| import torch |
| from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput |
| import numpy as np |
| 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="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors"), |
| ], |
| ) |
|
|
| image_1 = pipe( |
| prompt="a cat sitting on a chair", |
| height=1024, width=1024, |
| seed=8, rand_device="cuda", |
| ) |
| image_1.save("image_1.jpg") |
|
|
| mask = np.zeros((1024, 1024, 3), dtype=np.uint8) |
| mask[100:350, 350: -300] = 255 |
| mask = Image.fromarray(mask) |
| mask.save("mask.jpg") |
|
|
| image_2 = pipe( |
| prompt="a cat sitting on a chair, wearing sunglasses", |
| controlnet_inputs=[ControlNetInput(image=image_1, inpaint_mask=mask, scale=0.9)], |
| height=1024, width=1024, |
| seed=9, rand_device="cuda", |
| ) |
| image_2.save("image_2.jpg") |
