diff --git a/modules/devices.py b/modules/devices.py index 52c3e7cd..3bc86a6a 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -92,14 +92,18 @@ def cond_cast_float(input): def randn(seed, shape): + from modules.shared import opts + torch.manual_seed(seed) - if device.type == 'mps': + if opts.use_cpu_randn or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) def randn_without_seed(shape): - if device.type == 'mps': + from modules.shared import opts + + if opts.use_cpu_randn or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index a1aac7cf..e6a372d5 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -60,3 +60,12 @@ def store_latent(decoded): class InterruptedException(BaseException): pass + +if opts.use_cpu_randn: + import torchsde._brownian.brownian_interval + + def torchsde_randn(size, dtype, device, seed): + generator = torch.Generator(devices.cpu).manual_seed(int(seed)) + return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device) + + torchsde._brownian.brownian_interval._randn = torchsde_randn diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index e9f08518..13f4567a 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -190,7 +190,7 @@ class TorchHijack: if noise.shape == x.shape: return noise - if x.device.type == 'mps': + if opts.use_cpu_randn or x.device.type == 'mps': return torch.randn_like(x, device=devices.cpu).to(x.device) else: return torch.randn_like(x) diff --git a/modules/shared.py b/modules/shared.py index 725f58d1..b5b401fe 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -334,6 +334,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"), + "use_cpu_randn": OptionInfo(False, "Use CPU for random number generation to make manual seeds generate the same image across platforms. This may change existing seeds."), })) options_templates.update(options_section(('compatibility', "Compatibility"), {