Merge remote-tracking branch 'origin/master'

modules/extras.py

@@ -111,8 +111,9 @@ def run_pnginfo(image):
 
         items['exif comment'] = exif_comment
 
-        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif']:
-            del items[field]
+        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+                      'loop', 'background', 'timestamp', 'duration']:
+            items.pop(field, None)
 
 
     info = ''

modules/processing.py

@@ -188,7 +188,11 @@ def fix_seed(p):
 def process_images(p: StableDiffusionProcessing) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
-    assert p.prompt is not None
+    if type(p.prompt) == list:
+        assert(len(p.prompt) > 0)
+    else:
+        assert p.prompt is not None
+        
     devices.torch_gc()
 
     fix_seed(p)
@@ -265,6 +269,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
+            if (len(prompts) == 0):
+                break
+
             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
             #c = p.sd_model.get_learned_conditioning(prompts)
             uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)

script.js

@@ -76,6 +76,41 @@ function gradioApp(){
 
 global_progressbar = null
 
+function closeModal() {
+  gradioApp().getElementById("lightboxModal").style.display = "none";
+}
+
+function showModal(elem) {
+  gradioApp().getElementById("modalImage").src = elem.src
+  gradioApp().getElementById("lightboxModal").style.display = "block";
+}
+
+function showGalleryImage(){
+    setTimeout(function() {
+        fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
+        
+        if(fullImg_preview != null){
+            fullImg_preview.forEach(function function_name(e) {
+                if(e && e.parentElement.tagName == 'DIV'){
+                    e.style.cursor='pointer'
+
+                    elemfunc = function(elem){
+                        elem.onclick = function(){showModal(elem)};
+                    }
+                    elemfunc(e)
+                }
+            });
+        }
+
+    }, 100);
+}
+
+function galleryImageHandler(e){
+    if(e && e.parentElement.tagName == 'BUTTON'){
+        e.onclick = showGalleryImage;
+    }
+}
+
 function addTitles(root){
 	root.querySelectorAll('span, button, select').forEach(function(span){
 		tooltip = titles[span.textContent];
@@ -117,13 +152,18 @@ function addTitles(root){
                 img2img_preview.style.width = img2img_gallery.clientWidth + "px"
                 img2img_preview.style.height = img2img_gallery.clientHeight + "px"
             }
-
-
+		
             window.setTimeout(requestProgress, 500)
         });
         mutationObserver.observe( progressbar, { childList:true, subtree:true })
 	}
+	
+	fullImg_preview = gradioApp().querySelectorAll('img.w-full')
 
+	    if(fullImg_preview != null){
+		fullImg_preview.forEach(galleryImageHandler);
+	}
+	
 }
 
 document.addEventListener("DOMContentLoaded", function() {
@@ -131,6 +171,27 @@ document.addEventListener("DOMContentLoaded", function() {
         addTitles(gradioApp());
     });
     mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
+	
+    const modalFragment = document.createDocumentFragment();
+    const modal = document.createElement('div')
+    modal.onclick = closeModal;
+    
+    const modalClose = document.createElement('span')
+    modalClose.className = 'modalClose cursor';
+    modalClose.innerHTML = '×'
+    modalClose.onclick = closeModal;
+    modal.id = "lightboxModal";
+    modal.appendChild(modalClose)
+
+    const modalImage = document.createElement('img')
+    modalImage.id = 'modalImage';
+    modalImage.onclick = closeModal;
+    modal.appendChild(modalImage)
+
+    gradioApp().getRootNode().appendChild(modal)
+    
+    document.body.appendChild(modalFragment);
+	
 });
 
 function selected_gallery_index(){

scripts/prompts_from_file.py

@@ -13,28 +13,42 @@ from modules.shared import opts, cmd_opts, state
 
 class Script(scripts.Script):
     def title(self):
-        return "Prompts from file"
+        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]
 
-        return [file]
-
-    def run(self, p, data: bytes):
-        lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
+    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")]
         lines = [x for x in lines if len(x) > 0]
 
-        batch_count = math.ceil(len(lines) / p.batch_size)
-        print(f"Will process {len(lines) * p.n_iter} images in {batch_count * p.n_iter} batches.")
+        img_count = len(lines) * p.n_iter
+        batch_count = math.ceil(img_count / p.batch_size)
+        loop_count = math.ceil(batch_count / p.n_iter)
+        print(f"Will process {img_count} images in {batch_count} batches.")
 
         p.do_not_save_grid = True
 
         state.job_count = batch_count
 
         images = []
-        for batch_no in range(batch_count):
-            state.job = f"{batch_no + 1} out of {batch_count * p.n_iter}"
-            p.prompt = lines[batch_no*p.batch_size:(batch_no+1)*p.batch_size] * p.n_iter
+        for loop_no in range(loop_count):
+            state.job = f"{loop_no + 1} out of {loop_count}"
+            p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
             proc = process_images(p)
             images += proc.images
 

style.css

@@ -196,3 +196,40 @@ input[type="range"]{
   border-radius: 8px;
 }
 
+#lightboxModal{
+  display: none;
+  position: fixed;
+  z-index: 900;
+  padding-top: 100px;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  overflow: auto;
+  background-color: rgba(20, 20, 20, 0.95);
+}
+
+.modalClose {
+  color: white;
+  position: absolute;
+  top: 10px;
+  right: 25px;
+  font-size: 35px;
+  font-weight: bold;
+}
+
+.modalClose:hover,
+.modalClose:focus {
+  color: #999;
+  text-decoration: none;
+  cursor: pointer;
+}
+
+#modalImage {
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+    margin-top: auto;
+    width: auto;
+}
+