Diffusers documentation
Text-Guided Image-to-Image Generation
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Loading Pipelines, Models, and SchedulersUsing different SchedulersConfiguring Pipelines, Models, and SchedulersLoading and Adding Custom PipelinesUsing KerasCV Stable Diffusion Checkpoints in Diffusers
Pipelines for Inference
Unconditional Image GenerationText-to-Image GenerationText-Guided Image-to-ImageText-Guided Image-InpaintingText-Guided Depth-to-ImageControlling generationReusing seeds for deterministic generationReproducibilityCommunity PipelinesHow to contribute a PipelineUsing safetensors
Taking Diffusers Beyond Images
Optimization/Special Hardware
Training
OverviewUnconditional Image GenerationTextual InversionDreamboothText-to-image fine-tuningLoRA Support in Diffusers
Conceptual Guides
API
Main Classes
Pipelines
OverviewAltDiffusionAudio DiffusionCycle DiffusionDance DiffusionDDIMDDPMDiTLatent DiffusionPaintByExamplePNDMRePaintSafe Stable DiffusionScore SDE VESemantic Guidance
Stable Diffusion
OverviewText-to-ImageImage-to-ImageInpaintDepth-to-ImageImage-VariationSuper-ResolutionStable-Diffusion-Latent-UpscalerInstructPix2PixAttend and ExcitePix2Pix ZeroSelf-Attention GuidanceMultiDiffusion PanoramaText-to-Image Generation with ControlNet Conditioning
Stable Diffusion 2Stable unCLIPStochastic Karras VEUnCLIPUnconditional Latent DiffusionVersatile DiffusionVQ DiffusionSchedulers
OverviewDDIMDDIMInverseDDPMDEISDPM Discrete SchedulerDPM Discrete Scheduler with ancestral samplingEuler Ancestral SchedulerEuler schedulerHeun SchedulerIPNDMLinear MultistepMultistep DPM-SolverPNDMRePaint SchedulerSinglestep DPM-SolverStochastic Kerras VEUniPCMultistepSchedulerVE-SDEVP-SDEVQDiffusionScheduler
Experimental Features
You are viewing v0.14.0 version. A newer version v0.38.0 is available.
Text-Guided Image-to-Image Generation
The StableDiffusionDepth2ImgPipeline lets you pass a text prompt and an initial image to condition the generation of new images as well as a depth_map to preserve the images’ structure. If no depth_map is provided, the pipeline will automatically predict the depth via an integrated depth-estimation model.
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")
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]