refactor: rename DavchaLoadLLM and DavchaLLM classes to OldDavchaLoadLLM and OldDavchaLLM; add OldDavchaPromptEnricher class with schema and execution logic
This commit is contained in:
David Chatel
@@ -737,7 +737,7 @@ if os.path.isdir(gguf_folder):
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else:
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gguf_files = []
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class DavchaLoadLLM:
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class OldDavchaLoadLLM:
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@classmethod
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def INPUT_TYPES(s):
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return {'required':{
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@@ -796,7 +796,7 @@ class DavchaLLMAdvanced:
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llm_result = model.create_chat_completion(msgs, **generate_kwargs)
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return (llm_result['choices'][0]['message']['content'].strip(),)
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class DavchaLLM:
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class OldDavchaLLM:
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@classmethod
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def INPUT_TYPES(s):
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return {'required':{
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@@ -1559,6 +1559,8 @@ class DavchaQwenVL3(io.ComfyNode):
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{"role": "system", "content": system},
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{"role": "user", "content": prompt}
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]
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response_format = json.loads(response_format) if response_format.strip() != "" else None
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result = llm.create_chat_completion(
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seed=seed,
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@@ -1574,7 +1576,201 @@ class DavchaQwenVL3(io.ComfyNode):
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return io.NodeOutput(result['choices'][0]['message']['content'])
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class OldDavchaPromptEnricher(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="OldDavchaPromptEnricher",
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category="davcha/llm",
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inputs=[
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io.Custom("DavchaQwenVL3Model").Input("llm"),
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io.String.Input("system", multiline=True, dynamic_prompts=False),
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io.String.Input("prompt", multiline=True, dynamic_prompts=False),
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io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
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io.Int.Input("max_tokens", min=1, default=512),
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io.Float.Input("temperature", min=0.0, max=2.0, default=0.9, step=0.01),
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io.Float.Input("top_p", min=0.0, max=1.0, default=0.95, step=0.01),
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io.Int.Input("top_k", min=0, max=100, default=40),
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io.Float.Input("repeat_penalty", min=0.5, max=2.0, default=1.2, step=0.01),
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io.Boolean.Input("force_json_output", default=True),
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io.Image.Input("images", optional=True),
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],
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outputs=[
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io.String.Output()
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]
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)
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@classmethod
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def execute(cls, llm, system, prompt, seed, max_tokens, temperature, top_p, top_k, repeat_penalty, force_json_output, images=None):
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if not re.search(r'\{([^}]+)\}', prompt):
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return io.NodeOutput(prompt)
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m = re.findall(r'\{([^}]+)\}', prompt)
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keys = '\n'.join([f'{x}:' for x in m])
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p = f"""PROMPT:\n{prompt}\n\nKEYS:\n{keys}"""
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if images is not None:
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content = []
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for image in images:
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array = (image*255).clamp(0, 255).byte().cpu().numpy()
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pil_img = Image.fromarray(array, mode="RGB")
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buf = BytesIO()
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pil_img.save(buf, format="PNG")
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b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
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content.append({'type': 'image_url', 'image_url': {'url': f"data:image/png;base64,{b64}"}})
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content.append({'type': 'text', 'text': p})
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messages = [
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{"role": "system", "content": system},
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{"role": "user", "content": content}
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]
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else:
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messages = [
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{"role": "system", "content": system},
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{"role": "user", "content": p}
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]
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if force_json_output:
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response_format = {
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"type": "json_object",
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"schema": {
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"type": "object",
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"properties": {key:{"type":"string"} for key in m},
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"required": m
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}
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}
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else:
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response_format = None
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result = llm.create_chat_completion(
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seed=seed,
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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repeat_penalty=repeat_penalty,
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response_format=response_format,
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stop=["<|im_end|>", "<|im_start|>", "<im_end>", "<im_start>", "</im_end>", "</im_start>"]
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)
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groups = result['choices'][0]['message']['content']
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import json_repair
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p = prompt
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for k, v in json_repair.loads(groups).items():
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p = p.replace(f"{{{k}}}", "\n".join([str(x) for x in v]) if isinstance(v, list) else str(v))
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return io.NodeOutput(p)
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class DavchaImageStack(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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template = io.Autogrow.TemplatePrefix(input=io.Image.Input("image", optional=True), prefix="image", min=1, max=10)
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return io.Schema(
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node_id="DavchaImageStack",
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category="image",
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inputs=[
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io.Autogrow.Input("autogrow", template=template, optional=True),
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],
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outputs=[
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io.Image.Output()
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]
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)
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@classmethod
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def execute(cls, autogrow) -> io.NodeOutput:
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images = [v for k, v in sorted(autogrow.items()) if v is not None]
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return io.NodeOutput(images)
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class DavchaScheduledReferenceLatent(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="DavchaScheduledReferenceLatent",
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category="latent",
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inputs=[
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io.Conditioning.Input("cond"),
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io.Latent.Input("latent", optional=True),
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io.Float.Input("start", min=0.0, max=1.0, default=0.0, step=0.01),
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io.Float.Input("end", min=0.0, max=1.0, default=1.0, step=0.01),
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],
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outputs=[
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io.Conditioning.Output()
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]
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)
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@classmethod
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def execute(cls, cond, latent, start, end) -> io.NodeOutput:
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ts = {0.0,1.0,start,end}
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for x in cond:
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if "start_percent" in x[1]:
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ts.add(x[1]['start_percent'])
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ts.add(x[1]['end_percent'])
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else:
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x[1]["start_percent"] = 0
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x[1]["end_percent"] = 1
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ts = sorted(list(ts))
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c = []
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for s, e in zip(ts, ts[1:]):
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midpoint = (s + e) / 2.0
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for x in cond:
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if x[1]['start_percent'] <= midpoint < x[1]['end_percent']:
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active_cond = [x]
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break
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if start <= midpoint < end:
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active_cond = node_helpers.conditioning_set_values(active_cond, {"reference_latents": [latent["samples"]]}, append=True)
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c.extend(node_helpers.conditioning_set_values(active_cond, {"start_percent": s, "end_percent": e}))
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return io.NodeOutput(c)
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class DavchaScaleImageToTotalPixelsMax(io.ComfyNode):
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upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
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crop_methods = ["disabled", "center"]
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="DavchaScaleImageToTotalPixelsMax",
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display_name="Scale Image to Total Pixels",
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category="image/upscaling",
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inputs=[
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io.Combo.Input("upscale_method", options=cls.upscale_methods),
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io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
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io.Int.Input("resolution_steps", default=1, min=1, max=256, advanced=True),
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io.Image.Input("image", optional=True),
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],
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outputs=[
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io.Image.Output(),
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],
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)
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@classmethod
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def execute(cls, upscale_method, megapixels, resolution_steps, image=None) -> io.NodeOutput:
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if image is None:
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return io.NodeOutput(None)
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samples = image.movedim(-1,1)
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total = megapixels * 1024 * 1024
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scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
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width = round(samples.shape[3] * scale_by / resolution_steps) * resolution_steps
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height = round(samples.shape[2] * scale_by / resolution_steps) * resolution_steps
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s = comfy.utils.common_upscale(samples, int(width), int(height), upscale_method, "disabled")
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s = s.movedim(1,-1)
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return io.NodeOutput(s)
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NODE_CLASS_MAPPINGS = {
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'DavchaScheduledReferenceLatent': DavchaScheduledReferenceLatent,
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'DavchaScaleImageToTotalPixelsMax': DavchaScaleImageToTotalPixelsMax,
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'DavchaImageStack': DavchaImageStack,
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'OldDavchaPromptEnricher': OldDavchaPromptEnricher,
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'DavchaQwenVL3Loader': DavchaQwenVL3Loader,
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'DavchaQwenVL3': DavchaQwenVL3,
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'DavchaScheduledSampler': DavchaScheduledSampler,
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@@ -1586,8 +1782,8 @@ NODE_CLASS_MAPPINGS = {
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'DavchaTextEncodeQwenImageEditPlus': DavchaTextEncodeQwenImageEditPlus,
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'DavchaWan22LoraTagLoader': DavchaWan22LoraTagLoader,
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'DavchaWan22LoraTagParser': DavchaWan22LoraTagParser,
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'DavchaLoadLLM': DavchaLoadLLM,
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'DavchaLLM': DavchaLLM,
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'OldDavchaLoadLLM': OldDavchaLoadLLM,
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'OldDavchaLLM': OldDavchaLLM,
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'DavchaLLMAdvanced': DavchaLLMAdvanced,
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'PadAndResize': PadAndResize,
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'SmartMask': SmartMask,
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@@ -1611,6 +1807,10 @@ NODE_CLASS_MAPPINGS = {
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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'DavchaScheduledReferenceLatent': 'Scheduled Reference Latent',
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'DavchaScaleImageToTotalPixelsMax': 'Scale Image To Total Pixels Max',
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'DavchaImageStack': 'Davcha Image Stack',
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'OldDavchaPromptEnricher': 'Davcha Prompt Enricher',
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'DavchaQwenVL3Loader': 'DavchaQwenVL3Loader',
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'DavchaQwenVL3': 'DavchaQwenVL3',
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'DavchaScheduledSampler': 'Scheduled Sampler',
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@@ -1622,8 +1822,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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'DavchaTextEncodeQwenImageEditPlus': 'Text Encode Qwen Image Edit Plus',
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'DavchaWan22LoraTagLoader': 'Wan22 Lora Tag Loader',
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'DavchaWan22LoraTagParser': 'Wan22 Lora Tag Parser',
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'DavchaLoadLLM': 'DavchaLoadLLM',
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'DavchaLLM': 'DavchaLLM',
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'OldDavchaLoadLLM': 'OldDavchaLoadLLM',
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'OldDavchaLLM': 'OldDavchaLLM',
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'DavchaLLMAdvanced': 'DavchaLLMAdvanced',
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'PadAndResize': 'PadAndResize',
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'SmartMask': 'SmartMask',
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+2
-1
@@ -1 +1,2 @@
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webp
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webp
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rapidfuzz
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