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comparison_workflow.json

@@ -0,0 +1,2209 @@
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+      "LATENT"
+    ],
+    [
+      75,
+      21,
+      0,
+      28,
+      0,
+      "LATENT"
+    ],
+    [
+      76,
+      19,
+      0,
+      26,
+      0,
+      "LATENT"
+    ],
+    [
+      77,
+      18,
+      0,
+      24,
+      0,
+      "LATENT"
+    ],
+    [
+      78,
+      3,
+      0,
+      32,
+      1,
+      "VAE"
+    ],
+    [
+      79,
+      32,
+      0,
+      33,
+      0,
+      "IMAGE"
+    ],
+    [
+      80,
+      20,
+      0,
+      32,
+      0,
+      "LATENT"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ksampler_sdxl.py

@@ -0,0 +1,290 @@
+import torch
+import comfy.model_management
+import comfy.sample
+import latent_preview
+
+def prepare_mask(mask, shape):
+    mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[2], shape[3]), 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
+}

simple_workflow.json

@@ -0,0 +1,844 @@
+{
+  "last_node_id": 34,
+  "last_link_id": 80,
+  "nodes": [
+    {
+      "id": 12,
+      "type": "VAEDecode",
+      "pos": [
+        2060,
+        240
+      ],
+      "size": {
+        "0": 180,
+        "1": 60
+      },
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 2
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            1
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 3,
+      "type": "VAELoader",
+      "pos": [
+        1640,
+        360
+      ],
+      "size": {
+        "0": 360,
+        "1": 60
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            3
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "sdxl_vae_fixed.safetensors"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        80,
+        400
+      ],
+      "size": {
+        "0": 360,
+        "1": 100
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            8,
+            9
+          ],
+          "shape": 3,
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "SDXL\\sd_xl_refiner_1.0.safetensors"
+      ]
+    },
+    {
+      "id": 14,
+      "type": "PrimitiveNode",
+      "pos": [
+        460,
+        260
+      ],
+      "size": {
+        "0": 340,
+        "1": 220
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            4,
+            5
+          ],
+          "slot_index": 0,
+          "widget": {
+            "name": "text",
+            "config": [
+              "STRING",
+              {
+                "multiline": true
+              }
+            ]
+          }
+        }
+      ],
+      "title": "Positive prompt",
+      "properties": {},
+      "widgets_values": [
+        "amateur photo of a 22yo girl eye, detailed face, long hair, close-up on the eye, detailed eye, at night"
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 15,
+      "type": "PrimitiveNode",
+      "pos": [
+        460,
+        520
+      ],
+      "size": {
+        "0": 340,
+        "1": 220
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            6,
+            7
+          ],
+          "slot_index": 0,
+          "widget": {
+            "name": "text",
+            "config": [
+              "STRING",
+              {
+                "multiline": true
+              }
+            ]
+          }
+        }
+      ],
+      "title": "Negative prompt",
+      "properties": {},
+      "widgets_values": [
+        "3d render, grayscale, too dark, doll, not realistic, bad quality, low quality, drawing, sketch, simple, backlit, backlight, underexposure, underexposed, repetitive, tiled, cloned, child, copied, cartoon, (low quality:1.3), blurry, ugly, tiling, poorly drawn"
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 16,
+      "type": "EmptyLatentImage",
+      "pos": [
+        900,
+        660
+      ],
+      "size": {
+        "0": 220,
+        "1": 120
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            18
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        1024,
+        1024,
+        1
+      ],
+      "color": "#323",
+      "bgcolor": "#535"
+    },
+    {
+      "id": 2,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        80,
+        260
+      ],
+      "size": {
+        "0": 360,
+        "1": 100
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            12
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            10,
+            11
+          ],
+          "shape": 3,
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "SDXL\\sd_xl_base_1.0.safetensors"
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