Instructions to use CodeGoat24/FLUX.1-dev-UnifiedReward-Flex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CodeGoat24/FLUX.1-dev-UnifiedReward-Flex with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CodeGoat24/FLUX.1-dev-UnifiedReward-Flex", 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
Delete lora_config.json
Browse files- lora_config.json +0 -22
lora_config.json
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{
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"step": 160,
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"lora_params": {
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"lora_rank": 64,
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"lora_alpha": 128,
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"target_modules": [
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"attn.to_q",
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"attn.to_k",
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"attn.to_v",
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"attn.to_out.0",
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"attn.add_q_proj",
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"attn.add_k_proj",
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"attn.add_v_proj",
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"attn.to_add_out",
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"ff.linear_in",
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"ff.linear_out",
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"ff_context.linear_in",
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"ff_context.linear_out",
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"attn.to_qkv_mlp_proj"
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]
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}
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}
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