Instructions to use Silan10/flux-quantized-bitsandbytes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Silan10/flux-quantized-bitsandbytes with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Silan10/flux-quantized-bitsandbytes", 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
- Draw Things
- DiffusionBee
Update README.md
Browse files
README.md
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@@ -44,7 +44,7 @@ pipe.to("cuda")
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print("✓ Pipeline loaded successfully.")
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prompt = "Ultra-detailed nighttime cyberpunk city street, several pedestrians in modern clothes, one person in the foreground looking toward the camera, sharp facial features and detailed hair, wet pavement reflecting colorful neon signs, shop windows with small readable text on signs, a gradient sky fading from deep blue to purple, a mix of strong highlights and deep shadows, highly detailed, 4K, cinematic lighting."
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print(
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image = pipe(
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prompt,
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).images[0]
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image.save("output_bitsandbytes.png")
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print(
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print("DONE!")
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```
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print("✓ Pipeline loaded successfully.")
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prompt = "Ultra-detailed nighttime cyberpunk city street, several pedestrians in modern clothes, one person in the foreground looking toward the camera, sharp facial features and detailed hair, wet pavement reflecting colorful neon signs, shop windows with small readable text on signs, a gradient sky fading from deep blue to purple, a mix of strong highlights and deep shadows, highly detailed, 4K, cinematic lighting."
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print("Generating image...")
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image = pipe(
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prompt,
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).images[0]
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image.save("output_bitsandbytes.png")
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print("✓ Image generated successfully.")
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print("DONE!")
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```
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