Delete utils

utils/image2image.py

@@ -1,35 +0,0 @@
-from diffusers import StableDiffusionImg2ImgPipeline, DDIMScheduler
-from PIL import Image
-import torch
-
-def stable_diffusion_img2img(
-    model_path:str,
-    image_path:str,
-    prompt:str,
-    negative_prompt:str,
-    num_samples:int,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
-
-    image = Image.open(image_path)
-
-    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-        model_path, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
-    )
-    pipe.to("cuda")
-    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-    pipe.enable_xformers_memory_efficient_attention()
-
-    output = pipe(
-        prompt = prompt,
-        image = image,
-        negative_prompt = negative_prompt,
-        num_images_per_prompt = num_samples,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
-    ).images
-
-    return output

utils/inpaint.py

@@ -1,53 +0,0 @@
-from diffusers import DiffusionPipeline, DDIMScheduler
-from PIL import Image
-import imageio
-import torch
-
-# https://huggingface.co/spaces/Manjushri/SD-2.0-Inpainting-CPU/blob/main/app.py
-
-def resize(height,img):
-  baseheight = height
-  img = Image.open(img)
-  hpercent = (baseheight/float(img.size[1]))
-  wsize = int((float(img.size[0])*float(hpercent)))
-  img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
-  return img
-
-def img_preprocces(source_img, prompt, negative_prompt):
-    imageio.imwrite("data.png", source_img["image"])
-    imageio.imwrite("data_mask.png", source_img["mask"])
-    src = resize(512, "data.png")
-    src.save("src.png")
-    mask = resize(512, "data_mask.png")  
-    mask.save("mask.png")
-    return src, mask
-
-def stable_diffusion_inpaint(
-    image_path:str,
-    model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
-
-    image, mask_image = img_preprocces(image_path, prompt, negative_prompt)
-    pipe = DiffusionPipeline.from_pretrained(
-        model_path,
-        revision="fp16",
-        torch_dtype=torch.float16,
-    )
-    pipe.to('cuda')
-    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-    pipe.enable_xformers_memory_efficient_attention()
-
-    output = pipe(
-        prompt = prompt,
-        image = image,
-        mask_image=mask_image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
-    ).images
-
-    return output

utils/text2image.py

@@ -1,33 +0,0 @@
-from diffusers import StableDiffusionPipeline, DDIMScheduler
-import torch
-
-def stable_diffusion_text2img(
-    model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    height:int,
-    width:int,
-    ):
-
-    pipe = StableDiffusionPipeline.from_pretrained(
-        model_path, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
-    ).to("cuda")
-
-    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-    pipe.enable_xformers_memory_efficient_attention()
-
-    images = pipe(
-        prompt,
-        height=height,
-        width=width,
-        negative_prompt=negative_prompt,
-        num_images_per_prompt=1,
-        num_inference_steps=num_inference_step,
-        guidance_scale=guidance_scale,
-    ).images
-
-    return images