add downloader

examples/ExVideo/ExVideo_svd_test.py

@@ -2,6 +2,8 @@ from diffsynth import save_video, ModelManager, SVDVideoPipeline, HunyuanDiTImag
 from diffsynth import ModelManager
 import torch, os
 
+# The models will be downloaded automatically.
+# You can also use the following urls to download them manually.
 
 # Download models (from Huggingface)
 #   Text-to-image model:
@@ -14,7 +16,6 @@ import torch, os
 #   ExVideo extension blocks:
 #     `models/stable_video_diffusion/model.fp16.safetensors`: [link](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1/resolve/main/model.fp16.safetensors)
 
-
 # Download models (from Modelscope)
 #   Text-to-image model:
 #     `models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin`: [link](https://www.modelscope.cn/api/v1/models/modelscope/HunyuanDiT/repo?Revision=master&FilePath=t2i%2Fclip_text_encoder%2Fpytorch_model.bin)
@@ -30,13 +31,7 @@ import torch, os
 def generate_image():
     # Load models
     os.environ["TOKENIZERS_PARALLELISM"] = "True"
-    model_manager = ModelManager(torch_dtype=torch.float16, device="cuda")
-    model_manager.load_models([
-        "models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin",
-        "models/HunyuanDiT/t2i/mt5/pytorch_model.bin",
-        "models/HunyuanDiT/t2i/model/pytorch_model_ema.pt",
-        "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"
-    ])
+    model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", model_id_list=["HunyuanDiT"])
     pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager)
 
     # Generate an image
@@ -46,16 +41,13 @@ def generate_image():
         negative_prompt="错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，",
         num_inference_steps=50, height=1024, width=1024,
     )
+    model_manager.to("cpu")
     return image
 
 
 def generate_video(image):
     # Load models
-    model_manager = ModelManager(torch_dtype=torch.float16, device="cuda")
-    model_manager.load_models([
-        "models/stable_video_diffusion/svd_xt.safetensors",
-        "models/stable_video_diffusion/model.fp16.safetensors"
-    ])
+    model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", model_id_list=["stable-video-diffusion-img2vid-xt", "ExVideo-SVD-128f-v1"])
     pipe = SVDVideoPipeline.from_model_manager(model_manager)
 
     # Generate a video
@@ -67,16 +59,13 @@ def generate_video(image):
         num_inference_steps=50,
         min_cfg_scale=2, max_cfg_scale=2, contrast_enhance_scale=1.2
     )
+    model_manager.to("cpu")
     return video
 
 
 def upscale_video(image, video):
     # Load models
-    model_manager = ModelManager(torch_dtype=torch.float16, device="cuda")
-    model_manager.load_models([
-        "models/stable_video_diffusion/svd_xt.safetensors",
-        "models/stable_video_diffusion/model.fp16.safetensors",
-    ])
+    model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", model_id_list=["stable-video-diffusion-img2vid-xt", "ExVideo-SVD-128f-v1"])
     pipe = SVDVideoPipeline.from_model_manager(model_manager)
 
     # Generate a video
@@ -89,19 +78,20 @@ def upscale_video(image, video):
         num_inference_steps=25,
         min_cfg_scale=2, max_cfg_scale=2, contrast_enhance_scale=1.2
     )
+    model_manager.to("cpu")
     return video
 
 
-# We use Hunyuan DiT to generate the first frame.
+# We use Hunyuan DiT to generate the first frame. 10GB VRAM is required.
 # If you want to use your own image,
 # please use `image = Image.open("your_image_file.png")` to replace the following code.
 image = generate_image()
 image.save("image.png")
 
-# Now, generate a video with resolution of 512.
+# Now, generate a video with resolution of 512. 20GB VRAM is required.
 video = generate_video(image)
 save_video(video, "video_512.mp4", fps=30)
 
-# Upscale the video.
+# Upscale the video. 52GB VRAM is required.
 video = upscale_video(image, video)
 save_video(video, "video_1024.mp4", fps=30)