update reforge

.gitignore

@@ -17,6 +17,7 @@ __pycache__
 /ui-config.json
 /outputs
 /config.json
+/config_linux.json
 /log
 /webui.settings.bat
 /styles.csv

README.md

@@ -4,13 +4,25 @@ Stable Diffusion WebUI Forge/reForge is a platform on top of [Stable Diffusion W
 
 The name "Forge" is inspired from "Minecraft Forge". This project is aimed at becoming SD WebUI's Forge.
 
-# Important: Branches
+# Forge2/reForge2
 
-* main: Has all the possible upstream changes from A1111, new samplers/schedulers/sd options/etc and now, comfy backend updated to stream, so this deprecated the old forge backend.
-* dev: At this point (2025-07-20), it is the same as main branch.
-* dev2 and experimental: More unstable than dev, for now same as dev.
+You can read more on https://github.com/Panchovix/stable-diffusion-webui-reForge/discussions/377#discussioncomment-14010687. You can tell me here if you want to keep these branches here or do something like "reForge2".
+
+* newmain_newforge: Based on latest forge2 (gradio4, flux, etc) with some small changes that I plan to add very slowly. For now it has python 3.12 support, sage/flash attention support, all the samplers and schedulers from reForge (1), and recently, support for CFG++ samplers.
+* newforge_dendev: Based on latest ersatzForge fork which is based on forge2 (gradio4, flux, chroma, cosmos, longclip, and a ton more) from @DenOfEquity (https://github.com/DenOfEquity/ersatzForge). Many thanks Den for letting me to work on base on your fork on reForge. I will try to add new features from old reforge as well, like all the samplers.
+
+# Suggestion: For stability based on old forge, use forge classic
+
+reForge(1) is not really stable for all tasks sadly.
+
+So if you want to keep using old forge backend as it is, for sd1.x,2.x and SDXL, I suggest to use forge classic by @Haoming02 instead https://github.com/Haoming02/sd-webui-forge-classic, as at the moment that is the real succesor to old forge.
+
+Other branches:
+* main: Main branch with multiple changes and updates. But not stable as main-old branch.
+* dev: Similar to main but with more unstable changes. I.e. using comfy/ldm_patched backend for sd1.x and sdxl instead of A1111.
+* dev2: More unstable than dev, for now same as dev.
 * experimental: same as dev2 but with gradio 4.
-* main-old: Branch with old forge backend. Kept as backup in any case, but it won't receive updates.
+* main-old: Branch with old forge backend. Possibly the most stable and older one (2025-03)
 
 # Installing Forge/reForge
 
@@ -183,609 +195,15 @@ Since the UI got really cluttered with built it extensions, I have removed some
 * StableCascade-for-webUI-main: https://github.com/Panchovix/StableCascade-for-webUI-main.git
 * StableDiffusion3-for-webUI-main: https://github.com/Panchovix/StableDiffusion3-for-webUI-main.git
 
-# Original "Old" Forge (commit https://github.com/lllyasviel/stable-diffusion-webui-forge/commit/bfee03d8d9415a925616f40ede030fe7a51cbcfd) information.
-
-# Screenshots of Comparison (by Illyasviel)
-
-I tested with several devices, and this is a typical result from 8GB VRAM (3070ti laptop) with SDXL.
-
-**This is original WebUI:**
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/16893937-9ed9-4f8e-b960-70cd5d1e288f)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/7bbc16fe-64ef-49e2-a595-d91bb658bd94)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/de1747fd-47bc-482d-a5c6-0728dd475943)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/96e5e171-2d74-41ba-9dcc-11bf68be7e16)
-
-(average about 7.4GB/8GB, peak at about 7.9GB/8GB)
-
-**This is WebUI Forge/reForge:**
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/ca5e05ed-bd86-4ced-8662-f41034648e8c)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/3629ee36-4a99-4d9b-b371-12efb260a283)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/6d13ebb7-c30d-4aa8-9242-c0b5a1af8c95)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/c4f723c3-6ea7-4539-980b-0708ed2a69aa)
-
-(average and peak are all 6.3GB/8GB)
-
-You can see that Forge/reForge does not change WebUI results. Installing Forge/reForge is not a seed breaking change. 
-
-Forge/reForge can perfectly keep WebUI unchanged even for most complicated prompts like `fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]`.
-
-All your previous works still work in Forge/reForge!
-
-# Contribution
-
-# UNet Patcher
-
-The full name of the backend is `Stable Diffusion WebUI with Forge/reForge backend`, or for simplicity, the `Forge backend`. The API and python symbols are made similar to previous software only for reducing the learning cost of developers. Backend has a high percentage of Comfy code, about 80-85% or so.
-
-Now developing an extension is super simple. We finally have a patchable UNet.
-
-Below is using one single file with 80 lines of codes to support FreeU:
-
-`extensions-builtin/sd_forge_freeu/scripts/forge_freeu.py`
-
-```python
-import torch
-import gradio as gr
-from modules import scripts
-
-
-def Fourier_filter(x, threshold, scale):
-    x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
-    x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))
-    B, C, H, W = x_freq.shape
-    mask = torch.ones((B, C, H, W), device=x.device)
-    crow, ccol = H // 2, W //2
-    mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
-    x_freq = x_freq * mask
-    x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
-    x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real
-    return x_filtered.to(x.dtype)
-
-
-def set_freeu_v2_patch(model, b1, b2, s1, s2):
-    model_channels = model.model.model_config.unet_config["model_channels"]
-    scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
-
-    def output_block_patch(h, hsp, *args, **kwargs):
-        scale = scale_dict.get(h.shape[1], None)
-        if scale is not None:
-            hidden_mean = h.mean(1).unsqueeze(1)
-            B = hidden_mean.shape[0]
-            hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
-            hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
-            hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / \
-                          (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
-            h[:, :h.shape[1] // 2] = h[:, :h.shape[1] // 2] * ((scale[0] - 1) * hidden_mean + 1)
-            hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
-        return h, hsp
-
-    m = model.clone()
-    m.set_model_output_block_patch(output_block_patch)
-    return m
-
-
-class FreeUForForge(scripts.Script):
-    def title(self):
-        return "FreeU Integrated"
-
-    def show(self, is_img2img):
-        # make this extension visible in both txt2img and img2img tab.
-        return scripts.AlwaysVisible
-
-    def ui(self, *args, **kwargs):
-        with gr.Accordion(open=False, label=self.title()):
-            freeu_enabled = gr.Checkbox(label='Enabled', value=False)
-            freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01)
-            freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02)
-            freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99)
-            freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)
-
-        return freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2
-
-    def process_before_every_sampling(self, p, *script_args, **kwargs):
-        # This will be called before every sampling.
-        # If you use highres fix, this will be called twice.
-        
-        freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2 = script_args
-
-        if not freeu_enabled:
-            return
-
-        unet = p.sd_model.forge_objects.unet
-
-        unet = set_freeu_v2_patch(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2)
-
-        p.sd_model.forge_objects.unet = unet
-
-        # Below codes will add some logs to the texts below the image outputs on UI.
-        # The extra_generation_params does not influence results.
-        p.extra_generation_params.update(dict(
-            freeu_enabled=freeu_enabled,
-            freeu_b1=freeu_b1,
-            freeu_b2=freeu_b2,
-            freeu_s1=freeu_s1,
-            freeu_s2=freeu_s2,
-        ))
-
-        return
-```
-
-It looks like this:
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/277bac6e-5ea7-4bff-b71a-e55a60cfc03c)
-
-Similar components like HyperTile, KohyaHighResFix, SAG, can all be implemented within 100 lines of codes (see also the codes).
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/06472b03-b833-4816-ab47-70712ac024d3)
-
-ControlNets can finally be called by different extensions.
-
-Implementing Stable Video Diffusion and Zero123 are also super simple now (see also the codes). 
-
-*Stable Video Diffusion:*
-
-`extensions-builtin/sd_forge_svd/scripts/forge_svd.py`
-
-```python
-import torch
-import gradio as gr
-import os
-import pathlib
-
-from modules import script_callbacks
-from modules.paths import models_path
-from modules.ui_common import ToolButton, refresh_symbol
-from modules import shared
-
-from modules_forge.forge_util import numpy_to_pytorch, pytorch_to_numpy
-from ldm_patched.modules.sd import load_checkpoint_guess_config
-from ldm_patched.contrib.external_video_model import VideoLinearCFGGuidance, SVD_img2vid_Conditioning
-from ldm_patched.contrib.external import KSampler, VAEDecode
-
-
-opVideoLinearCFGGuidance = VideoLinearCFGGuidance()
-opSVD_img2vid_Conditioning = SVD_img2vid_Conditioning()
-opKSampler = KSampler()
-opVAEDecode = VAEDecode()
-
-svd_root = os.path.join(models_path, 'svd')
-os.makedirs(svd_root, exist_ok=True)
-svd_filenames = []
-
-
-def update_svd_filenames():
-    global svd_filenames
-    svd_filenames = [
-        pathlib.Path(x).name for x in
-        shared.walk_files(svd_root, allowed_extensions=[".pt", ".ckpt", ".safetensors"])
-    ]
-    return svd_filenames
-
-
-@torch.inference_mode()
-@torch.no_grad()
-def predict(filename, width, height, video_frames, motion_bucket_id, fps, augmentation_level,
-            sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler,
-            sampling_denoise, guidance_min_cfg, input_image):
-    filename = os.path.join(svd_root, filename)
-    model_raw, _, vae, clip_vision = \
-        load_checkpoint_guess_config(filename, output_vae=True, output_clip=False, output_clipvision=True)
-    model = opVideoLinearCFGGuidance.patch(model_raw, guidance_min_cfg)[0]
-    init_image = numpy_to_pytorch(input_image)
-    positive, negative, latent_image = opSVD_img2vid_Conditioning.encode(
-        clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level)
-    output_latent = opKSampler.sample(model, sampling_seed, sampling_steps, sampling_cfg,
-                                      sampling_sampler_name, sampling_scheduler, positive,
-                                      negative, latent_image, sampling_denoise)[0]
-    output_pixels = opVAEDecode.decode(vae, output_latent)[0]
-    outputs = pytorch_to_numpy(output_pixels)
-    return outputs
-
-
-def on_ui_tabs():
-    with gr.Blocks() as svd_block:
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(label='Input Image', source='upload', type='numpy', height=400)
-
-                with gr.Row():
-                    filename = gr.Dropdown(label="SVD Checkpoint Filename",
-                                           choices=svd_filenames,
-                                           value=svd_filenames[0] if len(svd_filenames) > 0 else None)
-                    refresh_button = ToolButton(value=refresh_symbol, tooltip="Refresh")
-                    refresh_button.click(
-                        fn=lambda: gr.update(choices=update_svd_filenames),
-                        inputs=[], outputs=filename)
-
-                width = gr.Slider(label='Width', minimum=16, maximum=8192, step=8, value=1024)
-                height = gr.Slider(label='Height', minimum=16, maximum=8192, step=8, value=576)
-                video_frames = gr.Slider(label='Video Frames', minimum=1, maximum=4096, step=1, value=14)
-                motion_bucket_id = gr.Slider(label='Motion Bucket Id', minimum=1, maximum=1023, step=1, value=127)
-                fps = gr.Slider(label='Fps', minimum=1, maximum=1024, step=1, value=6)
-                augmentation_level = gr.Slider(label='Augmentation Level', minimum=0.0, maximum=10.0, step=0.01,
-                                               value=0.0)
-                sampling_steps = gr.Slider(label='Sampling Steps', minimum=1, maximum=200, step=1, value=20)
-                sampling_cfg = gr.Slider(label='CFG Scale', minimum=0.0, maximum=50.0, step=0.1, value=2.5)
-                sampling_denoise = gr.Slider(label='Sampling Denoise', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
-                guidance_min_cfg = gr.Slider(label='Guidance Min Cfg', minimum=0.0, maximum=100.0, step=0.5, value=1.0)
-                sampling_sampler_name = gr.Radio(label='Sampler Name',
-                                                 choices=['euler', 'euler_ancestral', 'heun', 'heunpp2', 'dpm_2',
-                                                          'dpm_2_ancestral', 'lms', 'dpm_fast', 'dpm_adaptive',
-                                                          'dpmpp_2s_ancestral', 'dpmpp_sde', 'dpmpp_sde_gpu',
-                                                          'dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu',
-                                                          'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu', 'ddpm', 'lcm', 'ddim',
-                                                          'uni_pc', 'uni_pc_bh2'], value='euler')
-                sampling_scheduler = gr.Radio(label='Scheduler',
-                                              choices=['normal', 'karras', 'exponential', 'sgm_uniform', 'simple',
-                                                       'ddim_uniform'], value='karras')
-                sampling_seed = gr.Number(label='Seed', value=12345, precision=0)
-
-                generate_button = gr.Button(value="Generate")
-
-                ctrls = [filename, width, height, video_frames, motion_bucket_id, fps, augmentation_level,
-                         sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler,
-                         sampling_denoise, guidance_min_cfg, input_image]
-
-            with gr.Column():
-                output_gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain',
-                                            visible=True, height=1024, columns=4)
-
-        generate_button.click(predict, inputs=ctrls, outputs=[output_gallery])
-    return [(svd_block, "SVD", "svd")]
-
-
-update_svd_filenames()
-script_callbacks.on_ui_tabs(on_ui_tabs)
-```
-
-Note that although the above codes look like independent codes, they actually will automatically offload/unload any other models. For example, below is me opening webui, load SDXL, generated an image, then go to SVD, then generated image frames. You can see that the GPU memory is perfectly managed and the SDXL is moved to RAM then SVD is moved to GPU. 
-
-Note that this management is fully automatic. This makes writing extensions super simple.
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/de1a2d05-344a-44d7-bab8-9ecc0a58a8d3)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/14bcefcf-599f-42c3-bce9-3fd5e428dd91)
-
-Similarly, Zero123:
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/7685019c-7239-47fb-9cb5-2b7b33943285)
-
-### Write a simple ControlNet:
-
-Below is a simple extension to have a completely independent pass of ControlNet that never conflicts any other extensions:
-
-`extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py`
-
-Note that this extension is hidden because it is only for developers. To see it in UI, use `--show-controlnet-example`.
-
-The memory optimization in this example is fully automatic. You do not need to care about memory and inference speed, but you may want to cache objects if you wish.
-
-```python
-# Use --show-controlnet-example to see this extension.
-
-import cv2
-import gradio as gr
-import torch
-
-from modules import scripts
-from modules.shared_cmd_options import cmd_opts
-from modules_forge.shared import supported_preprocessors
-from modules.modelloader import load_file_from_url
-from ldm_patched.modules.controlnet import load_controlnet
-from modules_forge.controlnet import apply_controlnet_advanced
-from modules_forge.forge_util import numpy_to_pytorch
-from modules_forge.shared import controlnet_dir
-
-
-class ControlNetExampleForge(scripts.Script):
-    model = None
-
-    def title(self):
-        return "ControlNet Example for Developers"
-
-    def show(self, is_img2img):
-        # make this extension visible in both txt2img and img2img tab.
-        return scripts.AlwaysVisible
-
-    def ui(self, *args, **kwargs):
-        with gr.Accordion(open=False, label=self.title()):
-            gr.HTML('This is an example controlnet extension for developers.')
-            gr.HTML('You see this extension because you used --show-controlnet-example')
-            input_image = gr.Image(source='upload', type='numpy')
-            funny_slider = gr.Slider(label='This slider does nothing. It just shows you how to transfer parameters.',
-                                     minimum=0.0, maximum=1.0, value=0.5)
-
-        return input_image, funny_slider
-
-    def process(self, p, *script_args, **kwargs):
-        input_image, funny_slider = script_args
+# Last "Old" Forge commit (https://github.com/lllyasviel/stable-diffusion-webui-forge/commit/bfee03d8d9415a925616f40ede030fe7a51cbcfd) before forge2.
 
-        # This slider does nothing. It just shows you how to transfer parameters.
-        del funny_slider
-
-        if input_image is None:
-            return
-
-        # controlnet_canny_path = load_file_from_url(
-        #     url='https://huggingface.co/lllyasviel/sd_control_collection/resolve/main/sai_xl_canny_256lora.safetensors',
-        #     model_dir=model_dir,
-        #     file_name='sai_xl_canny_256lora.safetensors'
-        # )
-        controlnet_canny_path = load_file_from_url(
-            url='https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/control_v11p_sd15_canny_fp16.safetensors',
-            model_dir=controlnet_dir,
-            file_name='control_v11p_sd15_canny_fp16.safetensors'
-        )
-        print('The model [control_v11p_sd15_canny_fp16.safetensors] download finished.')
-
-        self.model = load_controlnet(controlnet_canny_path)
-        print('Controlnet loaded.')
-
-        return
-
-    def process_before_every_sampling(self, p, *script_args, **kwargs):
-        # This will be called before every sampling.
-        # If you use highres fix, this will be called twice.
-
-        input_image, funny_slider = script_args
-
-        if input_image is None or self.model is None:
-            return
-
-        B, C, H, W = kwargs['noise'].shape  # latent_shape
-        height = H * 8
-        width = W * 8
-        batch_size = p.batch_size
-
-        preprocessor = supported_preprocessors['canny']
-
-        # detect control at certain resolution
-        control_image = preprocessor(
-            input_image, resolution=512, slider_1=100, slider_2=200, slider_3=None)
-
-        # here we just use nearest neighbour to align input shape.
-        # You may want crop and resize, or crop and fill, or others.
-        control_image = cv2.resize(
-            control_image, (width, height), interpolation=cv2.INTER_NEAREST)
-
-        # Output preprocessor result. Now called every sampling. Cache in your own way.
-        p.extra_result_images.append(control_image)
-
-        print('Preprocessor Canny finished.')
-
-        control_image_bchw = numpy_to_pytorch(control_image).movedim(-1, 1)
-
-        unet = p.sd_model.forge_objects.unet
-
-        # Unet has input, middle, output blocks, and we can give different weights
-        # to each layers in all blocks.
-        # Below is an example for stronger control in middle block.
-        # This is helpful for some high-res fix passes. (p.is_hr_pass)
-        positive_advanced_weighting = {
-            'input': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2],
-            'middle': [1.0],
-            'output': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]
-        }
-        negative_advanced_weighting = {
-            'input': [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25],
-            'middle': [1.05],
-            'output': [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25]
-        }
-
-        # The advanced_frame_weighting is a weight applied to each image in a batch.
-        # The length of this list must be same with batch size
-        # For example, if batch size is 5, the below list is [0.2, 0.4, 0.6, 0.8, 1.0]
-        # If you view the 5 images as 5 frames in a video, this will lead to
-        # progressively stronger control over time.
-        advanced_frame_weighting = [float(i + 1) / float(batch_size) for i in range(batch_size)]
-
-        # The advanced_sigma_weighting allows you to dynamically compute control
-        # weights given diffusion timestep (sigma).
-        # For example below code can softly make beginning steps stronger than ending steps.
-        sigma_max = unet.model.model_sampling.sigma_max
-        sigma_min = unet.model.model_sampling.sigma_min
-        advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
-
-        # You can even input a tensor to mask all control injections
-        # The mask will be automatically resized during inference in UNet.
-        # The size should be B 1 H W and the H and W are not important
-        # because they will be resized automatically
-        advanced_mask_weighting = torch.ones(size=(1, 1, 512, 512))
-
-        # But in this simple example we do not use them
-        positive_advanced_weighting = None
-        negative_advanced_weighting = None
-        advanced_frame_weighting = None
-        advanced_sigma_weighting = None
-        advanced_mask_weighting = None
-
-        unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bchw=control_image_bchw,
-                                         strength=0.6, start_percent=0.0, end_percent=0.8,
-                                         positive_advanced_weighting=positive_advanced_weighting,
-                                         negative_advanced_weighting=negative_advanced_weighting,
-                                         advanced_frame_weighting=advanced_frame_weighting,
-                                         advanced_sigma_weighting=advanced_sigma_weighting,
-                                         advanced_mask_weighting=advanced_mask_weighting)
-
-        p.sd_model.forge_objects.unet = unet
-
-        # Below codes will add some logs to the texts below the image outputs on UI.
-        # The extra_generation_params does not influence results.
-        p.extra_generation_params.update(dict(
-            controlnet_info='You should see these texts below output images!',
-        ))
-
-        return
-
-
-# Use --show-controlnet-example to see this extension.
-if not cmd_opts.show_controlnet_example:
-    del ControlNetExampleForge
-
-```
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/822fa2fc-c9f4-4f58-8669-4b6680b91063)
-
-
-### Add a preprocessor
-
-Below is the full codes to add a normalbae preprocessor with perfect memory managements.
-
-You can use arbitrary independent extensions to add a preprocessor.
-
-Your preprocessor will be read by all other extensions using `modules_forge.shared.preprocessors`
-
-Below codes are in `extensions-builtin\forge_preprocessor_normalbae\scripts\preprocessor_normalbae.py`
-
-```python
-from modules_forge.supported_preprocessor import Preprocessor, PreprocessorParameter
-from modules_forge.shared import preprocessor_dir, add_supported_preprocessor
-from modules_forge.forge_util import resize_image_with_pad
-from modules.modelloader import load_file_from_url
-
-import types
-import torch
-import numpy as np
-
-from einops import rearrange
-from annotator.normalbae.models.NNET import NNET
-from annotator.normalbae import load_checkpoint
-from torchvision import transforms
-
-
-class PreprocessorNormalBae(Preprocessor):
-    def __init__(self):
-        super().__init__()
-        self.name = 'normalbae'
-        self.tags = ['NormalMap']
-        self.model_filename_filters = ['normal']
-        self.slider_resolution = PreprocessorParameter(
-            label='Resolution', minimum=128, maximum=2048, value=512, step=8, visible=True)
-        self.slider_1 = PreprocessorParameter(visible=False)
-        self.slider_2 = PreprocessorParameter(visible=False)
-        self.slider_3 = PreprocessorParameter(visible=False)
-        self.show_control_mode = True
-        self.do_not_need_model = False
-        self.sorting_priority = 100  # higher goes to top in the list
-
-    def load_model(self):
-        if self.model_patcher is not None:
-            return
-
-        model_path = load_file_from_url(
-            "https://huggingface.co/lllyasviel/Annotators/resolve/main/scannet.pt",
-            model_dir=preprocessor_dir)
-
-        args = types.SimpleNamespace()
-        args.mode = 'client'
-        args.architecture = 'BN'
-        args.pretrained = 'scannet'
-        args.sampling_ratio = 0.4
-        args.importance_ratio = 0.7
-        model = NNET(args)
-        model = load_checkpoint(model_path, model)
-        self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-        self.model_patcher = self.setup_model_patcher(model)
-
-    def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
-        input_image, remove_pad = resize_image_with_pad(input_image, resolution)
-
-        self.load_model()
-
-        self.move_all_model_patchers_to_gpu()
-
-        assert input_image.ndim == 3
-        image_normal = input_image
-
-        with torch.no_grad():
-            image_normal = self.send_tensor_to_model_device(torch.from_numpy(image_normal))
-            image_normal = image_normal / 255.0
-            image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
-            image_normal = self.norm(image_normal)
-
-            normal = self.model_patcher.model(image_normal)
-            normal = normal[0][-1][:, :3]
-            normal = ((normal + 1) * 0.5).clip(0, 1)
-
-            normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
-            normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)
-
-        return remove_pad(normal_image)
-
-
-add_supported_preprocessor(PreprocessorNormalBae())
-
-```
-
-# New features (that are not available in original WebUI)
-
-Thanks to Unet Patcher, many new things are possible now and supported in Forge/reForge, including SVD, Z123, masked Ip-adapter, masked controlnet, photomaker, etc.
-
-Masked Ip-Adapter
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/d26630f9-922d-4483-8bf9-f364dca5fd50)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/03580ef7-235c-4b03-9ca6-a27677a5a175)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/d9ed4a01-70d4-45b4-a6a7-2f765f158fae)
-
-Masked ControlNet
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/872d4785-60e4-4431-85c7-665c781dddaa)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/335a3b33-1ef8-46ff-a462-9f1b4f2c49fc)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/b3684a15-8895-414e-8188-487269dfcada)
-
-PhotoMaker
-
-(Note that photomaker is a special control that need you to add the trigger word "photomaker". Your prompt should be like "a photo of photomaker")
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/07b0b626-05b5-473b-9d69-3657624d59be)
-
-Marigold Depth
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/bdf54148-892d-410d-8ed9-70b4b121b6e7)
-
-# New Sampler (that is not in origin)
-
-    DDPM
-
-# Others samplers may be available, but after the schedulers merge, they shouldn't be needed.
-
-# About Extensions
-
-ControlNet and TiledVAE are integrated, and you should uninstall these two extensions:
-
-    sd-webui-controlnet
-    multidiffusion-upscaler-for-automatic1111
-
-Note that **AnimateDiff** is under construction by [continue-revolution](https://github.com/continue-revolution) at [sd-webui-animatediff forge/master branch](https://github.com/continue-revolution/sd-webui-animatediff/tree/forge/master) and [sd-forge-animatediff](https://github.com/continue-revolution/sd-forge-animatediff) (they are in sync). (continue-revolution original words: prompt travel, inf t2v, controlnet v2v have been proven to work well; motion lora, i2i batch still under construction and may be finished in a week")
+# Support
 
-Other extensions should work without problems, like:
+Some people have been asking how to donate or support the project, and I'm really grateful for that! I did this buymeacoffe link from some suggestions!
 
-    canvas-zoom
-    translations/localizations
-    Dynamic Prompts
-    Adetailer
-    Ultimate SD Upscale
-    Reactor
+[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/Panchovix)
 
-However, if newer extensions use Forge/reForge, their codes can be much shorter. 
 
-Usually if an old extension rework using Forge/reForge's unet patcher, 80% codes can be removed, especially when they need to call controlnet.
 
-# Support
 
-Some people have been asking how to donate or support the project, and I'm really grateful for that! I did this buymeacoffe link from some suggestions!
 
-[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/Panchovix)

ldm_patched/modules/model_management.py

@@ -238,6 +238,7 @@ except:
 
 XFORMERS_VERSION = ""
 XFORMERS_ENABLED_VAE = True
+XFORMERS_IS_AVAILABLE = True
 if args.disable_xformers:
     XFORMERS_IS_AVAILABLE = False
 else:
@@ -1088,6 +1089,8 @@ def flash_attention_enabled():
 def xformers_enabled():
     global directml_enabled
     global cpu_state
+    if sys.modules.get("xformers") is None:
+        return False
     if cpu_state != CPUState.GPU:
         return False
     if is_intel_xpu():

modules/devices.py

@@ -61,6 +61,8 @@ device_omnisr: torch.device = model_management.get_torch_device()  # will be man
 device_span: torch.device = model_management.get_torch_device()  # will be managed by memory management system
 device_compact: torch.device = model_management.get_torch_device()  # will be managed by memory management system
 device_codeformer: torch.device = model_management.get_torch_device()  # will be managed by memory management system
+device_rcan: torch.device = model_management.get_torch_device()  # will be managed by memory management system
+device_plksr: torch.device = model_management.get_torch_device()  # will be managed by memory management system
 dtype: torch.dtype = model_management.unet_dtype()
 dtype_vae: torch.dtype = model_management.vae_dtype()
 dtype_unet: torch.dtype = model_management.unet_dtype()

modules/modelloader.py

@@ -142,7 +142,7 @@ def load_spandrel_model(
     path: str | os.PathLike,
     *,
     device: str | torch.device | None,
-    prefer_half: bool = False,
+    prefer_half: bool = None,
     dtype: str | torch.dtype | None = None,
     expected_architecture: str | None = None,
 ) -> spandrel.ModelDescriptor:
@@ -157,18 +157,24 @@ def load_spandrel_model(
         logger.warning(
             f"Model {path!r} is not a {expected_architecture!r} model (got {arch.name!r})",
         )
-    half = False
+    float16 = False
+    bfloat16 = False
     if prefer_half:
         if model_descriptor.supports_half:
             model_descriptor.model.half()
-            half = True
+            float16 = True
+            logger.info("Model %s converted to float16 precision", path)
+        #elif model_descriptor.supports_bfloat16:
+        #    model_descriptor.model.bfloat16()
+        #    bfloat16 = True
+        #    logger.info("Model %s converted to bfloat16 precision", path)
         else:
             logger.info("Model %s does not support half precision, ignoring --half", path)
     if dtype:
         model_descriptor.model.to(dtype=dtype)
     model_descriptor.model.eval()
     logger.debug(
-        "Loaded %s from %s (device=%s, half=%s, dtype=%s)",
-        arch, path, device, half, dtype,
+        "Loaded %s from %s (device=%s, float16=%s, bfloat16=%s, dtype=%s)",
+        model_descriptor, path, device, float16, bfloat16, dtype,
     )
     return model_descriptor

modules/prompt_parser.py

@@ -383,7 +383,7 @@ re_attention = re.compile(r"""
 re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
 
 def parse_prompt_attention(text):
-    """
+    r"""
     Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
     Accepted tokens are:
       (abc) - increases attention to abc by a multiplier of 1.1

modules/upscaler.py

@@ -1,3 +1,4 @@
+import logging
 import os
 from abc import abstractmethod
 
@@ -5,7 +6,10 @@ import PIL
 from PIL import Image
 
 import modules.shared
-from modules import modelloader, shared
+from modules import modelloader, shared, devices
+
+
+logger = logging.getLogger(__name__)
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
@@ -25,8 +29,8 @@ class Upscaler:
 
     def __init__(self, create_dirs=False):
         self.mod_pad_h = None
-        self.tile_size = modules.shared.opts.ESRGAN_tile
-        self.tile_pad = modules.shared.opts.ESRGAN_tile_overlap
+        self.tile_size = None
+        self.tile_pad = None
         self.device = modules.shared.device
         self.img = None
         self.output = None
@@ -56,8 +60,16 @@ class Upscaler:
         dest_w = int((img.width * scale) // 8 * 8)
         dest_h = int((img.height * scale) // 8 * 8)
 
+        if shared.opts.unload_sd_during_upscale: #is highly possible this doesn't work
+            shared.sd_model.to(devices.cpu)
+            devices.torch_gc()
+            logger.info("Stable Diffusion Model weights are being unloaded from VRAM to RAM prior to upscale")
+
         for i in range(3):
-            if img.width >= dest_w and img.height >= dest_h and (i > 0 or scale != 1):
+            # Do not break the loop prior to do_upscale when img and dest are the same size.
+            # This is required for 1x scale post-processing models to produce an output image.
+            # FIXME: Only allow this behavior when a 1x scale model is selected.
+            if img.width > dest_w and img.height > dest_h and scale != 1:
                 break
 
             if shared.state.interrupted:
@@ -70,9 +82,16 @@ class Upscaler:
             if shape == (img.width, img.height):
                 break
 
+            if img.width >= dest_w and img.height >= dest_h:
+                break
+
         if img.width != dest_w or img.height != dest_h:
             img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)
 
+        if shared.opts.unload_sd_during_upscale:
+            shared.sd_model.to(shared.device)
+            logger.info("Stable Diffusion Model weights are being reloaded from RAM to VRAM after upscale")
+
         return img
 
     @abstractmethod