update image link

Author: a-r-r-o-w

src/diffusers/models/transformers/cogvideox_transformer_3d.py

@@ -466,8 +466,8 @@ def custom_forward(*inputs):
 
         # 5. Unpatchify
         # Note: we use `-1` instead of `channels`:
-        #   - It is okay to use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
-        #   - However, for CogVideoX-5b-I2V, input image (number of input channels is twice the output channels)
+        #   - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
+        #   - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
         p = self.config.patch_size
         output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
         output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py

@@ -17,11 +17,11 @@
 import math
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
-import PIL
 import torch
 from transformers import T5EncoderModel, T5Tokenizer
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...image_processor import PipelineImageInput
 from ...models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
 from ...models.embeddings import get_3d_rotary_pos_embed
 from ...pipelines.pipeline_utils import DiffusionPipeline
@@ -49,7 +49,9 @@
         >>> pipe.to("cuda")
 
         >>> prompt = "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
-        >>> image = load_image("astronaut.jpg")  # TODO: Add link to 720x480 image from HF Docs repo
+        >>> image = load_image(
+        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+        ... )
         >>> video = pipe(image, prompt, use_dynamic_cfg=True)
         >>> export_to_video(video.frames[0], "output.mp4", fps=8)
         ```
@@ -548,7 +550,7 @@ def interrupt(self):
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
         self,
-        image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],
+        image: PipelineImageInput,
         prompt: Optional[Union[str, List[str]]] = None,
         negative_prompt: Optional[Union[str, List[str]]] = None,
         height: int = 480,
@@ -576,8 +578,8 @@ def __call__(
         Function invoked when calling the pipeline for generation.
 
         Args:
-            image (`List[PIL.Image.Image]`):
-                The input video to condition the generation on. Must be a list of images/frames of the video.
+            image (`PipelineImageInput`):
+                The input video to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
             prompt (`str` or `List[str]`, *optional*):
                 The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                 instead.