mirror of
https://github.com/comfyanonymous/ComfyUI.git
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Merge branch 'comfyanonymous:master' into feat/is_change_object_storage
This commit is contained in:
Dr.Lt.Data
GitHub
commit
5fdf07ecfa
@ -38,6 +38,7 @@ parser.add_argument("--port", type=int, default=8188, help="Set the listen port.
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parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
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parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
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parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
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parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).")
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parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
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parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
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parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
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@ -631,23 +631,78 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
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elif solver_type == 'midpoint':
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x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
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x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
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if eta:
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x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
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old_denoised = denoised
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h_last = h
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return x
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@torch.no_grad()
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def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
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"""DPM-Solver++(3M) SDE."""
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seed = extra_args.get("seed", None)
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sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
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noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
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extra_args = {} if extra_args is None else extra_args
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s_in = x.new_ones([x.shape[0]])
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denoised_1, denoised_2 = None, None
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h_1, h_2 = None, None
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for i in trange(len(sigmas) - 1, disable=disable):
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denoised = model(x, sigmas[i] * s_in, **extra_args)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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if sigmas[i + 1] == 0:
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# Denoising step
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x = denoised
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else:
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t, s = -sigmas[i].log(), -sigmas[i + 1].log()
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h = s - t
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h_eta = h * (eta + 1)
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x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
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if h_2 is not None:
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r0 = h_1 / h
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r1 = h_2 / h
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d1_0 = (denoised - denoised_1) / r0
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d1_1 = (denoised_1 - denoised_2) / r1
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d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
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d2 = (d1_0 - d1_1) / (r0 + r1)
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phi_2 = h_eta.neg().expm1() / h_eta + 1
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phi_3 = phi_2 / h_eta - 0.5
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x = x + phi_2 * d1 - phi_3 * d2
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elif h_1 is not None:
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r = h_1 / h
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d = (denoised - denoised_1) / r
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phi_2 = h_eta.neg().expm1() / h_eta + 1
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x = x + phi_2 * d
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if eta:
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x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
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denoised_1, denoised_2 = denoised, denoised_1
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h_1, h_2 = h, h_1
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return x
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@torch.no_grad()
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def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
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sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
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noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
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return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
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@torch.no_grad()
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def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
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sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
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noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
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return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
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@torch.no_grad()
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def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
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sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
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noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
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return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
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@ -113,6 +113,7 @@ def model_config_from_unet_config(unet_config):
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if model_config.matches(unet_config):
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return model_config(unet_config)
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print("no match", unet_config)
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return None
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def model_config_from_unet(state_dict, unet_key_prefix, use_fp16):
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@ -189,12 +189,13 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
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continue
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to_run += [(p, COND)]
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for x in uncond:
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p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
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if p is None:
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continue
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if uncond is not None:
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for x in uncond:
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p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
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if p is None:
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continue
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to_run += [(p, UNCOND)]
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to_run += [(p, UNCOND)]
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while len(to_run) > 0:
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first = to_run[0]
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@ -282,6 +283,9 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
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max_total_area = model_management.maximum_batch_area()
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if math.isclose(cond_scale, 1.0):
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uncond = None
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cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat, model_options)
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if "sampler_cfg_function" in model_options:
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args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
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@ -343,6 +347,17 @@ def ddim_scheduler(model, steps):
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sigs += [0.0]
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return torch.FloatTensor(sigs)
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def sgm_scheduler(model, steps):
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sigs = []
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timesteps = torch.linspace(model.inner_model.inner_model.num_timesteps - 1, 0, steps + 1)[:-1].type(torch.int)
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for x in range(len(timesteps)):
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ts = timesteps[x]
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if ts > 999:
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ts = 999
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sigs.append(model.t_to_sigma(torch.tensor(ts)))
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sigs += [0.0]
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return torch.FloatTensor(sigs)
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def blank_inpaint_image_like(latent_image):
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blank_image = torch.ones_like(latent_image)
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# these are the values for "zero" in pixel space translated to latent space
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@ -521,10 +536,10 @@ def encode_adm(model, conds, batch_size, width, height, device, prompt_type):
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class KSampler:
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SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
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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", "ddim", "uni_pc", "uni_pc_bh2"]
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"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
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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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@ -566,6 +581,8 @@ class KSampler:
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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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29
comfy/sd.py
29
comfy/sd.py
@ -72,6 +72,7 @@ def load_lora(lora, to_load):
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regular_lora = "{}.lora_up.weight".format(x)
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diffusers_lora = "{}_lora.up.weight".format(x)
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transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
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A_name = None
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if regular_lora in lora.keys():
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@ -82,6 +83,10 @@ def load_lora(lora, to_load):
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A_name = diffusers_lora
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B_name = "{}_lora.down.weight".format(x)
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mid_name = None
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elif transformers_lora in lora.keys():
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A_name = transformers_lora
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B_name ="{}.lora_linear_layer.down.weight".format(x)
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mid_name = None
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if A_name is not None:
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mid = None
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@ -181,20 +186,29 @@ def model_lora_keys_clip(model, key_map={}):
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key_map[lora_key] = k
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lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c])
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key_map[lora_key] = k
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lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
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key_map[lora_key] = k
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k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
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if k in sdk:
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lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
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key_map[lora_key] = k
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clip_l_present = True
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lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
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key_map[lora_key] = k
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k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
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if k in sdk:
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if clip_l_present:
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lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
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key_map[lora_key] = k
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lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
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key_map[lora_key] = k
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else:
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lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner
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key_map[lora_key] = k
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key_map[lora_key] = k
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lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
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key_map[lora_key] = k
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return key_map
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@ -209,13 +223,16 @@ def model_lora_keys_unet(model, key_map={}):
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diffusers_keys = utils.unet_to_diffusers(model.model_config.unet_config)
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for k in diffusers_keys:
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if k.endswith(".weight"):
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unet_key = "diffusion_model.{}".format(diffusers_keys[k])
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key_lora = k[:-len(".weight")].replace(".", "_")
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key_map["lora_unet_{}".format(key_lora)] = "diffusion_model.{}".format(diffusers_keys[k])
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key_map["lora_unet_{}".format(key_lora)] = unet_key
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diffusers_lora_key = "unet.{}".format(k[:-len(".weight")].replace(".to_", ".processor.to_"))
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if diffusers_lora_key.endswith(".to_out.0"):
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diffusers_lora_key = diffusers_lora_key[:-2]
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key_map[diffusers_lora_key] = "diffusion_model.{}".format(diffusers_keys[k])
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diffusers_lora_prefix = ["", "unet."]
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for p in diffusers_lora_prefix:
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diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_"))
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if diffusers_lora_key.endswith(".to_out.0"):
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diffusers_lora_key = diffusers_lora_key[:-2]
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key_map[diffusers_lora_key] = unet_key
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return key_map
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def set_attr(obj, attr, value):
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@ -2,6 +2,35 @@ import torch
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from nodes import MAX_RESOLUTION
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def composite(destination, source, x, y, mask = None, multiplier = 8):
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x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
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y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))
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left, top = (x // multiplier, y // multiplier)
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right, bottom = (left + source.shape[3], top + source.shape[2],)
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if mask is None:
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mask = torch.ones_like(source)
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else:
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mask = mask.clone()
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mask = torch.nn.functional.interpolate(mask[None, None], size=(source.shape[2], source.shape[3]), mode="bilinear")
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mask = mask.repeat((source.shape[0], source.shape[1], 1, 1))
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# calculate the bounds of the source that will be overlapping the destination
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# this prevents the source trying to overwrite latent pixels that are out of bounds
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# of the destination
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visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
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mask = mask[:, :, :visible_height, :visible_width]
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inverse_mask = torch.ones_like(mask) - mask
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source_portion = mask * source[:, :, :visible_height, :visible_width]
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destination_portion = inverse_mask * destination[:, :, top:bottom, left:right]
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destination[:, :, top:bottom, left:right] = source_portion + destination_portion
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return destination
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class LatentCompositeMasked:
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@classmethod
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def INPUT_TYPES(s):
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@ -25,36 +54,31 @@ class LatentCompositeMasked:
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output = destination.copy()
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destination = destination["samples"].clone()
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source = source["samples"]
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output["samples"] = composite(destination, source, x, y, mask, 8)
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return (output,)
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x = max(-source.shape[3] * 8, min(x, destination.shape[3] * 8))
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y = max(-source.shape[2] * 8, min(y, destination.shape[2] * 8))
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class ImageCompositeMasked:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"destination": ("IMAGE",),
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"source": ("IMAGE",),
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"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
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"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
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},
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"optional": {
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"mask": ("MASK",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "composite"
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left, top = (x // 8, y // 8)
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right, bottom = (left + source.shape[3], top + source.shape[2],)
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if mask is None:
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mask = torch.ones_like(source)
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else:
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mask = mask.clone()
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mask = torch.nn.functional.interpolate(mask[None, None], size=(source.shape[2], source.shape[3]), mode="bilinear")
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mask = mask.repeat((source.shape[0], source.shape[1], 1, 1))
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# calculate the bounds of the source that will be overlapping the destination
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# this prevents the source trying to overwrite latent pixels that are out of bounds
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# of the destination
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visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
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mask = mask[:, :, :visible_height, :visible_width]
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inverse_mask = torch.ones_like(mask) - mask
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source_portion = mask * source[:, :, :visible_height, :visible_width]
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destination_portion = inverse_mask * destination[:, :, top:bottom, left:right]
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destination[:, :, top:bottom, left:right] = source_portion + destination_portion
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output["samples"] = destination
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CATEGORY = "image"
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def composite(self, destination, source, x, y, mask = None):
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destination = destination.clone().movedim(-1, 1)
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output = composite(destination, source.movedim(-1, 1), x, y, mask, 1).movedim(1, -1)
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return (output,)
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class MaskToImage:
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@ -253,6 +277,7 @@ class FeatherMask:
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NODE_CLASS_MAPPINGS = {
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"LatentCompositeMasked": LatentCompositeMasked,
|
||||
"ImageCompositeMasked": ImageCompositeMasked,
|
||||
"MaskToImage": MaskToImage,
|
||||
"ImageToMask": ImageToMask,
|
||||
"SolidMask": SolidMask,
|
||||
|
||||
@ -36,13 +36,15 @@ def get_gpu_names():
|
||||
else:
|
||||
return set()
|
||||
|
||||
def cuda_malloc_supported():
|
||||
blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M",
|
||||
"GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620",
|
||||
"Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000",
|
||||
"Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000",
|
||||
"GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M"}
|
||||
blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M",
|
||||
"GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620",
|
||||
"Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000",
|
||||
"Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000",
|
||||
"GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M",
|
||||
"GeForce GTX 1650", "GeForce GTX 1630"
|
||||
}
|
||||
|
||||
def cuda_malloc_supported():
|
||||
try:
|
||||
names = get_gpu_names()
|
||||
except:
|
||||
|
||||
@ -43,6 +43,10 @@ def set_output_directory(output_dir):
|
||||
global output_directory
|
||||
output_directory = output_dir
|
||||
|
||||
def set_temp_directory(temp_dir):
|
||||
global temp_directory
|
||||
temp_directory = temp_dir
|
||||
|
||||
def get_output_directory():
|
||||
global output_directory
|
||||
return output_directory
|
||||
@ -111,6 +115,8 @@ def add_model_folder_path(folder_name, full_folder_path):
|
||||
global folder_names_and_paths
|
||||
if folder_name in folder_names_and_paths:
|
||||
folder_names_and_paths[folder_name][0].append(full_folder_path)
|
||||
else:
|
||||
folder_names_and_paths[folder_name] = ([full_folder_path], set())
|
||||
|
||||
def get_folder_paths(folder_name):
|
||||
return folder_names_and_paths[folder_name][0][:]
|
||||
|
||||
20
main.py
20
main.py
@ -72,6 +72,17 @@ from server import BinaryEventTypes
|
||||
from nodes import init_custom_nodes
|
||||
import comfy.model_management
|
||||
|
||||
def cuda_malloc_warning():
|
||||
device = comfy.model_management.get_torch_device()
|
||||
device_name = comfy.model_management.get_torch_device_name(device)
|
||||
cuda_malloc_warning = False
|
||||
if "cudaMallocAsync" in device_name:
|
||||
for b in cuda_malloc.blacklist:
|
||||
if b in device_name:
|
||||
cuda_malloc_warning = True
|
||||
if cuda_malloc_warning:
|
||||
print("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
|
||||
|
||||
def prompt_worker(q, server):
|
||||
e = execution.PromptExecutor(server)
|
||||
while True:
|
||||
@ -100,7 +111,7 @@ def hijack_progress(server):
|
||||
|
||||
|
||||
def cleanup_temp():
|
||||
temp_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
|
||||
temp_dir = folder_paths.get_temp_directory()
|
||||
if os.path.exists(temp_dir):
|
||||
shutil.rmtree(temp_dir, ignore_errors=True)
|
||||
|
||||
@ -127,6 +138,10 @@ def load_extra_path_config(yaml_path):
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if args.temp_directory:
|
||||
temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
|
||||
print(f"Setting temp directory to: {temp_dir}")
|
||||
folder_paths.set_temp_directory(temp_dir)
|
||||
cleanup_temp()
|
||||
|
||||
loop = asyncio.new_event_loop()
|
||||
@ -143,6 +158,9 @@ if __name__ == "__main__":
|
||||
load_extra_path_config(config_path)
|
||||
|
||||
init_custom_nodes()
|
||||
|
||||
cuda_malloc_warning()
|
||||
|
||||
server.add_routes()
|
||||
hijack_progress(server)
|
||||
|
||||
|
||||
@ -9766,6 +9766,7 @@ LGraphNode.prototype.executeAction = function(action)
|
||||
|
||||
switch (w.type) {
|
||||
case "button":
|
||||
ctx.fillStyle = background_color;
|
||||
if (w.clicked) {
|
||||
ctx.fillStyle = "#AAA";
|
||||
w.clicked = false;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user