mirror of
https://github.com/invoke-ai/InvokeAI
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68 lines
2.5 KiB
Python
68 lines
2.5 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
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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,perlin=0.0,
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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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uc, c, extra_conditioning_info = conditioning
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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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def make_image(x_T) -> PIL.Image.Image:
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pipeline_output = pipeline.image_from_embeddings(
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latents=x_T,
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num_inference_steps=steps,
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text_embeddings=c,
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unconditioned_embeddings=uc,
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guidance_scale=cfg_scale,
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callback=step_callback,
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extra_conditioning_info=extra_conditioning_info,
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# TODO: eta = ddim_eta,
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# TODO: threshold = threshold,
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)
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return pipeline.numpy_to_pil(pipeline_output.images)[0]
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return make_image
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# returns a tensor filled with random numbers from a normal distribution
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def get_noise(self,width,height):
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device = self.model.device
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if self.use_mps_noise or device.type == 'mps':
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x = torch.randn([1,
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self.latent_channels,
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height // self.downsampling_factor,
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width // self.downsampling_factor],
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device='cpu').to(device)
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else:
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x = torch.randn([1,
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self.latent_channels,
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height // self.downsampling_factor,
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width // self.downsampling_factor],
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device=device)
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if self.perlin > 0.0:
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x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor)
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return x
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