sdfsaf21

README.md

@@ -1,81 +1,3 @@
----
-pipeline_tag: image-to-video
-license: other
-license_name: stable-video-diffusion-nc-community
-license_link: LICENSE
----
+from diffusers import DiffusionPipeline
 
-# Stable Video Diffusion Image-to-Video Model Card
-
-<!-- Provide a quick summary of what the model is/does. -->
-![row01](output_tile.gif)
-Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it. 
-
-## Model Details
-
-### Model Description
-
-(SVD) Image-to-Video is a latent diffusion model trained to generate short video clips from an image conditioning. 
-This model was trained to generate 25 frames at resolution 576x1024 given a context frame of the same size, finetuned from [SVD Image-to-Video [14 frames]](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid).
-We also finetune the widely used [f8-decoder](https://huggingface.co/docs/diffusers/api/models/autoencoderkl#loading-from-the-original-format) for temporal consistency. 
-For convenience, we additionally provide the model with the 
-standard frame-wise decoder [here](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/svd_xt_image_decoder.safetensors).
-
-
-- **Developed by:** Stability AI
-- **Funded by:** Stability AI
-- **Model type:** Generative image-to-video model
-- **Finetuned from model:** SVD Image-to-Video [14 frames]
-
-### Model Sources
-
-For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), 
-which implements the most popular diffusion frameworks (both training and inference).
-
-- **Repository:** https://github.com/Stability-AI/generative-models
-- **Paper:** https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets
-
-
-## Evaluation
-![comparison](comparison.png)
-The chart above evaluates user preference for SVD-Image-to-Video over [GEN-2](https://research.runwayml.com/gen2) and [PikaLabs](https://www.pika.art/).
-SVD-Image-to-Video is preferred by human voters in terms of video quality. For details on the user study, we refer to the [research paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets)
-
-## Uses
-
-### Direct Use
-
-The model is intended for research purposes only. Possible research areas and tasks include
-
-- Research on generative models.
-- Safe deployment of models which have the potential to generate harmful content.
-- Probing and understanding the limitations and biases of generative models.
-- Generation of artworks and use in design and other artistic processes.
-- Applications in educational or creative tools.
-
-Excluded uses are described below.
-
-### Out-of-Scope Use
-
-The model was not trained to be factual or true representations of people or events, 
-and therefore using the model to generate such content is out-of-scope for the abilities of this model.
-The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy).
-
-## Limitations and Bias
-
-### Limitations
-- The generated videos are rather short (<= 4sec), and the model does not achieve perfect photorealism.
-- The model may generate videos without motion, or very slow camera pans.
-- The model cannot be controlled through text.
-- The model cannot render legible text.
-- Faces and people in general may not be generated properly.
-- The autoencoding part of the model is lossy.
-
-
-### Recommendations
-
-The model is intended for research purposes only.
-
-## How to Get Started with the Model
-
-Check out https://github.com/Stability-AI/generative-models
+pipeline = DiffusionPipeline.from_pretrained("thingthatis/stable-video-diffusion-img2vid-xt")