mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-22 04:10:15 +08:00
Remove DeleteAll, Remove Custom KSampler, Remove Image List
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Silversith
parent
6ad3ae1731
commit
00a3897197
@ -37,206 +37,6 @@ class Note:
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CATEGORY = "silver_custom"
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CATEGORY = "silver_custom"
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class SaveImageList:
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def __init__(self):
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current_dir = os.path.abspath(os.getcwd())
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self.output_dir = os.path.join(current_dir, "output")
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self.type = "output"
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"images": ("IMAGE",),
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"filename_prefix": ("STRING", {"default": "ComfyUI"})},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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RETURN_TYPES = ()
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FUNCTION = "save_images_list"
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OUTPUT_NODE = True
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CATEGORY = "silver_custom"
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def save_images_list(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
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def map_filename(filename):
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prefix_len = len(os.path.basename(filename_prefix))
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prefix = filename[:prefix_len + 1]
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try:
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digits = int(filename[prefix_len + 1:].split('_')[0])
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except:
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digits = 0
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return (digits, prefix)
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subfolder = os.path.dirname(os.path.normpath(filename_prefix))
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filename = os.path.basename(os.path.normpath(filename_prefix))
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full_output_folder = os.path.join(self.output_dir, subfolder)
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if os.path.commonpath((self.output_dir, os.path.realpath(full_output_folder))) != self.output_dir:
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print("Saving image outside the output folder is not allowed.")
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return {}
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try:
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counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_",
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map(map_filename, os.listdir(full_output_folder))))[0] + 1
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except ValueError:
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counter = 1
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except FileNotFoundError:
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os.makedirs(full_output_folder, exist_ok=True)
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counter = 1
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if not os.path.exists(self.output_dir):
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os.makedirs(self.output_dir)
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results = list()
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for image in images:
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i = 255. * image.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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metadata = PngInfo()
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if prompt is not None:
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metadata.add_text("prompt", json.dumps(prompt))
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if extra_pnginfo is not None:
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for x in extra_pnginfo:
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metadata.add_text(x, json.dumps(extra_pnginfo[x]))
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now = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
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file = f"{filename}-{now}_{counter:05}_.png"
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img.save(os.path.join(full_output_folder, file), pnginfo=metadata, optimize=True)
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results.append({
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"filename": file,
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"subfolder": subfolder,
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"type": self.type
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})
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counter += 1
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return self.get_all_files()
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def get_all_files(self):
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results = []
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for root, dirs, files in os.walk(self.output_dir):
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for file in files:
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subfolder = os.path.relpath(root, self.output_dir)
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results.append({
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"filename": file,
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"subfolder": subfolder,
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"type": self.type
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})
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sorted_results = sorted(results, key=lambda x: x["filename"])
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return {"ui": {"images": sorted_results}}
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def custom_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
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disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, in_seed=None):
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latent_image = latent["samples"]
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noise_mask = None
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device = model_management.get_torch_device()
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if in_seed is not None:
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seed = in_seed
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print(seed)
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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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noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout,
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generator=torch.manual_seed(seed), device="cpu")
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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]),
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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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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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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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if 'control' in p[1]:
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control_nets += [p[1]['control']]
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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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if 'control' in n[1]:
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control_nets += [n[1]['control']]
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negative_copy += [[t] + n[1:]]
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control_net_models = []
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for x in control_nets:
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control_net_models += x.get_control_models()
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model_management.load_controlnet_gpu(control_net_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,
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scheduler=scheduler, denoise=denoise)
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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,
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start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise,
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denoise_mask=noise_mask)
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samples = samples.cpu()
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for c in control_nets:
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c.cleanup()
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out = latent.copy()
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out["samples"] = samples
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return (out, seed,)
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class CustomKSampler:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required":
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{
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"model": ("MODEL",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"latent_image": ("LATENT",),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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},
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"optional":
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{
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"in_seed": ()
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}
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}
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RETURN_TYPES = ("LATENT", "seed",)
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FUNCTION = "sample"
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CATEGORY = "silver_custom"
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def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0,
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in_seed=None):
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return custom_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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denoise=denoise, in_seed=in_seed)
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NODE_CLASS_MAPPINGS = {
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NODE_CLASS_MAPPINGS = {
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"Note": Note,
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"Note": Note,
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"SaveImageList": SaveImageList,
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"CustomKSampler": CustomKSampler,
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}
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}
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@ -106,13 +106,6 @@ class ComfyApi extends EventTarget {
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return await resp.json();
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return await resp.json();
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}
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}
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async deleteAllImages() {
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const confirmDelete = confirm("Are you sure you want to delete all images?");
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if (confirmDelete) {
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await this.#postItem("delete", { delete: "all" })
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}
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}
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/**
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/**
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* Gets a list of embedding names
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* Gets a list of embedding names
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* @returns An array of script urls to import
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* @returns An array of script urls to import
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@ -329,7 +329,6 @@ export class ComfyUI {
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$el("button", { textContent: "Load", onclick: () => fileInput.click() }),
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$el("button", { textContent: "Load", onclick: () => fileInput.click() }),
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$el("button", { textContent: "Clear", onclick: () => app.graph.clear() }),
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$el("button", { textContent: "Clear", onclick: () => app.graph.clear() }),
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$el("button", { textContent: "Load Default", onclick: () => app.loadGraphData() }),
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$el("button", { textContent: "Load Default", onclick: () => app.loadGraphData() }),
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$el("button", { textContent: "Delete Images", onclick: () => api.deleteAllImages() }),
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]);
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]);
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this.setStatus({ exec_info: { queue_remaining: "X" } });
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this.setStatus({ exec_info: { queue_remaining: "X" } });
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