Revert "Merge pull request #4 from xraxra/master"

This reverts commit eae4ec82be, reversing
changes made to 5ae104bd99.

webui.py

@@ -386,7 +386,7 @@ def check_prompt_length(prompt, comments):
     comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
 
 
-def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, skip_grid, skip_save, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False, normalize_prompt_weights=True):
+def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, skip_grid, skip_save, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False):
     """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 prompt is not None
     torch_gc()
@@ -452,26 +452,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
                 uc = model.get_learned_conditioning(len(prompts) * [""])
             if isinstance(prompts, tuple):
                 prompts = list(prompts)
-                
-            # split the prompt if it has : for weighting
-            # TODO for speed it might help to have this occur when all_prompts filled??
-            subprompts,weights = split_weighted_subprompts(prompts[0])
-            # get total weight for normalizing, this gets weird if large negative values used
-            totalPromptWeight = sum(weights)
-
-            # sub-prompt weighting used if more than 1
-            if len(subprompts) > 1:
-                c = torch.zeros_like(uc) # i dont know if this is correct.. but it works
-                for i in range(0,len(subprompts)): # normalize each prompt and add it
-                    weight = weights[i]
-                    if normalize_prompt_weights:
-                        weight = weight / totalPromptWeight
-                    #print(f"{subprompts[i]} {weight*100.0}%")
-                    # note if alpha negative, it functions same as torch.sub
-                    c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight) 
-            else: # just behave like usual
-                c = model.get_learned_conditioning(prompts)
-                
+            c = model.get_learned_conditioning(prompts)
             shape = [opt_C, height // opt_f, width // opt_f]
 
             # we manually generate all input noises because each one should have a specific seed
@@ -537,7 +518,7 @@ Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_0
     return output_images, seed, info, stats
 
 
-def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, skip_grid: bool, skip_save: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, normalize_prompt_weights: bool):
+def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, skip_grid: bool, skip_save: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int):
     outpath = opt.outdir or "outputs/txt2img-samples"
     err = False
     seed = seed_to_int(seed)
@@ -575,8 +556,8 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, p
             width=width,
             height=height,
             prompt_matrix=prompt_matrix,
-            use_GFPGAN=use_GFPGAN,
-            normalize_prompt_weights=normalize_prompt_weights
+            use_GFPGAN=use_GFPGAN
+            
         )
 
         del sampler
@@ -650,7 +631,6 @@ txt2img_interface = gr.Interface(
         gr.Textbox(label="Seed ('random' to randomize)", lines=1, value="random"),
         gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
         gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
-        gr.Checkbox(label="Normalize Prompt Weights (ensure sum of weights add up to 1.0)", value=True),
     ],
     outputs=[
         gr.Gallery(label="Images"),
@@ -665,7 +645,7 @@ txt2img_interface = gr.Interface(
 )
 
 
-def img2img(prompt: str, init_img, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix, loopback: bool, skip_grid: bool, skip_save: bool,  n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, normalize_prompt_weights: bool):
+def img2img(prompt: str, init_img, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix, loopback: bool, skip_grid: bool, skip_save: bool,  n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
     outpath = opt.outdir or "outputs/img2img-samples"
     err = False
     seed = seed_to_int(seed)
@@ -778,8 +758,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, sampler_name: str, use_GFPGA
                 width=width,
                 height=height,
                 prompt_matrix=prompt_matrix,
-                use_GFPGAN=use_GFPGAN,
-                normalize_prompt_weights=normalize_prompt_weights
+                use_GFPGAN=use_GFPGAN
             )
         
         del sampler
@@ -818,8 +797,7 @@ img2img_interface = gr.Interface(
         gr.Textbox(label="Seed ('random' to randomize)", lines=1, value="random"),
         gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
         gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
-        gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize"),
-        gr.Checkbox(label="Normalize Prompt Weights (ensure sum of weights add up to 1.0)", value=True),
+        gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
     ],
     outputs=[
         gr.Gallery(),
@@ -838,52 +816,6 @@ interfaces = [
     (img2img_interface, "img2img")
 ]
 
-# grabs all text up to the first occurrence of ':' as sub-prompt
-# takes the value following ':' as weight
-# if ':' has no value defined, defaults to 1.0
-# repeats until no text remaining
-# TODO this could probably be done with less code
-def split_weighted_subprompts(text):
-    print(text)
-    remaining = len(text)
-    prompts = []
-    weights = []
-    while remaining > 0:
-        if ":" in text:
-            idx = text.index(":") # first occurrence from start
-            # grab up to index as sub-prompt
-            prompt = text[:idx]
-            remaining -= idx
-            # remove from main text
-            text = text[idx+1:]
-            # find value for weight, assume it is followed by a space or comma
-            idx = len(text) # default is read to end of text
-            if " " in text:
-                idx = min(idx,text.index(" ")) # want the closer idx
-            if "," in text:
-                idx = min(idx,text.index(",")) # want the closer idx
-            if idx != 0:
-                try:
-                    weight = float(text[:idx])
-                except: # couldn't treat as float
-                    print(f"Warning: '{text[:idx]}' is not a value, are you missing a space or comma after a value?")
-                    weight = 1.0
-            else: # no value found
-                weight = 1.0
-            # remove from main text
-            remaining -= idx
-            text = text[idx+1:]
-            # append the sub-prompt and its weight
-            prompts.append(prompt)
-            weights.append(weight)
-        else: # no : found
-            if len(text) > 0: # there is still text though
-                # take remainder as weight 1
-                prompts.append(text)
-                weights.append(1.0)
-            remaining = 0
-    return prompts, weights
-
 def run_GFPGAN(image, strength):
     image = image.convert("RGB")