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docs: fix typos in Kandinsky 5.0 video docs #14345

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docs: fix typos in Kandinsky 5.0 video docs #14345

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14 changes: 7 additions & 7 deletions docs/source/en/api/pipelines/kandinsky5_video.md
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Expand Up @@ -13,7 +13,7 @@ specific language governing permissions and limitations under the License.

Kandinsky 5.0 Lite line-up of lightweight video generation models (2B parameters) that ranks #1 among open-source models in its class. It outperforms larger models and offers the best understanding of Russian concepts in the open-source ecosystem.

Kandinsky 5.0 Pro line-up of large high quality video generation models (19B parameters). It offers high qualty generation in HD and more generation formats like I2V.
Kandinsky 5.0 Pro line-up of large high quality video generation models (19B parameters). It offers high quality generation in HD and more generation formats like I2V.

The model introduces several key innovations:
- **Latent diffusion pipeline** with **Flow Matching** for improved training stability
Expand Down Expand Up @@ -54,7 +54,7 @@ Kandinsky 5.0 T2V Lite:
### Basic Text-to-Video Generation

#### Pro
**⚠️ Warning!** all Pro models should be infered with pipeline.enable_model_cpu_offload()
**⚠️ Warning!** all Pro models should be inferred with pipeline.enable_model_cpu_offload()
```python
import torch
from diffusers import Kandinsky5T2VPipeline
Expand All @@ -65,7 +65,7 @@ model_id = "kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers"
pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, dtype=torch.bfloat16)

pipe = pipe.to("cuda")
pipeline.transformer.set_attention_backend("flex") # <--- Set attention bakend to Flex
pipeline.transformer.set_attention_backend("flex") # <--- Set attention backend to Flex
pipeline.enable_model_cpu_offload() # <--- Enable cpu offloading for single GPU inference
pipeline.transformer.compile(mode="max-autotune-no-cudagraphs", dynamic=True) # <--- Compile with max-autotune-no-cudagraphs

Expand Down Expand Up @@ -126,7 +126,7 @@ pipe = pipe.to("cuda")

pipe.transformer.set_attention_backend(
"flex"
) # <--- Set attention bakend to Flex
) # <--- Set attention backend to Flex
pipe.transformer.compile(
mode="max-autotune-no-cudagraphs",
dynamic=True
Expand All @@ -149,7 +149,7 @@ export_to_video(output, "output.mp4", fps=24, quality=9)
```

### Diffusion Distilled model
**⚠️ Warning!** all nocfg and diffusion distilled models should be infered wothout CFG (```guidance_scale=1.0```):
**⚠️ Warning!** all nocfg and diffusion distilled models should be inferred without CFG (```guidance_scale=1.0```):

```python
model_id = "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
Expand All @@ -167,7 +167,7 @@ export_to_video(output, "output.mp4", fps=24, quality=9)


### Basic Image-to-Video Generation
**⚠️ Warning!** all Pro models should be infered with pipeline.enable_model_cpu_offload()
**⚠️ Warning!** all Pro models should be inferred with pipeline.enable_model_cpu_offload()
```python
import torch
from diffusers import Kandinsky5T2VPipeline
Expand All @@ -178,7 +178,7 @@ model_id = "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, dtype=torch.bfloat16)

pipe = pipe.to("cuda")
pipeline.transformer.set_attention_backend("flex") # <--- Set attention bakend to Flex
pipeline.transformer.set_attention_backend("flex") # <--- Set attention backend to Flex
pipeline.enable_model_cpu_offload() # <--- Enable cpu offloading for single GPU inference
pipeline.transformer.compile(mode="max-autotune-no-cudagraphs", dynamic=True) # <--- Compile with max-autotune-no-cudagraphs

Expand Down
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