Merge branch 'development' of github.com:deNULL/stable-diffusion into deNULL-development

Author: lstein

ldm/dream/server.py

@@ -1,11 +1,59 @@
+import argparse
 import json
 import base64
 import mimetypes
 import os
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
-from ldm.dream.pngwriter import PngWriter
+from ldm.dream.pngwriter import PngWriter, PromptFormatter
 from threading import Event
 
+def build_opt(post_data, seed, gfpgan_model_exists):
+    opt = argparse.Namespace()
+    setattr(opt, 'prompt', post_data['prompt'])
+    setattr(opt, 'init_img', post_data['initimg'])
+    setattr(opt, 'strength', float(post_data['strength']))
+    setattr(opt, 'iterations', int(post_data['iterations']))
+    setattr(opt, 'steps', int(post_data['steps']))
+    setattr(opt, 'width', int(post_data['width']))
+    setattr(opt, 'height', int(post_data['height']))
+    setattr(opt, 'seamless', 'seamless' in post_data)
+    setattr(opt, 'fit', 'fit' in post_data)
+    setattr(opt, 'mask', 'mask' in post_data)
+    setattr(opt, 'invert_mask', 'invert_mask' in post_data)
+    setattr(opt, 'cfg_scale', float(post_data['cfg_scale']))
+    setattr(opt, 'sampler_name', post_data['sampler_name'])
+    setattr(opt, 'gfpgan_strength', float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0)
+    setattr(opt, 'upscale', [int(post_data['upscale_level']), float(post_data['upscale_strength'])] if post_data['upscale_level'] != '' else None)
+    setattr(opt, 'progress_images', 'progress_images' in post_data)
+    setattr(opt, 'seed', seed if int(post_data['seed']) == -1 else int(post_data['seed']))
+    setattr(opt, 'variation_amount', float(post_data['variation_amount']) if int(post_data['seed']) != -1 else 0)
+    setattr(opt, 'with_variations', [])
+
+    broken = False
+    if int(post_data['seed']) != -1 and post_data['with_variations'] != '':
+        for part in post_data['with_variations'].split(','):
+            seed_and_weight = part.split(':')
+            if len(seed_and_weight) != 2:
+                print(f'could not parse with_variation part "{part}"')
+                broken = True
+                break
+            try:
+                seed = int(seed_and_weight[0])
+                weight = float(seed_and_weight[1])
+            except ValueError:
+                print(f'could not parse with_variation part "{part}"')
+                broken = True
+                break
+            opt.with_variations.append([seed, weight])
+    
+    if broken:
+        raise CanceledException
+
+    if len(opt.with_variations) == 0:
+        opt.with_variations = None
+
+    return opt
+
 class CanceledException(Exception):
     pass
 
@@ -81,57 +129,15 @@ def do_POST(self):
 
         content_length = int(self.headers['Content-Length'])
         post_data = json.loads(self.rfile.read(content_length))
-        prompt = post_data['prompt']
-        initimg = post_data['initimg']
-        strength = float(post_data['strength'])
-        iterations = int(post_data['iterations'])
-        steps = int(post_data['steps'])
-        width = int(post_data['width'])
-        height = int(post_data['height'])
-        fit      = 'fit' in post_data
-        seamless = 'seamless' in post_data
-        cfgscale = float(post_data['cfgscale'])
-        sampler_name  = post_data['sampler']
-        variation_amount = float(post_data['variation_amount'])
-        with_variations = post_data['with_variations']
-        gfpgan_strength = float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0
-        upscale_level    = post_data['upscale_level']
-        upscale_strength = post_data['upscale_strength']
-        upscale = [int(upscale_level),float(upscale_strength)] if upscale_level != '' else None
-        progress_images = 'progress_images' in post_data
-        seed = None if int(post_data['seed']) == -1 else int(post_data['seed'])
-
-        if with_variations != '':
-            parts = []
-            broken = False
-            for part in with_variations.split(','):
-                seed_and_weight = part.split(':')
-                if len(seed_and_weight) != 2:
-                    print(f'could not parse with_variation part "{part}"')
-                    broken = True
-                    break
-                try:
-                    vseed = int(seed_and_weight[0])
-                    vweight = float(seed_and_weight[1])
-                except ValueError:
-                    print(f'could not parse with_variation part "{part}"')
-                    broken = True
-                    break
-                parts.append([vseed, vweight])
-            if broken:
-                raise CanceledException
-            if len(parts) > 0:
-                with_variations = parts
-            else:
-                with_variations = None
+        opt = build_opt(post_data, self.model.seed, gfpgan_model_exists)
 
         self.canceled.clear()
-        print(f">> Request to generate with prompt: {prompt}")
+        print(f">> Request to generate with prompt: {opt.prompt}")
         # In order to handle upscaled images, the PngWriter needs to maintain state
         # across images generated by each call to prompt2img(), so we define it in
         # the outer scope of image_done()
         config = post_data.copy() # Shallow copy
-        config['initimg'] = config.pop('initimg_name','')
+        config['initimg'] = config.pop('initimg_name', '')
 
         images_generated = 0    # helps keep track of when upscaling is started
         images_upscaled = 0     # helps keep track of when upscaling is completed
@@ -144,9 +150,21 @@ def do_POST(self):
         # entry should not be inserted into the image list.
         def image_done(image, seed, upscaled=False):
             name = f'{prefix}.{seed}.png'
-            path = pngwriter.save_image_and_prompt_to_png(image, f'{prompt} -S{seed}', name)
-
-            config['seed'] = seed
+            iter_opt = argparse.Namespace(**vars(opt)) # copy
+            if opt.variation_amount > 0:
+                this_variation = [[seed, opt.variation_amount]]
+                if opt.with_variations is None:
+                    iter_opt.with_variations = this_variation
+                else:
+                    iter_opt.with_variations = opt.with_variations + this_variation
+                iter_opt.variation_amount = 0
+            elif opt.with_variations is None:
+                iter_opt.seed = seed
+            normalized_prompt = PromptFormatter(self.model, iter_opt).normalize_prompt()
+            path = pngwriter.save_image_and_prompt_to_png(image, f'{normalized_prompt} -S{iter_opt.seed}', name)
+
+            if int(config['seed']) == -1:
+                config['seed'] = seed
             # Append post_data to log, but only once!
             if not upscaled:
                 with open(os.path.join(self.outdir, "dream_web_log.txt"), "a") as log:
@@ -157,24 +175,24 @@ def image_done(image, seed, upscaled=False):
                 ) + '\n',"utf-8"))
 
             # control state of the "postprocessing..." message
-            upscaling_requested = upscale or gfpgan_strength>0
+            upscaling_requested = opt.upscale or opt.gfpgan_strength > 0
             nonlocal images_generated # NB: Is this bad python style? It is typical usage in a perl closure.
             nonlocal images_upscaled  # NB: Is this bad python style? It is typical usage in a perl closure.
             if upscaled:
                 images_upscaled += 1
             else:
-                images_generated +=1
+                images_generated += 1
             if upscaling_requested:
                 action = None
-                if images_generated >= iterations:
-                    if images_upscaled < iterations:
+                if images_generated >= opt.iterations:
+                    if images_upscaled < opt.iterations:
                         action = 'upscaling-started'
                     else:
                         action = 'upscaling-done'
                 if action:
-                    x = images_upscaled+1
+                    x = images_upscaled + 1
                     self.wfile.write(bytes(json.dumps(
-                        {'event':action,'processed_file_cnt':f'{x}/{iterations}'}
+                        {'event': action, 'processed_file_cnt': f'{x}/{opt.iterations}'}
                     ) + '\n',"utf-8"))
 
         step_writer = PngWriter(os.path.join(self.outdir, "intermediates"))
@@ -187,60 +205,31 @@ def image_progress(sample, step):
             # since rendering images is moderately expensive, only render every 5th image
             # and don't bother with the last one, since it'll render anyway
             nonlocal step_index
-            if progress_images and step % 5 == 0 and step < steps - 1:
+            if opt.progress_images and step % 5 == 0 and step < opt.steps - 1:
                 image = self.model.sample_to_image(sample)
-                name = f'{prefix}.{seed}.{step_index}.png'
-                metadata = f'{prompt} -S{seed} [intermediate]'
+                name = f'{prefix}.{opt.seed}.{step_index}.png'
+                metadata = f'{opt.prompt} -S{opt.seed} [intermediate]'
                 path = step_writer.save_image_and_prompt_to_png(image, metadata, name)
                 step_index += 1
             self.wfile.write(bytes(json.dumps(
                 {'event': 'step', 'step': step + 1, 'url': path}
             ) + '\n',"utf-8"))
 
         try:
-            if initimg is None:
+            if opt.init_img is None:
                 # Run txt2img
-                self.model.prompt2image(prompt,
-                                        iterations=iterations,
-                                        cfg_scale = cfgscale,
-                                        width = width,
-                                        height = height,
-                                        seed = seed,
-                                        steps = steps,
-                                        variation_amount = variation_amount,
-                                        with_variations = with_variations,
-                                        gfpgan_strength = gfpgan_strength,
-                                        upscale         = upscale,
-                                        sampler_name    = sampler_name,
-                                        seamless        = seamless,
-                                        step_callback=image_progress,
-                                        image_callback=image_done)
+                self.model.prompt2image(**vars(opt), step_callback=image_progress, image_callback=image_done)
             else:
                 # Decode initimg as base64 to temp file
                 with open("./img2img-tmp.png", "wb") as f:
-                    initimg = initimg.split(",")[1] # Ignore mime type
+                    initimg = opt.init_img.split(",")[1] # Ignore mime type
                     f.write(base64.b64decode(initimg))
+                opt1 = argparse.Namespace(**vars(opt))
+                opt1.init_img = "./img2img-tmp.png"
 
                 try:
                     # Run img2img
-                    self.model.prompt2image(prompt,
-                                            init_img = "./img2img-tmp.png",
-                                            strength = strength,
-                                            iterations = iterations,
-                                            cfg_scale  = cfgscale,
-                                            seed       = seed,
-                                            steps      = steps,
-                                            variation_amount = variation_amount,
-                                            with_variations = with_variations,
-                                            sampler_name    = sampler_name,
-                                            width      = width,
-                                            height     = height,
-                                            fit        = fit,
-                                            seamless   = seamless,
-                                            gfpgan_strength=gfpgan_strength,
-                                            upscale         = upscale,
-                                            step_callback=image_progress,
-                                            image_callback=image_done)
+                    self.model.prompt2image(**vars(opt1), step_callback=image_progress, image_callback=image_done)
                 finally:
                     # Remove the temp file
                     os.remove("./img2img-tmp.png")

static/dream_web/index.html

@@ -30,21 +30,21 @@ <h1>Stable Diffusion Dream Server</h1>
           <input value="1" type="number" id="iterations" name="iterations" size="4">
           <label for="steps">Steps:</label>
           <input value="50" type="number" id="steps" name="steps">
-          <label for="cfgscale">Cfg Scale:</label>
-          <input value="7.5" type="number" id="cfgscale" name="cfgscale" step="any">
-          <label for="sampler">Sampler:</label>
-          <select id="sampler" name="sampler" value="k_lms">
+          <label for="cfg_scale">Cfg Scale:</label>
+          <input value="7.5" type="number" id="cfg_scale" name="cfg_scale" step="any">
+          <label for="sampler_name">Sampler:</label>
+          <select id="sampler_name" name="sampler_name" value="k_lms">
             <option value="ddim">DDIM</option>
             <option value="plms">PLMS</option>
             <option value="k_lms" selected>KLMS</option>
             <option value="k_dpm_2">KDPM_2</option>
             <option value="k_dpm_2_a">KDPM_2A</option>
             <option value="k_euler">KEULER</option>
-	    <option value="k_euler_a">KEULER_A</option>
+	          <option value="k_euler_a">KEULER_A</option>
             <option value="k_heun">KHEUN</option>
           </select>
           <input type="checkbox" name="seamless" id="seamless">
-	  <label for="seamless">Seamless circular tiling</label>
+	        <label for="seamless">Seamless circular tiling</label>
           <br>
           <label title="Set to multiple of 64" for="width">Width:</label>
           <select id="width" name="width" value="512">

static/dream_web/index.js

@@ -9,7 +9,13 @@ function toBase64(file) {
 
 function appendOutput(src, seed, config) {
     let outputNode = document.createElement("figure");
-    let altText = seed.toString() + " | " + config.prompt;
+    
+    let variations = config.with_variations;
+    if (config.variation_amount > 0) {
+        variations = (variations ? variations + ',' : '') + seed + ':' + config.variation_amount;
+    }
+    let baseseed = (config.with_variations || config.variation_amount > 0) ? config.seed : seed;
+    let altText = baseseed + ' | ' + (variations ? variations + ' | ' : '') + config.prompt;
 
     // img needs width and height for lazy loading to work
     const figureContents = `
@@ -25,7 +31,7 @@ function appendOutput(src, seed, config) {
     `;
 
     outputNode.innerHTML = figureContents;
-    let figcaption = outputNode.querySelector('figcaption')
+    let figcaption = outputNode.querySelector('figcaption');
 
     // Reload image config
     figcaption.addEventListener('click', () => {
@@ -34,21 +40,11 @@ function appendOutput(src, seed, config) {
             if (k == 'initimg') { continue; }
             form.querySelector(`*[name=${k}]`).value = config[k];
         }
-        if (config.variation_amount > 0 || config.with_variations != '') {
-            document.querySelector("#seed").value = config.seed;
-        } else {
-            document.querySelector("#seed").value = seed;
-        }
 
-        if (config.variation_amount > 0) {
-            let oldVarAmt = document.querySelector("#variation_amount").value
-            let oldVariations = document.querySelector("#with_variations").value
-            let varSep = ''
-            document.querySelector("#variation_amount").value = 0;
-            if (document.querySelector("#with_variations").value != '') {
-                varSep = ","
-            }
-            document.querySelector("#with_variations").value = oldVariations + varSep + seed + ':' + config.variation_amount
+        document.querySelector("#seed").value = baseseed;
+        document.querySelector("#with_variations").value = variations || '';
+        if (document.querySelector("#variation_amount").value <= 0) {
+            document.querySelector("#variation_amount").value = 0.2;
         }
 
         saveFields(document.querySelector("#generate-form"));