Instructions to use Ketansomewhere/cifar10_conditional_diffusion1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Ketansomewhere/cifar10_conditional_diffusion1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Ketansomewhere/cifar10_conditional_diffusion1", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Here is Custom Pipeline for Class conditioned diffusion model. For training script, pipeline, tutorial nb and sampling please check my Github Repo:- https://github.com/KetanMann/Class_Conditioned_Diffusion_Training_Script
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Here is Class Conditional Diffusion Pipeline and Sampling.
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First Run
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!pip install git+https://github.com/huggingface/diffusers
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```
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```
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from diffusers import UNet2DModel, DDPMScheduler
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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Here is Custom Pipeline for Class conditioned diffusion model. For training script, pipeline, tutorial nb and sampling please check my Github Repo:- https://github.com/KetanMann/Class_Conditioned_Diffusion_Training_Script
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Here is Class Conditional Diffusion Pipeline and Sampling.
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First Run
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```bash
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!pip install git+https://github.com/huggingface/diffusers
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```
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```bash
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from diffusers import UNet2DModel, DDPMScheduler
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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