bbae307ef2
* Rework many patches and some UI details. * Speed up processing. * Move Colab to independent branch. * Implemented CFG Scale and TSNR correction when CFG is bigger than 10. * Implemented Developer Mode with more options to debug.
444 lines
22 KiB
Python
444 lines
22 KiB
Python
from comfy.samplers import *
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import comfy.model_management
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class KSamplerBasic:
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SCHEDULERS = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
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SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
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"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
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"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "ddim", "uni_pc", "uni_pc_bh2", "dpmpp_fooocus_2m_sde_inpaint_seamless"]
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def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
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self.model = model
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self.model_denoise = CFGNoisePredictor(self.model)
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if self.model.model_type == model_base.ModelType.V_PREDICTION:
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self.model_wrap = CompVisVDenoiser(self.model_denoise, quantize=True)
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else:
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self.model_wrap = k_diffusion_external.CompVisDenoiser(self.model_denoise, quantize=True)
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self.model_k = KSamplerX0Inpaint(self.model_wrap)
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self.device = device
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if scheduler not in self.SCHEDULERS:
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scheduler = self.SCHEDULERS[0]
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if sampler not in self.SAMPLERS:
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sampler = self.SAMPLERS[0]
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self.scheduler = scheduler
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self.sampler = sampler
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self.sigma_min=float(self.model_wrap.sigma_min)
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self.sigma_max=float(self.model_wrap.sigma_max)
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self.set_steps(steps, denoise)
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self.denoise = denoise
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self.model_options = model_options
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def calculate_sigmas(self, steps):
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sigmas = None
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discard_penultimate_sigma = False
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if self.sampler in ['dpm_2', 'dpm_2_ancestral']:
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steps += 1
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discard_penultimate_sigma = True
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if self.scheduler == "karras":
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sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
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elif self.scheduler == "exponential":
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sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
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elif self.scheduler == "normal":
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sigmas = self.model_wrap.get_sigmas(steps)
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elif self.scheduler == "simple":
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sigmas = simple_scheduler(self.model_wrap, steps)
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elif self.scheduler == "ddim_uniform":
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sigmas = ddim_scheduler(self.model_wrap, steps)
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elif self.scheduler == "sgm_uniform":
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sigmas = sgm_scheduler(self.model_wrap, steps)
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else:
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print("error invalid scheduler", self.scheduler)
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if discard_penultimate_sigma:
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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return sigmas
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def set_steps(self, steps, denoise=None):
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self.steps = steps
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if denoise is None or denoise > 0.9999:
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self.sigmas = self.calculate_sigmas(steps).to(self.device)
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else:
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new_steps = int(steps/denoise)
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sigmas = self.calculate_sigmas(new_steps).to(self.device)
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self.sigmas = sigmas[-(steps + 1):]
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def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
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if sigmas is None:
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sigmas = self.sigmas
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sigma_min = self.sigma_min
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if last_step is not None and last_step < (len(sigmas) - 1):
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sigma_min = sigmas[last_step]
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sigmas = sigmas[:last_step + 1]
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if force_full_denoise:
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sigmas[-1] = 0
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if start_step is not None:
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if start_step < (len(sigmas) - 1):
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sigmas = sigmas[start_step:]
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else:
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if latent_image is not None:
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return latent_image
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else:
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return torch.zeros_like(noise)
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positive = positive[:]
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negative = negative[:]
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resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], self.device)
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resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], self.device)
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calculate_start_end_timesteps(self.model_wrap, negative)
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calculate_start_end_timesteps(self.model_wrap, positive)
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#make sure each cond area has an opposite one with the same area
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for c in positive:
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create_cond_with_same_area_if_none(negative, c)
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for c in negative:
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create_cond_with_same_area_if_none(positive, c)
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pre_run_control(self.model_wrap, negative + positive)
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apply_empty_x_to_equal_area(list(filter(lambda c: c[1].get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
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apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
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if self.model.is_adm():
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positive = encode_adm(self.model, positive, noise.shape[0], noise.shape[3], noise.shape[2], self.device, "positive")
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negative = encode_adm(self.model, negative, noise.shape[0], noise.shape[3], noise.shape[2], self.device, "negative")
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if latent_image is not None:
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latent_image = self.model.process_latent_in(latent_image)
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extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options, "seed":seed}
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cond_concat = None
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if hasattr(self.model, 'concat_keys'): #inpaint
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cond_concat = []
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for ck in self.model.concat_keys:
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if denoise_mask is not None:
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if ck == "mask":
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cond_concat.append(denoise_mask[:,:1])
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elif ck == "masked_image":
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cond_concat.append(latent_image) #NOTE: the latent_image should be masked by the mask in pixel space
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else:
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if ck == "mask":
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cond_concat.append(torch.ones_like(noise)[:,:1])
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elif ck == "masked_image":
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cond_concat.append(blank_inpaint_image_like(noise))
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extra_args["cond_concat"] = cond_concat
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if sigmas[0] != self.sigmas[0] or (self.denoise is not None and self.denoise < 1.0):
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max_denoise = False
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else:
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max_denoise = True
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if self.sampler == "uni_pc":
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samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar)
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elif self.sampler == "uni_pc_bh2":
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samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
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elif self.sampler == "ddim":
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timesteps = []
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for s in range(sigmas.shape[0]):
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timesteps.insert(0, self.model_wrap.sigma_to_discrete_timestep(sigmas[s]))
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noise_mask = None
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if denoise_mask is not None:
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noise_mask = 1.0 - denoise_mask
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ddim_callback = None
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if callback is not None:
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total_steps = len(timesteps) - 1
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ddim_callback = lambda pred_x0, i: callback(i, pred_x0, None, total_steps)
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sampler = DDIMSampler(self.model, device=self.device)
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sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
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z_enc = sampler.stochastic_encode(latent_image, torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(self.device), noise=noise, max_denoise=max_denoise)
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samples, _ = sampler.sample_custom(ddim_timesteps=timesteps,
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conditioning=positive,
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batch_size=noise.shape[0],
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shape=noise.shape[1:],
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verbose=False,
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unconditional_guidance_scale=cfg,
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unconditional_conditioning=negative,
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eta=0.0,
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x_T=z_enc,
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x0=latent_image,
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img_callback=ddim_callback,
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denoise_function=self.model_wrap.predict_eps_discrete_timestep,
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extra_args=extra_args,
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mask=noise_mask,
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to_zero=sigmas[-1]==0,
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end_step=sigmas.shape[0] - 1,
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disable_pbar=disable_pbar)
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else:
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extra_args["denoise_mask"] = denoise_mask
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self.model_k.latent_image = latent_image
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self.model_k.noise = noise
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if max_denoise:
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noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
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else:
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noise = noise * sigmas[0]
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k_callback = None
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total_steps = len(sigmas) - 1
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if callback is not None:
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k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
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if latent_image is not None:
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noise += latent_image
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if self.sampler == "dpm_fast":
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samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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elif self.sampler == "dpm_adaptive":
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samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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else:
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samples = getattr(k_diffusion_sampling, "sample_{}".format(self.sampler))(self.model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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return self.model.process_latent_out(samples.to(torch.float32))
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class KSamplerWithRefiner:
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SCHEDULERS = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
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SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
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"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
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"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "ddim", "uni_pc", "uni_pc_bh2", "dpmpp_fooocus_2m_sde_inpaint_seamless"]
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def __init__(self, model, refiner_model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
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self.model_patcher = model
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self.refiner_model_patcher = refiner_model
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self.model = model.model
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self.refiner_model = refiner_model.model
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self.model_denoise = CFGNoisePredictor(self.model)
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self.refiner_model_denoise = CFGNoisePredictor(self.refiner_model)
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if self.model.model_type == model_base.ModelType.V_PREDICTION:
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self.model_wrap = CompVisVDenoiser(self.model_denoise, quantize=True)
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else:
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self.model_wrap = k_diffusion_external.CompVisDenoiser(self.model_denoise, quantize=True)
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if self.refiner_model.model_type == model_base.ModelType.V_PREDICTION:
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self.refiner_model_wrap = CompVisVDenoiser(self.refiner_model_denoise, quantize=True)
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else:
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self.refiner_model_wrap = k_diffusion_external.CompVisDenoiser(self.refiner_model_denoise, quantize=True)
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self.model_k = KSamplerX0Inpaint(self.model_wrap)
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self.refiner_model_k = KSamplerX0Inpaint(self.refiner_model_wrap)
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self.device = device
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if scheduler not in self.SCHEDULERS:
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scheduler = self.SCHEDULERS[0]
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if sampler not in self.SAMPLERS:
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sampler = self.SAMPLERS[0]
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self.scheduler = scheduler
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self.sampler = sampler
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self.sigma_min = float(self.model_wrap.sigma_min)
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self.sigma_max = float(self.model_wrap.sigma_max)
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self.set_steps(steps, denoise)
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self.denoise = denoise
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self.model_options = model_options
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def calculate_sigmas(self, steps):
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sigmas = None
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discard_penultimate_sigma = False
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if self.sampler in ['dpm_2', 'dpm_2_ancestral']:
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steps += 1
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discard_penultimate_sigma = True
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if self.scheduler == "karras":
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sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
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elif self.scheduler == "exponential":
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sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=self.sigma_min,
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sigma_max=self.sigma_max)
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elif self.scheduler == "normal":
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sigmas = self.model_wrap.get_sigmas(steps)
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elif self.scheduler == "simple":
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sigmas = simple_scheduler(self.model_wrap, steps)
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elif self.scheduler == "ddim_uniform":
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sigmas = ddim_scheduler(self.model_wrap, steps)
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elif self.scheduler == "sgm_uniform":
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sigmas = sgm_scheduler(self.model_wrap, steps)
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else:
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print("error invalid scheduler", self.scheduler)
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if discard_penultimate_sigma:
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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return sigmas
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def set_steps(self, steps, denoise=None):
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self.steps = steps
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if denoise is None or denoise > 0.9999:
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self.sigmas = self.calculate_sigmas(steps).to(self.device)
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else:
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new_steps = int(steps / denoise)
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sigmas = self.calculate_sigmas(new_steps).to(self.device)
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self.sigmas = sigmas[-(steps + 1):]
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def sample(self, noise, positive, negative, refiner_positive, refiner_negative, cfg, latent_image=None,
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start_step=None, last_step=None, refiner_switch_step=None,
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force_full_denoise=False, denoise_mask=None, sigmas=None, callback_function=None, disable_pbar=False, seed=None):
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if sigmas is None:
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sigmas = self.sigmas
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sigma_min = self.sigma_min
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if last_step is not None and last_step < (len(sigmas) - 1):
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sigma_min = sigmas[last_step]
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sigmas = sigmas[:last_step + 1]
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if force_full_denoise:
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sigmas[-1] = 0
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if start_step is not None:
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if start_step < (len(sigmas) - 1):
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sigmas = sigmas[start_step:]
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else:
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if latent_image is not None:
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return latent_image
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else:
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return torch.zeros_like(noise)
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positive = positive[:]
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negative = negative[:]
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resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], self.device)
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resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], self.device)
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calculate_start_end_timesteps(self.model_wrap, negative)
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calculate_start_end_timesteps(self.model_wrap, positive)
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# make sure each cond area has an opposite one with the same area
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for c in positive:
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create_cond_with_same_area_if_none(negative, c)
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for c in negative:
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create_cond_with_same_area_if_none(positive, c)
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pre_run_control(self.model_wrap, negative + positive)
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apply_empty_x_to_equal_area(
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list(filter(lambda c: c[1].get('control_apply_to_uncond', False) == True, positive)), negative, 'control',
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lambda cond_cnets, x: cond_cnets[x])
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apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
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if self.model.is_adm():
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positive = encode_adm(self.model, positive, noise.shape[0], noise.shape[3], noise.shape[2], self.device,
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"positive")
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negative = encode_adm(self.model, negative, noise.shape[0], noise.shape[3], noise.shape[2], self.device,
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"negative")
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refiner_positive = refiner_positive[:]
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refiner_negative = refiner_negative[:]
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resolve_areas_and_cond_masks(refiner_positive, noise.shape[2], noise.shape[3], self.device)
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resolve_areas_and_cond_masks(refiner_negative, noise.shape[2], noise.shape[3], self.device)
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calculate_start_end_timesteps(self.refiner_model_wrap, refiner_positive)
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calculate_start_end_timesteps(self.refiner_model_wrap, refiner_negative)
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# make sure each cond area has an opposite one with the same area
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for c in refiner_positive:
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create_cond_with_same_area_if_none(refiner_negative, c)
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for c in refiner_negative:
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create_cond_with_same_area_if_none(refiner_positive, c)
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if self.model.is_adm():
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refiner_positive = encode_adm(self.refiner_model, refiner_positive, noise.shape[0],
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noise.shape[3], noise.shape[2], self.device, "positive")
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refiner_negative = encode_adm(self.refiner_model, refiner_negative, noise.shape[0],
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noise.shape[3], noise.shape[2], self.device, "negative")
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def refiner_switch():
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comfy.model_management.load_model_gpu(self.refiner_model_patcher)
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self.model_denoise.inner_model = self.refiner_model_denoise.inner_model
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for i in range(len(positive)):
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positive[i] = refiner_positive[i]
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for i in range(len(negative)):
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negative[i] = refiner_negative[i]
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print('Refiner swapped.')
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return
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def callback(step, x0, x, total_steps):
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if step == refiner_switch_step:
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refiner_switch()
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if callback_function is not None:
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callback_function(step, x0, x, total_steps)
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if latent_image is not None:
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latent_image = self.model.process_latent_in(latent_image)
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extra_args = {"cond": positive, "uncond": negative, "cond_scale": cfg, "model_options": self.model_options,
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"seed": seed}
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cond_concat = None
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if hasattr(self.model, 'concat_keys'): # inpaint
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cond_concat = []
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for ck in self.model.concat_keys:
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if denoise_mask is not None:
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if ck == "mask":
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cond_concat.append(denoise_mask[:, :1])
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elif ck == "masked_image":
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cond_concat.append(
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latent_image) # NOTE: the latent_image should be masked by the mask in pixel space
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else:
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if ck == "mask":
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cond_concat.append(torch.ones_like(noise)[:, :1])
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elif ck == "masked_image":
|
|
cond_concat.append(blank_inpaint_image_like(noise))
|
|
extra_args["cond_concat"] = cond_concat
|
|
|
|
if sigmas[0] != self.sigmas[0] or (self.denoise is not None and self.denoise < 1.0):
|
|
max_denoise = False
|
|
else:
|
|
max_denoise = True
|
|
|
|
if self.sampler == "uni_pc":
|
|
samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas,
|
|
sampling_function=sampling_function, max_denoise=max_denoise,
|
|
extra_args=extra_args, noise_mask=denoise_mask, callback=callback,
|
|
disable=disable_pbar)
|
|
elif self.sampler == "uni_pc_bh2":
|
|
samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas,
|
|
sampling_function=sampling_function, max_denoise=max_denoise,
|
|
extra_args=extra_args, noise_mask=denoise_mask, callback=callback,
|
|
variant='bh2', disable=disable_pbar)
|
|
elif self.sampler == "ddim":
|
|
raise NotImplementedError('Swapped Refiner Does not support DDIM.')
|
|
else:
|
|
extra_args["denoise_mask"] = denoise_mask
|
|
self.model_k.latent_image = latent_image
|
|
self.model_k.noise = noise
|
|
|
|
if max_denoise:
|
|
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
|
|
if self.sampler == "dpm_fast":
|
|
samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], total_steps,
|
|
extra_args=extra_args, callback=k_callback,
|
|
disable=disable_pbar)
|
|
elif self.sampler == "dpm_adaptive":
|
|
samples = k_diffusion_sampling.sample_dpm_adaptive(self.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(self.sampler))(self.model_k, noise, sigmas,
|
|
extra_args=extra_args,
|
|
callback=k_callback,
|
|
disable=disable_pbar)
|
|
|
|
return self.model.process_latent_out(samples.to(torch.float32))
|