mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
f3050fefce
- txt2img2img back to using DDIM as img2img sampler; results produced by some k* samplers are just not reliable enough for good user experience - img2img progress message clarifies why img2img steps taken != steps requested - warn of potential problems when user tries to run img2img on a small init image
133 lines
4.8 KiB
Python
133 lines
4.8 KiB
Python
'''
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ldm.dream.generator.txt2img inherits from ldm.dream.generator
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'''
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import torch
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import numpy as np
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import math
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from ldm.dream.generator.base import Generator
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from ldm.models.diffusion.ddim import DDIMSampler
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class Txt2Img2Img(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 # for get_noise()
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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,strength,step_callback=None,**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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uc, c = conditioning
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@torch.no_grad()
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def make_image(x_T):
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trained_square = 512 * 512
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actual_square = width * height
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scale = math.sqrt(trained_square / actual_square)
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init_width = math.ceil(scale * width / 64) * 64
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init_height = math.ceil(scale * height / 64) * 64
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shape = [
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self.latent_channels,
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init_height // self.downsampling_factor,
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init_width // self.downsampling_factor,
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]
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sampler.make_schedule(
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ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
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)
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#x = self.get_noise(init_width, init_height)
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x = x_T
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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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samples, _ = sampler.sample(
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batch_size = 1,
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S = steps,
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x_T = x,
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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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eta = ddim_eta,
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img_callback = step_callback
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)
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print(
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f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
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)
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# resizing
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samples = torch.nn.functional.interpolate(
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samples,
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size=(height // self.downsampling_factor, width // self.downsampling_factor),
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mode="bilinear"
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)
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t_enc = int(strength * steps)
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ddim_sampler = DDIMSampler(self.model, device=self.model.device)
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ddim_sampler.make_schedule(
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ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
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)
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z_enc = ddim_sampler.stochastic_encode(
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samples,
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torch.tensor([t_enc]).to(self.model.device),
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noise=self.get_noise(width,height,False)
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)
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# decode it
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samples = ddim_sampler.decode(
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z_enc,
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c,
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t_enc,
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img_callback = step_callback,
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,
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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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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,scale = True):
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# print(f"Get noise: {width}x{height}")
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if scale:
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trained_square = 512 * 512
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actual_square = width * height
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scale = math.sqrt(trained_square / actual_square)
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scaled_width = math.ceil(scale * width / 64) * 64
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scaled_height = math.ceil(scale * height / 64) * 64
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else:
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scaled_width = width
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scaled_height = height
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device = self.model.device
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if device.type == 'mps':
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return torch.randn([1,
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self.latent_channels,
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scaled_height // self.downsampling_factor,
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scaled_width // self.downsampling_factor],
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device='cpu').to(device)
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else:
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return torch.randn([1,
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self.latent_channels,
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scaled_height // self.downsampling_factor,
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scaled_width // self.downsampling_factor],
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device=device)
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