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https://github.com/comfyanonymous/ComfyUI.git
synced 2025-09-18 08:35:03 +00:00
Merge branch 'comfyanonymous:master' into weightedConditionCombine
This commit is contained in:
FizzleDorf
GitHub
116
nodes.py
116
nodes.py
@@ -16,6 +16,7 @@ sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "co
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import comfy.diffusers_convert
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import comfy.samplers
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import comfy.sample
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import comfy.sd
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import comfy.utils
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@@ -203,24 +204,24 @@ class VAEEncodeForInpaint:
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def encode(self, vae, pixels, mask):
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x = (pixels.shape[1] // 64) * 64
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y = (pixels.shape[2] // 64) * 64
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mask = torch.nn.functional.interpolate(mask[None,None,], size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")[0][0]
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mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
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pixels = pixels.clone()
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if pixels.shape[1] != x or pixels.shape[2] != y:
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pixels = pixels[:,:x,:y,:]
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mask = mask[:x,:y]
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mask = mask[:,:,:x,:y]
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#grow mask by a few pixels to keep things seamless in latent space
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kernel_tensor = torch.ones((1, 1, 6, 6))
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mask_erosion = torch.clamp(torch.nn.functional.conv2d((mask.round())[None], kernel_tensor, padding=3), 0, 1)
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m = (1.0 - mask.round())
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mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=3), 0, 1)
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m = (1.0 - mask.round()).squeeze(1)
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for i in range(3):
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pixels[:,:,:,i] -= 0.5
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pixels[:,:,:,i] *= m
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pixels[:,:,:,i] += 0.5
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t = vae.encode(pixels)
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return ({"samples":t, "noise_mask": (mask_erosion[0][:x,:y].round())}, )
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return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
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class CheckpointLoader:
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@classmethod
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@@ -771,79 +772,23 @@ class SetLatentNoiseMask:
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s["noise_mask"] = mask
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return (s,)
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def common_ksampler(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):
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latent_image = latent["samples"]
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noise_mask = None
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device = comfy.model_management.get_torch_device()
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latent_image = latent["samples"]
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if disable_noise:
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noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
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else:
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batch_index = 0
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if "batch_index" in latent:
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batch_index = latent["batch_index"]
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generator = torch.manual_seed(seed)
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for i in range(batch_index):
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noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
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noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
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skip = latent["batch_index"] if "batch_index" in latent else 0
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noise = comfy.sample.prepare_noise(latent_image, seed, skip)
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noise_mask = None
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if "noise_mask" in latent:
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noise_mask = latent['noise_mask']
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noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear")
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noise_mask = noise_mask.round()
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noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)
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noise_mask = torch.cat([noise_mask] * noise.shape[0])
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noise_mask = noise_mask.to(device)
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real_model = None
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comfy.model_management.load_model_gpu(model)
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real_model = model.model
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noise = noise.to(device)
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latent_image = latent_image.to(device)
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positive_copy = []
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negative_copy = []
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control_nets = []
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def get_models(cond):
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models = []
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for c in cond:
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if 'control' in c[1]:
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models += [c[1]['control']]
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if 'gligen' in c[1]:
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models += [c[1]['gligen'][1]]
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return models
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for p in positive:
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t = p[0]
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if t.shape[0] < noise.shape[0]:
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t = torch.cat([t] * noise.shape[0])
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t = t.to(device)
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positive_copy += [[t] + p[1:]]
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for n in negative:
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t = n[0]
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if t.shape[0] < noise.shape[0]:
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t = torch.cat([t] * noise.shape[0])
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t = t.to(device)
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negative_copy += [[t] + n[1:]]
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models = get_models(positive) + get_models(negative)
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comfy.model_management.load_controlnet_gpu(models)
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if sampler_name in comfy.samplers.KSampler.SAMPLERS:
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sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
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else:
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#other samplers
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pass
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samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask)
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samples = samples.cpu()
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for m in models:
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m.cleanup()
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noise_mask = latent["noise_mask"]
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samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
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force_full_denoise=force_full_denoise, noise_mask=noise_mask)
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out = latent.copy()
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out["samples"] = samples
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return (out, )
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@@ -1006,8 +951,7 @@ class LoadImage:
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RETURN_TYPES = ("IMAGE", "MASK")
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FUNCTION = "load_image"
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def load_image(self, image):
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input_dir = folder_paths.get_input_directory()
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image_path = os.path.join(input_dir, image)
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image_path = folder_paths.get_annotated_filepath(image)
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i = Image.open(image_path)
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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@@ -1021,20 +965,27 @@ class LoadImage:
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@classmethod
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def IS_CHANGED(s, image):
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input_dir = folder_paths.get_input_directory()
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image_path = os.path.join(input_dir, image)
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image_path = folder_paths.get_annotated_filepath(image)
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m = hashlib.sha256()
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with open(image_path, 'rb') as f:
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m.update(f.read())
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return m.digest().hex()
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@classmethod
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def VALIDATE_INPUTS(s, image):
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if not folder_paths.exists_annotated_filepath(image):
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return "Invalid image file: {}".format(image)
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return True
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class LoadImageMask:
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_color_channels = ["alpha", "red", "green", "blue"]
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@classmethod
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def INPUT_TYPES(s):
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input_dir = folder_paths.get_input_directory()
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return {"required":
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{"image": (sorted(os.listdir(input_dir)), ),
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"channel": (["alpha", "red", "green", "blue"], ),}
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"channel": (s._color_channels, ),}
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}
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CATEGORY = "mask"
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@@ -1042,8 +993,7 @@ class LoadImageMask:
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RETURN_TYPES = ("MASK",)
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FUNCTION = "load_image"
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def load_image(self, image, channel):
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input_dir = folder_paths.get_input_directory()
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image_path = os.path.join(input_dir, image)
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image_path = folder_paths.get_annotated_filepath(image)
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i = Image.open(image_path)
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if i.getbands() != ("R", "G", "B", "A"):
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i = i.convert("RGBA")
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@@ -1060,13 +1010,22 @@ class LoadImageMask:
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@classmethod
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def IS_CHANGED(s, image, channel):
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input_dir = folder_paths.get_input_directory()
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image_path = os.path.join(input_dir, image)
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image_path = folder_paths.get_annotated_filepath(image)
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m = hashlib.sha256()
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with open(image_path, 'rb') as f:
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m.update(f.read())
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return m.digest().hex()
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@classmethod
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def VALIDATE_INPUTS(s, image, channel):
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if not folder_paths.exists_annotated_filepath(image):
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return "Invalid image file: {}".format(image)
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if channel not in s._color_channels:
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return "Invalid color channel: {}".format(channel)
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return True
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class ImageScale:
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upscale_methods = ["nearest-exact", "bilinear", "area"]
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crop_methods = ["disabled", "center"]
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@@ -1302,6 +1261,7 @@ def load_custom_nodes():
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def init_custom_nodes():
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load_custom_nodes()
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_hypernetwork.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))
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