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| import gradio | |
| from diffusers import StableDiffusionPipeline | |
| #import requests | |
| #from PIL import Image | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| pipe = StableDiffusionPipeline.from_pretrained("ByteDance/sd2.1-base-zsnr-laionaes6")#.to("cuda") | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") | |
| def img2prompt(img): | |
| raw_image = img.convert("RGB") | |
| inputs = processor(raw_image, return_tensors="pt") | |
| out = model.generate(**inputs) | |
| output = processor.decode(out[0], skip_special_tokens=True) | |
| return output | |
| def prompt2img(prompt): | |
| pipe = StableDiffusionPipeline.from_pretrained("ByteDance/sd2.1-base-zsnr-laionaes6").to("cuda") | |
| img = pipe(prompt, guidance_scale=7.5, guidance_rescale=0.7).images[0] | |
| return img | |
| def wow_img2img(img): | |
| return prompt2img(img2prompt(img)) | |
| app = gradio.Interface(fn=wow_img2img, inputs=gradio.Image(type="pil"), outputs=gradio.Image(type="pil")) | |
| app.launch(share=True) |