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ComfyUI
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Clean up and refactor sampler code. 

This should make it much easier to write custom nodes with kdiffusion type
samplers.
This commit is contained in:
comfyanonymous 2023-11-14 00:39:34 -05:00
parent 94cc718e9c
commit 420beeeb05
2 changed files with 56 additions and 39 deletions
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89
comfy/samplers.py
View File

@ -522,42 +522,59 @@ KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral"
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
class KSAMPLER(Sampler):
def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
self.sampler_function = sampler_function
self.extra_options = extra_options
self.inpaint_options = inpaint_options
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
extra_args["denoise_mask"] = denoise_mask
model_k = KSamplerX0Inpaint(model_wrap)
model_k.latent_image = latent_image
if self.inpaint_options.get("random", False): #TODO: Should this be the default?
generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
else:
model_k.noise = noise
if self.max_denoise(model_wrap, sigmas):
noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
else:
noise = noise * sigmas[0]
k_callback = None
total_steps = len(sigmas) - 1
if callback is not None:
k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
if latent_image is not None:
noise += latent_image
samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
return samples
def ksampler(sampler_name, extra_options={}, inpaint_options={}):
class KSAMPLER(Sampler):
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
extra_args["denoise_mask"] = denoise_mask
model_k = KSamplerX0Inpaint(model_wrap)
model_k.latent_image = latent_image
if inpaint_options.get("random", False): #TODO: Should this be the default?
generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
else:
model_k.noise = noise
if self.max_denoise(model_wrap, sigmas):
noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
else:
noise = noise * sigmas[0]
k_callback = None
total_steps = len(sigmas) - 1
if callback is not None:
k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
if sampler_name == "dpm_fast":
def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable):
sigma_min = sigmas[-1]
if sigma_min == 0:
sigma_min = sigmas[-2]
total_steps = len(sigmas) - 1
return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable)
sampler_function = dpm_fast_function
elif sampler_name == "dpm_adaptive":
def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable):
sigma_min = sigmas[-1]
if sigma_min == 0:
sigma_min = sigmas[-2]
return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable)
sampler_function = dpm_adaptive_function
else:
sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
if latent_image is not None:
noise += latent_image
if sampler_name == "dpm_fast":
samples = k_diffusion_sampling.sample_dpm_fast(model_k, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
elif sampler_name == "dpm_adaptive":
samples = k_diffusion_sampling.sample_dpm_adaptive(model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar)
else:
samples = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **extra_options)
return samples
return KSAMPLER
return KSAMPLER(sampler_function, extra_options, inpaint_options)
def wrap_model(model):
model_denoise = CFGNoisePredictor(model)
@ -618,11 +635,11 @@ def calculate_sigmas_scheduler(model, scheduler_name, steps):
print("error invalid scheduler", self.scheduler)
return sigmas
def sampler_class(name):
def sampler_object(name):
if name == "uni_pc":
sampler = UNIPC
sampler = UNIPC()
elif name == "uni_pc_bh2":
sampler = UNIPCBH2
sampler = UNIPCBH2()
elif name == "ddim":
sampler = ksampler("euler", inpaint_options={"random": True})
else:
@ -687,6 +704,6 @@ class KSampler:
else:
return torch.zeros_like(noise)
sampler = sampler_class(self.sampler)
sampler = sampler_object(self.sampler)
return sample(self.model, noise, positive, negative, cfg, self.device, sampler(), sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)

6
comfy_extras/nodes_custom_sampler.py
View File

@ -149,7 +149,7 @@ class KSamplerSelect:
FUNCTION = "get_sampler"
def get_sampler(self, sampler_name):
sampler = comfy.samplers.sampler_class(sampler_name)()
sampler = comfy.samplers.sampler_object(sampler_name)
return (sampler, )
class SamplerDPMPP_2M_SDE:
@ -172,7 +172,7 @@ class SamplerDPMPP_2M_SDE:
sampler_name = "dpmpp_2m_sde"
else:
sampler_name = "dpmpp_2m_sde_gpu"
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})()
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})
return (sampler, )
@ -196,7 +196,7 @@ class SamplerDPMPP_SDE:
sampler_name = "dpmpp_sde"
else:
sampler_name = "dpmpp_sde_gpu"
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})()
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
return (sampler, )
class SamplerCustom: