Stable Diffusion

Type: huggingface.text_to_image.StableDiffusion

Namespace: huggingface.text_to_image

Description

Generates images from text prompts using Stable Diffusion. image, generation, AI, text-to-image, SD

Use cases:
- Creating custom illustrations for various projects
- Generating concept art for creative endeavors
- Producing unique visual content for marketing materials
- Exploring AI-generated art for personal or professional use

Properties

Property Type Description Default
model hf.stable_diffusion The model to use for image generation. {'type': 'hf.stable_diffusion', 'repo_id': '', 'path': None, 'variant': None, 'allow_patterns': None, 'ignore_patterns': None}
variant Enum['default', 'fp16', 'fp32', 'bf16'] The variant of the model to use for generation. fp16
prompt str The prompt for image generation. ``
negative_prompt str The negative prompt to guide what should not appear in the generated image. ``
seed int Seed for the random number generator. Use -1 for a random seed. -1
num_inference_steps int Number of denoising steps. 25
guidance_scale float Guidance scale for generation. 7.5
scheduler Enum['DPMSolverSDEScheduler', 'EulerDiscreteScheduler', 'LMSDiscreteScheduler', 'DDIMScheduler', 'DDPMScheduler', 'HeunDiscreteScheduler', 'DPMSolverMultistepScheduler', 'DEISMultistepScheduler', 'PNDMScheduler', 'EulerAncestralDiscreteScheduler', 'UniPCMultistepScheduler', 'KDPM2DiscreteScheduler', 'DPMSolverSinglestepScheduler', 'KDPM2AncestralDiscreteScheduler'] The scheduler to use for the diffusion process. EulerDiscreteScheduler
loras List[hf.lora_sd_config] The LoRA models to use for image processing []
ip_adapter_model hf.ip_adapter The IP adapter model to use for image processing {'type': 'hf.ip_adapter', 'repo_id': '', 'path': None, 'variant': None, 'allow_patterns': None, 'ignore_patterns': None}
ip_adapter_image image When provided the image will be fed into the IP adapter {'type': 'image', 'uri': '', 'asset_id': None, 'data': None}
ip_adapter_scale float The strength of the IP adapter 0.5
pag_scale float Scale of the Perturbed-Attention Guidance applied to the image. 3.0
latents torch_tensor Optional initial latents to start generation from. {'type': 'torch_tensor', 'value': None, 'dtype': '<i8', 'shape': [1]}
enable_attention_slicing bool Enable attention slicing for the pipeline. This can reduce VRAM usage. True
enable_tiling bool Enable tiling for the VAE. This can reduce VRAM usage. True
enable_cpu_offload bool Enable CPU offload for the pipeline. This can reduce VRAM usage. True
output_type Enum['Image', 'Latent'] The type of output to generate. Image
width int Width of the generated image. 512
height int Height of the generated image 512

Outputs

Output Type Description
image image  
latent torch_tensor  

Metadata

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