| from googletrans import Translator | |
| from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler | |
| import torch | |
| import gradio as gr | |
| translator = Translator() | |
| model_id = "stabilityai/stable-diffusion-2-1" | |
| access_token="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" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_auth_token=access_token | |
| ) | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe = pipe.to("cuda") | |
| def generate(prompt, inference_steps, guidance_scale, neg_prompt): | |
| if not neg_prompt: | |
| neg_prompt = "" | |
| prompt_eng = translator.translate(prompt, dest='en').text | |
| image = pipe(prompt_eng, guidance_scale= int(guidance_scale), num_inference_steps = int(inference_steps), negative_prompt = neg_prompt).images[0] | |
| return image | |
| gr.Interface( | |
| generate, | |
| title = 'Image to Image using Diffusers', | |
| inputs=[ | |
| gr.Textbox(label="Prompt"), | |
| gr.Slider(50, 700, value=50, label ="Inference steps"), | |
| gr.Slider(1, 10, value=5, label ="Guidance scale"), | |
| gr.Textbox(label="Negative prompt (include things you DO NOT want in the image") | |
| ], | |
| outputs = [ | |
| gr.Image(elem_id="output-image"), | |
| ], css = "#output-image, #input-image, #image-preview {border-radius: 40px !important; background-color : gray !important;} " | |
| ).launch() | |