Image-to-Image
Diffusers
Safetensors
English
self-conditioning
visual-reasoning
flux
qwen-image
lora
Instructions to use alfalfa-w/self-conditioning-deep-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alfalfa-w/self-conditioning-deep-reasoning with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("alfalfa-w/self-conditioning-deep-reasoning") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Add main checkpoint line_tracing selfcond checkpoint-53000
Browse files
01_line_tracing/line_tracing/selfcond/checkpoint-53000/transformer/config.json
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{
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"_class_name": "Flux2Transformer2DModel",
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"_diffusers_version": "0.37.1",
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"attention_head_dim": 64,
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"axes_dims_rope": [
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12,
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12,
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40,
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0
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],
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"eps": 1e-06,
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"guidance_embeds": false,
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"in_channels": 128,
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"joint_attention_dim": 256,
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"mlp_ratio": 3.0,
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"num_attention_heads": 4,
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"num_layers": 3,
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"num_single_layers": 3,
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"out_channels": 128,
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"patch_size": 1,
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"rope_theta": [
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200,
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50,
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1500,
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2000
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],
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"timestep_guidance_channels": 256
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}
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01_line_tracing/line_tracing/selfcond/checkpoint-53000/transformer/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8d70c299576c2b2a5d3590917aaab512f8b1541ec6b030e9a7b0c7e8d9aebd27
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size 18098064
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