Delete app.py

app.py

@@ -1,125 +0,0 @@
-#@title
-import numpy as np
-import gradio as gr
-
-import argparse
-import itertools
-import math
-import os
-import random
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-import PIL
-from diffusers import StableDiffusionImg2ImgPipeline # 修正箇所
-from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, UNet2DConditionModel
-from diffusers.hub_utils import init_git_repo, push_to_hub
-from diffusers.optimization import get_scheduler
-from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
-YOUR_TOKEN=MY_SECRET_TOKEN
-device="cpu"
-
-pretrained_model_name_or_path = "CompVis/stable-diffusion-v1-4" #@param {type:"string"}
-
-from IPython.display import Markdown
-from huggingface_hub import hf_hub_download
-#@title Load your concept here
-#@markdown Enter the `repo_id` for a concept you like (you can find pre-learned concepts in the public [SD Concepts Library](https://huggingface.co/sd-concepts-library))
-repo_id_embeds = "sd-concepts-library/mikako-methodi2i" #@param {type:"string"}
-
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows*cols
-
-    w, h = imgs[0].size
-    grid = Image.new('RGB', size=(cols*w, rows*h))
-    grid_w, grid_h = grid.size
-    
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i%cols*w, i//cols*h))
-    return grid
-
-#@title Set up the Tokenizer and the Text Encoder
-tokenizer = CLIPTokenizer.from_pretrained(
-    pretrained_model_name_or_path,
-    subfolder="tokenizer",
-    use_auth_token=YOUR_TOKEN,
-)
-text_encoder = CLIPTextModel.from_pretrained(
-    pretrained_model_name_or_path, subfolder="text_encoder", use_auth_token=YOUR_TOKEN
-)
-
-#@title Load the newly learned embeddings into CLIP
-def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
-  loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
-  
-  # separate token and the embeds
-  trained_token = list(loaded_learned_embeds.keys())[0]
-  embeds = loaded_learned_embeds[trained_token]
-
-  # cast to dtype of text_encoder
-  dtype = text_encoder.get_input_embeddings().weight.dtype
-  embeds.to(dtype)
-
-  # add the token in tokenizer
-  token = token if token is not None else trained_token
-  num_added_tokens = tokenizer.add_tokens(token)
-  if num_added_tokens == 0:
-    raise ValueError(f"The tokenizer already contains the token {token}. Please pass a different `token` that is not already in the tokenizer.")
-  
-  # resize the token embeddings
-  text_encoder.resize_token_embeddings(len(tokenizer))
-  
-  # get the id for the token and assign the embeds
-  token_id = tokenizer.convert_tokens_to_ids(token)
-  text_encoder.get_input_embeddings().weight.data[token_id] = embeds
-load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer)
-
-def crop_center(pil_img, crop_width, crop_height):
-    img_width, img_height = pil_img.size
-    return pil_img.crop(((img_width - crop_width) // 2,
-                         (img_height - crop_height) // 2,
-                         (img_width + crop_width) // 2,
-                         (img_height + crop_height) // 2))
-                         
-#@title Run the Stable Diffusion pipeline
-#@markdown Don't forget to use the placeholder token in your prompt
-
-from torch import autocast
-
-#pipe = StableDiffusionPipeline.from_pretrained(
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    pretrained_model_name_or_path,
-    torch_dtype=torch.float16,
-    text_encoder=text_encoder,
-    tokenizer=tokenizer,
-    use_auth_token=True,
-    crop = True,
-).to(device)
-pipe.enable_attention_slicing()
-
-
-
-def inmm(init_image, prompt):
-    (w,h) = init_image.size
-    if w>h :
-      init_image = init_image.crop(((w - h) // 2,0,(w-h)//2 + h,h))
-      init_image = init_image.resize(512,512)
-
-    with autocast(device):
-      image = pipe([prompt], num_inference_steps=50, guidance_scale=7, init_image=init_image)["sample"]
-    return image[0]
-
-demo = gr.Interface(inmm, inputs=[gr.Image(shape=(512, 512),type="pil"),gr.Textbox(lines=2, placeholder="どんな絵が欲しいか",value ="a heartwarming and calming landscape drawing in <m-mi2i> style")], outputs="image",
-      examples=[["a_img.png", "A heartwarming Canadian wheat field scene in <m-mi2i> style, some houses, silos, and a lake in the distance"],
-                ["c_img.png","A heartwarming Landscape on the lake, scenery mirrored on the lake, in <m-mi2i> style"]])
-demo.launch()