nodes/kijai-ComfyUI-BrushNet-Wrapper
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Batching support for powerpaint

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Kijai
2024-04-18 16:53:29 +03:00
parent cb13235b19
commit af6dbbcbb6
1 changed files with 24 additions and 38 deletions
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nodes.py
+24 -38
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@@ -406,15 +406,15 @@ class powerpaint_brushnet_sampler(brushnet_sampler):
"default": "text-guided"
})
base_inputs["required"]["fitting_degree"] = (
"FLOAT", {"default": 1.0, "min": 0.3, "max": 1.0, "step": 0.05},
"FLOAT", {"default": 1.0, "min": 0.3, "max": 1.0, "step": 0.01},
)
print(base_inputs)
return base_inputs
def process(self, brushnet, image, mask, prompt, n_prompt, steps, cfg, guess_mode, clip_skip,
cfg_brushnet, control_guidance_start, control_guidance_end, seed, scheduler, task, fitting_degree):
# Call the parent class's process method to reuse its functionality
if 'ip_adapter' in brushnet:
raise Exception("This node doesn't currently support using IPAdapter.")
device = mm.get_torch_device()
mm.soft_empty_cache()
pipe=brushnet["pipe"]
@@ -456,8 +456,6 @@ class powerpaint_brushnet_sampler(brushnet_sampler):
noise_scheduler = TCDScheduler(**scheduler_config)
pipe.scheduler = noise_scheduler
B, H, W, C = image.shape
image = image.permute(0, 3, 1, 2).to(device)
@@ -473,45 +471,33 @@ class powerpaint_brushnet_sampler(brushnet_sampler):
image = image * (1-mask)
if 'ip_adapter' in brushnet:
print("Using IP adapter")
prompt_embeds, negative_prompt_embeds = brushnet['ip_adapter'].get_prompt_embeds(
brushnet['ip_adapter_image'],
prompt=prompt,
negative_prompt=n_prompt,
weight=[brushnet['ip_adapter_weight']]
)
prompt_embeds = torch.repeat_interleave(prompt_embeds, B, dim=0)
negative_prompt_embeds = torch.repeat_interleave(negative_prompt_embeds, B, dim=0)
use_ipadapter = True
prompt_list = None
n_prompt_list = None
else:
prompt_list = []
prompt_list.append(prompt)
if len(prompt_list) < B:
prompt_list += [prompt_list[-1]] * (B - len(prompt_list))
promptA, promptB, negative_promptA, negative_promptB = add_task(task)
prompt_list = []
n_prompt_list = []
promptA_list = []
negative_promptA_list = []
promptB_list = []
negative_promptB_list = []
n_prompt_list = []
n_prompt_list.append(n_prompt)
if len(n_prompt_list) < B:
n_prompt_list += [n_prompt_list[-1]] * (B - len(n_prompt_list))
prompt_list = [prompt] * (B - len(prompt_list)) if len(prompt_list) < B else prompt_list
n_prompt_list = [n_prompt] * (B - len(n_prompt_list)) if len(n_prompt_list) < B else n_prompt_list
prompt_embeds, negative_prompt_embeds = None, None
use_ipadapter = False
promptA_list = [promptA] * (B - len(promptA_list)) if len(promptA_list) < B else promptA_list
negative_promptA_list = [negative_promptA] * (B - len(negative_promptA_list)) if len(negative_promptA_list) < B else negative_promptA_list
promptB_list = [promptB] * (B - len(promptB_list)) if len(promptB_list) < B else promptB_list
negative_promptB_list = [negative_promptB] * (B - len(negative_promptB_list)) if len(negative_promptB_list) < B else negative_promptB_list
#sample
generator = torch.Generator(device).manual_seed(seed)
promptA, promptB, negative_promptA, negative_promptB = add_task(task)
images = pipe(
promptA = promptA,
promptB = promptB,
promptU = prompt,
negative_promptA = negative_promptA,
negative_promptB = negative_promptB,
negative_promptU = n_prompt,
promptA = promptA_list,
promptB = promptB_list,
promptU = prompt_list,
negative_promptA = negative_promptA_list,
negative_promptB = negative_promptB_list,
negative_promptU = n_prompt_list,
tradoff=fitting_degree,
tradoff_nag=fitting_degree,
image=image,