Merge branch 'AUTOMATIC1111:master' into master

3 files changed, 40 insertions, 24 deletions

diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index d438175c..88abc093 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -34,6 +34,9 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
         sigma_in = torch.cat([sigmas[i] * s_in] * 2)

         cond_in = torch.cat([uncond, cond])

 

+        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)]

         t = dnw.sigma_to_t(sigma_in)

 

@@ -78,6 +81,9 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)

         cond_in = torch.cat([uncond, cond])

 

+        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)]

 

         if i == 1:

@@ -194,7 +200,7 @@ class Script(scripts.Script):
             

             p.seed = p.seed + 1

             

-            return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning)

+            return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)

 

         p.sample = sample_extra

 

diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 1266be6f..1be22960 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -1,6 +1,7 @@
 import copy

 import math

 import os

+import random

 import sys

 import traceback

 import shlex

@@ -81,32 +82,34 @@ def cmdargs(line):
     return res

 

 

+def load_prompt_file(file):

+    if (file is None):

+        lines = []

+    else:

+        lines = [x.strip() for x in file.decode('utf8', errors='ignore').split("\n")]

+

+    return None, "\n".join(lines), gr.update(lines=7)

+

 class Script(scripts.Script):

     def title(self):

         return "Prompts from file or textbox"

 

     def ui(self, is_img2img):

-        # This checkbox would look nicer as two tabs, but there are two problems:

-        # 1) There is a bug in Gradio 3.3 that prevents visibility from working on Tabs

-        # 2) Even with Gradio 3.3.1, returning a control (like Tabs) that can't be used as input

-        #    causes a AttributeError: 'Tabs' object has no attribute 'preprocess' assert,

-        #    due to the way Script assumes all controls returned can be used as inputs.

-        # Therefore, there's no good way to use grouping components right now,

-        # so we will use a checkbox! :)

-        checkbox_txt = gr.Checkbox(label="Show Textbox", value=False)

-        file = gr.File(label="File with inputs", type='bytes')

-        prompt_txt = gr.TextArea(label="Prompts")

-        checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])

-        return [checkbox_txt, file, prompt_txt]

-

-    def on_show(self, checkbox_txt, file, prompt_txt):

-        return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]

-

-    def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):

-        if checkbox_txt:

-            lines = [x.strip() for x in prompt_txt.splitlines()]

-        else:

-            lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]

+        checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False)

+

+        prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1)

+        file = gr.File(label="Upload prompt inputs", type='bytes')

+

+        file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt])

+

+        # We start at one line. When the text changes, we jump to seven lines, or two lines if no \n.

+        # We don't shrink back to 1, because that causes the control to ignore [enter], and it may

+        # be unclear to the user that shift-enter is needed.

+        prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])

+        return [checkbox_iterate, file, prompt_txt]

+

+    def run(self, p, checkbox_iterate, file, prompt_txt: str):

+        lines = [x.strip() for x in prompt_txt.splitlines()]

         lines = [x for x in lines if len(x) > 0]

 

         p.do_not_save_grid = True

@@ -134,6 +137,9 @@ class Script(scripts.Script):
             jobs.append(args)

 

         print(f"Will process {len(lines)} lines in {job_count} jobs.")

+        if (checkbox_iterate and p.seed == -1):

+            p.seed = int(random.randrange(4294967294))

+

         state.job_count = job_count

 

         images = []

@@ -146,5 +152,9 @@ class Script(scripts.Script):
 

             proc = process_images(copy_p)

             images += proc.images

+            

+            if (checkbox_iterate):

+                p.seed = p.seed + (p.batch_size * p.n_iter)

+

 

-        return Processed(p, images, p.seed, "")

+        return Processed(p, images, p.seed, "")
\ No newline at end of file
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index eff0c942..f5255786 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -153,7 +153,6 @@ def str_permutations(x):
     """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""

     return x

 

-

 AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])

 AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])

 

@@ -178,6 +177,7 @@ axis_options = [
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),

     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),

     AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),

+    AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),

 ]