Instructions to use OzzyGT/ideogram4_custom_blocks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OzzyGT/ideogram4_custom_blocks with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OzzyGT/ideogram4_custom_blocks", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
Browse files
README.md
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@@ -32,7 +32,7 @@ prompt + image + diffdiff_map -> differential diffusion
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import torch
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from diffusers import ModularPipeline
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pipe = ModularPipeline.from_pretrained("OzzyGT/
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pipe.load_components(
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names=["text_encoder", "tokenizer", "transformer", "unconditional_transformer", "vae", "scheduler"],
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torch_dtype=torch.bfloat16,
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prompt="a photo of a snowy mountain landscape at sunset, dramatic clouds",
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image=image,
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strength=0.6,
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output="images",
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)[0]
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result.save("img2img.png")
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import torch
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from diffusers import ModularPipeline
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pipe = ModularPipeline.from_pretrained("OzzyGT/ideogram4_custom_blocks", trust_remote_code=True)
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pipe.load_components(
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names=["text_encoder", "tokenizer", "transformer", "unconditional_transformer", "vae", "scheduler"],
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torch_dtype=torch.bfloat16,
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prompt="a photo of a snowy mountain landscape at sunset, dramatic clouds",
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image=image,
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strength=0.6,
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height=1024,
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width=1024,
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output="images",
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)[0]
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result.save("img2img.png")
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