fix bugs

mindone/diffusers/pipelines/animatediff/pipeline_animatediff_video2video_controlnet.py

@@ -86,6 +86,7 @@
         >>> from controlnet_aux.processor import OpenposeDetector
 
         >>> open_pose = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
+        >>> conditioning_frames = []
         >>> for frame in tqdm(video):
         ...     conditioning_frames.append(open_pose(frame))
 

mindone/diffusers/pipelines/cogview3/pipeline_cogview3plus.py

@@ -42,7 +42,7 @@
         >>> import mindspore as ms
         >>> from mindone.diffusers import CogView3PlusPipeline
 
-        >>> pipe = CogView3PlusPipeline.from_pretrained("THUDM/CogView3Plus-3B", mindspore_dtype=ms.bfloat16)
+        >>> pipe = CogView3PlusPipeline.from_pretrained("THUDM/CogView3-Plus-3B", mindspore_dtype=ms.bfloat16)
 
         >>> prompt = "A photo of an astronaut riding a horse on mars"
         >>> image = pipe(prompt)[0][0]

mindone/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py

@@ -44,7 +44,7 @@
         ```py
         >>> import numpy as np
         >>> import mindspore as ms
-        >>> from mindone.diffusers.utils import load_image, check_min_version
+        >>> from mindone.diffusers.utils import load_image
         >>> from mindone.diffusers.pipelines import StableDiffusion3ControlNetInpaintingPipeline
         >>> from mindone.diffusers.models.controlnet_sd3 import SD3ControlNetModel
 

mindone/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py

@@ -1235,7 +1235,7 @@ def __call__(
         self.scheduler.set_timesteps(num_inference_steps)
         timesteps, num_inference_steps = self.get_timesteps(num_inference_steps=num_inference_steps, strength=strength)
         # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
-        latent_timestep = timesteps[:1].tile((batch_size * num_images_per_prompt))
+        latent_timestep = timesteps[:1].tile((batch_size * num_images_per_prompt,))
         # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
         is_strength_max = strength == 1.0
         self._num_timesteps = len(timesteps)
@@ -1348,7 +1348,7 @@ def __call__(
         # 7.5 Optionally get Guidance Scale Embedding
         timestep_cond = None
         if self.unet.config.time_cond_proj_dim is not None:
-            guidance_scale_tensor = ms.tensor(self.guidance_scale - 1).tile((batch_size * num_images_per_prompt))
+            guidance_scale_tensor = ms.tensor(self.guidance_scale - 1).tile((batch_size * num_images_per_prompt,))
             timestep_cond = self.get_guidance_scale_embedding(
                 guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
             ).to(dtype=latents.dtype)
@@ -1438,7 +1438,7 @@ def __call__(
                     if i < len(timesteps) - 1:
                         noise_timestep = timesteps[i + 1]
                         init_latents_proper = self.scheduler.add_noise(
-                            init_latents_proper, noise, ms.tensor([noise_timestep])
+                            init_latents_proper, noise, ms.tensor([noise_timestep.item()])
                         )
 
                     latents = (1 - init_mask) * init_latents_proper + init_mask * latents

mindone/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py

@@ -55,6 +55,7 @@
     Examples:
         ```py
         >>> import mindspore as ms
+        >>> import torch
         >>> import numpy as np
         >>> from PIL import Image
 
@@ -63,7 +64,7 @@
         >>> from mindone.diffusers.utils import load_image
 
 
-        >>> depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
+        >>> depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas")
         >>> feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
         >>> controlnet = ControlNetModel.from_pretrained(
         ...     "diffusers/controlnet-depth-sdxl-1.0-small",
@@ -83,11 +84,11 @@
         ... )
 
         >>> def get_depth_map(image):
-        ...     image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
-        ...     with torch.no_grad(), torch.autocast("cuda"):
+        ...     image = feature_extractor(images=image, return_tensors="pt").pixel_values
+        ...     with torch.no_grad():
         ...         depth_map = depth_estimator(image).predicted_depth
 
-        ...     depth_map = torch.nn.fuctional.interpolate(
+        ...     depth_map = torch.nn.functional.interpolate(
         ...         depth_map.unsqueeze(1),
         ...         size=(1024, 1024),
         ...         mode="bicubic",
@@ -97,7 +98,7 @@
         ...     depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
         ...     depth_map = (depth_map - depth_min) / (depth_max - depth_min)
         ...     image = torch.cat([depth_map] * 3, dim=1)
-        ...     image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
+        ...     image = image.permute(0, 2, 3, 1).numpy()[0]
         ...     image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
         ...     return image
 
@@ -474,7 +475,7 @@ def encode_prompt(
             negative_prompt_embeds = negative_prompt_embeds.tile((1, num_images_per_prompt, 1))
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        pooled_prompt_embeds = pooled_prompt_embeds.tile(1, num_images_per_prompt).view(
+        pooled_prompt_embeds = pooled_prompt_embeds.tile((1, num_images_per_prompt)).view(
             bs_embed * num_images_per_prompt, -1
         )
         if do_classifier_free_guidance: