From bf0508bd5945c6e301070fa92b65b5ddbae9a8f1 Mon Sep 17 00:00:00 2001 From: matt3o Date: Wed, 8 Nov 2023 20:54:08 +0100 Subject: [PATCH] attention masking --- IPAdapterPlus.py | 37 +- README.md | 16 +- examples/IPAdapter_2_masks.json | 1151 +++++++++++++++++++++++++++++++ examples/IPAdapter_mask.json | 665 ++++++++++++++++++ examples/masking.jpg | Bin 0 -> 118659 bytes 5 files changed, 1862 insertions(+), 7 deletions(-) create mode 100644 examples/IPAdapter_2_masks.json create mode 100644 examples/IPAdapter_mask.json create mode 100644 examples/masking.jpg diff --git a/IPAdapterPlus.py b/IPAdapterPlus.py index 63ba4aa..03a26c8 100644 --- a/IPAdapterPlus.py +++ b/IPAdapterPlus.py @@ -214,14 +214,16 @@ class CrossAttentionPatch: def __call__(self, n, context_attn2, value_attn2, extra_options): org_dtype = n.dtype - cond_or_uncond = extra_options["cond_or_uncond"] if "cond_or_uncond" in extra_options else None + cond_or_uncond = extra_options["cond_or_uncond"] with torch.autocast(device_type=self.device, dtype=self.dtype): q = n k = context_attn2 v = value_attn2 b = q.shape[0] + qs = q.shape[1] batch_prompt = b // len(cond_or_uncond) out = optimized_attention(q, k, v, extra_options["n_heads"]) + _, _, lh, lw = extra_options["original_shape"] k = v = [] for weight, cond, uncond, ipadapter, mask, weight_type in zip(self.weights, self.conds, self.unconds, self.ipadapters, self.masks, self.weight_type): @@ -242,16 +244,29 @@ class CrossAttentionPatch: # please read licensing notes https://github.com/lllyasviel/Fooocus/blob/main/fooocus_extras/ip_adapter.py#L225 ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True) ip_v_offset = ip_v - ip_v_mean - B, F, C = ip_k.shape + _, _, C = ip_k.shape channel_penalty = float(C) / 1280.0 W = weight * channel_penalty ip_k = ip_k * W ip_v = ip_v_offset + ip_v_mean * W - out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"]) + out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"]) if weight_type.startswith("original"): out_ip = out_ip * weight - + + if mask is not None: + # TODO: needs testing + for rate in [1, 2, 4, 8]: + mask_h = -(-lh//rate) # fancy ceil + mask_w = -(-lw//rate) + + if mask_h*mask_w == qs: + break + + mask_downsample = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(mask_h, mask_w), mode="bilinear").squeeze(0) + mask_downsample = mask_downsample.view(1, -1, 1).repeat(out.shape[0], 1, out.shape[2]) + out_ip = out_ip * mask_downsample + out = out + out_ip return out.to(dtype=org_dtype) @@ -303,13 +318,16 @@ class IPAdapterApply: "noise": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01 }), "weight_type": (["original", "linear", "channel penalty"], ), }, + "optional": { + "attn_mask": ("MASK",), + } } RETURN_TYPES = ("MODEL",) FUNCTION = "apply_ipadapter" CATEGORY = "ipadapter" - def apply_ipadapter(self, ipadapter, model, weight, clip_vision=None, image=None, weight_type="original", noise=None, embeds=None): + def apply_ipadapter(self, ipadapter, model, weight, clip_vision=None, image=None, weight_type="original", noise=None, embeds=None, attn_mask=None): self.dtype = model.model.diffusion_model.dtype self.device = comfy.model_management.get_torch_device() self.weight = weight @@ -364,6 +382,9 @@ class IPAdapterApply: work_model = model.clone() + if attn_mask is not None: + attn_mask = attn_mask.squeeze().to(self.device) + patch_kwargs = { "number": 0, "weight": self.weight, @@ -371,7 +392,8 @@ class IPAdapterApply: "dtype": self.dtype, "cond": image_prompt_embeds, "uncond": uncond_image_prompt_embeds, - "weight_type": weight_type + "weight_type": weight_type, + "mask": attn_mask } if not self.is_sdxl: @@ -541,6 +563,9 @@ class IPAdapterApplyEncoded(IPAdapterApply): "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }), "weight_type": (["original", "linear", "channel penalty"], ), }, + "optional": { + "attn_mask": ("MASK",), + } } class IPAdapterSaveEmbeds: diff --git a/README.md b/README.md index 35baae7..d2a6311 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,8 @@ IPAdapter implementation that follows the ComfyUI way of doing things. The code ## Important updates +**2023/11/08**: Added attention masking. + **2023/11/07**: Added three ways to apply the weight. [See below](#weight-types) for more info. **This might break things!** Please let me know if you are having issues. When loading an old workflow try to reload the page a couple of times or delete the `IPAdapter Apply` node and insert a new one. **2023/11/02**: Added compatibility with the new models in safetensors format (available on [huggingface](https://huggingface.co/h94/IP-Adapter)). @@ -139,7 +141,7 @@ You can choose how the IPAdapter weight is applied to the image embeds. Options - **original**: The weight is applied to the aggregated tensors. The weight works predictably for values greater and lower than 1. - **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. -- **channel penalty**: This method is developed by Lvmin Zhang at Stanford University and implemented in the Fooocus UI. Results are sometimes sharper but the image burns a little. It works very well also when weight is >1. +- **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. The image below shows the difference (zoom in). @@ -149,6 +151,18 @@ In the examples directory you can find [a workflow](examples/IPAdapter_weight_ty **Note:** I'm not still sure whether all methods will stay. `Linear` seems the most sensible but I wanted to keep the `original` for backward compatibility. `channel penalty` has a weird non-commercial clause but it's still part of a GNU GPLv3 software (ie: there's a licensing clash) so I'm trying to understand how to deal with that. +### Attention masking + +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. + +It is suggested to use a mask of the same size of the final generated image. + +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). + +masking + +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). + ## Troubleshooting **Error: 'CLIPVisionModelOutput' object has no attribute 'penultimate_hidden_states'** diff --git a/examples/IPAdapter_2_masks.json b/examples/IPAdapter_2_masks.json new file mode 100644 index 0000000..d150ecd --- /dev/null +++ b/examples/IPAdapter_2_masks.json @@ -0,0 +1,1151 @@ +{ + "last_node_id": 51, + "last_link_id": 90, + "nodes": [ + { + "id": 5, + "type": "CLIPTextEncode", + "pos": [ + 550, + 510 + ], + "size": { + "0": 400, + "1": 160 + }, + "flags": {}, + "order": 11, + "mode": 0, + "inputs": [ + { + "name": "clip", + "type": "CLIP", + "link": 6 + } + ], + "outputs": [ + { + "name": "CONDITIONING", + "type": "CONDITIONING", + "links": [ + 3 + ], + "slot_index": 0 + } + ], + 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