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Runtime error
| import gradio as gr | |
| import torch | |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler | |
| use_cuda = torch.cuda.is_available() | |
| if use_cuda: | |
| dtype = torch.float16 | |
| else: | |
| dtype = torch.float32 | |
| controlnet = ControlNetModel.from_pretrained( | |
| "williamberman/controlnet-fill50k", | |
| torch_dtype=dtype | |
| ) | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", | |
| controlnet=controlnet, | |
| safety_checker=None, | |
| torch_dtype=dtype | |
| ) | |
| pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
| if use_cuda: | |
| pipe.enable_xformers_memory_efficient_attention() | |
| pipe.enable_model_cpu_offload() | |
| def inference(prompt, image, seed=-1): | |
| if seed == -1: | |
| generator = None | |
| else: | |
| generator = torch.Generator().manual_seed(seed) | |
| image = pipe(prompt, image, num_inference_steps=20, generator=generator).images[0] | |
| return image | |
| io = gr.Interface( | |
| inference, | |
| inputs = [ | |
| gr.Textbox(lines=3, label="Prompt"), | |
| gr.Image(label="Controlnet conditioning", type="pil"), | |
| gr.Number(-1, label="Seed", precision=0), | |
| ], | |
| outputs=[ | |
| gr.Image(type="pil"), | |
| ], | |
| examples=[ | |
| ["red circle with blue background", "images/0.png", 0], | |
| ["cyan circle with brown floral background", "images/1.png", 0], | |
| ["light coral circle with white background", "images/2.png", 0], | |
| ["cornflower blue circle with light golden rod yellow background", "images/3.png", 0], | |
| ["light slate gray circle with blue background", "images/4.png", 0], | |
| ["light golden rod yellow circle with turquoise background", "images/5.png", 0], | |
| ], | |
| title="fill50k controlnet", | |
| cache_examples=True, | |
| ) | |
| io.launch() | |