
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- *hunyuan_i2v*: Hunyuan Video image 2 video
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## The Model Subtree
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- *name* : name of the finetune used to select
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- *base* : Id of the base model of the finetune (see previous section)
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- *description*: description of the finetune that will appear at the top
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- *URLs*: URLs of all the finetune versions (quantized / non quantized). WanGP will pick the version that is the closest to the user preferences. You will need to follow a naming convention to help WanGP identify the content of each version (see next section). Right now WanGP supports only 8 bits quantized model that have been quantized using **quanto**. WanGP offers a command switch to build easily such a quantized model (see below). *URLs* can contain also paths to local file to allow testing.
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- *preload_URLs* : URLs of files to download no matter what (used to load quantization maps for instance)
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- *auto_quantize*: if set to True and no quantized model URL is provided, WanGP will perform on the fly quantization if the user expects a quantized model
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Example of **model** subtree
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```
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"model":
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{
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"name": "Wan text2video FusioniX 14B",
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"base" : "t2v",
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"description": "A powerful merged text-to-video model based on the original WAN 2.1 T2V model, enhanced using multiple open-source components and LoRAs to boost motion realism, temporal consistency, and expressive detail. multiple open-source models and LoRAs to boost temporal quality, expressiveness, and motion realism.",
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"URLs": [
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"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_fp16.safetensors",
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"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_quanto_fp16_int8.safetensors",
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"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_quanto_bf16_int8.safetensors"
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],
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"preload_URLs": [
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],
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"auto_quantize": true
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},
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```
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## Finetune Model Naming Convention
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If a model is not quantized, it is assumed to be mostly 16 bits (with maybe a few 32 bits weights), so *bf16* or *fp16* should appear somewhere in the name. If you need examples just look at the **ckpts** subfolder, the naming convention for the base models is the same.
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If a model is quantized the term *quanto* should also be included since WanGP supports for the moment only *quanto* quantized model, most specically you should replace *fp16* by *quanto_fp16_int8* or *bf6* by *quanto_bf16_int8*.
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## Creating a Quanto Quantized file
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If you launch the app with the *--save-quantized* switch, WanGP will create a quantized file in the **ckpts** subfolder just after the model has been loaded. Please note that the model will *bf16* or *fp16* quantized depending on what you chose in the configuration menu.
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1) Make sure that in the finetune definition json file there is only a URL or filepath that points to the non quantized model
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2) Launch WanGP *python wgp.py --save-quantized*
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3) In the configuration menu *Transformer Data Type* property choose either *BF16* of *FP16*
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4) Launch a generation (settings used do not matter). As soon as the model is loaded, a new quantized model will be created in the **ckpts** subfolder it doesn't already exist.
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5) To test that this works properly set the local path in the "URLs" key of the finetune definition file. For instance *URLs = ["ckpts/finetune_quanto_fp16_int8.safetensors"]*
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6) Restart WanGP and select *Scaled Int8 Quantization* in the *Transformer Model Quantization* property
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7) Launch a new generation an verify in the terminal window that the right quantized model is loaded
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8) In order to share the finetune definition file will need to store the fine model weights in the cloud. You can upload them for instance on *Huggingface*. You can now replace in the definition file the local path by a URL (on Huggingface to get the URL of the model file click *Copy download link* when accessing the model properties)
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30
finetunes/hunyuan_t2v_accvideo.json
Normal file
30
finetunes/hunyuan_t2v_accvideo.json
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{
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"model": {
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"name": "Hunyuan AccVideo 720p 13B",
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"base": "hunyuan",
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"description": " AccVideo is a novel efficient distillation method to accelerate video diffusion models with synthetic datset. Our method is 8.5x faster than HunyuanVideo.",
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"URLs": [
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"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/accvideo_hunyuan_video_720_quanto_int8.safetensors"
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],
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"preload_URLs": [
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],
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"auto_quantize": true
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},
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"negative_prompt": "",
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"resolution": "832x480",
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"video_length": 81,
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"seed": 42,
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"num_inference_steps": 5,
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"flow_shift": 7,
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"embedded_guidance_scale": 6,
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"repeat_generation": 1,
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"loras_multipliers": "",
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"temporal_upsampling": "",
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"spatial_upsampling": "",
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"RIFLEx_setting": 0,
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"slg_start_perc": 10,
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"slg_end_perc": 90,
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"prompt_enhancer": "",
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"activated_loras": [
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]
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}
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31
finetunes/hunyuan_t2v_fast.json
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31
finetunes/hunyuan_t2v_fast.json
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{
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"model": {
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"name": "Hunyuan Fast Video 720p 13B",
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"base": "hunyuan",
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"description": "Fast Hunyuan is an accelerated HunyuanVideo model. It can sample high quality videos with 6 diffusion steps.",
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"URLs": [
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"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/fast_hunyuan_video_720_quanto_int8.safetensors"
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],
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"preload_URLs": [
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"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/fast_hunyuan_video_720_quanto_int8_map.json"
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],
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"auto_quantize": true
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},
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"negative_prompt": "",
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"resolution": "832x480",
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"video_length": 81,
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"seed": 42,
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"num_inference_steps": 6,
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"flow_shift": 17,
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"embedded_guidance_scale": 6,
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"repeat_generation": 1,
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"loras_multipliers": "",
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"temporal_upsampling": "",
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"spatial_upsampling": "",
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"RIFLEx_setting": 0,
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"slg_start_perc": 10,
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"slg_end_perc": 90,
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"prompt_enhancer": "",
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"activated_loras": [
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]
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}
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38
finetunes/t2v_fusionix.json
Normal file
38
finetunes/t2v_fusionix.json
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{
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"model":
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{
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"name": "Wan text2video FusioniX 14B",
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"base" : "t2v",
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"description": "A powerful merged text-to-video model based on the original WAN 2.1 T2V model, enhanced using multiple open-source components and LoRAs to boost motion realism, temporal consistency, and expressive detail.",
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"URLs": [
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"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_fp16.safetensors",
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"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_quanto_fp16_int8.safetensors",
|
||||
"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_quanto_bf16_int8.safetensors"
|
||||
],
|
||||
"auto_quantize": true
|
||||
},
|
||||
"negative_prompt": "",
|
||||
"prompt": "",
|
||||
"resolution": "832x480",
|
||||
"video_length": 81,
|
||||
"seed": -1,
|
||||
"num_inference_steps": 8,
|
||||
"guidance_scale": 1,
|
||||
"flow_shift": 5,
|
||||
"embedded_guidance_scale": 6,
|
||||
"repeat_generation": 1,
|
||||
"multi_images_gen_type": 0,
|
||||
"tea_cache_setting": 0,
|
||||
"tea_cache_start_step_perc": 0,
|
||||
"loras_multipliers": "",
|
||||
"temporal_upsampling": "",
|
||||
"spatial_upsampling": "",
|
||||
"RIFLEx_setting": 0,
|
||||
"slg_switch": 0,
|
||||
"slg_start_perc": 10,
|
||||
"slg_end_perc": 90,
|
||||
"cfg_star_switch": 0,
|
||||
"cfg_zero_step": -1,
|
||||
"prompt_enhancer": "",
|
||||
"activated_loras": []
|
||||
}
|
||||
38
finetunes/vace_14B_fusionix.json
Normal file
38
finetunes/vace_14B_fusionix.json
Normal file
@ -0,0 +1,38 @@
|
||||
{
|
||||
"model":
|
||||
{
|
||||
"name": "Vace FusioniX 14B",
|
||||
"base" : "vace_14B",
|
||||
"description": "Vace control model enhanced using multiple open-source components and LoRAs to boost motion realism, temporal consistency, and expressive detail.",
|
||||
"URLs": [
|
||||
"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_Vace_FusioniX_14B_mfp16.safetensors",
|
||||
"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_Vace_FusioniX_14B_quanto_mfp16_int8.safetensors",
|
||||
"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_Vace_FusioniX_14B_quanto_mbf16_int8.safetensors"
|
||||
],
|
||||
"auto_quantize": true
|
||||
},
|
||||
"negative_prompt": "",
|
||||
"prompt": "",
|
||||
"resolution": "832x480",
|
||||
"video_length": 81,
|
||||
"seed": -1,
|
||||
"num_inference_steps": 10,
|
||||
"guidance_scale": 1,
|
||||
"flow_shift": 5,
|
||||
"embedded_guidance_scale": 6,
|
||||
"repeat_generation": 1,
|
||||
"multi_images_gen_type": 0,
|
||||
"tea_cache_setting": 0,
|
||||
"tea_cache_start_step_perc": 0,
|
||||
"loras_multipliers": "",
|
||||
"temporal_upsampling": "",
|
||||
"spatial_upsampling": "",
|
||||
"RIFLEx_setting": 0,
|
||||
"slg_switch": 0,
|
||||
"slg_start_perc": 10,
|
||||
"slg_end_perc": 90,
|
||||
"cfg_star_switch": 0,
|
||||
"cfg_zero_step": -1,
|
||||
"prompt_enhancer": "",
|
||||
"activated_loras": []
|
||||
}
|
||||
@ -315,7 +315,7 @@ class Inference(object):
|
||||
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, model_filepath, text_encoder_filepath, dtype = torch.bfloat16, VAE_dtype = torch.float16, mixed_precision_transformer =torch.bfloat16 , **kwargs):
|
||||
def from_pretrained(cls, model_filepath, base_model_type, text_encoder_filepath, dtype = torch.bfloat16, VAE_dtype = torch.float16, mixed_precision_transformer =torch.bfloat16 , quantizeTransformer = False, save_quantized = False, **kwargs):
|
||||
|
||||
device = "cuda"
|
||||
|
||||
@ -331,23 +331,25 @@ class Inference(object):
|
||||
embedded_cfg_scale = 6
|
||||
filepath = model_filepath[0]
|
||||
i2v_condition_type = None
|
||||
i2v_mode = "i2v" in filepath
|
||||
i2v_mode = False
|
||||
custom = False
|
||||
custom_audio = False
|
||||
avatar = False
|
||||
if i2v_mode:
|
||||
if base_model_type == "hunyuan_i2v":
|
||||
model_id = "HYVideo-T/2"
|
||||
i2v_condition_type = "token_replace"
|
||||
elif "custom" in filepath:
|
||||
if "audio" in filepath:
|
||||
model_id = "HYVideo-T/2-custom-audio"
|
||||
custom_audio = True
|
||||
elif "edit" in filepath:
|
||||
model_id = "HYVideo-T/2-custom-edit"
|
||||
else:
|
||||
i2v_mode = True
|
||||
elif base_model_type == "hunyuan_custom":
|
||||
model_id = "HYVideo-T/2-custom"
|
||||
custom = True
|
||||
elif "avatar" in filepath :
|
||||
elif base_model_type == "hunyuan_custom_audio":
|
||||
model_id = "HYVideo-T/2-custom-audio"
|
||||
custom_audio = True
|
||||
custom = True
|
||||
elif base_model_type == "hunyuan_custom_edit":
|
||||
model_id = "HYVideo-T/2-custom-edit"
|
||||
custom = True
|
||||
elif base_model_type == "hunyuan_avatar":
|
||||
model_id = "HYVideo-T/2-avatar"
|
||||
text_len = 256
|
||||
avatar = True
|
||||
@ -385,10 +387,13 @@ class Inference(object):
|
||||
# model = Inference.load_state_dict(args, model, model_filepath)
|
||||
|
||||
# model_filepath ="c:/temp/hc/mp_rank_00_model_states_video.pt"
|
||||
offload.load_model_data(model, model_filepath, pinToMemory = pinToMemory, partialPinning = partialPinning)
|
||||
offload.load_model_data(model, model_filepath, quantizeTransformer = quantizeTransformer and not save_quantized, pinToMemory = pinToMemory, partialPinning = partialPinning)
|
||||
pass
|
||||
# offload.save_model(model, "hunyuan_video_avatar_edit_720_bf16.safetensors")
|
||||
# offload.save_model(model, "hunyuan_video_avatar_edit_720_quanto_bf16_int8.safetensors", do_quantize= True)
|
||||
if save_quantized:
|
||||
from wan.utils.utils import save_quantized_model
|
||||
save_quantized_model(model, filepath, dtype, None)
|
||||
|
||||
model.mixed_precision = mixed_precision_transformer
|
||||
|
||||
|
||||
@ -29,6 +29,8 @@ class DTT2V:
|
||||
checkpoint_dir,
|
||||
rank=0,
|
||||
model_filename = None,
|
||||
base_model_type = None,
|
||||
save_quantized = False,
|
||||
text_encoder_filename = None,
|
||||
quantizeTransformer = False,
|
||||
dtype = torch.bfloat16,
|
||||
@ -61,6 +63,7 @@ class DTT2V:
|
||||
from mmgp import offload
|
||||
# model_filename = "model.safetensors"
|
||||
# model_filename = "c:/temp/diffusion_pytorch_model-00001-of-00006.safetensors"
|
||||
base_config_file = f"configs/{base_model_type}.json"
|
||||
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer, writable_tensors= False) # , forcedConfigPath="c:/temp/config _df720.json")
|
||||
# offload.load_model_data(self.model, "recam.ckpt")
|
||||
# self.model.cpu()
|
||||
@ -72,6 +75,9 @@ class DTT2V:
|
||||
# offload.save_model(self.model, "rtfp16_int8.safetensors", do_quantize= "config.json")
|
||||
|
||||
self.model.eval().requires_grad_(False)
|
||||
if save_quantized:
|
||||
from wan.utils.utils import save_quantized_model
|
||||
save_quantized_model(self.model, model_filename[-1], dtype, base_config_file)
|
||||
|
||||
self.scheduler = FlowUniPCMultistepScheduler()
|
||||
|
||||
|
||||
@ -48,11 +48,13 @@ class WanI2V:
|
||||
self,
|
||||
config,
|
||||
checkpoint_dir,
|
||||
model_filename ="",
|
||||
text_encoder_filename="",
|
||||
model_filename = None,
|
||||
base_model_type= None,
|
||||
text_encoder_filename= None,
|
||||
quantizeTransformer = False,
|
||||
dtype = torch.bfloat16,
|
||||
VAE_dtype = torch.float32,
|
||||
save_quantized = False,
|
||||
mixed_precision_transformer = False
|
||||
):
|
||||
self.device = torch.device(f"cuda")
|
||||
@ -101,7 +103,8 @@ class WanI2V:
|
||||
# model_filename = [model_filename, "audio_processor_bf16.safetensors"]
|
||||
# model_filename = "c:/temp/i2v480p/diffusion_pytorch_model-00001-of-00007.safetensors"
|
||||
# dtype = torch.float16
|
||||
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer, writable_tensors= False) #, forcedConfigPath= "c:/temp/i2v720p/config.json")
|
||||
base_config_file = f"configs/{base_model_type}.json"
|
||||
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, writable_tensors= False, defaultConfigPath= base_config_file) #, forcedConfigPath= "c:/temp/i2v720p/config.json")
|
||||
self.model.lock_layers_dtypes(torch.float32 if mixed_precision_transformer else dtype)
|
||||
offload.change_dtype(self.model, dtype, True)
|
||||
# offload.save_model(self.model, "wan2.1_image2video_720p_14B_mbf16.safetensors", config_file_path="c:/temp/i2v720p/config.json")
|
||||
@ -110,6 +113,9 @@ class WanI2V:
|
||||
|
||||
# offload.save_model(self.model, "wan2.1_Fun_InP_1.3B_bf16_bis.safetensors")
|
||||
self.model.eval().requires_grad_(False)
|
||||
if save_quantized:
|
||||
from wan.utils.utils import save_quantized_model
|
||||
save_quantized_model(self.model, model_filename[-1], dtype, base_config_file)
|
||||
|
||||
|
||||
self.sample_neg_prompt = config.sample_neg_prompt
|
||||
|
||||
@ -50,8 +50,10 @@ class WanT2V:
|
||||
checkpoint_dir,
|
||||
rank=0,
|
||||
model_filename = None,
|
||||
base_model_type = None,
|
||||
text_encoder_filename = None,
|
||||
quantizeTransformer = False,
|
||||
save_quantized = False,
|
||||
dtype = torch.bfloat16,
|
||||
VAE_dtype = torch.float32,
|
||||
mixed_precision_transformer = False
|
||||
@ -81,21 +83,24 @@ class WanT2V:
|
||||
logging.info(f"Creating WanModel from {model_filename[-1]}")
|
||||
from mmgp import offload
|
||||
# model_filename = "c:/temp/vace1.3/diffusion_pytorch_model.safetensors"
|
||||
# model_filename = "vace14B_quanto_bf16_int8.safetensors"
|
||||
# model_filename = "Vacefusionix_quanto_fp16_int8.safetensors"
|
||||
# model_filename = "c:/temp/phantom/Phantom_Wan_14B-00001-of-00006.safetensors"
|
||||
# config_filename= "c:/temp/phantom/config.json"
|
||||
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer, writable_tensors= False)#, forcedConfigPath= config_filename)
|
||||
# offload.load_model_data(self.model, "e:/vace.safetensors")
|
||||
base_config_file = f"configs/{base_model_type}.json"
|
||||
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, writable_tensors= False, defaultConfigPath=base_config_file)#, forcedConfigPath= config_filename)
|
||||
# offload.load_model_data(self.model, "c:/temp/Phantom-Wan-1.3B.pth")
|
||||
# self.model.to(torch.bfloat16)
|
||||
# self.model.cpu()
|
||||
self.model.lock_layers_dtypes(torch.float32 if mixed_precision_transformer else dtype)
|
||||
# dtype = torch.bfloat16
|
||||
# offload.load_model_data(self.model, "ckpts/Wan14BT2VFusioniX_fp16.safetensors")
|
||||
offload.change_dtype(self.model, dtype, True)
|
||||
# offload.save_model(self.model, "wan2.1_phantom_14B_mbf16.safetensors", config_file_path=config_filename)
|
||||
# offload.save_model(self.model, "wan2.1_phantom_14B_quanto_fp16_int8.safetensors", do_quantize= True, config_file_path=config_filename)
|
||||
# offload.save_model(self.model, "wanfusionix_fp16.safetensors", config_file_path=base_config_file)
|
||||
# offload.save_model(self.model, "wanfusionix_quanto_fp16_int8.safetensors", do_quantize=True, config_file_path=base_config_file)
|
||||
self.model.eval().requires_grad_(False)
|
||||
|
||||
if save_quantized:
|
||||
from wan.utils.utils import save_quantized_model
|
||||
save_quantized_model(self.model, model_filename[-1], dtype, base_config_file)
|
||||
|
||||
self.sample_neg_prompt = config.sample_neg_prompt
|
||||
|
||||
|
||||
@ -226,3 +226,131 @@ def str2bool(v):
|
||||
return False
|
||||
else:
|
||||
raise argparse.ArgumentTypeError('Boolean value expected (True/False)')
|
||||
|
||||
|
||||
import sys, time
|
||||
|
||||
# Global variables to track download progress
|
||||
_start_time = None
|
||||
_last_time = None
|
||||
_last_downloaded = 0
|
||||
_speed_history = []
|
||||
_update_interval = 0.5 # Update speed every 0.5 seconds
|
||||
|
||||
def progress_hook(block_num, block_size, total_size, filename=None):
|
||||
"""
|
||||
Simple progress bar hook for urlretrieve
|
||||
|
||||
Args:
|
||||
block_num: Number of blocks downloaded so far
|
||||
block_size: Size of each block in bytes
|
||||
total_size: Total size of the file in bytes
|
||||
filename: Name of the file being downloaded (optional)
|
||||
"""
|
||||
global _start_time, _last_time, _last_downloaded, _speed_history, _update_interval
|
||||
|
||||
current_time = time.time()
|
||||
downloaded = block_num * block_size
|
||||
|
||||
# Initialize timing on first call
|
||||
if _start_time is None or block_num == 0:
|
||||
_start_time = current_time
|
||||
_last_time = current_time
|
||||
_last_downloaded = 0
|
||||
_speed_history = []
|
||||
|
||||
# Calculate download speed only at specified intervals
|
||||
speed = 0
|
||||
if current_time - _last_time >= _update_interval:
|
||||
if _last_time > 0:
|
||||
current_speed = (downloaded - _last_downloaded) / (current_time - _last_time)
|
||||
_speed_history.append(current_speed)
|
||||
# Keep only last 5 speed measurements for smoothing
|
||||
if len(_speed_history) > 5:
|
||||
_speed_history.pop(0)
|
||||
# Average the recent speeds for smoother display
|
||||
speed = sum(_speed_history) / len(_speed_history)
|
||||
|
||||
_last_time = current_time
|
||||
_last_downloaded = downloaded
|
||||
elif _speed_history:
|
||||
# Use the last calculated average speed
|
||||
speed = sum(_speed_history) / len(_speed_history)
|
||||
# Format file sizes and speed
|
||||
def format_bytes(bytes_val):
|
||||
for unit in ['B', 'KB', 'MB', 'GB']:
|
||||
if bytes_val < 1024:
|
||||
return f"{bytes_val:.1f}{unit}"
|
||||
bytes_val /= 1024
|
||||
return f"{bytes_val:.1f}TB"
|
||||
|
||||
file_display = filename if filename else "Unknown file"
|
||||
|
||||
if total_size <= 0:
|
||||
# If total size is unknown, show downloaded bytes
|
||||
speed_str = f" @ {format_bytes(speed)}/s" if speed > 0 else ""
|
||||
line = f"\r{file_display}: {format_bytes(downloaded)}{speed_str}"
|
||||
# Clear any trailing characters by padding with spaces
|
||||
sys.stdout.write(line.ljust(80))
|
||||
sys.stdout.flush()
|
||||
return
|
||||
|
||||
downloaded = block_num * block_size
|
||||
percent = min(100, (downloaded / total_size) * 100)
|
||||
|
||||
# Create progress bar (40 characters wide to leave room for other info)
|
||||
bar_length = 40
|
||||
filled = int(bar_length * percent / 100)
|
||||
bar = '█' * filled + '░' * (bar_length - filled)
|
||||
|
||||
# Format file sizes and speed
|
||||
def format_bytes(bytes_val):
|
||||
for unit in ['B', 'KB', 'MB', 'GB']:
|
||||
if bytes_val < 1024:
|
||||
return f"{bytes_val:.1f}{unit}"
|
||||
bytes_val /= 1024
|
||||
return f"{bytes_val:.1f}TB"
|
||||
|
||||
speed_str = f" @ {format_bytes(speed)}/s" if speed > 0 else ""
|
||||
|
||||
# Display progress with filename first
|
||||
line = f"\r{file_display}: [{bar}] {percent:.1f}% ({format_bytes(downloaded)}/{format_bytes(total_size)}){speed_str}"
|
||||
# Clear any trailing characters by padding with spaces
|
||||
sys.stdout.write(line.ljust(100))
|
||||
sys.stdout.flush()
|
||||
|
||||
# Print newline when complete
|
||||
if percent >= 100:
|
||||
print()
|
||||
|
||||
# Wrapper function to include filename in progress hook
|
||||
def create_progress_hook(filename):
|
||||
"""Creates a progress hook with the filename included"""
|
||||
global _start_time, _last_time, _last_downloaded, _speed_history
|
||||
# Reset timing variables for new download
|
||||
_start_time = None
|
||||
_last_time = None
|
||||
_last_downloaded = 0
|
||||
_speed_history = []
|
||||
|
||||
def hook(block_num, block_size, total_size):
|
||||
return progress_hook(block_num, block_size, total_size, filename)
|
||||
return hook
|
||||
|
||||
def save_quantized_model(model, model_filename, dtype, config_file):
|
||||
from mmgp import offload
|
||||
if dtype == torch.bfloat16:
|
||||
model_filename = model_filename.replace("fp16", "bf16")
|
||||
elif dtype == torch.float16:
|
||||
model_filename = model_filename.replace("bf16", "fp16")
|
||||
|
||||
if "_fp16" in model_filename:
|
||||
model_filename = model_filename.replace("_fp16", "_quanto_fp16_int8")
|
||||
elif "_bf16" in model_filename:
|
||||
model_filename = model_filename.replace("_bf16", "_quanto_bf16_int8")
|
||||
else:
|
||||
pos = model_filename.rfind(".")
|
||||
model_filename = model_filename[:pos] + "_quanto_int8" + model_filename[pos+1:]
|
||||
|
||||
if not os.path.isfile(model_filename):
|
||||
offload.save_model(model, model_filename, do_quantize= True, config_file_path=config_file)
|
||||
|
||||
169
wgp.py
169
wgp.py
@ -43,8 +43,8 @@ global_queue_ref = []
|
||||
AUTOSAVE_FILENAME = "queue.zip"
|
||||
PROMPT_VARS_MAX = 10
|
||||
|
||||
target_mmgp_version = "3.4.8"
|
||||
WanGP_version = "5.5"
|
||||
target_mmgp_version = "3.4.9"
|
||||
WanGP_version = "5.6"
|
||||
prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None
|
||||
|
||||
from importlib.metadata import version
|
||||
@ -161,6 +161,7 @@ def process_prompt_and_add_tasks(state, model_choice):
|
||||
return
|
||||
|
||||
inputs["model_filename"] = model_filename
|
||||
model_filename = get_base_model_filename(model_filename)
|
||||
prompts = prompt.replace("\r", "").split("\n")
|
||||
prompts = [prompt.strip() for prompt in prompts if len(prompt.strip())>0 and not prompt.startswith("#")]
|
||||
if len(prompts) ==0:
|
||||
@ -1159,6 +1160,12 @@ def _parse_args():
|
||||
help="Prevent switch models"
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--save-quantized",
|
||||
action="store_true",
|
||||
help="Save a quantized version of the current model"
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--preload",
|
||||
type=str,
|
||||
@ -1429,7 +1436,7 @@ def _parse_args():
|
||||
return args
|
||||
|
||||
def get_lora_dir(model_filename):
|
||||
|
||||
model_filename = get_base_model_filename(model_filename)
|
||||
model_family = get_model_family(model_filename)
|
||||
i2v = test_class_i2v(model_filename)
|
||||
if model_family == "wan":
|
||||
@ -1543,6 +1550,8 @@ for path in ["wan2.1_Vace_1.3B_preview_bf16.safetensors", "sky_reels2_diffusion
|
||||
print(f"Removing old version of model '{path}'. A new version of this model will be downloaded next time you use it.")
|
||||
os.remove( os.path.join("ckpts" , path))
|
||||
|
||||
finetunes = {}
|
||||
finetunes_filemap = {}
|
||||
|
||||
wan_choices_t2v=["ckpts/wan2.1_text2video_1.3B_bf16.safetensors", "ckpts/wan2.1_text2video_14B_bf16.safetensors", "ckpts/wan2.1_text2video_14B_quanto_int8.safetensors",
|
||||
"ckpts/wan2.1_recammaster_1.3B_bf16.safetensors", "ckpts/sky_reels2_diffusion_forcing_1.3B_mbf16.safetensors", "ckpts/sky_reels2_diffusion_forcing_14B_bf16.safetensors",
|
||||
@ -1581,14 +1590,32 @@ model_signatures = {"t2v": "text2video_14B", "t2v_1.3B" : "text2video_1.3B", "
|
||||
"hunyuan" : "hunyuan_video_720", "hunyuan_i2v" : "hunyuan_video_i2v_720", "hunyuan_custom" : "hunyuan_video_custom_720", "hunyuan_custom_audio" : "hunyuan_video_custom_audio", "hunyuan_custom_edit" : "hunyuan_video_custom_edit",
|
||||
"hunyuan_avatar" : "hunyuan_video_avatar" }
|
||||
|
||||
def get_model_finetune_def(model_filename):
|
||||
model_type = finetunes_filemap.get(model_filename, None )
|
||||
if model_type == None:
|
||||
return None
|
||||
return finetunes.get(model_type, None )
|
||||
|
||||
def get_base_model_filename(model_filename):
|
||||
finetune_def = get_model_finetune_def(model_filename)
|
||||
if finetune_def == None:
|
||||
return model_filename
|
||||
else:
|
||||
return finetune_def["base_filename"]
|
||||
|
||||
def get_model_type(model_filename):
|
||||
model_type = finetunes_filemap.get(model_filename, None )
|
||||
if model_type != None:
|
||||
return model_type
|
||||
for model_type, signature in model_signatures.items():
|
||||
if signature in model_filename:
|
||||
return model_type
|
||||
raise Exception("Unknown model:" + model_filename)
|
||||
|
||||
def get_model_family(model_filename):
|
||||
finetune_def = get_model_finetune_def(model_filename)
|
||||
if finetune_def != None:
|
||||
return finetune_def["model_family"]
|
||||
if "wan" in model_filename or "sky" in model_filename:
|
||||
return "wan"
|
||||
elif "ltxv" in model_filename:
|
||||
@ -1599,10 +1626,15 @@ def get_model_family(model_filename):
|
||||
raise Exception(f"Unknown model family for model'{model_filename}'")
|
||||
|
||||
def test_class_i2v(model_filename):
|
||||
model_filename = get_base_model_filename(model_filename)
|
||||
return "image2video" in model_filename or "Fun_InP" in model_filename or "FLF2V" in model_filename or "fantasy" in model_filename or "hunyuan_video_i2v" in model_filename
|
||||
|
||||
def get_model_name(model_filename, description_container = [""]):
|
||||
if "Fun" in model_filename:
|
||||
finetune_def = get_model_finetune_def(model_filename)
|
||||
if finetune_def != None:
|
||||
model_name = finetune_def["name"]
|
||||
description = finetune_def["description"]
|
||||
elif "Fun" in model_filename:
|
||||
model_name = "Fun InP image2video"
|
||||
model_name += " 14B" if "14B" in model_filename else " 1.3B"
|
||||
description = "The Fun model is an alternative image 2 video that supports out the box End Image fixing (contrary to the original Wan image 2 video model). The 1.3B adds also image 2 to video capability to the 1.3B model."
|
||||
@ -1684,6 +1716,10 @@ def get_model_name(model_filename, description_container = [""]):
|
||||
|
||||
|
||||
def get_model_filename(model_type, quantization ="int8", dtype_policy = ""):
|
||||
finetune_def = finetunes.get(model_type, None)
|
||||
if finetune_def != None:
|
||||
choices = [ "ckpts/" + os.path.basename(path) for path in finetune_def["URLs"] ]
|
||||
else:
|
||||
signature = model_signatures[model_type]
|
||||
choices = [ name for name in transformer_choices if signature in name]
|
||||
if len(quantization) == 0:
|
||||
@ -1694,7 +1730,11 @@ def get_model_filename(model_type, quantization ="int8", dtype_policy = ""):
|
||||
if len(choices) <= 1:
|
||||
raw_filename = choices[0]
|
||||
else:
|
||||
if quantization in ("int8", "fp8"):
|
||||
sub_choices = [ name for name in choices if quantization in name]
|
||||
else:
|
||||
sub_choices = [ name for name in choices if "quanto" not in name]
|
||||
|
||||
if len(sub_choices) > 0:
|
||||
dtype_str = "fp16" if dtype == torch.float16 else "bf16"
|
||||
new_sub_choices = [ name for name in sub_choices if dtype_str in name]
|
||||
@ -1703,7 +1743,7 @@ def get_model_filename(model_type, quantization ="int8", dtype_policy = ""):
|
||||
else:
|
||||
raw_filename = choices[0]
|
||||
|
||||
if dtype == torch.float16 and not "fp16" in raw_filename and model_family == "wan" :
|
||||
if dtype == torch.float16 and not "fp16" in raw_filename and model_family == "wan" and finetune_def != None :
|
||||
if "quanto_int8" in raw_filename:
|
||||
raw_filename = raw_filename.replace("quanto_int8", "quanto_fp16_int8")
|
||||
elif "quanto_bf16_int8" in raw_filename:
|
||||
@ -1739,6 +1779,12 @@ def get_default_settings(filename):
|
||||
i2v = test_class_i2v(filename)
|
||||
defaults_filename = get_settings_file_name(filename)
|
||||
if not Path(defaults_filename).is_file():
|
||||
finetune_def = get_model_finetune_def(filename)
|
||||
if finetune_def != None:
|
||||
ui_defaults = finetune_def["settings"]
|
||||
if len(ui_defaults.get("prompt","")) == 0:
|
||||
ui_defaults["prompt"]= get_default_prompt(i2v)
|
||||
else:
|
||||
ui_defaults = {
|
||||
"prompt": get_default_prompt(i2v),
|
||||
"resolution": "1280x720" if "720p" in filename else "832x480",
|
||||
@ -1839,6 +1885,30 @@ def get_default_settings(filename):
|
||||
ui_defaults["num_inference_steps"] = default_number_steps
|
||||
return ui_defaults
|
||||
|
||||
|
||||
finetunes_paths = glob.glob( os.path.join("finetunes", "*.json") )
|
||||
finetunes_paths.sort()
|
||||
for file_path in finetunes_paths:
|
||||
finetune_id = os.path.basename(file_path)[:-5]
|
||||
with open(file_path, "r", encoding="utf-8") as f:
|
||||
try:
|
||||
json_def = json.load(f)
|
||||
except Exception as e:
|
||||
raise Exception(f"Error while parsing Finetune Definition File '{file_path}': {str(e)}")
|
||||
finetune_def = json_def["model"]
|
||||
del json_def["model"]
|
||||
finetune_def["settings"] = json_def
|
||||
base_filename = get_model_filename(finetune_def["base"])
|
||||
finetune_def["base_filename"] = base_filename
|
||||
finetune_def["model_family"] = get_model_family(base_filename)
|
||||
finetunes[finetune_id] = finetune_def
|
||||
for url in finetune_def["URLs"]:
|
||||
url = url.split("/")[-1]
|
||||
finetunes_filemap["ckpts/" + url] = finetune_id
|
||||
|
||||
model_types += finetunes.keys()
|
||||
|
||||
|
||||
transformer_types = server_config.get("transformer_types", [])
|
||||
transformer_type = transformer_types[0] if len(transformer_types) > 0 else model_types[0]
|
||||
|
||||
@ -1904,6 +1974,7 @@ model_filename = ""
|
||||
# compile = "transformer"
|
||||
|
||||
def get_loras_preprocessor(transformer, model_filename):
|
||||
model_filename = get_base_model_filename(model_filename)
|
||||
preprocessor = getattr(transformer, "preprocess_loras", None)
|
||||
if preprocessor == None:
|
||||
return None
|
||||
@ -1946,6 +2017,8 @@ def get_hunyuan_text_encoder_filename(text_encoder_quantization):
|
||||
|
||||
def download_models(transformer_filename):
|
||||
def computeList(filename):
|
||||
if filename == None:
|
||||
return []
|
||||
pos = filename.rfind("/")
|
||||
filename = filename[pos+1:]
|
||||
return [filename]
|
||||
@ -1986,6 +2059,32 @@ def download_models(transformer_filename):
|
||||
|
||||
|
||||
model_family = get_model_family(transformer_filename)
|
||||
finetune_def = get_model_finetune_def(transformer_filename)
|
||||
if finetune_def != None:
|
||||
from urllib.request import urlretrieve
|
||||
from wan.utils.utils import create_progress_hook
|
||||
if not os.path.isfile(transformer_filename ):
|
||||
for url in finetune_def["URLs"]:
|
||||
if transformer_filename in url:
|
||||
break
|
||||
if not url.startswith("http"):
|
||||
raise Exception(f"Model '{transformer_filename}' was not found locally and no URL was provided to download it. Please add an URL in the finetune definition file.")
|
||||
try:
|
||||
urlretrieve(url,transformer_filename, create_progress_hook(transformer_filename))
|
||||
except Exception as e:
|
||||
if os.path.isfile(filename): os.remove(transformer_filename)
|
||||
raise Exception(f"URL '{url}' is invalid for Model '{transformer_filename}' : {str(e)}'")
|
||||
for url in finetune_def.get("preload_URLs", []):
|
||||
filename = "ckpts/" + url.split("/")[-1]
|
||||
if not os.path.isfile(filename ):
|
||||
if not url.startswith("http"):
|
||||
raise Exception(f"File '{filename}' to preload was not found locally and no URL was provided to download it. Please add an URL in the finetune definition file.")
|
||||
try:
|
||||
urlretrieve(url,filename, create_progress_hook(filename))
|
||||
except Exception as e:
|
||||
if os.path.isfile(filename): os.remove(filename)
|
||||
raise Exception(f"Preload URL '{url}' is invalid: {str(e)}'")
|
||||
transformer_filename = None
|
||||
if model_family == "wan":
|
||||
text_encoder_filename = get_wan_text_encoder_filename(text_encoder_quantization)
|
||||
model_def = {
|
||||
@ -2023,8 +2122,6 @@ def download_models(transformer_filename):
|
||||
offload.default_verboseLevel = verbose_level
|
||||
|
||||
|
||||
# download_models(transformer_filename)
|
||||
|
||||
def sanitize_file_name(file_name, rep =""):
|
||||
return file_name.replace("/",rep).replace("\\",rep).replace(":",rep).replace("|",rep).replace("?",rep).replace("<",rep).replace(">",rep).replace("\"",rep).replace("\n",rep).replace("\r",rep)
|
||||
|
||||
@ -2111,7 +2208,7 @@ def setup_loras(model_filename, transformer, lora_dir, lora_preselected_preset,
|
||||
return loras, loras_names, loras_presets, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset
|
||||
|
||||
|
||||
def load_wan_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
|
||||
def load_wan_model(model_filename, base_model_type, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False, save_quantized= False):
|
||||
filename = model_filename[-1]
|
||||
print(f"Loading '{filename}' model...")
|
||||
|
||||
@ -2130,11 +2227,13 @@ def load_wan_model(model_filename, quantizeTransformer = False, dtype = torch.bf
|
||||
config=cfg,
|
||||
checkpoint_dir="ckpts",
|
||||
model_filename=model_filename,
|
||||
base_model_type=base_model_type,
|
||||
text_encoder_filename= get_wan_text_encoder_filename(text_encoder_quantization),
|
||||
quantizeTransformer = quantizeTransformer,
|
||||
dtype = dtype,
|
||||
VAE_dtype = VAE_dtype,
|
||||
mixed_precision_transformer = mixed_precision_transformer
|
||||
mixed_precision_transformer = mixed_precision_transformer,
|
||||
save_quantized = save_quantized
|
||||
)
|
||||
|
||||
pipe = {"transformer": wan_model.model, "text_encoder" : wan_model.text_encoder.model, "vae": wan_model.vae.model }
|
||||
@ -2142,7 +2241,7 @@ def load_wan_model(model_filename, quantizeTransformer = False, dtype = torch.bf
|
||||
pipe["text_encoder_2"] = wan_model.clip.model
|
||||
return wan_model, pipe
|
||||
|
||||
def load_ltxv_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
|
||||
def load_ltxv_model(model_filename, quantizeTransformer = False, base_model_type = None, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False, save_quantized = False):
|
||||
filename = model_filename[-1]
|
||||
print(f"Loading '{filename}' model...")
|
||||
from ltx_video.ltxv import LTXV
|
||||
@ -2161,18 +2260,20 @@ def load_ltxv_model(model_filename, quantizeTransformer = False, dtype = torch.b
|
||||
|
||||
return ltxv_model, pipe
|
||||
|
||||
def load_hunyuan_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
|
||||
def load_hunyuan_model(model_filename, base_model_type = None, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False, save_quantized = False):
|
||||
filename = model_filename[-1]
|
||||
print(f"Loading '{filename}' model...")
|
||||
from hyvideo.hunyuan import HunyuanVideoSampler
|
||||
|
||||
hunyuan_model = HunyuanVideoSampler.from_pretrained(
|
||||
model_filepath = model_filename,
|
||||
base_model_type = base_model_type,
|
||||
text_encoder_filepath = get_hunyuan_text_encoder_filename(text_encoder_quantization),
|
||||
dtype = dtype,
|
||||
# quantizeTransformer = quantizeTransformer,
|
||||
quantizeTransformer = quantizeTransformer,
|
||||
VAE_dtype = VAE_dtype,
|
||||
mixed_precision_transformer = mixed_precision_transformer
|
||||
mixed_precision_transformer = mixed_precision_transformer,
|
||||
save_quantized = save_quantized
|
||||
)
|
||||
|
||||
pipe = { "transformer" : hunyuan_model.model, "text_encoder" : hunyuan_model.text_encoder, "text_encoder_2" : hunyuan_model.text_encoder_2, "vae" : hunyuan_model.vae }
|
||||
@ -2205,9 +2306,15 @@ def get_transformer_model(model):
|
||||
|
||||
def load_models(model_filename):
|
||||
global transformer_filename, transformer_loras_filenames
|
||||
base_filename = get_base_model_filename(model_filename)
|
||||
base_model_type = get_model_type(base_filename)
|
||||
finetune_def = get_model_finetune_def(model_filename)
|
||||
quantizeTransformer = finetune_def !=None and finetune_def.get("auto_quantize", False) and not "quanto" in model_filename
|
||||
|
||||
model_family = get_model_family(model_filename)
|
||||
perc_reserved_mem_max = args.perc_reserved_mem_max
|
||||
preload =int(args.preload)
|
||||
save_quantized = args.save_quantized
|
||||
if preload == 0:
|
||||
preload = server_config.get("preload_in_VRAM", 0)
|
||||
new_transformer_loras_filenames = None
|
||||
@ -2217,17 +2324,20 @@ def load_models(model_filename):
|
||||
for filename in model_filelist:
|
||||
download_models(filename)
|
||||
transformer_dtype = get_transformer_dtype(model_family, transformer_dtype_policy)
|
||||
if quantizeTransformer:
|
||||
transformer_dtype = torch.bfloat16 if "bf16" in model_filename else transformer_dtype
|
||||
transformer_dtype = torch.float16 if "fp16" in model_filename else transformer_dtype
|
||||
VAE_dtype = torch.float16 if server_config.get("vae_precision","16") == "16" else torch.float
|
||||
mixed_precision_transformer = server_config.get("mixed_precision","0") == "1"
|
||||
transformer_filename = None
|
||||
transformer_loras_filenames = None
|
||||
new_transformer_filename = model_filelist[-1]
|
||||
if model_family == "wan" :
|
||||
wan_model, pipe = load_wan_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
|
||||
wan_model, pipe = load_wan_model(model_filelist, base_model_type, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer, save_quantized = save_quantized)
|
||||
elif model_family == "ltxv":
|
||||
wan_model, pipe = load_ltxv_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
|
||||
wan_model, pipe = load_ltxv_model(model_filelist, base_model_type, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer, save_quantized = save_quantized)
|
||||
elif model_family == "hunyuan":
|
||||
wan_model, pipe = load_hunyuan_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
|
||||
wan_model, pipe = load_hunyuan_model(model_filelist, base_model_type, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer, save_quantized = save_quantized)
|
||||
else:
|
||||
raise Exception(f"Model '{new_transformer_filename}' not supported.")
|
||||
wan_model._model_file_name = new_transformer_filename
|
||||
@ -2517,6 +2627,7 @@ def refresh_gallery(state): #, msg
|
||||
prompt = task["prompt"]
|
||||
params = task["params"]
|
||||
model_filename = params["model_filename"]
|
||||
model_filename = get_base_model_filename(model_filename)
|
||||
onemorewindow_visible = "Vace" in model_filename or "diffusion_forcing" in model_filename or "ltxv" in model_filename
|
||||
enhanced = False
|
||||
if prompt.startswith("!enhanced!\n"):
|
||||
@ -2942,6 +3053,9 @@ def generate_video(
|
||||
raise gr.Error("Error while loading Loras: " + ", ".join(error_files))
|
||||
seed = None if seed == -1 else seed
|
||||
# negative_prompt = "" # not applicable in the inference
|
||||
original_filename = model_filename
|
||||
model_filename = get_base_model_filename(model_filename)
|
||||
|
||||
image2video = test_class_i2v(model_filename)
|
||||
current_video_length = video_length
|
||||
enable_RIFLEx = RIFLEx_setting == 0 and current_video_length > (6* 16) or RIFLEx_setting == 1
|
||||
@ -3198,7 +3312,11 @@ def generate_video(
|
||||
fit_into_canvas = fit_canvas
|
||||
)
|
||||
elif hunyuan_custom_edit:
|
||||
if "P" in video_prompt_type:
|
||||
progress_args = [0, get_latest_status(state,"Extracting Open Pose Information and Expanding Mask")]
|
||||
else:
|
||||
progress_args = [0, get_latest_status(state,"Extracting Video and Mask")]
|
||||
|
||||
send_cmd("progress", progress_args)
|
||||
src_video, src_mask = preprocess_video_with_mask(video_guide, video_mask, height=height, width = width, max_frames= current_video_length if window_no == 1 else current_video_length - reuse_frames, start_frame = guide_start_frame, fit_canvas = fit_canvas, target_fps = fps, pose_enhance = "P" in video_prompt_type)
|
||||
if window_no == 1:
|
||||
@ -3432,6 +3550,7 @@ def generate_video(
|
||||
inputs = get_function_arguments(generate_video, locals())
|
||||
inputs.pop("send_cmd")
|
||||
inputs.pop("task")
|
||||
inputs["model_filename"] = original_filename
|
||||
configs = prepare_inputs_dict("metadata", inputs)
|
||||
configs["prompt"] = "\n".join(original_prompts)
|
||||
if prompt_enhancer_image_caption_model != None and prompt_enhancer !=None and len(prompt_enhancer)>0:
|
||||
@ -4197,6 +4316,7 @@ def prepare_inputs_dict(target, inputs ):
|
||||
|
||||
if target == "settings":
|
||||
return inputs
|
||||
model_filename = get_base_model_filename(model_filename)
|
||||
|
||||
if not test_class_i2v(model_filename):
|
||||
inputs.pop("image_prompt_type")
|
||||
@ -4250,7 +4370,7 @@ def export_settings(state):
|
||||
settings["model_filename"] = model_filename
|
||||
text = json.dumps(settings, indent=4)
|
||||
text_base64 = base64.b64encode(text.encode('utf8')).decode('utf-8')
|
||||
return text_base64
|
||||
return text_base64, sanitize_file_name(model_type + "_" + datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%Hh%Mm%Ss") + ".json")
|
||||
|
||||
def use_video_settings(state, files):
|
||||
gen = get_gen_info(state)
|
||||
@ -4581,6 +4701,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
gen["queue"] = []
|
||||
state_dict["gen"] = gen
|
||||
|
||||
model_filename = get_base_model_filename(model_filename)
|
||||
preset_to_load = lora_preselected_preset if lora_preset_model == model_filename else ""
|
||||
|
||||
loras, loras_names, loras_presets, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset = setup_loras(model_filename, None, get_lora_dir(model_filename), preset_to_load, None)
|
||||
@ -5001,7 +5122,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
choices=[
|
||||
(str(i), i ) for i in range(40)
|
||||
],
|
||||
value=ui_defaults.get("slg_layers", ["9"]),
|
||||
value=ui_defaults.get("slg_layers", [9]),
|
||||
multiselect= True,
|
||||
label="Skip Layers",
|
||||
scale= 3
|
||||
@ -5024,7 +5145,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
label="CFG Star"
|
||||
)
|
||||
with gr.Row():
|
||||
cfg_zero_step = gr.Slider(-1, 39, value=ui_defaults.get("cfg_zero_step",-1), step=1, label="CFG Zero below this Layer (Extra Process)")
|
||||
cfg_zero_step = gr.Slider(-1, 39, value=ui_defaults.get("cfg_zero_step",-1), step=1, label="CFG Zero below this Layer (Extra Process)", visible = not (hunyuan_i2v or hunyuan_video_avatar or hunyuan_i2v or hunyuan_video_custom))
|
||||
with gr.Tab("Sliding Window", visible= sliding_window_enabled) as sliding_window_tab:
|
||||
|
||||
with gr.Column():
|
||||
@ -5071,6 +5192,8 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
with gr.Row():
|
||||
settings_file = gr.File(height=41,label="Load Settings From Video / Json")
|
||||
settings_base64_output = gr.Text(interactive= False, visible=False, value = "")
|
||||
settings_filename = gr.Text(interactive= False, visible=False, value = "")
|
||||
|
||||
if not update_form:
|
||||
with gr.Column():
|
||||
gen_status = gr.Text(interactive= False, label = "Status")
|
||||
@ -5214,10 +5337,10 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
outputs= None
|
||||
).then(fn=export_settings,
|
||||
inputs =[state],
|
||||
outputs= [settings_base64_output]
|
||||
outputs= [settings_base64_output, settings_filename]
|
||||
).then(
|
||||
fn=None,
|
||||
inputs=[settings_base64_output],
|
||||
inputs=[settings_base64_output, settings_filename],
|
||||
outputs=None,
|
||||
js=trigger_settings_download_js
|
||||
)
|
||||
@ -5799,7 +5922,7 @@ def get_js():
|
||||
"""
|
||||
|
||||
trigger_settings_download_js = """
|
||||
(base64String) => {
|
||||
(base64String, filename) => {
|
||||
if (!base64String) {
|
||||
console.log("No base64 settings data received, skipping download.");
|
||||
return;
|
||||
@ -5817,7 +5940,7 @@ def get_js():
|
||||
const a = document.createElement('a');
|
||||
a.style.display = 'none';
|
||||
a.href = url;
|
||||
a.download = 'settings.json';
|
||||
a.download = filename;
|
||||
document.body.appendChild(a);
|
||||
a.click();
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user