diffusers/v0.16.0/en/_app/pages/using-diffusers/depth2img.mdx-hf-doc-builder.js

import{S as st,i as rt,s as nt,e as o,k as d,w as pe,t as n,M as pt,c as i,d as t,m as h,a as s,x as le,h as p,b as l,N as Oe,G as a,g as m,y as me,L as lt,q as fe,o as de,B as he,v as mt}from"../../chunks/vendor-hf-doc-builder.js";import{I as ft}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as it}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as dt}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function ht(Le){let c,z,b,_,M,$,ue,N,ge,J,D,U,f,ce,T,be,_e,C,ve,we,O,ye,$e,E,De,Ee,K,v,Ie,q,ke,Pe,Q,I,V,w,Se,L,Te,qe,W,k,X,y,B,P,H,je,xe,R,Ae,Me,F,S,G,j,Be,Ne,Y,x,He,Z,A,Ce,ee,u,Re,te;return $=new ft({}),D=new dt({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/depth2img.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/depth2img.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/depth2img.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/depth2img.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/depth2img.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/depth2img.ipynb"}]}}),I=new it({props:{code:`import torch
import requests
from PIL import Image

from diffusers import StableDiffusionDepth2ImgPipeline

pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-depth",
    torch_dtype=torch.float16,
).to("cuda")`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionDepth2ImgPipeline

pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-depth&quot;</span>,
    torch_dtype=torch.float16,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)`}}),k=new it({props:{code:`url = "http://images.cocodataset.org/val2017/000000039769.jpg"
init_image = Image.open(requests.get(url, stream=True).raw)
prompt = "two tigers"
n_prompt = "bad, deformed, ugly, bad anatomy"
image = pipe(prompt=prompt, image=init_image, negative_prompt=n_prompt, strength=0.7).images[0]
image`,highlighted:`url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
init_image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
prompt = <span class="hljs-string">&quot;two tigers&quot;</span>
n_prompt = <span class="hljs-string">&quot;bad, deformed, ugly, bad anatomy&quot;</span>
image = pipe(prompt=prompt, image=init_image, negative_prompt=n_prompt, strength=<span class="hljs-number">0.7</span>).images[<span class="hljs-number">0</span>]
image`}}),{c(){c=o("meta"),z=d(),b=o("h1"),_=o("a"),M=o("span"),pe($.$$.fragment),ue=d(),N=o("span"),ge=n("Text-guided depth-to-image generation"),J=d(),pe(D.$$.fragment),U=d(),f=o("p"),ce=n("The "),T=o("a"),be=n("StableDiffusionDepth2ImgPipeline"),_e=n(" lets you pass a text prompt and an initial image to condition the generation of new images. In addition, you can also pass a "),C=o("code"),ve=n("depth_map"),we=n(" to preserve the image structure. If no "),O=o("code"),ye=n("depth_map"),$e=n(" is provided, the pipeline automatically predicts the depth via an integrated "),E=o("a"),De=n("depth-estimation model"),Ee=n("."),K=d(),v=o("p"),Ie=n("Start by creating an instance of the "),q=o("a"),ke=n("StableDiffusionDepth2ImgPipeline"),Pe=n(":"),Q=d(),pe(I.$$.fragment),V=d(),w=o("p"),Se=n("Now pass your prompt to the pipeline. 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