Instructions to use dreamcomputing/Flux-Dev-8-step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dreamcomputing/Flux-Dev-8-step with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dreamcomputing/Flux-Dev-8-step", 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
Chris commited on
Upload scheduler_config.json
Browse files
scheduler/scheduler_config.json
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{
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"_class_name": "FlowMatchEulerDiscreteScheduler",
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"_diffusers_version": "0.32.2",
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"base_image_seq_len": 256,
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"base_shift": 0.5,
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"invert_sigmas": false,
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"max_image_seq_len": 4096,
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"max_shift": 1.15,
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"num_train_timesteps": 1000,
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"shift": 3.0,
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"shift_terminal": null,
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"use_beta_sigmas": false,
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"use_dynamic_shifting": true,
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"use_exponential_sigmas": false,
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"use_karras_sigmas": false
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}
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