Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| #import torch | |
| #from torch import autocast // only for GPU | |
| from PIL import Image | |
| import numpy as np | |
| from io import BytesIO | |
| import os | |
| MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD') | |
| from diffusers import StableDiffusionImg2ImgPipeline | |
| print("hello sylvain") | |
| YOUR_TOKEN=MY_SECRET_TOKEN | |
| device="cpu" | |
| #prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) | |
| #prompt_pipe.to(device) | |
| img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) | |
| img_pipe.to(device) | |
| source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px") | |
| gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto") | |
| def resize(w_val,l_val,img): | |
| #baseheight = value | |
| 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) | |
| img = img.resize((w_val,l_val), Image.Resampling.LANCZOS) | |
| return img | |
| #init_image = init_image.resize((768, 512)) | |
| def infer(prompt, source_img): | |
| source_image = resize(512, 512, source_img) | |
| source_image.save('source.png') | |
| images_list = img_pipe([prompt] * 2, init_image=source_image, strength=0.75) | |
| images = [] | |
| safe_image = Image.open(r"unsafe.png") | |
| for i, image in enumerate(images_list["sample"]): | |
| if(images_list["nsfw_content_detected"][i]): | |
| images.append(safe_image) | |
| else: | |
| images.append(image) | |
| return images | |
| print("Great sylvain ! Everything is working fine !") | |
| title="Img2Img Stable Diffusion CPU" | |
| description="Img2Img Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>" | |
| gr.Interface(fn=infer, inputs=["text", source_img], outputs=gallery,title=title,description=description).queue(max_size=100).launch(enable_queue=True) | |
| #from torch import autocast | |
| #import requests | |
| #import torch | |
| #from PIL import Image | |
| #from io import BytesIO | |
| #import os | |
| #MY_SECRET_TOKEN = os.environ.get('HF_TOKEN_SD') | |
| #from diffusers import StableDiffusionImg2ImgPipeline | |
| #YOUR_TOKEN = MY_SECRET_TOKEN | |
| # load the pipeline | |
| #device = "cuda" | |
| #model_id_or_path = "CompVis/stable-diffusion-v1-4" | |
| # pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token = YOUR_TOKEN) | |
| #pipe = StableDiffusionImg2ImgPipeline.from_pretrained( | |
| # model_id_or_path, | |
| # revision="fp16", | |
| # torch_dtype=torch.float16, | |
| # use_auth_token=YOUR_TOKEN | |
| #) | |
| # or download via git clone https://huggingface.co/CompVis/stable-diffusion-v1-4 | |
| # and pass `model_id_or_path="./stable-diffusion-v1-4"` without having to use `use_auth_token=True`. | |
| #pipe = pipe.to(device) | |
| # let's download an initial image | |
| #url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" | |
| #response = requests.get(url) | |
| #init_image = Image.open(BytesIO(response.content)).convert("RGB") | |
| #init_image = init_image.resize((768, 512)) | |
| #prompt = "Lively, illustration of a [[[<king::4>]]], portrait, fantasy, intricate, Scenic, hyperdetailed, hyper realistic <king-hearthstone>, unreal engine, 4k, smooth, sharp focus, intricate, cinematic lighting, highly detailed, octane, digital painting, artstation, concept art, vibrant colors, Cinema4D, WLOP, 3d render, in the style of hearthstone::5 art by Artgerm and greg rutkowski and magali villeneuve, martina jackova, Giger" | |
| #with autocast("cuda"): | |
| # images = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5).images | |
| #images[0].save("fantasy_landscape.png") |