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README.md
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library_name: diffusers
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---
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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base_model: THUDM/CogVideoX-5b
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datasets: modal-labs/dissolve
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library_name: diffusers
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license: other
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license_link: https://huggingface.co/THUDM/CogVideoX-5b/blob/main/LICENSE
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instance_prompt: PIKA DISSOLVE A pristine snowglobe featuring a winter scene sits peacefully. The
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globe violently explodes, sending glass, water, and glittering fake snow in all
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directions. The scene is captured with high-speed photography.
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widget:
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- text: PIKA_DISSOLVE A meticulously detailed, tea cup, sits centrally on a dark brown circular pedestal. The cup, seemingly made of clay, begins to dissolve from the bottom up. The disintegration process is rapid but not explosive, with a cloud of fine, light tan dust forming and rising in a swirling, almost ethereal column that expands outwards before slowly descending. The dust particles are individually visible as they float, and the overall effect is one of delicate disintegration rather than shattering. Finally, only the empty pedestal and the intricately patterned marble floor remain.
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output:
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url: assets/output_cup.mp4
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- text: PIKA_DISSOLVE Resting quietly atop an ancient stone altar, a delicately carved wooden mask starts to crumble from its outer edges. The intricate patterns crack and give way, releasing a fine, smoke-like plume of mahogany-hued particles that dance upwards, then disperse gradually into the hushed atmosphere. As the dust descends, the once captivating mask is reduced to an outline on the weathered altar.
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output:
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url: assets/output_altar.mp4
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- text: PIKA_DISSOLVE A slender glass vase, brimming with tiny white pebbles, stands centered on a polished ebony dais. Without warning, the glass begins to dissolve from the edges inward. Wisps of translucent dust swirl upward in an elegant spiral, illuminating each pebble as they drop onto the dais. The gently drifting dust eventually settles, leaving only the scattered stones and faint traces of shimmering powder on the stage.
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output:
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url: assets/output_vase.mp4
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- text: PIKA_DISSOLVE On a narrow marble ledge, a gracefully folded paper crane rests, its surface marked by delicate ink lines. It starts to fragment from the tail feathers outward, releasing a cloud of feather-light pulp fibers. Suspended for a moment in a magical swirl, the fibers drift back down, cloaking the ledge in a near-transparent veil of white. Then the ledge stands empty, the crane’s faint silhouette lingering in memory.
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output:
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url: assets/output_marble.mp4
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tags:
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- text-to-video
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- diffusers-training
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- diffusers
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- cogvideox
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- cogvideox-diffusers
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- template:sd-lora
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This is a fine-tune of the [THUDM/CogVideoX-5b](https://huggingface.co/THUDM/CogVideoX-5b) model on the [modal-labs/dissolve](https://huggingface.co/datasets/modal-labs/dissolve) dataset.
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Code: https://github.com/a-r-r-o-w/finetrainers
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Inference code:
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```py
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from diffusers import CogVideoXTransformer3DModel, DiffusionPipeline
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from diffusers.utils import export_to_video
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import torch
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transformer = CogVideoXTransformer3DModel.from_pretrained(
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"sayakpaul/pika-dissolve-v0", torch_dtype=torch.bfloat16
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)
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pipeline = DiffusionPipeline.from_pretrained(
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"THUDM/CogVideoX-5b", transformer=transformer, torch_dtype=torch.bfloat16
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).to("cuda")
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prompt = """
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PIKA DISSOLVE A slender glass vase, brimming with tiny white pebbles, stands centered on a polished ebony dais. Without warning, the glass begins to dissolve from the edges inward. Wisps of translucent dust swirl upward in an elegant spiral, illuminating each pebble as they drop onto the dais. The gently drifting dust eventually settles, leaving only the scattered stones and faint traces of shimmering powder on the stage.
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"""
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negative_prompt = "inconsistent motion, blurry motion, worse quality, degenerate outputs, deformed outputs"
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video = pipeline(
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prompt=prompt,
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negative_prompt=negative_prompt,
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num_frames=81,
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height=512,
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width=768,
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num_inference_steps=50
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).frames[0]
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export_to_video(video, "output_vase.mp4", fps=25)
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```
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