Add support for the stable diffusion x4 upscaling model.

This is an old model.

Load the checkpoint like a regular one and use the new
SD_4XUpscale_Conditioning node.

comfy/latent_formats.py

@@ -33,3 +33,7 @@ class SDXL(LatentFormat):
                     [-0.3112, -0.2359, -0.2076]
                 ]
         self.taesd_decoder_name = "taesdxl_decoder"
+
+class SD_X4(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 0.08333

comfy/model_base.py

@@ -364,3 +364,24 @@ class Stable_Zero123(BaseModel):
                 cross_attn = self.cc_projection(cross_attn)
             out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
         return out
+
+class SD_X4Upscaler(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
+        super().__init__(model_config, model_type, device=device)
+
+    def extra_conds(self, **kwargs):
+        out = {}
+
+        image = kwargs.get("concat_image", None)
+        noise = kwargs.get("noise", None)
+
+        if image is None:
+            image = torch.zeros_like(noise)[:,:3]
+
+        if image.shape[1:] != noise.shape[1:]:
+            image = utils.common_upscale(image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
+
+        image = utils.resize_to_batch_size(image, noise.shape[0])
+
+        out['c_concat'] = comfy.conds.CONDNoiseShape(image)
+        return out

comfy/sd.py

@@ -174,6 +174,11 @@ class VAE:
             else:
                 #default SD1.x/SD2.x VAE parameters
                 ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
+
+                if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
+                    ddconfig['ch_mult'] = [1, 2, 4]
+                    self.downscale_ratio = 4
+
                 self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)
         else:
             self.first_stage_model = AutoencoderKL(**(config['params']))

comfy/supported_models.py

@@ -278,6 +278,32 @@ class Stable_Zero123(supported_models_base.BASE):
     def clip_target(self):
         return None
 
+class SD_X4Upscaler(SD20):
+    unet_config = {
+        "context_dim": 1024,
+        "model_channels": 256,
+        'in_channels': 7,
+        "use_linear_in_transformer": True,
+        "adm_in_channels": None,
+        "use_temporal_attention": False,
+    }
 
-models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega]
+    unet_extra_config = {
+        "disable_self_attentions": [True, True, True, False],
+        "num_heads": 8,
+        "num_head_channels": -1,
+    }
+
+    latent_format = latent_formats.SD_X4
+
+    sampling_settings = {
+        "linear_start": 0.0001,
+        "linear_end": 0.02,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.SD_X4Upscaler(self, device=device)
+        return out
+
+models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler]
 models += [SVD_img2vid]

comfy_extras/nodes_sdupscale.py

@@ -0,0 +1,45 @@
+import torch
+import nodes
+import comfy.utils
+
+class SD_4XUpscale_Conditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "images": ("IMAGE",),
+                              "positive": ("CONDITIONING",),
+                              "negative": ("CONDITIONING",),
+                              "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              # "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}), #TODO
+                             }}
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/upscale_diffusion"
+
+    def encode(self, images, positive, negative, scale_ratio):
+        width = max(1, round(images.shape[-2] * scale_ratio))
+        height = max(1, round(images.shape[-3] * scale_ratio))
+
+        pixels = comfy.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center")
+
+        out_cp = []
+        out_cn = []
+
+        for t in positive:
+            n = [t[0], t[1].copy()]
+            n[1]['concat_image'] = pixels
+            out_cp.append(n)
+
+        for t in negative:
+            n = [t[0], t[1].copy()]
+            n[1]['concat_image'] = pixels
+            out_cn.append(n)
+
+        latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])
+        return (out_cp, out_cn, {"samples":latent})
+
+NODE_CLASS_MAPPINGS = {
+    "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning,
+}

nodes.py

@@ -1880,6 +1880,7 @@ def init_custom_nodes():
         "nodes_sag.py",
         "nodes_perpneg.py",
         "nodes_stable3d.py",
+        "nodes_sdupscale.py",
     ]
 
     for node_file in extras_files: