Merge remote-tracking branch 'origin' into frontendrefactor

comfy/ldm/models/diffusion/ddpm.py

@@ -522,8 +522,8 @@ class LatentDiffusion(DDPM):
     """main class"""
 
     def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
+                 first_stage_config={},
+                 cond_stage_config={},
                  num_timesteps_cond=None,
                  cond_stage_key="image",
                  cond_stage_trainable=False,
@@ -562,8 +562,6 @@ class LatentDiffusion(DDPM):
 
         # self.instantiate_first_stage(first_stage_config)
         # self.instantiate_cond_stage(cond_stage_config)
-        self.first_stage_config = first_stage_config
-        self.cond_stage_config = cond_stage_config
 
         self.cond_stage_forward = cond_stage_forward
         self.clip_denoised = False

comfy/ldm/modules/attention.py

@@ -9,6 +9,8 @@ from typing import Optional, Any
 from ldm.modules.diffusionmodules.util import checkpoint
 from .sub_quadratic_attention import efficient_dot_product_attention
 
+import model_management
+
 try:
     import xformers
     import xformers.ops
@@ -189,12 +191,8 @@ class CrossAttentionBirchSan(nn.Module):
         _, _, k_tokens = key_t.shape
         qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
 
-        stats = torch.cuda.memory_stats(query.device)
-        mem_active = stats['active_bytes.all.current']
-        mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-        mem_free_torch = mem_reserved - mem_active
-        mem_free_total = mem_free_cuda + mem_free_torch
+        mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
+
         chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
 
         kv_chunk_size_min = None
@@ -276,12 +274,7 @@ class CrossAttentionDoggettx(nn.Module):
 
         r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
 
-        stats = torch.cuda.memory_stats(q.device)
-        mem_active = stats['active_bytes.all.current']
-        mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-        mem_free_torch = mem_reserved - mem_active
-        mem_free_total = mem_free_cuda + mem_free_torch
+        mem_free_total = model_management.get_free_memory(q.device)
 
         gb = 1024 ** 3
         tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()

comfy/model_management.py

@@ -145,14 +145,25 @@ def unload_if_low_vram(model):
     return model
 
 
-def get_free_memory():
+def get_free_memory(dev=None, torch_free_too=False):
+    if dev is None:
         dev = torch.cuda.current_device()
+
+    if hasattr(dev, 'type') and dev.type == 'cpu':
+        mem_free_total = psutil.virtual_memory().available
+        mem_free_torch = mem_free_total
+    else:
         stats = torch.cuda.memory_stats(dev)
         mem_active = stats['active_bytes.all.current']
         mem_reserved = stats['reserved_bytes.all.current']
         mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
         mem_free_torch = mem_reserved - mem_active
-    return mem_free_cuda + mem_free_torch
+        mem_free_total = mem_free_cuda + mem_free_torch
+
+    if torch_free_too:
+        return (mem_free_total, mem_free_torch)
+    else:
+        return mem_free_total
 
 def maximum_batch_area():
     global vram_state
@@ -162,6 +173,30 @@ def maximum_batch_area():
     memory_free = get_free_memory() / (1024 * 1024)
     area = ((memory_free - 1024) * 0.9) / (0.6)
     return int(max(area, 0))
+
+def cpu_mode():
+    global vram_state
+    return vram_state == CPU
+
+def should_use_fp16():
+    if cpu_mode():
+        return False #TODO ?
+
+    if torch.cuda.is_bf16_supported():
+        return True
+
+    props = torch.cuda.get_device_properties("cuda")
+    if props.major < 7:
+        return False
+
+    #FP32 is faster on those cards?
+    nvidia_16_series = ["1660", "1650", "1630"]
+    for x in nvidia_16_series:
+        if x in props.name:
+            return False
+
+    return True
+
 #TODO: might be cleaner to put this somewhere else
 import threading
 

comfy/sd.py

@@ -266,6 +266,7 @@ class CLIP:
         self.cond_stage_model = clip(**(params))
         self.tokenizer = tokenizer(embedding_directory=embedding_directory)
         self.patcher = ModelPatcher(self.cond_stage_model)
+        self.layer_idx = -1
 
     def clone(self):
         n = CLIP(no_init=True)
@@ -273,6 +274,7 @@ class CLIP:
         n.patcher = self.patcher.clone()
         n.cond_stage_model = self.cond_stage_model
         n.tokenizer = self.tokenizer
+        n.layer_idx = self.layer_idx
         return n
 
     def load_from_state_dict(self, sd):
@@ -282,9 +284,10 @@ class CLIP:
         return self.patcher.add_patches(patches, strength)
 
     def clip_layer(self, layer_idx):
-        return self.cond_stage_model.clip_layer(layer_idx)
+        self.layer_idx = layer_idx
 
     def encode(self, text):
+        self.cond_stage_model.clip_layer(self.layer_idx)
         tokens = self.tokenizer.tokenize_with_weights(text)
         try:
             self.patcher.patch_model()
@@ -317,9 +320,7 @@ class VAE:
         pixel_samples = pixel_samples.cpu().movedim(1,-1)
         return pixel_samples
 
-    def decode_tiled(self, samples):
-        tile_x = tile_y = 64
-        overlap = 8
+    def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 8):
         model_management.unload_model()
         output = torch.empty((samples.shape[0], 3, samples.shape[2] * 8, samples.shape[3] * 8), device="cpu")
         self.first_stage_model = self.first_stage_model.to(self.device)
@@ -656,3 +657,103 @@ def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, e
     sd = load_torch_file(ckpt_path)
     model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
     return (ModelPatcher(model), clip, vae)
+
+
+def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=None):
+    sd = load_torch_file(ckpt_path)
+    sd_keys = sd.keys()
+    clip = None
+    vae = None
+
+    fp16 = model_management.should_use_fp16()
+
+    class WeightsLoader(torch.nn.Module):
+        pass
+
+    w = WeightsLoader()
+    load_state_dict_to = []
+    if output_vae:
+        vae = VAE()
+        w.first_stage_model = vae.first_stage_model
+        load_state_dict_to = [w]
+
+    if output_clip:
+        clip_config = {}
+        if "cond_stage_model.model.transformer.resblocks.22.attn.out_proj.weight" in sd_keys:
+            clip_config['target'] = 'ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
+        else:
+            clip_config['target'] = 'ldm.modules.encoders.modules.FrozenCLIPEmbedder'
+        clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
+        w.cond_stage_model = clip.cond_stage_model
+        load_state_dict_to = [w]
+
+    sd_config = {
+        "linear_start": 0.00085,
+        "linear_end": 0.012,
+        "num_timesteps_cond": 1,
+        "log_every_t": 200,
+        "timesteps": 1000,
+        "first_stage_key": "jpg",
+        "cond_stage_key": "txt",
+        "image_size": 64,
+        "channels": 4,
+        "cond_stage_trainable": False,
+        "monitor": "val/loss_simple_ema",
+        "scale_factor": 0.18215,
+        "use_ema": False,
+    }
+
+    unet_config = {
+        "use_checkpoint": True,
+        "image_size": 32,
+        "out_channels": 4,
+        "attention_resolutions": [
+            4,
+            2,
+            1
+        ],
+        "num_res_blocks": 2,
+        "channel_mult": [
+            1,
+            2,
+            4,
+            4
+        ],
+        "use_spatial_transformer": True,
+        "transformer_depth": 1,
+        "legacy": False
+    }
+
+    if len(sd['model.diffusion_model.input_blocks.1.1.proj_in.weight'].shape) == 2:
+        unet_config['use_linear_in_transformer'] = True
+
+    unet_config["use_fp16"] = fp16
+    unet_config["model_channels"] = sd['model.diffusion_model.input_blocks.0.0.weight'].shape[0]
+    unet_config["in_channels"] = sd['model.diffusion_model.input_blocks.0.0.weight'].shape[1]
+    unet_config["context_dim"] = sd['model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight'].shape[1]
+
+    sd_config["unet_config"] = {"target": "ldm.modules.diffusionmodules.openaimodel.UNetModel", "params": unet_config}
+    model_config = {"target": "ldm.models.diffusion.ddpm.LatentDiffusion", "params": sd_config}
+
+    if unet_config["in_channels"] > 4: #inpainting model
+        sd_config["conditioning_key"] = "hybrid"
+        sd_config["finetune_keys"] = None
+        model_config["target"] = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
+    else:
+        sd_config["conditioning_key"] = "crossattn"
+
+    if unet_config["context_dim"] == 1024:
+        unet_config["num_head_channels"] = 64 #SD2.x
+    else:
+        unet_config["num_heads"] = 8 #SD1.x
+
+    if unet_config["context_dim"] == 1024 and unet_config["in_channels"] == 4: #only SD2.x non inpainting models are v prediction
+        k = "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm1.bias"
+        out = sd[k]
+        if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
+            sd_config["parameterization"] = 'v'
+
+    model = instantiate_from_config(model_config)
+    model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
+
+    return (ModelPatcher(model), clip, vae)

nodes.py

@@ -202,6 +202,40 @@ class CheckpointLoader:
         ckpt_path = os.path.join(self.ckpt_dir, ckpt_name)
         return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=self.embedding_directory)
 
+class CheckpointLoaderSimple:
+    models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
+    ckpt_dir = os.path.join(models_dir, "checkpoints")
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "ckpt_name": (filter_files_extensions(recursive_search(s.ckpt_dir), supported_ckpt_extensions), ),
+                             }}
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "loaders"
+
+    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+        ckpt_path = os.path.join(self.ckpt_dir, ckpt_name)
+        out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=CheckpointLoader.embedding_directory)
+        return out
+
+class CLIPSetLastLayer:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip": ("CLIP", ),
+                              "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
+                              }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "set_last_layer"
+
+    CATEGORY = "conditioning"
+
+    def set_last_layer(self, clip, stop_at_clip_layer):
+        clip = clip.clone()
+        clip.clip_layer(stop_at_clip_layer)
+        return (clip,)
+
 class LoraLoader:
     models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
     lora_dir = os.path.join(models_dir, "loras")
@@ -325,17 +359,15 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (filter_files_extensions(recursive_search(s.clip_dir), supported_pt_extensions), ),
-                              "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
     CATEGORY = "loaders"
 
-    def load_clip(self, clip_name, stop_at_clip_layer):
+    def load_clip(self, clip_name):
         clip_path = os.path.join(self.clip_dir, clip_name)
         clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=CheckpointLoader.embedding_directory)
-        clip.clip_layer(stop_at_clip_layer)
         return (clip,)
 
 class EmptyLatentImage:
@@ -810,7 +842,9 @@ class ImageInvert:
 NODE_CLASS_MAPPINGS = {
     "KSampler": KSampler,
     "CheckpointLoader": CheckpointLoader,
+    "CheckpointLoaderSimple": CheckpointLoaderSimple,
     "CLIPTextEncode": CLIPTextEncode,
+    "CLIPSetLastLayer": CLIPSetLastLayer,
     "VAEDecode": VAEDecode,
     "VAEEncode": VAEEncode,
     "VAEEncodeForInpaint": VAEEncodeForInpaint,