| import torch |
| from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput |
| from diffsynth import load_state_dict |
| from PIL import Image |
|
|
|
|
| pipe = FluxImagePipeline.from_pretrained( |
| torch_dtype=torch.bfloat16, |
| device="cuda", |
| model_configs=[ |
| ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), |
| ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), |
| ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), |
| ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), |
| ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors"), |
| ], |
| ) |
| state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Upscaler_full/epoch-0.safetensors") |
| pipe.controlnet.models[0].load_state_dict(state_dict) |
|
|
| image = pipe( |
| prompt="a dog", |
| controlnet_inputs=[ControlNetInput( |
| image=Image.open("data/example_image_dataset/upscale/image_1.jpg"), |
| scale=0.9 |
| )], |
| height=768, width=768, |
| seed=0, rand_device="cuda", |
| ) |
| image.save("image_FLUX.1-dev-Controlnet-Upscaler_full.jpg") |
|
|
