Merge pull request #57 from AUTOMATIC1111/master

merge from upstream
2026-09-19 09:14:35 +02:00 · 2023-03-25 08:47:00 -04:00
parent 7dc717cd55 aba5d639fb
commit f181885f0c
39 changed files with 763 additions and 800 deletions
@@ -6,23 +6,21 @@ from tqdm import trange
 import modules.scripts as scripts
 import gradio as gr

-from modules import processing, shared, sd_samplers, prompt_parser, sd_samplers_common
-from modules.processing import Processed
-from modules.shared import opts, cmd_opts, state
+from modules import processing, shared, sd_samplers, sd_samplers_common

 import torch
 import k_diffusion as K

-from PIL import Image
-from torch import autocast
-from einops import rearrange, repeat
-
-
 def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
    x = p.init_latent

    s_in = x.new_ones([x.shape[0]])
-    dnw = K.external.CompVisDenoiser(shared.sd_model)
+    if shared.sd_model.parameterization == "v":
+        dnw = K.external.CompVisVDenoiser(shared.sd_model)
+        skip = 1
+    else:
+        dnw = K.external.CompVisDenoiser(shared.sd_model)
+        skip = 0
    sigmas = dnw.get_sigmas(steps).flip(0)

    shared.state.sampling_steps = steps
@@ -37,7 +35,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
        image_conditioning = torch.cat([p.image_conditioning] * 2)
        cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}

-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
        t = dnw.sigma_to_t(sigma_in)

        eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
@@ -69,7 +67,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
    x = p.init_latent

    s_in = x.new_ones([x.shape[0]])
-    dnw = K.external.CompVisDenoiser(shared.sd_model)
+    if shared.sd_model.parameterization == "v":
+        dnw = K.external.CompVisVDenoiser(shared.sd_model)
+        skip = 1
+    else:
+        dnw = K.external.CompVisDenoiser(shared.sd_model)
+        skip = 0
    sigmas = dnw.get_sigmas(steps).flip(0)

    shared.state.sampling_steps = steps
@@ -84,7 +87,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
        image_conditioning = torch.cat([p.image_conditioning] * 2)
        cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}

-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]

        if i == 1:
            t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
@@ -125,7 +128,7 @@ class Script(scripts.Script):
    def show(self, is_img2img):
        return is_img2img

-    def ui(self, is_img2img):     
+    def ui(self, is_img2img):
        info = gr.Markdown('''
        * `CFG Scale` should be 2 or lower.
        ''')
@@ -213,4 +216,3 @@ class Script(scripts.Script):
        processed = processing.process_images(p)

        return processed
-
@@ -1,14 +1,10 @@
-import numpy as np
-from tqdm import trange
+import math

-import modules.scripts as scripts
 import gradio as gr
-
-from modules import processing, shared, sd_samplers, images
+import modules.scripts as scripts
+from modules import deepbooru, images, processing, shared
 from modules.processing import Processed
-from modules.sd_samplers import samplers
-from modules.shared import opts, cmd_opts, state
-from modules import deepbooru
+from modules.shared import opts, state


 class Script(scripts.Script):
@@ -20,39 +16,68 @@ class Script(scripts.Script):

    def ui(self, is_img2img):        
        loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
-        denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor"))
+        final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
+        denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
        append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")

-        return [loops, denoising_strength_change_factor, append_interrogation]
+        return [loops, final_denoising_strength, denoising_curve, append_interrogation]

-    def run(self, p, loops, denoising_strength_change_factor, append_interrogation):
+    def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
        processing.fix_seed(p)
        batch_count = p.n_iter
        p.extra_generation_params = {
-            "Denoising strength change factor": denoising_strength_change_factor,
+            "Final denoising strength": final_denoising_strength,
+            "Denoising curve": denoising_curve
        }

        p.batch_size = 1
        p.n_iter = 1

-        output_images, info = None, None
+        info = None
        initial_seed = None
        initial_info = None
+        initial_denoising_strength = p.denoising_strength

        grids = []
        all_images = []
        original_init_image = p.init_images
        original_prompt = p.prompt
+        original_inpainting_fill = p.inpainting_fill
        state.job_count = loops * batch_count

        initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]

-        for n in range(batch_count):
-            history = []
+        def calculate_denoising_strength(loop):
+            strength = initial_denoising_strength

+            if loops == 1:
+                return strength
+
+            progress = loop / (loops - 1)
+            match denoising_curve:
+                case "Aggressive":
+                    strength = math.sin((progress) * math.pi * 0.5)
+
+                case "Lazy":
+                    strength = 1 - math.cos((progress) * math.pi * 0.5)
+
+                case _:
+                    strength = progress
+
+            change = (final_denoising_strength - initial_denoising_strength) * strength
+            return initial_denoising_strength + change
+
+        history = []
+
+        for n in range(batch_count):
            # Reset to original init image at the start of each batch
            p.init_images = original_init_image

+            # Reset to original denoising strength
+            p.denoising_strength = initial_denoising_strength
+
+            last_image = None
+
            for i in range(loops):
                p.n_iter = 1
                p.batch_size = 1
@@ -72,26 +97,46 @@ class Script(scripts.Script):

                processed = processing.process_images(p)

+                # Generation cancelled.
+                if state.interrupted:
+                    break
+
                if initial_seed is None:
                    initial_seed = processed.seed
                    initial_info = processed.info

-                init_img = processed.images[0]
-
-                p.init_images = [init_img]
                p.seed = processed.seed + 1
-                p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
-                history.append(processed.images[0])
+                p.denoising_strength = calculate_denoising_strength(i + 1)
+                
+                if state.skipped:
+                    break

+                last_image = processed.images[0]
+                p.init_images = [last_image]
+                p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
+
+                if batch_count == 1:
+                    history.append(last_image)
+                    all_images.append(last_image)
+
+            if batch_count > 1 and not state.skipped and not state.interrupted:
+                history.append(last_image)
+                all_images.append(last_image)
+
+            p.inpainting_fill = original_inpainting_fill
+                
+            if state.interrupted:
+                    break
+
+        if len(history) > 1:
            grid = images.image_grid(history, rows=1)
            if opts.grid_save:
                images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)

-            grids.append(grid)
-            all_images += history
-
-        if opts.return_grid:
-            all_images = grids + all_images
+            if opts.return_grid:
+                grids.append(grid)
+                
+        all_images = grids + all_images

        processed = Processed(p, all_images, initial_seed, initial_info)

@@ -17,22 +17,24 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
    def ui(self):
        selected_tab = gr.State(value=0)

-        with gr.Tabs(elem_id="extras_resize_mode"):
-            with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
-                upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
+        with gr.Column():
+            with FormRow():
+                with gr.Tabs(elem_id="extras_resize_mode"):
+                    with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")

-            with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
-                with FormRow():
-                    upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
-                    upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
-                    upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+                    with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
+                        with FormRow():
+                            upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
+                            upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")

-        with FormRow():
-            extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+            with FormRow():
+                extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)

-        with FormRow():
-            extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
-            extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
+            with FormRow():
+                extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+                extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")

        tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
        tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
@@ -247,7 +247,7 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend

        state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"

-        processed: Processed = cell(x, y, z)
+        processed: Processed = cell(x, y, z, ix, iy, iz)

        if processed_result is None:
            # Use our first processed result object as a template container to hold our full results
@@ -559,8 +559,6 @@ class Script(scripts.Script):
        print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
        shared.total_tqdm.updateTotal(total_steps)

-        grid_infotext = [None]
-
        state.xyz_plot_x = AxisInfo(x_opt, xs)
        state.xyz_plot_y = AxisInfo(y_opt, ys)
        state.xyz_plot_z = AxisInfo(z_opt, zs)
@@ -589,7 +587,9 @@ class Script(scripts.Script):
            else:
                second_axes_processed = 'y'

-        def cell(x, y, z):
+        grid_infotext = [None] * (1 + len(zs))
+
+        def cell(x, y, z, ix, iy, iz):
            if shared.state.interrupted:
                return Processed(p, [], p.seed, "")

@@ -601,7 +601,9 @@ class Script(scripts.Script):

            res = process_images(pc)

-            if grid_infotext[0] is None:
+            # Sets subgrid infotexts
+            subgrid_index = 1 + iz
+            if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
                pc.extra_generation_params = copy(pc.extra_generation_params)
                pc.extra_generation_params['Script'] = self.title()

@@ -617,6 +619,12 @@ class Script(scripts.Script):
                    if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
                        pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])

+                grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
+
+            # Sets main grid infotext
+            if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0:
+                pc.extra_generation_params = copy(pc.extra_generation_params)
+
                if z_opt.label != 'Nothing':
                    pc.extra_generation_params["Z Type"] = z_opt.label
                    pc.extra_generation_params["Z Values"] = z_values
@@ -651,6 +659,9 @@ class Script(scripts.Script):

        z_count = len(zs)

+        # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
+        processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
+
        if not include_lone_images:
            # Don't need sub-images anymore, drop from list:
            processed.images = processed.images[:z_count+1]