Code for init image?
#24
by
Softology
- opened
How do I use an init/seed image with the example diffusers code? ie feed in an image and a strength that influences the output.
Can anyone post a minimal example modification to the example code on the model card?
Also, the link to "the documentation" is 404.
hi do you ask about " Image-To-Image " ?
Btw here's the doc: https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_3
Yes, image to image. Input an image, use that image to change the output.
I don't see any relevant code at that documentation link.
what about "Image-To-Image" section in https://huggingface.co/blog/sd3 ?
Here's the pipeline in the diffusers library and it's call method:
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
prompt_3: Optional[Union[str, List[str]]] = None,
image: PipelineImageInput = None,
strength: float = 0.6,
num_inference_steps: int = 50,
timesteps: List[int] = None,
guidance_scale: float = 7.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
negative_prompt_2: Optional[Union[str, List[str]]] = None,
negative_prompt_3: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
):
Thank you. Working fine now.
Softology
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