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README.md

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+# IC-Light
+
+IC-Light is a project to manipulate the illumination of images.
+
+The name "IC-Light" stands for **"Imposing Consistent Light"** (we will briefly describe this at the end of this page).
+
+Currently, we release two types of models: text-conditioned relighting model and background-conditioned model. Both types take foreground images as inputs.
+
+**Note that "iclightai dot com" is a scam website. They have no relationship with us. Do not give scam websites money! This GitHub repo is the only official IC-Light.**
+
+# News
+
+[Alternative model](https://github.com/lllyasviel/IC-Light/discussions/109) for stronger illumination modifications.
+
+Some news about flux is [here](https://github.com/lllyasviel/IC-Light/discussions/98). (A fix [update](https://github.com/lllyasviel/IC-Light/discussions/98#discussioncomment-11370266) is added at Nov 25, more demos will be uploaded soon.)
+
+# Get Started
+
+Below script will run the text-conditioned relighting model:
+
+    git clone https://github.com/lllyasviel/IC-Light.git
+    cd IC-Light
+    conda create -n iclight python=3.10
+    conda activate iclight
+    pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
+    pip install -r requirements.txt
+    python gradio_demo.py
+
+Or, to use background-conditioned demo:
+
+    python gradio_demo_bg.py
+
+Model downloading is automatic.
+
+Note that the "gradio_demo.py" has an official [huggingFace Space here](https://huggingface.co/spaces/lllyasviel/IC-Light).
+
+# Screenshot
+
+### Text-Conditioned Model
+
+(Note that the "Lighting Preference" are just initial latents - eg., if the Lighting Preference is "Left" then initial latent is left white right black.)
+
 ---
-title: Myspace
-emoji: 🦀
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.13.1
-app_file: app.py
-pinned: false
+
+**Prompt: beautiful woman, detailed face, warm atmosphere, at home, bedroom**
+
+Lighting Preference: Left
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/87265483-aa26-4d2e-897d-b58892f5fdd7)
+
+---
+
+**Prompt: beautiful woman, detailed face, sunshine from window**
+
+Lighting Preference: Left
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/148c4a6d-82e7-4e3a-bf44-5c9a24538afc)
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+**beautiful woman, detailed face, neon, Wong Kar-wai, warm**
+
+Lighting Preference: Left
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/f53c9de2-534a-42f4-8272-6d16a021fc01)
+
+---
+
+**Prompt: beautiful woman, detailed face, sunshine, outdoor, warm atmosphere**
+
+Lighting Preference: Right
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/25d6ea24-a736-4a0b-b42d-700fe8b2101e)
+
+---
+
+**Prompt: beautiful woman, detailed face, sunshine, outdoor, warm atmosphere**
+
+Lighting Preference: Left
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/dd30387b-0490-46ee-b688-2191fb752e68)
+
+---
+
+**Prompt: beautiful woman, detailed face, sunshine from window**
+
+Lighting Preference: Right
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/6c9511ca-f97f-401a-85f3-92b4442000e3)
+
+---
+
+**Prompt: beautiful woman, detailed face, shadow from window**
+
+Lighting Preference: Left
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/e73701d5-890e-4b15-91ee-97f16ea3c450)
+
+---
+
+**Prompt: beautiful woman, detailed face, sunset over sea**
+
+Lighting Preference: Right
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/ff26ac3d-1b12-4447-b51f-73f7a5122a05)
+
+---
+
+**Prompt: handsome boy, detailed face, neon light, city**
+
+Lighting Preference: Left
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/d7795e02-46f7-444f-93e7-4d6460840437)
+
+---
+
+**Prompt: beautiful woman, detailed face, light and shadow**
+
+Lighting Preference: Left
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/706f70a8-d1a0-4e0b-b3ac-804e8e231c0f)
+
+(beautiful woman, detailed face, soft studio lighting)
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/fe0a72df-69d4-4e11-b661-fb8b84d0274d)
+
+---
+
+**Prompt: Buddha, detailed face, sci-fi RGB glowing, cyberpunk**
+
+Lighting Preference: Left
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/68d60c68-ce23-4902-939e-11629ccaf39a)
+
+---
+
+**Prompt: Buddha, detailed face, natural lighting**
+
+Lighting Preference: Left
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/1841d23d-0a0d-420b-a5ab-302da9c47c17)
+
+---
+
+**Prompt: toy, detailed face, shadow from window**
+
+Lighting Preference: Bottom
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/dcb97439-ea6b-483e-8e68-cf5d320368c7)
+
+---
+
+**Prompt: toy, detailed face, sunset over sea**
+
+Lighting Preference: Right
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/4f78b897-621d-4527-afa7-78d62c576100)
+
+---
+
+**Prompt: dog, magic lit, sci-fi RGB glowing, studio lighting**
+
+Lighting Preference: Bottom
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/1db9cac9-8d3f-4f40-82e2-e3b0cafd8613)
+
+---
+
+**Prompt: mysteriou human, warm atmosphere, warm atmosphere, at home, bedroom**
+
+Lighting Preference: Right
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/5d5aa7e5-8cbd-4e1f-9f27-2ecc3c30563a)
+
+---
+
+### Background-Conditioned Model
+
+The background conditioned model does not require careful prompting. One can just use simple prompts like "handsome man, cinematic lighting".
+
+---
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/0b2a889f-682b-4393-b1ec-2cabaa182010)
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/477ca348-bd47-46ff-81e6-0ffc3d05feb2)
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/5bc9d8d9-02cd-442e-a75c-193f115f2ad8)
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/a35e4c57-e199-40e2-893b-cb1c549612a9)
+
+---
+
+A more structured visualization:
+
+![r1](https://github.com/lllyasviel/IC-Light/assets/19834515/c1daafb5-ac8b-461c-bff2-899e4c671ba3)
+
+# Imposing Consistent Light
+
+In HDR space, illumination has a property that all light transports are independent. 
+
+As a result, the blending of appearances of different light sources is equivalent to the appearance with mixed light sources:
+
+![cons](https://github.com/lllyasviel/IC-Light/assets/19834515/27c67787-998e-469f-862f-047344e100cd)
+
+Using the above [light stage](https://www.pauldebevec.com/Research/LS/) as an example, the two images from the "appearance mixture" and "light source mixture" are consistent (mathematically equivalent in HDR space, ideally).
+
+We imposed such consistency (using MLPs in latent space) when training the relighting models.
+
+As a result, the model is able to produce highly consistent relight - **so** consistent that different relightings can even be merged as normal maps! Despite the fact that the models are latent diffusion.
+
+![r2](https://github.com/lllyasviel/IC-Light/assets/19834515/25068f6a-f945-4929-a3d6-e8a152472223)
+
+From left to right are inputs, model outputs relighting, devided shadow image, and merged normal maps. Note that the model is not trained with any normal map data. This normal estimation comes from the consistency of relighting.
+
+You can reproduce this experiment using this button (it is 4x slower because it relight image 4 times)
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/d9c37bf7-2136-446c-a9a5-5a341e4906de)
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/fcf5dd55-0309-4e8e-9721-d55931ea77f0)
+
+Below are bigger images (feel free to try yourself to get more results!)
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/12335218-186b-4c61-b43a-79aea9df8b21)
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/2daab276-fdfa-4b0c-abcb-e591f575598a)
+
+For reference, [geowizard](https://fuxiao0719.github.io/projects/geowizard/) (geowizard is a really great work!):
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/4ba1a96d-e218-42ab-83ae-a7918d56ee5f)
+
+And, [switchlight](https://arxiv.org/pdf/2402.18848) (switchlight is another great work!):
+
+![image](https://github.com/lllyasviel/IC-Light/assets/19834515/fbdd961f-0b26-45d2-802e-ffd734affab8)
+
+# Model Notes
+
+* **iclight_sd15_fc.safetensors** - The default relighting model, conditioned on text and foreground. You can use initial latent to influence the relighting.
+
+* **iclight_sd15_fcon.safetensors** - Same as "iclight_sd15_fc.safetensors" but trained with offset noise. Note that the default "iclight_sd15_fc.safetensors" outperform this model slightly in a user study. And this is the reason why the default model is the model without offset noise.
+
+* **iclight_sd15_fbc.safetensors** - Relighting model conditioned with text, foreground, and background.
+
+Also, note that the original [BRIA RMBG 1.4](https://huggingface.co/briaai/RMBG-1.4) is for non-commercial use. If you use IC-Light in commercial projects, replace it with other background replacer like [BiRefNet](https://github.com/ZhengPeng7/BiRefNet).
+
+# Cite
+
+    @Misc{iclight,
+      author = {Lvmin Zhang and Anyi Rao and Maneesh Agrawala},
+      title  = {IC-Light GitHub Page},
+      year   = {2024},
+    }
+
+# Related Work
+
+Also read ...
+
+[Total Relighting: Learning to Relight Portraits for Background Replacement](https://augmentedperception.github.io/total_relighting/)
+
+[Relightful Harmonization: Lighting-aware Portrait Background Replacement](https://arxiv.org/abs/2312.06886)
+
+[SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting](https://arxiv.org/pdf/2402.18848)

briarmbg.py

@@ -0,0 +1,462 @@
+# RMBG1.4 (diffusers implementation)
+# Found on huggingface space of several projects
+# Not sure which project is the source of this file
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub import PyTorchModelHubMixin
+
+
+class REBNCONV(nn.Module):
+    def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1):
+        super(REBNCONV, self).__init__()
+
+        self.conv_s1 = nn.Conv2d(
+            in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride
+        )
+        self.bn_s1 = nn.BatchNorm2d(out_ch)
+        self.relu_s1 = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        hx = x
+        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
+
+        return xout
+
+
+def _upsample_like(src, tar):
+    src = F.interpolate(src, size=tar.shape[2:], mode="bilinear")
+    return src
+
+
+### RSU-7 ###
+class RSU7(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
+        super(RSU7, self).__init__()
+
+        self.in_ch = in_ch
+        self.mid_ch = mid_ch
+        self.out_ch = out_ch
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)  ## 1 -> 1/2
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
+
+        self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
+
+        self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+
+        hx = x
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+        hx = self.pool5(hx5)
+
+        hx6 = self.rebnconv6(hx)
+
+        hx7 = self.rebnconv7(hx6)
+
+        hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
+        hx6dup = _upsample_like(hx6d, hx5)
+
+        hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
+        hx5dup = _upsample_like(hx5d, hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
+        hx4dup = _upsample_like(hx4d, hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
+
+        return hx1d + hxin
+
+
+### RSU-6 ###
+class RSU6(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU6, self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
+
+        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
+
+        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+
+        hx6 = self.rebnconv6(hx5)
+
+        hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
+        hx5dup = _upsample_like(hx5d, hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
+        hx4dup = _upsample_like(hx4d, hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
+
+        return hx1d + hxin
+
+
+### RSU-5 ###
+class RSU5(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU5, self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
+
+        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
+
+        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+
+        hx5 = self.rebnconv5(hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
+        hx4dup = _upsample_like(hx4d, hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
+
+        return hx1d + hxin
+
+
+### RSU-4 ###
+class RSU4(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4, self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
+
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
+
+        return hx1d + hxin
+
+
+### RSU-4F ###
+class RSU4F(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4F, self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
+
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx2 = self.rebnconv2(hx1)
+        hx3 = self.rebnconv3(hx2)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
+        hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
+        hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
+
+        return hx1d + hxin
+
+
+class myrebnconv(nn.Module):
+    def __init__(
+        self,
+        in_ch=3,
+        out_ch=1,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        dilation=1,
+        groups=1,
+    ):
+        super(myrebnconv, self).__init__()
+
+        self.conv = nn.Conv2d(
+            in_ch,
+            out_ch,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+        )
+        self.bn = nn.BatchNorm2d(out_ch)
+        self.rl = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        return self.rl(self.bn(self.conv(x)))
+
+
+class BriaRMBG(nn.Module, PyTorchModelHubMixin):
+    def __init__(self, config: dict = {"in_ch": 3, "out_ch": 1}):
+        super(BriaRMBG, self).__init__()
+        in_ch = config["in_ch"]
+        out_ch = config["out_ch"]
+        self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1)
+        self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage1 = RSU7(64, 32, 64)
+        self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage2 = RSU6(64, 32, 128)
+        self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage3 = RSU5(128, 64, 256)
+        self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage4 = RSU4(256, 128, 512)
+        self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage5 = RSU4F(512, 256, 512)
+        self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage6 = RSU4F(512, 256, 512)
+
+        # decoder
+        self.stage5d = RSU4F(1024, 256, 512)
+        self.stage4d = RSU4(1024, 128, 256)
+        self.stage3d = RSU5(512, 64, 128)
+        self.stage2d = RSU6(256, 32, 64)
+        self.stage1d = RSU7(128, 16, 64)
+
+        self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
+        self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
+        self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
+        self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
+        self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
+        self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)
+
+        # self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.conv_in(hx)
+        # hx = self.pool_in(hxin)
+
+        # stage 1
+        hx1 = self.stage1(hxin)
+        hx = self.pool12(hx1)
+
+        # stage 2
+        hx2 = self.stage2(hx)
+        hx = self.pool23(hx2)
+
+        # stage 3
+        hx3 = self.stage3(hx)
+        hx = self.pool34(hx3)
+
+        # stage 4
+        hx4 = self.stage4(hx)
+        hx = self.pool45(hx4)
+
+        # stage 5
+        hx5 = self.stage5(hx)
+        hx = self.pool56(hx5)
+
+        # stage 6
+        hx6 = self.stage6(hx)
+        hx6up = _upsample_like(hx6, hx5)
+
+        # -------------------- decoder --------------------
+        hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
+        hx5dup = _upsample_like(hx5d, hx4)
+
+        hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
+        hx4dup = _upsample_like(hx4d, hx3)
+
+        hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
+
+        # side output
+        d1 = self.side1(hx1d)
+        d1 = _upsample_like(d1, x)
+
+        d2 = self.side2(hx2d)
+        d2 = _upsample_like(d2, x)
+
+        d3 = self.side3(hx3d)
+        d3 = _upsample_like(d3, x)
+
+        d4 = self.side4(hx4d)
+        d4 = _upsample_like(d4, x)
+
+        d5 = self.side5(hx5d)
+        d5 = _upsample_like(d5, x)
+
+        d6 = self.side6(hx6)
+        d6 = _upsample_like(d6, x)
+
+        return [
+            F.sigmoid(d1),
+            F.sigmoid(d2),
+            F.sigmoid(d3),
+            F.sigmoid(d4),
+            F.sigmoid(d5),
+            F.sigmoid(d6),
+        ], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6]

db_examples.py

@@ -0,0 +1,217 @@
+foreground_conditioned_examples = [
+    [
+        "imgs/i1.webp",
+        "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
+        "Right Light",
+        512,
+        960,
+        12345,
+        "imgs/o1.png",
+    ],
+    [
+        "imgs/i1.webp",
+        "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
+        "Left Light",
+        512,
+        960,
+        50,
+        "imgs/o2.png",
+    ],
+    [
+        "imgs/i3.png",
+        "beautiful woman, detailed face, neon, Wong Kar-wai, warm",
+        "Left Light",
+        512,
+        768,
+        12345,
+        "imgs/o3.png",
+    ],
+    [
+        "imgs/i3.png",
+        "beautiful woman, detailed face, sunshine from window",
+        "Left Light",
+        512,
+        768,
+        12345,
+        "imgs/o4.png",
+    ],
+    [
+        "imgs/i5.png",
+        "beautiful woman, detailed face, warm atmosphere, at home, bedroom",
+        "Left Light",
+        512,
+        768,
+        123,
+        "imgs/o5.png",
+    ],
+    [
+        "imgs/i6.jpg",
+        "beautiful woman, detailed face, sunshine from window",
+        "Right Light",
+        512,
+        768,
+        42,
+        "imgs/o6.png",
+    ],
+    [
+        "imgs/i7.jpg",
+        "beautiful woman, detailed face, shadow from window",
+        "Left Light",
+        512,
+        768,
+        8888,
+        "imgs/o7.png",
+    ],
+    [
+        "imgs/i8.webp",
+        "beautiful woman, detailed face, sunset over sea",
+        "Right Light",
+        512,
+        640,
+        42,
+        "imgs/o8.png",
+    ],
+    [
+        "imgs/i9.png",
+        "handsome boy, detailed face, neon light, city",
+        "Left Light",
+        512,
+        640,
+        12345,
+        "imgs/o9.png",
+    ],
+    [
+        "imgs/i10.png",
+        "beautiful woman, detailed face, light and shadow",
+        "Left Light",
+        512,
+        960,
+        8888,
+        "imgs/o10.png",
+    ],
+    [
+        "imgs/i11.png",
+        "Buddha, detailed face, sci-fi RGB glowing, cyberpunk",
+        "Left Light",
+        512,
+        768,
+        8888,
+        "imgs/o11.png",
+    ],
+    [
+        "imgs/i11.png",
+        "Buddha, detailed face, natural lighting",
+        "Left Light",
+        512,
+        768,
+        12345,
+        "imgs/o12.png",
+    ],
+    [
+        "imgs/i13.png",
+        "toy, detailed face, shadow from window",
+        "Bottom Light",
+        512,
+        704,
+        12345,
+        "imgs/o13.png",
+    ],
+    [
+        "imgs/i14.png",
+        "toy, detailed face, sunset over sea",
+        "Right Light",
+        512,
+        704,
+        100,
+        "imgs/o14.png",
+    ],
+    [
+        "imgs/i15.png",
+        "dog, magic lit, sci-fi RGB glowing, studio lighting",
+        "Bottom Light",
+        512,
+        768,
+        12345,
+        "imgs/o15.png",
+    ],
+    [
+        "imgs/i16.png",
+        "mysteriou human, warm atmosphere, warm atmosphere, at home, bedroom",
+        "Right Light",
+        512,
+        768,
+        100,
+        "imgs/o16.png",
+    ],
+]
+
+bg_samples = [
+    'imgs/bgs/1.webp',
+    'imgs/bgs/2.webp',
+    'imgs/bgs/3.webp',
+    'imgs/bgs/4.webp',
+    'imgs/bgs/5.webp',
+    'imgs/bgs/6.webp',
+    'imgs/bgs/7.webp',
+    'imgs/bgs/8.webp',
+    'imgs/bgs/9.webp',
+    'imgs/bgs/10.webp',
+    'imgs/bgs/11.png',
+    'imgs/bgs/12.png',
+    'imgs/bgs/13.png',
+    'imgs/bgs/14.png',
+    'imgs/bgs/15.png',
+]
+
+background_conditioned_examples = [
+    [
+        "imgs/alter/i3.png",
+        "imgs/bgs/7.webp",
+        "beautiful woman, cinematic lighting",
+        "Use Background Image",
+        512,
+        768,
+        12345,
+        "imgs/alter/o1.png",
+    ],
+    [
+        "imgs/alter/i2.png",
+        "imgs/bgs/11.png",
+        "statue of an angel, natural lighting",
+        "Use Flipped Background Image",
+        512,
+        768,
+        12345,
+        "imgs/alter/o2.png",
+    ],
+    [
+        "imgs/alter/i1.jpeg",
+        "imgs/bgs/2.webp",
+        "beautiful woman, cinematic lighting",
+        "Use Background Image",
+        512,
+        768,
+        12345,
+        "imgs/alter/o3.png",
+    ],
+    [
+        "imgs/alter/i1.jpeg",
+        "imgs/bgs/3.webp",
+        "beautiful woman, cinematic lighting",
+        "Use Background Image",
+        512,
+        768,
+        12345,
+        "imgs/alter/o4.png",
+    ],
+    [
+        "imgs/alter/i6.webp",
+        "imgs/bgs/15.png",
+        "handsome man, cinematic lighting",
+        "Use Background Image",
+        512,
+        768,
+        12345,
+        "imgs/alter/o5.png",
+    ],
+]

gradio_demo.py

@@ -0,0 +1,433 @@
+import os
+import math
+import gradio as gr
+import numpy as np
+import torch
+import safetensors.torch as sf
+import db_examples
+
+from PIL import Image
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
+from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
+from diffusers.models.attention_processor import AttnProcessor2_0
+from transformers import CLIPTextModel, CLIPTokenizer
+from briarmbg import BriaRMBG
+from enum import Enum
+from torch.hub import download_url_to_file
+
+
+# 'stablediffusionapi/realistic-vision-v51'
+# 'runwayml/stable-diffusion-v1-5'
+sd15_name = 'stablediffusionapi/realistic-vision-v51'
+tokenizer = CLIPTokenizer.from_pretrained(sd15_name, subfolder="tokenizer")
+text_encoder = CLIPTextModel.from_pretrained(sd15_name, subfolder="text_encoder")
+vae = AutoencoderKL.from_pretrained(sd15_name, subfolder="vae")
+unet = UNet2DConditionModel.from_pretrained(sd15_name, subfolder="unet")
+rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
+
+# Change UNet
+
+with torch.no_grad():
+    new_conv_in = torch.nn.Conv2d(8, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
+    new_conv_in.weight.zero_()
+    new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
+    new_conv_in.bias = unet.conv_in.bias
+    unet.conv_in = new_conv_in
+
+unet_original_forward = unet.forward
+
+
+def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
+    c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
+    c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
+    new_sample = torch.cat([sample, c_concat], dim=1)
+    kwargs['cross_attention_kwargs'] = {}
+    return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)
+
+
+unet.forward = hooked_unet_forward
+
+# Load
+
+model_path = './models/iclight_sd15_fc.safetensors'
+
+if not os.path.exists(model_path):
+    download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fc.safetensors', dst=model_path)
+
+sd_offset = sf.load_file(model_path)
+sd_origin = unet.state_dict()
+keys = sd_origin.keys()
+sd_merged = {k: sd_origin[k] + sd_offset[k] for k in sd_origin.keys()}
+unet.load_state_dict(sd_merged, strict=True)
+del sd_offset, sd_origin, sd_merged, keys
+
+# Device
+
+device = torch.device('cuda')
+text_encoder = text_encoder.to(device=device, dtype=torch.float16)
+vae = vae.to(device=device, dtype=torch.bfloat16)
+unet = unet.to(device=device, dtype=torch.float16)
+rmbg = rmbg.to(device=device, dtype=torch.float32)
+
+# SDP
+
+unet.set_attn_processor(AttnProcessor2_0())
+vae.set_attn_processor(AttnProcessor2_0())
+
+# Samplers
+
+ddim_scheduler = DDIMScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    clip_sample=False,
+    set_alpha_to_one=False,
+    steps_offset=1,
+)
+
+euler_a_scheduler = EulerAncestralDiscreteScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    steps_offset=1
+)
+
+dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    algorithm_type="sde-dpmsolver++",
+    use_karras_sigmas=True,
+    steps_offset=1
+)
+
+# Pipelines
+
+t2i_pipe = StableDiffusionPipeline(
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    unet=unet,
+    scheduler=dpmpp_2m_sde_karras_scheduler,
+    safety_checker=None,
+    requires_safety_checker=False,
+    feature_extractor=None,
+    image_encoder=None
+)
+
+i2i_pipe = StableDiffusionImg2ImgPipeline(
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    unet=unet,
+    scheduler=dpmpp_2m_sde_karras_scheduler,
+    safety_checker=None,
+    requires_safety_checker=False,
+    feature_extractor=None,
+    image_encoder=None
+)
+
+
+@torch.inference_mode()
+def encode_prompt_inner(txt: str):
+    max_length = tokenizer.model_max_length
+    chunk_length = tokenizer.model_max_length - 2
+    id_start = tokenizer.bos_token_id
+    id_end = tokenizer.eos_token_id
+    id_pad = id_end
+
+    def pad(x, p, i):
+        return x[:i] if len(x) >= i else x + [p] * (i - len(x))
+
+    tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"]
+    chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)]
+    chunks = [pad(ck, id_pad, max_length) for ck in chunks]
+
+    token_ids = torch.tensor(chunks).to(device=device, dtype=torch.int64)
+    conds = text_encoder(token_ids).last_hidden_state
+
+    return conds
+
+
+@torch.inference_mode()
+def encode_prompt_pair(positive_prompt, negative_prompt):
+    c = encode_prompt_inner(positive_prompt)
+    uc = encode_prompt_inner(negative_prompt)
+
+    c_len = float(len(c))
+    uc_len = float(len(uc))
+    max_count = max(c_len, uc_len)
+    c_repeat = int(math.ceil(max_count / c_len))
+    uc_repeat = int(math.ceil(max_count / uc_len))
+    max_chunk = max(len(c), len(uc))
+
+    c = torch.cat([c] * c_repeat, dim=0)[:max_chunk]
+    uc = torch.cat([uc] * uc_repeat, dim=0)[:max_chunk]
+
+    c = torch.cat([p[None, ...] for p in c], dim=1)
+    uc = torch.cat([p[None, ...] for p in uc], dim=1)
+
+    return c, uc
+
+
+@torch.inference_mode()
+def pytorch2numpy(imgs, quant=True):
+    results = []
+    for x in imgs:
+        y = x.movedim(0, -1)
+
+        if quant:
+            y = y * 127.5 + 127.5
+            y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8)
+        else:
+            y = y * 0.5 + 0.5
+            y = y.detach().float().cpu().numpy().clip(0, 1).astype(np.float32)
+
+        results.append(y)
+    return results
+
+
+@torch.inference_mode()
+def numpy2pytorch(imgs):
+    h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0  # so that 127 must be strictly 0.0
+    h = h.movedim(-1, 1)
+    return h
+
+
+def resize_and_center_crop(image, target_width, target_height):
+    pil_image = Image.fromarray(image)
+    original_width, original_height = pil_image.size
+    scale_factor = max(target_width / original_width, target_height / original_height)
+    resized_width = int(round(original_width * scale_factor))
+    resized_height = int(round(original_height * scale_factor))
+    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
+    left = (resized_width - target_width) / 2
+    top = (resized_height - target_height) / 2
+    right = (resized_width + target_width) / 2
+    bottom = (resized_height + target_height) / 2
+    cropped_image = resized_image.crop((left, top, right, bottom))
+    return np.array(cropped_image)
+
+
+def resize_without_crop(image, target_width, target_height):
+    pil_image = Image.fromarray(image)
+    resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
+    return np.array(resized_image)
+
+
+@torch.inference_mode()
+def run_rmbg(img, sigma=0.0):
+    H, W, C = img.shape
+    assert C == 3
+    k = (256.0 / float(H * W)) ** 0.5
+    feed = resize_without_crop(img, int(64 * round(W * k)), int(64 * round(H * k)))
+    feed = numpy2pytorch([feed]).to(device=device, dtype=torch.float32)
+    alpha = rmbg(feed)[0][0]
+    alpha = torch.nn.functional.interpolate(alpha, size=(H, W), mode="bilinear")
+    alpha = alpha.movedim(1, -1)[0]
+    alpha = alpha.detach().float().cpu().numpy().clip(0, 1)
+    result = 127 + (img.astype(np.float32) - 127 + sigma) * alpha
+    return result.clip(0, 255).astype(np.uint8), alpha
+
+
+@torch.inference_mode()
+def process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
+    bg_source = BGSource(bg_source)
+    input_bg = None
+
+    if bg_source == BGSource.NONE:
+        pass
+    elif bg_source == BGSource.LEFT:
+        gradient = np.linspace(255, 0, image_width)
+        image = np.tile(gradient, (image_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.RIGHT:
+        gradient = np.linspace(0, 255, image_width)
+        image = np.tile(gradient, (image_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.TOP:
+        gradient = np.linspace(255, 0, image_height)[:, None]
+        image = np.tile(gradient, (1, image_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.BOTTOM:
+        gradient = np.linspace(0, 255, image_height)[:, None]
+        image = np.tile(gradient, (1, image_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    else:
+        raise 'Wrong initial latent!'
+
+    rng = torch.Generator(device=device).manual_seed(int(seed))
+
+    fg = resize_and_center_crop(input_fg, image_width, image_height)
+
+    concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
+    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+
+    conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt)
+
+    if input_bg is None:
+        latents = t2i_pipe(
+            prompt_embeds=conds,
+            negative_prompt_embeds=unconds,
+            width=image_width,
+            height=image_height,
+            num_inference_steps=steps,
+            num_images_per_prompt=num_samples,
+            generator=rng,
+            output_type='latent',
+            guidance_scale=cfg,
+            cross_attention_kwargs={'concat_conds': concat_conds},
+        ).images.to(vae.dtype) / vae.config.scaling_factor
+    else:
+        bg = resize_and_center_crop(input_bg, image_width, image_height)
+        bg_latent = numpy2pytorch([bg]).to(device=vae.device, dtype=vae.dtype)
+        bg_latent = vae.encode(bg_latent).latent_dist.mode() * vae.config.scaling_factor
+        latents = i2i_pipe(
+            image=bg_latent,
+            strength=lowres_denoise,
+            prompt_embeds=conds,
+            negative_prompt_embeds=unconds,
+            width=image_width,
+            height=image_height,
+            num_inference_steps=int(round(steps / lowres_denoise)),
+            num_images_per_prompt=num_samples,
+            generator=rng,
+            output_type='latent',
+            guidance_scale=cfg,
+            cross_attention_kwargs={'concat_conds': concat_conds},
+        ).images.to(vae.dtype) / vae.config.scaling_factor
+
+    pixels = vae.decode(latents).sample
+    pixels = pytorch2numpy(pixels)
+    pixels = [resize_without_crop(
+        image=p,
+        target_width=int(round(image_width * highres_scale / 64.0) * 64),
+        target_height=int(round(image_height * highres_scale / 64.0) * 64))
+    for p in pixels]
+
+    pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype)
+    latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor
+    latents = latents.to(device=unet.device, dtype=unet.dtype)
+
+    image_height, image_width = latents.shape[2] * 8, latents.shape[3] * 8
+
+    fg = resize_and_center_crop(input_fg, image_width, image_height)
+    concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
+    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+
+    latents = i2i_pipe(
+        image=latents,
+        strength=highres_denoise,
+        prompt_embeds=conds,
+        negative_prompt_embeds=unconds,
+        width=image_width,
+        height=image_height,
+        num_inference_steps=int(round(steps / highres_denoise)),
+        num_images_per_prompt=num_samples,
+        generator=rng,
+        output_type='latent',
+        guidance_scale=cfg,
+        cross_attention_kwargs={'concat_conds': concat_conds},
+    ).images.to(vae.dtype) / vae.config.scaling_factor
+
+    pixels = vae.decode(latents).sample
+
+    return pytorch2numpy(pixels)
+
+
+@torch.inference_mode()
+def process_relight(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
+    input_fg, matting = run_rmbg(input_fg)
+    results = process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source)
+    return input_fg, results
+
+
+quick_prompts = [
+    'sunshine from window',
+    'neon light, city',
+    'sunset over sea',
+    'golden time',
+    'sci-fi RGB glowing, cyberpunk',
+    'natural lighting',
+    'warm atmosphere, at home, bedroom',
+    'magic lit',
+    'evil, gothic, Yharnam',
+    'light and shadow',
+    'shadow from window',
+    'soft studio lighting',
+    'home atmosphere, cozy bedroom illumination',
+    'neon, Wong Kar-wai, warm'
+]
+quick_prompts = [[x] for x in quick_prompts]
+
+
+quick_subjects = [
+    'beautiful woman, detailed face',
+    'handsome man, detailed face',
+]
+quick_subjects = [[x] for x in quick_subjects]
+
+
+class BGSource(Enum):
+    NONE = "None"
+    LEFT = "Left Light"
+    RIGHT = "Right Light"
+    TOP = "Top Light"
+    BOTTOM = "Bottom Light"
+
+
+block = gr.Blocks().queue()
+with block:
+    with gr.Row():
+        gr.Markdown("## IC-Light (Relighting with Foreground Condition)")
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                input_fg = gr.Image(source='upload', type="numpy", label="Image", height=480)
+                output_bg = gr.Image(type="numpy", label="Preprocessed Foreground", height=480)
+            prompt = gr.Textbox(label="Prompt")
+            bg_source = gr.Radio(choices=[e.value for e in BGSource],
+                                 value=BGSource.NONE.value,
+                                 label="Lighting Preference (Initial Latent)", type='value')
+            example_quick_subjects = gr.Dataset(samples=quick_subjects, label='Subject Quick List', samples_per_page=1000, components=[prompt])
+            example_quick_prompts = gr.Dataset(samples=quick_prompts, label='Lighting Quick List', samples_per_page=1000, components=[prompt])
+            relight_button = gr.Button(value="Relight")
+
+            with gr.Group():
+                with gr.Row():
+                    num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+                    seed = gr.Number(label="Seed", value=12345, precision=0)
+
+                with gr.Row():
+                    image_width = gr.Slider(label="Image Width", minimum=256, maximum=1024, value=512, step=64)
+                    image_height = gr.Slider(label="Image Height", minimum=256, maximum=1024, value=640, step=64)
+
+            with gr.Accordion("Advanced options", open=False):
+                steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1)
+                cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=2, step=0.01)
+                lowres_denoise = gr.Slider(label="Lowres Denoise (for initial latent)", minimum=0.1, maximum=1.0, value=0.9, step=0.01)
+                highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=3.0, value=1.5, step=0.01)
+                highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=1.0, value=0.5, step=0.01)
+                a_prompt = gr.Textbox(label="Added Prompt", value='best quality')
+                n_prompt = gr.Textbox(label="Negative Prompt", value='lowres, bad anatomy, bad hands, cropped, worst quality')
+        with gr.Column():
+            result_gallery = gr.Gallery(height=832, object_fit='contain', label='Outputs')
+    with gr.Row():
+        dummy_image_for_outputs = gr.Image(visible=False, label='Result')
+        gr.Examples(
+            fn=lambda *args: ([args[-1]], None),
+            examples=db_examples.foreground_conditioned_examples,
+            inputs=[
+                input_fg, prompt, bg_source, image_width, image_height, seed, dummy_image_for_outputs
+            ],
+            outputs=[result_gallery, output_bg],
+            run_on_click=True, examples_per_page=1024
+        )
+    ips = [input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source]
+    relight_button.click(fn=process_relight, inputs=ips, outputs=[output_bg, result_gallery])
+    example_quick_prompts.click(lambda x, y: ', '.join(y.split(', ')[:2] + [x[0]]), inputs=[example_quick_prompts, prompt], outputs=prompt, show_progress=False, queue=False)
+    example_quick_subjects.click(lambda x: x[0], inputs=example_quick_subjects, outputs=prompt, show_progress=False, queue=False)
+
+
+block.launch(server_name='0.0.0.0')

gradio_demo_bg.py

@@ -0,0 +1,465 @@
+import os
+import math
+import gradio as gr
+import numpy as np
+import torch
+import safetensors.torch as sf
+import db_examples
+
+from PIL import Image
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
+from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
+from diffusers.models.attention_processor import AttnProcessor2_0
+from transformers import CLIPTextModel, CLIPTokenizer
+from briarmbg import BriaRMBG
+from enum import Enum
+from torch.hub import download_url_to_file
+
+
+# 'stablediffusionapi/realistic-vision-v51'
+# 'runwayml/stable-diffusion-v1-5'
+sd15_name = 'stablediffusionapi/realistic-vision-v51'
+tokenizer = CLIPTokenizer.from_pretrained(sd15_name, subfolder="tokenizer")
+text_encoder = CLIPTextModel.from_pretrained(sd15_name, subfolder="text_encoder")
+vae = AutoencoderKL.from_pretrained(sd15_name, subfolder="vae")
+unet = UNet2DConditionModel.from_pretrained(sd15_name, subfolder="unet")
+rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
+
+# Change UNet
+
+with torch.no_grad():
+    new_conv_in = torch.nn.Conv2d(12, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
+    new_conv_in.weight.zero_()
+    new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
+    new_conv_in.bias = unet.conv_in.bias
+    unet.conv_in = new_conv_in
+
+unet_original_forward = unet.forward
+
+
+def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
+    c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
+    c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
+    new_sample = torch.cat([sample, c_concat], dim=1)
+    kwargs['cross_attention_kwargs'] = {}
+    return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)
+
+
+unet.forward = hooked_unet_forward
+
+# Load
+
+model_path = './models/iclight_sd15_fbc.safetensors'
+
+if not os.path.exists(model_path):
+    download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fbc.safetensors', dst=model_path)
+
+sd_offset = sf.load_file(model_path)
+sd_origin = unet.state_dict()
+keys = sd_origin.keys()
+sd_merged = {k: sd_origin[k] + sd_offset[k] for k in sd_origin.keys()}
+unet.load_state_dict(sd_merged, strict=True)
+del sd_offset, sd_origin, sd_merged, keys
+
+# Device
+
+device = torch.device('cuda')
+text_encoder = text_encoder.to(device=device, dtype=torch.float16)
+vae = vae.to(device=device, dtype=torch.bfloat16)
+unet = unet.to(device=device, dtype=torch.float16)
+rmbg = rmbg.to(device=device, dtype=torch.float32)
+
+# SDP
+
+unet.set_attn_processor(AttnProcessor2_0())
+vae.set_attn_processor(AttnProcessor2_0())
+
+# Samplers
+
+ddim_scheduler = DDIMScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    clip_sample=False,
+    set_alpha_to_one=False,
+    steps_offset=1,
+)
+
+euler_a_scheduler = EulerAncestralDiscreteScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    steps_offset=1
+)
+
+dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    algorithm_type="sde-dpmsolver++",
+    use_karras_sigmas=True,
+    steps_offset=1
+)
+
+# Pipelines
+
+t2i_pipe = StableDiffusionPipeline(
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    unet=unet,
+    scheduler=dpmpp_2m_sde_karras_scheduler,
+    safety_checker=None,
+    requires_safety_checker=False,
+    feature_extractor=None,
+    image_encoder=None
+)
+
+i2i_pipe = StableDiffusionImg2ImgPipeline(
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    unet=unet,
+    scheduler=dpmpp_2m_sde_karras_scheduler,
+    safety_checker=None,
+    requires_safety_checker=False,
+    feature_extractor=None,
+    image_encoder=None
+)
+
+
+@torch.inference_mode()
+def encode_prompt_inner(txt: str):
+    max_length = tokenizer.model_max_length
+    chunk_length = tokenizer.model_max_length - 2
+    id_start = tokenizer.bos_token_id
+    id_end = tokenizer.eos_token_id
+    id_pad = id_end
+
+    def pad(x, p, i):
+        return x[:i] if len(x) >= i else x + [p] * (i - len(x))
+
+    tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"]
+    chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)]
+    chunks = [pad(ck, id_pad, max_length) for ck in chunks]
+
+    token_ids = torch.tensor(chunks).to(device=device, dtype=torch.int64)
+    conds = text_encoder(token_ids).last_hidden_state
+
+    return conds
+
+
+@torch.inference_mode()
+def encode_prompt_pair(positive_prompt, negative_prompt):
+    c = encode_prompt_inner(positive_prompt)
+    uc = encode_prompt_inner(negative_prompt)
+
+    c_len = float(len(c))
+    uc_len = float(len(uc))
+    max_count = max(c_len, uc_len)
+    c_repeat = int(math.ceil(max_count / c_len))
+    uc_repeat = int(math.ceil(max_count / uc_len))
+    max_chunk = max(len(c), len(uc))
+
+    c = torch.cat([c] * c_repeat, dim=0)[:max_chunk]
+    uc = torch.cat([uc] * uc_repeat, dim=0)[:max_chunk]
+
+    c = torch.cat([p[None, ...] for p in c], dim=1)
+    uc = torch.cat([p[None, ...] for p in uc], dim=1)
+
+    return c, uc
+
+
+@torch.inference_mode()
+def pytorch2numpy(imgs, quant=True):
+    results = []
+    for x in imgs:
+        y = x.movedim(0, -1)
+
+        if quant:
+            y = y * 127.5 + 127.5
+            y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8)
+        else:
+            y = y * 0.5 + 0.5
+            y = y.detach().float().cpu().numpy().clip(0, 1).astype(np.float32)
+
+        results.append(y)
+    return results
+
+
+@torch.inference_mode()
+def numpy2pytorch(imgs):
+    h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0  # so that 127 must be strictly 0.0
+    h = h.movedim(-1, 1)
+    return h
+
+
+def resize_and_center_crop(image, target_width, target_height):
+    pil_image = Image.fromarray(image)
+    original_width, original_height = pil_image.size
+    scale_factor = max(target_width / original_width, target_height / original_height)
+    resized_width = int(round(original_width * scale_factor))
+    resized_height = int(round(original_height * scale_factor))
+    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
+    left = (resized_width - target_width) / 2
+    top = (resized_height - target_height) / 2
+    right = (resized_width + target_width) / 2
+    bottom = (resized_height + target_height) / 2
+    cropped_image = resized_image.crop((left, top, right, bottom))
+    return np.array(cropped_image)
+
+
+def resize_without_crop(image, target_width, target_height):
+    pil_image = Image.fromarray(image)
+    resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
+    return np.array(resized_image)
+
+
+@torch.inference_mode()
+def run_rmbg(img, sigma=0.0):
+    H, W, C = img.shape
+    assert C == 3
+    k = (256.0 / float(H * W)) ** 0.5
+    feed = resize_without_crop(img, int(64 * round(W * k)), int(64 * round(H * k)))
+    feed = numpy2pytorch([feed]).to(device=device, dtype=torch.float32)
+    alpha = rmbg(feed)[0][0]
+    alpha = torch.nn.functional.interpolate(alpha, size=(H, W), mode="bilinear")
+    alpha = alpha.movedim(1, -1)[0]
+    alpha = alpha.detach().float().cpu().numpy().clip(0, 1)
+    result = 127 + (img.astype(np.float32) - 127 + sigma) * alpha
+    return result.clip(0, 255).astype(np.uint8), alpha
+
+
+@torch.inference_mode()
+def process(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source):
+    bg_source = BGSource(bg_source)
+
+    if bg_source == BGSource.UPLOAD:
+        pass
+    elif bg_source == BGSource.UPLOAD_FLIP:
+        input_bg = np.fliplr(input_bg)
+    elif bg_source == BGSource.GREY:
+        input_bg = np.zeros(shape=(image_height, image_width, 3), dtype=np.uint8) + 64
+    elif bg_source == BGSource.LEFT:
+        gradient = np.linspace(224, 32, image_width)
+        image = np.tile(gradient, (image_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.RIGHT:
+        gradient = np.linspace(32, 224, image_width)
+        image = np.tile(gradient, (image_height, 1))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.TOP:
+        gradient = np.linspace(224, 32, image_height)[:, None]
+        image = np.tile(gradient, (1, image_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    elif bg_source == BGSource.BOTTOM:
+        gradient = np.linspace(32, 224, image_height)[:, None]
+        image = np.tile(gradient, (1, image_width))
+        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+    else:
+        raise 'Wrong background source!'
+
+    rng = torch.Generator(device=device).manual_seed(seed)
+
+    fg = resize_and_center_crop(input_fg, image_width, image_height)
+    bg = resize_and_center_crop(input_bg, image_width, image_height)
+    concat_conds = numpy2pytorch([fg, bg]).to(device=vae.device, dtype=vae.dtype)
+    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+    concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1)
+
+    conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt)
+
+    latents = t2i_pipe(
+        prompt_embeds=conds,
+        negative_prompt_embeds=unconds,
+        width=image_width,
+        height=image_height,
+        num_inference_steps=steps,
+        num_images_per_prompt=num_samples,
+        generator=rng,
+        output_type='latent',
+        guidance_scale=cfg,
+        cross_attention_kwargs={'concat_conds': concat_conds},
+    ).images.to(vae.dtype) / vae.config.scaling_factor
+
+    pixels = vae.decode(latents).sample
+    pixels = pytorch2numpy(pixels)
+    pixels = [resize_without_crop(
+        image=p,
+        target_width=int(round(image_width * highres_scale / 64.0) * 64),
+        target_height=int(round(image_height * highres_scale / 64.0) * 64))
+    for p in pixels]
+
+    pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype)
+    latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor
+    latents = latents.to(device=unet.device, dtype=unet.dtype)
+
+    image_height, image_width = latents.shape[2] * 8, latents.shape[3] * 8
+    fg = resize_and_center_crop(input_fg, image_width, image_height)
+    bg = resize_and_center_crop(input_bg, image_width, image_height)
+    concat_conds = numpy2pytorch([fg, bg]).to(device=vae.device, dtype=vae.dtype)
+    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+    concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1)
+
+    latents = i2i_pipe(
+        image=latents,
+        strength=highres_denoise,
+        prompt_embeds=conds,
+        negative_prompt_embeds=unconds,
+        width=image_width,
+        height=image_height,
+        num_inference_steps=int(round(steps / highres_denoise)),
+        num_images_per_prompt=num_samples,
+        generator=rng,
+        output_type='latent',
+        guidance_scale=cfg,
+        cross_attention_kwargs={'concat_conds': concat_conds},
+    ).images.to(vae.dtype) / vae.config.scaling_factor
+
+    pixels = vae.decode(latents).sample
+    pixels = pytorch2numpy(pixels, quant=False)
+
+    return pixels, [fg, bg]
+
+
+@torch.inference_mode()
+def process_relight(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source):
+    input_fg, matting = run_rmbg(input_fg)
+    results, extra_images = process(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source)
+    results = [(x * 255.0).clip(0, 255).astype(np.uint8) for x in results]
+    return results + extra_images
+
+
+@torch.inference_mode()
+def process_normal(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source):
+    input_fg, matting = run_rmbg(input_fg, sigma=16)
+
+    print('left ...')
+    left = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.LEFT.value)[0][0]
+
+    print('right ...')
+    right = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.RIGHT.value)[0][0]
+
+    print('bottom ...')
+    bottom = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.BOTTOM.value)[0][0]
+
+    print('top ...')
+    top = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.TOP.value)[0][0]
+
+    inner_results = [left * 2.0 - 1.0, right * 2.0 - 1.0, bottom * 2.0 - 1.0, top * 2.0 - 1.0]
+
+    ambient = (left + right + bottom + top) / 4.0
+    h, w, _ = ambient.shape
+    matting = resize_and_center_crop((matting[..., 0] * 255.0).clip(0, 255).astype(np.uint8), w, h).astype(np.float32)[..., None] / 255.0
+
+    def safa_divide(a, b):
+        e = 1e-5
+        return ((a + e) / (b + e)) - 1.0
+
+    left = safa_divide(left, ambient)
+    right = safa_divide(right, ambient)
+    bottom = safa_divide(bottom, ambient)
+    top = safa_divide(top, ambient)
+
+    u = (right - left) * 0.5
+    v = (top - bottom) * 0.5
+
+    sigma = 10.0
+    u = np.mean(u, axis=2)
+    v = np.mean(v, axis=2)
+    h = (1.0 - u ** 2.0 - v ** 2.0).clip(0, 1e5) ** (0.5 * sigma)
+    z = np.zeros_like(h)
+
+    normal = np.stack([u, v, h], axis=2)
+    normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
+    normal = normal * matting + np.stack([z, z, 1 - z], axis=2) * (1 - matting)
+
+    results = [normal, left, right, bottom, top] + inner_results
+    results = [(x * 127.5 + 127.5).clip(0, 255).astype(np.uint8) for x in results]
+    return results
+
+
+quick_prompts = [
+    'beautiful woman',
+    'handsome man',
+    'beautiful woman, cinematic lighting',
+    'handsome man, cinematic lighting',
+    'beautiful woman, natural lighting',
+    'handsome man, natural lighting',
+    'beautiful woman, neo punk lighting, cyberpunk',
+    'handsome man, neo punk lighting, cyberpunk',
+]
+quick_prompts = [[x] for x in quick_prompts]
+
+
+class BGSource(Enum):
+    UPLOAD = "Use Background Image"
+    UPLOAD_FLIP = "Use Flipped Background Image"
+    LEFT = "Left Light"
+    RIGHT = "Right Light"
+    TOP = "Top Light"
+    BOTTOM = "Bottom Light"
+    GREY = "Ambient"
+
+
+block = gr.Blocks().queue()
+with block:
+    with gr.Row():
+        gr.Markdown("## IC-Light (Relighting with Foreground and Background Condition)")
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                input_fg = gr.Image(source='upload', type="numpy", label="Foreground", height=480)
+                input_bg = gr.Image(source='upload', type="numpy", label="Background", height=480)
+            prompt = gr.Textbox(label="Prompt")
+            bg_source = gr.Radio(choices=[e.value for e in BGSource],
+                                 value=BGSource.UPLOAD.value,
+                                 label="Background Source", type='value')
+
+            example_prompts = gr.Dataset(samples=quick_prompts, label='Prompt Quick List', components=[prompt])
+            bg_gallery = gr.Gallery(height=450, object_fit='contain', label='Background Quick List', value=db_examples.bg_samples, columns=5, allow_preview=False)
+            relight_button = gr.Button(value="Relight")
+
+            with gr.Group():
+                with gr.Row():
+                    num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+                    seed = gr.Number(label="Seed", value=12345, precision=0)
+                with gr.Row():
+                    image_width = gr.Slider(label="Image Width", minimum=256, maximum=1024, value=512, step=64)
+                    image_height = gr.Slider(label="Image Height", minimum=256, maximum=1024, value=640, step=64)
+
+            with gr.Accordion("Advanced options", open=False):
+                steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
+                cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=7.0, step=0.01)
+                highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=3.0, value=1.5, step=0.01)
+                highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=0.9, value=0.5, step=0.01)
+                a_prompt = gr.Textbox(label="Added Prompt", value='best quality')
+                n_prompt = gr.Textbox(label="Negative Prompt",
+                                      value='lowres, bad anatomy, bad hands, cropped, worst quality')
+                normal_button = gr.Button(value="Compute Normal (4x Slower)")
+        with gr.Column():
+            result_gallery = gr.Gallery(height=832, object_fit='contain', label='Outputs')
+    with gr.Row():
+        dummy_image_for_outputs = gr.Image(visible=False, label='Result')
+        gr.Examples(
+            fn=lambda *args: [args[-1]],
+            examples=db_examples.background_conditioned_examples,
+            inputs=[
+                input_fg, input_bg, prompt, bg_source, image_width, image_height, seed, dummy_image_for_outputs
+            ],
+            outputs=[result_gallery],
+            run_on_click=True, examples_per_page=1024
+        )
+    ips = [input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source]
+    relight_button.click(fn=process_relight, inputs=ips, outputs=[result_gallery])
+    normal_button.click(fn=process_normal, inputs=ips, outputs=[result_gallery])
+    example_prompts.click(lambda x: x[0], inputs=example_prompts, outputs=prompt, show_progress=False, queue=False)
+
+    def bg_gallery_selected(gal, evt: gr.SelectData):
+        return gal[evt.index]['name']
+
+    bg_gallery.select(bg_gallery_selected, inputs=bg_gallery, outputs=input_bg)
+
+
+block.launch(server_name='0.0.0.0')

requirements.txt

@@ -1,14 +1,10 @@
---extra-index-url https://download.pytorch.org/whl/cu124
-torch
-torchvision
-diffusers==0.31.0
-accelerate==1.1.1
-transformers==4.46.2
-sentencepiece==0.2.0
+diffusers==0.27.2
+transformers==4.36.2
 opencv-python
 safetensors
-pillow
+pillow==10.2.0
 einops
+torch
 peft
-pyzipper
-python-multipart==0.0.12
+gradio==3.41.2
+protobuf==3.20