added resize seeds and variation seeds features

modules/images.py

@@ -136,7 +136,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
     color_active = (0, 0, 0)
     color_inactive = (153, 153, 153)
 
-    pad_left = width * 3 // 4 if len(ver_texts) > 0 else 0
+    pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
 
     cols = im.width // width
     rows = im.height // height

modules/img2img.py

@@ -11,7 +11,7 @@ from modules.ui import plaintext_to_html
 import modules.images as images
 import modules.scripts
 
-def img2img(prompt: str, negative_prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, denoising_strength_change_factor: float, seed: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
+def img2img(prompt: str, negative_prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, denoising_strength_change_factor: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
     is_inpaint = mode == 1
     is_loopback = mode == 2
     is_upscale = mode == 3
@@ -34,6 +34,10 @@ def img2img(prompt: str, negative_prompt: str, init_img, init_img_with_mask, ste
         prompt=prompt,
         negative_prompt=negative_prompt,
         seed=seed,
+        subseed=subseed,
+        subseed_strength=subseed_strength,
+        seed_resize_from_h=seed_resize_from_h,
+        seed_resize_from_w=seed_resize_from_w,
         sampler_index=sampler_index,
         batch_size=batch_size,
         n_iter=n_iter,

modules/processing.py

@@ -29,7 +29,7 @@ def torch_gc():
 
 
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -37,6 +37,10 @@ class StableDiffusionProcessing:
         self.prompt_for_display: str = None
         self.negative_prompt: str = (negative_prompt or "")
         self.seed: int = seed
+        self.subseed: int = subseed
+        self.subseed_strength: float = subseed_strength
+        self.seed_resize_from_h: int = seed_resize_from_h
+        self.seed_resize_from_w: int = seed_resize_from_w
         self.sampler_index: int = sampler_index
         self.batch_size: int = batch_size
         self.n_iter: int = n_iter
@@ -84,23 +88,67 @@ class Processed:
 
         return json.dumps(obj)
 
+# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
+def slerp(val, low, high):
+    low_norm = low/torch.norm(low, dim=1, keepdim=True)
+    high_norm = high/torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm*high_norm).sum(1))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    return res
 
-def create_random_tensors(shape, seeds):
+
+def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
     xs = []
-    for seed in seeds:
-        torch.manual_seed(seed)
+    for i, seed in enumerate(seeds):
+        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
+
+        subnoise = None
+        if subseeds is not None:
+            subseed = 0 if i >= len(subseeds) else subseeds[i]
+            torch.manual_seed(subseed)
+            subnoise = torch.randn(noise_shape, device=shared.device)
 
         # randn results depend on device; gpu and cpu get different results for same seed;
         # the way I see it, it's better to do this on CPU, so that everyone gets same result;
-        # but the original script had it like this so I do not dare change it for now because
+        # but the original script had it like this, so I do not dare change it for now because
         # it will break everyone's seeds.
-        xs.append(torch.randn(shape, device=shared.device))
-    x = torch.stack(xs)
+        torch.manual_seed(seed)
+        noise = torch.randn(noise_shape, device=shared.device)
+
+        if subnoise is not None:
+            #noise = subnoise * subseed_strength + noise * (1 - subseed_strength)
+            noise = slerp(subseed_strength, noise, subnoise)
+
+        if noise_shape != shape:
+            #noise = torch.nn.functional.interpolate(noise.unsqueeze(1), size=shape[1:], mode="bilinear").squeeze()
+            # noise_shape = (64, 80)
+            # shape = (64, 72)
+
+            torch.manual_seed(seed)
+            x = torch.randn(shape, device=shared.device)
+            dx = (shape[2] - noise_shape[2]) // 2 # -4
+            dy = (shape[1] - noise_shape[1]) // 2
+            w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
+            h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
+            tx = 0 if dx < 0 else dx
+            ty = 0 if dy < 0 else dy
+            dx = max(-dx, 0)
+            dy = max(-dy, 0)
+
+            x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
+            noise = x
+
+
+
+        xs.append(noise)
+    x = torch.stack(xs).to(shared.device)
     return x
 
 
-def set_seed(seed):
-    return int(random.randrange(4294967294)) if seed is None or seed == -1 else seed
+def fix_seed(p):
+    p.seed = int(random.randrange(4294967294)) if p.seed is None or p.seed == -1 else p.seed
+    p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == -1 else p.subseed
 
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
@@ -111,7 +159,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     assert p.prompt is not None
     torch_gc()
 
-    seed = set_seed(p.seed)
+    fix_seed(p)
 
     os.makedirs(p.outpath_samples, exist_ok=True)
     os.makedirs(p.outpath_grids, exist_ok=True)
@@ -125,20 +173,31 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     else:
         all_prompts = p.batch_size * p.n_iter * [prompt]
 
-    if type(seed) == list:
-        all_seeds = seed
+    if type(p.seed) == list:
+        all_seeds = int(p.seed)
     else:
-        all_seeds = [int(seed + x) for x in range(len(all_prompts))]
+        all_seeds = [int(p.seed + x) for x in range(len(all_prompts))]
+
+    if type(p.subseed) == list:
+        all_subseeds = p.subseed
+    else:
+        all_subseeds = [int(p.subseed + x) for x in range(len(all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0):
+        index = position_in_batch + iteration * p.batch_size
+
         generation_params = {
             "Steps": p.steps,
             "Sampler": samplers[p.sampler_index].name,
             "CFG scale": p.cfg_scale,
-            "Seed": all_seeds[position_in_batch + iteration * p.batch_size],
+            "Seed": all_seeds[index],
             "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
+            "Size": f"{p.width}x{p.height}",
             "Batch size": (None if p.batch_size < 2 else p.batch_size),
             "Batch pos": (None if p.batch_size < 2 else position_in_batch),
+            "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
+            "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
+            "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         }
 
         if p.extra_generation_params is not None:
@@ -174,7 +233,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 comments += model_hijack.comments
 
             # we manually generate all input noises because each one should have a specific seed
-            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds)
+            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=all_subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
 
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
@@ -231,10 +290,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 output_images.insert(0, grid)
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", seed, all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
+                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
 
     torch_gc()
-    return Processed(p, output_images, seed, infotext())
+    return Processed(p, output_images, all_seeds[0], infotext())
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

modules/shared.py

@@ -62,7 +62,6 @@ class State:
     current_image = None
     current_image_sampling_step = 0
 
-
     def interrupt(self):
         self.interrupted = True
 

modules/txt2img.py

@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, *args):
+def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -14,6 +14,10 @@ def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, r
         prompt=prompt,
         negative_prompt=negative_prompt,
         seed=seed,
+        subseed=subseed,
+        subseed_strength=subseed_strength,
+        seed_resize_from_h=seed_resize_from_h,
+        seed_resize_from_w=seed_resize_from_w,
         sampler_index=sampler_index,
         batch_size=batch_size,
         n_iter=n_iter,

modules/ui.py

@@ -192,6 +192,40 @@ def visit(x, func, path=""):
         func(path + "/" + str(x.label), x)
 
 
+def create_seed_inputs():
+    with gr.Row():
+        seed = gr.Number(label='Seed', value=-1)
+        subseed = gr.Number(label='Variation seed', value=-1, visible=False)
+        seed_checkbox = gr.Checkbox(label="Extra", elem_id="subseed_show", value=False)
+
+    with gr.Row():
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, visible=False)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0, visible=False)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0, visible=False)
+
+    def change_visiblity(show):
+
+        return {
+            subseed: gr_show(show),
+            subseed_strength: gr_show(show),
+            seed_resize_from_h: gr_show(show),
+            seed_resize_from_w: gr_show(show),
+        }
+
+    seed_checkbox.change(
+        change_visiblity,
+        inputs=[seed_checkbox],
+        outputs=[
+            subseed,
+            subseed_strength,
+            seed_resize_from_h,
+            seed_resize_from_w
+        ]
+    )
+
+    return seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w
+
+
 def create_ui(txt2img, img2img, run_extras, run_pnginfo):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         with gr.Row():
@@ -220,7 +254,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
 
-                seed = gr.Number(label='Seed', value=-1)
+                seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w = create_seed_inputs()
 
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
@@ -260,6 +294,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     batch_size,
                     cfg_scale,
                     seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
                     height,
                     width,
                 ] + custom_inputs,
@@ -357,7 +392,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
 
-                seed = gr.Number(label='Seed', value=-1)
+                seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w = create_seed_inputs()
 
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
@@ -440,6 +475,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     denoising_strength,
                     denoising_strength_change_factor,
                     seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
                     height,
                     width,
                     resize_mode,