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https://github.com/invoke-ai/InvokeAI
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bd0c0d77d2
* Enhance free_gpu_mem option Unload cond_stage_model on free_gpu_mem option is setted * Enhance free_gpu_mem option Unload cond_stage_model on free_gpu_mem option is setted
89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
'''
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ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
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'''
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import torch
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import numpy as np
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from ldm.invoke.generator.base import Generator
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from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
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import gc
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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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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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uc, c, extra_conditioning_info = conditioning
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@torch.no_grad()
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def make_image(x_T):
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shape = [
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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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]
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if self.free_gpu_mem and self.model.model.device != self.model.device:
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self.model.model.to(self.model.device)
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sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
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samples, _ = sampler.sample(
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batch_size = 1,
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S = steps,
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x_T = x_T,
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conditioning = c,
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shape = shape,
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verbose = False,
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unconditional_guidance_scale = cfg_scale,
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unconditional_conditioning = uc,
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extra_conditioning_info = extra_conditioning_info,
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eta = ddim_eta,
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img_callback = step_callback,
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threshold = threshold,
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attention_maps_callback = attention_maps_callback,
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)
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if self.free_gpu_mem:
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self.model.model.to('cpu')
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self.model.cond_stage_model.device = 'cpu'
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self.model.cond_stage_model.to('cpu')
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gc.collect()
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torch.cuda.empty_cache()
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return self.sample_to_image(samples)
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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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