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VCM07_style.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8de463223c45d273ec77449808d47bce0b6987678ccc71cf3d413beba6ad3a17
+size 25515

VCM07_style2.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f1aec4732e93aa0943a30f1d8c8ec666abc9e94b0d0136d43c58740bd3d510f
+size 25515

prompt_blending.py

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+import modules.scripts as scripts
+import modules.prompt_parser as prompt_parser
+import itertools
+import torch
+
+
+def hijacked_get_learned_conditioning(model, prompts, steps):
+    global real_get_learned_conditioning
+
+    if not hasattr(model, '__hacked'):
+        real_model_func = model.get_learned_conditioning
+
+        def hijacked_model_func(texts):
+            weighted_prompts = list(map(lambda t: get_weighted_prompt((t, 1)), texts))
+            all_texts = []
+            for weighted_prompt in weighted_prompts:
+                for (prompt, weight) in weighted_prompt:
+                    all_texts.append(prompt)
+
+            if len(all_texts) > len(texts):
+                all_conds = real_model_func(all_texts)
+                offset = 0
+
+                conds = []
+
+                for weighted_prompt in weighted_prompts:
+                    c = torch.zeros_like(all_conds[offset])
+                    for (i, (prompt, weight)) in enumerate(weighted_prompt):
+                        c = torch.add(c, all_conds[i+offset], alpha=weight)
+                    conds.append(c)
+                    offset += len(weighted_prompt)
+                return conds
+            else:
+                return real_model_func(texts)
+
+        model.get_learned_conditioning = hijacked_model_func
+        model.__hacked = True
+
+    switched_prompts = list(map(lambda p: switch_syntax(p), prompts))
+    return real_get_learned_conditioning(model, switched_prompts, steps)
+
+
+real_get_learned_conditioning = hijacked_get_learned_conditioning  # no really, overriden below
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "Prompt Blending"
+
+    def show(self, is_img2img):
+        global real_get_learned_conditioning
+        if real_get_learned_conditioning == hijacked_get_learned_conditioning:
+            real_get_learned_conditioning = prompt_parser.get_learned_conditioning
+            prompt_parser.get_learned_conditioning = hijacked_get_learned_conditioning
+        return False
+
+    def ui(self, is_img2img):
+        return []
+
+    def run(self, p, seeds):
+        return
+
+
+OPEN = '{'
+CLOSE = '}'
+SEPARATE = '|'
+MARK = '@'
+REAL_MARK = ':'
+
+
+def combine(left, right):
+    return map(lambda p: (p[0][0] + p[1][0], p[0][1] * p[1][1]), itertools.product(left, right))
+
+
+def get_weighted_prompt(prompt_weight):
+    (prompt, full_weight) = prompt_weight
+    results = [('', full_weight)]
+    alts = []
+    start = 0
+    mark = -1
+    open_count = 0
+    first_open = 0
+    nested = False
+
+    for i, c in enumerate(prompt):
+        add_alt = False
+        do_combine = False
+        if c == OPEN:
+            open_count += 1
+            if open_count == 1:
+                first_open = i
+                results = list(combine(results, [(prompt[start:i], 1)]))
+                start = i + 1
+            else:
+                nested = True
+
+        if c == MARK and open_count == 1:
+            mark = i
+
+        if c == SEPARATE and open_count == 1:
+            add_alt = True
+
+        if c == CLOSE:
+            open_count -= 1
+            if open_count == 0:
+                add_alt = True
+                do_combine = True
+        if i == len(prompt) - 1 and open_count > 0:
+            add_alt = True
+            do_combine = True
+
+        if add_alt:
+            end = i
+            weight = 1
+            if mark != -1:
+                weight_str = prompt[mark + 1:i]
+                try:
+                    weight = float(weight_str)
+                    end = mark
+                except ValueError:
+                    print("warning, not a number:", weight_str)
+
+
+
+            alt = (prompt[start:end], weight)
+            alts += get_weighted_prompt(alt) if nested else [alt]
+            nested = False
+            mark = -1
+            start = i + 1
+
+        if do_combine:
+            if len(alts) <= 1:
+                alts = [(prompt[first_open:i + 1], 1)]
+
+            results = list(combine(results, alts))
+            alts = []
+
+    # rest of the prompt
+    results = list(combine(results, [(prompt[start:], 1)]))
+    weight_sum = sum(map(lambda r: r[1], results))
+    results = list(map(lambda p: (p[0], p[1] / weight_sum * full_weight), results))
+
+    return results
+
+
+def switch_syntax(prompt):
+    p = list(prompt)
+    stack = []
+    for i, c in enumerate(p):
+        if c == '{' or c == '[' or c == '(':
+            stack.append(c)
+
+        if len(stack) > 0:
+            if c == '}' or c == ']' or c == ')':
+                stack.pop()
+
+        if c == REAL_MARK and stack[-1] == '{':
+            p[i] = MARK
+
+    return "".join(p)
+
+# def test(p, w=1):
+#     print('')
+#     print(p)
+#     result = get_weighted_prompt((p, w))
+#     print(result)
+#     print(sum(map(lambda x: x[1], result)))
+#
+#
+# test("fantasy landscape")
+# test("fantasy {landscape|city}, dark")
+# test("fantasy {landscape|city}, {fire|ice} ")
+# test("fantasy {landscape|city}, {fire|ice}, {dark|light} ")
+# test("fantasy landscape, {{fire|lava}|ice}")
+# test("fantasy landscape, {{fire@4|lava@1}|ice@2}")
+# test("fantasy landscape, {{fire@error|lava@1}|ice@2}")
+# test("fantasy landscape, {{fire|lava}|ice@2")
+# test("fantasy landscape, {fire|lava} {cool} {ice,water}")
+# test("fantasy landscape, {fire|lava} {cool} {ice,water")
+# test("{lava|ice|water@5}")
+# test("{fire@4|lava@1}", 5)
+# test("{{fire@4|lava@1}|ice@2|water@5}")
+# test("{fire|[email protected]}")