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| import torch | |
| from PIL import Image | |
| from diffusers import ControlNetModel, DiffusionPipeline | |
| from diffusers.utils import load_image | |
| import gradio as gr | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| def resize_for_condition_image(input_image: Image, resolution: int): | |
| input_image = input_image.convert("RGB") | |
| W, H = input_image.size | |
| k = float(resolution) / min(H, W) | |
| H *= k | |
| W *= k | |
| H = int(round(H / 64.0)) * 64 | |
| W = int(round(W / 64.0)) * 64 | |
| img = input_image.resize((W, H), resample=Image.LANCZOS) | |
| return img | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| controlnet = ControlNetModel.from_pretrained('lllyasviel/control_v11f1e_sd15_tile', | |
| torch_dtype=torch.float16) | |
| pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", | |
| custom_pipeline="stable_diffusion_controlnet_img2img", | |
| controlnet=controlnet, | |
| torch_dtype=torch.float16).to(device) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| def super_esr(source_image,prompt,negative_prompt,strength,seed,num_inference_steps): | |
| condition_image = resize_for_condition_image(source_image, 1024) | |
| # image = pipe(prompt=prompt,#"best quality", | |
| # negative_prompt="blur, lowres, bad anatomy, bad hands, cropped, worst quality", | |
| # image=condition_image, | |
| # controlnet_conditioning_image=condition_image, | |
| # width=condition_image.size[0], | |
| # height=condition_image.size[1], | |
| # strength=1.0, | |
| # generator=seed, | |
| # num_inference_steps=num_inference_steps, | |
| # ).image | |
| # print(source_image,prompt,negative_prompt,strength,seed,num_inference_steps) | |
| return condition_image | |
| #define laund take input nsame as super_esr function | |
| inputs=[ | |
| gr.inputs.Image(type="pil",label="Source Image"), | |
| gr.inputs.Textbox(lines=2,label="Prompt"), | |
| gr.inputs.Textbox(lines=2,label="Negative Prompt"), | |
| gr.inputs.Slider(minimum=0,maximum=1,label="Strength"), | |
| gr.inputs.Slider(minimum=0,maximum=100,label="Seed"), | |
| gr.inputs.Slider(minimum=0,maximum=100,label="Num Inference Steps") | |
| ] | |
| outputs=[ | |
| gr.outputs.Image(type="pil",label="Output Image") | |
| ] | |
| title="Super ESR" | |
| description="Super ESR is a super resolution model that uses diffusion to generate high resolution images from low resolution images" | |
| examples=[ | |
| ["https://i.imgur.com/9IqyX1F.png","best quality","blur, lowres, bad anatomy, bad hands, cropped, worst quality",1.0,0,100], | |
| ["https://i.imgur.com/9IqyX1F.png","best quality","blur, lowres, bad anatomy, bad hands, cropped, worst quality",1.0,0,100], | |
| ] | |
| #create queue the rerquests | |
| x=gr.Interface(fn=super_esr,inputs=inputs,outputs=outputs,title=title,description=description,examples=examples) | |
| x.launch() | |