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mode="bilinear") + mask = mask.expand((-1,shape[1],-1,-1)) + if mask.shape[0] < shape[0]: + mask = mask.repeat((shape[0] -1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]] + return mask +def remap_range(value, minIn, MaxIn, minOut, maxOut): + if value > MaxIn: value = MaxIn; + if value < minIn: value = minIn; + finalValue = ((value - minIn) / (MaxIn - minIn)) * (maxOut - minOut) + minOut; + return finalValue; + +class KSamplerSDXLAdvanced: + @classmethod + def INPUT_TYPES(s): + ui_widgets = {"required": + { + "model_model": ("MODEL",), + "model_refiner": ("MODEL",), + "CONDITIONING_model_pos": ("CONDITIONING", ), + "CONDITIONING_model_neg": ("CONDITIONING", ), + "CONDITIONING_refiner_pos": ("CONDITIONING", ), + "CONDITIONING_refiner_neg": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "cfg_scale": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 100.0}), + # "cfg_rescale_multiplier": ("FLOAT", {"default": 1, "min": -1.0, "max": 2.0, "step": 0.1}), + + "sampler": (comfy.samplers.KSampler.SAMPLERS, {"default": "dpmpp_2m"}), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "karras"}), + + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "base_steps": ("INT", {"default": 12, "min": 0, "max": 10000}), + "refiner_steps": ("INT", {"default": 4, "min": 0, "max": 10000}), + "detail_level": ("FLOAT", {"default": 1, "min": 0.0, "max": 2.0, "step": 0.1}), + "detail_from": (["penultimate_step","base_sample"], {"default": "penultimate_step"}), + "noise_source": (["CPU","GPU"], {"default": "CPU"}), + "auto_rescale_tonemap": (["enable","disable"], {"default": "enable"}), + "rescale_tonemap_to": ("FLOAT", {"default": 7.5, "min": 0, "max": 30.0, "step": 0.5}), + # "refiner_extra_noise": (["enable","disable"], {"default": "disable"}), + # "base_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step": 0.01}), + # "noise_shift_end_refiner": ("INT", {"default": -1, "min": -10000, "max": 0}) + }, + "optional": + { + "SD15VAE": ("VAE", ), + "SDXLVAE": ("VAE", ), + } + } + return ui_widgets + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample_sdxl" + CATEGORY = "sampling" + + + def patch_tonemap(self, model, multiplier): + def sampler_tonemap_reinhard(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + noise_pred = (cond - uncond) + noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:,None] + noise_pred /= noise_pred_vector_magnitude + + mean = torch.mean(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True) + std = torch.std(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True) + top = (std * 3 + mean) * multiplier + + #reinhard + noise_pred_vector_magnitude *= (1.0 / top) + new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0) + new_magnitude *= top + + return uncond + noise_pred * new_magnitude * cond_scale + + m = model.clone() + m.set_model_sampler_cfg_function(sampler_tonemap_reinhard) + return m + + # def patch_model(self, model, multiplier): + # def rescale_cfg(args): + # cond = args["cond"] + # uncond = args["uncond"] + # cond_scale = args["cond_scale"] + + # x_cfg = uncond + cond_scale * (cond - uncond) + # ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) + # ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) + + # x_rescaled = x_cfg * (ro_pos / ro_cfg) + # x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg + + # return x_final + + # m = model.clone() + # m.set_model_sampler_cfg_function(rescale_cfg) + # return m + + def common_ksampler(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): + device = comfy.model_management.get_torch_device() + latent_image = latent["samples"] + + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + + previewer = latent_preview.get_previewer(device, model.model.latent_format) + + pbar = comfy.utils.ProgressBar(steps) + def callback(step, x0, x, total_steps): + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(step + 1, total_steps, preview_bytes) + + 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, seed=seed) + out = latent.copy() + out["samples"] = samples + return out + + def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0): + force_full_denoise = True + if return_with_leftover_noise == "enable": + force_full_denoise = False + disable_noise = False + if add_noise == "disable": + disable_noise = True + return self.common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) + + def calc_sigma(self, model, sampler_name, scheduler, steps, start_at_step, end_at_step): + device = comfy.model_management.get_torch_device() + end_at_step = min(steps, end_at_step) + start_at_step = min(start_at_step, end_at_step) + real_model = None + comfy.model_management.load_model_gpu(model) + real_model = model.model + sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=1.0, model_options=model.model_options) + sigmas = sampler.sigmas + sigma = sigmas[start_at_step] - sigmas[end_at_step] + sigma /= model.model.latent_format.scale_factor + sigma_output = sigma.cpu().numpy() + print("Calculated sigma:",sigma_output) + return sigma_output + + def create_noisy_latents(self, source, seed, width, height, batch_size): + torch.manual_seed(seed) + if source == "CPU": + device = "cpu" + else: + device = comfy.model_management.get_torch_device() + noise = torch.randn((batch_size, 4, height // 8, width // 8), dtype=torch.float32, device=device).cpu() + return {"samples":noise} + + def inject_noise(self, latents, strength, noise=None, mask=None): + s = latents.copy() + if noise is None: + return s + if latents["samples"].shape != noise["samples"].shape: + print("warning, shapes in InjectNoise not the same, ignoring") + return s + noised = s["samples"].clone() + noise["samples"].clone() * strength + if mask is not None: + mask = prepare_mask(mask, noised.shape) + noised = mask * noised + (1-mask) * latents["samples"] + s["samples"] = noised + return s + + # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475 + def slerp(self, val, low, high): + dims = low.shape + + #flatten to batches + low = low.reshape(dims[0], -1) + high = high.reshape(dims[0], -1) + + low_norm = low/torch.norm(low, dim=1, keepdim=True) + high_norm = high/torch.norm(high, dim=1, keepdim=True) + + # in case we divide by zero + low_norm[low_norm != low_norm] = 0.0 + high_norm[high_norm != high_norm] = 0.0 + + omega = torch.acos((low_norm*high_norm).sum(1)) + so = torch.sin(omega) + res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high + return res.reshape(dims) + + def slerp_latents(self, latents1, factor, latents2=None, mask=None): + s = latents1.copy() + if latents2 is None: + return (s,) + if latents1["samples"].shape != latents2["samples"].shape: + print("warning, shapes in LatentSlerp not the same, ignoring") + return (s,) + slerped = self.slerp(factor, latents1["samples"].clone(), latents2["samples"].clone()) + if mask is not None: + mask = prepare_mask(mask, slerped.shape) + slerped = mask * slerped + (1-mask) * latents1["samples"] + s["samples"] = slerped + return s + + def compute_and_generate_noise(self,samples,seed,width,height,batch_size,model,sampler,scheduler,total_steps,start_at,end_at,source): + noisy_latent = self.create_noisy_latents(source,seed,width,height,batch_size) + sigma_balls = self.calc_sigma(model,sampler,scheduler,total_steps,start_at,end_at) + samples_output = self.inject_noise(samples,sigma_balls,noisy_latent) + return samples_output + + def sample_sdxl(self, model_model, model_refiner, CONDITIONING_model_pos, CONDITIONING_model_neg, CONDITIONING_refiner_pos, CONDITIONING_refiner_neg, latent_image, seed, cfg_scale, sampler, scheduler, start_at_step, base_steps, refiner_steps,detail_level,detail_from,noise_source,auto_rescale_tonemap,rescale_tonemap_to,SD15VAE=None, SDXLVAE=None): + + # if cfg_rescale_multiplier != 1: + # model_model = self.patch_model(model_model,cfg_rescale_multiplier) + # model_refiner = self.patch_model(model_refiner,cfg_rescale_multiplier) + if auto_rescale_tonemap == "enable" and cfg_scale!=rescale_tonemap_to: + scale_model = 1/cfg_scale*rescale_tonemap_to + model_model = self.patch_tonemap(model_model,scale_model) + if sampler == "uni_pc" or sampler == "uni_pc_bh2": + scale_model = 1/cfg_scale*7.5 + model_refiner = self.patch_tonemap(model_refiner,scale_model) + + for lat in latent_image['samples']: + d, y, x = lat.size() + break + + batch_size = len(latent_image['samples']) + width = x*8 + height = y*8 + + base_start_at = start_at_step + base_end_at = base_steps + base_total_steps = base_steps + refiner_steps + refiner_start_at = base_steps + refiner_end_at = base_steps + refiner_steps + refiner_total_steps = base_steps + refiner_steps + + if sampler == "uni_pc" or sampler == "uni_pc_bh2": + noisy_base = self.compute_and_generate_noise(latent_image,seed,width,height,batch_size,model_model,sampler,scheduler,base_end_at-1,base_start_at,base_end_at-1,noise_source) + else: + noisy_base = self.compute_and_generate_noise(latent_image,seed,width,height,batch_size,model_model,sampler,scheduler,base_end_at,base_start_at,base_end_at,noise_source) + sample_model = self.sample(model_model,"disable",seed,base_total_steps,cfg_scale,sampler,scheduler,CONDITIONING_model_pos,CONDITIONING_model_neg,noisy_base,base_start_at,base_end_at,"disable") + + if SD15VAE is not None and SDXLVAE is not None: + sample_model["samples"] = SD15VAE.decode(sample_model["samples"]) + sample_model["samples"] = SDXLVAE.encode(sample_model["samples"]) + + if sampler == "uni_pc" or sampler == "uni_pc_bh2": + sampler = "dpmpp_2m" + scheduler = "karras" + + if detail_level < 0.9999 or detail_level > 1: + if detail_from == "penultimate_step": + if detail_level > 1: + noisy_latent_1 = self.compute_and_generate_noise(sample_model,seed,width,height,batch_size,model_refiner,sampler,scheduler,refiner_total_steps+1,refiner_start_at,refiner_end_at+1,noise_source) + else: + noisy_latent_1 = self.compute_and_generate_noise(sample_model,seed,width,height,batch_size,model_refiner,sampler,scheduler,refiner_total_steps-1,refiner_start_at,refiner_end_at-1,noise_source) + else: + noisy_latent_1 = sample_model + noisy_latent_2 = self.compute_and_generate_noise(sample_model,seed,width,height,batch_size,model_refiner,sampler,scheduler,refiner_total_steps, refiner_start_at,refiner_end_at,noise_source) + if detail_level > 1: + noisy_latent_3 = self.slerp_latents(noisy_latent_1,remap_range(detail_level,1,2,1,0),noisy_latent_2) + else: + noisy_latent_3 = self.slerp_latents(noisy_latent_1,detail_level,noisy_latent_2) + else: + noisy_latent_3 = self.compute_and_generate_noise(sample_model,seed,width,height,batch_size,model_refiner,sampler,scheduler,refiner_total_steps, refiner_start_at,refiner_end_at,noise_source) + + sample_refiner = self.sample(model_refiner,"disable",seed,refiner_total_steps,cfg_scale,sampler,scheduler,CONDITIONING_refiner_pos,CONDITIONING_refiner_neg,noisy_latent_3,refiner_start_at,refiner_end_at,"disable") + + return (sample_refiner,) + +NODE_CLASS_MAPPINGS = { + "KSamplerSDXLAdvanced": KSamplerSDXLAdvanced +} diff --git a/.a_Workflows/comfyuiExtremeDetail_v10/simple_workflow.json b/.a_Workflows/comfyuiExtremeDetail_v10/simple_workflow.json new file mode 100644 index 000000000..7378f780f --- /dev/null +++ b/.a_Workflows/comfyuiExtremeDetail_v10/simple_workflow.json @@ -0,0 +1,844 @@ +{ + "last_node_id": 34, + "last_link_id": 80, + "nodes": [ + { + "id": 12, + "type": "VAEDecode", + "pos": [ + 2060, + 240 + ], + 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