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https://github.com/invoke-ai/InvokeAI
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Merge branch 'development' into model-switching
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@ -35,6 +35,24 @@ from ldm.invoke.devices import choose_torch_device, choose_precision
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from ldm.invoke.conditioning import get_uc_and_c
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from ldm.invoke.model_cache import ModelCache
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def fix_func(orig):
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if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
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def new_func(*args, **kw):
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device = kw.get("device", "mps")
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kw["device"]="cpu"
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return orig(*args, **kw).to(device)
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return new_func
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return orig
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torch.rand = fix_func(torch.rand)
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torch.rand_like = fix_func(torch.rand_like)
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torch.randn = fix_func(torch.randn)
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torch.randn_like = fix_func(torch.randn_like)
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torch.randint = fix_func(torch.randint)
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torch.randint_like = fix_func(torch.randint_like)
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torch.bernoulli = fix_func(torch.bernoulli)
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torch.multinomial = fix_func(torch.multinomial)
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"""Simplified text to image API for stable diffusion/latent diffusion
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Example Usage:
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@ -137,6 +155,7 @@ class Generate:
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self.precision = precision
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self.strength = 0.75
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self.seamless = False
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self.hires_fix = False
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self.embedding_path = embedding_path
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self.model = None # empty for now
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self.model_hash = None
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@ -156,6 +175,7 @@ class Generate:
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# device to Generate(). However the device was then ignored, so
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# it wasn't actually doing anything. This logic could be reinstated.
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device_type = choose_torch_device()
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print(f'>> Using device_type {device_type}')
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self.device = torch.device(device_type)
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if full_precision:
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if self.precision != 'auto':
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@ -236,7 +256,7 @@ class Generate:
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embiggen_tiles = None,
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# these are specific to GFPGAN/ESRGAN
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facetool = None,
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gfpgan_strength = 0,
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facetool_strength = 0,
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codeformer_fidelity = None,
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save_original = False,
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upscale = None,
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@ -256,9 +276,10 @@ class Generate:
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height // height of image, in multiples of 64 (512)
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cfg_scale // how strongly the prompt influences the image (7.5) (must be >1)
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seamless // whether the generated image should tile
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hires_fix // whether the Hires Fix should be applied during generation
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init_img // path to an initial image
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strength // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
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gfpgan_strength // strength for GFPGAN. 0.0 preserves image exactly, 1.0 replaces it completely
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facetool_strength // strength for GFPGAN/CodeFormer. 0.0 preserves image exactly, 1.0 replaces it completely
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ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image)
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step_callback // a function or method that will be called each step
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image_callback // a function or method that will be called each time an image is generated
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@ -289,6 +310,7 @@ class Generate:
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width = width or self.width
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height = height or self.height
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seamless = seamless or self.seamless
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hires_fix = hires_fix or self.hires_fix
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cfg_scale = cfg_scale or self.cfg_scale
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ddim_eta = ddim_eta or self.ddim_eta
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iterations = iterations or self.iterations
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@ -405,11 +427,11 @@ class Generate:
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reference_image_path = init_color,
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image_callback = image_callback)
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if upscale is not None or gfpgan_strength > 0:
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if upscale is not None or facetool_strength > 0:
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self.upscale_and_reconstruct(results,
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upscale = upscale,
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facetool = facetool,
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strength = gfpgan_strength,
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strength = facetool_strength,
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codeformer_fidelity = codeformer_fidelity,
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save_original = save_original,
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image_callback = image_callback)
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@ -452,7 +474,7 @@ class Generate:
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self,
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image_path,
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tool = 'gfpgan', # one of 'upscale', 'gfpgan', 'codeformer', 'outpaint', or 'embiggen'
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gfpgan_strength = 0.0,
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facetool_strength = 0.0,
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codeformer_fidelity = 0.75,
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upscale = None,
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out_direction = None,
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@ -499,11 +521,11 @@ class Generate:
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facetool = 'codeformer'
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elif tool == 'upscale':
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facetool = 'gfpgan' # but won't be run
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gfpgan_strength = 0
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facetool_strength = 0
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return self.upscale_and_reconstruct(
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[[image,seed]],
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facetool = facetool,
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strength = gfpgan_strength,
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strength = facetool_strength,
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codeformer_fidelity = codeformer_fidelity,
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save_original = save_original,
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upscale = upscale,
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