rename

README.md

@@ -26,9 +26,9 @@ detailed usage:
 ```python
 import torch
 import kiui
-from mvdream.pipeline_mvdream import MVDreamStableDiffusionPipeline
+from mvdream.pipeline_mvdream import MVDreamPipeline
 
-pipe = MVDreamStableDiffusionPipeline.from_pretrained('./weights', torch_dtype=torch.float16)
+pipe = MVDreamPipeline.from_pretrained('./weights', torch_dtype=torch.float16)
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"

convert_mvdream_to_diffusers.py

@@ -16,7 +16,7 @@ from typing import Any
 from accelerate import init_empty_weights
 from accelerate.utils import set_module_tensor_to_device
 from mvdream.models import MultiViewUNetModel
-from mvdream.pipeline_mvdream import MVDreamStableDiffusionPipeline
+from mvdream.pipeline_mvdream import MVDreamPipeline
 from transformers import CLIPTokenizer, CLIPTextModel
 
 logger = logging.get_logger(__name__)
@@ -460,7 +460,7 @@ def convert_from_original_mvdream_ckpt(checkpoint_path, original_config_file, de
             f"Unknown context_dim: {original_config.model.paams.unet_config.params.context_dim}"
         )
 
-    pipe = MVDreamStableDiffusionPipeline(
+    pipe = MVDreamPipeline(
         vae=vae,
         unet=unet,
         tokenizer=tokenizer,
@@ -547,7 +547,7 @@ if __name__ == "__main__":
                 image.save(f"image_{i}.png")  # type: ignore
 
             print(f"Testing entire pipeline...")
-            loaded_pipe: MVDreamStableDiffusionPipeline = MVDreamStableDiffusionPipeline.from_pretrained(args.dump_path, safe_serialization=args.to_safetensors)  # type: ignore
+            loaded_pipe: MVDreamPipeline = MVDreamPipeline.from_pretrained(args.dump_path, safe_serialization=args.to_safetensors)  # type: ignore
             images = loaded_pipe(
                 prompt="Head of Hatsune Miku",
                 negative_prompt="painting, bad quality, flat",

main.py

@@ -2,9 +2,9 @@ import torch
 import kiui
 import numpy as np
 import argparse
-from mvdream.pipeline_mvdream import MVDreamStableDiffusionPipeline
+from mvdream.pipeline_mvdream import MVDreamPipeline
 
-pipe = MVDreamStableDiffusionPipeline.from_pretrained(
+pipe = MVDreamPipeline.from_pretrained(
     "./weights", # local weights
     # "ashawkey/mvdream-sd2.1-diffusers",
     torch_dtype=torch.float16

mvdream/pipeline_mvdream.py

@@ -74,7 +74,7 @@ def get_camera(
     return torch.tensor(np.stack(cameras, 0)).float()
 
 
-class MVDreamStableDiffusionPipeline(DiffusionPipeline):
+class MVDreamPipeline(DiffusionPipeline):
     def __init__(
         self,
         vae: AutoencoderKL,