diff --git a/README.md b/README.md index 6adeb54..d08beb4 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,32 @@ -# Comfyui-PanoCard +# ComfyUI-PanoCard A plugin application that utilizes ComfyUI to generate 360-degree panoramic images. It primarily works by converting between flat images and equidistant cylindrical projections, stretching flat images into a curved format, and setting regional conditions, while harnessing the power of large models to produce 360-degree panoramic views. + + +## Features +- 支持360度全景图和平面图像的相互转换 Support the mutual conversion between 360 degree panoramic images and flat images +![image1](workflows\img\image4.png) + +- 支持平面图片进行圆弧拉伸和极轴变换 Support arc stretching and polar axis transformation for flat images +![image3](workflows\img\image3.png) + +- 将平面图像按照全景的方式分割为6个面 Split flat images into 6 faces according to the panoramic way +![image3](workflows\img\image5.png) + +- 编写6个面的提示词一次性生成6个面 Write 6 prompts to generate 6 faces at once +![image1](workflows\img\image1.png) + +- 组合6张图片通过手动调整来生成一张360全景图 Combine 6 images to generate a 360 panoramic image by manually adjusting +![image5](workflows\img\image6.png) + +- 利用区域提示词生成360度全景图 Utilize regional prompts to generate 360 degree panoramic images +![image5](workflows\img\image7.png) + +## 反馈交流 +- 如果有使用上的问题、建议、Bug等，可以通过抖音(抖音号：ycmiyesic)，或者加我V：beliveWeC +- 如果需要VR全景后台可以联系我 + +## 参考链接 +- Pano立方到等距、等距到立方、等距到图片核心的算法来自py360convert，链接地址 https://github.com/sunset1995/py360convert +- 横向扩展和纵向扩展参考了imageMagic的想法 +- Pano查看器参考：https://github.com/gokayfem/ComfyUI-Dream-Interpreter +- 图像基础变换参考ComfyUI-ALLOR: https://github.com/Nourepide/ComfyUI-Allor \ No newline at end of file diff --git a/__init__.py b/__init__.py index 818069f..05629b2 100644 --- a/__init__.py +++ b/__init__.py @@ -21,6 +21,7 @@ NODE_CLASS_MAPPINGS = { "PanoImageRoll": PanoImageRoll, "PanoCondAllBatch":PanoMaskCondBatch, "PanoCondFaceBatch":PanoClipBatch, + "PanoCondFaceClamp":PanoCondClipClamp, "PanoPromptSplit":PanoPromptSplit, "PanoRegionalPrompt":PanoRegionalPrompt, "PanoImage2FaceSplit":PanoImageSplit, diff --git a/pano.py b/pano.py index 29d3416..9c541b2 100644 --- a/pano.py +++ b/pano.py @@ -124,13 +124,65 @@ def DepackClip(cond_face): else: print("no guidance") + cnet_list = None + if 'pano_control' in b_pooleds_dict: + cnet_list = b_pooleds_dict["pano_control"] + print("has pano_control number:",len(cnet_list)) + else: + print("no pano_control") + for i in range(6): if key_pooleds > 0.0: - res.append([(conds[i], {"pooled_output": pooleds[i], "guidance": key_pooleds})]) + cond_dict = {"pooled_output": pooleds[i], "guidance": key_pooleds} else: - res.append([(conds[i], {"pooled_output": pooleds[i]})]) + cond_dict = {"pooled_output": pooleds[i]} + + if cnet_list is not None: + if 'control' in cnet_list[i] and 'control_apply_to_uncond' in cnet_list[i]: + cond_dict['control'] = cnet_list[i]['control'] + cond_dict['control_apply_to_uncond'] = cnet_list[i]['control_apply_to_uncond'] + + res.append([(conds[i], cond_dict)]) return res - + + +def pad_conditions(conds, num_tokens=0): + if len(conds) == 0: + return [] + + # 找到最大的 token 数量 + if num_tokens > 0: + max_num_tokens = num_tokens + else: + max_num_tokens = max(tensor.shape[1] for tensor in conds) + + # 创建一个新的列表来存储填充后的张量 + padded_conds = [] + + for cond in conds: + current_num_tokens = cond.shape[1] + if current_num_tokens < max_num_tokens: + # 填充较短的张量 + padding_size = max_num_tokens - current_num_tokens + # 创建填充张量 + padding = torch.zeros( + (cond.shape[0], padding_size, cond.shape[2]), # [batch_size, padding_size, token_dim] + dtype=cond.dtype, + device=cond.device + ) + # 拼接原始张量和填充张量 + padded_cond = torch.cat((cond, padding), dim=1) + elif current_num_tokens > max_num_tokens: + # 截断较长的张量 + padded_cond = cond[:, :max_num_tokens, :] + else: + # 如果长度相同，直接使用原始张量 + padded_cond = cond + + padded_conds.append(padded_cond) + + return padded_conds + class PanoViewer: @classmethod # 定义输入类型 @@ -1058,7 +1110,7 @@ class PanoFaceToLong: def __init__(self): pass - CATEGORY = "PanoCard/split" + CATEGORY = "PanoCard/convert" @classmethod def INPUT_TYPES(cls): @@ -1200,6 +1252,8 @@ class PanoImageOutClamp: return (images_list[0],images_list[1],images_list[2],images_list[3],images_list[4],images_list[5]) + + class PanoMaskCondBatch: CATEGORY = "PanoCard/conditioning" @classmethod @@ -1207,7 +1261,8 @@ class PanoMaskCondBatch: return { "required": { "clip": ("CLIP", ), - "strength_face": ("FLOAT",{"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01},), + "strength_mask": ("FLOAT",{"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01},), + "combine_up_down": ("BOOLEAN", {"default": True, "label_on": "true", "label_off": "false"}), "total": ("STRING", {"multiline": True}), "prefix": ("STRING", {"multiline": True}), "front": ("STRING", {"multiline": True}), @@ -1222,13 +1277,14 @@ class PanoMaskCondBatch: "cond_face": ("CONDITIONING",), } } - RETURN_TYPES = ("CONDITIONING","CONDITIONING","CONDITIONING","CONDITIONING") - RETURN_NAMES = ("cond_mix","cond_face","cond_total","cond_mask") + RETURN_TYPES = ("CONDITIONING","CONDITIONING","CONDITIONING","CONDITIONING","MASK") + RETURN_NAMES = ("cond_mix","cond_face","cond_total","cond_mask","masks") FUNCTION = "encode" def encode(self, clip, - strength_face, + strength_mask, + combine_up_down, total, prefix, front, right, back, left, up, down, @@ -1236,28 +1292,29 @@ class PanoMaskCondBatch: ): #调整局部和整体的强度 - strength_face = strength_face * 2 - strength_total = 2.001 - strength_face + strength_mask = strength_mask + strength_total = 1.0 - strength_mask #全局编码 tokens = clip.tokenize(total + prefix) t_cond, t_pooled = clip.encode_from_tokens(tokens, return_pooled=True) conditioning_total = [(t_cond, {"pooled_output": t_pooled})] - num_tokens = [] if cond_face is None: #局部编码 conds = [] pooleds = [] - texts = [front,right,back,left,up,down] + if combine_up_down: + texts = [front,right,back,left,total,up+down] + else: + texts = [front,right,back,left,up,down] for i, text in enumerate(texts): tokens = clip.tokenize(prefix + text) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) conds.append(cond) pooleds.append(pooled) num_tokens.append(cond.shape[1]) - else: # 解包外层列表 [b_conds, face_pooled_dict] = cond_face[0] @@ -1265,6 +1322,10 @@ class PanoMaskCondBatch: b_pooleds = face_pooled_dict["pooled_output"] conds = get_original_tensors(b_conds) + if combine_up_down: + conds[5] = torch.cat((conds[4], conds[5]), 1) + conds[4] = t_cond + pooleds[4] = t_pooled pooleds = get_original_tensors(b_pooleds) num_tokens = [cond.shape[1] for cond in conds] @@ -1272,54 +1333,42 @@ class PanoMaskCondBatch: num_tokens2 = num_tokens + [t_cond.shape[1]] max_num_tokens = max(num_tokens2) - # 重复截断填充face token - for i in range(len(conds)): - current_num_tokens = conds[i].shape[1] - if current_num_tokens > max_num_tokens: - conds[i] = conds[i][:, :max_num_tokens, :] - elif current_num_tokens < max_num_tokens: - repeat_times = max_num_tokens // current_num_tokens + 1 - repeated_tokens = conds[i].repeat(1, repeat_times, 1) - conds[i] = repeated_tokens[:, :max_num_tokens, :] - - # 填充totol token - padding_size = max_num_tokens - t_cond.shape[1] - if padding_size > 0: - padding = torch.zeros( - (1, padding_size, t_cond.shape[2]), - dtype=t_cond.dtype, - device=t_cond.device) - t_cond = torch.cat((t_cond, padding), dim=1) - + # 填充face token + conds = pad_conditions(conds, max_num_tokens) b_conds = torch.cat(conds) b_pooleds = torch.cat(pooleds) - - # 将张量列表中的所有张量进行拼接 - t_cond_scaled = [torch.mul(t_cond, strength_total)] - conds_scaled = [torch.mul(cond, strength_face) for cond in conds] - t_conds = torch.cat(t_cond_scaled + conds_scaled, dim=1) + conditioning_face = [(b_conds, {"pooled_output": b_pooleds})] - t_pooled_scaled = [torch.mul(t_pooled, strength_total)] - pooleds_scaled = [torch.mul(pooled, strength_face) for pooled in pooleds] - t_pooleds = torch.cat(t_pooled_scaled + pooleds_scaled, dim=0) + # 填充totol token + t_cond = pad_conditions([t_cond], max_num_tokens)[0] + + # 混合条件 + conditioning_mix = conditioning_total + for i in range(6): + face_conds = [(conds[i], {"pooled_output": pooleds[i]})] + conditioning_mix = nodes.ConditioningConcat().concat(conditioning_mix, face_conds)[0] # 计算遮罩合并 - conditioning = [] + conditioning_mask = [] if masks is not None: if masks.shape[0] != 6: raise ValueError("The number of masks must be 6.") - mask_a = torch.ones_like(masks[0]).unsqueeze(0) + mask_a = torch.ones_like(masks[0]).unsqueeze(0) print(mask_a.shape) t_conditioning = [(t_cond, { "pooled_output": t_pooled, - "mask": mask_a, + "mask": mask_a , "set_area_to_bounds": False, "mask_strength": strength_total })] - conditioning = t_conditioning - + conditioning_mask = t_conditioning + + if combine_up_down: + masks[5] = masks[4] + masks[5] + masks[4] = mask_a - (masks[0] + masks[1] + masks[2] + masks[3] + masks[5]) + for i in range(6): mask_i = masks[i] if len(mask_i.shape) < 3: @@ -1329,15 +1378,20 @@ class PanoMaskCondBatch: "pooled_output": pooleds[i], "mask": mask_i, "set_area_to_bounds": False, - "mask_strength": strength_face + "mask_strength": strength_mask })] - conditioning += b_conditioning + conditioning_mask += b_conditioning else: - conditioning = [(t_conds, {"pooled_output": t_pooleds})] - - return ([(t_conds, {"pooled_output": t_pooleds})], - [(b_conds, {"pooled_output": b_pooleds})], - conditioning_total, conditioning) + conditioning_mask = conditioning_total + for i in range(6): + face_conds = [(conds[i], {"pooled_output": pooleds[i]})] + conditioning_mask = nodes.ConditioningCombine().combine(conditioning_mask, face_conds)[0] + + return (conditioning_mix, + conditioning_face, + conditioning_total, + conditioning_mask, + masks) class PanoClipBatch: @@ -1363,7 +1417,6 @@ class PanoClipBatch: def encode(self, clip, prefix, front, right, back, left, up, down): conds = [] pooleds = [] - num_tokens = [] front = prefix + front right = prefix + right back = prefix + back @@ -1377,30 +1430,77 @@ class PanoClipBatch: cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) conds.append(cond) pooleds.append(pooled) - num_tokens.append(cond.shape[1]) - - # 找到最大的 token 数量 - max_num_tokens = max(num_tokens) - - # 修改padding的生成方式： - for i in range(len(conds)): - current_num_tokens = num_tokens[i] - if current_num_tokens < max_num_tokens: - padding_size = max_num_tokens - current_num_tokens - # 修正padding创建方式 - padding = torch.zeros( - (1, padding_size, conds[i].shape[2]), # [batch_size, padding_size, token_dim] - dtype=conds[i].dtype, - device=conds[i].device - ) - conds[i] = torch.cat((conds[i], padding), dim=1) - + + conds = pad_conditions(conds) b_conds = torch.cat(conds) b_pooleds = torch.cat(pooleds) return ([(b_conds, {"pooled_output": b_pooleds})],) +class PanoCondClipClamp: + def __init__(self): + pass + + CATEGORY = "PanoCard/conditioning" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "front": ("CONDITIONING",), + "right": ("CONDITIONING",), + "back": ("CONDITIONING",), + "left": ("CONDITIONING",), + "up": ("CONDITIONING",), + "down": ("CONDITIONING",), + "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}), + }, + } + + RETURN_TYPES = ("CONDITIONING",) + RETURN_NAMES = ("cond_face",) + FUNCTION = "node" + def node(self, front, right, back, left, up, down, guidance): + # 将输入的图像转换为 PyTorch 的 Tensor + cond_list = [front, right, back, left, up, down] + cnet_list = [] + conds = [] + pooleds = [] + for cond_face in cond_list: + if cond_face is not None: + [c, t] = cond_face[0] + conds.append(c) + pooleds.append(t['pooled_output']) + if 'control' in t and 'control_apply_to_uncond' in t: + cnet_dict = { + 'control': t['control'], + 'control_apply_to_uncond': t['control_apply_to_uncond'] + } + cnet_list.append(cnet_dict) + else: + cnet_list.append({}) + else: + raise Exception("Error: The input is not a valid condition.") + + cond_face = pad_conditions(conds) + b_conds = torch.cat(cond_face) + b_pooleds = torch.cat(pooleds) + + if len(cnet_list) > 0: + res_dict = {"pooled_output": b_pooleds, "pano_control": cnet_list} + else: + res_dict = {"pooled_output": b_pooleds} + + if guidance > 0: + res_dict["guidance_scale"] = guidance + + res = [(b_conds, res_dict)] + + # 返回结果 + return (res,) + + class PanoPromptSplit: CATEGORY = "PanoCard/conditioning" @@ -1523,21 +1623,10 @@ class PanoRegionalPrompt: if masks.shape[0] != 6: raise Exception("Mask shape must be 6") - bkap = nodes.NODE_CLASS_MAPPINGS['KSamplerAdvancedProvider']() base_sampler = bkap.doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=sigma_factor, scheduler_func_opt=scheduler_func_opt)[0] res = [] - - [b_conds, b_pooleds_dict] = cond_face[0] - if "pooled_output" in b_pooleds_dict: - b_pooleds = b_pooleds_dict["pooled_output"] - else: - raise Exception("no pooled_output") - - conds = get_original_tensors(b_conds) - pooleds = get_original_tensors(b_pooleds) - model, clip, vae, positive, negative = basic_pipe def rdoit(basic_pipe, mask, cfg, sampler_name, scheduler, sigma_factor=1.0, variation_seed=0, variation_strength=0.0, variation_method='linear', scheduler_func_opt=None): @@ -1546,9 +1635,7 @@ class PanoRegionalPrompt: kap = nodes.NODE_CLASS_MAPPINGS['KSamplerAdvancedProvider']() rp = nodes.NODE_CLASS_MAPPINGS['RegionalPrompt']() - sampler = kap.doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=sigma_factor, scheduler_func_opt=scheduler_func_opt)[0] - try: regional_prompts = rp.doit(mask, sampler, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method)[0] except: @@ -1556,8 +1643,10 @@ class PanoRegionalPrompt: return regional_prompts + conds = DepackClip(cond_face) + for i, mask in enumerate(masks): - positive = [(conds[i], {"pooled_output": pooleds[i]})] + positive = conds[i] pipe = model, clip, vae, positive, negative rp = rdoit(pipe, mask.unsqueeze(0), cfg, sampler_name, scheduler, sigma_factor=sigma_factor, @@ -1705,7 +1794,7 @@ class PanoImageSplit: return (images,) class PanoClipOutClamp: - CATEGORY = "PanoCard/split" + CATEGORY = "PanoCard/conditioning" @classmethod def INPUT_TYPES(s): return { @@ -1776,21 +1865,23 @@ class DetailerHook(PixelKSampleHook): class FaceCondScheduleHook(DetailerHook): - def __init__(self, cond, face_skip, seed): + def __init__(self, cond, face_niose, seed): super().__init__() self.cond = cond self.seed = seed - self.face_skip = face_skip + self.face_niose = face_niose def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise): index = abs(seed - self.seed) % 6 if index == 0 and self.cond is None: self.cond = DepackClip(positive) positive = self.cond[index] - if self.face_skip[index]: + if self.face_niose[index] < 0.1: steps = 0 - - print("index", index) + denoise = 0 + else: + denoise = self.face_niose[index] + print("index:", index, " denoise:",denoise) return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise class CondFaceScheduleHookProvider: @@ -1801,12 +1892,12 @@ class CondFaceScheduleHookProvider: return { "required": { "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), - "skip_face1": ("BOOLEAN", {"default": False,"label_on": "ture", "label_off": "false"}), - "skip_face2": ("BOOLEAN", {"default": False,"label_on": "ture", "label_off": "false"}), - "skip_face3": ("BOOLEAN", {"default": False,"label_on": "ture", "label_off": "false"}), - "skip_face4": ("BOOLEAN", {"default": False,"label_on": "ture", "label_off": "false"}), - "skip_face5": ("BOOLEAN", {"default": False,"label_on": "ture", "label_off": "false"}), - "skip_face6": ("BOOLEAN", {"default": False,"label_on": "ture", "label_off": "false"}), + "niose_face1": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "niose_face2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "niose_face3": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "niose_face4": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "niose_face5": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "niose_face6": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), }, "optional": { "cond_face": ("CONDITIONING",), @@ -1818,11 +1909,11 @@ class CondFaceScheduleHookProvider: CATEGORY = "PanoCard/conditioning" - def doit(self, seed, skip_face1, skip_face2, skip_face3, skip_face4, skip_face5, skip_face6, cond_face=None ): - face_skip = [skip_face1, skip_face2, skip_face3, skip_face4, skip_face5, skip_face6] + def doit(self, seed, niose_face1, niose_face2, niose_face3, niose_face4, niose_face5, niose_face6, cond_face=None ): + face_niose = [niose_face1, niose_face2, niose_face3, niose_face4, niose_face5, niose_face6] if cond_face is None: cond = None else: cond = DepackClip(cond_face) - hook = FaceCondScheduleHook(cond, face_skip, seed) + hook = FaceCondScheduleHook(cond, face_niose, seed) return (hook, seed) \ No newline at end of file diff --git a/sss.png b/sss.png new file mode 100644 index 0000000..c6d27d3 Binary files /dev/null and b/sss.png differ diff --git a/workflows/1_单次生成全景图6个面.json b/workflows/1_单次生成全景图6个面.json new file mode 100644 index 0000000..fcf4087 --- /dev/null +++ b/workflows/1_单次生成全景图6个面.json @@ -0,0 +1 @@ +{"id":"79cd7d62-4a33-4f00-b554-08b1da9ed400","revision":0,"last_node_id":72,"last_link_id":99,"nodes":[{"id":42,"type":"PanoCardViewer","pos":[8417.8173828125,4318.06201171875],"size":[600,500],"flags":{},"order":26,"mode":0,"inputs":[{"localized_name":"pano_image","name":"pano_image","type":"IMAGE","link":46}],"outputs":[],"properties":{"aux_id":"Gue-e/ComfyUI-PanoCard","ver":"9668e2faa307b571428eb919b3c992e3b5f15e1b","Node name for S&R":"PanoCardViewer"},"widgets_values":[null]},{"id":40,"type":"ToBasicPipe","pos":[6565.79833984375,4001.199951171875],"size":[241.79998779296875,106],"flags":{},"order":10,"mode":0,"inputs":[{"localized_name":"model","name":"model","type":"MODEL","link":38},{"localized_name":"clip","name":"clip","type":"CLIP","link":39},{"localized_name":"vae","name":"vae","type":"VAE","link":47},{"localized_name":"positive","name":"positive","type":"CONDITIONING","link":99},{"localized_name":"negative","name":"negative","type":"CONDITIONING","link":40}],"outputs":[{"localized_name":"basic_pipe","name":"basic_pipe","type":"BASIC_PIPE","slot_index":0,"links":[63]}],"properties":{"cnr_id":"comfyui-impact-pack","ver":"782c6f439eda79d88f22908bc83f57db77d44097","Node name for S&R":"ToBasicPipe"},"widgets_values":[]},{"id":4,"type":"UnetLoaderGGUF","pos":[5666.416015625,3997.285888671875],"size":[315,58],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"localized_name":"模型","name":"MODEL","type":"MODEL","slot_index":0,"links":[38]}],"properties":{"cnr_id":"comfyui-gguf","ver":"298192ed60f8ca821c6fe5f8030cae23424cada5","Node name for S&R":"UnetLoaderGGUF"},"widgets_values":["flux1-schnell-Q4_K_S.gguf"]},{"id":2,"type":"PanoCondFaceBatch","pos":[6044.4169921875,3993.569580078125],"size":[457.7218933105469,723.6602783203125],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"clip","name":"clip","type":"CLIP","link":2}],"outputs":[{"localized_name":"cond_face","name":"cond_face","type":"CONDITIONING","slot_index":0,"links":[99]}],"properties":{"aux_id":"Gue-e/ComfyUI-PanoCard","ver":"9668e2faa307b571428eb919b3c992e3b5f15e1b","Node name for S&R":"PanoCondFaceBatch"},"widgets_values":["The overall style of the picture is a soft pink illustration.","A girl wearing a bright red dress stood elegantly in the lush green grass, her hair swaying gently in the breeze. 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