complete code rewrite. there will be blood
This commit is contained in:
matt3o
@@ -1,3 +1,4 @@
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/__pycache__/
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/models/*.bin
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/models/*.safetensors
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.directory
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@@ -0,0 +1,142 @@
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import torch
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import math
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import torch.nn.functional as F
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from comfy.ldm.modules.attention import optimized_attention
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from .utils import tensor_to_size
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class CrossAttentionPatch:
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# forward for patching
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def __init__(self, ipadapter=None, number=0, weight=1.0, cond=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
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self.weights = [weight]
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self.ipadapters = [ipadapter]
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self.conds = [cond]
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self.unconds = [uncond]
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self.weight_types = [weight_type]
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self.masks = [mask]
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self.sigma_starts = [sigma_start]
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self.sigma_ends = [sigma_end]
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self.unfold_batch = [unfold_batch]
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self.number = number
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self.layers = 10 if '101_to_k_ip' in ipadapter.ip_layers.to_kvs else 15
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self.k_key = str(self.number*2+1) + "_to_k_ip"
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self.v_key = str(self.number*2+1) + "_to_v_ip"
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def set_new_condition(self, ipadapter=None, number=0, weight=1.0, cond=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
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self.weights.append(weight)
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self.ipadapters.append(ipadapter)
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self.conds.append(cond)
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self.unconds.append(uncond)
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self.weight_types.append(weight_type)
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self.masks.append(mask)
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self.sigma_starts.append(sigma_start)
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self.sigma_ends.append(sigma_end)
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self.unfold_batch.append(unfold_batch)
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def __call__(self, q, k, v, extra_options):
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dtype = q.dtype
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cond_or_uncond = extra_options["cond_or_uncond"]
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sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9
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block_type = extra_options["block"][0]
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#block_id = extra_options["block"][1]
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t_idx = extra_options["transformer_index"]
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# extra options for AnimateDiff
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ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None
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b = q.shape[0]
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seq_len = q.shape[1]
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batch_prompt = b // len(cond_or_uncond)
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out = optimized_attention(q, k, v, extra_options["n_heads"])
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_, _, oh, ow = extra_options["original_shape"]
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for weight, cond, uncond, ipadapter, mask, weight_type, sigma_start, sigma_end, unfold_batch in zip(self.weights, self.conds, self.unconds, self.ipadapters, self.masks, self.weight_types, self.sigma_starts, self.sigma_ends, self.unfold_batch):
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if sigma <= sigma_start and sigma >= sigma_end:
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if unfold_batch and cond.shape[0] > 1:
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# Check AnimateDiff context window
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if ad_params is not None and ad_params["sub_idxs"] is not None:
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# if image length matches or exceeds full_length get sub_idx images
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if cond.shape[0] >= ad_params["full_length"]:
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cond = torch.Tensor(cond[ad_params["sub_idxs"]])
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uncond = torch.Tensor(uncond[ad_params["sub_idxs"]])
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# otherwise get sub_idxs images
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else:
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cond = tensor_to_size(cond, ad_params["full_length"])
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uncond = tensor_to_size(uncond, ad_params["full_length"])
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cond = cond[ad_params["sub_idxs"]]
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uncond = uncond[ad_params["sub_idxs"]]
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cond = tensor_to_size(cond, batch_prompt)
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uncond = tensor_to_size(uncond, batch_prompt)
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k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond)
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k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond)
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v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond)
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v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond)
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else:
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k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond).repeat(batch_prompt, 1, 1)
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k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond).repeat(batch_prompt, 1, 1)
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v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond).repeat(batch_prompt, 1, 1)
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v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond).repeat(batch_prompt, 1, 1)
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ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0)
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ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0)
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out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
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if weight_type == 'ease in':
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weight = weight * (0.05 + 0.95 * (1 - t_idx / self.layers))
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elif weight_type == 'ease out':
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weight = weight * (0.05 + 0.95 * (t_idx / self.layers))
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elif weight_type == 'ease in-out':
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weight = weight * (0.05 + 0.95 * (1 - abs(t_idx - (self.layers/2)) / (self.layers/2)))
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elif weight_type == 'reverse in-out':
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weight = weight * (0.05 + 0.95 * (abs(t_idx - (self.layers/2)) / (self.layers/2)))
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elif weight_type == 'weak input' and block_type == 'input':
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weight = weight * 0.2
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elif weight_type == 'weak middle' and block_type == 'middle':
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weight = weight * 0.2
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elif weight_type == 'weak output' and block_type == 'output':
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weight = weight * 0.2
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elif weight_type == 'strong middle' and (block_type == 'input' or block_type == 'output'):
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weight = weight * 0.2
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if mask is not None:
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mask_h = oh / math.sqrt(oh * ow / seq_len)
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mask_h = int(mask_h) + int((seq_len % int(mask_h)) != 0)
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mask_w = seq_len // mask_h
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# check if using AnimateDiff and sliding context window
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if (mask.shape[0] > 1 and ad_params is not None and ad_params["sub_idxs"] is not None):
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# if mask length matches or exceeds full_length, get sub_idx masks
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if mask.shape[0] >= ad_params["full_length"]:
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mask = torch.Tensor(mask[ad_params["sub_idxs"]])
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mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
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else:
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mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
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mask = tensor_to_size(mask, ad_params["full_length"])
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mask = mask[ad_params["sub_idxs"]]
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else:
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mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
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mask = tensor_to_size(mask, batch_prompt)
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mask = mask.repeat(len(cond_or_uncond), 1, 1)
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mask = mask.view(mask.shape[0], -1, 1).repeat(1, 1, out.shape[2])
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# covers cases where extreme aspect ratios can cause the mask to have a wrong size
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mask_len = mask_h * mask_w
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if mask_len < seq_len:
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pad_len = seq_len - mask_len
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pad1 = pad_len // 2
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pad2 = pad_len - pad1
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mask = F.pad(mask, (0, 0, pad1, pad2), value=0.0)
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elif mask_len > seq_len:
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crop_start = (mask_len - seq_len) // 2
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mask = mask[:, crop_start:crop_start+seq_len, :]
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out_ip = out_ip * mask
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out_ip = out_ip * weight
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out = out + out_ip
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return out.to(dtype=dtype)
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+921
-951
File diff suppressed because it is too large
Load Diff
@@ -3,38 +3,50 @@
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IPAdapter implementation that follows the ComfyUI way of doing things. The code is memory efficient, fast, and shouldn't break with Comfy updates.
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# Open source for you but not free for me...
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I started working on IPAdapter because I needed it for my work. As the project evolved I'm inevitably receiving feature requests, bug reports and support requests.
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I'm an open source advocate and I'm happy to share all my code for free but maintaining the IPAdapter, the [Essentials](https://github.com/cubiq/ComfyUI_essentials), [InstantID](https://github.com/cubiq/ComfyUI_InstantID) and [Face Analysis](https://github.com/cubiq/ComfyUI_FaceAnalysis) takes time.
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**I'm not expecting donations but if you are making a profit from my projects it is only fair that you give something back.** I'm talking especially to companies here, I know the struggles of being a freelancer.
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Please contact me if you are interested in a sponsorship at _matt3o@gmail_ or consider a contribution via [PayPal](paypal.me/matt3o) (Matteo "matt3o" Spinelli, Firenze, IT). That will help mainining the code, adding new features and working on better documentation.
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And in that regard I really need to thank [Nathan Shipley](https://www.nathanshipley.com/) for his generous donation. Go check his website, he's terribly talented.
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## :warning: IPAdapter V2: complete Code rewrite warning
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A code cleanup was long overdue and with the occasion I also added a few new important features. The code should be faster and should take less resources but with such an important code rewrite it's inevitable to have introduced some new bugs.
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**At the moment I'm releasing this completely undocumented!** I will post better documentation and video tutorials in the coming days. In the meantime you can check the `example` directory for most of the old and new features.
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## Important updates
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**2024/03/23**: Complete code rewrite!. **This is a breaking update!** Your previous workflows won't work and you'll need to recreate them. You've been warned! After the update, refresh your browser, delete the old IPAdapter nodes and create the new ones.
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**2024/02/02**: Added experimental [tiled IPAdapter](#tiled-ipadapter). It lets you easily handle reference images that are not square. Can be useful for upscaling.
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**2024/01/19**: Support for FaceID Portrait models.
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**2024/01/16**: Notably increased quality of FaceID Plus/v2 models. Check the [comparison](https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/195) of all face models.
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**2023/12/30**: Added support for FaceID Plus v2 models. **Important:** this update again breaks the previous implementation. This time I had to make a new node just for FaceID. The base `IPAdapter Apply` node will work with all previous models; for all FaceID models you'll find an `IPAdapter Apply FaceID` node. **When using v2 remember to check the `v2` options otherwise it won't work as expected!** As always the examples directory is full of workflows for you to play with.
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**2023/12/28**: Added support for FaceID Plus models. **Important:** this update breaks the previous implementation of FaceID. Check the updated workflows in the example directory! Remember to refresh the browser ComfyUI page to clear up the local cache.
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**2023/12/22**: Added support for FaceID models. [Read the documentation](#faceid) for details.
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**2023/12/05**: Added `batch embeds` node. This lets you encode images in batches and merge them together into an `IPAdapter Apply Encoded` node. Useful mostly for animations because the clip vision encoder takes a lot of VRAM. My suggestion is to split the animation in batches of about 120 frames.
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**2023/11/29**: Added `unfold_batch` option to send the reference images sequentially to a latent batch. Useful for animations.
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*(previous updates removed for better readability)*
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## What is it?
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The IPAdapter are very powerful models for image-to-image conditioning. Given a reference image you can do variations augmented by text prompt, controlnets and masks. Think of it as a 1-image lora.
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The IPAdapter are very powerful models for image-to-image conditioning. Given one or more reference images you can do variations augmented by text prompt, controlnets and masks. Think of it as a 1-image lora.
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## Example workflow
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The [example directory](./examples/) has many workflows that cover all IPAdapter functionalities.
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## Video Tutorials
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The following are mostly outdated now but the overall concepts are still valid.
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<a href="https://youtu.be/7m9ZZFU3HWo" target="_blank">
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<img src="https://img.youtube.com/vi/7m9ZZFU3HWo/hqdefault.jpg" alt="Watch the video" />
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</a>
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@@ -51,9 +63,13 @@ The [example directory](./examples/) has many workflows that cover all IPAdapter
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Download or git clone this repository inside `ComfyUI/custom_nodes/` directory or use the Manager. Beware that the automatic update of the manager sometimes doesn't work and you may need to upgrade manually.
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IPAdapter always requires the latest version of ComfyUI. If something doesn't work be sure to upgrade!
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There's now an *Unified Model Loader*, for it to work you need to name the files exactly how it is described below.
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The pre-trained models are available on [huggingface](https://huggingface.co/h94/IP-Adapter), download and place them in the `ComfyUI/models/ipadapter` directory (create it if not present). You can also use any custom location setting an `ipadapter` entry in the `extra_model_paths.yaml` file.
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IPAdapter also needs the image encoders. You need the [CLIP-**ViT-H**-14-laion2B-s32B-b79K](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors) and [CLIP-**ViT-bigG**-14-laion2B-39B-b160k](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors) image encoders, you may already have them. If you don't, download them but **be careful because the file name is the same!** Rename them to something easy to remember and place them in the `ComfyUI/models/clip_vision/` directory.
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IPAdapter also needs the image encoders. You need the [CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors) and [CLIP-ViT-bigG-14-laion2B-39B-b160k.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors) image encoders, you may already have them. If you don't, download them but **be careful because the file name is the same for both!** Rename them and place them in the `ComfyUI/models/clip_vision/` directory.
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The following table shows the combination of Checkpoint and Image encoder to use for each IPAdapter Model. Any Tensor size mismatch you may get it is likely caused by a wrong combination.
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@@ -88,156 +104,20 @@ When the dependencies are satisfied you need:
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The loras need to be placed into `ComfyUI/models/loras/` directory.
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## How to
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## Generic suggestions
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There's a basic workflow included in this repo and a few examples in the [examples](./examples/) directory. Usually it's a good idea to lower the `weight` to at least `0.8`.
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There's a basic workflow included in this repo and a few examples in the [examples](./examples/) directory. Usually it's a good idea to lower the `weight` to at least `0.8` and increase the steps a little.
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The `noise` parameter is an experimental exploitation of the IPAdapter models. You can set it as low as `0.01` for an arguably better result.
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## Documentation soon to come...
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<details>
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<summary><strong>More info about the noise option</strong></summary>
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<img src="./examples/noise_example.jpg" width="100%" alt="canny controlnet" />
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Basically the IPAdapter sends two pictures for the conditioning, one is the reference the other --that you don't see-- is an empty image that could be considered like a negative conditioning.
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What I'm doing is to send a very noisy image instead of an empty one. The `noise` parameter determines the amount of noise that is added. A value of `0.01` adds a lot of noise (more noise == less impact becaue the model doesn't get it); a value of `1.0` removes most of noise so the generated image gets conditioned more.
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</details>
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### Preparing the reference image
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The reference image needs to be encoded by the CLIP vision model. The encoder resizes the image to 224×224 **and crops it to the center!**. It's not an IPAdapter thing, it's how the clip vision works. This means that if you use a portrait or landscape image and the main attention (eg: the face of a character) is not in the middle you'll likely get undesired results. Use square pictures as reference for more predictable results.
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I've added a `PrepImageForClipVision` node that does all the required operations for you. You just have to select the crop position (top/left/center/etc...) and a sharpening amount if you want.
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In the image below you can see the difference between prepped and not prepped images.
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<img src="./examples/prep_images.jpg" width="100%" alt="prepped images" />
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### KSampler configuration suggestions
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The IPAdapter generally requires a few more `steps` than usual, if the result is underwhelming try to add 10+ steps. The model tends to burn the images a little. If needed lower the CFG scale.
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The `noise` option generally grants better results, experiment with it.
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### IPAdapter + ControlNet
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The model is very effective when paired with a ControlNet. In the example below I experimented with Canny. [The workflow](./examples/IPAdapter_Canny.json) is in the examples directory.
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<img src="./examples/canny_controlnet.jpg" width="100%" alt="canny controlnet" />
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### IPAdapter Face
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IPAdapter offers an interesting model for a kind of "face swap" effect. [The workflow is provided](./examples/IPAdapter_face.json). Set a close up face as reference image and then input your text prompt as always. The generated character should have the face of the reference. It also works with img2img given a high denoise.
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<img src="./examples/face_swap.jpg" width="50%" alt="face swap" />
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**Note:** there's a new `full-face` model available that's arguably better.
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### Masking (Inpainting)
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The most effective way to apply the IPAdapter to a region is by an [inpainting workflow](./examples/IPAdapter_inpaint.json). Remeber to use a specific checkpoint for inpainting otherwise it won't work. Even if you are inpainting a face I find that the *IPAdapter-Plus* (not the *face* one), works best.
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<img src="./examples/inpainting.jpg" width="100%" alt="inpainting" />
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### Image Batches
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It is possible to pass multiple images for the conditioning with the `Batch Images` node. An [example workflow](./examples/IPAdapter_batch_images.json) is provided; in the picture below you can see the result of one and two images conditioning.
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<img src="./examples/batch_images.jpg" width="100%" alt="batcg images" />
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It seems to be effective with 2-3 images, beyond that it tends to *blur* the information too much.
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### Image Weighting
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When sending multiple images you can increase/decrease the weight of each image by using the `IPAdapterEncoder` node. The workflow ([included in the examples](examples/IPAdapter_weighted.json)) looks like this:
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<img src="./examples/image_weighting.jpg" width="100%" alt="image weighting" />
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The node accepts 4 images, but remember that you can send batches of images to each slot.
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### Weight types
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You can choose how the IPAdapter weight is applied to the image embeds. Options are:
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- **original**: The weight is applied to the aggregated tensors. The weight works predictably for values greater and lower than 1.
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- **linear**: The weight is applied to the individual tensors before aggretating them. Compared to `original` the influence is weaker when weight is <1 and stronger when >1. **Note:** at weight `1` the two methods are equivalent.
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- **channel penalty**: This method is a modified version of Lvmin Zhang's (Fooocus). Results are sometimes sharper. It works very well also when weight is >1. Still experimental, may change in the future.
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The image below shows the difference (zoom in).
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<img src="./examples/weight_types.jpg" width="100%" alt="weight types" />
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In the examples directory you can find [a workflow](examples/IPAdapter_weight_types.json) that lets you easily compare the three methods.
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### Attention masking
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It's possible to add a mask to define the area where the IPAdapter will be applied to. Everything outside the mask will ignore the reference images and will only listen to the text prompt.
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It is suggested to use a mask of the same size of the final generated image.
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In the picture below I use two reference images masked one on the left and the other on the right. The image is generated only with IPAdapter and one ksampler (without in/outpainting or area conditioning).
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<img src="./examples/masking.jpg" width="512" alt="masking" />
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It is also possible to send a batch of masks that will be applied to a batch of latents, one per frame. The size should be the same but if needed some normalization will be performed to avoid errors. This feature also supports (experimentally) AnimateDiff including context sliding.
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||||
|
||||
In the examples directory you'll find a couple of masking workflows: [simple](examples/IPAdapter_mask.json) and [two masks](examples/IPAdapter_2_masks.json).
|
||||
|
||||
### Timestepping
|
||||
|
||||
In the `Apply IPAdapter` node you can set a start and an end point. The IPAdapter will be applied exclusively in that timeframe of the generation. This is a very powerful tool to modulate the intesity of IPAdapter models.
|
||||
|
||||
<img src="./examples/timestepping.jpg" width="100%" alt="timestepping" />
|
||||
|
||||
### Tiled IPAdapter
|
||||
|
||||
This is an experimental node that automatically splits a reference image in quadrants. It can be especially useful when the reference image is not in 1:1 ratio as the Clip Vision encoder only works with 224x224 square images.
|
||||
|
||||
The `short_side_tiles` parameter defines the number of tiles to use for ther shorter side of the reference image; the number of tiles for the other side are calculated automatically. If the image is in landscape or portrait mode that generally means that only 2 tiles are created. If the image is a square, the value must be at least `2` for the node to have any meaninful effect.
|
||||
|
||||
**If the aspect ratio of the refence image and the latent are very diffirent the image will almost certainly be stretched or squished.**
|
||||
|
||||
`tile_weight` is very important any time the `short_side_tiles` value is greater than 1. The default value of `0.6` should be good for most scenarios but you may need to lower it a little (0.5) for SDXL. **This parameter has no effect if the `short_side_tiles` is = 1.**
|
||||
|
||||
The main IPAdapter `weight` should also be lowered, `0.7` is a good starting point.
|
||||
|
||||
**Important:** With a high number of tiles we are going to reiterate on the same _concept_ multiple times so I suggest to lower the CFG or better use the CFG Rescale node. Please check the workflow in the examples directory for reference.
|
||||
|
||||
The node is experimental and will likely change in the future.
|
||||
|
||||
### FaceID
|
||||
|
||||
FaceID is a new IPAdapter model that takes the embeddings from [InsightFace](https://github.com/deepinsight/insightface). As such you need to install `insightface` in your ComfyUI python environment. You may also need `onnxruntime` and `onnxruntime-gpu`. Note that your CUDA version might not be compatible with onnxruntime, in that case you can select the "CPU" provider from the `Load InsightFace model` node.
|
||||
|
||||
The first time you use InsightFace the model will be downloaded automatically, check the console to see the progress. If you get an error you need to donwload the [buffalo_l](https://github.com/deepinsight/insightface/releases) model manually inside the `ComfyUI/models/insightface/models` directory. Also every time you run the workflow for the first time InsightFace will take quite a few seconds to load.
|
||||
|
||||
**The FaceID model is used in conjuction with its Lora!** Check the [installation instructions](#installation) for the links to all models.
|
||||
|
||||
The reference image needs to be prepared differently compared to the other IPAdapter face models. While standard face models expect the face to take basically the whole frame, FaceID prefers the subject to be a little further away. Don't cut the face too close and leave hair, beard, ears, neck in the picture.
|
||||
|
||||
**InsightFace will often fail to detect the face** and it will throw an error. Try with a different picture possibly cut to half-bust. FaceID generally works with drawings/illustrations too and the result is often very nice.
|
||||
|
||||
I just implemented the FaceID code so I don't have best practices yet and more testing is needed. It's important to understand that **FaceID can (and should) be used as a first pass for an additional IPAdapter Face model**.
|
||||
|
||||
In the [examples directory](./examples/) you'll find a few workflows to get you started with FaceID.
|
||||
|
||||
The following would be a basic workflow that includes FaceID enhanced by a Plus Face model.
|
||||
|
||||
<img src="./examples/face_id_wf.jpg" width="100%" alt="timestepping" />
|
||||
Working on it!
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
Please check the [troubleshooting](https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/108) before posting a new issue.
|
||||
|
||||
## Diffusers version
|
||||
|
||||
If you are interested I've also implemented the same features for [Huggingface Diffusers](https://github.com/cubiq/Diffusers_IPAdapter).
|
||||
Please check the [troubleshooting](https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/108) before posting a new issue. Alse remember to check the previous closed issues.
|
||||
|
||||
## Credits
|
||||
|
||||
- [IPAdapter](https://github.com/tencent-ailab/IP-Adapter/)
|
||||
- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
|
||||
- [laksjdjf](https://github.com/laksjdjf/IPAdapter-ComfyUI/)
|
||||
- [fooocus](https://github.com/lllyasviel/Fooocus/blob/main/fooocus_extras/ip_adapter.py)
|
||||
|
||||
|
||||
+16
@@ -1,3 +1,19 @@
|
||||
"""
|
||||
██▓ ██▓███ ▄▄▄ ▓█████▄ ▄▄▄ ██▓███ ▄▄▄█████▓▓█████ ██▀███
|
||||
▓██▒▓██░ ██▒▒████▄ ▒██▀ ██▌▒████▄ ▓██░ ██▒▓ ██▒ ▓▒▓█ ▀ ▓██ ▒ ██▒
|
||||
▒██▒▓██░ ██▓▒▒██ ▀█▄ ░██ █▌▒██ ▀█▄ ▓██░ ██▓▒▒ ▓██░ ▒░▒███ ▓██ ░▄█ ▒
|
||||
░██░▒██▄█▓▒ ▒░██▄▄▄▄██ ░▓█▄ ▌░██▄▄▄▄██ ▒██▄█▓▒ ▒░ ▓██▓ ░ ▒▓█ ▄ ▒██▀▀█▄
|
||||
░██░▒██▒ ░ ░ ▓█ ▓██▒░▒████▓ ▓█ ▓██▒▒██▒ ░ ░ ▒██▒ ░ ░▒████▒░██▓ ▒██▒
|
||||
░▓ ▒▓▒░ ░ ░ ▒▒ ▓▒█░ ▒▒▓ ▒ ▒▒ ▓▒█░▒▓▒░ ░ ░ ▒ ░░ ░░ ▒░ ░░ ▒▓ ░▒▓░
|
||||
▒ ░░▒ ░ ▒ ▒▒ ░ ░ ▒ ▒ ▒ ▒▒ ░░▒ ░ ░ ░ ░ ░ ░▒ ░ ▒░
|
||||
▒ ░░░ ░ ▒ ░ ░ ░ ░ ▒ ░░ ░ ░ ░░ ░
|
||||
░ ░ ░ ░ ░ ░ ░ ░ ░
|
||||
░
|
||||
·-—+ IPAdapter Plus Extension for ComfyUI +—-· ·
|
||||
Brought to you by Matteo "Matt3o/Cubiq" Spinelli
|
||||
https://github.com/cubiq/ComfyUI_IPAdapter_plus/
|
||||
"""
|
||||
|
||||
from .IPAdapterPlus import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
||||
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
|
||||
|
||||
@@ -1,705 +0,0 @@
|
||||
{
|
||||
"last_node_id": 36,
|
||||
"last_link_id": 93,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 6,
|
||||
"type": "VAEDecode",
|
||||
"pos": [
|
||||
1800,
|
||||
610
|
||||
],
|
||||
"size": {
|
||||
"0": 140,
|
||||
"1": 50
|
||||
},
|
||||
"flags": {},
|
||||
"order": 12,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "samples",
|
||||
"type": "LATENT",
|
||||
"link": 7
|
||||
},
|
||||
{
|
||||
"name": "vae",
|
||||
"type": "VAE",
|
||||
"link": 8
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
72
|
||||
],
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "VAEDecode"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 7,
|
||||
"type": "VAELoader",
|
||||
"pos": [
|
||||
1510,
|
||||
910
|
||||
],
|
||||
"size": {
|
||||
"0": 240,
|
||||
"1": 60
|
||||
},
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "VAE",
|
||||
"type": "VAE",
|
||||
"links": [
|
||||
8
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "VAELoader"
|
||||
},
|
||||
"widgets_values": [
|
||||
"vae-ft-mse-840000-ema-pruned.safetensors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "CheckpointLoaderSimple",
|
||||
"pos": [
|
||||
50,
|
||||
700
|
||||
],
|
||||
"size": {
|
||||
"0": 290,
|
||||
"1": 100
|
||||
},
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "MODEL",
|
||||
"type": "MODEL",
|
||||
"links": [
|
||||
15
|
||||
],
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "CLIP",
|
||||
"type": "CLIP",
|
||||
"links": [
|
||||
5,
|
||||
6
|
||||
],
|
||||
"slot_index": 1
|
||||
},
|
||||
{
|
||||
"name": "VAE",
|
||||
"type": "VAE",
|
||||
"links": [],
|
||||
"slot_index": 2
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CheckpointLoaderSimple"
|
||||
},
|
||||
"widgets_values": [
|
||||
"sd15/realisticVisionV51_v51VAE.safetensors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 4,
|
||||
"type": "CLIPTextEncode",
|
||||
"pos": [
|
||||
710,
|
||||
570
|
||||
],
|
||||
"size": {
|
||||
"0": 400,
|
||||
"1": 160
|
||||
},
|
||||
"flags": {},
|
||||
"order": 8,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"link": 5
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "CONDITIONING",
|
||||
"type": "CONDITIONING",
|
||||
"links": [
|
||||
2
|
||||
],
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CLIPTextEncode"
|
||||
},
|
||||
"widgets_values": [
|
||||
"closeup photo of a woman wearing a white spring dress in a garden\n\nhigh quality, diffuse light, highly detailed, 4k"
|
||||
],
|
||||
"color": "#232",
|
||||
"bgcolor": "#353"
|
||||
},
|
||||
{
|
||||
"id": 5,
|
||||
"type": "CLIPTextEncode",
|
||||
"pos": [
|
||||
710,
|
||||
777
|
||||
],
|
||||
"size": {
|
||||
"0": 400,
|
||||
"1": 160
|
||||
},
|
||||
"flags": {},
|
||||
"order": 9,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"link": 6
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "CONDITIONING",
|
||||
"type": "CONDITIONING",
|
||||
"links": [
|
||||
3
|
||||
],
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CLIPTextEncode"
|
||||
},
|
||||
"widgets_values": [
|
||||
"blurry, malformed, distorted, naked"
|
||||
],
|
||||
"color": "#322",
|
||||
"bgcolor": "#533"
|
||||
},
|
||||
{
|
||||
"id": 3,
|
||||
"type": "EmptyLatentImage",
|
||||
"pos": [
|
||||
892,
|
||||
994
|
||||
],
|
||||
"size": {
|
||||
"0": 210,
|
||||
"1": 110
|
||||
},
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "LATENT",
|
||||
"type": "LATENT",
|
||||
"links": [
|
||||
4
|
||||
],
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "EmptyLatentImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
512,
|
||||
512,
|
||||
1
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 14,
|
||||
"type": "LoraLoaderModelOnly",
|
||||
"pos": [
|
||||
770,
|
||||
410
|
||||
],
|
||||
"size": {
|
||||
"0": 210,
|
||||
"1": 82
|
||||
},
|
||||
"flags": {},
|
||||
"order": 7,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "model",
|
||||
"type": "MODEL",
|
||||
"link": 15
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "MODEL",
|
||||
"type": "MODEL",
|
||||
"links": [
|
||||
85
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LoraLoaderModelOnly"
|
||||
},
|
||||
"widgets_values": [
|
||||
"ip-adapter-faceid_sd15_lora.safetensors",
|
||||
0.6
|
||||
],
|
||||
"color": "#223",
|
||||
"bgcolor": "#335"
|
||||
},
|
||||
{
|
||||
"id": 22,
|
||||
"type": "PreviewImage",
|
||||
"pos": [
|
||||
1990,
|
||||
530
|
||||
],
|
||||
"size": {
|
||||
"0": 527.009765625,
|
||||
"1": 548.077880859375
|
||||
},
|
||||
"flags": {},
|
||||
"order": 13,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 72
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "PreviewImage"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 32,
|
||||
"type": "InsightFaceLoader",
|
||||
"pos": [
|
||||
691,
|
||||
291
|
||||
],
|
||||
"size": {
|
||||
"0": 290,
|
||||
"1": 60
|
||||
},
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "INSIGHTFACE",
|
||||
"type": "INSIGHTFACE",
|
||||
"links": [
|
||||
84
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "InsightFaceLoader"
|
||||
},
|
||||
"widgets_values": [
|
||||
"CPU"
|
||||
],
|
||||
"color": "#223",
|
||||
"bgcolor": "#335"
|
||||
},
|
||||
{
|
||||
"id": 31,
|
||||
"type": "CLIPVisionLoader",
|
||||
"pos": [
|
||||
691,
|
||||
172
|
||||
],
|
||||
"size": {
|
||||
"0": 290,
|
||||
"1": 60
|
||||
},
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "CLIP_VISION",
|
||||
"type": "CLIP_VISION",
|
||||
"links": [
|
||||
82
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CLIPVisionLoader"
|
||||
},
|
||||
"widgets_values": [
|
||||
"IPAdapter_image_encoder_sd15.safetensors"
|
||||
],
|
||||
"color": "#223",
|
||||
"bgcolor": "#335"
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"type": "LoadImage",
|
||||
"pos": [
|
||||
180,
|
||||
-50
|
||||
],
|
||||
"size": {
|
||||
"0": 390.733154296875,
|
||||
"1": 482.0174560546875
|
||||
},
|
||||
"flags": {},
|
||||
"order": 5,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
83
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "MASK",
|
||||
"type": "MASK",
|
||||
"links": null,
|
||||
"shape": 3
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LoadImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
"cwf_portrait.jpg",
|
||||
"image"
|
||||
],
|
||||
"color": "#223",
|
||||
"bgcolor": "#335"
|
||||
},
|
||||
{
|
||||
"id": 9,
|
||||
"type": "IPAdapterModelLoader",
|
||||
"pos": [
|
||||
691,
|
||||
58
|
||||
],
|
||||
"size": {
|
||||
"0": 290,
|
||||
"1": 60
|
||||
},
|
||||
"flags": {},
|
||||
"order": 6,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IPADAPTER",
|
||||
"type": "IPADAPTER",
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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Before Width: | Height: | Size: 116 KiB |
Binary file not shown.
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Before Width: | Height: | Size: 116 KiB |
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Before Width: | Height: | Size: 383 KiB |
Binary file not shown.
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@@ -1,8 +1,4 @@
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# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
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# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
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|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from einops import rearrange
|
||||
@@ -156,3 +152,124 @@ def masked_mean(t, *, dim, mask=None):
|
||||
masked_t = t.masked_fill(~mask, 0.0)
|
||||
|
||||
return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)
|
||||
|
||||
|
||||
class FacePerceiverResampler(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
dim=768,
|
||||
depth=4,
|
||||
dim_head=64,
|
||||
heads=16,
|
||||
embedding_dim=1280,
|
||||
output_dim=768,
|
||||
ff_mult=4,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.proj_in = nn.Linear(embedding_dim, dim)
|
||||
self.proj_out = nn.Linear(dim, output_dim)
|
||||
self.norm_out = nn.LayerNorm(output_dim)
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(
|
||||
nn.ModuleList(
|
||||
[
|
||||
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
|
||||
FeedForward(dim=dim, mult=ff_mult),
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
def forward(self, latents, x):
|
||||
x = self.proj_in(x)
|
||||
for attn, ff in self.layers:
|
||||
latents = attn(x, latents) + latents
|
||||
latents = ff(latents) + latents
|
||||
latents = self.proj_out(latents)
|
||||
return self.norm_out(latents)
|
||||
|
||||
|
||||
class MLPProjModel(nn.Module):
|
||||
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
|
||||
super().__init__()
|
||||
|
||||
self.proj = nn.Sequential(
|
||||
nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
|
||||
nn.GELU(),
|
||||
nn.Linear(clip_embeddings_dim, cross_attention_dim),
|
||||
nn.LayerNorm(cross_attention_dim)
|
||||
)
|
||||
|
||||
def forward(self, image_embeds):
|
||||
clip_extra_context_tokens = self.proj(image_embeds)
|
||||
return clip_extra_context_tokens
|
||||
|
||||
class MLPProjModelFaceId(nn.Module):
|
||||
def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
|
||||
super().__init__()
|
||||
|
||||
self.cross_attention_dim = cross_attention_dim
|
||||
self.num_tokens = num_tokens
|
||||
|
||||
self.proj = nn.Sequential(
|
||||
nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
|
||||
nn.GELU(),
|
||||
nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
|
||||
)
|
||||
self.norm = nn.LayerNorm(cross_attention_dim)
|
||||
|
||||
def forward(self, id_embeds):
|
||||
x = self.proj(id_embeds)
|
||||
x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
class ProjModelFaceIdPlus(nn.Module):
|
||||
def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, clip_embeddings_dim=1280, num_tokens=4):
|
||||
super().__init__()
|
||||
|
||||
self.cross_attention_dim = cross_attention_dim
|
||||
self.num_tokens = num_tokens
|
||||
|
||||
self.proj = nn.Sequential(
|
||||
nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
|
||||
nn.GELU(),
|
||||
nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
|
||||
)
|
||||
self.norm = nn.LayerNorm(cross_attention_dim)
|
||||
|
||||
self.perceiver_resampler = FacePerceiverResampler(
|
||||
dim=cross_attention_dim,
|
||||
depth=4,
|
||||
dim_head=64,
|
||||
heads=cross_attention_dim // 64,
|
||||
embedding_dim=clip_embeddings_dim,
|
||||
output_dim=cross_attention_dim,
|
||||
ff_mult=4,
|
||||
)
|
||||
|
||||
def forward(self, id_embeds, clip_embeds, scale=1.0, shortcut=False):
|
||||
x = self.proj(id_embeds)
|
||||
x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
|
||||
x = self.norm(x)
|
||||
out = self.perceiver_resampler(x, clip_embeds)
|
||||
if shortcut:
|
||||
out = x + scale * out
|
||||
return out
|
||||
|
||||
class ImageProjModel(nn.Module):
|
||||
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
|
||||
super().__init__()
|
||||
|
||||
self.cross_attention_dim = cross_attention_dim
|
||||
self.clip_extra_context_tokens = clip_extra_context_tokens
|
||||
self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
|
||||
self.norm = nn.LayerNorm(cross_attention_dim)
|
||||
|
||||
def forward(self, image_embeds):
|
||||
embeds = image_embeds
|
||||
x = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
|
||||
x = self.norm(x)
|
||||
return x
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 478 KiB |
@@ -0,0 +1,222 @@
|
||||
import re
|
||||
import torch
|
||||
import os
|
||||
import folder_paths
|
||||
from comfy.clip_vision import clip_preprocess, Output
|
||||
import comfy.utils
|
||||
import comfy.model_management as model_management
|
||||
import torchvision.transforms.v2 as T
|
||||
|
||||
def get_clipvision_file(preset):
|
||||
preset = preset.lower()
|
||||
clipvision_list = folder_paths.get_filename_list("clip_vision")
|
||||
|
||||
if preset.startswith("vit-g"):
|
||||
pattern = '(ViT.bigG.14.*39B.b160k|ipadapter.*sdxl|sdxl*.model\.(bin|safetensors))'
|
||||
else:
|
||||
pattern = '(ViT.H.14.*s32B.b79K|ipadapter.*sd15|sd15*.model\.(bin|safetensors))'
|
||||
clipvision_file = [e for e in clipvision_list if re.search(pattern, e, re.IGNORECASE)]
|
||||
|
||||
clipvision_file = folder_paths.get_full_path("clip_vision", clipvision_file[0]) if clipvision_file else None
|
||||
|
||||
return clipvision_file
|
||||
|
||||
def get_ipadapter_file(preset, is_sdxl):
|
||||
preset = preset.lower()
|
||||
ipadapter_list = folder_paths.get_filename_list("ipadapter")
|
||||
is_insightface = False
|
||||
lora_pattern = None
|
||||
|
||||
if preset.startswith("light"):
|
||||
if is_sdxl:
|
||||
raise Exception("light model is not supported for SDXL")
|
||||
pattern = 'sd15.light\.(safetensors|bin)$'
|
||||
elif preset.startswith("standard"):
|
||||
if is_sdxl:
|
||||
pattern = 'ip.adapter.sdxl.vit.h\.(safetensors|bin)$'
|
||||
else:
|
||||
pattern = 'ip.adapter.sd15\.(safetensors|bin)$'
|
||||
elif preset.startswith("vit-g"):
|
||||
if is_sdxl:
|
||||
pattern = 'ip.adapter.sdxl\.(safetensors|bin)$'
|
||||
else:
|
||||
pattern = 'sd15.vit.g\.(safetensors|bin)$'
|
||||
elif preset.startswith("plus ("):
|
||||
if is_sdxl:
|
||||
pattern = 'plus.sdxl.vit.h\.(safetensors|bin)$'
|
||||
else:
|
||||
pattern = 'ip.adapter.plus.sd15\.(safetensors|bin)$'
|
||||
elif preset.startswith("plus face"):
|
||||
if is_sdxl:
|
||||
pattern = 'plus.face.sdxl.vit.h\.(safetensors|bin)$'
|
||||
else:
|
||||
pattern = 'plus.face.sd15\.(safetensors|bin)$'
|
||||
elif preset.startswith("full"):
|
||||
if is_sdxl:
|
||||
raise Exception("full face model is not supported for SDXL")
|
||||
pattern = 'full.face.sd15\.(safetensors|bin)$'
|
||||
elif preset.startswith("faceid portrait"):
|
||||
if is_sdxl:
|
||||
raise Exception("portrait model is not supported for SDXL")
|
||||
pattern = 'portrait.sd15\.(safetensors|bin)$'
|
||||
is_insightface = True
|
||||
elif preset == "faceid":
|
||||
if is_sdxl:
|
||||
pattern = 'faceid.sdxl\.(safetensors|bin)$'
|
||||
lora_pattern = 'faceid.sdxl.lora\.safetensors$'
|
||||
else:
|
||||
pattern = 'faceid.sd15\.(safetensors|bin)$'
|
||||
lora_pattern = 'faceid.sd15.lora\.safetensors$'
|
||||
is_insightface = True
|
||||
elif preset.startswith("faceid plus -"):
|
||||
if is_sdxl:
|
||||
raise Exception("faceid plus model is not supported for SDXL")
|
||||
pattern = 'faceid.plus.sd15\.(safetensors|bin)$'
|
||||
lora_pattern = 'faceid.plus.sd15.lora\.safetensors$'
|
||||
is_insightface = True
|
||||
elif preset.startswith("faceid plus v2"):
|
||||
if is_sdxl:
|
||||
pattern = 'faceid.plusv2.sdxl\.(safetensors|bin)$'
|
||||
lora_pattern = 'faceid.plusv2.sdxl.lora\.safetensors$'
|
||||
else:
|
||||
pattern = 'faceid.plusv2.sd15\.(safetensors|bin)$'
|
||||
lora_pattern = 'faceid.plusv2.sd15.lora\.safetensors$'
|
||||
is_insightface = True
|
||||
else:
|
||||
raise Exception(f"invalid type '{preset}'")
|
||||
|
||||
ipadapter_file = [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]
|
||||
ipadapter_file = folder_paths.get_full_path("ipadapter", ipadapter_file[0]) if ipadapter_file else None
|
||||
|
||||
return ipadapter_file, is_insightface, lora_pattern
|
||||
|
||||
def get_lora_file(pattern):
|
||||
lora_list = folder_paths.get_filename_list("loras")
|
||||
lora_file = [e for e in lora_list if re.search(pattern, e, re.IGNORECASE)]
|
||||
lora_file = folder_paths.get_full_path("loras", lora_file[0]) if lora_file else None
|
||||
|
||||
return lora_file
|
||||
|
||||
def ipadapter_model_loader(file):
|
||||
model = comfy.utils.load_torch_file(file, safe_load=True)
|
||||
|
||||
if file.lower().endswith(".safetensors"):
|
||||
st_model = {"image_proj": {}, "ip_adapter": {}}
|
||||
for key in model.keys():
|
||||
if key.startswith("image_proj."):
|
||||
st_model["image_proj"][key.replace("image_proj.", "")] = model[key]
|
||||
elif key.startswith("ip_adapter."):
|
||||
st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key]
|
||||
model = st_model
|
||||
del st_model
|
||||
|
||||
if not "ip_adapter" in model.keys() or not model["ip_adapter"]:
|
||||
raise Exception("invalid IPAdapter model {}".format(file))
|
||||
|
||||
if 'plusv2' in file.lower():
|
||||
model["faceidplusv2"] = True
|
||||
|
||||
return model
|
||||
|
||||
def insightface_loader(provider):
|
||||
try:
|
||||
from insightface.app import FaceAnalysis
|
||||
except ImportError as e:
|
||||
raise Exception(e)
|
||||
|
||||
path = os.path.join(folder_paths.models_dir, "insightface")
|
||||
model = FaceAnalysis(name="buffalo_l", root=path, providers=[provider + 'ExecutionProvider',])
|
||||
model.prepare(ctx_id=0, det_size=(640, 640))
|
||||
return model
|
||||
|
||||
def encode_image_masked(clip_vision, image, mask=None):
|
||||
model_management.load_model_gpu(clip_vision.patcher)
|
||||
image = image.to(clip_vision.load_device)
|
||||
|
||||
pixel_values = clip_preprocess(image.to(clip_vision.load_device)).float()
|
||||
|
||||
if mask is not None:
|
||||
pixel_values = pixel_values * mask.to(clip_vision.load_device)
|
||||
|
||||
out = clip_vision.model(pixel_values=pixel_values, intermediate_output=-2)
|
||||
|
||||
outputs = Output()
|
||||
outputs["last_hidden_state"] = out[0].to(model_management.intermediate_device())
|
||||
outputs["image_embeds"] = out[2].to(model_management.intermediate_device())
|
||||
outputs["penultimate_hidden_states"] = out[1].to(model_management.intermediate_device())
|
||||
return outputs
|
||||
|
||||
def tensor_to_size(source, dest_size):
|
||||
if isinstance(dest_size, torch.Tensor):
|
||||
dest_size = dest_size.shape[0]
|
||||
source_size = source.shape[0]
|
||||
|
||||
if source_size < dest_size:
|
||||
shape = [dest_size - source_size] + [1]*(source.dim()-1)
|
||||
source = torch.cat((source, source[-1:].repeat(shape)), dim=0)
|
||||
elif source_size > dest_size:
|
||||
source = source[:dest_size]
|
||||
|
||||
return source
|
||||
|
||||
def min_(tensor_list):
|
||||
# return the element-wise min of the tensor list.
|
||||
x = torch.stack(tensor_list)
|
||||
mn = x.min(axis=0)[0]
|
||||
return torch.clamp(mn, min=0)
|
||||
|
||||
def max_(tensor_list):
|
||||
# return the element-wise max of the tensor list.
|
||||
x = torch.stack(tensor_list)
|
||||
mx = x.max(axis=0)[0]
|
||||
return torch.clamp(mx, max=1)
|
||||
|
||||
# From https://github.com/Jamy-L/Pytorch-Contrast-Adaptive-Sharpening/
|
||||
def contrast_adaptive_sharpening(image, amount):
|
||||
img = T.functional.pad(image, (1, 1, 1, 1)).cpu()
|
||||
|
||||
a = img[..., :-2, :-2]
|
||||
b = img[..., :-2, 1:-1]
|
||||
c = img[..., :-2, 2:]
|
||||
d = img[..., 1:-1, :-2]
|
||||
e = img[..., 1:-1, 1:-1]
|
||||
f = img[..., 1:-1, 2:]
|
||||
g = img[..., 2:, :-2]
|
||||
h = img[..., 2:, 1:-1]
|
||||
i = img[..., 2:, 2:]
|
||||
|
||||
# Computing contrast
|
||||
cross = (b, d, e, f, h)
|
||||
mn = min_(cross)
|
||||
mx = max_(cross)
|
||||
|
||||
diag = (a, c, g, i)
|
||||
mn2 = min_(diag)
|
||||
mx2 = max_(diag)
|
||||
mx = mx + mx2
|
||||
mn = mn + mn2
|
||||
|
||||
# Computing local weight
|
||||
inv_mx = torch.reciprocal(mx)
|
||||
amp = inv_mx * torch.minimum(mn, (2 - mx))
|
||||
|
||||
# scaling
|
||||
amp = torch.sqrt(amp)
|
||||
w = - amp * (amount * (1/5 - 1/8) + 1/8)
|
||||
div = torch.reciprocal(1 + 4*w)
|
||||
|
||||
output = ((b + d + f + h)*w + e) * div
|
||||
output = torch.nan_to_num(output)
|
||||
output = output.clamp(0, 1)
|
||||
|
||||
return output
|
||||
|
||||
def tensor_to_image(tensor):
|
||||
image = tensor.mul(255).clamp(0, 255).byte().cpu()
|
||||
image = image[..., [2, 1, 0]].numpy()
|
||||
return image
|
||||
|
||||
def image_to_tensor(image):
|
||||
tensor = torch.clamp(torch.from_numpy(image).float() / 255., 0, 1)
|
||||
tensor = tensor[..., [2, 1, 0]]
|
||||
return tensor
|
||||
Reference in New Issue
Block a user