diff --git a/CHANGELOG.md b/CHANGELOG.md
index b7fe8d620..cb18b1db7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,8 @@ Major refactor of [FLUX.1](https://blackforestlabs.ai/announcing-black-forest-la
- Full **ControlNet** support, better **LoRA** support, full **prompt attention** support,
- faster, more flexible loading, with additional quantization options, and more...
+Oh, as a sidenote, and also new auto **HDR** image create for SD and SDXL ;)
+
### Details
**Major refactor of FLUX.1 support:**
@@ -40,6 +42,10 @@ Major refactor of [FLUX.1](https://blackforestlabs.ai/announcing-black-forest-la
enable via *settings -> compute -> fused projections*
**Other improvements:**
+- **HDR** high-dynamic-range image create for SD and SDXL
+ create HDR images from in multiple exposures by latent-space modifications during generation
+ use via *scripts -> hdr*
+ *note*: save hdr saves image in standard 8bit/channel *and* 16bit/channel PNG format
- **taesd** configurable number of layers
can be used to speed-up taesd decoding by reducing number of ops
e.g. if generating 1024px image, reducing layers by 1 will result in preview being 512px
diff --git a/modules/processing_correction.py b/modules/processing_correction.py
index 6afaaa8d3..c52f30ab3 100644
--- a/modules/processing_correction.py
+++ b/modules/processing_correction.py
@@ -85,8 +85,7 @@ def correction(p, timestep, latent):
if timestep > 950 and p.hdr_clamp:
p.extra_generation_params["HDR clamp"] = f'{p.hdr_threshold}/{p.hdr_boundary}'
latent = soft_clamp_tensor(latent, threshold=p.hdr_threshold, boundary=p.hdr_boundary)
- if 500 < timestep < 800 and (p.hdr_brightness != 0 or p.hdr_color != 0 or p.hdr_tint_ratio != 0):
- p.extra_generation_params["HDR center"] = f'{p.hdr_color}/{p.hdr_brightness}'
+ if 600 < timestep < 900 and (p.hdr_color != 0 or p.hdr_tint_ratio != 0):
if p.hdr_brightness != 0:
latent[0:1] = center_tensor(latent[0:1], full_shift=float(p.hdr_mode), offset=2*p.hdr_brightness) # Brightness
p.extra_generation_params["HDR brightness"] = f'{p.hdr_brightness}'
@@ -98,6 +97,11 @@ def correction(p, timestep, latent):
if p.hdr_tint_ratio != 0:
latent = color_adjust(latent, p.hdr_color_picker, p.hdr_tint_ratio)
p.hdr_tint_ratio = 0
+ if timestep < 200 and (p.hdr_brightness != 0): # do it late so it doesn't change the composition
+ if p.hdr_brightness != 0:
+ latent[0:1] = center_tensor(latent[0:1], full_shift=float(p.hdr_mode), offset=2*p.hdr_brightness) # Brightness
+ p.extra_generation_params["HDR brightness"] = f'{p.hdr_brightness}'
+ p.hdr_brightness = 0
if timestep < 350 and p.hdr_sharpen != 0:
p.extra_generation_params["HDR sharpen"] = f'{p.hdr_sharpen}'
per_step_ratio = 2 ** (timestep / 250) * p.hdr_sharpen / 16
diff --git a/scripts/hdr.py b/scripts/hdr.py
new file mode 100644
index 000000000..d18f28a2b
--- /dev/null
+++ b/scripts/hdr.py
@@ -0,0 +1,101 @@
+import os
+import cv2
+import numpy as np
+import gradio as gr
+from PIL import Image
+import modules.scripts as scripts
+from modules import images, processing, shared
+from modules.processing import Processed
+from modules.shared import opts, state
+
+
+class Script(scripts.Script):
+ def title(self):
+ return "HDR"
+
+ def show(self, is_img2img):
+ return True
+
+ def ui(self, is_img2img):
+ with gr.Row():
+ gr.HTML(" High Dynamic Range
")
+ with gr.Row():
+ save_hdr = gr.Checkbox(label="Save HDR image", value=True)
+ hdr_range = gr.Slider(minimum=0, maximum=1, step=0.05, value=0.65, label='HDR range')
+ with gr.Row():
+ is_tonemap = gr.Checkbox(label="Enable tonemap", value=False)
+ gamma = gr.Slider(minimum=0, maximum=2, step=0.05, value=1.0, label='Gamma', visible=False)
+ with gr.Row():
+ scale = gr.Slider(minimum=0, maximum=2, step=0.05, value=1.0, label='Scale', visible=False)
+ saturation = gr.Slider(minimum=0, maximum=2, step=0.05, value=1.0, label='Saturation', visible=False)
+ is_tonemap.change(fn=self.change_tonemap, inputs=[is_tonemap], outputs=[gamma, scale, saturation])
+ return [hdr_range, save_hdr, is_tonemap, gamma, scale, saturation]
+
+ def change_tonemap(self, is_tonemap):
+ return [gr.update(visible=is_tonemap), gr.update(visible=is_tonemap), gr.update(visible=is_tonemap)]
+
+ def merge(self, imgs: list, is_tonemap: bool, gamma, scale, saturation):
+ shared.log.info(f'HDR: merge images={len(imgs)} tonemap={is_tonemap} sgamma={gamma} scale={scale} saturation={saturation}')
+ imgs_np = [np.asarray(img).astype(np.uint8) for img in imgs]
+
+ align = cv2.createAlignMTB()
+ align.process(imgs_np, imgs_np)
+
+ # cv2.createMergeRobertson()
+ # cv2.createMergeDebevec()
+ merge = cv2.createMergeMertens()
+ hdr = merge.process(imgs_np)
+
+ # cv2.createTonemapDrago()
+ # cv2.createTonemapReinhard()
+ if is_tonemap:
+ tonemap = cv2.createTonemapMantiuk(gamma, scale, saturation)
+ hdr = tonemap.process(hdr)
+
+ ldr = np.clip(hdr * 255, 0, 255).astype(np.uint8)
+ hdr = np.clip(hdr * 65535, 0, 65535).astype(np.uint16)
+ hdr = cv2.cvtColor(hdr, cv2.COLOR_BGR2RGB)
+ return hdr, ldr
+
+ def run(self, p, hdr_range, save_hdr, is_tonemap, gamma, scale, saturation): # pylint: disable=arguments-differ
+ if shared.sd_model_type != 'sd' and shared.sd_model_type != 'sdxl':
+ shared.log.error(f'HDR: incorrect base model: {shared.sd_model.__class__.__name__}')
+ return
+ p.extra_generation_params = {
+ "HDR range": hdr_range,
+ }
+ shared.log.info(f'HDR: range={hdr_range}')
+ processing.fix_seed(p)
+ imgs = []
+ info = ''
+ for i in range(3):
+ p.n_iter = 1
+ p.batch_size = 1
+ p.do_not_save_grid = True
+ p.hdr_brightness = (i - 1) * (2.0 * hdr_range)
+ p.hdr_mode = 0
+ p.task_args['seed'] = p.seed
+ processed: processing.Processed = processing.process_images(p)
+ imgs += processed.images
+ if i == 1:
+ info = processed.info
+ if state.interrupted:
+ break
+
+ if len(imgs) > 1:
+ hdr, ldr = self.merge(imgs, is_tonemap, gamma, scale, saturation)
+ img = Image.fromarray(ldr)
+ imgs.insert(0, img)
+ if save_hdr:
+ fn, _txt = images.save_image(img, shared.opts.outdir_save, "", p.seed, p.prompt, opts.grid_format, info=processed.info, p=p)
+ fn = os.path.splitext(fn)[0] + '-hdr.png'
+ shared.log.debug(f'Save: image="{fn}" type=PNG channels=16')
+ cv2.imwrite(fn, hdr)
+ # if opts.grid_save:
+ # images.save_image(grid, p.outpath_grids, "grid", p.seed, p.prompt, opts.grid_format, info=processed.info, grid=True, p=p)
+ if opts.return_grid:
+ grid = images.image_grid(imgs, rows=1)
+ imgs.append(grid)
+
+ processed = Processed(p, images_list=imgs, seed=p.seed, info=info)
+ return processed