Merge pull request #326 from yolain/v1.2.2-beta
Fix fluxLoader ckpt can not load all-in-one ckpt beyond nf4
This commit is contained in:
yolain
+195
-188
@@ -918,7 +918,7 @@ class fullLoader:
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positive, positive_token_normalization, positive_weight_interpretation,
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negative, negative_token_normalization, negative_weight_interpretation,
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batch_size, model_override=None, clip_override=None, vae_override=None, optional_lora_stack=None, optional_controlnet_stack=None, a1111_prompt_style=False, prompt=None,
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my_unique_id=None, nf4=False
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my_unique_id=None
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):
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# Clean models from loaded_objects
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@@ -926,7 +926,7 @@ class fullLoader:
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# Load models
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log_node_warn("正在加载模型...")
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model, clip, vae, clip_vision, lora_stack = easyCache.load_main(ckpt_name, config_name, vae_name, lora_name, lora_model_strength, lora_clip_strength, optional_lora_stack, model_override, clip_override, vae_override, prompt, nf4=nf4)
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model, clip, vae, clip_vision, lora_stack = easyCache.load_main(ckpt_name, config_name, vae_name, lora_name, lora_model_strength, lora_clip_strength, optional_lora_stack, model_override, clip_override, vae_override, prompt)
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# Create Empty Latent
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model_type = get_sd_version(model)
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@@ -1982,11 +1982,10 @@ class fluxLoader(fullLoader):
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batch_size, model_override, clip_override, vae_override, optional_lora_stack=optional_lora_stack,
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optional_controlnet_stack=optional_controlnet_stack,
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a1111_prompt_style=a1111_prompt_style, prompt=prompt,
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my_unique_id=my_unique_id, nf4=True)
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my_unique_id=my_unique_id)
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# Dit Loader
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from .dit.utils import string_to_dtype
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from .dit.pixArt.config import pixart_conf, pixart_res
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class pixArtLoader:
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@@ -4254,81 +4253,6 @@ class samplerCustomSettings:
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FUNCTION = "settings"
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CATEGORY = "EasyUse/PreSampling"
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def ip2p(self, positive, negative, vae=None, pixels=None, latent=None):
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if latent is not None:
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concat_latent = latent
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else:
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x = (pixels.shape[1] // 8) * 8
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y = (pixels.shape[2] // 8) * 8
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if pixels.shape[1] != x or pixels.shape[2] != y:
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x_offset = (pixels.shape[1] % 8) // 2
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y_offset = (pixels.shape[2] % 8) // 2
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pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
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concat_latent = vae.encode(pixels)
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out_latent = {}
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out_latent["samples"] = torch.zeros_like(concat_latent)
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out = []
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for conditioning in [positive, negative]:
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c = []
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for t in conditioning:
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d = t[1].copy()
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d["concat_latent_image"] = concat_latent
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n = [t[0], d]
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c.append(n)
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out.append(c)
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return (out[0], out[1], out_latent)
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def get_inversed_euler_sampler(self):
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@torch.no_grad()
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def sample_inversed_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0.,
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s_tmax=float('inf'), s_noise=1.):
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"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
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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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for i in trange(1, len(sigmas), disable=disable):
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sigma_in = sigmas[i - 1]
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if i == 1:
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sigma_t = sigmas[i]
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else:
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sigma_t = sigma_in
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denoised = model(x, sigma_t * s_in, **extra_args)
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if i == 1:
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d = (x - denoised) / (2 * sigmas[i])
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else:
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d = (x - denoised) / sigmas[i - 1]
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dt = sigmas[i] - sigmas[i - 1]
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x = x + d * dt
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if callback is not None:
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callback(
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{'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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return x / sigmas[-1]
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ksampler = comfy.samplers.KSAMPLER(sample_inversed_euler)
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return (ksampler,)
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def get_custom_cls(self, sampler_name):
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try:
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cls = custom_samplers.__dict__[sampler_name]
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return cls()
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except:
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raise Exception(f"Custom sampler {sampler_name} not found, Please updated your ComfyUI")
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def add_model_patch_option(self, model):
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if 'transformer_options' not in model.model_options:
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model.model_options['transformer_options'] = {}
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to = model.model_options['transformer_options']
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if "model_patch" not in to:
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to["model_patch"] = {}
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return to
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def settings(self, pipe, guider, cfg, cfg_negative, sampler_name, scheduler, coeff, steps, sigma_max, sigma_min, rho, beta_d, beta_min, eps_s, flip_sigmas, denoise, add_noise, seed, image_to_latent=None, latent=None, optional_sampler=None, optional_sigmas=None, prompt=None, extra_pnginfo=None, my_unique_id=None):
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# 图生图转换
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@@ -4337,60 +4261,6 @@ class samplerCustomSettings:
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positive = pipe['positive']
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negative = pipe['negative']
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batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1
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_guider, sigmas = None, None
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# sigmas
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if optional_sigmas is not None:
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sigmas = optional_sigmas
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else:
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match scheduler:
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case 'vp':
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sigmas, = self.get_custom_cls('VPScheduler').get_sigmas(steps, beta_d, beta_min, eps_s)
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case 'karrasADV':
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sigmas, = self.get_custom_cls('KarrasScheduler').get_sigmas(steps, sigma_max, sigma_min, rho)
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case 'exponentialADV':
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sigmas, = self.get_custom_cls('ExponentialScheduler').get_sigmas(steps, sigma_max, sigma_min)
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case 'polyExponential':
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sigmas, = self.get_custom_cls('PolyexponentialScheduler').get_sigmas(steps, sigma_max, sigma_min,
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rho)
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case 'sdturbo':
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sigmas, = self.get_custom_cls('SDTurboScheduler').get_sigmas(model, steps, denoise)
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case 'alignYourSteps':
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model_type = get_sd_version(model)
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if model_type == 'unknown':
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model_type = 'sdxl'
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# raise Exception("This Model not supported")
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sigmas, = alignYourStepsScheduler().get_sigmas(model_type.upper(), steps, denoise)
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case 'gits':
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sigmas, = gitsScheduler().get_sigmas(coeff, steps, denoise)
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case _:
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sigmas, = self.get_custom_cls('BasicScheduler').get_sigmas(model, scheduler, steps, denoise)
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# filp_sigmas
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if flip_sigmas:
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sigmas, = self.get_custom_cls('FlipSigmas').get_sigmas(sigmas)
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#######################################################################################
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# brushnet
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to = None
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transformer_options = model.model_options['transformer_options'] if "transformer_options" in model.model_options else {}
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if 'model_patch' in transformer_options and 'brushnet' in transformer_options['model_patch']:
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to = self.add_model_patch_option(model)
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mp = to['model_patch']
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if isinstance(model.model.model_config, comfy.supported_models.SD15):
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mp['SDXL'] = False
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elif isinstance(model.model.model_config, comfy.supported_models.SDXL):
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mp['SDXL'] = True
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else:
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print('Base model type: ', type(model.model.model_config))
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raise Exception("Unsupported model type: ", type(model.model.model_config))
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mp['all_sigmas'] = sigmas
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mp['unet'] = model.model.diffusion_model
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mp['step'] = 0
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mp['total_steps'] = 1
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#
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#######################################################################################
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if image_to_latent is not None:
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_, height, width, _ = image_to_latent.shape
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@@ -4416,33 +4286,11 @@ class samplerCustomSettings:
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samples = pipe["samples"]
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images = pipe["images"]
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# guider
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if guider == 'CFG':
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_guider, = self.get_custom_cls('CFGGuider').get_guider(model, positive, negative, cfg)
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elif guider in ['DualCFG', 'IP2P+DualCFG']:
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_guider, = self.get_custom_cls('DualCFGGuider').get_guider(model, positive, middle, negative, cfg, cfg_negative)
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else:
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_guider, = self.get_custom_cls('BasicGuider').get_guider(model, positive)
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# sampler
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if optional_sampler:
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sampler = optional_sampler
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else:
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if sampler_name == 'inversed_euler':
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sampler, = self.get_inversed_euler_sampler()
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else:
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sampler, = self.get_custom_cls('KSamplerSelect').get_sampler(sampler_name)
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# noise
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if add_noise == 'disable':
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noise, = self.get_custom_cls('DisableNoise').get_noise()
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else:
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noise, = self.get_custom_cls('RandomNoise').get_noise(seed)
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new_pipe = {
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"model": pipe['model'],
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"positive": pipe['positive'],
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"negative": pipe['negative'],
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"model": model,
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"positive": positive,
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"negative": negative,
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"vae": pipe['vae'],
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"clip": pipe['clip'],
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@@ -4452,17 +4300,27 @@ class samplerCustomSettings:
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"loader_settings": {
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**pipe["loader_settings"],
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"middle": pipe['negative'],
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"steps": steps,
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"cfg": cfg,
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"cfg_negative": cfg_negative,
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"sampler_name": sampler_name,
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"scheduler": scheduler,
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"denoise": denoise,
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"add_noise": add_noise,
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"custom": {
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"noise": noise,
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"guider": _guider,
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"sampler": sampler,
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"sigmas": sigmas,
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}
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"guider": guider,
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"coeff": coeff,
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"sigma_max": sigma_max,
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"sigma_min": sigma_min,
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"rho": rho,
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"beta_d": beta_d,
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"beta_min": beta_min,
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"eps_s": beta_min,
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"flip_sigmas": flip_sigmas
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},
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"optional_sampler": optional_sampler,
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"optional_sigmas": optional_sigmas
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}
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}
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@@ -5022,6 +4880,177 @@ class samplerFull:
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FUNCTION = "run"
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CATEGORY = "EasyUse/Sampler"
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def ip2p(self, positive, negative, vae=None, pixels=None, latent=None):
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if latent is not None:
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concat_latent = latent
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else:
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x = (pixels.shape[1] // 8) * 8
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y = (pixels.shape[2] // 8) * 8
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if pixels.shape[1] != x or pixels.shape[2] != y:
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x_offset = (pixels.shape[1] % 8) // 2
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y_offset = (pixels.shape[2] % 8) // 2
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pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
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concat_latent = vae.encode(pixels)
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out_latent = {}
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out_latent["samples"] = torch.zeros_like(concat_latent)
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out = []
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for conditioning in [positive, negative]:
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c = []
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for t in conditioning:
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d = t[1].copy()
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d["concat_latent_image"] = concat_latent
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n = [t[0], d]
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c.append(n)
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out.append(c)
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return (out[0], out[1], out_latent)
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def get_inversed_euler_sampler(self):
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@torch.no_grad()
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def sample_inversed_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0.,s_tmax=float('inf'), s_noise=1.):
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"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
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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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for i in trange(1, len(sigmas), disable=disable):
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sigma_in = sigmas[i - 1]
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if i == 1:
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sigma_t = sigmas[i]
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else:
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sigma_t = sigma_in
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denoised = model(x, sigma_t * s_in, **extra_args)
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if i == 1:
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d = (x - denoised) / (2 * sigmas[i])
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else:
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d = (x - denoised) / sigmas[i - 1]
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dt = sigmas[i] - sigmas[i - 1]
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x = x + d * dt
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if callback is not None:
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callback(
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{'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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return x / sigmas[-1]
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ksampler = comfy.samplers.KSAMPLER(sample_inversed_euler)
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return (ksampler,)
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def get_custom_cls(self, sampler_name):
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try:
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cls = custom_samplers.__dict__[sampler_name]
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return cls()
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except:
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raise Exception(f"Custom sampler {sampler_name} not found, Please updated your ComfyUI")
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def add_model_patch_option(self, model):
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if 'transformer_options' not in model.model_options:
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model.model_options['transformer_options'] = {}
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to = model.model_options['transformer_options']
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if "model_patch" not in to:
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to["model_patch"] = {}
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return to
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def get_sampler_custom(self, model, positive, negative, loader_settings):
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_guider = None
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middle = loader_settings['middle'] if "middle" in loader_settings else negative
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steps = loader_settings['steps'] if "steps" in loader_settings else 20
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cfg = loader_settings['cfg'] if "cfg" in loader_settings else 8.0
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cfg_negative = loader_settings['cfg_negative'] if "cfg_negative" in loader_settings else 8.0
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sampler_name = loader_settings['sampler_name'] if "sampler_name" in loader_settings else "euler"
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scheduler = loader_settings['scheduler'] if "scheduler" in loader_settings else "normal"
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guider = loader_settings['custom']['guider'] if "guider" in loader_settings['custom'] else "CFG"
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beta_d = loader_settings['custom']['beta_d'] if "beta_d" in loader_settings['custom'] else 0.1
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beta_min = loader_settings['custom']['beta_min'] if "beta_min" in loader_settings['custom'] else 0.1
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eps_s = loader_settings['custom']['eps_s'] if "eps_s" in loader_settings['custom'] else 0.1
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sigma_max = loader_settings['custom']['sigma_max'] if "sigma_max" in loader_settings['custom'] else 14.61
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sigma_min = loader_settings['custom']['sigma_min'] if "sigma_min" in loader_settings['custom'] else 0.03
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rho = loader_settings['custom']['rho'] if "rho" in loader_settings['custom'] else 7.0
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coeff = loader_settings['custom']['coeff'] if "coeff" in loader_settings['custom'] else 1.2
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flip_sigmas = loader_settings['custom']['flip_sigmas'] if "flip_sigmas" in loader_settings['custom'] else False
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denoise = loader_settings['denoise'] if "denoise" in loader_settings else 1.0
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add_noise = loader_settings['add_noise'] if "add_noise" in loader_settings else "enable"
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seed = loader_settings['seed'] if "seed" in loader_settings else 0
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optional_sigmas = loader_settings['optional_sigmas'] if "optional_sigmas" in loader_settings else None
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optional_sampler = loader_settings['optional_sampler'] if "optional_sampler" in loader_settings else None
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# sigmas
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if optional_sigmas is not None:
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sigmas = optional_sigmas
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else:
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if scheduler == 'vp':
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sigmas, = self.get_custom_cls('VPScheduler').get_sigmas(steps, beta_d, beta_min, eps_s)
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elif scheduler == 'karrasADV':
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sigmas, = self.get_custom_cls('KarrasScheduler').get_sigmas(steps, sigma_max, sigma_min, rho)
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elif scheduler == 'exponentialADV':
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sigmas, = self.get_custom_cls('ExponentialScheduler').get_sigmas(steps, sigma_max, sigma_min)
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elif scheduler == 'polyExponential':
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sigmas, = self.get_custom_cls('PolyexponentialScheduler').get_sigmas(steps, sigma_max, sigma_min, rho)
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elif scheduler == 'sdturbo':
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sigmas, = self.get_custom_cls('SDTurboScheduler').get_sigmas(model, steps, denoise)
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elif scheduler == 'alignYourSteps':
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model_type = get_sd_version(model)
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if model_type == 'unknown':
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model_type = 'sdxl'
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sigmas, = alignYourStepsScheduler().get_sigmas(model_type.upper(), steps, denoise)
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elif scheduler == 'gits':
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sigmas, = gitsScheduler().get_sigmas(coeff, steps, denoise)
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else:
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sigmas, = self.get_custom_cls('BasicScheduler').get_sigmas(model, scheduler, steps, denoise)
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# filp_sigmas
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if flip_sigmas:
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sigmas, = self.get_custom_cls('FlipSigmas').get_sigmas(sigmas)
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#######################################################################################
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# brushnet
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to = None
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transformer_options = model.model_options['transformer_options'] if "transformer_options" in model.model_options else {}
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if 'model_patch' in transformer_options and 'brushnet' in transformer_options['model_patch']:
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to = self.add_model_patch_option(model)
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mp = to['model_patch']
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if isinstance(model.model.model_config, comfy.supported_models.SD15):
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mp['SDXL'] = False
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elif isinstance(model.model.model_config, comfy.supported_models.SDXL):
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mp['SDXL'] = True
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else:
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print('Base model type: ', type(model.model.model_config))
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raise Exception("Unsupported model type: ", type(model.model.model_config))
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mp['all_sigmas'] = sigmas
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mp['unet'] = model.model.diffusion_model
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mp['step'] = 0
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mp['total_steps'] = 1
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#######################################################################################
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# guider
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if guider == 'CFG':
|
||||
_guider, = self.get_custom_cls('CFGGuider').get_guider(model, positive, negative, cfg)
|
||||
elif guider in ['DualCFG', 'IP2P+DualCFG']:
|
||||
_guider, = self.get_custom_cls('DualCFGGuider').get_guider(model, positive, middle,
|
||||
negative, cfg, cfg_negative)
|
||||
else:
|
||||
_guider, = self.get_custom_cls('BasicGuider').get_guider(model, positive)
|
||||
|
||||
# sampler
|
||||
if optional_sampler:
|
||||
_sampler = optional_sampler
|
||||
else:
|
||||
if sampler_name == 'inversed_euler':
|
||||
_sampler, = self.get_inversed_euler_sampler()
|
||||
else:
|
||||
_sampler, = self.get_custom_cls('KSamplerSelect').get_sampler(sampler_name)
|
||||
|
||||
# noise
|
||||
if add_noise == 'disable':
|
||||
noise, = self.get_custom_cls('DisableNoise').get_noise()
|
||||
else:
|
||||
noise, = self.get_custom_cls('RandomNoise').get_noise(seed)
|
||||
|
||||
return (noise, _guider, _sampler, sigmas)
|
||||
|
||||
def run(self, pipe, steps, cfg, sampler_name, scheduler, denoise, image_output, link_id, save_prefix, seed=None, model=None, positive=None, negative=None, latent=None, vae=None, clip=None, xyPlot=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False, downscale_options=None, image=None):
|
||||
|
||||
samp_model = model if model is not None else pipe["model"]
|
||||
@@ -5033,7 +5062,7 @@ class samplerFull:
|
||||
|
||||
samp_seed = seed if seed is not None else pipe['seed']
|
||||
|
||||
samp_custom = pipe["loader_settings"]["custom"] if "custom" in pipe["loader_settings"] else None
|
||||
samp_custom = pipe["loader_settings"] if "custom" in pipe["loader_settings"] else None
|
||||
|
||||
steps = steps if steps is not None else pipe['loader_settings']['steps']
|
||||
start_step = pipe['loader_settings']['start_step'] if 'start_step' in pipe['loader_settings'] else 0
|
||||
@@ -5052,8 +5081,6 @@ class samplerFull:
|
||||
if add_noise == "disable":
|
||||
disable_noise = True
|
||||
|
||||
|
||||
# When model is colors
|
||||
def downscale_model_unet(samp_model):
|
||||
# 获取Unet参数
|
||||
if "PatchModelAddDownscale" in ALL_NODE_CLASS_MAPPINGS:
|
||||
@@ -5124,16 +5151,12 @@ class samplerFull:
|
||||
start_time = int(time.time() * 1000)
|
||||
# 开始推理
|
||||
if samp_custom is not None:
|
||||
guider = samp_custom['guider'] if 'guider' in samp_custom else None
|
||||
_sampler = samp_custom['sampler'] if 'sampler' in samp_custom else None
|
||||
sigmas = samp_custom['sigmas'] if 'sigmas' in samp_custom else None
|
||||
noise = samp_custom['noise'] if 'noise' in samp_custom else None
|
||||
samp_samples, _ = sampler.custom_advanced_ksampler(noise, guider, _sampler, sigmas, samp_samples)
|
||||
noise, _guider, _sampler, sigmas = self.get_sampler_custom(samp_model, samp_positive, samp_negative, samp_custom)
|
||||
samp_samples, _ = sampler.custom_advanced_ksampler(noise, _guider, _sampler, sigmas, samp_samples)
|
||||
elif scheduler == 'align_your_steps':
|
||||
model_type = get_sd_version(samp_model)
|
||||
if model_type == 'unknown':
|
||||
model_type = 'sdxl'
|
||||
# raise Exception("This Model not supported")
|
||||
sigmas, = alignYourStepsScheduler().get_sigmas(model_type.upper(), steps, denoise)
|
||||
_sampler = comfy.samplers.sampler_object(sampler_name)
|
||||
samp_samples = sampler.custom_ksampler(samp_model, samp_seed, steps, cfg, _sampler, sigmas, samp_positive, samp_negative, samp_samples, disable_noise=disable_noise, preview_latent=preview_latent)
|
||||
@@ -7564,22 +7587,6 @@ class stableDiffusion3API:
|
||||
|
||||
#---------------------------------------------------------------API 结束----------------------------------------------------------------------
|
||||
|
||||
class CheckpointLoaderNF4:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
|
||||
}}
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
|
||||
FUNCTION = "load_checkpoint"
|
||||
|
||||
CATEGORY = "loaders"
|
||||
|
||||
def load_checkpoint(self, ckpt_name):
|
||||
from .bitsandbytes_NF4 import OPS
|
||||
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
|
||||
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"), model_options={"custom_operations": OPS})
|
||||
return out[:3]
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
# seed 随机种
|
||||
"easy seed": easySeed,
|
||||
|
||||
+5
-5
@@ -1,4 +1,4 @@
|
||||
import time, os, psutil
|
||||
import re, time, os, psutil
|
||||
import folder_paths
|
||||
import comfy.utils
|
||||
import comfy.sd
|
||||
@@ -221,7 +221,7 @@ class easyLoader:
|
||||
del self.loaded_objects[obj_type][item[0]]
|
||||
current_memory = self.get_memory_usage()
|
||||
|
||||
def load_checkpoint(self, ckpt_name, config_name=None, load_vision=False, nf4=False):
|
||||
def load_checkpoint(self, ckpt_name, config_name=None, load_vision=False):
|
||||
cache_name = ckpt_name
|
||||
if config_name not in [None, "Default"]:
|
||||
cache_name = ckpt_name + "_" + config_name
|
||||
@@ -239,7 +239,7 @@ class easyLoader:
|
||||
loaded_ckpt = comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
|
||||
else:
|
||||
model_options = {}
|
||||
if nf4:
|
||||
if re.search("nf4", ckpt_name):
|
||||
from ..bitsandbytes_NF4 import OPS
|
||||
model_options = {"custom_operations": OPS}
|
||||
loaded_ckpt = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=output_clip, output_clipvision=output_clipvision, embedding_directory=folder_paths.get_folder_paths("embeddings"), model_options=model_options)
|
||||
@@ -442,7 +442,7 @@ class easyLoader:
|
||||
node = prompt[xy_model_id]
|
||||
if "ckpt_name_1" in node["inputs"]:
|
||||
ckpt_name_1 = node["inputs"]["ckpt_name_1"]
|
||||
model, clip, vae, clip_vision = self.load_checkpoint(ckpt_name_1, nf4=nf4)
|
||||
model, clip, vae, clip_vision = self.load_checkpoint(ckpt_name_1)
|
||||
can_load_lora = False
|
||||
# Load models
|
||||
elif model_override is not None and clip_override is not None and vae_override is not None:
|
||||
@@ -456,7 +456,7 @@ class easyLoader:
|
||||
elif clip_override is not None:
|
||||
raise Exception(f"[ERROR] model or vae is missing")
|
||||
else:
|
||||
model, clip, vae, clip_vision = self.load_checkpoint(ckpt_name, config_name, nf4=nf4)
|
||||
model, clip, vae, clip_vision = self.load_checkpoint(ckpt_name, config_name)
|
||||
|
||||
if optional_lora_stack is not None and can_load_lora:
|
||||
for lora in optional_lora_stack:
|
||||
|
||||
@@ -111,7 +111,6 @@ class easySampler:
|
||||
# add model patch
|
||||
# brushnet
|
||||
add_model_patch(model)
|
||||
# kolors
|
||||
#######################################################################################
|
||||
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative,
|
||||
latent_image,
|
||||
|
||||
Reference in New Issue
Block a user