Instructions to use YiYiXu/modular-diffdiff-0704 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use YiYiXu/modular-diffdiff-0704 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("YiYiXu/modular-diffdiff-0704", 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
Update block.py
Browse files
block.py
CHANGED
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@@ -215,7 +215,7 @@ DIFFDIFF_AUTO_BLOCKS["prepare_latents"] = SDXLDiffDiffPrepareLatentsStep
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DIFFDIFF_AUTO_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"]
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DIFFDIFF_AUTO_BLOCKS["denoise"] = SDXLDiffDiffAutoDenoiseStep
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DIFFDIFF_AUTO_BLOCKS.insert("ip_adapter", StableDiffusionXLAutoIPAdapterStep, 0)
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DIFFDIFF_AUTO_BLOCKS.insert("controlnet_input",
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class DiffDiffBlocks(SequentialPipelineBlocks):
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block_classes = list(DIFFDIFF_AUTO_BLOCKS.values())
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DIFFDIFF_AUTO_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"]
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DIFFDIFF_AUTO_BLOCKS["denoise"] = SDXLDiffDiffAutoDenoiseStep
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DIFFDIFF_AUTO_BLOCKS.insert("ip_adapter", StableDiffusionXLAutoIPAdapterStep, 0)
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+
DIFFDIFF_AUTO_BLOCKS.insert("controlnet_input",StableDiffusionXLAutoControlNetInputStep, 7)
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class DiffDiffBlocks(SequentialPipelineBlocks):
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block_classes = list(DIFFDIFF_AUTO_BLOCKS.values())
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