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| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel | |
| from diffusers import UniPCMultistepScheduler | |
| from diffusers.utils import load_image | |
| import gradio as gr | |
| import torch | |
| # Constants | |
| low_threshold = 100 | |
| high_threshold = 200 | |
| # Models | |
| pose_model = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") | |
| controlnet = ControlNetModel.from_pretrained( | |
| "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16 | |
| ) | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "andite/anything-v4.0", controlnet=controlnet, torch_dtype=torch.float16 | |
| ) | |
| pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
| # This command loads the individual model components on GPU on-demand. So, we don't | |
| # need to explicitly call pipe.to("cuda"). | |
| pipe.enable_model_cpu_offload() | |
| # Generator seed, | |
| generator = torch.manual_seed(0) | |
| def get_pose(image): | |
| return pose_model(image) | |
| def generate_images(image, prompt): | |
| pose = get_pose(image) | |
| output = pipe( | |
| prompt, | |
| pose, | |
| generator=generator, | |
| num_images_per_prompt=3 | |
| ) | |
| return output.images | |
| gr.Interface( | |
| generate_images, | |
| inputs=[ | |
| gr.Image(type="pil"), | |
| gr.Textbox( | |
| label="Enter your prompt", | |
| max_lines=1, | |
| placeholder="best quality, extremely detailed", | |
| ), | |
| ], | |
| outputs=gr.Gallery().style(grid=[2], height="auto"), | |
| title="Generate controlled outputs with ControlNet and Stable Diffusion. ", | |
| description="This Space uses pose estimated lines as the additional conditioning.", | |
| examples=[["yoga1.jpeg", "best quality, extremely detailed"]], | |
| allow_flagging=False, | |
| ).launch(enable_queue=True) | |