mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-10 13:32:36 +08:00
Merge branch 'comfyanonymous:master' into fix/secure-combo
This commit is contained in:
Dr.Lt.Data
GitHub
commit
73f09ee268
@ -193,7 +193,7 @@ You can set this command line setting to disable the upcasting to fp32 in some c
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Use ```--preview-method auto``` to enable previews.
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The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_encoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_encoder.pth) and [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
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The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) (for SD1.x and SD2.x) and [taesdxl_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth) (for SDXL) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
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## Support and dev channel
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@ -77,7 +77,7 @@ class BatchedBrownianTree:
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except TypeError:
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seed = [seed]
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self.batched = False
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self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
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self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
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@staticmethod
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def sort(a, b):
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@ -85,7 +85,7 @@ class BatchedBrownianTree:
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def __call__(self, t0, t1):
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t0, t1, sign = self.sort(t0, t1)
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w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
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w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
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return w if self.batched else w[0]
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@ -543,7 +543,8 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
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def sample_dpmpp_sde(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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"""DPM-Solver++ (stochastic)."""
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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) if noise_sampler is None else noise_sampler
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seed = extra_args.get("seed", None)
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noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) 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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sigma_fn = lambda t: t.neg().exp()
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@ -613,8 +614,9 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
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if solver_type not in {'heun', 'midpoint'}:
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raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
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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) if noise_sampler is None else noise_sampler
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noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) 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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@ -9,8 +9,23 @@ class LatentFormat:
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class SD15(LatentFormat):
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def __init__(self, scale_factor=0.18215):
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self.scale_factor = scale_factor
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self.latent_rgb_factors = [
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# R G B
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[0.298, 0.207, 0.208], # L1
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[0.187, 0.286, 0.173], # L2
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[-0.158, 0.189, 0.264], # L3
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[-0.184, -0.271, -0.473], # L4
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]
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self.taesd_decoder_name = "taesd_decoder.pth"
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class SDXL(LatentFormat):
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def __init__(self):
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self.scale_factor = 0.13025
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self.latent_rgb_factors = [ #TODO: these are the factors for SD1.5, need to estimate new ones for SDXL
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# R G B
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[0.298, 0.207, 0.208], # L1
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[0.187, 0.286, 0.173], # L2
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[-0.158, 0.189, 0.264], # L3
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[-0.184, -0.271, -0.473], # L4
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]
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self.taesd_decoder_name = "taesdxl_decoder.pth"
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@ -65,7 +65,7 @@ def cleanup_additional_models(models):
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for m in models:
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m.cleanup()
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def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False):
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def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
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device = comfy.model_management.get_torch_device()
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if noise_mask is not None:
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@ -85,7 +85,7 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
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sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
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samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar)
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samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
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samples = samples.cpu()
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cleanup_additional_models(models)
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@ -13,7 +13,7 @@ def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
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#The main sampling function shared by all the samplers
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#Returns predicted noise
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def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}):
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def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}, seed=None):
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def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in):
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area = (x_in.shape[2], x_in.shape[3], 0, 0)
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strength = 1.0
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@ -292,8 +292,8 @@ class CFGNoisePredictor(torch.nn.Module):
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super().__init__()
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self.inner_model = model
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self.alphas_cumprod = model.alphas_cumprod
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def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}):
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out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options)
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def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}, seed=None):
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out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options, seed=seed)
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return out
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@ -301,11 +301,11 @@ class KSamplerX0Inpaint(torch.nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}):
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def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}, seed=None):
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if denoise_mask is not None:
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latent_mask = 1. - denoise_mask
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x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
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out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options)
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out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options, seed=seed)
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if denoise_mask is not None:
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out *= denoise_mask
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@ -542,7 +542,7 @@ class KSampler:
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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):
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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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@ -589,7 +589,7 @@ class KSampler:
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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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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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41
comfy/sd.py
41
comfy/sd.py
@ -19,6 +19,7 @@ from . import model_detection
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from . import sd1_clip
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from . import sd2_clip
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from . import sdxl_clip
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def load_model_weights(model, sd):
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m, u = model.load_state_dict(sd, strict=False)
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@ -524,7 +525,7 @@ class CLIP:
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return n
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def load_from_state_dict(self, sd):
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self.cond_stage_model.transformer.load_state_dict(sd, strict=False)
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self.cond_stage_model.load_sd(sd)
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def add_patches(self, patches, strength=1.0):
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return self.patcher.add_patches(patches, strength)
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@ -555,6 +556,8 @@ class CLIP:
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tokens = self.tokenize(text)
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return self.encode_from_tokens(tokens)
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def load_sd(self, sd):
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return self.cond_stage_model.load_sd(sd)
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class VAE:
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def __init__(self, ckpt_path=None, device=None, config=None):
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@ -959,22 +962,42 @@ def load_style_model(ckpt_path):
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return StyleModel(model)
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def load_clip(ckpt_path, embedding_directory=None):
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clip_data = utils.load_torch_file(ckpt_path, safe_load=True)
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def load_clip(ckpt_paths, embedding_directory=None):
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clip_data = []
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for p in ckpt_paths:
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clip_data.append(utils.load_torch_file(p, safe_load=True))
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class EmptyClass:
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pass
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for i in range(len(clip_data)):
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if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
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clip_data[i] = utils.transformers_convert(clip_data[i], "", "text_model.", 32)
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clip_target = EmptyClass()
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clip_target.params = {}
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if "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data:
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clip_target.clip = sd2_clip.SD2ClipModel
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clip_target.tokenizer = sd2_clip.SD2Tokenizer
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if len(clip_data) == 1:
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if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
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clip_target.clip = sdxl_clip.SDXLRefinerClipModel
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clip_target.tokenizer = sdxl_clip.SDXLTokenizer
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elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
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clip_target.clip = sd2_clip.SD2ClipModel
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clip_target.tokenizer = sd2_clip.SD2Tokenizer
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else:
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clip_target.clip = sd1_clip.SD1ClipModel
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clip_target.tokenizer = sd1_clip.SD1Tokenizer
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else:
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clip_target.clip = sd1_clip.SD1ClipModel
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clip_target.tokenizer = sd1_clip.SD1Tokenizer
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clip_target.clip = sdxl_clip.SDXLClipModel
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clip_target.tokenizer = sdxl_clip.SDXLTokenizer
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clip = CLIP(clip_target, embedding_directory=embedding_directory)
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clip.load_from_state_dict(clip_data)
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for c in clip_data:
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m, u = clip.load_sd(c)
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if len(m) > 0:
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print("clip missing:", m)
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if len(u) > 0:
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print("clip unexpected:", u)
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return clip
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def load_gligen(ckpt_path):
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@ -128,6 +128,9 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
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def encode(self, tokens):
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return self(tokens)
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def load_sd(self, sd):
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return self.transformer.load_state_dict(sd, strict=False)
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def parse_parentheses(string):
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result = []
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current_item = ""
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@ -31,6 +31,11 @@ class SDXLClipG(sd1_clip.SD1ClipModel):
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self.layer = "hidden"
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self.layer_idx = layer_idx
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def load_sd(self, sd):
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if "text_projection" in sd:
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self.text_projection[:] = sd.pop("text_projection")
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return super().load_sd(sd)
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class SDXLClipGTokenizer(sd1_clip.SD1Tokenizer):
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def __init__(self, tokenizer_path=None, embedding_directory=None):
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super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280)
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@ -68,6 +73,12 @@ class SDXLClipModel(torch.nn.Module):
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l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
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return torch.cat([l_out, g_out], dim=-1), g_pooled
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def load_sd(self, sd):
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if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
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return self.clip_g.load_sd(sd)
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else:
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return self.clip_l.load_sd(sd)
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class SDXLRefinerClipModel(torch.nn.Module):
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def __init__(self, device="cpu"):
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super().__init__()
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@ -81,3 +92,5 @@ class SDXLRefinerClipModel(torch.nn.Module):
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g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
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return g_out, g_pooled
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def load_sd(self, sd):
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return self.clip_g.load_sd(sd)
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@ -49,14 +49,8 @@ class TAESDPreviewerImpl(LatentPreviewer):
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class Latent2RGBPreviewer(LatentPreviewer):
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def __init__(self):
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self.latent_rgb_factors = torch.tensor([
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# R G B
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[0.298, 0.207, 0.208], # L1
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[0.187, 0.286, 0.173], # L2
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[-0.158, 0.189, 0.264], # L3
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[-0.184, -0.271, -0.473], # L4
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], device="cpu")
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def __init__(self, latent_rgb_factors):
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self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
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def decode_latent_to_preview(self, x0):
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latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors
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@ -69,12 +63,12 @@ class Latent2RGBPreviewer(LatentPreviewer):
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return Image.fromarray(latents_ubyte.numpy())
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def get_previewer(device):
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def get_previewer(device, latent_format):
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previewer = None
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method = args.preview_method
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if method != LatentPreviewMethod.NoPreviews:
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# TODO previewer methods
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taesd_decoder_path = folder_paths.get_full_path("vae_approx", "taesd_decoder.pth")
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taesd_decoder_path = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
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if method == LatentPreviewMethod.Auto:
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method = LatentPreviewMethod.Latent2RGB
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@ -86,10 +80,10 @@ def get_previewer(device):
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taesd = TAESD(None, taesd_decoder_path).to(device)
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previewer = TAESDPreviewerImpl(taesd)
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else:
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print("Warning: TAESD previews enabled, but could not find models/vae_approx/taesd_decoder.pth")
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print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
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if previewer is None:
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previewer = Latent2RGBPreviewer()
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previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
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return previewer
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25
nodes.py
25
nodes.py
@ -520,11 +520,27 @@ class CLIPLoader:
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RETURN_TYPES = ("CLIP",)
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FUNCTION = "load_clip"
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CATEGORY = "loaders"
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CATEGORY = "advanced/loaders"
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def load_clip(self, clip_name):
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clip_path = folder_paths.get_full_path("clip", clip_name)
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clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"))
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return (clip,)
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class DualCLIPLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ),
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}}
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RETURN_TYPES = ("CLIP",)
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FUNCTION = "load_clip"
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||||
CATEGORY = "advanced/loaders"
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||||
|
||||
def load_clip(self, clip_name1, clip_name2):
|
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clip_path1 = folder_paths.get_full_path("clip", clip_name1)
|
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clip_path2 = folder_paths.get_full_path("clip", clip_name2)
|
||||
clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
|
||||
return (clip,)
|
||||
|
||||
class CLIPVisionLoader:
|
||||
@ -938,7 +954,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
|
||||
if preview_format not in ["JPEG", "PNG"]:
|
||||
preview_format = "JPEG"
|
||||
|
||||
previewer = latent_preview.get_previewer(device)
|
||||
previewer = latent_preview.get_previewer(device, model.model.latent_format)
|
||||
|
||||
pbar = comfy.utils.ProgressBar(steps)
|
||||
def callback(step, x0, x, total_steps):
|
||||
@ -949,7 +965,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
|
||||
|
||||
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
|
||||
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
|
||||
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback)
|
||||
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, seed=seed)
|
||||
out = latent.copy()
|
||||
out["samples"] = samples
|
||||
return (out, )
|
||||
@ -1315,6 +1331,7 @@ NODE_CLASS_MAPPINGS = {
|
||||
"LatentCrop": LatentCrop,
|
||||
"LoraLoader": LoraLoader,
|
||||
"CLIPLoader": CLIPLoader,
|
||||
"DualCLIPLoader": DualCLIPLoader,
|
||||
"CLIPVisionEncode": CLIPVisionEncode,
|
||||
"StyleModelApply": StyleModelApply,
|
||||
"unCLIPConditioning": unCLIPConditioning,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user