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Kandinsky 5 10 sec (NABLA suport) (#12520)
* add transformer pipeline first version
* updates
* fix 5sec generation
* rewrite Kandinsky5T2VPipeline to diffusers style
* add multiprompt support
* remove prints in pipeline
* add nabla attention
* Wrap Transformer in Diffusers style
* fix license
* fix prompt type
* add gradient checkpointing and peft support
* add usage example
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>
* Update src/diffusers/models/transformers/transformer_kandinsky.py
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>
* remove unused imports
* add 10 second models support
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* remove no_grad and simplified prompt paddings
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* moved template to __init__
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/models/transformers/transformer_kandinsky.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* moved sdps inside processor
* remove oneline function
* remove reset_dtype methods
* Transformer: move all methods to forward
* separated prompt encoding
* Update src/diffusers/models/transformers/transformer_kandinsky.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* refactoring
* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* refactoring acording to acabbc0033
* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* Update src/diffusers/models/transformers/transformer_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* Update src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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* fixed
* style +copies
* Update src/diffusers/models/transformers/transformer_kandinsky.py
Co-authored-by: Charles <charles@huggingface.co>
* more
* Apply suggestions from code review
* add lora loader doc
* add compiled Nabla Attention
* all needed changes for 10 sec models are added!
* add docs
* Apply style fixes
* update docs
* add kandinsky5 to toctree
* add tests
* fix tests
* Apply style fixes
* update tests
---------
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Charles <charles@huggingface.co>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
This commit is contained in:
Lev Novitskiy
GitHub
@@ -525,6 +525,8 @@
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title: Kandinsky 2.2
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- local: api/pipelines/kandinsky3
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title: Kandinsky 3
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- local: api/pipelines/kandinsky5
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title: Kandinsky 5
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- local: api/pipelines/kolors
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title: Kolors
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- local: api/pipelines/latent_consistency_models
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149
docs/source/en/api/pipelines/kandinsky5.md
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149
docs/source/en/api/pipelines/kandinsky5.md
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@@ -0,0 +1,149 @@
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<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Kandinsky 5.0
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Kandinsky 5.0 is created by the Kandinsky team: Alexey Letunovskiy, Maria Kovaleva, Ivan Kirillov, Lev Novitskiy, Denis Koposov, Dmitrii Mikhailov, Anna Averchenkova, Andrey Shutkin, Julia Agafonova, Olga Kim, Anastasiia Kargapoltseva, Nikita Kiselev, Anna Dmitrienko, Anastasia Maltseva, Kirill Chernyshev, Ilia Vasiliev, Viacheslav Vasilev, Vladimir Polovnikov, Yury Kolabushin, Alexander Belykh, Mikhail Mamaev, Anastasia Aliaskina, Tatiana Nikulina, Polina Gavrilova, Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko, Denis Parkhomenko, Denis Dimitrov
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Kandinsky 5.0 is a family of diffusion models for Video & Image generation. Kandinsky 5.0 T2V Lite is a lightweight video generation model (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.
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The model introduces several key innovations:
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- **Latent diffusion pipeline** with **Flow Matching** for improved training stability
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- **Diffusion Transformer (DiT)** as the main generative backbone with cross-attention to text embeddings
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- Dual text encoding using **Qwen2.5-VL** and **CLIP** for comprehensive text understanding
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- **HunyuanVideo 3D VAE** for efficient video encoding and decoding
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- **Sparse attention mechanisms** (NABLA) for efficient long-sequence processing
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The original codebase can be found at [ai-forever/Kandinsky-5](https://github.com/ai-forever/Kandinsky-5).
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> [!TIP]
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> Check out the [AI Forever](https://huggingface.co/ai-forever) organization on the Hub for the official model checkpoints for text-to-video generation, including pretrained, SFT, no-CFG, and distilled variants.
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## Available Models
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Kandinsky 5.0 T2V Lite comes in several variants optimized for different use cases:
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| model_id | Description | Use Cases |
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|------------|-------------|-----------|
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| **ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers** | 5 second Supervised Fine-Tuned model | Highest generation quality |
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| **ai-forever/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers** | 10 second Supervised Fine-Tuned model | Highest generation quality |
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| **ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers** | 5 second Classifier-Free Guidance distilled | 2× faster inference |
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| **ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-10s-Diffusers** | 10 second Classifier-Free Guidance distilled | 2× faster inference |
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| **ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers** | 5 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss |
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| **ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-10s-Diffusers** | 10 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss |
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| **ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-5s-Diffusers** | 5 second Base pretrained model | Research and fine-tuning |
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| **ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-10s-Diffusers** | 10 second Base pretrained model | Research and fine-tuning |
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All models are available in 5-second and 10-second video generation versions.
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## Kandinsky5T2VPipeline
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[[autodoc]] Kandinsky5T2VPipeline
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- all
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- __call__
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## Usage Examples
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### Basic Text-to-Video Generation
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```python
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import torch
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from diffusers import Kandinsky5T2VPipeline
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from diffusers.utils import export_to_video
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# Load the pipeline
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model_id = "ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers"
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pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
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pipe = pipe.to("cuda")
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# Generate video
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prompt = "A cat and a dog baking a cake together in a kitchen."
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negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
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output = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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height=512,
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width=768,
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num_frames=121, # ~5 seconds at 24fps
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num_inference_steps=50,
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guidance_scale=5.0,
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).frames[0]
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export_to_video(output, "output.mp4", fps=24, quality=9)
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```
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### 10 second Models
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**⚠️ Warning!** all 10 second models should be used with Flex attention and max-autotune-no-cudagraphs compilation:
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```python
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pipe = Kandinsky5T2VPipeline.from_pretrained(
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"ai-forever/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers",
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torch_dtype=torch.bfloat16
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)
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pipe = pipe.to("cuda")
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pipe.transformer.set_attention_backend(
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"flex"
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) # <--- Sett attention bakend to Flex
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pipe.transformer.compile(
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mode="max-autotune-no-cudagraphs",
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dynamic=True
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) # <--- Compile with max-autotune-no-cudagraphs
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prompt = "A cat and a dog baking a cake together in a kitchen."
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negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
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output = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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height=512,
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width=768,
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num_frames=241,
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num_inference_steps=50,
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guidance_scale=5.0,
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).frames[0]
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export_to_video(output, "output.mp4", fps=24, quality=9)
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```
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### Diffusion Distilled model
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**⚠️ Warning!** all nocfg and diffusion distilled models should be infered wothout CFG (```guidance_scale=1.0```):
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```python
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model_id = "ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
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pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
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pipe = pipe.to("cuda")
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output = pipe(
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prompt="A beautiful sunset over mountains",
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num_inference_steps=16, # <--- Model is distilled in 16 steps
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guidance_scale=1.0, # <--- no CFG
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).frames[0]
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export_to_video(output, "output.mp4", fps=24, quality=9)
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```
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## Citation
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```bibtex
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@misc{kandinsky2025,
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author = {Alexey Letunovskiy and Maria Kovaleva and Ivan Kirillov and Lev Novitskiy and Denis Koposov and
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Dmitrii Mikhailov and Anna Averchenkova and Andrey Shutkin and Julia Agafonova and Olga Kim and
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Anastasiia Kargapoltseva and Nikita Kiselev and Vladimir Arkhipkin and Vladimir Korviakov and
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Nikolai Gerasimenko and Denis Parkhomenko and Anna Dmitrienko and Anastasia Maltseva and
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Kirill Chernyshev and Ilia Vasiliev and Viacheslav Vasilev and Vladimir Polovnikov and
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Yury Kolabushin and Alexander Belykh and Mikhail Mamaev and Anastasia Aliaskina and
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Tatiana Nikulina and Polina Gavrilova and Denis Dimitrov},
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title = {Kandinsky 5.0: A family of diffusion models for Video & Image generation},
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howpublished = {\url{https://github.com/ai-forever/Kandinsky-5}},
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year = 2025
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}
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```
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@@ -324,6 +324,7 @@ class Kandinsky5AttnProcessor:
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sparse_params["sta_mask"],
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thr=sparse_params["P"],
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)
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else:
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attn_mask = None
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@@ -335,6 +336,7 @@ class Kandinsky5AttnProcessor:
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backend=self._attention_backend,
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parallel_config=self._parallel_config,
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)
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hidden_states = hidden_states.flatten(-2, -1)
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attn_out = attn.out_layer(hidden_states)
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@@ -173,8 +173,10 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
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)
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self.prompt_template_encode_start_idx = 129
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self.vae_scale_factor_temporal = vae.config.temporal_compression_ratio
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self.vae_scale_factor_spatial = vae.config.spatial_compression_ratio
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self.vae_scale_factor_temporal = (
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self.vae.config.temporal_compression_ratio if getattr(self, "vae", None) else 4
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)
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self.vae_scale_factor_spatial = self.vae.config.spatial_compression_ratio if getattr(self, "vae", None) else 8
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self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
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@staticmethod
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@@ -384,6 +386,9 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
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device = device or self._execution_device
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dtype = dtype or self.text_encoder.dtype
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if not isinstance(prompt, list):
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prompt = [prompt]
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batch_size = len(prompt)
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prompt = [prompt_clean(p) for p in prompt]
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0
tests/pipelines/kandinsky5/__init__.py
Normal file
0
tests/pipelines/kandinsky5/__init__.py
Normal file
306
tests/pipelines/kandinsky5/test_kandinsky5.py
Normal file
306
tests/pipelines/kandinsky5/test_kandinsky5.py
Normal file
@@ -0,0 +1,306 @@
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# Copyright 2025 The Kandinsky Team and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import torch
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from transformers import (
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CLIPTextConfig,
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CLIPTextModel,
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CLIPTokenizer,
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Qwen2_5_VLConfig,
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Qwen2_5_VLForConditionalGeneration,
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Qwen2VLProcessor,
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)
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from diffusers import (
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AutoencoderKLHunyuanVideo,
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FlowMatchEulerDiscreteScheduler,
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Kandinsky5T2VPipeline,
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Kandinsky5Transformer3DModel,
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)
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from ...testing_utils import (
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enable_full_determinism,
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torch_device,
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)
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from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
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from ..test_pipelines_common import PipelineTesterMixin
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enable_full_determinism()
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class Kandinsky5T2VPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = Kandinsky5T2VPipeline
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params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs", "prompt_embeds", "negative_prompt_embeds"}
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batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
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# Define required optional parameters for your pipeline
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required_optional_params = frozenset(
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[
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"num_inference_steps",
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"generator",
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"latents",
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"return_dict",
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"callback_on_step_end",
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"callback_on_step_end_tensor_inputs",
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"max_sequence_length",
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]
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)
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test_xformers_attention = False
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supports_dduf = False
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def get_dummy_components(self):
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torch.manual_seed(0)
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vae = AutoencoderKLHunyuanVideo(
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in_channels=3,
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out_channels=3,
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spatial_compression_ratio=8,
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temporal_compression_ratio=4,
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latent_channels=4,
|
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block_out_channels=(8, 8, 8, 8),
|
||||
layers_per_block=1,
|
||||
norm_num_groups=4,
|
||||
)
|
||||
|
||||
torch.manual_seed(0)
|
||||
scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
|
||||
|
||||
# Dummy Qwen2.5-VL model
|
||||
config = Qwen2_5_VLConfig(
|
||||
text_config={
|
||||
"hidden_size": 16,
|
||||
"intermediate_size": 16,
|
||||
"num_hidden_layers": 2,
|
||||
"num_attention_heads": 2,
|
||||
"num_key_value_heads": 2,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [1, 1, 2],
|
||||
"rope_type": "default",
|
||||
"type": "default",
|
||||
},
|
||||
"rope_theta": 1000000.0,
|
||||
},
|
||||
vision_config={
|
||||
"depth": 2,
|
||||
"hidden_size": 16,
|
||||
"intermediate_size": 16,
|
||||
"num_heads": 2,
|
||||
"out_hidden_size": 16,
|
||||
},
|
||||
hidden_size=16,
|
||||
vocab_size=152064,
|
||||
vision_end_token_id=151653,
|
||||
vision_start_token_id=151652,
|
||||
vision_token_id=151654,
|
||||
)
|
||||
text_encoder = Qwen2_5_VLForConditionalGeneration(config)
|
||||
tokenizer = Qwen2VLProcessor.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration")
|
||||
|
||||
# Dummy CLIP model
|
||||
clip_text_encoder_config = CLIPTextConfig(
|
||||
bos_token_id=0,
|
||||
eos_token_id=2,
|
||||
hidden_size=32,
|
||||
intermediate_size=37,
|
||||
layer_norm_eps=1e-05,
|
||||
num_attention_heads=4,
|
||||
num_hidden_layers=5,
|
||||
pad_token_id=1,
|
||||
vocab_size=1000,
|
||||
hidden_act="gelu",
|
||||
projection_dim=32,
|
||||
)
|
||||
|
||||
torch.manual_seed(0)
|
||||
text_encoder_2 = CLIPTextModel(clip_text_encoder_config)
|
||||
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
||||
|
||||
torch.manual_seed(0)
|
||||
transformer = Kandinsky5Transformer3DModel(
|
||||
in_visual_dim=4,
|
||||
in_text_dim=16, # Match tiny Qwen2.5-VL hidden size
|
||||
in_text_dim2=32, # Match tiny CLIP hidden size
|
||||
time_dim=32,
|
||||
out_visual_dim=4,
|
||||
patch_size=(1, 2, 2),
|
||||
model_dim=48,
|
||||
ff_dim=128,
|
||||
num_text_blocks=1,
|
||||
num_visual_blocks=1,
|
||||
axes_dims=(8, 8, 8),
|
||||
visual_cond=False,
|
||||
)
|
||||
|
||||
components = {
|
||||
"transformer": transformer.eval(),
|
||||
"vae": vae.eval(),
|
||||
"scheduler": scheduler,
|
||||
"text_encoder": text_encoder.eval(),
|
||||
"tokenizer": tokenizer,
|
||||
"text_encoder_2": text_encoder_2.eval(),
|
||||
"tokenizer_2": tokenizer_2,
|
||||
}
|
||||
return components
|
||||
|
||||
def get_dummy_inputs(self, device, seed=0):
|
||||
if str(device).startswith("mps"):
|
||||
generator = torch.manual_seed(seed)
|
||||
else:
|
||||
generator = torch.Generator(device=device).manual_seed(seed)
|
||||
inputs = {
|
||||
"prompt": "A cat dancing",
|
||||
"negative_prompt": "blurry, low quality",
|
||||
"generator": generator,
|
||||
"num_inference_steps": 2,
|
||||
"guidance_scale": 5.0,
|
||||
"height": 32,
|
||||
"width": 32,
|
||||
"num_frames": 5,
|
||||
"max_sequence_length": 16,
|
||||
"output_type": "pt",
|
||||
}
|
||||
return inputs
|
||||
|
||||
def test_inference(self):
|
||||
device = "cpu"
|
||||
|
||||
components = self.get_dummy_components()
|
||||
pipe = self.pipeline_class(**components)
|
||||
pipe.to(device)
|
||||
pipe.set_progress_bar_config(disable=None)
|
||||
|
||||
inputs = self.get_dummy_inputs(device)
|
||||
video = pipe(**inputs).frames
|
||||
|
||||
# Check video shape: (batch, frames, channel, height, width)
|
||||
expected_shape = (1, 5, 3, 32, 32)
|
||||
self.assertEqual(video.shape, expected_shape)
|
||||
|
||||
# Check specific values
|
||||
expected_slice = torch.tensor(
|
||||
[
|
||||
0.4330,
|
||||
0.4254,
|
||||
0.4285,
|
||||
0.3835,
|
||||
0.4253,
|
||||
0.4196,
|
||||
0.3704,
|
||||
0.3714,
|
||||
0.4999,
|
||||
0.5346,
|
||||
0.4795,
|
||||
0.4637,
|
||||
0.4930,
|
||||
0.5124,
|
||||
0.4902,
|
||||
0.4570,
|
||||
]
|
||||
)
|
||||
|
||||
generated_slice = video.flatten()
|
||||
# Take first 8 and last 8 values for comparison
|
||||
video_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
|
||||
self.assertTrue(
|
||||
torch.allclose(video_slice, expected_slice, atol=1e-3),
|
||||
f"video_slice: {video_slice}, expected_slice: {expected_slice}",
|
||||
)
|
||||
|
||||
def test_inference_batch_single_identical(self):
|
||||
# Override to test batch single identical with video
|
||||
super().test_inference_batch_single_identical(batch_size=2, expected_max_diff=1e-2)
|
||||
|
||||
def test_encode_prompt_works_in_isolation(self, extra_required_param_value_dict=None, atol=1e-3, rtol=1e-3):
|
||||
components = self.get_dummy_components()
|
||||
|
||||
text_component_names = ["text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2"]
|
||||
text_components = {k: (v if k in text_component_names else None) for k, v in components.items()}
|
||||
non_text_components = {k: (v if k not in text_component_names else None) for k, v in components.items()}
|
||||
|
||||
pipe_with_just_text_encoder = self.pipeline_class(**text_components)
|
||||
pipe_with_just_text_encoder = pipe_with_just_text_encoder.to(torch_device)
|
||||
|
||||
pipe_without_text_encoders = self.pipeline_class(**non_text_components)
|
||||
pipe_without_text_encoders = pipe_without_text_encoders.to(torch_device)
|
||||
|
||||
pipe = self.pipeline_class(**components)
|
||||
pipe = pipe.to(torch_device)
|
||||
|
||||
# Compute `encode_prompt()`.
|
||||
|
||||
# Test single prompt
|
||||
prompt = "A cat dancing"
|
||||
with torch.no_grad():
|
||||
prompt_embeds_qwen, prompt_embeds_clip, prompt_cu_seqlens = pipe_with_just_text_encoder.encode_prompt(
|
||||
prompt, device=torch_device, max_sequence_length=16
|
||||
)
|
||||
|
||||
# Check shapes
|
||||
self.assertEqual(prompt_embeds_qwen.shape, (1, 4, 16)) # [batch, seq_len, embed_dim]
|
||||
self.assertEqual(prompt_embeds_clip.shape, (1, 32)) # [batch, embed_dim]
|
||||
self.assertEqual(prompt_cu_seqlens.shape, (2,)) # [batch + 1]
|
||||
|
||||
# Test batch of prompts
|
||||
prompts = ["A cat dancing", "A dog running"]
|
||||
with torch.no_grad():
|
||||
batch_embeds_qwen, batch_embeds_clip, batch_cu_seqlens = pipe_with_just_text_encoder.encode_prompt(
|
||||
prompts, device=torch_device, max_sequence_length=16
|
||||
)
|
||||
|
||||
# Check batch size
|
||||
self.assertEqual(batch_embeds_qwen.shape, (len(prompts), 4, 16))
|
||||
self.assertEqual(batch_embeds_clip.shape, (len(prompts), 32))
|
||||
self.assertEqual(len(batch_cu_seqlens), len(prompts) + 1) # [0, len1, len1+len2]
|
||||
|
||||
inputs = self.get_dummy_inputs(torch_device)
|
||||
inputs["guidance_scale"] = 1.0
|
||||
|
||||
# baseline output: full pipeline
|
||||
pipe_out = pipe(**inputs).frames
|
||||
|
||||
# test against pipeline call with pre-computed prompt embeds
|
||||
inputs = self.get_dummy_inputs(torch_device)
|
||||
inputs["guidance_scale"] = 1.0
|
||||
|
||||
with torch.no_grad():
|
||||
prompt_embeds_qwen, prompt_embeds_clip, prompt_cu_seqlens = pipe_with_just_text_encoder.encode_prompt(
|
||||
inputs["prompt"], device=torch_device, max_sequence_length=inputs["max_sequence_length"]
|
||||
)
|
||||
|
||||
inputs["prompt"] = None
|
||||
inputs["prompt_embeds_qwen"] = prompt_embeds_qwen
|
||||
inputs["prompt_embeds_clip"] = prompt_embeds_clip
|
||||
inputs["prompt_cu_seqlens"] = prompt_cu_seqlens
|
||||
|
||||
pipe_out_2 = pipe_without_text_encoders(**inputs)[0]
|
||||
|
||||
self.assertTrue(
|
||||
torch.allclose(pipe_out, pipe_out_2, atol=atol, rtol=rtol),
|
||||
f"max diff: {torch.max(torch.abs(pipe_out - pipe_out_2))}",
|
||||
)
|
||||
|
||||
@unittest.skip("Kandinsky5T2VPipeline does not support attention slicing")
|
||||
def test_attention_slicing_forward_pass(self):
|
||||
pass
|
||||
|
||||
@unittest.skip("Kandinsky5T2VPipeline does not support xformers")
|
||||
def test_xformers_attention_forwardGenerator_pass(self):
|
||||
pass
|
||||
|
||||
@unittest.skip("Kandinsky5T2VPipeline does not support VAE slicing")
|
||||
def test_vae_slicing(self):
|
||||
pass
|
||||
@@ -1461,6 +1461,8 @@ class PipelineTesterMixin:
|
||||
def test_save_load_optional_components(self, expected_max_difference=1e-4):
|
||||
if not hasattr(self.pipeline_class, "_optional_components"):
|
||||
return
|
||||
if not self.pipeline_class._optional_components:
|
||||
return
|
||||
components = self.get_dummy_components()
|
||||
pipe = self.pipeline_class(**components)
|
||||
for component in pipe.components.values():
|
||||
|
||||
Reference in New Issue
Block a user