mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
ca8d9fb885
Added symmetry to Invoke based on discussions with @damian0815. This can currently only be activated via the CLI with the `--h_symmetry_time_pct` and `--v_symmetry_time_pct` options. Those take values from 0.0-1.0, exclusive, indicating the percentage through generation at which symmetry is applied as a one-time operation. To have symmetry in either axis applied after the first step, use a very low value like 0.001.
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
'''
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ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
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'''
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import PIL.Image
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import torch
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from .base import Generator
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from .diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
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from ...models.diffusion.shared_invokeai_diffusion import PostprocessingSettings
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class Txt2Img(Generator):
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def __init__(self, model, precision):
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super().__init__(model, precision)
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@torch.no_grad()
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def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
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conditioning,width,height,step_callback=None,threshold=0.0,warmup=0.2,perlin=0.0,
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h_symmetry_time_pct=None,v_symmetry_time_pct=None,attention_maps_callback=None,
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**kwargs):
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"""
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Returns a function returning an image derived from the prompt and the initial image
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Return value depends on the seed at the time you call it
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kwargs are 'width' and 'height'
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"""
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self.perlin = perlin
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# noinspection PyTypeChecker
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pipeline: StableDiffusionGeneratorPipeline = self.model
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pipeline.scheduler = sampler
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uc, c, extra_conditioning_info = conditioning
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conditioning_data = (
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ConditioningData(
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uc, c, cfg_scale, extra_conditioning_info,
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postprocessing_settings=PostprocessingSettings(
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threshold=threshold,
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warmup=warmup,
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h_symmetry_time_pct=h_symmetry_time_pct,
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v_symmetry_time_pct=v_symmetry_time_pct
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)
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).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
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def make_image(x_T) -> PIL.Image.Image:
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pipeline_output = pipeline.image_from_embeddings(
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latents=torch.zeros_like(x_T,dtype=self.torch_dtype()),
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noise=x_T,
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num_inference_steps=steps,
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conditioning_data=conditioning_data,
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callback=step_callback,
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)
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if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
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attention_maps_callback(pipeline_output.attention_map_saver)
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return pipeline.numpy_to_pil(pipeline_output.images)[0]
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return make_image
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