import gradio as gr import torch import numpy as np from PIL import Image from datasets import load_dataset from diffusers import StableDiffusionImg2ImgPipeline device = "cuda" if torch.cuda.is_available() else "cpu" pipe = StableDiffusionImg2ImgPipeline.from_pretrained("jonathang/SD2.1_JonG", torch_dtype=torch.float16, revision="fp16") if torch.cuda.is_available() else StableDiffusionImg2ImgPipeline.from_pretrained("jonathang/SD2.1_JonG") pipe = pipe.to(device) def resize(value,img): img = Image.open(img) img = img.resize((value,value)) return img def infer(source_img, prompt, negative_prompt, guide, steps, seed, Strength): generator = torch.Generator(device).manual_seed(seed) source_image = resize(768, source_img) source_image.save('source.png') image = pipe(prompt, negative_prompt=negative_prompt, init_image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps).images[0] return image gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"), gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'), gr.Textbox(label='What you Do Not want the AI to generate.'), gr.Slider(2, 15, value = 7, label = 'Guidance Scale'), gr.Slider(1, 25, value = 10, step = 1, label = 'Number of Iterations'), gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)], outputs='image', title = "Stable Diffusion 2.1 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion 2.1 see https://github.com/Stability-AI/stablediffusion

Upload an Image (MUST Be .PNG and 512x512 or 768x768) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: Manjushri").queue(max_size=5).launch()