wangbo/ComfyUI
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Merge branch 'comfyanonymous:master' into batched_noise

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FizzleDorf 2023-12-03 12:50:27 -05:00 committed by GitHub
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3 changed files with 27 additions and 1 deletions
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2
comfy/model_base.py
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@ -303,7 +303,7 @@ class SVD_img2vid(BaseModel):
if latent_image.shape[1:] != noise.shape[1:]: if latent_image.shape[1:] != noise.shape[1:]:
latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
latent_image = utils.repeat_to_batch_size(latent_image, noise.shape[0]) latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])
out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image)

20
comfy/utils.py
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@ -239,6 +239,26 @@ def repeat_to_batch_size(tensor, batch_size):
return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size] return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size]
return tensor return tensor
def resize_to_batch_size(tensor, batch_size):
in_batch_size = tensor.shape[0]
if in_batch_size == batch_size:
return tensor
if batch_size <= 1:
return tensor[:batch_size]
output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device)
if batch_size < in_batch_size:
scale = (in_batch_size - 1) / (batch_size - 1)
for i in range(batch_size):
output[i] = tensor[min(round(i * scale), in_batch_size - 1)]
else:
scale = in_batch_size / batch_size
for i in range(batch_size):
output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]
return output
def convert_sd_to(state_dict, dtype): def convert_sd_to(state_dict, dtype):
keys = list(state_dict.keys()) keys = list(state_dict.keys())
for k in keys: for k in keys:

6
web/scripts/app.js
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@ -1878,6 +1878,8 @@ export class ComfyApp {
if (pngInfo) { if (pngInfo) {
if (pngInfo.workflow) { if (pngInfo.workflow) {
await this.loadGraphData(JSON.parse(pngInfo.workflow)); await this.loadGraphData(JSON.parse(pngInfo.workflow));
} else if (pngInfo.prompt) {
this.loadApiJson(JSON.parse(pngInfo.prompt));
} else if (pngInfo.parameters) { } else if (pngInfo.parameters) {
importA1111(this.graph, pngInfo.parameters); importA1111(this.graph, pngInfo.parameters);
} }
@ -1889,6 +1891,8 @@ export class ComfyApp {
this.loadGraphData(JSON.parse(pngInfo.workflow)); this.loadGraphData(JSON.parse(pngInfo.workflow));
} else if (pngInfo.Workflow) { } else if (pngInfo.Workflow) {
this.loadGraphData(JSON.parse(pngInfo.Workflow)); // Support loading workflows from that webp custom node. this.loadGraphData(JSON.parse(pngInfo.Workflow)); // Support loading workflows from that webp custom node.
} else if (pngInfo.prompt) {
this.loadApiJson(JSON.parse(pngInfo.prompt));
} }
} }
} else if (file.type === "application/json" || file.name?.endsWith(".json")) { } else if (file.type === "application/json" || file.name?.endsWith(".json")) {
@ -1908,6 +1912,8 @@ export class ComfyApp {
const info = await getLatentMetadata(file); const info = await getLatentMetadata(file);
if (info.workflow) { if (info.workflow) {
await this.loadGraphData(JSON.parse(info.workflow)); await this.loadGraphData(JSON.parse(info.workflow));
} else if (info.prompt) {
this.loadApiJson(JSON.parse(info.prompt));
} }
} }
} }