mirror of
https://github.com/invoke-ai/InvokeAI
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72 lines
2.9 KiB
Python
72 lines
2.9 KiB
Python
'''
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invokeai.backend.generator.img2img descends from ldm.invoke.generator
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'''
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import torch
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from diffusers import logging
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from .base import Generator
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from ..stable_diffusion import (StableDiffusionGeneratorPipeline,
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ConditioningData,
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PostprocessingSettings
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)
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class Img2Img(Generator):
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def __init__(self, model, precision):
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super().__init__(model, precision)
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self.init_latent = None # by get_noise()
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def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
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conditioning,init_image,strength,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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"""
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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):
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# FIXME: use x_T for initial seeded noise
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# We're not at the moment because the pipeline automatically resizes init_image if
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# necessary, which the x_T input might not match.
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logging.set_verbosity_error() # quench safety check warnings
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pipeline_output = pipeline.img2img_from_embeddings(
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init_image, strength, steps, conditioning_data,
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noise_func=self.get_noise_like,
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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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def get_noise_like(self, like: torch.Tensor):
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device = like.device
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if device.type == 'mps':
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x = torch.randn_like(like, device='cpu').to(device)
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else:
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x = torch.randn_like(like, device=device)
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if self.perlin > 0.0:
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shape = like.shape
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x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
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return x
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